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Sample records for learning time elt

  1. Enrichment in Massachusetts Expanded Learning Time (ELT) Schools. Issue Brief

    Science.gov (United States)

    Caven, Meghan; Checkoway, Amy; Gamse, Beth; Luck, Rachel; Wu, Sally

    2012-01-01

    This brief highlights key information about enrichment activities, which represent one of the main components of the Massachusetts Expanded Learning Time (ELT) initiative. Over time, the ELT initiative has supported over two dozen schools across the Commonwealth. A comprehensive evaluation of the ELT initiative found that implementation of the…

  2. Reflection of Learning Theories in Iranian ELT Textbooks

    Science.gov (United States)

    Neghad, Hossein Hashem

    2014-01-01

    This study was undertaken to evaluate Iranian ELT English textbooks (Senior High school and Pre-University) in the light of three learning theories i.e., behaviourism, cognitivism, and constructivism. Each of these learning theories embedding an array of instructional strategies and techniques acted as evaluation checklist. That is, Iranian ELT…

  3. The Positive Effects of Cognitive Learning Styles in ELT Classes

    Science.gov (United States)

    Yagcioglu, Ozlem

    2016-01-01

    In the EFL, ESL, ESP and in the ELT classes, students are taught their courses with different kinds of methods and approaches. Cognitive learning styles are the most essential styles in foreign language education. In this paper, the positive effects of cognitive learning styles will be handled. The benefits of these styles will be highlighted.…

  4. THE POSITIVE EFFECTS OF COGNITIVE LEARNING STYLES IN ELT CLASSES

    OpenAIRE

    Ozlem Yagcioglu

    2016-01-01

    In the EFL, ESL, ESP and in the ELT classes, students are taught their courses with different kinds of methods and approaches. Cognitive learning styles are the most essential styles in foreign language education. In this paper, the positive effects of cognitive learning styles will be handled. The benefits of these styles will be highlighted. Games on cognitive learning styles will be explained. Sample classroom activities will be shared. Useful books, videos and websites on cognitive learni...

  5. Feeding the ELT Students' Needs Through Kolb's Learning Styles Inventory

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    Ayfer SU BERGİL

    2017-12-01

    Full Text Available Contrary to learning styles seem the same as what abilities refer, they are related to them in the sense that they decipher how individuals desire to use their capabilities. There have been diverse learning styles theories intent to explain the individual differences on account of the acceleration and the amount of absorbed knowledge. Learning styles have been defined under the notions of cognitive, affective and physiological attributes that serve as nearly strong indicators of how learners distinguish, combine, and reciprocate to the learning phenomena which gains importance and provide basis for language education process as well. Thus, this study aims to determine the learning styles of English language teaching (ELT students studying at Amasya University, Faculty of Education in 2017-2018 academic year. The participants of the study consist of totally 109 out of 122 from 1st, 2nd, 3rd and 4th grade students of English Language Teaching Department. The data collection instrument was Kolb’s Learning Style Inventory including four sets of work labeled as Concrete Experience, Reflective Observation, Abstract Conceptualization, and Active Experimentation and the students were expected to rank order the 12 items listed for each category via assigning a 4 to the word which best characterizes their learning style, a 3 to the next best, a 2 to the next, and a 1 to the least characteristic word. By this way, ELT students’ dominant learning styles which refer to their learning profiles has been specified descriptively. Furthermore, the learning styles of ELT students has been interconnected with the content of the courses they need to take during their teacher education process and suggestions for the members of ELT departments has been provided based on the findings of these learning styles.

  6. Teacher as Learning Facilitator in ELT

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    Badea Elena Codruta

    2012-05-01

    Full Text Available The classroom is the magic active scenery where many educational things take place simultaneously.Intellectual, emotional, socio-cultural, motivational and curricular factors corroborate their influence onclassroom environments, whether we deal with traditional models of teaching or with the constructivistapproaches. The growing demand for language teachers, English in particular, has determined a new vision oflanguage teaching strategies. The cutting-edge technology has created a fertile ground which successfullyfosters the teacher –student communication, emphasizing the teacher’s role to guide students and to generate achange in their learning approach and in eliciting useable knowledge. This way, the teacher has a larger abilityto convert knowledge into practical information that is of real help and value to students. Students are involvedin a continuous educational scheme and are tested on what they have learned. This ensures they can alwaysenjoy the benefits of active learning from expert teachers. The present paper deals with a brief analysis of therole of teacher as learning facilitator and its importance for student acquisition process, eliciting some strategiesin support of collaborative and student-centered learning.

  7. UTILIZING A MOODLE-BASED E-LEARNING PLATFORM IN ELT: AN AUTOETHNOGRAPHY

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    Ismail Anas

    2017-04-01

    Full Text Available The digitalization in education has created opportunities and challenges for teacher of English in incorporating and utilizing emerging technologies into online teaching and learning environment. This article presents a two year experiencein utilizing moodle in English Language Teaching (ELT in a Vocational Higher Education (VHE context-so called polytechnic. This e-learning platform, hosted in a subdomain of author‘s personal website, has been in-service since 2014 to present. An autoethnographical approach with a self-reflection of the author‘s experience and exploration on moodle 2.3 has fundamentally underpinned this report. The experiment began to work on how the platform was constructed, organized, and implemented in ELT. The process of construction, organization, and implementation haswidespread implications forteachertechnology and digital literacy competence. Best practices, barriers, and challenges are also outlined as well as recommendations for future implementation and development.

  8. Digital technology use in ELT classrooms and self-directed learning

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    Nehir Sert

    2016-04-01

    Full Text Available The digital era is a new challenge for teachers. While children get acquainted with digital technology before the age of six, teachers, who have encountered the digital world at a later time in their lives, struggle with it. Self-directed learning, which is crucial for lifelong learning, can be enhanced by the use of technology within and beyond classroom settings. The aim of this study was to examine the difference between the perceptions of students in low- and high-income groups about their use of technology in a general sense and their teachers’ use of technology in ELT classrooms. It also tested the correlation between the perceptions of their self-directed learning behaviours and their own/their teachers’ technology use. The population of the study consisted of 75 students from high- and 70 students from low-income groups. Causal comparative and correlational research methods were adopted in the study. The surveys to measure the students’ perceptions about technology use were developed by the researchers. A scale, established by Demirtas and Sert (2010, was used to identify the level of self-directed learning views of the students. The data were collected at the beginning of the first term of the 2015-2016 school year. The results indicated that there was no significant difference between perceptions of the low- and high-income students regarding their own technology use. Likewise, perceptions of the low- and high-income students did not differ regarding their teachers’ technology use. There was no correlation between the perceptions of the low-/high-income mixed group regarding their use of technology and their teachers’ use of technology. Lastly, self-directed learning perceptions of the low-/high-income mixed group did not correlate with their perceptions on any aspects of technology use. The educational implications of these results were discussed and suggestions were put forward in order to produce more effective learning

  9. Handwriting as a Tool for Learning in ELT

    Science.gov (United States)

    Lund, Ragnhild Elisabeth

    2016-01-01

    This article discusses the role that handwriting can have when writing is used as a tool for learning in English language education. Nineteen Norwegian EFL teacher training students were interviewed in focus groups about their own practices and their thoughts about writing-to-learn activities. All the students said that they prefer to write by…

  10. ELT-scale Adaptive Optics real-time control with thes Intel Xeon Phi Many Integrated Core Architecture

    Science.gov (United States)

    Jenkins, David R.; Basden, Alastair; Myers, Richard M.

    2018-05-01

    We propose a solution to the increased computational demands of Extremely Large Telescope (ELT) scale adaptive optics (AO) real-time control with the Intel Xeon Phi Knights Landing (KNL) Many Integrated Core (MIC) Architecture. The computational demands of an AO real-time controller (RTC) scale with the fourth power of telescope diameter and so the next generation ELTs require orders of magnitude more processing power for the RTC pipeline than existing systems. The Xeon Phi contains a large number (≥64) of low power x86 CPU cores and high bandwidth memory integrated into a single socketed server CPU package. The increased parallelism and memory bandwidth are crucial to providing the performance for reconstructing wavefronts with the required precision for ELT scale AO. Here, we demonstrate that the Xeon Phi KNL is capable of performing ELT scale single conjugate AO real-time control computation at over 1.0kHz with less than 20μs RMS jitter. We have also shown that with a wavefront sensor camera attached the KNL can process the real-time control loop at up to 966Hz, the maximum frame-rate of the camera, with jitter remaining below 20μs RMS. Future studies will involve exploring the use of a cluster of Xeon Phis for the real-time control of the MCAO and MOAO regimes of AO. We find that the Xeon Phi is highly suitable for ELT AO real time control.

  11. Muusika : Olari Elts juhatab Frankfurdis. Financial Time märkis meie rahvusooperit. "Diplomaatilised noodid" Tallinnas. 100 mustlasviiulit / Heili Vaus-Tamm

    Index Scriptorium Estoniae

    Vaus-Tamm, Heili, 1961-

    2002-01-01

    Olari Elts juhatab Fankfurdi Raadio Sümfooniaorkestrit. 4. jaan. Financial Time'is kirjutatakse Estonia uuslavastusest "Tark naine". Tallinna Filharmoonia sarja "Diplomaatilised noodid" lähimatest kontsertidest Tallinnas. 100 mustlasviiulit esineb 27. juulil Põltsamaa lossihoovis

  12. Quasi-real-time end-to-end simulations of ELT-scale adaptive optics systems on GPUs

    Science.gov (United States)

    Gratadour, Damien

    2011-09-01

    Our team has started the development of a code dedicated to GPUs for the simulation of AO systems at the E-ELT scale. It uses the CUDA toolkit and an original binding to Yorick (an open source interpreted language) to provide the user with a comprehensive interface. In this paper we present the first performance analysis of our simulation code, showing its ability to provide Shack-Hartmann (SH) images and measurements at the kHz scale for VLT-sized AO system and in quasi-real-time (up to 70 Hz) for ELT-sized systems on a single top-end GPU. The simulation code includes multiple layers atmospheric turbulence generation, ray tracing through these layers, image formation at the focal plane of every sub-apertures of a SH sensor using either natural or laser guide stars and centroiding on these images using various algorithms. Turbulence is generated on-the-fly giving the ability to simulate hours of observations without the need of loading extremely large phase screens in the global memory. Because of its performance this code additionally provides the unique ability to test real-time controllers for future AO systems under nominal conditions.

  13. Digital technology use in ELT classrooms and self-directed learning

    OpenAIRE

    Nehir Sert; Ebru Boynueğri

    2016-01-01

    The digital era is a new challenge for teachers. While children get acquainted with digital technology before the age of six, teachers, who have encountered the digital world at a later time in their lives, struggle with it. Self-directed learning, which is crucial for lifelong learning, can be enhanced by the use of technology within and beyond classroom settings. The aim of this study was to examine the difference between the perceptions of students in low- and high-income groups about thei...

  14. Neoliberalism and ELT Coursebook Content

    Science.gov (United States)

    Copley, Keith

    2018-01-01

    The widespread use of commercially produced coursebooks tailored to a global market remains a reality within English language teaching (ELT) across a broad range of teaching contexts. Most of these coursebooks profess to being vaguely communicative in their approach, while at the same time attempting to package and present language as a…

  15. PENERAPAN MODEL PEMBELAJARAN GROUP INVESTIGATION DAN LECTURING UNTUK MENINGKATKAN PEMAHAMAN MAHASISWA TERHADAP MATERI ENGLISH TEACHING AND LEARNING THEORIES (ELT

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    Eva Nikmatul Rabbiyanti

    2016-06-01

    Full Text Available Merging two Methods between lecturing and Group Investigation are expected not only to develop active participation of students in the lecturing process but also to develop the ability of creative thinking, collaboration and presenting themselves in public, but the writer keep maintaining the use of lecturing methods to give control to the  delivery of the key points of the materials. The research was conducted on students of D class of the 4th semester, TBI Programs of STAIN Pamekasan at 2013/2014 year, totaling 34 students. The method used was classroom action research by using two cycles, in each cycle consists of Planning, Implementation, and the reflection. The results of this study indicate that, the ability of students' understanding of the course material ELT tends to increase in each cycle, with the average value of pre-cycle test was 45.29, and in the first cycle was 73.09, while in the second cycle was 79.00. with the percentage of completeness in cycle 1 was 85.29% while in the second cycle was 88.23%. Besides, the collaboration of this two models were also able to improve students analytical skills, the ability to express ideas and opinions in English, and also the ability to work together and present themselves in public. Based on the results of the study finally concluded, successful mechanism developed in this study consists of eleven stages, namely 1 the preparation phase, 2 the learning objective delivery, 3 aperseption delivery, 4 the new material delivery, 5 brain storming, 6 topics identification and grouping, 7 distribution of job decriptions, 8 conducting the investigation, 9 preparation of the final report, 10 Present the final report, 11 Evaluation of the achievement

  16. ELT Materials: The Key to Fostering Effective Teaching and Learning Settings

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    Astrid Núñez Pardo

    2009-10-01

    Full Text Available Our article aims at providing teachers with an overview for materials development, taking into account the experience gained by two teachers in the English Programme of the School of Education at Universidad Externado de Colombia in Bogotá. This experience has helped us achieve better teaching and learning conditions for our university students in their quest to learn a foreign language. This paper addresses the issue of the role of teachers as textbook developers, and how they can meet materials development demands by integrating a clear conceptualisation and set of principles as well as their essential components.

  17. Literature Teaching in ELT

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    To show the importance of literature teaching in English language teaching (ELT),this paper explores the relations between language, culture and literature,examines the present problems in literature teaching and possible solutions are suggested as well.

  18. Linguistic and Cultural Strategies in ELT Dictionaries

    Science.gov (United States)

    Corrius, Montse; Pujol, Didac

    2010-01-01

    There are three main types of ELT dictionaries: monolingual, bilingual, and bilingualized. Each type of dictionary, while having its own advantages, also hinders the learning of English as a foreign language and culture in so far as it is written from a homogenizing (linguistic- and culture-centric) perspective. This paper presents a new type of…

  19. Teaching Unplugged: Applications of Dogme ELT in India

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    Sadeqa Ghazal

    2014-03-01

    Full Text Available The socio-political changes across the world indicate that it is, increasingly, becoming a questioning world. It is simple common sense that this change must also be reflected in the education system. A critical pedagogy that foregrounds dialogue and encourages questioning is therefore more relevant in the present times. The top-down approach of teaching only hampers the development of learners by silencing their voice and agency. In this respect, this paper focuses on the Dogme, or unplugged teaching, approach in English language teaching (ELT in Indian context. The paper explores theoretical reasons, based upon the views of Lev Vygotsky, Paulo Freire, and Charles Taylor, in support of adopting Dogme in ELT for radically changing the face of prevalent second language teaching scenario in India. An analysis of existing literature and empirical evidences strongly suggest that implementing this approach would be appropriate for multiple reasons. Being a dialogue based approach, it gives the learner, as well as the teacher, a chance to grow and learn together. It creates a zone of proximal development which helps learners to recognize their own voice and leads to self-discovery. Dogme in ELT can be motivating and empowering. As it is a pedagogy, of bare essentials, it is pro-poor and can be used even in under-equipped classrooms. Moreover, as it is grounded in the personal experience of the learner, it can fit well into a multicultural context. Therefore, it is implied and assumed that this approach would work very well in Indian context which is multicultural and economically diverse.

  20. Electron Technology: ELTE 2016

    Science.gov (United States)

    Pisarkiewicz, Tadeusz; Kucewicz, Wojciech

    2016-12-01

    In this paper we present a review of research results and technical accomplishments presented by researchers from technical universities, governmental institutes and research companies during the XIIth Scientific Conference Electron Technology, ELTE 2016. This review is based on materials presented at four topical conference sessions: Microelectronics and Nanoelectronics, Photonics, Materials and Technologies, and Microsystems and also on materials presented by invited speakers at two dedicated sessions. Oral sessions were accompanied by the poster sessions. In effect about 50 papers gathered in this volume reflect the topics discussed at the Conference. A short description of technological and measurement possibilities in the laboratories of Academic Centre for Materials and Nanotechnology and also in the Department of Electronics of the Faculty of Computer Science, Electronics and Telecommunications AGH UST are given.

  1. ELT in a changing world innovative approaches to new challenges

    CERN Document Server

    Ahmed, Azra; Saleem, Faiza; Cane, Graeme

    2013-01-01

    A novel ELT resource for language specialists and teachers across the world, this selection of papers is a collection of the most compelling and innovative ideas presented at a seminar hosted by the Centre of English Language, Aga Khan University, Pakistan, in January 2011, entitled 'ELT in a Changing World: Innovative Approaches to New Challenges'.The book is divided into three sections, the first of which is 'Global change and language learning'. This section offers a guided tour of language teaching evolution, highlighting the merits of enhanced language awareness, self-immersive and input/

  2. Learning During Stressful Times

    Science.gov (United States)

    Shors, Tracey J.

    2012-01-01

    Stressful life events can have profound effects on our cognitive and motor abilities, from those that could be construed as adaptive to those not so. In this review, I discuss the general notion that acute stressful experience necessarily impairs our abilities to learn and remember. The effects of stress on operant conditioning, that is, learned helplessness, as well as those on classical conditioning procedures are discussed in the context of performance and adaptation. Studies indicating sex differences in learning during stressful times are discussed, as are those attributing different responses to the existence of multiple memory systems and nonlinear relationships. The intent of this review is to highlight the apparent plasticity of the stress response, how it might have evolved to affect both performance and learning processes, and the potential problems with interpreting stress effects on learning as either good or bad. An appreciation for its plasticity may provide new avenues for investigating its underlying neuronal mechanisms. PMID:15054128

  3. MICADO: first light imager for the E-ELT

    NARCIS (Netherlands)

    Davies, R.; Schubert, J.; Hartl, M.; Alves, J.; Clénet, Y.; Lang-Bardl, F.; Nicklas, H.; Pott, J. -U; Ragazzoni, R.; Tolstoy, E.; Agocs, T.; Anwand-Heerwart, H.; Barboza, S.; Baudoz, P.; Bender, R.; Bizenberger, P.; Boccaletti, A.; Boland, W.; Bonifacio, P.; Briegel, F.; Buey, T.; Chapron, F.; Cohen, M.; Czoske, O.; Dreizler, S.; Falomo, R.; Feautrier, P.; Förster Schreiber, N.; Gendron, E.; Genzel, R.; Glück, M.; Gratadour, D.; Greimel, R.; Grupp, F.; Häuser, M.; Haug, M.; Hennawi, J.; Hess, H. J.; Hörmann, V.; Hofferbert, R.; Hopp, U.; Hubert, Z.; Ives, D.; Kausch, W.; Kerber, F.; Kravcar, H.; Kuijken, K.; Leitzinger, M.; Leschinski, K.; Massari, D.; Mei, S.; Merlin, F.; Mohr, L.; Monna, A.; Müller, F.; Navarro, R.; Plattner, M.; Przybilla, N.; Ramlau, R.; Ramsay, S.; Ratzka, T.; Rhode, P.; Richter, J.; Rix, H. -W; Rodeghiero, G.; Rohloff, R. -R; Rousset, G.; Ruddenklau, R.; Schaffenroth, V.; Schlichter, J.; Sevin, A.; Stuik, R.; Sturm, E.; Thomas, J.; Tromp, N.; Turatto, M.; Verdoes-Kleijn, G.; Vidal, F.; Wagner, R.; Wegner, M.; Zeilinger, W.; Ziegler, B.; Zins, G.

    2016-01-01

    MICADO will equip the E-ELT with a first light capability for diffraction limited imaging at near-infrared wavelengths. The instrument's observing modes focus on various flavours of imaging, including astrometric, high contrast, and time resolved. There is also a single object spectroscopic mode

  4. Learning Time and Educational Effectiveness.

    Science.gov (United States)

    Anderson, Lorin W.

    1980-01-01

    To explore the relationship between time and school learning, this paper defines the three kinds of learning time identified by researchers--allocated time, time-on-task, and academic learning time--and relates them to curriculum development. The author cites evidence that time-on-task is related to student achievement and describes two…

  5. The GTC: a convenient test bench for ELT demonstrations

    Science.gov (United States)

    Rodriguez Espinosa, Jose M.; Hammersley, Peter L.; Martinez-Roger, Carlos

    2004-07-01

    The Gran Telescopio Canarias (GTC) is, being assembled at the Observatorio del Roque de los Muchachos (ORM) in the island of La Palma. First light is expected for early 2005 with the first science observations late in 2005. The GTC, being a segmented primary mirror telescope, could be employed for testing several technological aspects relevant to the future generation of Extremely Large Telescopes (ELT). In the short term, the mass production of aespheric mirror segments can be examined in detail and improvements made along the way, or planned for the future. Indeed the GTC segments are now entering into a chain production scheme. Later on, different strategies for the control aspects of the primary mirror can be explored to optimize the optical performance of segmented telescopes. Moreover, the entire GTC active optics can offer a learning tool for testing various strategies and their application to ELTs.

  6. An Analytical Evaluation of Iranian High School ELT Textbooks from 1970 to 2010

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    Akbar Azizifar

    2009-10-01

    Full Text Available Textbooks play a very crucial role in the process of language teaching and learning. The present study carries out an evaluation of two series of ELT textbooks used for teaching English language in Iranian high schools since 1970 to 2010. For this purpose, Tucker’s (1975 textbook evaluation model (see Appendix has been employed. The results suggest that one of the main factors for the students’ achievement in English language is the ELT textbooks. The researcher suggests that in the textbooks, there should be enough opportunity for the learners to communicatively practice the language they are learning.

  7. Prime Time for Learning.

    Science.gov (United States)

    Leidy, Vivian; And Others

    1981-01-01

    Five elementary teachers explain how they orient pupils and get learning started on the first day of school--whether or not their supplies or textbooks have arrived--by building learning activities around a common interest like dogs, earthworms, football, or the Statue of Liberty. (Editor/SJL)

  8. Intelligent vibration control of ELTs and large AO hardware

    Science.gov (United States)

    Pott, J.-U.; Kürster, M.; Trowitzsch, J.; Borelli, J.; Rohloff, R.-R.; Herbst, T.; Böhm, M.; Keck, A.; Ruppel, T.; Sawodny, O.

    2012-07-01

    MPIA leads the construction of the LINC-NIRVANA instrument, the MCAO-supported Fizeau imager for the LBT, serves as pathfinder for future ELT-AO imagers in terms of size and technology. In this contribution, we review recent results and significant progress made on the development of key items of our stratgey to achieve a piston stability of up to 100nm during a science exposure. We present an overview of our vibration control strategies for optical path and tip-tilt stabilization, involving accelerometer based real-time vibration measurements, vibration sensitive active control of actuators, and the development of a dynamical model of the LBT. MPIA also co-develops the E-ELT first-light NIR imager MICADO (both SCAO and MCAO assisted). Our experiences, made with LINC-NIRVANA, will be fed into the MICADO structural AO design to reach highest on-sky sensitivity.

  9. 77 FR 1779 - Emergency Locator Transmitters (ELTs)

    Science.gov (United States)

    2012-01-11

    ... INFORMATION CONTACT: Mr. Albert Sayadian, AIR-130, Federal Aviation Administration, 470 L'Enfant Plaza, Suite... nautical miles in radius. Additionally, 406 MHz ELTs which have a GPS position input can potentially reduce...

  10. Time and Associative Learning.

    Science.gov (United States)

    Balsam, Peter D; Drew, Michael R; Gallistel, C R

    2010-01-01

    In a basic associative learning paradigm, learning is said to have occurred when the conditioned stimulus evokes an anticipatory response. This learning is widely believed to depend on the contiguous presentation of conditioned and unconditioned stimulus. However, what it means to be contiguous has not been rigorously defined. Here we examine the empirical bases for these beliefs and suggest an alternative view based on the hypothesis that learning about the temporal relationships between events determines the speed of emergence, vigor and form of conditioned behavior. This temporal learning occurs very rapidly and prior to the appearance of the anticipatory response. The temporal relations are learned even when no anticipatory response is evoked. The speed with which an anticipatory response emerges is proportional to the informativeness of the predictive cue (CS) regarding the rate of occurrence of the predicted event (US). This analysis gives an account of what we mean by "temporal pairing" and is in accord with the data on speed of acquisition and basic findings in the cue competition literature. In this account, learning depends on perceiving and encoding temporal regularities rather than stimulus contiguities.

  11. Establishing Time for Professional Learning

    Science.gov (United States)

    Journal of Staff Development, 2013

    2013-01-01

    Time for collaborative learning is an essential resource for educators working to implement college- and career-ready standards. The pages in this article include tools from the workbook "Establishing Time for Professional Learning." The tools support a complete process to help educators effectively find and use time. The following…

  12. A QUEST FOR LITERATURE IN ELT COURSEBOOKS

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    Fatma Gümüşok

    2013-07-01

    Full Text Available The mission of literature in ELT has undergone a change for the last century. Once both a vehicle and an aim for teaching foreign language, literature now seems to remain out of center. Although it has various benefits for language learners, it reaches language classes only when the teacher is willing to make use of it, which is suggested by the studies conducted on teaching literature (Ross, 1991; Timuçin, 2001; Wang, 2009. Following the language teacher, coursebooks are the second primary input source in a classroom. In other words, if a teacher is reluctant to use literary texts in the lesson, the coursebook is the only provider for literature to be benefited. Therefore, this study aims at exploring to what extent literary texts and literary elements are used in ELT coursebooks. ELT coursebooks used in the preparatory schools of state universities in Ankara were analyzed for literary texts and elements. In addition, two ELT coursebook series which were published in the last 20 years and are not used anymore were analyzed in order to see whether ELT coursebooks differ with regard to the quantity of literary texts and elements in the last 20 years. In total, 22 coursebooks from different levels were analyzed. The finding reveals that ELT coursebooks contain bits and pieces of literature and there has been decrease in the number of literary texts in the currently used coursebooks.

  13. Can ELT in Higher Education Be Successful? The Current Status of ELT in Mexico

    Science.gov (United States)

    Vazquez, Alberto Mora; Guzman, Nelly Paulina Trejo; Roux, Ruth

    2013-01-01

    The purpose of this paper is to analyze the determinants of the current state of the ELT field in Mexican contexts. In particular, it explores the ways in which diverse social and political factors hamper the successful implementation of national and institutional ELT policies. Drawing on a case study carried out throughout a period of five years,…

  14. THE DEVELOPMENT OF ENGLISH LANGUAGE TEACHING (ELT COMPETENCY-BASED SYLLABUS IN SENIOR HIGH SCHOOL

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    Besral Besral

    2012-07-01

    Full Text Available Although competency has long been the major concern in ELT either in the EFL or ESL contexts, the rise of competency-based syllabus launched by the Ministry of National Education (2006 brought about significant issue among the English teachers in the country. One of the crucial issues is that how to transfer the concepts of competences into the syllabus design.  Since a syllabus does not only contain a list of subject content, but also how curriculum planners (teachers reflect their understanding and belief about nature of language and of language teaching and learning, the ELT must be carried out to achieve communicative competence. Current investigation on the practices of ELT, however, indicates that English teachers are still walking in place, leaving the CC as a big slogan in their jobs.

  15. E-ELT M1 test facility

    Science.gov (United States)

    Dimmler, M.; Marrero, J.; Leveque, S.; Barriga, P.; Sedghi, B.; Mueller, M.

    2012-09-01

    During the advanced design phase of the European Extremely Large Telescope (E-ELT) several critical components have been prototyped. During the last year some of them have been tested in dedicated test stands. In particular, a representative section of the E-ELT primary mirror has been assembled with 2 active and 2 passive segments. This test stand is equipped with complete prototype segment subunits, i.e. including support mechanisms, glass segments, edge sensors, position actuators as well as additional metrology for monitoring. The purpose is to test various procedures such as calibration, alignment and handling and to study control strategies. In addition the achievable component and subsystem performances are evaluated, and interface issues are identified. In this paper an overview of the activities related to the E-ELT M1 Test Facility will be given. Experiences and test results are presented.

  16. Prototyping the E-ELT M1 local control system communication infrastructure

    Science.gov (United States)

    Argomedo, J.; Kornweibel, N.; Grudzien, T.; Dimmler, M.; Andolfato, L.; Barriga, P.

    2016-08-01

    The primary mirror of the E-ELT is composed of 798 hexagonal segments of about 1.45 meters across. Each segment can be moved in piston and tip-tilt using three position actuators. Inductive edge sensors are used to provide feedback for global reconstruction of the mirror shape. The E-ELT M1 Local Control System will provide a deterministic infrastructure for collecting edge sensor and actuators readings and distribute the new position actuators references while at the same time providing failure detection, isolation and notification, synchronization, monitoring and configuration management. The present paper describes the prototyping activities carried out to verify the feasibility of the E-ELT M1 local control system communication architecture design and assess its performance and potential limitations.

  17. China English and ELT for English Majors

    Science.gov (United States)

    Zhang, Mingjuan

    2008-01-01

    This paper is a general study of one of varieties of English--China English and its influence on English Language Teaching (ELT) for English majors. The status of English as an International language breaks the situation in which British English or American English is the sole standard. English becomes World Englishes, taking on a plural form,…

  18. High-precision astrometry towards ELTs

    NARCIS (Netherlands)

    Massari, Davide; Fiorentino, Giuliana; Tolstoy, Eline; McConnachie, Alan; Stuik, Remko; Schreiber, Laura; Andersen, David; Clénet, Yann; Davies, Richard; Gratadour, Damien; Kuijken, Konrad; Navarro, Ramon; Pott, Jörg-Uwe; Rodeghiero, Gabriele; Turri, Paolo; Verdoes Kleijn, Gijs

    2016-01-01

    With the aim of paving the road for future accurate astrometry with MICADO at the European-ELT, we performed an astrometric study using two different but complementary approaches to investigate two critical components that contribute to the total astrometric accuracy. First, we tested the predicted

  19. A Thorough Scrutiny of ELT Textbook Evaluations: A Review Inquiry

    Directory of Open Access Journals (Sweden)

    Reza Gholami

    2017-07-01

    Full Text Available It is thoroughly agreed that English language textbooks stand amongst the foremost components in any language classrooms worldwide, being referred to as valid, beneficial and labor-saving tools to fulfill an extensive range of needs. An ELT textbook is not merely a set of sheets of paper fastened together to hinge at one side, but is the beating heart of any education system whereupon the whole learning revolves. Notwithstanding their interminable benefits, it is admitted that still the compiled textbooks, especially the ones prescribed in Educational systems have to be evaluated and assessed to confirm whether they fulfil the objectives they are meant for or not, as it is said no perfect textbook exists. Having dealt with evaluation in general, this research meticulously elaborates on textbook evaluation more specifically concluding that there is a dearth of inquiry on textbook selection and evaluation. Afterwards, this research introduces the most common approaches for evaluating ELT textbooks and materials. The paper culminates with concluding remarks and implications, hoping to shed light on how textbook evaluation is practiced worldwide.

  20. VIEW FROM THE TOP: ENJOYABLE AND EFFECTIVE APPROACHES TO ELT FOR FLIGHT ATTENDANT

    Directory of Open Access Journals (Sweden)

    Nanik Rianandita Sari,S.S.,M.A.

    2017-04-01

    Full Text Available Young people today are more exposed to popular culture than any other age group. Television, movies, music, magazines, fashion, and internet form a major psychological part of the lives and life styles of youngsters. Since there has been increasing interest in the use of popular culture as aids in learning English. This research will investigate the ways in which popular culture might be used in teaching English. Learning language through pop culture, which comes from the West. Music, songs, and movies are highly popular with youngster. The movie ‘View From The Top‘, in particular, was a popular movie, where it entered the fantasy world of most young girl who want to be a flight attendant class of 2011. Many of flight attendant watch this movie for several times, remembering the lines spoken by the actress, and reciting the lyrics of the theme songs. All of which seems to suggest that flight attendant student can learn English through their encounters with popular culture. A movie is one interesting way to learn English. Movie is one of popular culture which is part of oral literature which enhances ELT through elements such as authentic material, language in use and aesthetic representation of the spoken language, as well as language and cultural enrichment. Literature appeals to flight attendant students, it motivates them to become responsive and active learners.

  1. Analysis list: elt-3 [Chip-atlas[Archive

    Lifescience Database Archive (English)

    Full Text Available elt-3 Embryo,Larvae + ce10 http://dbarchive.biosciencedbc.jp/kyushu-u/ce10/target/elt-3.1.tsv http:...//dbarchive.biosciencedbc.jp/kyushu-u/ce10/target/elt-3.5.tsv http://dbarchive.biosciencedbc....jp/kyushu-u/ce10/target/elt-3.10.tsv http://dbarchive.biosciencedbc.jp/kyushu-u/ce10/colo/elt-3.Embryo.tsv,http:...//dbarchive.biosciencedbc.jp/kyushu-u/ce10/colo/elt-3.Larvae.tsv http://dbar...chive.biosciencedbc.jp/kyushu-u/ce10/colo/Embryo.gml,http://dbarchive.biosciencedbc.jp/kyushu-u/ce10/colo/Larvae.gml ...

  2. SKILLS-BASED ECLECTIC TECHNIQUES MATRIX FOR ELT MICROTEACHINGS

    Directory of Open Access Journals (Sweden)

    İskender Hakkı Sarıgöz

    2016-10-01

    Full Text Available Foreign language teaching undergoes constant changes due to the methodological improvement. This progress may be examined in two parts. They are the methods era and the post-methods era. It is not pragmatic today to propose a particular language teaching method and its techniques for all purposes. The holistic inflexibility of mid-century methods has long gone. In the present day, constructivist foreign language teaching trends attempt to see the learner as a whole person and an individual who may be different from the other students in many respects. At the same time, the individual differences should not keep the learners away from group harmony. For this reason, current teacher training programs require eclectic teaching matrixes for unit design considering the mixed ability student groups. These matrixes can be prepared in a multidimensional fashion because there are many functional techniques in different methods and other new techniques to be created by instructors freely in accordance with the teaching aims. The hypothesis in this argument is that the collection of foreign language teaching techniques compiled in ELT microteachings for a particular group of learners has to be arranged eclectically in order to update the teaching process. Nevertheless, designing a teaching format of this sort is a demanding and highly criticized task. This study briefly argues eclecticism in language-skills based methodological struggle from the perspective of ELT teacher education.

  3. Science Data Management for the E-ELT: usecase MICADO

    NARCIS (Netherlands)

    Verdoes Kleijn, Gijs

    2015-01-01

    The E-ELT First-light instrument MICADO will explore new parameter space in terms of precision astrometry, photometry and spectroscopy. This provides challenges for the data handling and reduction to ensure MICADO takes the observational capabilities of the AO-assisted E-ELT towards its limits. Our

  4. A Role for Soft Systems Methodology in ELT Projects.

    Science.gov (United States)

    Holliday, Adrian

    1990-01-01

    Discusses the uses for soft systems methodology (SSM) in English language training (ELT) projects. It is suggested that ethnographic techniques may help in achieving the understanding needed to start an ELT project while SSM may provide a useful means for structuring ethnographic findings. (Author/VWL)

  5. Timepiece: Extending and Enhancing Learning Time.

    Science.gov (United States)

    Anderson, Lorin W., Ed.; Walberg, Herbert J., Ed.

    This publication offers suggestions for making more productive use of time, a scarce and valued educational resource. The chapter authors, authorities on the use of educational time, write about how to extend and enhance learning time within and outside schools. In "Productive Use of Time," Herbert Walberg describes how learning time can be…

  6. Is there ELF in ELT coursebooks?

    Directory of Open Access Journals (Sweden)

    Paola Vettorel

    2013-10-01

    Full Text Available This article aims to explore whether well-attested findings in the fields of World Englishes (WE and of English as a lingua franca (ELF have determined a shift in perspective in the overall approach to English language teaching (ELT, and how far this shift has permeated teaching materials and coursebooks. The research study was carried out in Italy, a country where ELT coursebooks have often played a relevant role in introducing innovations in language teaching methodology. The research design included a corpus of ten coursebooks that have been published and adopted in Italian secondary schools in the last 6 years. The coursebooks were evaluated in terms of the presence or absence of references to WE and/or ELF, of awareness-raising activities, of the promotion of using English outside the school environment and of the use of effective English communication and intercultural strategies among nonnative speakers. Findings show that there have been no significant changes in the inclusion of WE- and ELForiented materials and related tasks, apart from the area of promotion of cultural and intercultural awareness.

  7. The E-ELT program status

    Science.gov (United States)

    Tamai, Roberto; Cirasuolo, Michele; González, Juan Carlos; Koehler, Bertrand; Tuti, Mauro

    2016-07-01

    ESO is now fully engaged in building the European Extremely Large Telescope (E-ELT), a 40-m class optical nearinfrared telescope to be installed on top of Cerro Armazones, Chile and become operational around 2025. The Programme was formally approved by ESO Council back in 2012. However the required funding level for starting construction was actually reached in 2014, leading to a Green Light to start large construction contracts in December of that year. Since then, the programme has entered a very busy phase leading to the signature of the first major industrial contracts as well as the agreements with scientific institutes in ESO Member States to design and built the first suite of science instruments. This paper summarizes the current status of the E-ELT Programme and presents some aspects related to scientific objectives, managerial organization, programmatic aspects and system engineering approach. It also outlines the procurement strategies put in place to achieve the goal of the Programme: building the 'world's biggest eye on the sky' within the next decade.

  8. AN EVALUATION OF SELECTED MOROCCAN ELT TEXTBOOKS: A STANDARDS-BASED APPROACH PERSPECTIVE

    Directory of Open Access Journals (Sweden)

    Hassan Ait Bouzid

    2017-05-01

    Full Text Available Standards-Based Approach to textbook evaluation has been blooming in recent decades. Nevertheless, this practice has received very little attention in Morocco.  The present study aims to bridge a gap in the literature of the Moroccan context by investigating the extent to which three locally designed ELT textbooks conform to the theoretical principles of the Standards-Based Approach which defines the teaching of English as a foreign language in the country (Ministry of National Education, 2007. Its objective is to examine whether and how these textbooks present contents that enable learners to meet the content standards included in the goal areas of Communications, Cultures, Connections and Comparisons. The study is informed by the theoretical framework of the Standards-Based Approach. It adopts a mixed-methods design that uses content analysis as a mixed data analysis method combining both quantitative and qualitative techniques. The findings reveal a number of shortcomings relevant to the representation of the content standards as several standards are not sufficiently addressed in the activities included in these textbooks. Eventually, some suggestions are addressed to policy makers, textbook designers and teachers to overcome the identified problems in current and future textbooks. The study is expected to sensitize ELT practitioners about the viability of using textbook evaluation in boosting both the quality of ELT textbooks and the quality of the teaching learning outcomes.

  9. Leading Role of Educators in ELT for Young Learners

    Directory of Open Access Journals (Sweden)

    Jiali Du

    2014-10-01

    Full Text Available This paper discusses the leading role of Chinese educators in ELT for young learners. English is a global language. ELT for children becomes especially popular in China when English was officially considered compulsory at primary school in 2001. National identity is the presentation of cultural identity, and alien culture helps children understand native culture from the outside perspective. Culture sensitive applications are required to be made in ELT by teachers. Educators’ excellent presentation and students’ well-established practice lead to full production, bringing the active intake from the passive input.

  10. E-Learning, Time and Unconscious Thinking

    Science.gov (United States)

    Mathew, David

    2014-01-01

    This article views the temporal dimensions of e-learning through a psychoanalytic lens, and asks the reader to consider links between online learning and psychoanalysis. It argues that time and its associated philosophical puzzles impinge on both psychoanalytic theory and on e-learning at two specific points. The first is in the distinction…

  11. Communication, timing, and common learning

    Czech Academy of Sciences Publication Activity Database

    Steiner, Jakub; Stewart, C.

    2011-01-01

    Roč. 146, č. 1 (2011), s. 230-247 ISSN 0022-0531 Institutional research plan: CEZ:AV0Z70850503 Keywords : common knowledge * learning * communication Subject RIV: AH - Economics Impact factor: 1.235, year: 2011

  12. Exploring the extent to which ELT students utilise smartphones for ...

    African Journals Online (AJOL)

    Zehra

    2015-11-09

    Nov 9, 2015 ... aimed to explore the extent to which English Language Teaching (ELT) students utilise ... Given the fact that almost all students have a personal smartphone, and use it ..... ears as a disadvantage for smartphones (Kétyi,.

  13. Leading Learning in Our Times

    Science.gov (United States)

    Trilling, Bernie

    2010-01-01

    Important tools that schools need to support a 21st century approach to teaching and learning include the usual suspects: the Internet, pen and paper, cell phones, educational games, tests and quizzes, good teachers, caring communities, educational funding, and loving parents. All of these items and more contribute to a 21st century education, but…

  14. Pragmatic Content in Global and Local ELT Textbooks

    Directory of Open Access Journals (Sweden)

    Hussein Meihami

    2015-11-01

    Full Text Available This study is an attempt to explore the frequency of pragmatic content occurrence represented as three speech acts of requesting, refusing, and apologizing in global and local English Language Teaching (ELT textbooks. Three global elementary ELT textbooks, namely Interchange, Top Notch, and American English File along with the local elementary textbooks of Iran Language Institute (ILI Series, were examined for their pragmatic content. To analyze the pragmatic content of these textbooks, the researchers used three different frameworks. The results indicated that while both global and local ELT textbooks shared a sufficient number of speech acts of request and refusal, they failed to pay enough attention to the speech act of apology regarding its frequency and the strategies through which it is performed. To sum, the findings of this study highlight the differences in the frequency of different speech acts and the strategies used to perform them in global and local elementary ELT textbooks, which bear some implications for ELT textbook developers and language instructors.

  15. Learned Interval Time Facilitates Associate Memory Retrieval

    Science.gov (United States)

    van de Ven, Vincent; Kochs, Sarah; Smulders, Fren; De Weerd, Peter

    2017-01-01

    The extent to which time is represented in memory remains underinvestigated. We designed a time paired associate task (TPAT) in which participants implicitly learned cue-time-target associations between cue-target pairs and specific cue-target intervals. During subsequent memory testing, participants showed increased accuracy of identifying…

  16. Learning about Learning: Action Learning in Times of Organisational Change

    Science.gov (United States)

    Hill, Robyn

    2009-01-01

    This paper explores the conduct and outcomes of an action learning activity during a period of intense organisational change in a medium-sized vocational education and training organisation in Victoria, Australia. This organisation was the subject of significant change due to government-driven and statewide amalgamation, downsizing and sector…

  17. Hard Times for HRD, Lean Times for Learning?: Workplace Participatory Practices as Enablers of Learning

    Science.gov (United States)

    Warhurst, Russell

    2013-01-01

    Purpose: This article aims to show how in times of austerity when formal HRD activity is curtailed and yet the need for learning is greatest, non-formal learning methods such as workplace involvement and participation initiated by line managers can compensate by enabling the required learning and change. Design/methodology/approach: A qualitative…

  18. Exploring ELT Students’ Perception of Mobile Phone through Figurative Language

    OpenAIRE

    ŞENEL, Müfit

    2016-01-01

    The aim of this study is to investigate, analyze and evaluate the mobile phone perceptions of ELT students by the help of figurative language. The participants of this study are Grade 3 and Grade 4 students of ELT Department at Samsun 19 Mayıs University, Faculty of Education, involving 139 students in total. In this research students were asked to use “simile” as a figurative language and complete the sentence “Mobile phone is as ……as…….”  in the target language.  According to content analys...

  19. Introducing Diversity of English into ELT: Student Teachers' Responses

    Science.gov (United States)

    Suzuki, Ayako

    2011-01-01

    The growing use of English by its L2 speakers for their international communication has started to suggest a need for many changes in ELT, particularly in traditional EFL countries. One of the changes discussed among some sociolinguists is the introduction of different varieties of English from the two traditionally highly regarded varieties, i.e.…

  20. Simulating a Direction-Finder Search for an ELT

    Science.gov (United States)

    Bream, Bruce

    2005-01-01

    A computer program simulates the operation of direction-finding equipment engaged in a search for an emergency locator transmitter (ELT) aboard an aircraft that has crashed. The simulated equipment is patterned after the equipment used by the Civil Air Patrol to search for missing aircraft. The program is designed to be used for training in radio direction-finding and/or searching for missing aircraft without incurring the expense and risk of using real aircraft and ground search resources. The program places a hidden ELT on a map and enables the user to search for the location of the ELT by moving a 14 NASA Tech Briefs, March 2005 small aircraft image around the map while observing signal-strength and direction readings on a simulated direction- finding locator instrument. As the simulated aircraft is turned and moved on the map, the program updates the readings on the direction-finding instrument to reflect the current position and heading of the aircraft relative to the location of the ELT. The software is distributed in a zip file that contains an installation program. The software runs on the Microsoft Windows 9x, NT, and XP operating systems.

  1. A Thorough Scrutiny of ELT Textbook Evaluations: A Review Inquiry

    Science.gov (United States)

    Gholami, Reza; Noordin, Nooreen; Rafik-Galea, Shameem

    2017-01-01

    It is thoroughly agreed that English language textbooks stand amongst the foremost components in any language classrooms worldwide, being referred to as valid, beneficial and labor-saving tools to fulfill an extensive range of needs. An ELT textbook is not merely a set of sheets of paper fastened together to hinge at one side, but is the beating…

  2. Students' Pressure, Time Management and Effective Learning

    Science.gov (United States)

    Sun, Hechuan; Yang, Xiaolin

    2009-01-01

    Purpose: This paper aims to survey the status quo of the student pressure and the relationship between their daily time management and their learning outcomes in three different types of higher secondary schools at Shenyang, the capital city of Liaoning Province in mainland China. Design/methodology/approach: An investigation was carried out in 14…

  3. Modeling Time Series Data for Supervised Learning

    Science.gov (United States)

    Baydogan, Mustafa Gokce

    2012-01-01

    Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

  4. Linear time relational prototype based learning.

    Science.gov (United States)

    Gisbrecht, Andrej; Mokbel, Bassam; Schleif, Frank-Michael; Zhu, Xibin; Hammer, Barbara

    2012-10-01

    Prototype based learning offers an intuitive interface to inspect large quantities of electronic data in supervised or unsupervised settings. Recently, many techniques have been extended to data described by general dissimilarities rather than Euclidean vectors, so-called relational data settings. Unlike the Euclidean counterparts, the techniques have quadratic time complexity due to the underlying quadratic dissimilarity matrix. Thus, they are infeasible already for medium sized data sets. The contribution of this article is twofold: On the one hand we propose a novel supervised prototype based classification technique for dissimilarity data based on popular learning vector quantization (LVQ), on the other hand we transfer a linear time approximation technique, the Nyström approximation, to this algorithm and an unsupervised counterpart, the relational generative topographic mapping (GTM). This way, linear time and space methods result. We evaluate the techniques on three examples from the biomedical domain.

  5. Overcoming Learning Time And Space Constraints Through Technological Tool

    Directory of Open Access Journals (Sweden)

    Nafiseh Zarei

    2015-08-01

    Full Text Available Today the use of technological tools has become an evolution in language learning and language acquisition. Many instructors and lecturers believe that integrating Web-based learning tools into language courses allows pupils to become active learners during learning process. This study investigate how the Learning Management Blog (LMB overcomes the learning time and space constraints that contribute to students’ language learning and language acquisition processes. The participants were 30 ESL students at National University of Malaysia. A qualitative approach comprising an open-ended questionnaire and a semi-structured interview was used to collect data. The results of the study revealed that the students’ language learning and acquisition processes were enhanced. The students did not face any learning time and space limitations while being engaged in the learning process via the LMB. They learned and acquired knowledge using the language learning materials and forum at anytime and anywhere. Keywords: learning time, learning space, learning management blog

  6. An Empirical Investigation of Individual Differences in Time to Learn

    Science.gov (United States)

    Anderson, Lorin W.

    1976-01-01

    Results show that student differences in time-on-task to learn to criterion are alterable and can be minimized over a sequence of learning units given appropriate adaptive learning strategies. (Author/DEP)

  7. Bilingualism--A Sanguine Step in ELT

    Science.gov (United States)

    Anil, Beena

    2014-01-01

    Bilingualism can be used as a teaching aid in teaching and learning English language in an Indian classroom and to improve the language accuracy, fluency, and clarity of learners. Bilingualism can aid the teaching and learning process productively in the classroom. In India, most of the students consider English as a subject rather than a tool of…

  8. The ELT in 2017: The Year of the Primary Mirror

    Science.gov (United States)

    Cirasuolo, M.; Tamai, R.; Cayrel, M.; Koehler, B.; Biancat Marchet, F..; González, J. C.; Dimmler, M.; Tuti, M.; ELT Team

    2018-03-01

    The Extremely Large Telescope (ELT) is at the core of ESO's vision to deliver the largest optical and infrared telescope in the world. With its unrivalled sensitivity and angular resolution the ELT will transform our view of the Universe: from exoplanets to resolved stellar populations, from galaxy evolution to cosmology and fundamental physics. This article focuses on one of the most challenging aspects of the entire programme, the 39-metre primary mirror (M1). 2017 was a particularly intense year for M1, the main highlight being the approval by ESO's Council to proceed with construction of the entire mirror. In addition, several contracts have been placed to ensure that the giant primary mirror will be operational at first light.

  9. Turkish ELT students' willingness to communicate in English

    OpenAIRE

    Şener, Sabriye

    2014-01-01

    This paper aims to present the willingness to communicate (WTC) in English of the English Language Teaching Department (ELT) students of Çanakkale Onsekiz Mart University inside and outside the class. Additionally, the relationships among students’ willingness to communicate in English, their linguistic self-confidence, motivation, attitudes toward international community, and personality will be presented. To this end, quantitative data were gathered from 274 students studying at the departm...

  10. Posters, Self-Directed Learning, and L2 Vocabulary Acquisition

    Science.gov (United States)

    Cetin, Yakup; Flamand, Lee

    2013-01-01

    Posters, either as promotions by various ELT publishing houses or prepared by ELT teachers and students, are widely used on the walls of many foreign language classrooms. Many of them consist of colourful pictures along with L2 vocabulary, grammar, and texts in order to contribute to the foreign language learning process. However, many ELT…

  11. Time factor in e-learning and assessment

    OpenAIRE

    Romero Velasco, Margarida

    2010-01-01

    Peer-reviewed Peer reviewed Time is probably one of the most polysemous words in education. In e-learning, characterization of the time factor is particularly relevant because of the high level of flexibility in the teaching and learning times, and the resulting responsibility of the e-learners in regulating their learning times.

  12. Blended learning pedagogy: the time is now!

    Science.gov (United States)

    Pizzi, Michael A

    2014-07-01

    Pedagogy is rapidly changing. To develop best practice in academia, it is important that we change with the changing needs of students. This article suggests that blended learning is one of the most important pedagogical formats that can enhance student learning, optimize the use of active learning strategies, and potentially improve student learning outcomes.

  13. Learning to stay ahead of time

    DEFF Research Database (Denmark)

    Staunæs, Dorthe; Raffnsøe, Sverre

    2014-01-01

    In the context of an ongoing change, management is required to take the form of a leadership that must be reignited over and over again. The article examines a new art of leadership that may be viewed as an attempt to keep up with these challenges and stay ahead of time. It emerges from...... a pilgrimage leadership learning laboratory on the road to Santiago de la Compostela. This moving lab creates situations of extraordinary intensity that border on hyperreality and force the leader to find him/herself anew on the verge of him/herself. Conceived as pilgrimage, leadership moves ahead of time...... as it reaches into and anticipates a future still unknown. In this setting, anticipatory affects and the virtual take up a predominant role. As it emerges here, leadership distinguishes itself not only from leadership in the traditional sense, but also from management and governmentality....

  14. Precarious Learning and Labour in Financialized Times

    Directory of Open Access Journals (Sweden)

    Jamie Magnusson

    2013-07-01

    Full Text Available Our current globalized economic regimes of financialized capital have systematically altered relations of learning and labour through the dynamics of precarity, debt, and the political economy of new wars. The risks of these regimes are absorbed unevenly across transnational landscapes, creating cartographies of violence and dispossession, particularly among youth, indigenous, working class, and racialized women. Presently there is surprisingly little discussion on the relevance of financialization for adult educators. Transnational resistances organizing against neoliberal restructuring, austerity policies, and debt crises are emerging at the same time that massive investments are being made into homeland security and the carceral state. This paper opens up discussion on the implications of financialized times for educators, and develops an analytic framework for examining how these global realities are best addressed at local sites of adult and higher education.

  15. Creativity and innovations in ELT materials development looking beyond the current design

    CERN Document Server

    Bao, Dat

    2018-01-01

    This book challenges current practices in ELT materials design in order to transform coursebook quality. It proposes ways to improve task design through resources such as drama, poetry, literature and online resources; and it maps out a number of unusual connections between theory and practice in the field of ELT materials development.

  16. In-Time On-Place Learning

    Science.gov (United States)

    Bauters, Merja; Purma, Jukka; Leinonen, Teemu

    2014-01-01

    The aim of this short paper is to look at how mobile video recording devices could support learning related to physical practices or places and situations at work. This paper discusses particular kind of workplace learning, namely learning using short video clips that are related to physical environment and tasks preformed in situ. The paper…

  17. End-to-End Operations in the ELT Era

    Science.gov (United States)

    Hainaut, O. R.; Bierwirth, T.; Brillant, S.; Mieske, S.; Patat, F.; Rejkuba, M.; Romaniello, M.; Sterzik, M.

    2018-03-01

    The Data Flow System is the infrastructure on which Very Large Telescope (VLT) observations are performed at the Observatory, before and after the observations themselves take place. Since its original conception in the late 1990s, it has evolved to accommodate new observing modes and new instruments on La Silla and Paranal. Several updates and upgrades are needed to overcome its obsolescence and to integrate requirements from the new instruments from the community and, of course, from ESO's Extremely Large Telescope (ELT), which will be integrated into Paranal's operations. We describe the end-to-end operations and the resulting roadmap guiding their further development.

  18. ELT-MELAS analyzer and its on-line programs

    International Nuclear Information System (INIS)

    Anikeev, V.B.; Berezhnoj, V.A.; Glupova

    1976-01-01

    ELT-MELAS device constructed for an automatic analysis of pictures from big bubble chambers is described. It is controlled by a medium-size ICL-1903A computer and has two measuring modes: analysis of the ''agreement'' signal and digitation of slice-scans. Main features of the hardware and of on-line controlling and diagnostic software are presented. The test results of the MELAS complex as well as preliminary results of the scan-slice measurements of pictures from 15sup(') chamber are given

  19. E-ELT M5 field stabilisation unit scale 1 demonstrator design and performances evaluation

    Science.gov (United States)

    Casalta, J. M.; Barriga, J.; Ariño, J.; Mercader, J.; San Andrés, M.; Serra, J.; Kjelberg, I.; Hubin, N.; Jochum, L.; Vernet, E.; Dimmler, M.; Müller, M.

    2010-07-01

    The M5 Field stabilization Unit (M5FU) for European Extremely Large Telescope (E-ELT) is a fast correcting optical system that shall provide tip-tilt corrections for the telescope dynamic pointing errors and the effect of atmospheric tiptilt and wind disturbances. A M5FU scale 1 demonstrator (M5FU1D) is being built to assess the feasibility of the key elements (actuators, sensors, mirror, mirror interfaces) and the real-time control algorithm. The strict constraints (e.g. tip-tilt control frequency range 100Hz, 3m ellipse mirror size, mirror first Eigen frequency 300Hz, maximum tip/tilt range +/- 30 arcsec, maximum tiptilt error < 40 marcsec) have been a big challenge for developing the M5FU Conceptual Design and its scale 1 demonstrator. The paper summarises the proposed design for the final unit and demonstrator and the measured performances compared to the applicable specifications.

  20. An algorithm for learning real-time automata

    NARCIS (Netherlands)

    Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.

    2007-01-01

    We describe an algorithm for learning simple timed automata, known as real-time automata. The transitions of real-time automata can have a temporal constraint on the time of occurrence of the current symbol relative to the previous symbol. The learning algorithm is similar to the redblue fringe

  1. EPISTEMOLOGICAL BELIEFS AND METACOGNITIVE STRATEGIES OF ELT PRE-SERVICE TEACHERS IN DISTANCE AND FORMAL EDUCATION

    Directory of Open Access Journals (Sweden)

    Meral GUVEN

    2012-04-01

    Full Text Available The epistemological beliefs in learning process have been investigated from different aspects in relation with many variables in literature. Such beliefs are defined as individuals’ beliefs regarding knowledge and learning. As another related, popular concept, the metacognitive strategies are identified as the strategies used to control the process of obtaining knowledge. Thus, it is seen that both of them are employed to make learning more effective. Within this framework, the aim of the present study was to determine the epistemological beliefs and metacognitive strategies of the pre-service teachers in the distance and formal education English Language Teaching program and to investigate whether there was any difference/ were any differences between them. To collect data, “Epistemological Belief Scale” developed by Schommer (1990 and translated and validated by Deryakulu and Büyüköztürk (2002 and “Metacognitive Strategy Inventory” which was adapted for university students by Yıldız, Akpınar and Ergin (2006 were used. Then through the descriptive method they were analyzed. As a result of study, it was determined that there was a significant relationship between the epistemological beliefs and metacognitive strategy use of ELT pre-service teachers in both formal and distance education programs.

  2. Quality of E-Learners’ Time and Learning Performance Beyond Quantitative Time-on-Task

    Directory of Open Access Journals (Sweden)

    Margarida Romero

    2011-06-01

    Full Text Available AbstractAlong with the amount of time spent learning (or time-on-task, the quality of learning time has a real influence on learning performance. Quality of time in online learning depends on students’ time availability and their willingness to devote quality cognitive time to learning activities. However, the quantity and quality of the time spent by adult e-learners on learning activities can be reduced by professional, family, and social commitments. Considering that the main time pattern followed by most adult e-learners is a professional one, it may be beneficial for online education programs to offer a certain degree of flexibility in instructional time that might allow adult learners to adjust their learning times to their professional constraints. However, using the time left over once professional and family requirements have been fulfilled could lead to a reduction in quality time for learning. This paper starts by introducing the concept of quality of learning time from an online student-centred perspective. The impact of students’ time-related variables (working hours, time-on-task engagement, time flexibility, time of day, day of week is then analyzed according to individual and collaborative grades achieved during an online master’s degree program. The data show that both students’ time flexibility (r = .98 and especially their availability to learn in the morning are related to better grades in individual (r = .93 and collaborative activities (r = .46.

  3. E-ELT constraints on runaway dilaton scenarios

    Energy Technology Data Exchange (ETDEWEB)

    Martinelli, M. [Institut für Theoretische Physik, Ruprecht-Karls-Universität Heidelberg, Philosophenweg 16, 69120, Heidelberg (Germany); Calabrese, E. [Sub-department of Astrophysics, University of Oxford, Keble Road, Oxford OX1 3RH (United Kingdom); Martins, C.J.A.P., E-mail: m.martinelli@thphys.uni-heidelberg.de, E-mail: erminia.calabrese@physics.ox.ac.uk, E-mail: carlos.martins@astro.up.pt [Centro de Astrofìsica, Universidade do Porto, Rua das Estrelas, 4150-762 Porto (Portugal)

    2015-11-01

    We use a combination of simulated cosmological probes and astrophysical tests of the stability of the fine-structure constant α, as expected from the forthcoming European Extremely Large Telescope (E-ELT), to constrain the class of string-inspired runaway dilaton models of Damour, Piazza and Veneziano. We consider three different scenarios for the dark sector couplings in the model and discuss the observational differences between them. We improve previously existing analyses investigating in detail the degeneracies between the parameters ruling the coupling of the dilaton field to the other components of the universe, and studying how the constraints on these parameters change for different fiducial cosmologies. We find that if the couplings are small (e.g., α{sub b} = α{sub V} ∼ 0) these degeneracies strongly affect the constraining power of future data, while if they are sufficiently large (e.g., α{sub b} ∼> 10{sup −5}−α{sub V} ∼> 0.05, as in agreement with current constraints) the degeneracies can be partially broken. We show that E-ELT will be able to probe some of this additional parameter space.

  4. Working and Learning in Times of Uncertainty

    DEFF Research Database (Denmark)

    This book analyses the challenges of globalisation and uncertainty impacting on working and learning at individual, organisational and societal levels. Each of the contributions addresses two overall questions: How is working and learning affected by uncertainty and globalisation? And, in what ways...... do individuals, organisations, political actors and education systems respond to these challenges? Part 1 focuses on the micro level of working and learning for understanding the learning processes from an individual point of view by reflecting on learners’ needs and situations at work and in school......). Finally, Part 3 addresses the macro level of working and learning by analysing how to govern, structure and organise vocational, professional and adult education at the boundaries of work, education and policy making....

  5. Learning Styles of Medical Students Change in Relation to Time

    Science.gov (United States)

    Gurpinar, Erol; Bati, Hilal; Tetik, Cihat

    2011-01-01

    The aim of the present study was to investigate if any changes exist in the learning styles of medical students over time and in relation to different curriculum models with these learning styles. This prospective cohort study was conducted in three different medical faculties, which implement problem-based learning (PBL), hybrid, and integrated…

  6. Real-time Color Codes for Assessing Learning Process

    OpenAIRE

    Dzelzkalēja, L; Kapenieks, J

    2016-01-01

    Effective assessment is an important way for improving the learning process. There are existing guidelines for assessing the learning process, but they lack holistic digital knowledge society considerations. In this paper the authors propose a method for real-time evaluation of students’ learning process and, consequently, for quality evaluation of teaching materials both in the classroom and in the distance learning environment. The main idea of the proposed Color code method (CCM) is to use...

  7. Time will tell: The role of mobile learning analytics in self-regulated learning

    NARCIS (Netherlands)

    Tabuenca, Bernardo; Kalz, Marco; Drachsler, Hendrik; Specht, Marcus

    2015-01-01

    This longitudinal study explores the effects of tracking and monitoring time devoted to learn with a mobile tool, on self-regulated learning. Graduate students (n = 36) from three different online courses used their own mobile devices to track how much time they devoted to learn over a period of

  8. Extrasolar Planets Observed with JWST and the ELTs

    Science.gov (United States)

    Deming, L. Drake

    2010-01-01

    The advent of cryogenic space-borne infrared observatories such as the Spitzer Space Telescope has lead to a revolution in the study of planets and planetary systems orbiting sun-like stars. Already Spitzer has characterized the emergent infrared spectra of close-in giant exoplanets using transit and eclipse techniques. The James Webb Space Telescope (JWST) will be able to extend these studies to superEarth exoplanets orbiting in the habitable zones of M-dwarf stars in the near solar neighborhood. The forthcoming ground-based Extremely Large Telescopes (ELTs) will playa key role in these studies, being especially valuable for spectroscopy at higher spectral resolving powers where large photon fluxes are needed. The culmination of this work within the next two decades will be the detection and spectral characterization of the major molecular constituents in the atmosphere of a habitable superEarth orbiting a nearby lower main sequence star.

  9. Precarious Learning and Labour in Financialized Times

    Science.gov (United States)

    Magnusson, Jamie

    2013-01-01

    Our current globalized economic regimes of financialized capital have systematically altered relations of learning and labour through the dynamics of precarity, debt, and the political economy of new wars. The risks of these regimes are absorbed unevenly across transnational landscapes, creating cartographies of violence and dispossession,…

  10. Learning to trust : network effects through time.

    NARCIS (Netherlands)

    Barrera, D.; Bunt, G. van de

    2009-01-01

    This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

  11. Learning to trust: network effects through time

    NARCIS (Netherlands)

    Barrera, D.; van de Bunt, G

    2009-01-01

    This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

  12. A profile on the methodology courses at the ELT departments of the education faculties in Turkey

    OpenAIRE

    Dalkılıç, Nilüfer

    1996-01-01

    Ankara : Institute of Economics and Social Sciences, Bilkent Univ., 1996. Thesis (Master's) -- Bilkent University, 1996. Includes bibliographical references leaves 78-80 In this study, the methodology courses at ELT departments in Turkey were examined in terms of design, content and delivery. In order to collect data, sample ELT Departments of the Education Faculties in Turkey were chosen from different parts of Turkey. Data were collected through questionnaires administered to two ...

  13. Reflections on practicum experiences of non-ELT student teachers in Turkey

    OpenAIRE

    Mirici, İsmail Hakkı; Ölmez-Çağlar, Funda

    2018-01-01

    In English language teacher education (hereafter ELT)programs of Turkish universities, teaching practicum has a critical value dueto its pivotal role in equipping student teachers with the necessarycompetences and preparing them for the teaching profession. Practicumexperience turns out to be of greater importance for the graduates of otherEnglish language departments (hereafter non-ELT) such as English Linguistics,English Language and Literature or American Culture and Literature who attendp...

  14. Moving and Learning: Expanding Style and Increasing Flexibility

    Science.gov (United States)

    Peterson, Kay; DeCato, Lisa; Kolb, David A.

    2015-01-01

    This article introduces ways in which movement can enhance one's understanding of how to learn using Experiential Learning Theory (ELT) concepts of the Learning Cycle, Learning Styles, and Learning Flexibility. The theoretical correspondence between the dialectic dimensions of the Learning Cycle and the dimensions of the Laban Movement Analysis…

  15. Applied ELT: A Paradigm Justifying Complex Adaptive System of Language Teaching?

    Directory of Open Access Journals (Sweden)

    Masoud Mahmoodzadeh

    2013-12-01

    Full Text Available In an endeavor to reflect on the advent of Applied ELT paradigm pioneered by Pishghadam (2011 in the area of second language education, this article delves into the unexplored nature of this emerging paradigm via a contemporary complexity-driven voice. The crux of the argument addressed in this article suggests that Applied ELT is a pragmatic manifestation of complex adaptive system of language teaching. To set the grounds expressly for such enquiry, firstly it draws on both premises and axioms associated with complexity theory and its existing literature in the circle of second language research. It then tracks down the evolutionary course of the new developed paradigm of Applied ELT within the realm of second language education and also elaborates the cornerstone and manifold tenets of this paradigm sufficiently. Finally, the article attempts to critically elucidate and rationalize the recent emergence of Applied ELT paradigm through the lens of complexity theory. To broaden our thinking and understanding about the potential and multi-directional influence of ELT field, the article ends by calling for a reshaped educational direction for ELT position in second language education.

  16. “That’s the biggest impact!” Pedagogical values of movies in ELT classrooms

    Directory of Open Access Journals (Sweden)

    Nyak Mutia Ismail

    2017-09-01

    Full Text Available Since many say watching movies can have a positive effect on language learning outcomes, this research was done to find out which skills movies can contribute most to, whether speaking, listening, reading, writing, vocabulary, grammar or cultural aspects. A qualitative research method was used for this study. This study was done to find out the teachers’ perceptions of using English movies in English Language Teaching (ELT processes. Teachers from three different levels: primary, secondary, and senior high-school teachers were asked to answer a questionnaire set in accordance with the research topic. Five of them were senior high school teachers, two of them were junior high school teachers, and three of them were elementary school teachers. Creative interviews were also used as an additional data source. The results showed that most of the teachers agreed that movies play their biggest role and considerable advantages in developing cultural aspects and listening skills. Furthermore, some integration is possible between listening and speaking as well as with reading and writing because watching movies works better with integration of skills. Apparently, even though vocabulary can develop with this technique, grammar is not enhanced alone without being accompanied by writing.

  17. Evaluation of Listening Skill of ELT Textbook at Secondary School Level

    Directory of Open Access Journals (Sweden)

    Mumtaz Ahmed

    2015-06-01

    Full Text Available Textbook evaluation means development of textbook that is based on rigorous research. In Pakistan text books are designed on communicative language teaching which focuses on communication. Morley (1991 has asserted that listening has a critical role in communication and in language acquisition because the better the students understand, the better they will be able to speak. In our text books, listening practices (text and activities are missing, and listening plays a secondary role as compared to speaking, as it is part of oral work that are dialogues and role play, neglecting that during conversation in English our students face hurdles in quick thinking and accurate predicting because of ignoring listening skill which help in learning sound, rhythm, intonation, pronunciation, vocabulary and grammatical details. The researchers’ intention here is to put different views on importance of listening skill and to evaluate English Text Books prescribed in Punjab government school whether they contain listening material, corresponding activities and related audio video material in text books. Keywords: Evaluation, listening skill, ELT, textbook, Punjab Textbook Board (PTB

  18. Active learning reduces annotation time for clinical concept extraction.

    Science.gov (United States)

    Kholghi, Mahnoosh; Sitbon, Laurianne; Zuccon, Guido; Nguyen, Anthony

    2017-10-01

    To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation. There are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation. First, all sequences within the 73 reports were manually annotated from scratch. Next, active learning models were built to generate pre-annotations for the sequences selected by a query strategy. The annotation/reviewing time per sequence was recorded. The 120 test reports were used to measure the effectiveness of the active learning models. When annotating from scratch, active learning reduced the annotation time up to 35% and 28% compared to a fully supervised approach and a random sampling baseline, respectively. Reviewing active learning-assisted pre-annotations resulted in 20% further reduction of the annotation time when compared to de novo annotation. The number of concepts that require manual annotation is a good indicator of the annotation time for various active learning approaches as demonstrated by high correlation between time rate and concept annotation rate. Active learning has a key role in reducing the time required to manually annotate domain concepts from clinical free text, either when annotating from scratch or reviewing active learning-assisted pre-annotations. Copyright © 2017 Elsevier B.V. All rights reserved.

  19. Recovering the colour-dependent albedo of exoplanets with high-resolution spectroscopy: from ESPRESSO to the ELT.

    Science.gov (United States)

    Martins, J. H. C.; Figueira, P.; Santos, N. C.; Melo, C.; Garcia Muñoz, A.; Faria, J.; Pepe, F.; Lovis, C.

    2018-05-01

    The characterization of planetary atmospheres is a daunting task, pushing current observing facilities to their limits. The next generation of high-resolution spectrographs mounted on large telescopes - such as ESPRESSO@VLT and HIRES@ELT - will allow us to probe and characterize exoplanetary atmospheres in greater detail than possible to this point. We present a method that permits the recovery of the colour-dependent reflectivity of exoplanets from high-resolution spectroscopic observations. Determining the wavelength-dependent albedo will provide insight into the chemical properties and weather of the exoplanet atmospheres. For this work, we simulated ESPRESSO@VLT and HIRES@ELT high-resolution observations of known planetary systems with several albedo configurations. We demonstrate how the cross correlation technique applied to theses simulated observations can be used to successfully recover the geometric albedo of exoplanets over a range of wavelengths. In all cases, we were able to recover the wavelength dependent albedo of the simulated exoplanets and distinguish between several atmospheric models representing different atmospheric configurations. In brief, we demonstrate that the cross correlation technique allows for the recovery of exoplanetary albedo functions from optical observations with the next generation of high-resolution spectrographs that will be mounted on large telescopes with reasonable exposure times. Its recovery will permit the characterization of exoplanetary atmospheres in terms of composition and dynamics and consolidates the cross correlation technique as a powerful tool for exoplanet characterization.

  20. Time to rethink the neural mechanisms of learning and memory.

    Science.gov (United States)

    Gallistel, Charles R; Balsam, Peter D

    2014-02-01

    Most studies in the neurobiology of learning assume that the underlying learning process is a pairing - dependent change in synaptic strength that requires repeated experience of events presented in close temporal contiguity. However, much learning is rapid and does not depend on temporal contiguity, which has never been precisely defined. These points are well illustrated by studies showing that the temporal relations between events are rapidly learned- even over long delays- and that this knowledge governs the form and timing of behavior. The speed with which anticipatory responses emerge in conditioning paradigms is determined by the information that cues provide about the timing of rewards. The challenge for understanding the neurobiology of learning is to understand the mechanisms in the nervous system that encode information from even a single experience, the nature of the memory mechanisms that can encode quantities such as time, and how the brain can flexibly perform computations based on this information. Copyright © 2013 Elsevier Inc. All rights reserved.

  1. Professional Learning in Part-time University Study

    DEFF Research Database (Denmark)

    Rasmussen, Palle

    2007-01-01

    The theme of this article is adult students' learning in part-time studies at university level in Denmark. One issue discussed is the interplay of research and teaching in this kind of study programme. Examples are presented from the Master of Learning Processes study programme at Aalborg...

  2. Radiologists' preferences for just-in-time learning.

    Science.gov (United States)

    Kahn, Charles E; Ehlers, Kevin C; Wood, Beverly P

    2006-09-01

    Effective learning can occur at the point of care, when opportunities arise to acquire information and apply it to a clinical problem. To assess interest in point-of-care learning, we conducted a survey to explore radiologists' attitudes and preferences regarding the use of just-in-time learning (JITL) in radiology. Following Institutional Review Board approval, we invited 104 current radiology residents and 86 radiologists in practice to participate in a 12-item Internet-based survey to assess their attitudes toward just-in-time learning. Voluntary participation in the survey was solicited by e-mail; respondents completed the survey on a web-based form. Seventy-nine physicians completed the questionnaire, including 47 radiology residents and 32 radiologists in practice; the overall response rate was 42%. Respondents generally expressed a strong interest for JITL: 96% indicated a willingness to try such a system, and 38% indicated that they definitely would use a JITL system. They expressed a preference for learning interventions of 5-10 min in length. Current and recent radiology trainees have expressed a strong interest in just-in-time learning. The information from this survey should be useful in pursuing the design of learning interventions and systems for delivering just-in-time learning to radiologists.

  3. Increasing instruction time in school does increase learning

    DEFF Research Database (Denmark)

    Andersen, Simon Calmar; Humlum, Maria; Nandrup, Anne Brink

    2016-01-01

    Increasing instruction time in school is a central element in the attempts of many governments to improve student learning, but prior research—mainly based on observational data—disputes the effect of this approach and points out the potential negative effects on student behavior. Based on a large......-scale, cluster-randomized trial, we find that increasing instruction time increases student learning and that a general increase in instruction time is at least as efficient as an expert-developed, detailed teaching program that increases instruction with the same amount of time. These findings support the value...... of increased instruction time....

  4. Online Quiz Time Limits and Learning Outcomes in Economics

    Science.gov (United States)

    Evans, Brent; Culp, Robert

    2015-01-01

    In an effort to better understand the impact of timing limits, the authors compare the learning outcomes of students who completed timed quizzes with students who took untimed quizzes in economics principles courses. Students were assigned two online quizzes--one timed and one untimed--and re-tested on the material the following class day. Our…

  5. Time for Learning: An Exploratory Analysis of NAEP Data

    Science.gov (United States)

    Ginsburg, Alan; Chudowsky, Naomi

    2012-01-01

    This report uses NAEP background data to track time and learning since the mid-1990s in three areas: student absenteeism; classroom instructional time in mathematics, reading, music and the visual arts; and homework time expected by teachers. Key report findings are: (1) Students with higher rates of "monthly absenteeism" score…

  6. A Model for Learning Over Time: The Big Picture

    Science.gov (United States)

    Amato, Herbert K.; Konin, Jeff G.; Brader, Holly

    2002-01-01

    Objective: To present a method of describing the concept of “learning over time” with respect to its implementation into an athletic training education program curriculum. Background: The formal process of learning over time has recently been introduced as a required way for athletic training educational competencies and clinical proficiencies to be delivered and mastered. Learning over time incorporates the documented cognitive, psychomotor, and affective skills associated with the acquisition, progression, and reflection of information. This method of academic preparation represents a move away from a quantitative-based learning module toward a proficiency-based mastery of learning. Little research or documentation can be found demonstrating either the specificity of this concept or suggestions for its application. Description: We present a model for learning over time that encompasses multiple indicators for assessment in a successive format. Based on a continuum approach, cognitive, psychomotor, and affective characteristics are assessed at different levels in classroom and clinical environments. Clinical proficiencies are a common set of entry-level skills that need to be integrated into the athletic training educational domains. Objective documentation is presented, including the skill breakdown of a task and a matrix to identify a timeline of competency and proficiency delivery. Clinical Advantages: The advantages of learning over time pertain to the integration of cognitive knowledge into clinical skill acquisition. Given the fact that learning over time has been implemented as a required concept for athletic training education programs, this model may serve to assist those program faculty who have not yet developed, or are in the process of developing, a method of administering this approach to learning. PMID:12937551

  7. Timing of quizzes during learning: Effects on motivation and retention.

    Science.gov (United States)

    Healy, Alice F; Jones, Matt; Lalchandani, Lakshmi A; Tack, Lindsay Anderson

    2017-06-01

    This article investigates how the timing of quizzes given during learning impacts retention of studied material. We investigated the hypothesis that interspersing quizzes among study blocks increases student engagement, thus improving learning. Participants learned 8 artificial facts about each of 8 plant categories, with the categories blocked during learning. Quizzes about 4 of the 8 facts from each category occurred either immediately after studying the facts for that category (standard) or after studying the facts from all 8 categories (postponed). In Experiment 1, participants were given tests shortly after learning and several days later, including both the initially quizzed and unquizzed facts. Test performance was better in the standard than in the postponed condition, especially for categories learned later in the sequence. This result held even for the facts not quizzed during learning, suggesting that the advantage cannot be due to any direct testing effects. Instead the results support the hypothesis that interrupting learning with quiz questions is beneficial because it can enhance learner engagement. Experiment 2 provided further support for this hypothesis, based on participants' retrospective ratings of their task engagement during the learning phase. These findings have practical implications for when to introduce quizzes in the classroom. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  8. Human learning: Power laws or multiple characteristic time scales?

    Directory of Open Access Journals (Sweden)

    Gottfried Mayer-Kress

    2006-09-01

    Full Text Available The central proposal of A. Newell and Rosenbloom (1981 was that the power law is the ubiquitous law of learning. This proposition is discussed in the context of the key factors that led to the acceptance of the power law as the function of learning. We then outline the principles of an epigenetic landscape framework for considering the role of the characteristic time scales of learning and an approach to system identification of the processes of performance dynamics. In this view, the change of performance over time is the product of a superposition of characteristic exponential time scales that reflect the influence of different processes. This theoretical approach can reproduce the traditional power law of practice – within the experimental resolution of performance data sets - but we hypothesize that this function may prove to be a special and perhaps idealized case of learning.

  9. What is the Place of English Literature in ELT Classrooms? A Review of Related Studies

    Directory of Open Access Journals (Sweden)

    Tarek A. Alkhaleefah

    2017-10-01

    Full Text Available The debate over the place and role of literature in language classrooms has long intrigued researchers and teachers’ interests over the years. Although there is an overall consensus that the teaching of literature in English language teaching (ELT classrooms can help foster L2 learners’ language skills and cognitive abilities, some researchers have suggested that integrating literature in ELT classrooms should be approached with caution due to EFL learners’ limited language proficiency. In this paper, the researcher reviews previous related studies on the place of literature in the English language teaching (ELT contexts. The aim of this review is to shed light on this researchers/teachers’ ongoing debate over the place of teaching English literature in ELT. In particular, the review examines how researchers perceive the role of literature and its authenticity in ELT classrooms as stimulating learners’ interests and personal engagement with literary texts, fostering L2 learners’ language skills (particularly their reading and creative writing skills, and enhancing their critical thinking skills and strategic processing of texts. Furthermore, the review covers issues related to how the integration of literature in language classrooms should be carefully task-designed and assessed.

  10. Time representation in reinforcement learning models of the basal ganglia

    Directory of Open Access Journals (Sweden)

    Samuel Joseph Gershman

    2014-01-01

    Full Text Available Reinforcement learning models have been influential in understanding many aspects of basal ganglia function, from reward prediction to action selection. Time plays an important role in these models, but there is still no theoretical consensus about what kind of time representation is used by the basal ganglia. We review several theoretical accounts and their supporting evidence. We then discuss the relationship between reinforcement learning models and the timing mechanisms that have been attributed to the basal ganglia. We hypothesize that a single computational system may underlie both reinforcement learning and interval timing—the perception of duration in the range of seconds to hours. This hypothesis, which extends earlier models by incorporating a time-sensitive action selection mechanism, may have important implications for understanding disorders like Parkinson's disease in which both decision making and timing are impaired.

  11. Hidden Curriculum: An Analysis of Cultural Content of the ELT Textbooks in Inner, Outer, and Expanding Circle Countries

    Science.gov (United States)

    Rashidi, Naser; Meihami, Hussein

    2016-01-01

    Despite the great body of work examining the cultural content of the international and local ELT textbooks, the cultural content and elements of the ELT textbooks in the inner, outer, and expanding circle countries have seldom been reported. That said, the purpose of this study was twofold: first, it was aimed to investigate the cultural content…

  12. 77 FR 41473 - Proposed Technical Standard Order (TSO)-C126b, 406 MHz Emergency Locator Transmitters (ELT) and...

    Science.gov (United States)

    2012-07-13

    ... to ensure proper retention of ELTs during airplane accidents.'' (2) NTSB Factual Report--Aviation... airplanes. This diversity in mounting techniques include improper and/or inadequate mounting of many ELT's... maintenance instructions for the article are revised to include the following information by June 30, 2013: a...

  13. E-learning for Part-Time Medical Studies

    Directory of Open Access Journals (Sweden)

    Półjanowicz Wiesław

    2016-12-01

    Full Text Available Distance education undoubtedly has many advantages, such as individualization of the learning process, unified transmission of teaching materials, the opportunity to study at any place and any time, reduction of financial costs for commuting to classes or accommodation of participants, etc. Adequate working conditions on the e-learning portal must also be present, eg. well-prepared, substantive courses and good communication between the participants. Therefore, an important element in the process of conducting e-learning courses is to measure the increase of knowledge and satisfaction of participants with distance learning. It allows for fine-tuning the content of the course and for classes to be properly organized. This paper presents the results of teaching and assessment of satisfaction with e-learning courses in “Problems of multiculturalism in medicine”, “Selected issues of visual rehabilitation” and “Ophthalmology and Ophthalmic Nursing”, which were carried out experimentally at the Faculty of Health Sciences at the Medical University of Bialystok for nursing students for the 2010/2011 academic year. The study group consisted of 72 part-time students who learnt in e-learning mode and the control group of 87 students who learnt in the traditional way. The students’ opinions about the teaching process and final exam scores were analyzed based on a specially prepared survey questionnaire. Organization of e-learning classes was rated positively by 90% of students. The average result on the final exams for all distance learning subjects was at the level of 82%, while for classes taught in the traditional form it was 81%. Based on these results, we conclude that distance learning is as effective as learning according to the traditional form in medical education studies.

  14. A novel time series link prediction method: Learning automata approach

    Science.gov (United States)

    Moradabadi, Behnaz; Meybodi, Mohammad Reza

    2017-09-01

    Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.

  15. Learning and Teaching Problems in Part-Time Higher Education.

    Science.gov (United States)

    Trotman-Dickenson, D. I.

    1988-01-01

    Results of a British survey of the administrations of six universities and six public colleges, employers, and employees who were part-time students are reported and discussed. The survey assessed the perceptions of those groups concerning problems in the instruction and learning of part-time students. (MSE)

  16. The Effects of Embedding Information Technologies within ELT on EFL Learners’ Motivation and Interest

    Directory of Open Access Journals (Sweden)

    Shaker Al-Mohammadi

    2014-01-01

    Full Text Available In today’s globalised world, technologies have been embedded in every aspect of daily activities and discourses. The field of education made no exception and hence technologies have become an integral part of all educational systems worldwide, but with different levels and layers. The presence of information technology in English language teaching has brought about notable changes for teachers and learners alike. Accordingly, this paper investigates the impact of integrating information technologies in ELT on EFL learners’ motivation and interest. Based on an authentic comparative case study, this paper explores the influence of information technology on EFL learners’ perceptions, motivation, and interest in the context of ELT in the Tunisian higher education. The findings of this study suggest that the integration of IT in ELT heavily affects EFL students’ motivation and academic performance and hence EFL instructors should take this variable into consideration.

  17. Examining the Perceptions of English Instructors Regarding the Incorporation of Global Citizenship Education into ELT

    Directory of Open Access Journals (Sweden)

    Fatma BAŞARIR

    2017-12-01

    Full Text Available The aim of this study is to explore the perceptions of ELT instructors working at a higher education institution in Turkey regarding integrating global citizenship education into ELT courses. The study was carried out by using phenomenological design, which is one of the qualitative studies. The data were collected using interview method and a semi-structured interview form was developed by the researcher as the data collection tool. The participants, selected on the basis of easily accessible sampling method, which is one of the purposeful sampling methods. The participants comprises of 13 English instructors who work at a higher education institution in the Central Anatolia Region in the academic year 2015-2016. Instructors’ opinions were taken regarding how they described global citizenship, what were their roles and responsibilities in educating students as global citizens, how they practiced global citizenship education in their classes, and the challenges they were facing in practicing global citizenship education in ELT courses. Data were analysed with content analysis technique. Findings revealed that participants mostly focused on the “value” dimension of global citizenship such as respect, sensitivity, sense of belonging, responsibility, openness, etc. The instructors deemed their roles and responsibilities in preparing students as global citizens as an informer and role model. While most of the participants stated that they did not involve any specific teaching practices in their classes to educate students as global citizens, as they thought ELT lessons and global citizenship education were irrelevant, addressing global issues in the courses and role modelling were conducted by few instructors to promote global citizenship. Predominantly grammar-based teaching and student unwillingness were found as challenges of integrating global citizenship into ELT. As a result, it was concluded that ELT instructors have insufficient levels

  18. Active controllers and the time duration to learn a task

    Science.gov (United States)

    Repperger, D. W.; Goodyear, C.

    1986-01-01

    An active controller was used to help train naive subjects involved in a compensatory tracking task. The controller is called active in this context because it moves the subject's hand in a direction to improve tracking. It is of interest here to question whether the active controller helps the subject to learn a task more rapidly than the passive controller. Six subjects, inexperienced to compensatory tracking, were run to asymptote root mean square error tracking levels with an active controller or a passive controller. The time required to learn the task was defined several different ways. The results of the different measures of learning were examined across pools of subjects and across controllers using statistical tests. The comparison between the active controller and the passive controller as to their ability to accelerate the learning process as well as reduce levels of asymptotic tracking error is reported here.

  19. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Årup; Frutiger, Sally A.

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel...... practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing. Hum. Brain Mapping 15...

  20. Polysynchronous: Dialogic Construction of Time in Online Learning

    Science.gov (United States)

    Oztok, Murat; Wilton, Lesley; Zingaro, Daniel; Mackinnon, Kim; Makos, Alexandra; Phirangee, Krystle; Brett, Clare; Hewitt, Jim

    2014-01-01

    Online learning has been conceptualized for decades as being delivered in one of two modes: synchronous or asynchronous. Technological determinism falls short in describing the role that the individuals' psychological, social and pedagogical factors play in their perception, experience and understanding of time online. This article explores…

  1. Probability Learning: Changes in Behavior across Time and Development

    Science.gov (United States)

    Plate, Rista C.; Fulvio, Jacqueline M.; Shutts, Kristin; Green, C. Shawn; Pollak, Seth D.

    2018-01-01

    Individuals track probabilities, such as associations between events in their environments, but less is known about the degree to which experience--within a learning session and over development--influences people's use of incoming probabilistic information to guide behavior in real time. In two experiments, children (4-11 years) and adults…

  2. Instructional Advice, Time Advice and Learning Questions in Computer Simulations

    Science.gov (United States)

    Rey, Gunter Daniel

    2010-01-01

    Undergraduate students (N = 97) used an introductory text and a computer simulation to learn fundamental concepts about statistical analyses (e.g., analysis of variance, regression analysis and General Linear Model). Each learner was randomly assigned to one cell of a 2 (with or without instructional advice) x 2 (with or without time advice) x 2…

  3. Adaptation and learning: characteristic time scales of performance dynamics.

    Science.gov (United States)

    Newell, Karl M; Mayer-Kress, Gottfried; Hong, S Lee; Liu, Yeou-Teh

    2009-12-01

    A multiple time scales landscape model is presented that reveals structures of performance dynamics that were not resolved in the traditional power law analysis of motor learning. It shows the co-existence of separate processes during and between practice sessions that evolve in two independent dimensions characterized by time scales that differ by about an order of magnitude. Performance along the slow persistent dimension of learning improves often as much and sometimes more during rest (memory consolidation and/or insight generation processes) than during a practice session itself. In contrast, the process characterized by the fast, transient dimension of adaptation reverses direction between practice sessions, thereby significantly degrading performance at the beginning of the next practice session (warm-up decrement). The theoretical model fits qualitatively and quantitatively the data from Snoddy's [Snoddy, G. S. (1926). Learning and stability. Journal of Applied Psychology, 10, 1-36] classic learning study of mirror tracing and other averaged and individual data sets, and provides a new account of the processes of change in adaptation and learning. 2009 Elsevier B.V. All rights reserved.

  4. Online gaming for learning optimal team strategies in real time

    Science.gov (United States)

    Hudas, Gregory; Lewis, F. L.; Vamvoudakis, K. G.

    2010-04-01

    This paper first presents an overall view for dynamical decision-making in teams, both cooperative and competitive. Strategies for team decision problems, including optimal control, zero-sum 2-player games (H-infinity control) and so on are normally solved for off-line by solving associated matrix equations such as the Riccati equation. However, using that approach, players cannot change their objectives online in real time without calling for a completely new off-line solution for the new strategies. Therefore, in this paper we give a method for learning optimal team strategies online in real time as team dynamical play unfolds. In the linear quadratic regulator case, for instance, the method learns the Riccati equation solution online without ever solving the Riccati equation. This allows for truly dynamical team decisions where objective functions can change in real time and the system dynamics can be time-varying.

  5. Time-sensitive Customer Churn Prediction based on PU Learning

    OpenAIRE

    Wang, Li; Chen, Chaochao; Zhou, Jun; Li, Xiaolong

    2018-01-01

    With the fast development of Internet companies throughout the world, customer churn has become a serious concern. To better help the companies retain their customers, it is important to build a customer churn prediction model to identify the customers who are most likely to churn ahead of time. In this paper, we propose a Time-sensitive Customer Churn Prediction (TCCP) framework based on Positive and Unlabeled (PU) learning technique. Specifically, we obtain the recent data by shortening the...

  6. Managing Innovation in Language Education: A Course for ELT Change Agents

    Science.gov (United States)

    Waters, Alan; Vilches, Ma. Luz C.

    2005-01-01

    As a steady stream of recent papers indicates, ELT curriculum reform projects are not always as successful as they might be. One overall reason for this situation appears to be a failure to adequately take into account concepts and practices from the world of innovation management. This paper describes an attempt to contribute towards ameliorating…

  7. Nanometre-accurate form measurement machine for E-ELT M1 segments

    NARCIS (Netherlands)

    Bos, A.; Henselmans, R.; Rosielle, P.C.J.N.; Steinbuch, M.

    2015-01-01

    To enable important scientific discoveries, ESO has defined a new ground-based telescope: the European Extremely Large Telescope (E-ELT). The baseline design features a telescope with a 39-m-class primary mirror (M1), making it the largest and most powerful telescope in the world. The M1 consists of

  8. Technology and the Human Dimension: An Interview with Elting E. Morison.

    Science.gov (United States)

    Bowser, Hal

    1985-01-01

    Elting E. Morison (a distinguished historian) discusses America's inventive past and ponders how Americans might benefit from understanding it better. The interview elicits his expertise as an interpreter of technology and his beliefs about teaching technology in schools, the role of technology in society, and future prospects. (DH)

  9. ELT Research in Turkey: A Content Analysis of Selected Features of Published Articles

    Science.gov (United States)

    Yagiz, Oktay; Aydin, Burcu; Akdemir, Ahmet Selçuk

    2016-01-01

    This study reviews a selected sample of 274 research articles on ELT, published between 2005 and 2015 in Turkish contexts. In the study, 15 journals in ULAKBIM database and articles from national and international journals accessed according to convenience sampling method were surveyed and relevant articles were obtained. A content analysis was…

  10. Moodle: A Way for Blending VLE and Face-to-Face Instruction in the ELT Context?

    Science.gov (United States)

    Ilin, Gulden

    2013-01-01

    This classroom research explores the probable consequences of a blended Teaching English to Young Learners (TEYLs) course comprised of Moodle applications and face to face instruction in the English Language Teaching (ELT) context. Contrary to previous face to face only procedure, the course was divided into two segments: traditional classroom…

  11. 77 FR 28668 - Technical Standard Order (TSO)-C91a, Emergency Locator Transmitters (ELTs)

    Science.gov (United States)

    2012-05-15

    ... Aviation Administration, 470 L'Enfant Plaza, Suite 4102, Washington, DC 20024. Telephone (202) 385-4652... ELTs can optionally include a GPS position which can potentially reduce the search area to within 100... a GPS locator instead of the 121.5 homing beacon. The FAA acknowledges the benefits of including GPS...

  12. The Science Case for Multi-Object Spectroscopy on the European ELT

    NARCIS (Netherlands)

    Evans, Chris; Puech, Mathieu; Afonso, Jose; Almaini, Omar; Amram, Philippe; Aussel, Hervé; Barbuy, Beatriz; Basden, Alistair; Bastian, Nate; Battaglia, Giuseppina; Biller, Beth; Bonifacio, Piercarlo; Bouché, Nicholas; Bunker, Andy; Caffau, Elisabetta; Charlot, Stephane; Cirasuolo, Michele; Clenet, Yann; Combes, Francoise; Conselice, Chris; Contini, Thierry; Cuby, Jean-Gabriel; Dalton, Gavin; Davies, Ben; de Koter, Alex; Disseau, Karen; Dunlop, Jim; Epinat, Benoît; Fiore, Fabrizio; Feltzing, Sofia; Ferguson, Annette; Flores, Hector; Fontana, Adriano; Fusco, Thierry; Gadotti, Dimitri; Gallazzi, Anna; Gallego, Jesus; Giallongo, Emanuele; Gonçalves, Thiago; Gratadour, Damien; Guenther, Eike; Hammer, Francois; Hill, Vanessa; Huertas-Company, Marc; Ibata, Roridgo; Kaper, Lex; Korn, Andreas; Larsen, Søren; Le Fèvre, Olivier; Lemasle, Bertrand; Maraston, Claudia; Mei, Simona; Mellier, Yannick; Morris, Simon; Östlin, Göran; Paumard, Thibaut; Pello, Roser; Pentericci, Laura; Peroux, Celine; Petitjean, Patrick; Rodrigues, Myriam; Rodríguez-Muñoz, Lucía; Rouan, Daniel; Sana, Hugues; Schaerer, Daniel; Telles, Eduardo; Trager, Scott; Tresse, Laurence; Welikala, Niraj; Zibetti, Stefano; Ziegler, Bodo

    2015-01-01

    This White Paper presents the scientific motivations for a multi-object spectrograph (MOS) on the European Extremely Large Telescope (E-ELT). The MOS case draws on all fields of contemporary astronomy, from extra-solar planets, to the study of the halo of the Milky Way and its satellites, and from

  13. SimCADO: an instrument data simulator package for MICADO at the E-ELT

    NARCIS (Netherlands)

    Leschinski, K.; Czoske, O.; Köhler, R.; Mach, M.; Zeilinger, W.; Verdoes Kleijn, G.; Alves, J.; Kausch, W.; Przybilla, N.

    2016-01-01

    MICADO will be the first-light wide-field imager for the European Extremely Large Telescope (E-ELT) and will provide diffraction limited imaging (7mas at 1.2mm) over a 53 arc-second field of view. In order to support various consortium activities we have developed a first version of SimCADO: an

  14. European Extremely Large Telescope (E-ELT) availability stochastic model: integrating failure mode and effect analysis (FMEA), influence diagram, and Bayesian network together

    Science.gov (United States)

    Verzichelli, Gianluca

    2016-08-01

    An Availability Stochastic Model for the E-ELT has been developed in GeNIE. The latter is a Graphical User Interface (GUI) for the Structural Modeling, Inference, and Learning Engine (SMILE), originally distributed by the Decision Systems Laboratory from the University of Pittsburgh, and now being a product of Bayes Fusion, LLC. The E-ELT will be the largest optical/near-infrared telescope in the world. Its design comprises an Alt-Azimuth mount reflecting telescope with a 39-metre-diameter segmented primary mirror, a 4-metre-diameter secondary mirror, a 3.75-metre-diameter tertiary mirror, adaptive optics and multiple instruments. This paper highlights how a Model has been developed for an earlier on assessment of the Telescope Avail- ability. It also describes the modular structure and the underlying assumptions that have been adopted for developing the model and demonstrates the integration of FMEA, Influence Diagram and Bayesian Network elements. These have been considered for a better characterization of the Model inputs and outputs and for taking into account Degraded-based Reliability (DBR). Lastly, it provides an overview of how the information and knowledge captured in the model may be used for an earlier on definition of the Failure, Detection, Isolation and Recovery (FDIR) Control Strategy and the Telescope Minimum Master Equipment List (T-MMEL).

  15. Spaced learning and innovative teaching: school time, pedagogy of attention and learning awareness

    Directory of Open Access Journals (Sweden)

    Garzia Maeca

    2016-06-01

    Full Text Available Currently, the ‘time’ variable has taken on the function of instructional and pedagogical innovation catalyst, after representing-over the years-a symbol of democratisation, learning opportunity and instruction quality, able to incorporate themes such as school dropout, personalisation and vocation into learning. Spaced Learning is a teaching methodology useful to quickly seize information in long-term memory based on a particular arrangement of the lesson time that comprises three input sessions and two intervals. Herein we refer to a teachers’ training initiative on Spaced Learning within the programme ‘DocentiInFormAzione’ in the EDOC@WORK3.0 Project in Apulia region in 2015. The training experience aimed at increasing teachers’ competencies in the Spaced Learning method implemented in a context of collaborative reflection and reciprocal enrichment. The intent of the article is to show how a process of rooting of the same culture of innovation, which opens to the discovery (or rediscovery of effective teaching practices sustained by scientific evidences, can be successfully implemented and to understand how or whether this innovation- based on the particular organisation of instructional time-links learning awareness to learning outcomes.

  16. A Parametric Learning and Identification Based Robust Iterative Learning Control for Time Varying Delay Systems

    Directory of Open Access Journals (Sweden)

    Lun Zhai

    2014-01-01

    Full Text Available A parametric learning based robust iterative learning control (ILC scheme is applied to the time varying delay multiple-input and multiple-output (MIMO linear systems. The convergence conditions are derived by using the H∞ and linear matrix inequality (LMI approaches, and the convergence speed is analyzed as well. A practical identification strategy is applied to optimize the learning laws and to improve the robustness and performance of the control system. Numerical simulations are illustrated to validate the above concepts.

  17. Learning of time series through neuron-to-neuron instruction

    Energy Technology Data Exchange (ETDEWEB)

    Miyazaki, Y [Department of Physics, Kyoto University, Kyoto 606-8502, (Japan); Kinzel, W [Institut fuer Theoretische Physik, Universitaet Wurzburg, 97074 Wurzburg (Germany); Shinomoto, S [Department of Physics, Kyoto University, Kyoto (Japan)

    2003-02-07

    A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space.

  18. Learning of time series through neuron-to-neuron instruction

    International Nuclear Information System (INIS)

    Miyazaki, Y; Kinzel, W; Shinomoto, S

    2003-01-01

    A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space

  19. Finite time convergent learning law for continuous neural networks.

    Science.gov (United States)

    Chairez, Isaac

    2014-02-01

    This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper. Copyright © 2013 Elsevier Ltd. All rights reserved.

  20. The Implications of Expanding the Instruction Time for the English Language Teaching Policy Implementation in the Sultanate of Oman: A Qualitative Study

    Science.gov (United States)

    Al-Issa, Ali S. M.

    2013-01-01

    This study asks questions and elicits answers about the importance of English language teaching (ELT) instruction time on the national curriculum in the Sultanate of Oman from an ideological perspective. It triangulates data from semi-structured interviews made with different agents involved in the Omani ELT system and representing different…

  1. Influence of learning strategy on response time during complex value-based learning and choice.

    Directory of Open Access Journals (Sweden)

    Shiva Farashahi

    Full Text Available Measurements of response time (RT have long been used to infer neural processes underlying various cognitive functions such as working memory, attention, and decision making. However, it is currently unknown if RT is also informative about various stages of value-based choice, particularly how reward values are constructed. To investigate these questions, we analyzed the pattern of RT during a set of multi-dimensional learning and decision-making tasks that can prompt subjects to adopt different learning strategies. In our experiments, subjects could use reward feedback to directly learn reward values associated with possible choice options (object-based learning. Alternatively, they could learn reward values of options' features (e.g. color, shape and combine these values to estimate reward values for individual options (feature-based learning. We found that RT was slower when the difference between subjects' estimates of reward probabilities for the two alternative objects on a given trial was smaller. Moreover, RT was overall faster when the preceding trial was rewarded or when the previously selected object was present. These effects, however, were mediated by an interaction between these factors such that subjects were faster when the previously selected object was present rather than absent but only after unrewarded trials. Finally, RT reflected the learning strategy (i.e. object-based or feature-based approach adopted by the subject on a trial-by-trial basis, indicating an overall faster construction of reward value and/or value comparison during object-based learning. Altogether, these results demonstrate that the pattern of RT can be informative about how reward values are learned and constructed during complex value-based learning and decision making.

  2. Real-time individualized training vectors for experiential learning.

    Energy Technology Data Exchange (ETDEWEB)

    Willis, Matt; Tucker, Eilish Marie; Raybourn, Elaine Marie; Glickman, Matthew R.; Fabian, Nathan

    2011-01-01

    Military training utilizing serious games or virtual worlds potentially generate data that can be mined to better understand how trainees learn in experiential exercises. Few data mining approaches for deployed military training games exist. Opportunities exist to collect and analyze these data, as well as to construct a full-history learner model. Outcomes discussed in the present document include results from a quasi-experimental research study on military game-based experiential learning, the deployment of an online game for training evidence collection, and results from a proof-of-concept pilot study on the development of individualized training vectors. This Lab Directed Research & Development (LDRD) project leveraged products within projects, such as Titan (Network Grand Challenge), Real-Time Feedback and Evaluation System, (America's Army Adaptive Thinking and Leadership, DARWARS Ambush! NK), and Dynamic Bayesian Networks to investigate whether machine learning capabilities could perform real-time, in-game similarity vectors of learner performance, toward adaptation of content delivery, and quantitative measurement of experiential learning.

  3. Cluster analysis of activity-time series in motor learning

    DEFF Research Database (Denmark)

    Balslev, Daniela; Nielsen, Finn Å; Futiger, Sally A

    2002-01-01

    Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel......-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show...

  4. Representation Learning from Time Labelled Heterogeneous Data for Mobile Crowdsensing

    Directory of Open Access Journals (Sweden)

    Chunmei Ma

    2016-01-01

    Full Text Available Mobile crowdsensing is a new paradigm that can utilize pervasive smartphones to collect and analyze data to benefit users. However, sensory data gathered by smartphone usually involves different data types because of different granularity and multiple sensor sources. Besides, the data are also time labelled. The heterogeneous and time sequential data raise new challenges for data analyzing. Some existing solutions try to learn each type of data one by one and analyze them separately without considering time information. In addition, the traditional methods also have to determine phone orientation because some sensors equipped in smartphone are orientation related. In this paper, we think that a combination of multiple sensors can represent an invariant feature for a crowdsensing context. Therefore, we propose a new representation learning method of heterogeneous data with time labels to extract typical features using deep learning. We evaluate that our proposed method can adapt data generated by different orientations effectively. Furthermore, we test the performance of the proposed method by recognizing two group mobile activities, walking/cycling and driving/bus with smartphone sensors. It achieves precisions of 98.6% and 93.7% in distinguishing cycling from walking and bus from driving, respectively.

  5. Effect of chronotype and student learning time on mathematical ability based on self-regulated learning

    Science.gov (United States)

    Ratnaningsih, N.; El Akbar, R. R.; Hidayat, E.

    2018-05-01

    One of ways to improve students' learning ability is conduct a research, with purpose to obtain a method to improve students' ability. Research often carried out on the modification of teaching methods, uses of teaching media, motivation, interests and talents of students. Research related to the internal condition of students becomes very interesting to studied, including research on circadian rhythms. Every person in circadian rhythms has its own Chronotype, which divided into two types namely early type and night late type. Chronotype affects the comfort in activity, for example a person with Chronotype category of early type tends to be more comfort in daytime activities. The purpose of this study is to examine the conditions of students, related Chronotype suitable or appropriate for student learning time. This suitability then studied in relation to the ability of learning mathematics with self- regulated learning approach. This study consists of three stages; (i) student Chronotype measurement, (ii) data retrieval, and (iii) analysis of research results. The results show the relationship between the students' learning ability in mathematics to learning time corresponding to Chronotype.

  6. Fixation and escape times in stochastic game learning

    International Nuclear Information System (INIS)

    Realpe-Gomez, John; Szczesny, Bartosz; Galla, Tobias; Dall’Asta, Luca

    2012-01-01

    Evolutionary dynamics in finite populations is known to fixate eventually in the absence of mutation. We here show that a similar phenomenon can be found in stochastic game dynamical batch learning, and investigate fixation in learning processes in a simple 2×2 game, for two-player games with cyclic interaction, and in the context of the best-shot network game. The analogues of finite populations in evolution are here finite batches of observations between strategy updates. We study when and how such fixation can occur, and present results on the average time-to-fixation from numerical simulations. Simple cases are also amenable to analytical approaches and we provide estimates of the behaviour of so-called escape times as a function of the batch size. The differences and similarities with escape and fixation in evolutionary dynamics are discussed. (paper)

  7. Uudised : Olari Elts peadirigendiks Riiga. Kitarriöö uudisteosega. Täna algab kirikufestival / Priit Kuusk

    Index Scriptorium Estoniae

    Kuusk, Priit, 1938-

    2001-01-01

    O. Elts allkirjastas 30. juulil lepingu, millega ta saab Läti RSO peadirigendiks. Duo Bagger-Varts esines festivalil "Klaaspärlimäng", esiettekandele tuli J. Räätsa teos "Fragment". IX rahvusvahelisest kirikumuusika festivalist

  8. The 3 R's of Learning Time: Rethink, Reshape, Reclaim

    Science.gov (United States)

    Sackey, Shera Carter

    2012-01-01

    The Learning School Alliance is a network of schools collaborating about professional practice. The network embodies Learning Forward's purpose to advance effective job-embedded professional learning that leads to student outcomes. A key component of Learning Forward's Standards for Professional Learning is a focus on collaborative learning,…

  9. Endoderm development in Caenorhabditis elegans: the synergistic action of ELT-2 and -7 mediates the specification→differentiation transition.

    Science.gov (United States)

    Sommermann, Erica M; Strohmaier, Keith R; Maduro, Morris F; Rothman, Joel H

    2010-11-01

    The transition from specification of cell identity to the differentiation of cells into an appropriate and enduring state is critical to the development of embryos. Transcriptional profiling in Caenorhabditis elegans has revealed a large number of genes that are expressed in the fully differentiated intestine; however, no regulatory factor has been found to be essential to initiate their expression once the endoderm has been specified. These gut-expressed genes possess a preponderance of GATA factor binding sites and one GATA factor, ELT-2, fulfills the expected characteristics of a key regulator of these genes based on its persistent expression exclusively in the developing and differentiated intestine and its ability to bind these regulatory sites. However, a striking characteristic of elt-2(0) knockout mutants is that while they die shortly after hatching owing to an obstructed gut passage, they nevertheless contain a gut that has undergone complete morphological differentiation. We have discovered a second gut-specific GATA factor, ELT-7, that profoundly synergizes with ELT-2 to create a transcriptional switch essential for gut cell differentiation. ELT-7 is first expressed in the early endoderm lineage and, when expressed ectopically, is sufficient to activate gut differentiation in nonendodermal progenitors. elt-7 is transcriptionally activated by the redundant endoderm-specifying factors END-1 and -3, and its product in turn activates both its own expression and that of elt-2, constituting an apparent positive feedback system. While elt-7 loss-of-function mutants lack a discernible phenotype, simultaneous loss of both elt-7 and elt-2 results in a striking all-or-none block to morphological differentiation of groups of gut cells with a region-specific bias, as well as reduced or abolished gut-specific expression of a number of terminal differentiation genes. ELT-2 and -7 synergize not only in activation of gene expression but also in repression of a gene that

  10. The race to learn: spike timing and STDP can coordinate learning and recall in CA3.

    Science.gov (United States)

    Nolan, Christopher R; Wyeth, Gordon; Milford, Michael; Wiles, Janet

    2011-06-01

    The CA3 region of the hippocampus has long been proposed as an autoassociative network performing pattern completion on known inputs. The dentate gyrus (DG) region is often proposed as a network performing the complementary function of pattern separation. Neural models of pattern completion and separation generally designate explicit learning phases to encode new information and assume an ideal fixed threshold at which to stop learning new patterns and begin recalling known patterns. Memory systems are significantly more complex in practice, with the degree of memory recall depending on context-specific goals. Here, we present our spike-timing separation and completion (STSC) model of the entorhinal cortex (EC), DG, and CA3 network, ascribing to each region a role similar to that in existing models but adding a temporal dimension by using a spiking neural network. Simulation results demonstrate that (a) spike-timing dependent plasticity in the EC-CA3 synapses provides a pattern completion ability without recurrent CA3 connections, (b) the race between activation of CA3 cells via EC-CA3 synapses and activation of the same cells via DG-CA3 synapses distinguishes novel from known inputs, and (c) modulation of the EC-CA3 synapses adjusts the learned versus test input similarity required to evoke a direct CA3 response prior to any DG activity, thereby adjusting the pattern completion threshold. These mechanisms suggest that spike timing can arbitrate between learning and recall based on the novelty of each individual input, ensuring control of the learn-recall decision resides in the same subsystem as the learned memories themselves. The proposed modulatory signal does not override this decision but biases the system toward either learning or recall. The model provides an explanation for empirical observations that a reduction in novelty produces a corresponding reduction in the latency of responses in CA3 and CA1. Copyright © 2010 Wiley-Liss, Inc.

  11. Oscillations, Timing, Plasticity, and Learning in the Cerebellum.

    Science.gov (United States)

    Cheron, G; Márquez-Ruiz, J; Dan, B

    2016-04-01

    The highly stereotyped, crystal-like architecture of the cerebellum has long served as a basis for hypotheses with regard to the function(s) that it subserves. Historically, most clinical observations and experimental work have focused on the involvement of the cerebellum in motor control, with particular emphasis on coordination and learning. Two main models have been suggested to account for cerebellar functioning. According to Llinás's theory, the cerebellum acts as a control machine that uses the rhythmic activity of the inferior olive to synchronize Purkinje cell populations for fine-tuning of coordination. In contrast, the Ito-Marr-Albus theory views the cerebellum as a motor learning machine that heuristically refines synaptic weights of the Purkinje cell based on error signals coming from the inferior olive. Here, we review the role of timing of neuronal events, oscillatory behavior, and synaptic and non-synaptic influences in functional plasticity that can be recorded in awake animals in various physiological and pathological models in a perspective that also includes non-motor aspects of cerebellar function. We discuss organizational levels from genes through intracellular signaling, synaptic network to system and behavior, as well as processes from signal production and processing to memory, delegation, and actual learning. We suggest an integrative concept for control and learning based on articulated oscillation templates.

  12. A Distance Learning Review--The Communicational Module "Learning on Demand--Anywhere at Any Time"

    Science.gov (United States)

    Tatkovic, Nevenka; Ruzic, Maja

    2004-01-01

    The society of knowledge refers to the society marked with the principle which requires that knowledge, information and life-time learning hold a key to success in the world of IT technology. Internet, World Wide Web, Web Based Education and ever so growing speed of IT and communicational technologies have enabled the application of new modes,…

  13. Overlay improvements using a real time machine learning algorithm

    Science.gov (United States)

    Schmitt-Weaver, Emil; Kubis, Michael; Henke, Wolfgang; Slotboom, Daan; Hoogenboom, Tom; Mulkens, Jan; Coogans, Martyn; ten Berge, Peter; Verkleij, Dick; van de Mast, Frank

    2014-04-01

    While semiconductor manufacturing is moving towards the 14nm node using immersion lithography, the overlay requirements are tightened to below 5nm. Next to improvements in the immersion scanner platform, enhancements in the overlay optimization and process control are needed to enable these low overlay numbers. Whereas conventional overlay control methods address wafer and lot variation autonomously with wafer pre exposure alignment metrology and post exposure overlay metrology, we see a need to reduce these variations by correlating more of the TWINSCAN system's sensor data directly to the post exposure YieldStar metrology in time. In this paper we will present the results of a study on applying a real time control algorithm based on machine learning technology. Machine learning methods use context and TWINSCAN system sensor data paired with post exposure YieldStar metrology to recognize generic behavior and train the control system to anticipate on this generic behavior. Specific for this study, the data concerns immersion scanner context, sensor data and on-wafer measured overlay data. By making the link between the scanner data and the wafer data we are able to establish a real time relationship. The result is an inline controller that accounts for small changes in scanner hardware performance in time while picking up subtle lot to lot and wafer to wafer deviations introduced by wafer processing.

  14. Cross-cultural Communication and ELT in China

    Institute of Scientific and Technical Information of China (English)

    ChengTongchun

    2004-01-01

    Culture plays a significant role in teaching and learning a language. The acquisition of cultural knowledge is an indispensable part of language learning. This paper discusses the importance and necessity of cross-cultural communication in the language teaching, and focuses on three parts:

  15. The Negative Impact of Community Stressors on Learning Time: Examining Inequalities between California High Schools

    Science.gov (United States)

    Mirra, Nicole; Rogers, John

    2015-01-01

    Allocated classroom time is not the same as time available for learning--a host of economic and social stressors undermine learning time in schools serving low-income students. When time is limited, it is hard to meet rigorous learning standards. The challenge is compounded in high-poverty schools where community stressors place additional demands…

  16. Characterization of exoplanet atmospheres using high-dispersion spectroscopy with the E-ELT and beyond

    Directory of Open Access Journals (Sweden)

    Snellen Ignas

    2013-04-01

    Full Text Available Ground-based high-dispersion (R ∼ 100,000 spectroscopy provides unique information on exoplanet atmospheres, inaccessible from space - even using the JWST or other future space telescopes. Recent successes in transmission- and dayside spectroscopy using CRIRES on the Very Large Telescope prelude the enormous discovery potential of high-dispersion spectrographs on the E-ELT, such as METIS in the thermal infrared, and HIRES in the optical/near-infrared. This includes the orbital inclination and masses of hundred(s of non-transiting planets, line-by-line molecular band spectra, planet rotation and global wind patterns, longitudinal spectral variations, and possibly isotopologue ratios. Thinking beyond the E-ELT, we advocate that ultimately a systematic search for oxygen in atmospheres of nearby Earth-like planets can be conducted using large arrays of relatively low-cost flux collector telescopes equipped with high-dispersion spectrographs.

  17. What time is it? Deep learning approaches for circadian rhythms.

    Science.gov (United States)

    Agostinelli, Forest; Ceglia, Nicholas; Shahbaba, Babak; Sassone-Corsi, Paolo; Baldi, Pierre

    2016-06-15

    Circadian rhythms date back to the origins of life, are found in virtually every species and every cell, and play fundamental roles in functions ranging from metabolism to cognition. Modern high-throughput technologies allow the measurement of concentrations of transcripts, metabolites and other species along the circadian cycle creating novel computational challenges and opportunities, including the problems of inferring whether a given species oscillate in circadian fashion or not, and inferring the time at which a set of measurements was taken. We first curate several large synthetic and biological time series datasets containing labels for both periodic and aperiodic signals. We then use deep learning methods to develop and train BIO_CYCLE, a system to robustly estimate which signals are periodic in high-throughput circadian experiments, producing estimates of amplitudes, periods, phases, as well as several statistical significance measures. Using the curated data, BIO_CYCLE is compared to other approaches and shown to achieve state-of-the-art performance across multiple metrics. We then use deep learning methods to develop and train BIO_CLOCK to robustly estimate the time at which a particular single-time-point transcriptomic experiment was carried. In most cases, BIO_CLOCK can reliably predict time, within approximately 1 h, using the expression levels of only a small number of core clock genes. BIO_CLOCK is shown to work reasonably well across tissue types, and often with only small degradation across conditions. BIO_CLOCK is used to annotate most mouse experiments found in the GEO database with an inferred time stamp. All data and software are publicly available on the CircadiOmics web portal: circadiomics.igb.uci.edu/ fagostin@uci.edu or pfbaldi@uci.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

  18. Forecasting air quality time series using deep learning.

    Science.gov (United States)

    Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse

    2018-04-13

    This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution

  19. Ensemble Deep Learning for Biomedical Time Series Classification

    Directory of Open Access Journals (Sweden)

    Lin-peng Jin

    2016-01-01

    Full Text Available Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost.

  20. The Science Case for Multi-Object Spectroscopy on the European ELT

    OpenAIRE

    Evans, Chris; Puech, Mathieu; Afonso, Jose; Almaini, Omar; Amram, Philippe; Aussel, Hervé; Barbuy, Beatriz; Basden, Alistair; Bastian, Nate; Battaglia, Giuseppina; Biller, Beth; Bonifacio, Piercarlo; Bouché, Nicholas; Bunker, Andy; Caffau, Elisabetta

    2015-01-01

    This White Paper presents the scientific motivations for a multi-object spectrograph (MOS) on the European Extremely Large Telescope (E-ELT). The MOS case draws on all fields of contemporary astronomy, from extra-solar planets, to the study of the halo of the Milky Way and its satellites, and from resolved stellar populations in nearby galaxies out to observations of the earliest 'first-light' structures in the partially-reionised Universe. The material presented here results from thorough di...

  1. Evidence for an alternation strategy in time-place learning.

    Science.gov (United States)

    Pizzo, Matthew J; Crystal, Jonathon D

    2004-11-30

    Many different conclusions concerning what type of mechanism rats use to solve a daily time-place task have emerged in the literature. The purpose of this study was to test three competing explanations of time-place discrimination. Rats (n = 10) were tested twice daily in a T-maze, separated by approximately 7 h. Food was available at one location in the morning and another location in the afternoon. After the rats learned to visit each location at the appropriate time, tests were omitted to evaluate whether the rats were utilizing time-of-day (i.e., a circadian oscillator) or an alternation strategy (i.e., visiting a correct location is a cue to visit the next location). Performance on this test was significantly lower than chance, ruling out the use of time-of-day. A phase advance of the light cycle was conducted to test the alternation strategy and timing with respect to the light cycle (i.e., an interval timer). There was no difference between probe and baseline performance. These results suggest that the rats used an alternation strategy to meet the temporal and spatial contingencies in the time-place task.

  2. The Impact of Students' Temporal Perspectives on Time-on-Task and Learning Performance in Game Based Learning

    Science.gov (United States)

    Romero, Margarida; Usart, Mireia

    2013-01-01

    The use of games for educational purposes has been considered as a learning methodology that attracts the students' attention and may allow focusing individuals on the learning activity through the [serious games] SG game dynamic. Based on the hypothesis that students' Temporal Perspective has an impact on learning performance and time-on-task,…

  3. Learning the language of time: Children's acquisition of duration words.

    Science.gov (United States)

    Tillman, Katharine A; Barner, David

    2015-05-01

    Children use time words like minute and hour early in development, but take years to acquire their precise meanings. Here we investigate whether children assign meaning to these early usages, and if so, how. To do this, we test their interpretation of seven time words: second, minute, hour, day, week, month, and year. We find that preschoolers infer the orderings of time words (e.g., hour>minute), but have little to no knowledge of the absolute durations they encode. Knowledge of absolute duration is learned much later in development - many years after children first start using time words in speech - and in many children does not emerge until they have acquired formal definitions for the words. We conclude that associating words with the perception of duration does not come naturally to children, and that early intuitive meanings of time words are instead rooted in relative orderings, which children may infer from their use in speech. Copyright © 2015 Elsevier Inc. All rights reserved.

  4. Hierarchical Meta-Learning in Time Series Forecasting for Improved Interference-Less Machine Learning

    Directory of Open Access Journals (Sweden)

    David Afolabi

    2017-11-01

    Full Text Available The importance of an interference-less machine learning scheme in time series prediction is crucial, as an oversight can have a negative cumulative effect, especially when predicting many steps ahead of the currently available data. The on-going research on noise elimination in time series forecasting has led to a successful approach of decomposing the data sequence into component trends to identify noise-inducing information. The empirical mode decomposition method separates the time series/signal into a set of intrinsic mode functions ranging from high to low frequencies, which can be summed up to reconstruct the original data. The usual assumption that random noises are only contained in the high-frequency component has been shown not to be the case, as observed in our previous findings. The results from that experiment reveal that noise can be present in a low frequency component, and this motivates the newly-proposed algorithm. Additionally, to prevent the erosion of periodic trends and patterns within the series, we perform the learning of local and global trends separately in a hierarchical manner which succeeds in detecting and eliminating short/long term noise. The algorithm is tested on four datasets from financial market data and physical science data. The simulation results are compared with the conventional and state-of-the-art approaches for time series machine learning, such as the non-linear autoregressive neural network and the long short-term memory recurrent neural network, respectively. Statistically significant performance gains are recorded when the meta-learning algorithm for noise reduction is used in combination with these artificial neural networks. For time series data which cannot be decomposed into meaningful trends, applying the moving average method to create meta-information for guiding the learning process is still better than the traditional approach. Therefore, this new approach is applicable to the forecasting

  5. Through Teachers’ Eyes: The Use of Virtual Classrooms in ELT

    Directory of Open Access Journals (Sweden)

    Jairo Enrique Castañeda

    2012-12-01

    Full Text Available The use of virtual environments to support as well as to complement language teaching and learning processes is becoming a recurrent practice and sometimes policy in several educational institutions. This paper reports the results of an inquiry carried out at the language center of a private university in Bogotá. Those results intended to describe EFL teachers’ viewpoints regarding the promotion of autonomous, collaborative, and meaningful learning through the use of virtual classrooms in the teaching of English as a foreign language. Findings show that the promotion of these three types of learning through the use of virtual classrooms still represents challenges in the context in which this study was carried out.

  6. Using Online Lectures to Make Time for Active Learning

    Science.gov (United States)

    Prunuske, Amy J.; Batzli, Janet; Howell, Evelyn; Miller, Sarah

    2012-01-01

    To make time in class for group activities devoted to critical thinking, we integrated a series of short online lectures into the homework assignments of a large, introductory biology course at a research university. The majority of students viewed the online lectures before coming to class and reported that the online lectures helped them to complete the in-class activity and did not increase the amount of time they devoted to the course. In addition, students who viewed the online lecture performed better on clicker questions designed to test lower-order cognitive skills. The in-class activities then gave the students practice analyzing the information in groups and provided the instructor with feedback about the students’ understanding of the material. On the basis of the results of this study, we support creating hybrid course models that allow students to learn the fundamental information outside of class time, thereby creating time during the class period to be dedicated toward the conceptual understanding of the material. PMID:22714412

  7. Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

    Science.gov (United States)

    Lee, Hanbong

    2016-01-01

    Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

  8. Learning English in the Periphery: A View from Myanmar (Burma)

    Science.gov (United States)

    Tin, Tan Bee

    2014-01-01

    Although researchers have called for the investigation of local vernacular learning and teaching practices in various ELT (English language teaching) contexts, studies conducted in the Periphery are fewer in number. This study attempts to understand English learning experiences of a group of students from the Periphery, who were studying English…

  9. In Pursuit of Alternatives in ELT Methodology: WebQuests

    Science.gov (United States)

    Sen, Ayfer; Neufeld, Steve

    2006-01-01

    Although the Internet has opened up a vast new source of information for university students to use and explore, many students lack the skills to find, critically evaluate and intelligently exploit web-based resources. This problem is accentuated in English-medium universities where students learn and use English as a foreign language. In these…

  10. An ELT's Solution to Combat Plagiarism: "Birth" of CALL.

    Science.gov (United States)

    Sabieh, Christine

    One English-as-a Second-Language professor fought plagiarism using computer assisted language learning (CALL). She succeeded in getting half of her class to write documented research papers free of plagiarism. Although all of the students claimed to know how to avoid plagiarizing, 35 percent presented the work with minor traces of plagiarism. The…

  11. EDUCATIONAL LEAPFROGGING IN THE mLEARNING TIME

    Directory of Open Access Journals (Sweden)

    Abdel Rahman IBRAHIM SULEIMAN

    2014-07-01

    Full Text Available In this theoretical study, researcher tries to shed light on the modern strategy of education, Mobile learning is this strategy, which has become a reality exists in the educational institutions and aims researcher of this study. Trying to figure out the reality of Mobil Determining if the mobile learning part of the E-Learning. Trying for identify future of mobile learning. And the researcher collect the information and the data from previous research in addition to what has been published on websites and blogs and has reached the researcher to achieve the successes of Mobile learning at the level of the educational process now , and that this strategy of mobile learning is not part of the e-learning, and generation of generations , but a new way for the development of the educational process educational , researcher is expected to evolve Mobile learning expands even at the all levels of educational.

  12. Car-following Behavior Model Learning Using Timed Automata

    NARCIS (Netherlands)

    Zhang, Yihuan; Lin, Q.; Wang, Jun; Verwer, S.E.; Dochain, D.; Henrion, D.; Peaucelle, D.

    Learning driving behavior is fundamental for autonomous vehicles to “understand” traffic situations. This paper proposes a novel method for learning a behavioral model of car-following using automata learning algorithms. The model is interpretable for car-following behavior analysis. Frequent common

  13. Mental Time Travel, Memory and the Social Learning Strategies Tournament

    Science.gov (United States)

    Fogarty, L.; Rendell, L.; Laland, K. N.

    2012-01-01

    The social learning strategies tournament was an open computer-based tournament investigating the best way to learn in a changing environment. Here we present an analysis of the impact of memory on the ability of strategies entered into the social learning strategies tournament (Rendell, Boyd, et al., 2010) to modify their own behavior to suit a…

  14. Understanding the Effects of Time on Collaborative Learning Processes in Problem Based Learning: A Mixed Methods Study

    Science.gov (United States)

    Hommes, J.; Van den Bossche, P.; de Grave, W.; Bos, G.; Schuwirth, L.; Scherpbier, A.

    2014-01-01

    Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning…

  15. A Lecture Supporting System Based on Real-Time Learning Analytics

    Science.gov (United States)

    Shimada, Atsushi; Konomi, Shin'ichi

    2017-01-01

    A new lecture supporting system based on real-time learning analytics is proposed. Our target is on-site classrooms where teachers give their lectures, and a lot of students listen to teachers' explanation, conduct exercises etc. We utilize not only an e-Learning system, but also an e-Book system to collect real-time learning activities during the…

  16. How to describe grammar and vocabulary in ELT

    CERN Document Server

    Liu, Dilin

    2013-01-01

    Language description plays an important role in language learning/teaching because it often determines what specific language forms, features, and usages are taught and how. A good understanding of language description is vital for language teachers and material writers and should constitute an important part of their knowledge. This book provides a balanced treatment of both theory and practice. It focuses on some of the most important and challenging grammar and vocabulary usage questions. Using these questions as examples, it shows how theory can inform practice and how grammar and vocab

  17. Machine learning in heart failure: ready for prime time.

    Science.gov (United States)

    Awan, Saqib Ejaz; Sohel, Ferdous; Sanfilippo, Frank Mario; Bennamoun, Mohammed; Dwivedi, Girish

    2018-03-01

    The aim of this review is to present an up-to-date overview of the application of machine learning methods in heart failure including diagnosis, classification, readmissions and medication adherence. Recent studies have shown that the application of machine learning techniques may have the potential to improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Recently developed deep learning methods are expected to yield even better performance than traditional machine learning techniques in performing complex tasks by learning the intricate patterns hidden in big medical data. The review summarizes the recent developments in the application of machine and deep learning methods in heart failure management.

  18. In real time: exploring nursing students' learning during an international experience.

    Science.gov (United States)

    Afriyie Asenso, Barbara; Reimer-Kirkham, Sheryl; Astle, Barbara

    2013-10-11

    Abstract Nursing education has increasingly turned to international learning experiences to educate students who are globally minded and aware of social injustices in local and global communities. To date, research with international learning experiences has focused on the benefits for the students participating, after they have completed the international experience. The purpose of this qualitative study was to explore how nursing students learn during the international experience. The sample consisted of eight nursing students who enrolled in an international learning experience, and data were collected in "real time" in Zambia. The students were observed during learning activities and were interviewed three times. Three major themes emerged from the thematic analysis: expectations shaped students' learning, engagement facilitated learning, and critical reflection enhanced learning. Implications are discussed, related to disrupting media representations of Africa that shape students' expectations, and educational strategies for transformative learning and global citizenship.

  19. Metrology requirements for the serial production of ELT primary mirror segments

    Science.gov (United States)

    Rees, Paul C. T.; Gray, Caroline

    2015-08-01

    The manufacture of the next generation of large astronomical telescopes, the extremely large telescopes (ELT), requires the rapid manufacture of greater than 500 1.44m hexagonal segments for the primary mirror of each telescope. Both leading projects, the Thirty Meter Telescope (TMT) and the European Extremely Large Telescope (E-ELT), have set highly demanding technical requirements for each fabricated segment. These technical requirements, when combined with the anticipated construction schedule for each telescope, suggest that more than one optical fabricator will be involved in the delivery of the primary mirror segments in order to meet the project schedule. For one supplier, the technical specification is challenging and requires highly consistent control of metrology in close coordination with the polishing technologies used in order to optimize production rates. For production using multiple suppliers, however the supply chain is structured, consistent control of metrology along the supply chain will be required. This requires a broader pattern of independent verification than is the case of a single supplier. This paper outlines the metrology requirements for a single supplier throughout all stages of the fabrication process. We identify and outline those areas where metrology accuracy and duration have a significant impact on production efficiency. We use the challenging ESO E-ELT technical specification as an example of our treatment, including actual process data. We further develop this model for the case of a supply chain consisting of multiple suppliers. Here, we emphasize the need to control metrology throughout the supply chain in order to optimize net production efficiency.

  20. Workshop “Science with the VLT in the ELT Era”

    CERN Document Server

    Astrophysics and Space Science Proceedings

    2008-01-01

    The Workshop ‘Science with the VLT in the ELT era’ was organised by ESO as a forum for the astronomical community to debate its expected future use of ESO’s Very Large Telescope ( and its VLTI interferometric mode) when other facilities such as ALMA, JWST and, hopefully, at least one extremely large 30-40m class telescope will be operating. VLT/I science highlights were presented, future science priorities argued, synergies between the VLT and the future facilities confirmed and specific new VLT/I instruments proposed.

  1. The E-ELT project: the telescope main structure detailed design study

    Science.gov (United States)

    Marchiori, Gianpietro; Busatta, Andrea; Ghedin, Leonardo; De Lorenzi, Simone

    2012-09-01

    The European Extremely Large Telescope (E-ELT) is the biggest telescope in the world. Within the Detailed Design activities, ESO has awarded EIE GROUP (European Industrial Engineering) a contract for the Design of the Main Structure to the point where the concept of the telescope has been consolidated, from a construction point of view. All the Design activities have been developed in order to create an integrated system in terms of functionality and performance, while the engineering activities have been performed with the aim of obtaining a telescope that can be built, transported, integrated, with a reduced maintainability.

  2. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity.

    Science.gov (United States)

    Pecevski, Dejan; Maass, Wolfgang

    2016-01-01

    Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p (*) that generates the examples it receives. This holds even if p (*) contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference.

  3. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity123

    Science.gov (United States)

    Pecevski, Dejan

    2016-01-01

    Abstract Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p* that generates the examples it receives. This holds even if p* contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference. PMID:27419214

  4. Museums as spaces and times for learning and social participation.

    Directory of Open Access Journals (Sweden)

    César M.

    2014-12-01

    Full Text Available A museum is valued according to its collections, communication and knowledge exchange with visitors (Primo, 1999. Museums should be in dialogue with the public, contributing to their development (Skramstad, 2004 and collective memory (Wertsch, 2004. Social interactions and working in participants’ zone of proximal development (Vygotsky, 1934/1962 play an important role in non-formal learning opportunities that take place at museums. The National Museum of Natural History and Science (Lisbon University offers weekly holiday programmes for children and teenagers, aiming at developing scientific literacy in intercultural and inclusive spaces and times, facilitating knowledge appropriation and social participation. We studied these programmes, assuming an interpretive approach (Denzin, 2002 and developing an intrinsic case study (Stake, 1995. The main participants were these children and teenagers, their parents, and museum educational agents. Data collecting instruments included observation, interviews, questionnaires, children and teenagers’ protocols and tasks inspired in projective techniques. Data treatment and analysis was based on a narrative content analysis (Clandinin & Connelly, 1998 from which inductive categories emerged (Hamido & César, 2009. Some examples illuminate participants’ expectancies, their engagement in activities, and the contributions of social interactions and non-formal education to the development of scientific literacy.

  5. Project Management in Real Time: A Service-Learning Project

    Science.gov (United States)

    Larson, Erik; Drexler, John A., Jr.

    2010-01-01

    This article describes a service-learning assignment for a project management course. It is designed to facilitate hands-on student learning of both the technical and the interpersonal aspects of project management, and it involves student engagement with real customers and real stakeholders in the creation of real events with real outcomes. As…

  6. Age and time effects on implicit and explicit learning

    NARCIS (Netherlands)

    Verneau, M.; Kamp, J. van der; Savelsbergh, G.J.P.; Looze, M.P. de

    2014-01-01

    Study Context: It has been proposed that effects of aging are more pronounced for explicit than for implicit motor learning. The authors evaluated this claim by comparing the efficacy of explicit and implicit learning of a movement sequence in young and older adults, and by testing the resilience

  7. Age and Time Effects on Implicit and Explicit Learning

    NARCIS (Netherlands)

    Verneau, M.M.N.; van der Kamp, J.; Savelsbergh, G.J.P.; de Looze, M.P.

    2014-01-01

    Study Context: It has been proposed that effects of aging are more pronounced for explicit than for implicit motor learning. The authors evaluated this claim by comparing the efficacy of explicit and implicit learning of a movement sequence in young and older adults, and by testing the resilience

  8. Language Learning Attitudes: Ingrained Or Shaped In Time?

    Directory of Open Access Journals (Sweden)

    Gökçe DİŞLEN DAĞGÖL

    2017-09-01

    Full Text Available Language learning has become an essential need in today’s world. From academic to social settings, humans need to communicate in a different language to survive in their community. However, despite this increasing importance of language, it is difficult to say we have attained successful language learning on a large scale since there are a lot of factors in language learning process. Language attitudes, one of these factors, influence this process both positively and negatively, depending on how we view learning a foreign language. Therefore, this study deals with the issue of language attitudes to uncover learners’ language conceptions and probable effects on their learning. Moreover, this study aims to reveal the potential role of past learning experiences on the development of language beliefs positively or negatively. Thus, 35 university students in their 1st, 2nd, 3rd and 4th years constitute the participants of the study. Based on mixed research design, the study is comprised of both quantitative and qualitative data. Quantitative data were gathered through Attitude Scale towards English Course, and the analyses were performed with Statistical Packages for Social Sciences (SPSS 17.0 version for Windows. The qualitative data were collected from students’ reports of their own autobiographies regarding their previous language learning experiences in elementary, secondary, high school and university years, and were subjected to the content analysis. The study showed language attitudes from behavioural, cognitive and affective perspectives and found out different factors in shaping their learning conceptions.

  9. The Relationship between Motivation, Learning Approaches, Academic Performance and Time Spent

    Science.gov (United States)

    Everaert, Patricia; Opdecam, Evelien; Maussen, Sophie

    2017-01-01

    Previous literature calls for further investigation in terms of precedents and consequences of learning approaches (deep learning and surface learning). Motivation as precedent and time spent and academic performance as consequences are addressed in this paper. The study is administered in a first-year undergraduate course. Results show that the…

  10. Incremental Impact of Time on Students' Use of E-Learning via Facebook

    Science.gov (United States)

    Moghavvemi, Sedigheh; Salarzadeh Janatabadi, Hashem

    2018-01-01

    The majority of studies utilised the cross-sectional method to measure students' intention to learn and investigate their corresponding learning behaviours. Only a few studies have measured the process of change in students' learning behaviour in the context of time. The main purpose of this study is to determine the effects of using a Facebook…

  11. Crumpled Molecules and Edible Plastic: Science Learning Activation in Out-of-School Time

    Science.gov (United States)

    Dorph, Rena; Schunn, Christian D.; Crowley, Kevin

    2017-01-01

    The Coalition for Science After School highlights the dual nature of outcomes for science learning during out-of- school time (OST): Learning experiences should not only be positive in the moment, but also position youth for future success. Several frameworks speak to the first set of immediate outcomes--what youth learn, think, and feel as the…

  12. EFFECTS OF COOPERATIVE LEARNING MODEL TYPE STAD JUST-IN TIME BASED ON THE RESULTS OF LEARNING TEACHING PHYSICS COURSE IN PHYSICS SCHOOL IN PHYSICS PROGRAM FACULTY UNIMED

    Directory of Open Access Journals (Sweden)

    Teguh Febri Sudarma

    2013-06-01

    Full Text Available Research was aimed to determine: (1 Students’ learning outcomes that was taught with just in time teaching based STAD cooperative learning method and STAD cooperative learning method (2 Students’ outcomes on Physics subject that had high learning activity compared with low learning activity. The research sample was random by raffling four classes to get two classes. The first class taught with just in time teaching based STAD cooperative learning method, while the second class was taught with STAD cooperative learning method. The instrument used was conceptual understanding that had been validated with 7 essay questions. The average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,47 higher than average gain values of students learning results with STAD cooperative learning method. The high learning activity and low learning activity gave different learning results. In this case the average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,48 higher than average gain values of students learning results with STAD cooperative learning method. There was interaction between learning model and learning activity to the physics learning result test in students

  13. DEVELOPING PODCAST OF ENGLISH SONG AS MEDIA FOR ELT LISTENING

    Directory of Open Access Journals (Sweden)

    Amirudin Latif

    2015-10-01

    Full Text Available Abstract: Listening is one of the fundamental language skills.Based on the pre survey research, many students at Senior High School were not interested in listening course.This research tried to develop podcast of English song (PES as the media in order to help the teacher in the teaching of listening and make the students interested in listening course. The type of the reseacher is developmental research.The steps of this research are self evaluation, expert review and one-to-one, small group, and field test. The subjects of this research are the students of SMAN 03 Metro and SMK Muhammadiyah 03 Metro. The researcher collected the data by giving some questionnaires to the expert review and students to find out whether the media is applicable and suitable or not. The result showed that: first, most of the studentsfelt fun and enjoyable in the learning process of listening course. Second, PES media was applicable, the students were active, very enthusiastic, excited with PES media.   Key Words: English song, Listening media, Podcast.

  14. Single machine total completion time minimization scheduling with a time-dependent learning effect and deteriorating jobs

    Science.gov (United States)

    Wang, Ji-Bo; Wang, Ming-Zheng; Ji, Ping

    2012-05-01

    In this article, we consider a single machine scheduling problem with a time-dependent learning effect and deteriorating jobs. By the effects of time-dependent learning and deterioration, we mean that the job processing time is defined by a function of its starting time and total normal processing time of jobs in front of it in the sequence. The objective is to determine an optimal schedule so as to minimize the total completion time. This problem remains open for the case of -1 < a < 0, where a denotes the learning index; we show that an optimal schedule of the problem is V-shaped with respect to job normal processing times. Three heuristic algorithms utilising the V-shaped property are proposed, and computational experiments show that the last heuristic algorithm performs effectively and efficiently in obtaining near-optimal solutions.

  15. Design of pre-optics for laser guide star wavefront sensor for the ELT

    Science.gov (United States)

    Muslimov, Eduard; Dohlen, Kjetil; Neichel, Benoit; Hugot, Emmanuel

    2017-12-01

    In the present paper, we consider the optical design of a zoom system for the active refocusing in laser guide star wavefront sensors. The system is designed according to the specifications coming from the Extremely Large Telescope (ELT)-HARMONI instrument, the first-light, integral field spectrograph for the European (E)-ELT. The system must provide a refocusing of the laser guide as a function of telescope pointing and large decentring of the incoming beam. The system considers four moving lens groups, each of them being a doublet with one aspherical surface. The advantages and shortcomings of such a solution in terms of the component displacements and complexity of the surfaces are described in detail. It is shown that the system can provide the median value of the residual wavefront error of 13.8-94.3 nm and the maximum value <206 nm, while the exit pupil distortion is 0.26-0.36% for each of the telescope pointing directions.

  16. First results of the wind evaluation breadboard for ELT primary mirror design

    Science.gov (United States)

    Reyes García-Talavera, Marcos; Viera, Teodora; Núñez, Miguel

    2010-07-01

    The Wind Evaluation Breadboard (WEB) is a primary mirror and telescope simulator formed by seven aluminium segments, including position sensors, electromechanical support systems and support structures. WEB has been developed to evaluate technologies for primary mirror wavefront control and to evaluate the performance of the control of wind buffeting disturbance on ELT segmented mirrors. For this purpose WEB electro-mechanical set-up simulates the real operational constrains applied to large segmented mirrors. This paper describes the WEB assembly, integration and verification, the instrument characterisation and close loop control design, including the dynamical characterization of the instrument and the control architecture. The performance of the new technologies developed for position sensing, acting and controlling is evaluated. The integration of the instrument in the observatory and the results of the first experiments are summarised, with different wind conditions, elevation and azimuth angles of incidence. Conclusions are extracted with respect the wind rejection performance and the control strategy for an ELT. WEB has been designed and developed by IAC, ESO, ALTRAN and JUPASA, with the integration of subsystems of FOGALE and TNO.

  17. Machine learning application in the life time of materials

    OpenAIRE

    Yu, Xiaojiao

    2017-01-01

    Materials design and development typically takes several decades from the initial discovery to commercialization with the traditional trial and error development approach. With the accumulation of data from both experimental and computational results, data based machine learning becomes an emerging field in materials discovery, design and property prediction. This manuscript reviews the history of materials science as a disciplinary the most common machine learning method used in materials sc...

  18. Exploring ELT Students' Awareness of the Differences between the British and American Varieties of English

    Science.gov (United States)

    Yaman, Ismail

    2015-01-01

    This study aims to find out the extent to which students attending the English Language Teaching Programme (ELT) at Ondokuz Mayis University are aware of the major spelling, vocabulary, and pronunciation differences between American and British English which constitute the most commonly used varieties of English. To this end, 42 randomly selected…

  19. Where the Difference Lies: Teachers' Perceptions toward Cultural Content of ELT Books in Three Circles of World Englishes

    Science.gov (United States)

    Monfared, Abbas; Mozaheb, Mohammad Amin; Shahiditabar, Mostafa

    2016-01-01

    Drawing on the literature on culture and intercultural communication, current discussions surrounding English as an international language (EIL), and cultural appropriation of ELT books in local communities, this article reports the findings of a qualitative and qualitative research study with English language teachers from Inner (40 American, 36…

  20. The nT1 translocation separates vulval regulatory elements from the egl-18 and elt-6 GATA factor genes.

    Science.gov (United States)

    Koh, Kyunghee; Bernstein, Yelena; Sundaram, Meera V

    2004-03-01

    egl-18 and elt-6 are partially redundant, adjacent genes encoding GATA factors essential for viability, seam cell development, and vulval development in Caenorhabditis elegans. The nT1 reciprocal translocation causes a strong Vulvaless phenotype, and an nT1 breakpoint was previously mapped to the left arm of LGIV, where egl-18/elt-6 are located. Here we present evidence that the nT1 vulval phenotype is due to a disruption of egl-18/elt-6 function specifically in the vulva. egl-18 mutations do not complement nT1 for vulval defects, and the nT1 breakpoint on LGIV is located within approximately 800 bp upstream of a potential transcriptional start site of egl-18. In addition, we have identified a approximately 350-bp cis-regulatory region sufficient for vulval expression just upstream of the nT1 breakpoint. By examining the fusion state and division patterns of the cells in the developing vulva of nT1 mutants, we demonstrate that egl-18/elt-6 prevent fusion and promote cell proliferation at multiple steps of vulval development.

  1. Project overview of OPTIMOS-EVE: the fibre-fed multi-object spectrograph for the E-ELT

    NARCIS (Netherlands)

    Navarro, R.; Chemla, F.; Bonifacio, P.; Flores, H.; Guinouard, I.; Huet, J.-M.; Puech, M.; Royer, F.; Pragt, J.H.; Wulterkens, G.; Sawyer, E.C.; Caldwell, M.E.; Tosh, I.A.J.; Whalley, M.S.; Woodhouse, G.F.W.; Spanò, P.; Di Marcantonio, P.; Andersen, M.I.; Dalton, G.B.; Kaper, L.; Hammer, F.

    2010-01-01

    OPTIMOS-EVE (OPTical Infrared Multi Object Spectrograph - Extreme Visual Explorer) is the fibre fed multi object spectrograph proposed for the European Extremely Large Telescope (E-ELT), planned to be operational in 2018 at Cerro Armazones (Chile). It is designed to provide a spectral resolution of

  2. Assessing ELT Pre-Service Teachers via Web 2.0 Tools: Perceptions toward Traditional, Online and Alternative Assessment

    Science.gov (United States)

    Cirit, Nazli Ceren

    2015-01-01

    The purpose of this study is to investigate the perceptions of the ELT pre-service teachers toward the traditional, alternative, and online assessment methods and examine whether the participants' attitudes change toward the types of assessment after the tasks via Web 2.0 tools are implemented. In the light of these aims, the study was conducted…

  3. ELT Teacher Education Flipped Classroom: An Analysis of Task Challenge and Student Teachers' Views and Expectations

    Science.gov (United States)

    Karaaslan, Hatice; Çelebi, Hatice

    2017-01-01

    In this study, we explore the interplay between task complexity, task conditions and task difficulty introduced by Robinson (2001) in flipped classroom instruction at tertiary level through the data we collected from undergraduate English Language Teaching (ELT) department students studying at an English-medium state university. For the…

  4. ELT Teacher Trainees' Self-Perceptions and Awareness of the Pronunciation Skill, and Their Attitudes towards Its Instruction

    Science.gov (United States)

    Gürsoy, Esim; Hüseyinoglu, Madina

    2017-01-01

    Having received the stamp of an "often neglected" element in second language teaching, opinions of English Language Teaching (ELT) Teacher Trainees (TT) taking pronunciation as an explicit instruction course seem to be neglected in research advocated in this field as well. Moreover, features of pronunciation (segmentals and…

  5. A matter of timing: harm reduction in learned helplessness.

    Science.gov (United States)

    Richter, Sophie Helene; Sartorius, Alexander; Gass, Peter; Vollmayr, Barbara

    2014-11-03

    Learned helplessness has excellent validity as an animal model for depression, but problems in reproducibility limit its use and the high degree of stress involved in the paradigm raises ethical concerns. We therefore aimed to identify which and how many trials of the learned helplessness paradigm are necessary to distinguish between helpless and non-helpless rats. A trial-by-trial reanalysis of tests from 163 rats with congenital learned helplessness or congenital non-learned helplessness and comparison of 82 rats exposed to inescapable shock with 38 shock-controls revealed that neither the first test trials, when rats showed unspecific hyperlocomotion, nor trials of the last third of the test, when almost all animals responded quickly to the stressor, contributed to sensitivity and specificity of the test. Considering only trials 3-10 improved the classification of helpless and non-helpless rats. The refined analysis allows abbreviation of the test for learned helplessness from 15 trials to 10 trials thereby reducing pain and stress of the experimental animals without losing statistical power.

  6. DeepRT: deep learning for peptide retention time prediction in proteomics

    OpenAIRE

    Ma, Chunwei; Zhu, Zhiyong; Ye, Jun; Yang, Jiarui; Pei, Jianguo; Xu, Shaohang; Zhou, Ruo; Yu, Chang; Mo, Fan; Wen, Bo; Liu, Siqi

    2017-01-01

    Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, pr...

  7. Towards Real-Time Speech Emotion Recognition for Affective E-Learning

    Science.gov (United States)

    Bahreini, Kiavash; Nadolski, Rob; Westera, Wim

    2016-01-01

    This paper presents the voice emotion recognition part of the FILTWAM framework for real-time emotion recognition in affective e-learning settings. FILTWAM (Framework for Improving Learning Through Webcams And Microphones) intends to offer timely and appropriate online feedback based upon learner's vocal intonations and facial expressions in order…

  8. Food Gardening and Intergenerational Learning in Times of ...

    African Journals Online (AJOL)

    The focus of discussion is the intergenerational interactions and learning ... pastoralism and, to a lesser degree, cultivation (Mayer, 1971; Mostert, 1992). ... discouraged about the hard physical work and rather limited economic ... in the Amanzi for Food project, a middle-aged female participant, Mrs Peters, has involved a.

  9. Real-Time Barcode Detection and Classification Using Deep Learning

    DEFF Research Database (Denmark)

    Hansen, Daniel Kold; Nasrollahi, Kamal; Rasmussen, Christoffer Bøgelund

    2017-01-01

    Barcodes, in their different forms, can be found on almost any packages available in the market. Detecting and then decoding of barcodes have therefore great applications. We describe how to adapt the state-of-the- art deep learning-based detector of You Only Look Once (YOLO) for the purpose...

  10. Food Gardening and Intergenerational Learning in Times of ...

    African Journals Online (AJOL)

    Uncertainty is a universal phenomenon, a lived experience, an unease about acting ... uncertainty through mediations of knowledge, the formation of new social relations and ... Environmental Affairs and Tourism, 53% of young people in the country are ... Bubomi learning network connected to the Amanzi for Food project.

  11. Studies and Suggestions on English Vocabulary Teaching and Learning

    Science.gov (United States)

    Zheng, Shigao

    2012-01-01

    To improve vocabulary learning and teaching in ELT settings, two questionnaires are designed and directed to more than 100 students and teachers in one of China's key universities. The findings suggest that an enhanced awareness of cultural difference, metaphorical competence, and learners' autonomy in vocabulary acquisition will effectively…

  12. A new test facility for the E-ELT infrared detector program

    Science.gov (United States)

    Lizon, Jean Louis; Amico, Paola; Brinkmann, Martin; Delabre, Bernard; Finger, Gert; Guidolin, Ivan Maria; Guzman, Ronald; Hinterschuster, Renate; Ives, Derek; Klein, Barbara; Quattri, Marco

    2016-08-01

    During the development of the VLT instrumentation program, ESO acquired considerable expertise in the area of infrared detectors, their testing and optimizing their performance. This can mainly be attributed to a very competent team and most importantly to the availability of a very well suited test facility, namely, IRATEC. This test facility was designed more than 15 years ago, specifically for 1K × 1K detectors such as the Aladdin device, with a maximum field of only 30 mm square. Unfortunately, this facility is no longer suited for the testing of the new larger format detectors that are going to be used to equip the future E-ELT instruments. It is projected that over the next 20 years, there will be of the order of 50-100 very large format detectors to be procured and tested for use with E-ELT first and second generation instruments and VLT third generation instruments. For this reason ESO has initiated the in-house design and construction of a dedicated new IR detector arrays test facility: the Facility for Infrared Array Testing (FIAT). It will be possible to mount up to four 60 mm square detectors in the facility, as well as mosaics of smaller detectors. It is being designed to have a very low thermal background such that detectors with 5.3 μm cut-off material can routinely be tested. The paper introduces the most important use cases for which FIAT is designed: they range from performing routine performance measurements on acquired devices, optimization setups for custom applications (like spot scan intra-pixel response, persistence and surface reflectivity measurements), test of new complex operation modes (e.g. high speed subwindowing mode for low order sensing, flexure control, etc.) and the development of new tests and calibration procedures to support the scientific requirements of the E-ELT and to allow troubleshooting the unexpected challenges that arise when a new detector system is brought online. The facility is also being designed to minimize

  13. A Computational Model of the Temporal Dynamics of Plasticity in Procedural Learning: Sensitivity to Feedback Timing

    Directory of Open Access Journals (Sweden)

    Vivian V. Valentin

    2014-07-01

    Full Text Available The evidence is now good that different memory systems mediate the learning of different types of category structures. In particular, declarative memory dominates rule-based (RB category learning and procedural memory dominates information-integration (II category learning. For example, several studies have reported that feedback timing is critical for II category learning, but not for RB category learning – results that have broad support within the memory systems literature. Specifically, II category learning has been shown to be best with feedback delays of 500ms compared to delays of 0 and 1000ms, and highly impaired with delays of 2.5 seconds or longer. In contrast, RB learning is unaffected by any feedback delay up to 10 seconds. We propose a neurobiologically detailed theory of procedural learning that is sensitive to different feedback delays. The theory assumes that procedural learning is mediated by plasticity at cortical-striatal synapses that are modified by dopamine-mediated reinforcement learning. The model captures the time-course of the biochemical events in the striatum that cause synaptic plasticity, and thereby accounts for the empirical effects of various feedback delays on II category learning.

  14. Beyond the Didactic Classroom: Educational Models to Encourage Active Student Involvement in Learning

    OpenAIRE

    Shreeve, Michael W.

    2008-01-01

    In a chiropractic college that utilizes a hybrid curriculum model composed of adult-based learning strategies along with traditional lecture-based course delivery, a literature search for educational delivery methods that would integrate the affective domain and the cognitive domain of learning provided some insights into the use of problem-based learning (PBL), experiential learning theory (ELT), and the emerging use of appreciative inquiry (AI) to enhance the learning experience. The purpos...

  15. Exploring Complex Engineering Learning Over Time with Epistemic Network Analysis

    OpenAIRE

    Svarovsky, Gina Navoa

    2011-01-01

    Recently, K-12 engineering education has received increased attention as a pathway to building stronger foundations in math andscience and introducing young people to the profession. However, the National Academy of Engineering found that many K-12engineering programs focus heavily on engineering design and science and math learning while minimizing the development ofengineering habits of mind. This narrowly-focused engineering activity can leave young people – and in particular, girls – with...

  16. Once upon a time.... Storytelling to enhance teaching and learning.

    Science.gov (United States)

    Lordly, Daphne

    2007-01-01

    The impact of storytelling in the classroom was examined, as was what motivates individuals to engage in storytelling. A storytelling methodology was introduced in an undergraduate nutrition course as an opportunity to enhance the teaching and learning environment. A 28-item, multi-part, self-administered survey was then distributed to the class (n=17). Survey responses (n=15, 88% response) indicate that educators' and students' storytelling can positively influence the learning environment. This occurs through the creation of a greater focus on personalized information, glimpses of real-life experience, a connection with a topic as participants recognize similarities in their own personal experience and knowledge, and connections between different topics and through the emphasis on key concepts. Stories initiate useful conversations about unexplored struggles within practice, such as the emotional dimension(s) of an issue or what it means to be professional. Students are motivated to participate in storytelling through an external focus on others (i.e., helping others to learn) and an internal focus on self (i.e., seeking a connection with others to promote social dialogue). Several challenges related to the use of storytelling in the classroom emerged. Storytelling develops ways of knowing and dialoguing about issues, which has the potential to influence how students will approach their professional practice.

  17. Understanding the effects of time on collaborative learning processes in problem based learning: a mixed methods study.

    Science.gov (United States)

    Hommes, J; Van den Bossche, P; de Grave, W; Bos, G; Schuwirth, L; Scherpbier, A

    2014-10-01

    Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning processes developed within and over three periods in the first 1,5 study years of an undergraduate curriculum. Next, a qualitative study using semi-structured individual interviews focused on detailed development of group processes driving collaborative learning during one period in seven tutorial groups. The hierarchic multilevel analyses of the quantitative data showed that a varying combination of group processes developed within and over the three observed periods. The qualitative study illustrated development in psychological safety, interdependence, potency, group learning behaviour, social and task cohesion. Two new processes emerged: 'transactive memory' and 'convergence in mental models'. The results indicate that groups are dynamic social systems with numerous contextual influences. Future research should thus include time as an important influence on collaborative learning. Practical implications are discussed.

  18. The native-speaker fever in English language teaching (ELT: Pitting pedagogical competence against historical origin

    Directory of Open Access Journals (Sweden)

    Anchimbe, Eric A.

    2006-01-01

    Full Text Available This paper discusses English language teaching (ELT around the world, and argues that as a profession, it should emphasise pedagogical competence rather than native-speaker requirement in the recruitment of teachers in English as a foreign language (EFL and English as a second language (ESL contexts. It establishes that being a native speaker does not make one automatically a competent speaker or, of that matter, a competent teacher of the language. It observes that on many grounds, including physical, sociocultural, technological and economic changes in the world as well as the status of English as official and national language in many post-colonial regions, the distinction between native and non-native speakers is no longer valid.

  19. Discovery of Potent and Selective Inhibitors for ADAMTS-4 through DNA-Encoded Library Technology (ELT).

    Science.gov (United States)

    Ding, Yun; O'Keefe, Heather; DeLorey, Jennifer L; Israel, David I; Messer, Jeffrey A; Chiu, Cynthia H; Skinner, Steven R; Matico, Rosalie E; Murray-Thompson, Monique F; Li, Fan; Clark, Matthew A; Cuozzo, John W; Arico-Muendel, Christopher; Morgan, Barry A

    2015-08-13

    The aggrecan degrading metalloprotease ADAMTS-4 has been identified as a novel therapeutic target for osteoarthritis. Here, we use DNA-encoded Library Technology (ELT) to identify novel ADAMTS-4 inhibitors from a DNA-encoded triazine library by affinity selection. Structure-activity relationship studies based on the selection information led to the identification of potent and highly selective inhibitors. For example, 4-(((4-(6,7-dimethoxy-3,4-dihydroisoquinolin-2(1H)-yl)-6-(((4-methylpiperazin-1-yl)methyl)amino)-1,3,5-triazin-2-yl)amino)methyl)-N-ethyl-N-(m-tolyl)benzamide has IC50 of 10 nM against ADAMTS-4, with >1000-fold selectivity over ADAMT-5, MMP-13, TACE, and ADAMTS-13. These inhibitors have no obvious zinc ligand functionality.

  20. Prefocal station mechanical design concept study for the E-ELT

    Science.gov (United States)

    Jolley, Paul; Brunetto, Enzo; Frank, Christoph; Lewis, Steffan; Marchetti, Enrico

    2016-07-01

    The Nasmyth platforms of the E-ELT will contain one Prefocal Station (PFS) each. The main PFS functional requirements are to provide a focal plane to the three Nasmyth focal stations and the Coudé focus, optical sensing supporting telescope low order optimisation and seeing limited image quality, and optical sensing supporting characterising and phasing of M1 and other telescope subsystems. The PFS user requirements are used to derive the PFS technical requirements specification that will form the basis for design, development and production of the system. This specification process includes high-level architectural decisions and technical performance budget allocations. The mechanical design concepts reported here have been developed in order to validate key system specifications and associated technical budgets.

  1. A mechanical design concept for EAGLE on the revised E-ELT

    Science.gov (United States)

    Dubbeldam, Cornelis M.

    2014-07-01

    ESO have recently revisited the design of the E-ELT with a view to reducing cost, leading to de-scopes which include a smaller primary aperture and removal of the Gravity Invariant Focal Station (GIFS). In its original concept, the EAGLE instrument was designed to be located at the GIFS and consequently a major mechanical re-design was required to enable the instrument to be placed on its side in a conventional straight-through Nasmyth configuration. In this paper, a conceptual design for a new instrument structure is presented. A preliminary finite element analysis was carried out to assess the structure's behaviour under operating loading conditions (e.g. gravity loads). The results of this analysis demonstrate that the proposed design is viable, without any significant degradation in performance compared to the original GIFS design.

  2. E-ELT Site Chosen - World's Biggest Eye on the Sky to be Located on Armazones, Chile

    Science.gov (United States)

    2010-04-01

    On 26 April 2010, the ESO Council selected Cerro Armazones as the baseline site for the planned 42-metre European Extremely Large Telescope (E-ELT). Cerro Armazones is a mountain at an altitude of 3060 metres in the central part of Chile's Atacama Desert, some 130 kilometres south of the town of Antofagasta and about 20 kilometres from Cerro Paranal, home of ESO's Very Large Telescope. "This is an important milestone that allows us to finalise the baseline design of this very ambitious project, which will vastly advance astronomical knowledge," says Tim de Zeeuw, ESO's Director General. "I thank the site selection team for the tremendous work they have done over the past few years." ESO's next step is to build a European extremely large optical/infrared telescope (E-ELT) with a primary mirror 42 metres in diameter. The E-ELT will be "the world's biggest eye on the sky" - the only such telescope in the world. ESO is drawing up detailed construction plans together with the community. The E-ELT will address many of the most pressing unsolved questions in astronomy, and may, eventually, revolutionise our perception of the Universe, much as Galileo's telescope did 400 years ago. The final go-ahead for construction is expected at the end of 2010, with the start of operations planned for 2018. The decision on the E-ELT site was taken by the ESO Council, which is the governing body of the Organisation composed of representatives of ESO's fourteen Member States, and is based on an extensive comparative meteorological investigation, which lasted several years. The majority of the data collected during the site selection campaigns will be made public in the course of the year 2010. Various factors needed to be considered in the site selection process. Obviously the "astronomical quality" of the atmosphere, for instance, the number of clear nights, the amount of water vapour, and the "stability" of the atmosphere (also known as seeing) played a crucial role. But other

  3. Time Spent, Workload, and Student and Faculty Perceptions in a Blended Learning Environment

    Science.gov (United States)

    Schumacher, Christie; Arif, Sally

    2016-01-01

    Objective. To evaluate student perception and time spent on asynchronous online lectures in a blended learning environment (BLE) and to assess faculty workload and perception. Methods. Students (n=427) time spent viewing online lectures was measured in three courses. Students and faculty members completed a survey to assess perceptions of a BLE. Faculty members recorded time spent creating BLEs. Results. Total time spent in the BLE was less than the allocated time for two of the three courses by 3-15%. Students preferred online lectures for their flexibility, students’ ability to apply information learned, and congruence with their learning styles. Faculty members reported the BLE facilitated higher levels of learning during class sessions but noted an increase in workload. Conclusion. A BLE increased faculty workload but was well received by students. Time spent viewing online lectures was less than what was allocated in two of the three courses. PMID:27667839

  4. Status of E-ELT M5 scale-one demonstrator

    Science.gov (United States)

    Barriga, Pablo; Sedghi, Babak; Dimmler, Martin; Kornweibel, Nick

    2014-07-01

    The fifth mirror of the European Extremely Large Telescope optical train is a field stabilization tip/tilt unit responsible for correcting the dynamical tip and tilt caused mainly by wind load on the telescope. A scale-one prototype including the inclined support, the fixed frame and a basic control system was designed and manufactured by NTE-SENER (Spain) and CSEM (Switzerland) as part of the prototyping and design activities. All interfaces to the mirror have been reproduced on a dummy structure reproducing the inertial characteristics of the optical element. The M5 unit is required to have sufficient bandwidth for tip/tilt reference commands coming from the wavefront control system. Such a bandwidth can be achieved using local active damping loop to damp the low frequency mechanical modes before closing a position loop. Prototyping on the M5 unit has been undertaken in order to demonstrate the E-ELT control system architecture, concepts and development standards and to further study active damping strategies. The control system consists of two nested loops: a local damping loop and a position loop. The development of this control system was undertaken following the E-ELT control system development standards in order to determine their applicability and performance and includes hardware selection, communication, synchronization, configuration, and data logging. In this paper we present the current status of the prototype M5 control system and the latest results on the active damping control strategy, in particular the promising results obtained with the method of positive position feedback.

  5. The right time to learn: mechanisms and optimization of spaced learning

    Science.gov (United States)

    Smolen, Paul; Zhang, Yili; Byrne, John H.

    2016-01-01

    For many types of learning, spaced training, which involves repeated long inter-trial intervals, leads to more robust memory formation than does massed training, which involves short or no intervals. Several cognitive theories have been proposed to explain this superiority, but only recently have data begun to delineate the underlying cellular and molecular mechanisms of spaced training, and we review these theories and data here. Computational models of the implicated signalling cascades have predicted that spaced training with irregular inter-trial intervals can enhance learning. This strategy of using models to predict optimal spaced training protocols, combined with pharmacotherapy, suggests novel ways to rescue impaired synaptic plasticity and learning. PMID:26806627

  6. Generalization bounds of ERM-based learning processes for continuous-time Markov chains.

    Science.gov (United States)

    Zhang, Chao; Tao, Dacheng

    2012-12-01

    Many existing results on statistical learning theory are based on the assumption that samples are independently and identically distributed (i.i.d.). However, the assumption of i.i.d. samples is not suitable for practical application to problems in which samples are time dependent. In this paper, we are mainly concerned with the empirical risk minimization (ERM) based learning process for time-dependent samples drawn from a continuous-time Markov chain. This learning process covers many kinds of practical applications, e.g., the prediction for a time series and the estimation of channel state information. Thus, it is significant to study its theoretical properties including the generalization bound, the asymptotic convergence, and the rate of convergence. It is noteworthy that, since samples are time dependent in this learning process, the concerns of this paper cannot (at least straightforwardly) be addressed by existing methods developed under the sample i.i.d. assumption. We first develop a deviation inequality for a sequence of time-dependent samples drawn from a continuous-time Markov chain and present a symmetrization inequality for such a sequence. By using the resultant deviation inequality and symmetrization inequality, we then obtain the generalization bounds of the ERM-based learning process for time-dependent samples drawn from a continuous-time Markov chain. Finally, based on the resultant generalization bounds, we analyze the asymptotic convergence and the rate of convergence of the learning process.

  7. Online Learning Solutions for Freeway Travel Time Prediction

    NARCIS (Netherlands)

    Van Lint, J.W.C.

    2008-01-01

    Providing travel time information to travelers on available route alternatives in traffic networks is widely believed to yield positive effects on individual drive behavior and (route/departure time) choice behavior, as well as on collective traffic operations in terms of, for example, overall time

  8. Business Faculty Time Management: Lessons Learned from the Trenches

    Science.gov (United States)

    Cummings, Richard G.; Holmes, Linda E.

    2009-01-01

    Teaching, research, and service expectations of the academic profession may sometimes seem overwhelming. Although much has been written about time management in general, there has not been much written about time management in the academic professions and even less written about time management for academics in the business disciplines. This paper…

  9. Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

    Directory of Open Access Journals (Sweden)

    Zhiming Gui

    2014-01-01

    Full Text Available Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

  10. A Quasi-Experimental Examination: Cognitive Sequencing of Instruction Using Experiential Learning Theory for STEM Concepts in Agricultural Education

    Science.gov (United States)

    Smith, Kasee L.; Rayfield, John

    2017-01-01

    Understanding methods for effectively instructing STEM education concepts is essential in the current climate of education (Freeman, Marginson, & Tyler 2014). Kolb's experiential learning theory (ELT) outlines four specific modes of learning, based on preferences for grasping and transforming information. This quasi-experimental study was…

  11. Learning of pitch and time structures in an artificial grammar setting.

    Science.gov (United States)

    Prince, Jon B; Stevens, Catherine J; Jones, Mari Riess; Tillmann, Barbara

    2018-04-12

    Despite the empirical evidence for the power of the cognitive capacity of implicit learning of structures and regularities in several modalities and materials, it remains controversial whether implicit learning extends to the learning of temporal structures and regularities. We investigated whether (a) an artificial grammar can be learned equally well when expressed in duration sequences as when expressed in pitch sequences, (b) learning of the artificial grammar in either duration or pitch (as the primary dimension) sequences can be influenced by the properties of the secondary dimension (invariant vs. randomized), and (c) learning can be boosted when the artificial grammar is expressed in both pitch and duration. After an exposure phase with grammatical sequences, learning in a subsequent test phase was assessed in a grammaticality judgment task. Participants in both the pitch and duration conditions showed incidental (not fully implicit) learning of the artificial grammar when the secondary dimension was invariant, but randomizing the pitch sequence prevented learning of the artificial grammar in duration sequences. Expressing the artificial grammar in both pitch and duration resulted in disproportionately better performance, suggesting an interaction between the learning of pitch and temporal structure. The findings are relevant to research investigating the learning of temporal structures and the learning of structures presented simultaneously in 2 dimensions (e.g., space and time, space and objects). By investigating learning, the findings provide further insight into the potential specificity of pitch and time processing, and their integrated versus independent processing, as previously debated in music cognition research. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  12. Identification of critical time-consuming student support activities in e-learning

    NARCIS (Netherlands)

    De Vries, Fred; Kester, Liesbeth; Sloep, Peter; Van Rosmalen, Peter; Pannekeet, Kees; Koper, Rob

    2005-01-01

    Please cite the original publication: De Vries, F., Kester, L., Sloep, P., Van Rosmalen, P., Pannekeet, K., & Koper, R. (2005). Identification of critical time-consuming student support activities in e-learning. Research in Learning Technology (ALT-J), 13(3), 219-229.

  13. The Role of Age and Occupational Future Time Perspective in Workers' Motivation to Learn

    Science.gov (United States)

    Kochoian, Nané; Raemdonck, Isabel; Frenay, Mariane; Zacher, Hannes

    2017-01-01

    The purpose of this paper is to better understand the relationship between employees' chronological age and their motivation to learn, by adopting a lifespan perspective. Based on socioemotional selectivity theory, we suggest that occupational future time perspective mediates the relationship between age and motivation to learn. In accordance with…

  14. Online discussion compensates for suboptimal timing of supportive information presentation in a digitally supported learning environment

    NARCIS (Netherlands)

    Noroozi, O.; Busstra, M.C.; Mulder, M.; Biemans, H.J.A.; Tobi, H.; Geelen, A.; Veer, van 't P.; Chizari, M.

    2012-01-01

    This study used a sequential set-up to investigate the consecutive effects of timing of supportive information presentation (information before vs. information during the learning task clusters) in interactive digital learning materials (IDLMs) and type of collaboration (personal discussion vs.

  15. Effects of Business School Student's Study Time on the Learning Process

    Science.gov (United States)

    Tetteh, Godson Ayertei

    2016-01-01

    Purpose: This paper aims to clarify the relationship between the student's study time and the learning process in the higher education system by adapting the total quality management (TQM) principles-process approach. Contrary to Deming's (1982) constancy of purpose to improve the learning process, some students in higher education postpone their…

  16. Flexible Learning and Teaching: Looking Beyond the Binary of Full-time/Part-time Provision in South African Higher Education

    Directory of Open Access Journals (Sweden)

    Barbara M Jones

    2015-06-01

    Full Text Available This paper engages with literature on flexible learning and teaching in order to explore whether it may be possible, within the South African context, to have flexible learning and teaching provide a third way which goes beyond the current practice of full-time/part-time provision. This binary classification of students is a proxy for day-time/after-hours delivery.  The argument is made that effective, flexible learning and teaching requires a fundamental shift in thinking about learning and teaching in higher education that moves us beyond such binaries. The paper proposes that in order to ensure access and success for students, ‘common knowledge’ (Edwards, 2010 will need to be co-constructed which understands flexible learning and teaching in ways which will meet needs of a diversity of students, including working students. It will require ‘resourceful leadership’ (Edwards, 2014 within the university that recognises, enhances and gives purpose to the capability of colleagues at every level of the systems they lead. Also, it will require the building of ‘common knowledge’ between certain sectors of universities and particular workplaces.

  17. Benefits for Voice Learning Caused by Concurrent Faces Develop over Time.

    Science.gov (United States)

    Zäske, Romi; Mühl, Constanze; Schweinberger, Stefan R

    2015-01-01

    Recognition of personally familiar voices benefits from the concurrent presentation of the corresponding speakers' faces. This effect of audiovisual integration is most pronounced for voices combined with dynamic articulating faces. However, it is unclear if learning unfamiliar voices also benefits from audiovisual face-voice integration or, alternatively, is hampered by attentional capture of faces, i.e., "face-overshadowing". In six study-test cycles we compared the recognition of newly-learned voices following unimodal voice learning vs. bimodal face-voice learning with either static (Exp. 1) or dynamic articulating faces (Exp. 2). Voice recognition accuracies significantly increased for bimodal learning across study-test cycles while remaining stable for unimodal learning, as reflected in numerical costs of bimodal relative to unimodal voice learning in the first two study-test cycles and benefits in the last two cycles. This was independent of whether faces were static images (Exp. 1) or dynamic videos (Exp. 2). In both experiments, slower reaction times to voices previously studied with faces compared to voices only may result from visual search for faces during memory retrieval. A general decrease of reaction times across study-test cycles suggests facilitated recognition with more speaker repetitions. Overall, our data suggest two simultaneous and opposing mechanisms during bimodal face-voice learning: while attentional capture of faces may initially impede voice learning, audiovisual integration may facilitate it thereafter.

  18. An Investigation Into the Culture and Social Actors Representation in Summit Series ELT Textbooks Within van Leeuwen’s 1996 Framework

    Directory of Open Access Journals (Sweden)

    Nasser Rashidi

    2015-03-01

    Full Text Available The current study aims at identifying particular ways through which social actors are represented in Summit Series ELT textbooks. It examines cultural load in the textbooks within critical discourse analysis framework, in this case van Leeuwen’s framework. Particularly, the study attempts to explore if values, norms, and roles are culture/context-bound. Results of the analyses showed that among discursive features, Inclusion, Genericization, and Indetermination were used more than Exclusion, Specification, and Determination. Activation was more observed than Passivation, and Categorization had an important function in the representation of some of the social actors along with Assimilation and Impersonalization. The analysis also indicated the impartiality toward the representation of social actors. Moral, social, and personal values were the most disseminated values, while social morality and traditions had the highest occurrence. However, a few discriminative cases were found regarding gender roles. The researchers proposed that Summit Series were less grounded in cultural assumptions/biases. This impartiality eases language learning by keeping learners away from misunderstanding and incomprehensibility.

  19. Engaged Journalism: Using Experiential Learning Theory (ELT) for In-Class Journaling Activities

    Science.gov (United States)

    Jenkins, J. Jacob; Clarke, Tracylee

    2017-01-01

    Educators have long recognized the value and import of class journaling. Traditional approaches to journaling, however, only engage students in one mode of communicative expression while allowing them to procrastinate in writing their entries. Typical journals are also read exclusively by the instructor, which overlooks the opportunity for…

  20. Geological Time, Biological Events and the Learning Transfer Problem

    Science.gov (United States)

    Johnson, Claudia C.; Middendorf, Joan; Rehrey, George; Dalkilic, Mehmet M.; Cassidy, Keely

    2014-01-01

    Comprehension of geologic time does not come easily, especially for students who are studying the earth sciences for the first time. This project investigated the potential success of two teaching interventions that were designed to help non-science majors enrolled in an introductory geology class gain a richer conceptual understanding of the…

  1. Learning for sustainability in times of accelerating change

    NARCIS (Netherlands)

    Wals, A.E.J.; Corcoran, P.B.

    2012-01-01

    We live in turbulent times, our world is changing at accelerating speed. Information is everywhere, but wisdom appears in short supply when trying to address key inter-related challenges of our time such as; runaway climate change, the loss of biodiversity, the depletion of natural resources, the

  2. Time series forecasting based on deep extreme learning machine

    NARCIS (Netherlands)

    Guo, Xuqi; Pang, Y.; Yan, Gaowei; Qiao, Tiezhu; Yang, Guang-Hong; Yang, Dan

    2017-01-01

    Multi-layer Artificial Neural Networks (ANN) has caught widespread attention as a new method for time series forecasting due to the ability of approximating any nonlinear function. In this paper, a new local time series prediction model is established with the nearest neighbor domain theory, in

  3. Impact of learning adaptability and time management disposition on study engagement among Chinese baccalaureate nursing students.

    Science.gov (United States)

    Liu, Jing-Ying; Liu, Yan-Hui; Yang, Ji-Peng

    2014-01-01

    The aim of this study was to explore the relationships among study engagement, learning adaptability, and time management disposition in a sample of Chinese baccalaureate nursing students. A convenient sample of 467 baccalaureate nursing students was surveyed in two universities in Tianjin, China. Students completed a questionnaire that included their demographic information, Chinese Utrecht Work Engagement Scale-Student Questionnaire, Learning Adaptability Scale, and Adolescence Time Management Disposition Scale. One-way analysis of variance tests were used to assess the relationship between certain characteristics of baccalaureate nursing students. Pearson correlation was performed to test the correlation among study engagement, learning adaptability, and time management disposition. Hierarchical linear regression analyses were performed to explore the mediating role of time management disposition. The results revealed that study engagement (F = 7.20, P < .01) and learning adaptability (F = 4.41, P < .01) differed across grade groups. Learning adaptability (r = 0.382, P < .01) and time management disposition (r = 0.741, P < .01) were positively related with study engagement. Time management disposition had a partially mediating effect on the relationship between study engagement and learning adaptability. The findings implicate that educators should not only promote interventions to increase engagement of baccalaureate nursing students but also focus on development, investment in adaptability, and time management. Copyright © 2014 Elsevier Inc. All rights reserved.

  4. Distributing learning over time: the spacing effect in children's acquisition and generalization of science concepts.

    Science.gov (United States)

    Vlach, Haley A; Sandhofer, Catherine M

    2012-01-01

    The spacing effect describes the robust finding that long-term learning is promoted when learning events are spaced out in time rather than presented in immediate succession. Studies of the spacing effect have focused on memory processes rather than for other types of learning, such as the acquisition and generalization of new concepts. In this study, early elementary school children (5- to 7-year-olds; N = 36) were presented with science lessons on 1 of 3 schedules: massed, clumped, and spaced. The results revealed that spacing lessons out in time resulted in higher generalization performance for both simple and complex concepts. Spaced learning schedules promote several types of learning, strengthening the implications of the spacing effect for educational practices and curriculum. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

  5. Giving English Language Learners the Time They Need to Succeed: Profiles of Three Expanded Learning Time Schools

    Science.gov (United States)

    Farbman, David A.

    2015-01-01

    With the number of students who are English language learners (ELLs) likely to double in coming years, it is more important than ever for schools across the U.S. to design and implement educational practices and strategies that best meet ELLs' learning needs, says the report, "Giving English Language Learners the Time They Need to…

  6. Dissociable effects of practice variability on learning motor and timing skills.

    Science.gov (United States)

    Caramiaux, Baptiste; Bevilacqua, Frédéric; Wanderley, Marcelo M; Palmer, Caroline

    2018-01-01

    Motor skill acquisition inherently depends on the way one practices the motor task. The amount of motor task variability during practice has been shown to foster transfer of the learned skill to other similar motor tasks. In addition, variability in a learning schedule, in which a task and its variations are interweaved during practice, has been shown to help the transfer of learning in motor skill acquisition. However, there is little evidence on how motor task variations and variability schedules during practice act on the acquisition of complex motor skills such as music performance, in which a performer learns both the right movements (motor skill) and the right time to perform them (timing skill). This study investigated the impact of rate (tempo) variability and the schedule of tempo change during practice on timing and motor skill acquisition. Complete novices, with no musical training, practiced a simple musical sequence on a piano keyboard at different rates. Each novice was assigned to one of four learning conditions designed to manipulate the amount of tempo variability across trials (large or small tempo set) and the schedule of tempo change (randomized or non-randomized order) during practice. At test, the novices performed the same musical sequence at a familiar tempo and at novel tempi (testing tempo transfer), as well as two novel (but related) sequences at a familiar tempo (testing spatial transfer). We found that practice conditions had little effect on learning and transfer performance of timing skill. Interestingly, practice conditions influenced motor skill learning (reduction of movement variability): lower temporal variability during practice facilitated transfer to new tempi and new sequences; non-randomized learning schedule improved transfer to new tempi and new sequences. Tempo (rate) and the sequence difficulty (spatial manipulation) affected performance variability in both timing and movement. These findings suggest that there is a

  7. Making time for learning-oriented leadership in multidisciplinary hospital management groups.

    Science.gov (United States)

    Singer, Sara J; Hayes, Jennifer E; Gray, Garry C; Kiang, Mathew V

    2015-01-01

    Although the clinical requirements of health care delivery imply the need for interdisciplinary management teams to work together to promote frontline learning, such interdisciplinary, learning-oriented leadership is atypical. We designed this study to identify behaviors enabling groups of diverse managers to perform as learning-oriented leadership teams on behalf of quality and safety. We randomly selected 12 of 24 intact groups of hospital managers from one hospital to participate in a Safety Leadership Team Training program. We collected primary data from March 2008 to February 2010 including pre- and post-staff surveys, multiple interviews, observations, and archival data from management groups. We examined the level and trend in frontline perceptions of managers' learning-oriented leadership following the intervention and ability of management groups to achieve objectives on targeted improvement projects. Among the 12 intervention groups, we identified higher- and lower-performing intervention groups and behaviors that enabled higher performers to work together more successfully. Management groups that achieved more of their performance goals and whose staff perceived more and greater improvement in their learning-oriented leadership after participation in Safety Leadership Team Training invested in structures that created learning capacity and conscientiously practiced prescribed learning-oriented management and problem-solving behaviors. They made the time to do these things because they envisioned the benefits of learning, valued the opportunity to learn, and maintained an environment of mutual respect and psychological safety within their group. Learning in management groups requires vision of what learning can accomplish; will to explore, practice, and build learning capacity; and mutual respect that sustains a learning environment.

  8. ULE design considerations for a 3m class light weighted mirror blank for E-ELT M5

    Science.gov (United States)

    Fox, Andrew; Hobbs, Tom; Edwards, Mary; Arnold, Matthew; Sawyer, Kent

    2016-07-01

    It is expected that the next generation of large ground based astronomical telescopes will need large fast-steering/tip-tilt mirrors made of ultra-lightweight construction. These fast-steering mirrors are used to continuously correct for atmospheric disturbances and telescope vibrations. An example of this is the European Extremely Large Telescope (E-ELT) M5 lightweight mirror, which is part of the Tip-Tilt/Field-Stabilization Unit. The baseline design for the E-ELT M5 mirror, as presented in the E-ELT Construction Proposal, is a closed-back ULE mirror with a lightweight core using square core cells. Corning Incorporated (Corning) has a long history of manufacturing lightweight mirror blanks using ULE in a closed-back construction, going back to the 1960's, and includes the Hubble Space Telescope primary mirror, Subaru Telescope secondary and tertiary mirrors, the Magellan I and II tertiary mirrors, and Kepler Space Telescope primary mirror, among many others. A parametric study of 1-meter class lightweight mirror designs showed that Corning's capability to seal a continuous back sheet to a light-weighted core structure provides superior mirror rigidity, in a near-zero thermal expansion material, relative to other existing technologies in this design space. Corning has investigated the parametric performance of several design characteristics for a 3-meter class lightweight mirror blank for the E-ELT M5. Finite Element Analysis was performed on several design scenarios to obtain weight, areal density, and first Eigen frequency. This paper presents an overview of Corning ULE and lightweight mirror manufacturing capabilities, the parametric performance of design characteristics for 1-meter class and 3-meter class lightweight mirrors, as well as the manufacturing advantages and disadvantages of those characteristics.

  9. Discovery of a Potent Class of PI3Kα Inhibitors with Unique Binding Mode via Encoded Library Technology (ELT).

    Science.gov (United States)

    Yang, Hongfang; Medeiros, Patricia F; Raha, Kaushik; Elkins, Patricia; Lind, Kenneth E; Lehr, Ruth; Adams, Nicholas D; Burgess, Joelle L; Schmidt, Stanley J; Knight, Steven D; Auger, Kurt R; Schaber, Michael D; Franklin, G Joseph; Ding, Yun; DeLorey, Jennifer L; Centrella, Paolo A; Mataruse, Sibongile; Skinner, Steven R; Clark, Matthew A; Cuozzo, John W; Evindar, Ghotas

    2015-05-14

    In the search of PI3K p110α wild type and H1047R mutant selective small molecule leads, an encoded library technology (ELT) campaign against the desired target proteins was performed which led to the discovery of a selective chemotype for PI3K isoforms from a three-cycle DNA encoded library. An X-ray crystal structure of a representative inhibitor from this chemotype demonstrated a unique binding mode in the p110α protein.

  10. ELT RESEARCH PAPERS AS AUTHENTIC MATERIALS IN TEACHING RESEARCH-BASED ARTICLE WRITING: A CASE IN INDONESIAN CONTEXT

    Directory of Open Access Journals (Sweden)

    M. Ali Ghufron

    2017-09-01

    Full Text Available There are strong shreds of evidence that the choice of instructional materials has large effects on students’ achievement. This study was to assess the efficacy of using ELT research papers as authentic materials in teaching research-based article writing. This study was aimed at revealing whether or not there is a significant difference in students’ writing skill in terms of ELT research paper writing between the students who were taught by using ELT research papers as authentic materials and those who were taught by using textbook materials provided by the faculty. This study belongs to a quasi-experimental study with an experimental and control group pretest-posttest design. The population of this study was 75 students from the fourth semester of English Education Study Program of IKIP PGRI Bojonegoro, East Java, Indonesia. The sample was selected through cluster random sampling and consisted of 50 students that were divided into two groups. The instrument used to collect the data was a writing test. Consequently, normality and homogeneity of the data were tested. A t-test was used to compare the mean of the two groups. The hypothesis was designed and tested at 0.05 level of significance. The results revealed that there is a significant difference in students’ academic writing skill between the students who were taught by using the ELT research papers as authentic materials and those who were taught by using textbook materials. The t-test revealed that t-value is higher than t-table (6.07>2.01. Therefore it is concluded that the authentic instructional materials could significantly improve students’ academic writing skill.

  11. Using Response Times to Assess Learning Progress: A Joint Model for Responses and Response Times

    Science.gov (United States)

    Wang, Shiyu; Zhang, Susu; Douglas, Jeff; Culpepper, Steven

    2018-01-01

    Analyzing students' growth remains an important topic in educational research. Most recently, Diagnostic Classification Models (DCMs) have been used to track skill acquisition in a longitudinal fashion, with the purpose to provide an estimate of students' learning trajectories in terms of the change of fine-grained skills overtime. Response time…

  12. Beyond the didactic classroom: educational models to encourage active student involvement in learning.

    Science.gov (United States)

    Shreeve, Michael W

    2008-01-01

    In a chiropractic college that utilizes a hybrid curriculum model composed of adult-based learning strategies along with traditional lecture-based course delivery, a literature search for educational delivery methods that would integrate the affective domain and the cognitive domain of learning provided some insights into the use of problem-based learning (PBL), experiential learning theory (ELT), and the emerging use of appreciative inquiry (AI) to enhance the learning experience. The purpose of this literature review is to provide a brief overview of key components of PBL, ELT, and AI in educational methodology and to discuss how these might be used within the chiropractic curriculum to supplement traditional didactic lecture courses. A growing body of literature describes the use of PBL and ELT in educational settings across many disciplines, both at the undergraduate and graduate levels. The use of appreciative inquiry as an instructional methodology presents a new area for exploration and study in the academic environment. Educational research in the chiropractic classroom incorporating ELT and appreciative inquiry might provide some valuable insights for future curriculum development.

  13. A Time to Define: Making the Specific Learning Disability Definition Prescribe Specific Learning Disability

    Science.gov (United States)

    Kavale, Kenneth A.; Spaulding, Lucinda S.; Beam, Andrea P.

    2009-01-01

    Unlike other special education categories defined in U.S. law (Individuals with Disabilities Education Act), the definition of specific learning disability (SLD) has not changed since first proposed in 1968. Thus, although the operational definition of SLD has responded to new knowledge and understanding about the construct, the formal definition…

  14. A presentation system for just-in-time learning in radiology.

    Science.gov (United States)

    Kahn, Charles E; Santos, Amadeu; Thao, Cheng; Rock, Jayson J; Nagy, Paul G; Ehlers, Kevin C

    2007-03-01

    There is growing interest in bringing medical educational materials to the point of care. We sought to develop a system for just-in-time learning in radiology. A database of 34 learning modules was derived from previously published journal articles. Learning objectives were specified for each module, and multiple-choice test items were created. A web-based system-called TEMPO-was developed to allow radiologists to select and view the learning modules. Web services were used to exchange clinical context information between TEMPO and the simulated radiology work station. Preliminary evaluation was conducted using the System Usability Scale (SUS) questionnaire. TEMPO identified learning modules that were relevant to the age, sex, imaging modality, and body part or organ system of the patient being viewed by the radiologist on the simulated clinical work station. Users expressed a high degree of satisfaction with the system's design and user interface. TEMPO enables just-in-time learning in radiology, and can be extended to create a fully functional learning management system for point-of-care learning in radiology.

  15. [A technological device for optimizing the time taken for blind people to learn Braille].

    Science.gov (United States)

    Hernández, Cesar; Pedraza, Luis F; López, Danilo

    2011-10-01

    This project was aimed at designing and putting an electronic prototype into practice for improving the initial time taken by visually handicapped people for learning Braille, especially children. This project was mainly based on a prototype digital electronic device which identifies and translates material written by a user in Braille by a voice synthesis system, producing artificial words to determine whether a handicapped person's writing in Braille has been correct. A global system for mobile communications (GSM) module was also incorporated into the device which allowed it to send text messages, thereby involving innovation in the field of articles for aiding visually handicapped people. This project's main result was an easily accessed and understandable prototype device which improved visually handicapped people's initial learning of Braille. The time taken for visually handicapped people to learn Braille became significantly reduced whilst their interest increased, as did their concentration time regarding such learning.

  16. Peer-assisted learning: time for nomenclature clarification

    Directory of Open Access Journals (Sweden)

    Alexander Olaussen

    2016-07-01

    Full Text Available Background: Peer-assisted learning (PAL is used throughout all levels of healthcare education. Lack of formalised agreement on different PAL programmes may confuse the literature. Given the increasing interest in PAL as an education philosophy, the terms need clarification. The aim of this review is to 1 describe different PAL programmes, 2 clarify the terminology surrounding PAL, and 3 propose a simple pragmatic way of defining PAL programmes based on their design. Methods: A review of current PAL programmes within the healthcare setting was conducted. Each programme was scrutinised based on two aspects: the relationship between student and teacher, and the student to teacher ratio. The studies were then shown to fit exclusively into the novel proposed classification. Results: The 34 programmes found, demonstrate a wide variety in terms used. We established six terms, which exclusively applied to the programmes. The relationship between student and teacher was categorised as peer-to-peer or near-peer. The student to teacher ratio suited three groupings, named intuitively ‘Mentoring’ (1:1 or 1:2, ‘Tutoring’ (1:3–10, and ‘Didactic’ (1:>10. From this, six novel terms – all under the heading of PAL – are suggested: ‘Peer Mentoring’, ‘Peer Tutoring’, ‘Peer Didactic’, ‘Near-Peer Mentoring’, ‘Near-Peer Tutoring’, and ‘Near-Peer Didactic’. Conclusions: We suggest herein a simple pragmatic terminology to overcome ambiguous terminology. Academically, clear terms will allow effective and efficient research, ensuring furthering of the educational philosophy.

  17. Peer-assisted learning: time for nomenclature clarification

    Science.gov (United States)

    Olaussen, Alexander; Reddy, Priya; Irvine, Susan; Williams, Brett

    2016-01-01

    Background Peer-assisted learning (PAL) is used throughout all levels of healthcare education. Lack of formalised agreement on different PAL programmes may confuse the literature. Given the increasing interest in PAL as an education philosophy, the terms need clarification. The aim of this review is to 1) describe different PAL programmes, 2) clarify the terminology surrounding PAL, and 3) propose a simple pragmatic way of defining PAL programmes based on their design. Methods A review of current PAL programmes within the healthcare setting was conducted. Each programme was scrutinised based on two aspects: the relationship between student and teacher, and the student to teacher ratio. The studies were then shown to fit exclusively into the novel proposed classification. Results The 34 programmes found, demonstrate a wide variety in terms used. We established six terms, which exclusively applied to the programmes. The relationship between student and teacher was categorised as peer-to-peer or near-peer. The student to teacher ratio suited three groupings, named intuitively ‘Mentoring’ (1:1 or 1:2), ‘Tutoring’ (1:3–10), and ‘Didactic’ (1:>10). From this, six novel terms – all under the heading of PAL – are suggested: ‘Peer Mentoring’, ‘Peer Tutoring’, ‘Peer Didactic’, ‘Near-Peer Mentoring’, ‘Near-Peer Tutoring’, and ‘Near-Peer Didactic’. Conclusions We suggest herein a simple pragmatic terminology to overcome ambiguous terminology. Academically, clear terms will allow effective and efficient research, ensuring furthering of the educational philosophy. PMID:27415590

  18. An algorithm for learning real-time automata (extended abstract)

    NARCIS (Netherlands)

    Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.

    2007-01-01

    A common model for discrete event systems is a deterministic finite automaton (DFA). An advantage of this model is that it can be interpreted by domain experts. When observing a real-world system, however, there often is more information than just the sequence of discrete events: the time at which

  19. No Time to Think: Policy, Pedagogy and Professional Learning

    Science.gov (United States)

    Leonard, Simon N.; Roberts, Philip

    2016-01-01

    In this study, we seek to illuminate the effects of the global policy convergence in education through a close study of its enactment within an Australian Teacher Education course. Building on an examination of the changing priorities of a cohort of pre-service teachers over a short space of time, we argue that the enactment of New Public…

  20. Time and Practice: Learning to Become a Geographer

    Science.gov (United States)

    Downs, Roger M.

    2014-01-01

    A goal of geography education is fostering geographic literacy for all and building significant expertise for some. How much time and practice do students need to become literate or expert in geography? There is not an answer to this question. Using two concepts from cognitive psychology--the ideas of ten thousand hours and deliberate…

  1. Problem based learning: the effect of real time data on the website to student independence

    Science.gov (United States)

    Setyowidodo, I.; Pramesti, Y. S.; Handayani, A. D.

    2018-05-01

    Learning science developed as an integrative science rather than disciplinary education, the reality of the nation character development has not been able to form a more creative and independent Indonesian man. Problem Based Learning based on real time data in the website is a learning method focuses on developing high-level thinking skills in problem-oriented situations by integrating technology in learning. The essence of this study is the presentation of authentic problems in the real time data situation in the website. The purpose of this research is to develop student independence through Problem Based Learning based on real time data in website. The type of this research is development research with implementation using purposive sampling technique. Based on the study there is an increase in student self-reliance, where the students in very high category is 47% and in the high category is 53%. This learning method can be said to be effective in improving students learning independence in problem-oriented situations.

  2. The Effect of Inquiry Training Learning Model Based on Just in Time Teaching for Problem Solving Skill

    Science.gov (United States)

    Turnip, Betty; Wahyuni, Ida; Tanjung, Yul Ifda

    2016-01-01

    One of the factors that can support successful learning activity is the use of learning models according to the objectives to be achieved. This study aimed to analyze the differences in problem-solving ability Physics student learning model Inquiry Training based on Just In Time Teaching [JITT] and conventional learning taught by cooperative model…

  3. Refined adaptive optics simulation with wide field of view for the E-ELT

    International Nuclear Information System (INIS)

    Chebbo, Manal

    2012-01-01

    Refined simulation tools for wide field AO systems (such as MOAO, MCAO or LTAO) on ELTs present new challenges. Increasing the number of degrees of freedom (scales as the square of the telescope diameter) makes the standard simulation's codes useless due to the huge number of operations to be performed at each step of the Adaptive Optics (AO) loop process. This computational burden requires new approaches in the computation of the DM voltages from WFS data. The classical matrix inversion and the matrix vector multiplication have to be replaced by a cleverer iterative resolution of the Least Square or Minimum Mean Square Error criterion (based on sparse matrices approaches). Moreover, for this new generation of AO systems, concepts themselves will become more complex: data fusion coming from multiple Laser and Natural Guide Stars (LGS / NGS) will have to be optimized, mirrors covering all the field of view associated to dedicated mirrors inside the scientific instrument itself will have to be coupled using split or integrated tomography schemes, differential pupil or/and field rotations will have to be considered, etc. All these new entries should be carefully simulated, analysed and quantified in terms of performance before any implementation in AO systems. For those reasons I developed, in collaboration with the ONERA, a full simulation code, based on iterative solution of linear systems with many parameters (use of sparse matrices). On this basis, I introduced new concepts of filtering and data fusion (LGS / NGS) to effectively manage modes such as tip, tilt and defocus in the entire process of tomographic reconstruction. The code will also eventually help to develop and test complex control laws (Multi-DM and multi-field) who have to manage a combination of adaptive telescope and post-focal instrument including dedicated deformable mirrors. The first application of this simulation tool has been studied in the framework of the EAGLE multi-object spectrograph

  4. Rhythm and timing in autism: Learning to dance

    Directory of Open Access Journals (Sweden)

    Pat eAmos

    2013-04-01

    Full Text Available In recent years, a significant body of research has focused on challenges to neural connectivity as a key to understanding autism. In contrast to attempts to identify a single static, primarily brain-based deficit, children and adults diagnosed with autism are increasingly perceived as out of sync with their internal and external environments in dynamic ways that must also involve operations of the peripheral nervous systems. The noisiness that seems to occur in both directions of neural flow may help explain challenges to movement and sensing, and ultimately to entrainment with circadian rhythms and social interactions. across the autism spectrum. Profound differences in the rhythm and timing of movement have been tracked to infancy. Difficulties with self-synchrony inhibit praxis, and can disrupt the dance of relationships through which caregiver and child build meaning. Different sensory aspects of a situation may fail to match up; ultimately, intentions and actions themselves may be uncoupled. This uncoupling may help explain the expressions of alienation from the actions of one’s body which recur in the autobiographical autism literature. Multi-modal/cross-modal coordination of different types of sensory information into coherent events may be difficult to achieve because amodal properties (e.g. rhythm and tempo that help unite perceptions are unreliable. One question posed to the connectivity research concerns the role of rhythm and timing in this operation, and whether these can be mobilized to reduce overload and enhance performance. A case is made for developmental research addressing how people with autism actively explore and make sense of their environments. The parent/author recommends investigating approaches such as scaffolding interactions via rhythm, following the person’s lead, slowing the pace, discriminating between intentional communication and stray motor patterns, and organizing information through one sensory mode at

  5. The time course of ethanol tolerance: associative learning

    Directory of Open Access Journals (Sweden)

    J.L.O. Bueno

    2007-11-01

    Full Text Available The effect of different contextual stimuli on different ethanol-induced internal states was investigated during the time course of both the hypothermic effect of the drug and of drug tolerance. Minimitters were surgically implanted in 16 Wistar rats to assess changes in their body temperature under the effect of ethanol. Rat groups were submitted to ethanol or saline trials every other day. The animals were divided into two groups, one receiving a constant dose (CD of ethanol injected intraperitoneally, and the other receiving increasing doses (ID during the 10 training sessions. During the ethanol training sessions, conditioned stimuli A (tone and B (buzzer were presented at "state +" (35 min after drug injection and "state -" (170 min after drug injection, respectively. Conditioned stimuli C (bip and D (white noise were presented at moments equivalent to stimuli A and B, respectively, but during the saline training sessions. All stimuli lasted 15 min. The CD group, but not the ID group, developed tolerance to the hypothermic effect of ethanol. Stimulus A (associated with drug "state +" induced hyperthermia with saline injection in the ID group. Stimulus B (associated with drug "state -" reduced ethanol tolerance in the CD group and modulated the hypothermic effect of the drug in the ID group. These results indicate that contextual stimuli acquire modulatory conditioned properties that are associated with the time course of both the action of the drug and the development of drug tolerance.

  6. TEXPLORE temporal difference reinforcement learning for robots and time-constrained domains

    CERN Document Server

    Hester, Todd

    2013-01-01

    This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuou...

  7. The time course of location-avoidance learning in fear of spiders.

    Science.gov (United States)

    Rinck, Mike; Koene, Marieke; Telli, Sibel; Moerman-van den Brink, Wiltine; Verhoeven, Barbara; Becker, Eni S

    2016-01-01

    Two experiments were designed to study the time course of avoidance learning in spider fearfuls (SFs) under controlled experimental conditions. To achieve this, we employed an immersive virtual environment (IVE): While walking freely through a virtual art museum to search for specific paintings, the participants were exposed to virtual spiders. Unbeknown to the participants, only two of four museum rooms contained spiders, allowing for avoidance learning. Indeed, the more SF the participants were, the faster they learned to avoid the rooms that contained spiders (Experiment. 1), and within the first six trials, high fearfuls already developed a preference for starting their search task in rooms without spiders (Experiment 2). These results illustrate the time course of avoidance learning in SFs, and they speak to the usefulness of IVEs in fundamental anxiety research.

  8. Time course influences transfer of visual perceptual learning across spatial location.

    Science.gov (United States)

    Larcombe, S J; Kennard, C; Bridge, H

    2017-06-01

    Visual perceptual learning describes the improvement of visual perception with repeated practice. Previous research has established that the learning effects of perceptual training may be transferable to untrained stimulus attributes such as spatial location under certain circumstances. However, the mechanisms involved in transfer have not yet been fully elucidated. Here, we investigated the effect of altering training time course on the transferability of learning effects. Participants were trained on a motion direction discrimination task or a sinusoidal grating orientation discrimination task in a single visual hemifield. The 4000 training trials were either condensed into one day, or spread evenly across five training days. When participants were trained over a five-day period, there was transfer of learning to both the untrained visual hemifield and the untrained task. In contrast, when the same amount of training was condensed into a single day, participants did not show any transfer of learning. Thus, learning time course may influence the transferability of perceptual learning effects. Copyright © 2017 Elsevier Ltd. All rights reserved.

  9. Enhancement of Online Robotics Learning Using Real-Time 3D Visualization Technology

    OpenAIRE

    Richard Chiou; Yongjin (james) Kwon; Tzu-Liang (bill) Tseng; Robin Kizirian; Yueh-Ting Yang

    2010-01-01

    This paper discusses a real-time e-Lab Learning system based on the integration of 3D visualization technology with a remote robotic laboratory. With the emergence and development of the Internet field, online learning is proving to play a significant role in the upcoming era. In an effort to enhance Internet-based learning of robotics and keep up with the rapid progression of technology, a 3- Dimensional scheme of viewing the robotic laboratory has been introduced in addition to the remote c...

  10. A real-time articulatory visual feedback approach with target presentation for second language pronunciation learning.

    Science.gov (United States)

    Suemitsu, Atsuo; Dang, Jianwu; Ito, Takayuki; Tiede, Mark

    2015-10-01

    Articulatory information can support learning or remediating pronunciation of a second language (L2). This paper describes an electromagnetic articulometer-based visual-feedback approach using an articulatory target presented in real-time to facilitate L2 pronunciation learning. This approach trains learners to adjust articulatory positions to match targets for a L2 vowel estimated from productions of vowels that overlap in both L1 and L2. Training of Japanese learners for the American English vowel /æ/ that included visual training improved its pronunciation regardless of whether audio training was also included. Articulatory visual feedback is shown to be an effective method for facilitating L2 pronunciation learning.

  11. Learning characteristics of a space-time neural network as a tether skiprope observer

    Science.gov (United States)

    Lea, Robert N.; Villarreal, James A.; Jani, Yashvant; Copeland, Charles

    1993-01-01

    The Software Technology Laboratory at the Johnson Space Center is testing a Space Time Neural Network (STNN) for observing tether oscillations present during retrieval of a tethered satellite. Proper identification of tether oscillations, known as 'skiprope' motion, is vital to safe retrieval of the tethered satellite. Our studies indicate that STNN has certain learning characteristics that must be understood properly to utilize this type of neural network for the tethered satellite problem. We present our findings on the learning characteristics including a learning rate versus momentum performance table.

  12. Learning Constructive Primitives for Real-time Dynamic Difficulty Adjustment in Super Mario Bros

    OpenAIRE

    Shi, Peizhi; Chen, Ke

    2017-01-01

    Among the main challenges in procedural content generation (PCG), content quality assurance and dynamic difficulty adjustment (DDA) of game content in real time are two major issues concerned in adaptive content generation. Motivated by the recent learning-based PCG framework, we propose a novel approach to seamlessly address two issues in Super Mario Bros (SMB). To address the quality assurance issue, we exploit the synergy between rule-based and learning-based methods to produce quality gam...

  13. Why Hong Kong students favour more face-to-face classroom time in blended learning

    OpenAIRE

    Henri,James; Lee,Sandra

    2007-01-01

    A three year study in student characteristics, needs and learning styles guided instructors at the University of Hong Kong Faculty of Education to improve teaching and learning in a core module: Information Literacy. A mixed-method approach analyzed data collected from undergraduate, in-service teachers in a BEd program, and helped instructors in the program to gain insight into the Hong Kong teacher working, post-service towards a BEd in Library and Information Science. Part-time students in...

  14. A Study of Time Spent Working at Learning Centers. Technical Report #17.

    Science.gov (United States)

    Omori, Sharon; And Others

    This study examined the proportion of time children in the Kamehameha Early Education Program schools spend at actual school work in learning centers. Systematic time-sampled observations using multiple observers were conducted in December-January and again in March-April. The subjects, 12 children (6 kindergarteners and 6 first graders) were…

  15. An Integrated Theory of Prospective Time Interval Estimation: The Role of Cognition, Attention, and Learning

    Science.gov (United States)

    Taatgen, Niels A.; van Rijn, Hedderik; Anderson, John

    2007-01-01

    A theory of prospective time perception is introduced and incorporated as a module in an integrated theory of cognition, thereby extending existing theories and allowing predictions about attention and learning. First, a time perception module is established by fitting existing datasets (interval estimation and bisection and impact of secondary…

  16. Learning Management System Calendar Reminders and Effects on Time Management and Academic Performance

    Science.gov (United States)

    Mei, Jianyang

    2016-01-01

    This research project uses a large research university in the Midwest as a research site to explore the time management skills of international students and analyzes how using the Course Hack, an online Learning Management System (LMS) calendar tool, improves participants' time management skills and positively impacts their academic performance,…

  17. Evaluation of Online Log Variables That Estimate Learners' Time Management in a Korean Online Learning Context

    Science.gov (United States)

    Jo, Il-Hyun; Park, Yeonjeong; Yoon, Meehyun; Sung, Hanall

    2016-01-01

    The purpose of this study was to identify the relationship between the psychological variables and online behavioral patterns of students, collected through a learning management system (LMS). As the psychological variable, time and study environment management (TSEM), one of the sub-constructs of MSLQ, was chosen to verify a set of time-related…

  18. Cerebellar motor learning versus cerebellar motor timing: the climbing fibre story

    Science.gov (United States)

    Llinás, Rodolfo R

    2011-01-01

    Abstract Theories concerning the role of the climbing fibre system in motor learning, as opposed to those addressing the olivocerebellar system in the organization of motor timing, are briefly contrasted. The electrophysiological basis for the motor timing hypothesis in relation to the olivocerebellar system is treated in detail. PMID:21486816

  19. Time-place learning over a lifetime : Absence of memory loss in trained old mice

    NARCIS (Netherlands)

    Mulder, Cornelis K; Reckman, Gerlof A R; Gerkema, Menno P; van der Zee, Eddy A

    Time-place learning (TPL) offers the possibility to study the functional interaction between cognition and the circadian system with aging. With TPL, animals link biological significant events with the location and the time of day. This what-where-when type of memory provides animals with an

  20. A longitudinal study on time perspectives: relations with academic delay of gratification and learning environment

    NARCIS (Netherlands)

    Peetsma, T.; Schuitema, J.; van der Veen, I.

    2012-01-01

    After they start secondary school (at age 12 in the Netherlands), students' time perspectives on school and professional career and self-regulated learning decrease, while their perspectives on leisure increase. We aimed to investigate relations in the developments in time perspectives and delay of

  1. An integrated theory of prospective time interval estimation : The role of cognition, attention, and learning

    NARCIS (Netherlands)

    Taatgen, Niels A.; van Rijn, Hedderik; Anderson, John

    A theory of prospective time perception is introduced and incorporated as a module in an integrated theory of cognition, thereby extending existing theories and allowing predictions about attention and learning. First, a time perception module is established by fitting existing datasets (interval

  2. Time-Place Learning over a Lifetime: Absence of Memory Loss in Trained Old Mice

    Science.gov (United States)

    Mulder, Cornelis K.; Reckman, Gerlof A. R.; Gerkema, Menno P.; Van der Zee, Eddy A.

    2015-01-01

    Time-place learning (TPL) offers the possibility to study the functional interaction between cognition and the circadian system with aging. With TPL, animals link biological significant events with the location and the time of day. This what-where-when type of memory provides animals with an experience-based daily schedule. Mice were tested for…

  3. Galaxy Mass Assembly with VLT & HST and lessons for E-ELT/MOSAIC

    Science.gov (United States)

    Hammer, François; Flores, Hector; Puech, Mathieu

    2015-02-01

    The fraction of distant disks and mergers is still debated, while 3D-spectroscopy is revolutionizing the field. However its limited spatial resolution imposes a complimentary HST imagery and a robust analysis procedure. When applied to observations of IMAGES galaxies at z = 0.4-0.8, it reveals that half of the spiral progenitors were in a merger phase, 6 billion year ago. The excellent correspondence between methodologically-based classifications of morphologies and kinematics definitively probes a violent origin of disk galaxies as proposed by Hammer et al. (2005). Examination of nearby galaxy outskirts reveals fossil imprints of such ancient merger events, under the form of well organized stellar streams. Perhaps our neighbor, M31, is the best illustration of an ancient merger, which modeling in 2010 leads to predict the gigantic plane of satellites discovered by Ibata et al. (2013). There are still a lot of discoveries to be done until the ELT era, which will open an avenue for detailed and accurate 3D-spectroscopy of galaxies from the earliest epochs to the present.

  4. Gender Representation under Critical Image Analysis: The Case of Iranian ELT Textbooks

    Directory of Open Access Journals (Sweden)

    Ali Dabbagh

    2016-12-01

    Full Text Available This paper aimed to identify and reveal gender positioning in the images used in the recent ELT nation-wide text books, i.e. Prospects 1, 2 & 3. To do so, the dimensions in Goffman (1976 were mixed with the image semiotic category of Kress and van Leeuwen (2006 to analyze the images in terms of determining active participant, gaze direction, visual techniques, body display, and the space in which the participants were shown. In order to elicit the hidden patterns, the data went over a thorough content analysis which revealed the following: First, males were presented more than females as active, looking at the viewer, and framed in a close–up format which signified their prominence and power in relation to females. Second, while both men and women were represented as fully clothed, no female character in the images were depicted in sparsely and lightly clothed. Third, a balanced representation was shown regarding the location in which males or females were present in the images, portraying at both home and workplace spaces despite not showing females in images related to open spaces such as streets and neighborhoods. Results promise implications for language teachers and teacher educators as to raise their awareness of gender bias, though in some aspects and not in others, in images of the text books. In addition, results have clear message for material developers not to selectively represent a gender-unbiased picture of males and females in only some limited aspects.

  5. Integrated Logistics Support approach: concept for the new big projects: E-ELT, SKA, CTA

    Science.gov (United States)

    Marchiori, G.; Rampini, F.; Formentin, F.

    2014-08-01

    The Integrated Logistic Support is a process supporting strategies and optimizing activities for a correct project management and system engineering development. From the design & engineering of complex technical systems, to the erection on site, acceptance and after-sales service, EIE GROUP covers all aspects of the Integrated Logistics Support (ILS) process that includes: costing process centered around the life cycle cost and Level of Repair Analyses; engineering process which influences the design via means of reliability, modularization, etc.; technical publishing process based on international specifications; ordering administration process for supply support. Through the ILS, EIE GROUP plans and directs the identification and development of logistics support and system requirements for its products, with the goal of creating systems that last longer and require less support, thereby reducing costs and increasing return on investments. ILS therefore, addresses these aspects of supportability not only during acquisition, but also throughout the operational life cycle of the system. The impact of the ILS is often measured in terms of metrics such as reliability, availability, maintainability and testability (RAMT), and System Safety (RAMS). Example of the criteria and approach adopted by EIE GROUP during the design, manufacturing and test of the ALMA European Antennas and during the design phase of the E-ELT telescope and Dome are presented.

  6. Evaluating Speech acts in ELT Textbooks: The Case of Compliments and Complaints in the Touchstone Series

    Directory of Open Access Journals (Sweden)

    Mahdieh Jalilian

    2016-07-01

    Full Text Available Textbooks play an important role in English Language Teaching (ELT, particularly in the English as a Foreign Language (EFL context where it provides the primary linguistic input. The present research was an attempt to comparatively evaluate the Touchstone series in terms of compliment and complaint speech acts. Four Touchstone textbooks (Book 1, Book 2, Book 3, and Book 4 were selected and content analysis was done using Olshtain and Weinbach’s (1993 complaint strategies and Wolfson and Manes’ (1980 classification of compliment. The frequencies and percentages of compliments and complaint speech acts were obtained. Data analysis showed that, first, the total frequency of the complaint speech act was higher in Touchstone, Book 4 than the other three textbooks; second, the frequency of complaint and compliment speech acts in the Writing section was quite low, but the Conversation section had a high frequency of compliment speech act in the Touchstone series; third, the expression of annoyance or disapproval complaint strategy was frequently used in the Touchstone series; fourth, the compliment strategy of ‘noun phrase + looks/is (intensifier adjective’ was very frequent in the Touchstone series; finally, there was a significant difference between the frequencies of the two speech acts, in general, in the four Touchstone textbooks. Considering the weaknesses and strengthens of Touchstone series, implications for teachers, material developers, and textbook writers are provided.

  7. Improved E-ELT subsystem and component specifications, thanks to M1 test facility

    Science.gov (United States)

    Dimmler, M.; Marrero, J.; Leveque, S.; Barriga, Pablo; Sedghi, B.; Kornweibel, N.

    2014-07-01

    During the last 2 years ESO has operated the "M1 Test Facility", a test stand consisting of a representative section of the E-ELT primary mirror equipped with 4 complete prototype segment subunits including sensors, actuators and control system. The purpose of the test facility is twofold: it serves to study and get familiar with component and system aspects like calibration, alignment and handling procedures and suitable control strategies on real hardware long before the primary mirror (hereafter M1) components are commissioned. Secondly, and of major benefit to the project, it offered the possibility to evaluate component and subsystem performance and interface issues in a system context in such detail, that issues could be identified early enough to feed back into the subsystem and component specifications. This considerably reduces risk and cost of the production units and allows refocusing the project team on important issues for the follow-up of the production contracts. Experiences are presented in which areas the results of the M1 Test Facility particularly helped to improve subsystem specifications and areas, where additional tests were adopted independent of the main test facility. Presented are the key experiences of the M1 Test Facility which lead to improved specifications or identified the need for additional testing outside of the M1 Test Facility.

  8. ERASMUS-F: pathfinder for an E-ELT 3D instrumentation

    Science.gov (United States)

    Kelz, Andreas; Roth, Martin M.; Bacon, Roland; Bland-Hawthorn, Joss; Nicklas, Harald E.; Bryant, Julia J.; Colless, Matthew; Croom, Scott; Ellis, Simon; Fleischmann, Andreas; Gillingham, Peter; Haynes, Roger; Hopkins, Andrew; Kosmalski, Johan; O'Byrne, John W.; Olaya, Jean-Christophe; Rambold, William N.; Robertson, Gordon

    2010-07-01

    ERASMUS-F is a pathfinder study for a possible E-ELT 3D-instrumentation, funded by the German Ministry for Education and Research (BMBF). The study investigates the feasibility to combine a broadband optical spectrograph with a new generation of multi-object deployable fibre bundles. The baseline approach is to modify the spectrograph of the Multi-Unit Spectroscopic Explorer (MUSE), which is a VLT integral-field instrument using slicers, with a fibre-fed input. Taking advantage of recent developments in astrophotonics, it is planed to equip such an instrument with fused fibre bundles (hexabundles) that offer larger filling factors than dense-packed classical fibres. The overall project involves an optical and mechanical design study, the specifications of a software package for 3Dspectrophotometry, based upon the experiences with the P3d Data Reduction Software and an investigation of the science case for such an instrument. As a proof-of-concept, the study also involves a pathfinder instrument for the VLT, called the FIREBALL project.

  9. Real-time yield estimation based on deep learning

    Science.gov (United States)

    Rahnemoonfar, Maryam; Sheppard, Clay

    2017-05-01

    Crop yield estimation is an important task in product management and marketing. Accurate yield prediction helps farmers to make better decision on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits is very time consuming and expensive process and it is not practical for big fields. Robotic systems including Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), provide an efficient, cost-effective, flexible, and scalable solution for product management and yield prediction. Recently huge data has been gathered from agricultural field, however efficient analysis of those data is still a challenging task. Computer vision approaches currently face diffident challenges in automatic counting of fruits or flowers including occlusion caused by leaves, branches or other fruits, variance in natural illumination, and scale. In this paper a novel deep convolutional network algorithm was developed to facilitate the accurate yield prediction and automatic counting of fruits and vegetables on the images. Our method is robust to occlusion, shadow, uneven illumination and scale. Experimental results in comparison to the state-of-the art show the effectiveness of our algorithm.

  10. Examining the Effect of Time Constraint on the Online Mastery Learning Approach towards Improving Postgraduate Students' Achievement

    Science.gov (United States)

    Ee, Mong Shan; Yeoh, William; Boo, Yee Ling; Boulter, Terry

    2018-01-01

    Time control plays a critical role within the online mastery learning (OML) approach. This paper examines the two commonly implemented mastery learning strategies--personalised system of instructions and learning for mastery (LFM)--by focusing on what occurs when there is an instructional time constraint. Using a large data set from a postgraduate…

  11. Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

    Directory of Open Access Journals (Sweden)

    Nicolas Frémaux

    2013-04-01

    Full Text Available Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD learning of Doya (2000 to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

  12. On learning science and pseudoscience from prime-time television programming

    Science.gov (United States)

    Whittle, Christopher Henry

    The purpose of the present dissertation is to determine whether the viewing of two particular prime-time television programs, ER and The X-Files, increases viewer knowledge of science and to identify factors that may influence learning from entertainment television programming. Viewer knowledge of scientific dialogue from two science-based prime-time television programs, ER, a serial drama in a hospital emergency room and The X-Files, a drama about two Federal Bureau of Investigation agents who pursue alleged extraterrestrial life and paranormal activity, is studied. Level of viewing, education level, science education level, experiential factors, level of parasocial interaction, and demographic characteristics are assessed as independent variables affecting learning from entertainment television viewing. The present research involved a nine-month long content analysis of target television program dialogue and data collection from an Internet-based survey questionnaire posted to target program-specific on-line "chat" groups. The present study demonstrated that entertainment television program viewers incidentally learn science from entertainment television program dialogue. The more they watch, the more they learn. Viewing a pseudoscientific fictional television program does necessarily influence viewer beliefs in pseudoscience. Higher levels of formal science study are reflected in more science learning and less learning of pseudoscience from entertainment television program viewing. Pseudoscience learning from entertainment television programming is significantly related to experience with paranormal phenomena, higher levels of viewer parasocial interaction, and specifically, higher levels of cognitive parasocial interaction. In summary, the greater a viewer's understanding of science the more they learn when they watch their favorite science-based prime-time television programs. Viewers of pseudoscience-based prime-time television programming with higher levels

  13. Why Hong Kong students favour more face-to-face classroom time in blended learning

    Directory of Open Access Journals (Sweden)

    James Henri

    Full Text Available A three year study in student characteristics, needs and learning styles guided instructors at the University of Hong Kong Faculty of Education to improve teaching and learning in a core module: Information Literacy. A mixed-method approach analyzed data collected from undergraduate, in-service teachers in a BEd program, and helped instructors in the program to gain insight into the Hong Kong teacher working, post-service towards a BEd in Library and Information Science. Part-time students indicated a preference for a combination of online and face-to-face teaching, with more face-to-face class time in that mix. These findings would also be informative for other part-time programs using blended teaching and learning models.

  14. Estimating the implicit component of visuomotor rotation learning by constraining movement preparation time.

    Science.gov (United States)

    Leow, Li-Ann; Gunn, Reece; Marinovic, Welber; Carroll, Timothy J

    2017-08-01

    When sensory feedback is perturbed, accurate movement is restored by a combination of implicit processes and deliberate reaiming to strategically compensate for errors. Here, we directly compare two methods used previously to dissociate implicit from explicit learning on a trial-by-trial basis: 1 ) asking participants to report the direction that they aim their movements, and contrasting this with the directions of the target and the movement that they actually produce, and 2 ) manipulating movement preparation time. By instructing participants to reaim without a sensory perturbation, we show that reaiming is possible even with the shortest possible preparation times, particularly when targets are narrowly distributed. Nonetheless, reaiming is effortful and comes at the cost of increased variability, so we tested whether constraining preparation time is sufficient to suppress strategic reaiming during adaptation to visuomotor rotation with a broad target distribution. The rate and extent of error reduction under preparation time constraints were similar to estimates of implicit learning obtained from self-report without time pressure, suggesting that participants chose not to apply a reaiming strategy to correct visual errors under time pressure. Surprisingly, participants who reported aiming directions showed less implicit learning according to an alternative measure, obtained during trials performed without visual feedback. This suggests that the process of reporting can affect the extent or persistence of implicit learning. The data extend existing evidence that restricting preparation time can suppress explicit reaiming and provide an estimate of implicit visuomotor rotation learning that does not require participants to report their aiming directions. NEW & NOTEWORTHY During sensorimotor adaptation, implicit error-driven learning can be isolated from explicit strategy-driven reaiming by subtracting self-reported aiming directions from movement directions, or

  15. Supervised Learning Using Spike-Timing-Dependent Plasticity of Memristive Synapses.

    Science.gov (United States)

    Nishitani, Yu; Kaneko, Yukihiro; Ueda, Michihito

    2015-12-01

    We propose a supervised learning model that enables error backpropagation for spiking neural network hardware. The method is modeled by modifying an existing model to suit the hardware implementation. An example of a network circuit for the model is also presented. In this circuit, a three-terminal ferroelectric memristor (3T-FeMEM), which is a field-effect transistor with a gate insulator composed of ferroelectric materials, is used as an electric synapse device to store the analog synaptic weight. Our model can be implemented by reflecting the network error to the write voltage of the 3T-FeMEMs and introducing a spike-timing-dependent learning function to the device. An XOR problem was successfully demonstrated as a benchmark learning by numerical simulations using the circuit properties to estimate the learning performance. In principle, the learning time per step of this supervised learning model and the circuit is independent of the number of neurons in each layer, promising a high-speed and low-power calculation in large-scale neural networks.

  16. Incoherent dictionary learning for reducing crosstalk noise in least-squares reverse time migration

    Science.gov (United States)

    Wu, Juan; Bai, Min

    2018-05-01

    We propose to apply a novel incoherent dictionary learning (IDL) algorithm for regularizing the least-squares inversion in seismic imaging. The IDL is proposed to overcome the drawback of traditional dictionary learning algorithm in losing partial texture information. Firstly, the noisy image is divided into overlapped image patches, and some random patches are extracted for dictionary learning. Then, we apply the IDL technology to minimize the coherency between atoms during dictionary learning. Finally, the sparse representation problem is solved by a sparse coding algorithm, and image is restored by those sparse coefficients. By reducing the correlation among atoms, it is possible to preserve most of the small-scale features in the image while removing much of the long-wavelength noise. The application of the IDL method to regularization of seismic images from least-squares reverse time migration shows successful performance.

  17. Learning Bounds of ERM Principle for Sequences of Time-Dependent Samples

    Directory of Open Access Journals (Sweden)

    Mingchen Yao

    2015-01-01

    Full Text Available Many generalization results in learning theory are established under the assumption that samples are independent and identically distributed (i.i.d.. However, numerous learning tasks in practical applications involve the time-dependent data. In this paper, we propose a theoretical framework to analyze the generalization performance of the empirical risk minimization (ERM principle for sequences of time-dependent samples (TDS. In particular, we first present the generalization bound of ERM principle for TDS. By introducing some auxiliary quantities, we also give a further analysis of the generalization properties and the asymptotical behaviors of ERM principle for TDS.

  18. Optimal critic learning for robot control in time-varying environments.

    Science.gov (United States)

    Wang, Chen; Li, Yanan; Ge, Shuzhi Sam; Lee, Tong Heng

    2015-10-01

    In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.

  19. Supervised spike-timing-dependent plasticity: a spatiotemporal neuronal learning rule for function approximation and decisions.

    Science.gov (United States)

    Franosch, Jan-Moritz P; Urban, Sebastian; van Hemmen, J Leo

    2013-12-01

    How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as "supervisor." Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

  20. Developing the metacognitive skill of noticing the gap through self-transcribing: The case of students enrolled in an ELT training program in Chile

    Directory of Open Access Journals (Sweden)

    Millaray D Salas

    2015-10-01

    Full Text Available This paper reports the preliminary results of the first phase of an ongoing educational intervention conducted with students enrolled in an ELT training program at PUCV. The study set out to explore the feasibility and effectiveness of the recording and self-transcription methodology (Lynch, 2001, 2007; Mennim, 2003, 2012 as a route to noticing the gap and defossilization. Students (N=20 transcribed the oral texts they produced during the speaking section of the diagnostic test for English 5. The tasks were: (1 transcribing three minutes of their speaking time, (2 highlighting all the errors they identified in their own speech (3 coding them (e.g. grammatical, lexical, phonological and (4 sending the annotated transcript to the instructor by email. Drawing on the theory of questionnaire design and processing (Dörnyei, 2003, a survey was designed and posted online (GoogleForm and emailed to the students. The questionnaire asked students to evaluate the perceived benefits of the self-transcription methodology. The study data consist of the annotated transcripts and the questionnaire responses. The results of this study were much less positive than what has been reported in the literature (Willis & Willis, 1996, Burns & Joyce, 1997; Lynch, 2001, 2007; Mennim, 2003, 2012; Thornbury & Slade, 2006; Boettinger, Park & Timmis 2010; Stillwell et al., 2010, so an attempt is made to see why this might have been the case. Some pedagogical implications of this approach in the Chilean context are discussed.

  1. Do learning collaboratives strengthen communication? A comparison of organizational team communication networks over time.

    Science.gov (United States)

    Bunger, Alicia C; Lengnick-Hall, Rebecca

    Collaborative learning models were designed to support quality improvements, such as innovation implementation by promoting communication within organizational teams. Yet the effect of collaborative learning approaches on organizational team communication during implementation is untested. The aim of this study was to explore change in communication patterns within teams from children's mental health organizations during a year-long learning collaborative focused on implementing a new treatment. We adopt a social network perspective to examine intraorganizational communication within each team and assess change in (a) the frequency of communication among team members, (b) communication across organizational hierarchies, and (c) the overall structure of team communication networks. A pretest-posttest design compared communication among 135 participants from 21 organizational teams at the start and end of a learning collaborative. At both time points, participants were asked to list the members of their team and rate the frequency of communication with each along a 7-point Likert scale. Several individual, pair-wise, and team level communication network metrics were calculated and compared over time. At the individual level, participants reported communicating with more team members by the end of the learning collaborative. Cross-hierarchical communication did not change. At the team level, these changes manifested differently depending on team size. In large teams, communication frequency increased, and networks grew denser and slightly less centralized. In small teams, communication frequency declined, growing more sparse and centralized. Results suggest that team communication patterns change minimally but evolve differently depending on size. Learning collaboratives may be more helpful for enhancing communication among larger teams; thus, managers might consider selecting and sending larger staff teams to learning collaboratives. This study highlights key future

  2. SU-F-P-20: Predicting Waiting Times in Radiation Oncology Using Machine Learning

    International Nuclear Information System (INIS)

    Joseph, A; Herrera, D; Hijal, T; Kildea, J; Hendren, L; Leung, A; Wainberg, J; Sawaf, M; Gorshkov, M; Maglieri, R; Keshavarz, M

    2016-01-01

    Purpose: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts of data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient’s waiting experience. Methods: In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested. Results: We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes

  3. SU-F-P-20: Predicting Waiting Times in Radiation Oncology Using Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Joseph, A; Herrera, D; Hijal, T; Kildea, J [McGill University Health Centre, Montreal, Quebec (Canada); Hendren, L; Leung, A; Wainberg, J; Sawaf, M; Gorshkov, M; Maglieri, R; Keshavarz, M [McGill University, Montreal, Quebec (Canada)

    2016-06-15

    Purpose: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts of data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient’s waiting experience. Methods: In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested. Results: We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes

  4. Time to learn: the outlook for renewal of patient-centred education in the digital age.

    Science.gov (United States)

    Glick, T H; Moore, G T

    2001-05-01

    Major forces in society and within health systems are fragmenting patient care and clinical learning. The distancing of physician and trainee from the patient undermines learning about the patient-doctor relationship. The disconnection of care and learning from one successive venue to another impedes the ability of trainees to learn about illness longitudinally. As a conceptual piece, our methods have been those of witnessing the experiences of patients, practitioners, and students over time and observing the impact of fragmented systems and changing expectations on care and learning. We have reflected on the opportunities created by digital information systems and interactive telemedicine to help renew essential relationships. Although there is, as yet, little in the literature on educational or health outcomes of this kind of technological enablement, we anticipate opportunities for a renewed focus on the patient in that patient's own space and time. Multimedia applications can achieve not only real-time connections, but can help construct a "virtual patient" as a platform for supervision and assessment, permitting preceptors to evaluate trainee-patient interactions, utilization of Web-based data and human resources, and on-line professionalism. Just as diverse elements in society are capitalizing upon digital technology to create advantageous relationships, all of the elements in the complex systems of health care and medical training can be better connected, so as to put the patient back in the centre of care and the trainee's ongoing relationship to the patient back in the centre of education.

  5. Pointillist, Cyclical, and Overlapping: Multidimensional Facets of Time in Online Learning

    Directory of Open Access Journals (Sweden)

    Pekka Ihanainen

    2011-11-01

    Full Text Available A linear, sequential time conception based on in-person meetings and pedagogical activities is not enough for those who practice and hope to enhance contemporary education, particularly where online interactions are concerned. In this article, we propose a new model for understanding time in pedagogical contexts. Conceptual parts of the model will be employed as a “cultural technology” to help us relate to evolving phenomena, both physical and virtual. We label these constructs as pointillist, cyclical, and overlapping times.Pointillist time and learning takes place in “dots” of actions that consist of small, discrete moments (e.g., tweeting. Producing, receiving, and sharing ideas in this context are separate points in each actor’s timeline. Cyclical time and learning emerges from intensive periods, which are highly visible in online forums. This construct reveals itself through interactions that often exist in multiple online environments. Overlapping time and learning involves various configurations of linear, pointillist, and cyclical layers, which are mainly evident through the simultaneous uses of social communication technologies.Pointillist, cyclical, and overlapping time constructs enable new orientations for conceptualizing time in pedagogy. In this article we also introduce de-, re-, and en- modes of these pedagogies that connect with approaches to meet the needs of learners for individualization, personalization, and cyborgization.

  6. Neuromodulated Spike-Timing-Dependent Plasticity and Theory of Three-Factor Learning Rules

    Directory of Open Access Journals (Sweden)

    Wulfram eGerstner

    2016-01-01

    Full Text Available Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulatorson synaptic plasticity is useful not just for action learning in classical conditioning, but also to decide 'when' to create new memories in response to a flow of sensory stimuli.In this review, we focus on timing requirements for pre- and postsynaptic activity in conjunction with one or several phasic neuromodulatory signals. While the emphasis of the text is on conceptual models and mathematical theories, we also discusssome experimental evidence for neuromodulation of Spike-Timing-Dependent Plasticity.We highlight the importance of synaptic mechanisms in bridging the temporal gap between sensory stimulation and neuromodulatory signals, and develop a framework for a class of neo-Hebbian three-factor learning rules that depend on presynaptic activity, postsynaptic variables as well as the influence of neuromodulators.

  7. Tensorial dynamic time warping with articulation index representation for efficient audio-template learning.

    Science.gov (United States)

    Le, Long N; Jones, Douglas L

    2018-03-01

    Audio classification techniques often depend on the availability of a large labeled training dataset for successful performance. However, in many application domains of audio classification (e.g., wildlife monitoring), obtaining labeled data is still a costly and laborious process. Motivated by this observation, a technique is proposed to efficiently learn a clean template from a few labeled, but likely corrupted (by noise and interferences), data samples. This learning can be done efficiently via tensorial dynamic time warping on the articulation index-based time-frequency representations of audio data. The learned template can then be used in audio classification following the standard template-based approach. Experimental results show that the proposed approach outperforms both (1) the recurrent neural network approach and (2) the state-of-the-art in the template-based approach on a wildlife detection application with few training samples.

  8. Learning of temporal motor patterns: An analysis of continuous vs. reset timing

    Directory of Open Access Journals (Sweden)

    Rodrigo eLaje

    2011-10-01

    Full Text Available Our ability to generate well-timed sequences of movements is critical to an array of behaviors, including the ability to play a musical instrument or a video game. Here we address two questions relating to timing with the goal of better understanding the neural mechanisms underlying temporal processing. First, how does accuracy and variance change over the course of learning of complex spatiotemporal patterns? Second, is the timing of sequential responses most consistent with starting and stopping an internal timer at each interval or with continuous timing?To address these questions we used a psychophysical task in which subjects learned to reproduce a sequence of finger taps in the correct order and at the correct times—much like playing a melody at the piano. This task allowed us to calculate the variance of the responses at different time points using data from the same trials. Our results show that while standard Weber’s law is clearly violated, variance does increase as a function of time squared, as expected according to the generalized form of Weber’s law—which separates the source of variance into time-dependent and time-independent components. Over the course of learning, both the time-independent variance and the coefficient of the time-dependent term decrease. Our analyses also suggest that timing of sequential events does not rely on the resetting of an internal timer at each event.We describe and interpret our results in the context of computer simulations that capture some of our psychophysical findings. Specifically, we show that continuous timing, as opposed to reset timing, is expected from population clock models in which timing emerges from the internal dynamics of recurrent neural networks.

  9. An attempt to elaborate a construct to measure the degree of explicitness and implicitness in ELT materials

    Directory of Open Access Journals (Sweden)

    Pascual Cantos Gómez

    2010-06-01

    Full Text Available The concepts of explicit and implicit (knowledge are at the core of SLA studies. We take explicit as conscious and declarative (knowledge; implicit as unconscious, automatic and procedural (knowledge (DeKeyser, 2003; R. Ellis, 2005a, 2005b, 2009; Hulstjin, 2005; Robinson, 1996; Schmidt, 1990, 1994. The importance of those concepts and components, we believe, must also be acknowledged in language teaching, and consequently in language teaching materials. However, explicitness and implicitness are rather complex constructs; such complexity allows for multiple nuances and perspectives in their analysis, and this fact poses a real challenge for their identification in the learning and teaching process and materials. We focus here on ELT materials and aim at the building of a reliable construct which may help in the identification of their potential for promoting implicit and explicit components. We first determined the features to identify the construct for implicitness and explicitness; next, we validated it and then we applied it to the analysis of the activities included in three sample units of three textbooks. The results were computed along a continuum ranging from 0 to 10 in each activity. The systematization and computation of results will hopefully offer a reliable figure regarding the identification of the degree of explicitness and/or implicitness in the materials analysed.Los conceptos de (conocimiento explícito e implícito constituyen uno de los puntos centrales en los estudios sobre la adquisición de lenguas extranjeras. Por explícito se entiende (conocimiento consciente o declarativo; por implícito, (conocimiento no consciente, automático y procedimentalizado (DeKeyser, 2003; R. Ellis, 2005a, 2005b, 2009; Hulstjin, 2005; Robinson, 1996; Schmidt, 1990, 1994. La importancia de ambos conceptos debe trasladarse también al campo de la enseñanza de idiomas y por lo tanto a los materiales docentes. Sin embargo, lo explícito e impl

  10. A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.

    Science.gov (United States)

    Halloran, John T; Rocke, David M

    2018-05-04

    Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide-spectrum matches based on the learned decision boundary between targets and decoys. To improve analysis time for large-scale data sets, we update Percolator's SVM learning engine through software and algorithmic optimizations rather than heuristic approaches that necessitate the careful study of their impact on learned parameters across different search settings and data sets. We show that by optimizing Percolator's original learning algorithm, l 2 -SVM-MFN, large-scale SVM learning requires nearly only a third of the original runtime. Furthermore, we show that by employing the widely used Trust Region Newton (TRON) algorithm instead of l 2 -SVM-MFN, large-scale Percolator SVM learning is reduced to nearly only a fifth of the original runtime. Importantly, these speedups only affect the speed at which Percolator converges to a global solution and do not alter recalibration performance. The upgraded versions of both l 2 -SVM-MFN and TRON are optimized within the Percolator codebase for multithreaded and single-thread use and are available under Apache license at bitbucket.org/jthalloran/percolator_upgrade .

  11. A Social Practice Theory of Learning and Becoming across Contexts and Time

    Science.gov (United States)

    Penuel, William R.; DiGiacomo, Daniela K.; Van Horne, Katie; Kirshner, Ben

    2016-01-01

    This paper presents a social practice theory of learning and becoming across contexts and time. Our perspective is rooted in the Danish tradition of critical psychology (Dreier, 1997; Mørck & Huniche, 2006; Nissen, 2005), and we use social practice theory to interpret the pathway of one adolescent whom we followed as part of a longitudinal…

  12. Active Learning and Just-in-Time Teaching in a Material and Energy Balances Course

    Science.gov (United States)

    Liberatore, Matthew W.

    2013-01-01

    The delivery of a material and energy balances course is enhanced through a series of in-class and out-of-class exercises. An active learning classroom is achieved, even at class sizes over 150 students, using multiple instructors in a single classroom, problem solving in teams, problems based on YouTube videos, and just-in-time teaching. To avoid…

  13. Assessment of Stand-Alone Displays for Time Management in a Creativity-Driven Learning Environment

    DEFF Research Database (Denmark)

    Frimodt-Møller, Søren

    2017-01-01

    This paper considers the pros and cons of stand-alone displays, analog (e.g. billboards, blackboards, whiteboards, large pieces of paper etc.) as well as digital (e.g. large shared screens, digital whiteboards or similar), as tools for time management processes in a creativity-driven learning...

  14. Part-Time Community College Instructors Teaching in Learning Communities: An Exploratory Multiple Case Study

    Science.gov (United States)

    Paterson, John W.

    2017-01-01

    Community colleges have a greater portion of students at-risk for college completion than four-year schools and faculty at these institutions are overwhelmingly and increasingly part-time. Learning communities have been identified as a high-impact practice with numerous benefits documented for community college instructors and students: a primary…

  15. Nuclear power plant monitoring using real-time learning neural network

    International Nuclear Information System (INIS)

    Nabeshima, Kunihiko; Tuerkcan, E.; Ciftcioglu, O.

    1994-01-01

    In the present research, artificial neural network (ANN) with real-time adaptive learning is developed for the plant wide monitoring of Borssele Nuclear Power Plant (NPP). Adaptive ANN learning capability is integrated to the monitoring system so that robust and sensitive on-line monitoring is achieved in real-time environment. The major advantages provided by ANN are that system modelling is formed by means of measurement information obtained from a multi-output process system, explicit modelling is not required and the modelling is not restricted to linear systems. Also ANN can respond very fast to anomalous operational conditions. The real-time ANN learning methodology with adaptive real-time monitoring capability is described below for the wide-range and plant-wide data from an operating nuclear power plant. The layered neural network with error backpropagation algorithm for learning has three layers. The network type is auto-associative, inputs and outputs are exactly the same, using 12 plant signals. (author)

  16. Focusing on Student Learning to Guide the Use of Staff Time

    Science.gov (United States)

    Bates, Imelda; Baume, David; Assinder, Susan

    2010-01-01

    The paper develops and illustrates a model for designing courses. The model gives explicit attention to educational considerations, principally to the importance of active, goal-directed student learning. It also explores economic considerations, principally how to make the best possible use of the time of the teacher in planning and running the…

  17. Pre-Service Post Graduate Teachers' First Time Experience with Constructivist Learning Environment (CLE) Using MOODLE

    Science.gov (United States)

    Boopathiraj, C.; Chellamani, K.

    2015-01-01

    The aim of this study is to enlighten and discuss Post Graduate student teachers' first time experiences and their level of satisfaction with the use of Moodle Learning Management System (LMS) during their "Research Methods in Education" course offered online. This study investigated 30 pre-service Post Graduate student teachers' to…

  18. Perceived Learning and Timely Graduation for Business Undergraduates Taking an Online or Hybrid Course

    Science.gov (United States)

    Blau, Gary; Drennan, Rob B.; Hochner, Arthur; Kapanjie, Darin

    2016-01-01

    An online survey tested the impact of background, technological, and course-related variables on perceived learning and timely graduation for a complete data sample of 263 business undergraduates taking at least one online or hybrid course in the fall of 2015. Hierarchical regression results showed that course-related variables (instructor…

  19. Examining the Relations of Time Management and Procrastination within a Model of Self-Regulated Learning

    Science.gov (United States)

    Wolters, Christopher A.; Won, Sungjun; Hussain, Maryam

    2017-01-01

    The primary goal of this study was to investigate whether college students' academic time management could be used to understand their engagement in traditional and active forms of procrastination within a model of self-regulated learning. College students (N = 446) completed a self-report survey that assessed motivational and strategic aspects of…

  20. Cerebral activation related to implicit sequence learning in a Double Serial Reaction Time task

    NARCIS (Netherlands)

    van der Graaf, FHCE; Maguire, RP; Leenders, KL; de Jong, BM

    2006-01-01

    Using functional magnetic resonance imaging (fMRI), we examined the distribution of cerebral activations related to implicitly learning a series of fixed stimulus-response combinations. In a novel - bimanual - variant of the Serial Reaction Time task (SRT), simultaneous finger movements of the two

  1. Feasibility of a real-time hand hygiene notification machine learning system in outpatient clinics.

    Science.gov (United States)

    Geilleit, R; Hen, Z Q; Chong, C Y; Loh, A P; Pang, N L; Peterson, G M; Ng, K C; Huis, A; de Korne, D F

    2018-04-09

    Various technologies have been developed to improve hand hygiene (HH) compliance in inpatient settings; however, little is known about the feasibility of machine learning technology for this purpose in outpatient clinics. To assess the effectiveness, user experiences, and costs of implementing a real-time HH notification machine learning system in outpatient clinics. In our mixed methods study, a multi-disciplinary team co-created an infrared guided sensor system to automatically notify clinicians to perform HH just before first patient contact. Notification technology effects were measured by comparing HH compliance at baseline (without notifications) with real-time auditory notifications that continued till HH was performed (intervention I) or notifications lasting 15 s (intervention II). User experiences were collected during daily briefings and semi-structured interviews. Costs of implementation of the system were calculated and compared to the current observational auditing programme. Average baseline HH performance before first patient contact was 53.8%. With real-time auditory notifications that continued till HH was performed, overall HH performance increased to 100% (P machine learning system were estimated to be 46% lower than the observational auditing programme. Machine learning technology that enables real-time HH notification provides a promising cost-effective approach to both improving and monitoring HH, and deserves further development in outpatient settings. Copyright © 2018 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

  2. Dynamic Hebbian Cross-Correlation Learning Resolves the Spike Timing Dependent Plasticity Conundrum

    Directory of Open Access Journals (Sweden)

    Tjeerd V. olde Scheper

    2018-01-01

    Full Text Available Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP curve, with one potentiating and one depressing window, is only one of many possible curves that describe synaptic learning using the STDP mechanism. It has been shown experimentally that STDP curves may contain multiple LTP and LTD windows of variable width, and even inverted windows. The underlying STDP mechanism that is capable of producing such an extensive, and apparently incompatible, range of learning curves is still under investigation. In this paper, it is shown that STDP originates from a combination of two dynamic Hebbian cross-correlations of local activity at the synapse. The correlation of the presynaptic activity with the local postsynaptic activity is a robust and reliable indicator of the discrepancy between the presynaptic neuron and the postsynaptic neuron's activity. The second correlation is between the local postsynaptic activity with dendritic activity which is a good indicator of matching local synaptic and dendritic activity. We show that this simple time-independent learning rule can give rise to many forms of the STDP learning curve. The rule regulates synaptic strength without the need for spike matching or other supervisory learning mechanisms. Local differences in dendritic activity at the synapse greatly affect the cross-correlation difference which determines the relative contributions of different neural activity sources. Dendritic activity due to nearby synapses, action potentials, both forward and back-propagating, as well as inhibitory synapses will dynamically modify the local activity at the synapse, and the resulting STDP learning rule. The dynamic Hebbian learning rule ensures furthermore, that the resulting synaptic strength is dynamically stable, and that interactions between synapses do not result in local instabilities. The rule clearly demonstrates that synapses function as independent localized

  3. Autonomous learning by simple dynamical systems with a discrete-time formulation

    Science.gov (United States)

    Bilen, Agustín M.; Kaluza, Pablo

    2017-05-01

    We present a discrete-time formulation for the autonomous learning conjecture. The main feature of this formulation is the possibility to apply the autonomous learning scheme to systems in which the errors with respect to target functions are not well-defined for all times. This restriction for the evaluation of functionality is a typical feature in systems that need a finite time interval to process a unit piece of information. We illustrate its application on an artificial neural network with feed-forward architecture for classification and a phase oscillator system with synchronization properties. The main characteristics of the discrete-time formulation are shown by constructing these systems with predefined functions.

  4. Concept of Best Practices in English Language Teaching to Pakistani ELT Fraternity

    Directory of Open Access Journals (Sweden)

    Muhammad Arif Soomro

    2016-08-01

    Full Text Available Teaching industry of English as a second or foreign language has grown massively in recent times in Pakistan. There are many public sectors universities and English academies established all over Pakistan offering English language proficiency courses. Therefore, this wave led to conduct this research. The purpose of conducting this study was to investigate contemporary pedagogical techniques used for teaching and learning English and to introduce the concept of ESL /EFL Best Practices for effective language teaching in Pakistan. Purposive Sampling method was used to collect the information from respondents regarding their contemporary-used teaching techniques in ESL/EFL class. The questionnaire was implied as the main tool for data collection among twenty English language teachers from two public sector universities. The results of the study indicated that teachers were attached  with some outdated techniques and activities secondly, they also faced problems applying new techniques while teaching in a large multilevel classrooms, thirdly, teachers’ had willingness to adopt and employ innovative techniques in classrooms and lastly, the notion of ESL best practices was uncommon among them. Most of the teaching strategies do not create better learning environment, and learners do not interestingly participate due outdated activities. Therefore, the suggested solution was utilizing best practices that are based on modern techniques, approaches considerable for multiple levels depending upon the needs and developmental state of the individual learners. Keywords: pedagogical strategies, ESL/EFL Best Practices, Pakistani teachers, English teaching/learning

  5. CAT-PUMA: CME Arrival Time Prediction Using Machine learning Algorithms

    Science.gov (United States)

    Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

    2018-04-01

    CAT-PUMA (CME Arrival Time Prediction Using Machine learning Algorithms) quickly and accurately predicts the arrival of Coronal Mass Ejections (CMEs) of CME arrival time. The software was trained via detailed analysis of CME features and solar wind parameters using 182 previously observed geo-effective partial-/full-halo CMEs and uses algorithms of the Support Vector Machine (SVM) to make its predictions, which can be made within minutes of providing the necessary input parameters of a CME.

  6. Real-time modeling of primitive environments through wavelet sensors and Hebbian learning

    Science.gov (United States)

    Vaccaro, James M.; Yaworsky, Paul S.

    1999-06-01

    Modeling the world through sensory input necessarily provides a unique perspective for the observer. Given a limited perspective, objects and events cannot always be encoded precisely but must involve crude, quick approximations to deal with sensory information in a real- time manner. As an example, when avoiding an oncoming car, a pedestrian needs to identify the fact that a car is approaching before ascertaining the model or color of the vehicle. In our methodology, we use wavelet-based sensors with self-organized learning to encode basic sensory information in real-time. The wavelet-based sensors provide necessary transformations while a rank-based Hebbian learning scheme encodes a self-organized environment through translation, scale and orientation invariant sensors. Such a self-organized environment is made possible by combining wavelet sets which are orthonormal, log-scale with linear orientation and have automatically generated membership functions. In earlier work we used Gabor wavelet filters, rank-based Hebbian learning and an exponential modulation function to encode textural information from images. Many different types of modulation are possible, but based on biological findings the exponential modulation function provided a good approximation of first spike coding of `integrate and fire' neurons. These types of Hebbian encoding schemes (e.g., exponential modulation, etc.) are useful for quick response and learning, provide several advantages over contemporary neural network learning approaches, and have been found to quantize data nonlinearly. By combining wavelets with Hebbian learning we can provide a real-time front-end for modeling an intelligent process, such as the autonomous control of agents in a simulated environment.

  7. Children benefit differently from night- and day-time sleep in motor learning.

    Science.gov (United States)

    Yan, Jin H

    2017-08-01

    Motor skill acquisition occurs while practicing (on-line) and when asleep or awake (off-line). However, developmental questions still remain about whether children of various ages benefit similarly or differentially from night- and day-time sleeping. The likely circadian effects (time-of-day) and the possible between-test-interference (order effects) associated with children's off-line motor learning are currently unknown. Therefore, this study examines the contributions of over-night sleeping and mid-day napping to procedural skill learning. One hundred and eight children were instructed to practice a finger sequence task using computer keyboards. After an equivalent 11-h interval in one of the three states (sleep, nap, wakefulness), children performed the same sequence in retention tests and a novel sequence in transfer tests. Changes in the movement time and sequence accuracy were evaluated between ages (6-7, 8-9, 10-11years) during practice, and from skill training to retrievals across three states. Results suggest that night-time sleeping and day-time napping improved the tapping speed, especially for the 6-year-olds. The circadian factor did not affect off-line motor learning in children. The interference between the two counter-balanced retrieval tests was not found for the off-line motor learning. This research offers possible evidence about the age-related motor learning characteristics in children and a potential means for enhancing developmental motor skills. The dynamics between age, experience, memory formation, and the theoretical implications of motor skill acquisition are discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. An investigation of fMRI time series stationarity during motor sequence learning foot tapping tasks.

    Science.gov (United States)

    Muhei-aldin, Othman; VanSwearingen, Jessie; Karim, Helmet; Huppert, Theodore; Sparto, Patrick J; Erickson, Kirk I; Sejdić, Ervin

    2014-04-30

    Understanding complex brain networks using functional magnetic resonance imaging (fMRI) is of great interest to clinical and scientific communities. To utilize advanced analysis methods such as graph theory for these investigations, the stationarity of fMRI time series needs to be understood as it has important implications on the choice of appropriate approaches for the analysis of complex brain networks. In this paper, we investigated the stationarity of fMRI time series acquired from twelve healthy participants while they performed a motor (foot tapping sequence) learning task. Since prior studies have documented that learning is associated with systematic changes in brain activation, a sequence learning task is an optimal paradigm to assess the degree of non-stationarity in fMRI time-series in clinically relevant brain areas. We predicted that brain regions involved in a "learning network" would demonstrate non-stationarity and may violate assumptions associated with some advanced analysis approaches. Six blocks of learning, and six control blocks of a foot tapping sequence were performed in a fixed order. The reverse arrangement test was utilized to investigate the time series stationarity. Our analysis showed some non-stationary signals with a time varying first moment as a major source of non-stationarity. We also demonstrated a decreased number of non-stationarities in the third block as a result of priming and repetition. Most of the current literature does not examine stationarity prior to processing. The implication of our findings is that future investigations analyzing complex brain networks should utilize approaches robust to non-stationarities, as graph-theoretical approaches can be sensitive to non-stationarities present in data. Copyright © 2014 Elsevier B.V. All rights reserved.

  9. Autism: Too eager to learn? Event related potential findings of increased dependency on intentional learning in a serial reaction time task.

    Science.gov (United States)

    Zwart, Fenny S; Vissers, Constance Th W M; van der Meij, Roemer; Kessels, Roy P C; Maes, Joseph H R

    2017-09-01

    It has been suggested that people with autism spectrum disorder (ASD) have an increased tendency to use explicit (or intentional) learning strategies. This altered learning may play a role in the development of the social communication difficulties characterizing ASD. In the current study, we investigated incidental and intentional sequence learning using a Serial Reaction Time (SRT) task in an adult ASD population. Response times and event related potentials (ERP) components (N2b and P3) were assessed as indicators of learning and knowledge. Findings showed that behaviorally, sequence learning and ensuing explicit knowledge were similar in ASD and typically developing (TD) controls. However, ERP findings showed that learning in the TD group was characterized by an enhanced N2b, while learning in the ASD group was characterized by an enhanced P3. These findings suggest that learning in the TD group might be more incidental in nature, whereas learning in the ASD group is more intentional or effortful. Increased intentional learning might serve as a strategy for individuals with ASD to control an overwhelming environment. Although this led to similar behavioral performances on the SRT task, it is very plausible that this intentional learning has adverse effects in more complex social situations, and hence contributes to the social impairments found in ASD. Autism Res 2017, 10: 1533-1543. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

  10. Experiments with Online Reinforcement Learning in Real-Time Strategy Games

    DEFF Research Database (Denmark)

    Toftgaard Andersen, Kresten; Zeng, Yifeng; Dahl Christensen, Dennis

    2009-01-01

    Real-time strategy (RTS) games provide a challenging platform to implement online reinforcement learning (RL) techniques in a real application. Computer, as one game player, monitors opponents' (human or other computers) strategies and then updates its own policy using RL methods. In this article......, we first examine the suitability of applying the online RL in various computer games. Reinforcement learning application depends on both RL complexity and the game features. We then propose a multi-layer framework for implementing online RL in an RTS game. The framework significantly reduces RL...... the effectiveness of our proposed framework and shed light on relevant issues in using online RL in RTS games....

  11. Event timing in associative learning: from biochemical reaction dynamics to behavioural observations.

    Directory of Open Access Journals (Sweden)

    Ayse Yarali

    Full Text Available Associative learning relies on event timing. Fruit flies for example, once trained with an odour that precedes electric shock, subsequently avoid this odour (punishment learning; if, on the other hand the odour follows the shock during training, it is approached later on (relief learning. During training, an odour-induced Ca(++ signal and a shock-induced dopaminergic signal converge in the Kenyon cells, synergistically activating a Ca(++-calmodulin-sensitive adenylate cyclase, which likely leads to the synaptic plasticity underlying the conditioned avoidance of the odour. In Aplysia, the effect of serotonin on the corresponding adenylate cyclase is bi-directionally modulated by Ca(++, depending on the relative timing of the two inputs. Using a computational approach, we quantitatively explore this biochemical property of the adenylate cyclase and show that it can generate the effect of event timing on associative learning. We overcome the shortage of behavioural data in Aplysia and biochemical data in Drosophila by combining findings from both systems.

  12. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

    Directory of Open Access Journals (Sweden)

    Christian Albers

    Full Text Available Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP. Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious and strong (teacher spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

  13. Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

    Science.gov (United States)

    Albers, Christian; Westkott, Maren; Pawelzik, Klaus

    2016-01-01

    Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

  14. Developing Mentors: Adult participation, practices, and learning in an out-of-school time STEM program

    Science.gov (United States)

    Scipio, Deana Aeolani

    This dissertation examines learning within an out-of-school time (OST) Science, Technology, Engineering, and Mathematics (STEM) broadening participation program. The dissertation includes an introduction, three empirical chapters (written as individual articles), and a conclusion. The dissertation context is a chemical oceanography OST program for middle school students called Project COOL---Chemical Oceanography Outside the Lab. The program was a collaboration between middle school OST programming, a learning sciences research laboratory, and a chemical oceanography laboratory. Both labs were located at a research-based university in the Pacific Northwest of the United States. Participants include 34 youth, 12 undergraduates, and five professional scientists. The dissertation data corpus includes six years of ethnographic field notes across three field sites, 400 hours of video and audio recordings, 40 hours of semi-structured interviews, and more than 100 participant generated artifacts. Analysis methods include comparative case analysis, cognitive mapping, semiotic cluster analysis, video interaction analysis, and discourse analysis. The first empirical article focuses on synthesizing productive programmatic features from four years of design-based research.. The second article is a comparative case study of three STEM mentors from non-dominant communities in the 2011 COOL OST Program. The third article is a comparative case study of undergraduates learning to be mentors in the 2014 COOL OST Program. Findings introduce Deep Hanging as a theory of learning in practice. Deep Hanging entails authentic tasks in rich contexts, providing access, capitalizing on opportunity, and building interpersonal relationships. Taken together, these three chapters illuminate the process of designing a rich OST learning environment and the kinds of learning in practice that occurred for adult learners learning to be mentors through their participation in the COOL OST program. In

  15. Real time eye tracking using Kalman extended spatio-temporal context learning

    Science.gov (United States)

    Munir, Farzeen; Minhas, Fayyaz ul Amir Asfar; Jalil, Abdul; Jeon, Moongu

    2017-06-01

    Real time eye tracking has numerous applications in human computer interaction such as a mouse cursor control in a computer system. It is useful for persons with muscular or motion impairments. However, tracking the movement of the eye is complicated by occlusion due to blinking, head movement, screen glare, rapid eye movements, etc. In this work, we present the algorithmic and construction details of a real time eye tracking system. Our proposed system is an extension of Spatio-Temporal context learning through Kalman Filtering. Spatio-Temporal Context Learning offers state of the art accuracy in general object tracking but its performance suffers due to object occlusion. Addition of the Kalman filter allows the proposed method to model the dynamics of the motion of the eye and provide robust eye tracking in cases of occlusion. We demonstrate the effectiveness of this tracking technique by controlling the computer cursor in real time by eye movements.

  16. Predicting the time of conversion to MCI in the elderly: role of verbal expression and learning.

    Science.gov (United States)

    Oulhaj, Abderrahim; Wilcock, Gordon K; Smith, A David; de Jager, Celeste A

    2009-11-03

    Increasing awareness that minimal or mild cognitive impairment (MCI) in the elderly may be a precursor of dementia has led to an increase in the number of people attending memory clinics. We aimed to develop a way of predicting the period of time before cognitive impairment occurs in community-dwelling elderly. The method is illustrated by the use of simple tests of different cognitive domains. A cohort of 241 normal elderly volunteers was followed for up to 20 years with regular assessments of cognitive abilities using the Cambridge Cognitive Examination (CAMCOG); 91 participants developed MCI. We used interval-censored survival analysis statistical methods to model which baseline cognitive tests best predicted the time to convert to MCI. Out of several baseline variables, only age and CAMCOG subscores for expression and learning/memory were predictors of the time to conversion. The time to conversion was 14% shorter for each 5 years of age, 17% shorter for each point lower in the expression score, and 15% shorter for each point lower in the learning score. We present in tabular form the probability of converting to MCI over intervals between 2 and 10 years for different combinations of expression and learning scores. In apparently normal elderly people, subtle measurable cognitive deficits that occur within the normal range on standard testing protocols reliably predict the time to clinically relevant cognitive impairment long before clinical symptoms are reported.

  17. Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities

    Science.gov (United States)

    Sadeghi, Alireza; Sheikholeslami, Fatemeh; Giannakis, Georgios B.

    2018-02-01

    Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this work, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.

  18. Adjustment to subtle time constraints and power law learning in rapid serial visual presentation

    Directory of Open Access Journals (Sweden)

    Jacqueline Chakyung Shin

    2015-11-01

    Full Text Available We investigated whether attention could be modulated through the implicit learning of temporal information in a rapid serial visual presentation (RSVP task. Participants identified two target letters among numeral distractors. The stimulus-onset asynchrony immediately following the first target (SOA1 varied at three levels (70, 98, and 126 ms randomly between trials or fixed within blocks of trials. Practice over three consecutive days resulted in a continuous improvement in the identification rate for both targets and attenuation of the attentional blink (AB, a decrement in target (T2 identification when presented 200-400 ms after another target (T1. Blocked SOA1s led to a faster rate of improvement in RSVP performance and more target order reversals relative to random SOA1s, suggesting that the implicit learning of SOA1 positively affected performance. The results also reveal power law learning curves for individual target identification as well as the reduction in the AB decrement. These learning curves reflect the spontaneous emergence of skill through subtle attentional modulations rather than general attentional distribution. Together, the results indicate that implicit temporal learning could improve high level and rapid cognitive processing and highlights the sensitivity and adaptability of the attentional system to subtle constraints in stimulus timing.

  19. Assessing learning styles of Saudi dental students using Kolb's Learning Style Inventory.

    Science.gov (United States)

    ALQahtani, Dalal A; Al-Gahtani, Sara M

    2014-06-01

    Experiential learning theory (ELT), a theory developed by David Kolb that considers experience to be very important for learning, classifies learners into four categories: Divergers, Assimilators, Convergers, and Accommodators. Kolb used his Learning Style Inventory (LSI) to validate ELT. Knowing the learning styles of students facilitates their understanding of themselves and thereby increases teaching efficiency. Few studies have been conducted that investigate learning preferences of students in the field of dentistry. This study was designed to distinguish learning styles among Saudi dental students and interns utilizing Kolb's LSI. The survey had a response rate of 62 percent (424 of 685 dental students), but surveys with incomplete answers or errors were excluded, resulting in 291 usable surveys (42 percent of the student population). The independent variables of this study were gender, clinical experience level, academic achievement as measured by grade point average (GPA), and specialty interest. The Diverging learning style was the dominant style among those in the sample. While the students preferred the Assimilating style during their early preclinical years, they preferred the Diverging style during their later clinical years. No associations were found between students' learning style and their gender, GPA, or specialty interest. Further research is needed to support these findings and demonstrate the impact of learning styles on dental students' learning.

  20. Touchscreen Facilitates Young Children’s Transfer of Learning to Tell Time

    Directory of Open Access Journals (Sweden)

    Fuxing Wang

    2016-11-01

    Full Text Available Young children are devoting increasing time to playing on handheld touchscreen devices (e.g., iPads. Though thousands of touchscreen apps are claimed to be educational, there is a lack of sufficient evidence examining the impact of touchscreens on children’s learning outcomes. In the present study, the two questions we focused on were (a whether using a touchscreen was helpful in teaching children to tell time, and (b to what extent young children could transfer what they had learned on the touchscreen to other media. A pre- and posttest design was adopted. After learning to read the time on the iPad touchscreen for 10 minutes, three groups of 5- to 6-year-old children (N = 65 were respectively tested with an iPad touchscreen, a toy clock or a drawing of a clock on paper. The results revealed that posttest scores in the iPad touchscreen test group were significantly higher than those at pretest, indicating that the touchscreen itself could provide support for young children’s learning. Similarly, regardless of being tested with a toy clock or paper drawing, children’s posttest performance was also better than pretest, suggesting that children could transfer what they had learned on an iPad touchscreen to other media. However, comparison among groups showed that children tested with the paper drawing underperformed those tested with the other two media. The theoretical and practical implications of the results, as well as limitations of the present study, are discussed.

  1. Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training

    Directory of Open Access Journals (Sweden)

    Alexandru D. Iordan

    2018-01-01

    Full Text Available Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on “resting-state” networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA and 20 older adults (OA were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of

  2. Uudised : EMA uus barokkorel. Olari Elts Soome Raadio SO ees. "Supreme Silence" Saksamaal. Kangro muusika Lätis ja Leedus / Priit Kuusk

    Index Scriptorium Estoniae

    Kuusk, Priit, 1938-

    2001-01-01

    EMA vastvalminud barokkoreli avamispidustustest. O.Elts juhatas 30. apr. Finlandia-talos toimunud kontserdil Soome Raadio SO-d. P.Vähi teos "Supreme Silence" kõlas kahel muusikafestivalil Saksamaal. Läti klaveriduo esitas rahvusvahelisel klaveriduode festivalil Lätis ja Leedus R.Kangro pala "O Sancta Simplicitas!"

  3. Kaks rauast nooleotsa Jägala Jõesuu linnamäelt: arheoloogiline ja ajalooline kontekst / Mari Lõhmus, Ester Oras, Martti Veldi

    Index Scriptorium Estoniae

    Lõhmus, Mari

    2010-01-01

    Jägala Jõesuu linnamäelt leitud vanemast rauaajast pärit kahest nooleotsast. Nooleotste avastamisest ja konserveerimisest ning olemasoleva info põhjal tehtud vaadetest minevikku. Nooleotsad viitavad rauaajal toimunud sõjalisele konfliktile Läänemere piirkonnas

  4. A comparison of the stochastic and machine learning approaches in hydrologic time series forecasting

    Science.gov (United States)

    Kim, T.; Joo, K.; Seo, J.; Heo, J. H.

    2016-12-01

    Hydrologic time series forecasting is an essential task in water resources management and it becomes more difficult due to the complexity of runoff process. Traditional stochastic models such as ARIMA family has been used as a standard approach in time series modeling and forecasting of hydrological variables. Due to the nonlinearity in hydrologic time series data, machine learning approaches has been studied with the advantage of discovering relevant features in a nonlinear relation among variables. This study aims to compare the predictability between the traditional stochastic model and the machine learning approach. Seasonal ARIMA model was used as the traditional time series model, and Random Forest model which consists of decision tree and ensemble method using multiple predictor approach was applied as the machine learning approach. In the application, monthly inflow data from 1986 to 2015 of Chungju dam in South Korea were used for modeling and forecasting. In order to evaluate the performances of the used models, one step ahead and multi-step ahead forecasting was applied. Root mean squared error and mean absolute error of two models were compared.

  5. EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

    Directory of Open Access Journals (Sweden)

    Ting Wang

    2014-01-01

    Full Text Available Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG is widely used in eye state classification to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches.

  6. Approach to the E-ELT dome and main structure challenges

    Science.gov (United States)

    Bilbao, Armando; Murga, Gaizka; Gómez, Celia; Llarena, Javier

    2014-07-01

    The E-ELT as a whole could be classified as an extremely challenging project. More precisely, it should be defined as an array of many different sub-challenges, which comprise technical, logistical and managerial matters. This paper reviews some of these critical challenges, in particular those related to the Dome and the Main Structure, suggesting ways to face them in the most pragmatic way possible. Technical challenges for the Dome and the Main Structure are mainly related to the need to upscale current design standards to an order of magnitude larger design. Trying a direct design escalation is not feasible; it would not work. A design effort is needed to cross hybridize current design standards with technologies coming from other different applications. Innovative design is therefore not a wish but a must. And innovative design comes along with design risk. Design risk needs to be tackled from two angles: on the one hand through thorough design validation analysis and on the other hand through extensive pre-assembly and testing. And, once again, full scale integrated pre-assembly and testing of extremely large subsystems is not always possible. Therefore, defining a comprehensive test plan for critical components, critical subsystems and critical subassemblies becomes essential. Logistical challenges are linked to the erection site. Cerro Armazones is a remote site and this needs to be considered when evaluating transport and erection requirements. But it is not only the remoteness of the site that needs to be considered. The size of both Dome and Main Structure require large construction cranes and a well defined erection plan taking into account pre-assembly strategies, limited plan area utilization, erection sequence, erection stability during intermediate stages and, very specifically, efficient coordination between the Dome and the Main Structure erection processes. Managerial issues pose another set of challenges in this project. Both the size of the

  7. Influencing Work-Related Learning: The Role of Job Characteristics and Self-Directed Learning Orientation in Part-Time Vocational Education

    Science.gov (United States)

    Gijbels, David; Raemdonck, Isabel; Vervecken, Dries

    2010-01-01

    Based on the Demand-Control-Support (DCS) model, the present paper aims to investigate the influence of job characteristics such as job demands, job control, social support at work and self-directed learning orientation on the work-related learning behaviour of workers. The present study was conducted in a centre for part-time vocational education…

  8. Real-time, adaptive machine learning for non-stationary, near chaotic gasoline engine combustion time series.

    Science.gov (United States)

    Vaughan, Adam; Bohac, Stanislav V

    2015-10-01

    Fuel efficient Homogeneous Charge Compression Ignition (HCCI) engine combustion timing predictions must contend with non-linear chemistry, non-linear physics, period doubling bifurcation(s), turbulent mixing, model parameters that can drift day-to-day, and air-fuel mixture state information that cannot typically be resolved on a cycle-to-cycle basis, especially during transients. In previous work, an abstract cycle-to-cycle mapping function coupled with ϵ-Support Vector Regression was shown to predict experimentally observed cycle-to-cycle combustion timing over a wide range of engine conditions, despite some of the aforementioned difficulties. The main limitation of the previous approach was that a partially acasual randomly sampled training dataset was used to train proof of concept offline predictions. The objective of this paper is to address this limitation by proposing a new online adaptive Extreme Learning Machine (ELM) extension named Weighted Ring-ELM. This extension enables fully causal combustion timing predictions at randomly chosen engine set points, and is shown to achieve results that are as good as or better than the previous offline method. The broader objective of this approach is to enable a new class of real-time model predictive control strategies for high variability HCCI and, ultimately, to bring HCCI's low engine-out NOx and reduced CO2 emissions to production engines. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

    Science.gov (United States)

    Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

    2016-01-01

    Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

  10. Sensitivity analysis of machine-learning models of hydrologic time series

    Science.gov (United States)

    O'Reilly, A. M.

    2017-12-01

    Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

  11. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

    2018-02-01

    Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  12. Kinesthetic Astronomy: Significant Upgrades to the Sky Time Lesson that Support Student Learning

    Science.gov (United States)

    Morrow, C. A.; Zawaski, M.

    2004-12-01

    This paper will report on a significant upgrade to the first in a series of innovative, experiential lessons we call Kinesthetic Astronomy. The Sky Time lesson reconnects students with the astronomical meaning of the day, year, and seasons. Like all Kinesthetic Astronomy lessons, it teaches basic astronomical concepts through choreographed bodily movements and positions that provide educational sensory experiences. They are intended for sixth graders up through adult learners in both formal and informal educational settings. They emphasize astronomical concepts and phenomenon that people can readily encounter in their "everyday" lives such as time, seasons, and sky motions of the Sun, Moon, stars, and planets. Kinesthetic Astronomy lesson plans are fully aligned with national science education standards, both in content and instructional practice. Our lessons offer a complete learning cycle with written assessment opportunities now embedded throughout the lesson. We have substantially strengthened the written assessment options for the Sky Time lesson to help students translate their kinesthetic and visual learning into the verbal-linguistic and mathematical-logical realms of expression. Field testing with non-science undergraduates, middle school science teachers and students, Junior Girl Scouts, museum education staff, and outdoor educators has been providing evidence that Kinesthetic Astronomy techniques allow learners to achieve a good grasp of concepts that are much more difficult to learn in more conventional ways such as via textbooks or even computer animation. Field testing of the Sky Time lesson has also led us to significant changes from the previous version to support student learning. We will report on the nature of these changes.

  13. Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

    Science.gov (United States)

    Wehmeyer, Christoph; Noé, Frank

    2018-06-01

    Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data. We can show that our time-lagged autoencoder reliably finds low-dimensional embeddings for high-dimensional feature spaces which capture the slow dynamics of the underlying stochastic processes—beyond the capabilities of linear dimension reduction techniques.

  14. Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

    Science.gov (United States)

    Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

    2018-02-01

    Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

  15. KOMPARASI KEMAMPUAN KOMUNIKASI MATEMATIS SISWA DENGAN MODEL LEARNING CYCLE DAN TIME TOKEN

    Directory of Open Access Journals (Sweden)

    Arin Ayundhita

    2014-11-01

    Full Text Available Tujuan penelitian ini untuk mengetahui apakah model pembelajaran Learning Cycle 5E dan model pembelajaran Time Token pada siswa kelas VIII materi keliling dan luas lingkaran dapat mencapai ketuntasan belajar dan untuk mengetahui manakah yang lebih baik antara model pembelajaran Learning Cycle 5E dan model pembelajaran Time Token. Populasi dalam penelitian ini adalah siswa kelas VIII SMP Negeri 1 Sine Kabupaten Ngawi tahun pelajaran 2013/2014. Dengan menggunakan teknik cluster random sampling, terpilih sampel yaitu siswa kelas VIII A sebagai kelas eksperimen 1 dan kelas VIII E sebagai kelas eksperimen 2. Pengumpulan data dilakukan dengan metode dokumentasi, tes, dan observasi. Analisis data menggunakan uji proporsi dan uji perbedaan dua rata-rata. Dari hasil uji ketuntasan belajar diperoleh siswa kelas eksperimen 1 mencapai ketuntasan belajar klasikal sementara kelas eksperimen 2 belum mencapai ketuntasan belajar klasikal. Dari hasil uji perbedaan rata-rata satu pihak diperoleh rata-rata kemampuan komunikasi matematis siswa kelas eksperimen 1 lebih baik daripada rata-rata kemampuan komunikasi matematis siswa kelas eksperimen 2. Simpulan yang diperoleh adalah model pembelajaran Learning Cycle 5E lebih baik dari pembelajaran dengan model Time Token.

  16. Staged Inference using Conditional Deep Learning for energy efficient real-time smart diagnosis.

    Science.gov (United States)

    Parsa, Maryam; Panda, Priyadarshini; Sen, Shreyas; Roy, Kaushik

    2017-07-01

    Recent progress in biosensor technology and wearable devices has created a formidable opportunity for remote healthcare monitoring systems as well as real-time diagnosis and disease prevention. The use of data mining techniques is indispensable for analysis of the large pool of data generated by the wearable devices. Deep learning is among the promising methods for analyzing such data for healthcare applications and disease diagnosis. However, the conventional deep neural networks are computationally intensive and it is impractical to use them in real-time diagnosis with low-powered on-body devices. We propose Staged Inference using Conditional Deep Learning (SICDL), as an energy efficient approach for creating healthcare monitoring systems. For smart diagnostics, we observe that all diagnoses are not equally challenging. The proposed approach thus decomposes the diagnoses into preliminary analysis (such as healthy vs unhealthy) and detailed analysis (such as identifying the specific type of cardio disease). The preliminary diagnosis is conducted real-time with a low complexity neural network realized on the resource-constrained on-body device. The detailed diagnosis requires a larger network that is implemented remotely in cloud and is conditionally activated only for detailed diagnosis (unhealthy individuals). We evaluated the proposed approach using available physiological sensor data from Physionet databases, and achieved 38% energy reduction in comparison to the conventional deep learning approach.

  17. Incorporating Real-time Earthquake Information into Large Enrollment Natural Disaster Course Learning

    Science.gov (United States)

    Furlong, K. P.; Benz, H.; Hayes, G. P.; Villasenor, A.

    2010-12-01

    Although most would agree that the occurrence of natural disaster events such as earthquakes, volcanic eruptions, and floods can provide effective learning opportunities for natural hazards-based courses, implementing compelling materials into the large-enrollment classroom environment can be difficult. These natural hazard events derive much of their learning potential from their real-time nature, and in the modern 24/7 news-cycle where all but the most devastating events are quickly out of the public eye, the shelf life for an event is quite limited. To maximize the learning potential of these events requires that both authoritative information be available and course materials be generated as the event unfolds. Although many events such as hurricanes, flooding, and volcanic eruptions provide some precursory warnings, and thus one can prepare background materials to place the main event into context, earthquakes present a particularly confounding situation of providing no warning, but where context is critical to student learning. Attempting to implement real-time materials into large enrollment classes faces the additional hindrance of limited internet access (for students) in most lecture classrooms. In Earth 101 Natural Disasters: Hollywood vs Reality, taught as a large enrollment (150+ students) general education course at Penn State, we are collaborating with the USGS’s National Earthquake Information Center (NEIC) to develop efficient means to incorporate their real-time products into learning activities in the lecture hall environment. Over time (and numerous events) we have developed a template for presenting USGS-produced real-time information in lecture mode. The event-specific materials can be quickly incorporated and updated, along with key contextual materials, to provide students with up-to-the-minute current information. In addition, we have also developed in-class activities, such as student determination of population exposure to severe ground

  18. Off-Policy Reinforcement Learning: Optimal Operational Control for Two-Time-Scale Industrial Processes.

    Science.gov (United States)

    Li, Jinna; Kiumarsi, Bahare; Chai, Tianyou; Lewis, Frank L; Fan, Jialu

    2017-12-01

    Industrial flow lines are composed of unit processes operating on a fast time scale and performance measurements known as operational indices measured at a slower time scale. This paper presents a model-free optimal solution to a class of two time-scale industrial processes using off-policy reinforcement learning (RL). First, the lower-layer unit process control loop with a fast sampling period and the upper-layer operational index dynamics at a slow time scale are modeled. Second, a general optimal operational control problem is formulated to optimally prescribe the set-points for the unit industrial process. Then, a zero-sum game off-policy RL algorithm is developed to find the optimal set-points by using data measured in real-time. Finally, a simulation experiment is employed for an industrial flotation process to show the effectiveness of the proposed method.

  19. Integral reinforcement learning for continuous-time input-affine nonlinear systems with simultaneous invariant explorations.

    Science.gov (United States)

    Lee, Jae Young; Park, Jin Bae; Choi, Yoon Ho

    2015-05-01

    This paper focuses on a class of reinforcement learning (RL) algorithms, named integral RL (I-RL), that solve continuous-time (CT) nonlinear optimal control problems with input-affine system dynamics. First, we extend the concepts of exploration, integral temporal difference, and invariant admissibility to the target CT nonlinear system that is governed by a control policy plus a probing signal called an exploration. Then, we show input-to-state stability (ISS) and invariant admissibility of the closed-loop systems with the policies generated by integral policy iteration (I-PI) or invariantly admissible PI (IA-PI) method. Based on these, three online I-RL algorithms named explorized I-PI and integral Q -learning I, II are proposed, all of which generate the same convergent sequences as I-PI and IA-PI under the required excitation condition on the exploration. All the proposed methods are partially or completely model free, and can simultaneously explore the state space in a stable manner during the online learning processes. ISS, invariant admissibility, and convergence properties of the proposed methods are also investigated, and related with these, we show the design principles of the exploration for safe learning. Neural-network-based implementation methods for the proposed schemes are also presented in this paper. Finally, several numerical simulations are carried out to verify the effectiveness of the proposed methods.

  20. Enhancement of Online Robotics Learning Using Real-Time 3D Visualization Technology

    Directory of Open Access Journals (Sweden)

    Richard Chiou

    2010-06-01

    Full Text Available This paper discusses a real-time e-Lab Learning system based on the integration of 3D visualization technology with a remote robotic laboratory. With the emergence and development of the Internet field, online learning is proving to play a significant role in the upcoming era. In an effort to enhance Internet-based learning of robotics and keep up with the rapid progression of technology, a 3- Dimensional scheme of viewing the robotic laboratory has been introduced in addition to the remote controlling of the robots. The uniqueness of the project lies in making this process Internet-based, and remote robot operated and visualized in 3D. This 3D system approach provides the students with a more realistic feel of the 3D robotic laboratory even though they are working remotely. As a result, the 3D visualization technology has been tested as part of a laboratory in the MET 205 Robotics and Mechatronics class and has received positive feedback by most of the students. This type of research has introduced a new level of realism and visual communications to online laboratory learning in a remote classroom.

  1. Learning motion concepts using real-time microcomputer-based laboratory tools

    Science.gov (United States)

    Thornton, Ronald K.; Sokoloff, David R.

    1990-09-01

    Microcomputer-based laboratory (MBL) tools have been developed which interface to Apple II and Macintosh computers. Students use these tools to collect physical data that are graphed in real time and then can be manipulated and analyzed. The MBL tools have made possible discovery-based laboratory curricula that embody results from educational research. These curricula allow students to take an active role in their learning and encourage them to construct physical knowledge from observation of the physical world. The curricula encourage collaborative learning by taking advantage of the fact that MBL tools present data in an immediately understandable graphical form. This article describes one of the tools—the motion detector (hardware and software)—and the kinematics curriculum. The effectiveness of this curriculum compared to traditional college and university methods for helping students learn basic kinematics concepts has been evaluated by pre- and post-testing and by observation. There is strong evidence for significantly improved learning and retention by students who used the MBL materials, compared to those taught in lecture.

  2. Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task.

    Directory of Open Access Journals (Sweden)

    Pavel Sanda

    2017-09-01

    Full Text Available Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making.

  3. Investigating the Relationship Between Self-Directed Learning Readiness and Time Management Skills in Turkish Undergraduate Nursing Students.

    Science.gov (United States)

    Ertuğ, Nurcan; Faydali, Saide

    The aims of this study were to determine self-directed learning and time management skills of undergraduate nursing students and to investigate the relationship between the concepts. The use of self-directed learning has increased as an educational strategy in recent years. This descriptive and correlational study was conducted with 383 undergraduate nursing students in Turkey. Data were collected using a sociodemographic questionnaire, the Self-Directed Learning Readiness Scale, and Time Management Questionnaire. Mean scores were as follows: self-directed learning readiness, 159.12 (SD = 20.8); time management, 87.75 (SD = 12.1). A moderate positive correlation was found between self-directed learning readiness and time management values. Time management scores were 78.42 when self-directed learning readiness was ≤149 and 90.82 when self-directed learning readiness was ≥ 150, with a statistically significant difference (p = .000). Level of self-directed learning and academic achievement were higher in students who managed their time well.

  4. INTEGRATING ICT IN ENGLISH LANGUAGE TEACHING AND LEARNING IN INDONESIA

    Directory of Open Access Journals (Sweden)

    Tuti Hidayati

    2016-11-01

    Full Text Available Indonesian ELT is complex for numerous reasons, and the level of students‟ outcome has been regarded unsatisfactory by a number of researchers and academics. This paper considers ICT as one of possible alternatives to deal with the complexity of Indonesian ELT and to improve its outcomes. It widely explores ICT integration in English LTL, especially on how ICT has been used in this field. It further investigates the benefits and challenges of integrating ICT in LTL. The paper argues that the integration of ICT is promising for changing and improving the effectiveness of the current Indonesian ELT condition when it is carried out in line with the effective LTL principles. The integration of ICT will enable teachers to vary teaching and learning activities, to gradually change the teaching style to be more student-centred, to train students to have more active role in learning, and to access a huge range of authentic learning materials. The paper also acknowledges the contraints that will emerge in an effort of integrating ICT in Indonesian English LTL. Hence, some recommedations for action are proposed at the end.

  5. Time to Engage? Texting to Support and Enhance First Year Undergraduate Learning

    Directory of Open Access Journals (Sweden)

    Geraldine Jones

    2009-04-01

    Full Text Available In this paper we discuss a case study investigating how the academic and personal development of first year students on an undergraduate sports education degree can be supported and enhanced with mobile SMS communication. SMS-based technologies were introduced in response to students’ particular needs (in transition to Higher Education and characteristics (‘digital natives’. Despite being unaccustomed to using their mobile phones for academic study, students willingly participated in SMS communication with their tutor via a texting management service. Drawing on evidence from two student surveys, focus groups and a tutor’s journal, we illustrate the potential that mobile SMS communication has to link and establish continuity between face to face teaching sessions and online learning activities in the Virtual Learning Environment (VLE. Many students perceived the SMS communication to have had a positive impact on their management of study time. We link our findings with the existing literature and argue that mobile text based communication has the potential to support the development of time management skills, an important component of self regulatory learning, a skill which has been shown to be key in making a successful transition.

  6. Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case

    International Nuclear Information System (INIS)

    Voyant, Cyril; Notton, Gilles; Darras, Christophe; Fouilloy, Alexis; Motte, Fabrice

    2017-01-01

    As global solar radiation forecasting is a very important challenge, several methods are devoted to this goal with different levels of accuracy and confidence. In this study we propose to better understand how the uncertainty is propagated in the context of global radiation time series forecasting using machine learning. Indeed we propose to decompose the error considering four kinds of uncertainties: the error due to the measurement, the variability of time series, the machine learning uncertainty and the error related to the horizon. All these components of the error allow to determinate a global uncertainty generating prediction bands related to the prediction efficiency. We also have defined a reliability index which could be very interesting for the grid manager in order to estimate the validity of predictions. We have experimented this method on a multilayer perceptron which is a popular machine learning technique. We have shown that the global error and its components are essential to quantify in order to estimate the reliability of the model outputs. The described method has been successfully applied to four meteorological stations in Mediterranean area. - Highlights: • Solar irradiation predictions require confidence bands. • There are a lot of kinds of uncertainties to take into account in order to propose prediction bands. • the ranking of different kinds of uncertainties is essential to propose an operational tool for the grid managers.

  7. Large-scale machine learning and evaluation platform for real-time traffic surveillance

    Science.gov (United States)

    Eichel, Justin A.; Mishra, Akshaya; Miller, Nicholas; Jankovic, Nicholas; Thomas, Mohan A.; Abbott, Tyler; Swanson, Douglas; Keller, Joel

    2016-09-01

    In traffic engineering, vehicle detectors are trained on limited datasets, resulting in poor accuracy when deployed in real-world surveillance applications. Annotating large-scale high-quality datasets is challenging. Typically, these datasets have limited diversity; they do not reflect the real-world operating environment. There is a need for a large-scale, cloud-based positive and negative mining process and a large-scale learning and evaluation system for the application of automatic traffic measurements and classification. The proposed positive and negative mining process addresses the quality of crowd sourced ground truth data through machine learning review and human feedback mechanisms. The proposed learning and evaluation system uses a distributed cloud computing framework to handle data-scaling issues associated with large numbers of samples and a high-dimensional feature space. The system is trained using AdaBoost on 1,000,000 Haar-like features extracted from 70,000 annotated video frames. The trained real-time vehicle detector achieves an accuracy of at least 95% for 1/2 and about 78% for 19/20 of the time when tested on ˜7,500,000 video frames. At the end of 2016, the dataset is expected to have over 1 billion annotated video frames.

  8. From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control.

    Science.gov (United States)

    Grossberg, Stephen

    2015-09-24

    This article provides an overview of neural models of synaptic learning and memory whose expression in adaptive behavior depends critically on the circuits and systems in which the synapses are embedded. It reviews Adaptive Resonance Theory, or ART, models that use excitatory matching and match-based learning to achieve fast category learning and whose learned memories are dynamically stabilized by top-down expectations, attentional focusing, and memory search. ART clarifies mechanistic relationships between consciousness, learning, expectation, attention, resonance, and synchrony. ART models are embedded in ARTSCAN architectures that unify processes of invariant object category learning, recognition, spatial and object attention, predictive remapping, and eye movement search, and that clarify how conscious object vision and recognition may fail during perceptual crowding and parietal neglect. The generality of learned categories depends upon a vigilance process that is regulated by acetylcholine via the nucleus basalis. Vigilance can get stuck at too high or too low values, thereby causing learning problems in autism and medial temporal amnesia. Similar synaptic learning laws support qualitatively different behaviors: Invariant object category learning in the inferotemporal cortex; learning of grid cells and place cells in the entorhinal and hippocampal cortices during spatial navigation; and learning of time cells in the entorhinal-hippocampal system during adaptively timed conditioning, including trace conditioning. Spatial and temporal processes through the medial and lateral entorhinal-hippocampal system seem to be carried out with homologous circuit designs. Variations of a shared laminar neocortical circuit design have modeled 3D vision, speech perception, and cognitive working memory and learning. A complementary kind of inhibitory matching and mismatch learning controls movement. This article is part of a Special Issue entitled SI: Brain and Memory

  9. CORRELATION OF INTEREST TO LEARN AND USE TIME LEARNING WITH LEARNING ACHIEVEMENT AUTOMOTIVE ELECTRICAL IN CLASS XII LIGHT VEHICLE ENGINEERING SMK PIRI I YOGYAKARTA ACADEMIC YEAR 2013/2014

    Directory of Open Access Journals (Sweden)

    Ari Pujiatmoko

    2014-06-01

    Full Text Available The purpose of this study were: 1 to determine whether there is a correlation between students' interest in learning and the learning achievement of automotive electrical, 2 to determine whether there is a correlation between the use of time studying the learning achievement of automotive electrical, 3 to determine whether there is a correlation between student interest and use the time to learn and the learning achievement of students of class XII automotive electrical TKR SMK PIRI 1 Yogyakarta academic year 2013/2014.  This research was conducted in class XII TKR SMK PIRI 1 Yogyakarta academic year 2013/2014. This study is an ex-post facto. This study used two independent variables and the interest in learning the use of learning time, while the dependent variable is the electrical automotive learning achievement. This study is a population study by the respondent amounted to 100 students. Techniques of data collection using questionnaire techniques and engineering documentation. Research instrument in this study is a questionnaire interest in learning, inquiry learning time management and documentation of student achievement. Trials using the instrument validity and reliability test. The analysis technique used is the prerequisite test for normality, linearity, and multicollinearity. Then test hypotheses using partial correlation analysis techniques and correlation.  The results showed that: 1 students' interest to have a strong positive correlation with school performance automotive electrical ρ value of 0.737; 2 the use of learning time have a low positive correlation with school performance automotive electrical ρ value of 0.275; 3 interest student learning and the use of study time has a very strong positive correlation with learning achievement of students of class XII automotive electrical TKR SMK PIRI I Yogyakarta academic year 2013/2014 as evidenced by the value of R = 0.811.

  10. Time-Contrastive Learning Based DNN Bottleneck Features for Text-Dependent Speaker Verification

    DEFF Research Database (Denmark)

    Sarkar, Achintya Kumar; Tan, Zheng-Hua

    2017-01-01

    In this paper, we present a time-contrastive learning (TCL) based bottleneck (BN) feature extraction method for speech signals with an application to text-dependent (TD) speaker verification (SV). It is well-known that speech signals exhibit quasi-stationary behavior in and only in a short interval......, and the TCL method aims to exploit this temporal structure. More specifically, it trains deep neural networks (DNNs) to discriminate temporal events obtained by uniformly segmenting speech signals, in contrast to existing DNN based BN feature extraction methods that train DNNs using labeled data...... to discriminate speakers or pass-phrases or phones or a combination of them. In the context of speaker verification, speech data of fixed pass-phrases are used for TCL-BN training, while the pass-phrases used for TCL-BN training are excluded from being used for SV, so that the learned features can be considered...

  11. Robust Monotonically Convergent Iterative Learning Control for Discrete-Time Systems via Generalized KYP Lemma

    Directory of Open Access Journals (Sweden)

    Jian Ding

    2014-01-01

    Full Text Available This paper addresses the problem of P-type iterative learning control for a class of multiple-input multiple-output linear discrete-time systems, whose aim is to develop robust monotonically convergent control law design over a finite frequency range. It is shown that the 2 D iterative learning control processes can be taken as 1 D state space model regardless of relative degree. With the generalized Kalman-Yakubovich-Popov lemma applied, it is feasible to describe the monotonically convergent conditions with the help of linear matrix inequality technique and to develop formulas for the control gain matrices design. An extension to robust control law design against systems with structured and polytopic-type uncertainties is also considered. Two numerical examples are provided to validate the feasibility and effectiveness of the proposed method.

  12. Unsupervised learning by spike timing dependent plasticity in phase change memory (PCM synapses

    Directory of Open Access Journals (Sweden)

    Stefano eAmbrogio

    2016-03-01

    Full Text Available We present a novel one-transistor/one-resistor (1T1R synapse for neuromorphic networks, based on phase change memory (PCM technology. The synapse is capable of spike-timing dependent plasticity (STDP, where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors.

  13. Information extraction from dynamic PS-InSAR time series using machine learning

    Science.gov (United States)

    van de Kerkhof, B.; Pankratius, V.; Chang, L.; van Swol, R.; Hanssen, R. F.

    2017-12-01

    Due to the increasing number of SAR satellites, with shorter repeat intervals and higher resolutions, SAR data volumes are exploding. Time series analyses of SAR data, i.e. Persistent Scatterer (PS) InSAR, enable the deformation monitoring of the built environment at an unprecedented scale, with hundreds of scatterers per km2, updated weekly. Potential hazards, e.g. due to failure of aging infrastructure, can be detected at an early stage. Yet, this requires the operational data processing of billions of measurement points, over hundreds of epochs, updating this data set dynamically as new data come in, and testing whether points (start to) behave in an anomalous way. Moreover, the quality of PS-InSAR measurements is ambiguous and heterogeneous, which will yield false positives and false negatives. Such analyses are numerically challenging. Here we extract relevant information from PS-InSAR time series using machine learning algorithms. We cluster (group together) time series with similar behaviour, even though they may not be spatially close, such that the results can be used for further analysis. First we reduce the dimensionality of the dataset in order to be able to cluster the data, since applying clustering techniques on high dimensional datasets often result in unsatisfying results. Our approach is to apply t-distributed Stochastic Neighbor Embedding (t-SNE), a machine learning algorithm for dimensionality reduction of high-dimensional data to a 2D or 3D map, and cluster this result using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The results show that we are able to detect and cluster time series with similar behaviour, which is the starting point for more extensive analysis into the underlying driving mechanisms. The results of the methods are compared to conventional hypothesis testing as well as a Self-Organising Map (SOM) approach. Hypothesis testing is robust and takes the stochastic nature of the observations into account

  14. Statistical Learning and Adaptive Decision-Making Underlie Human Response Time Variability in Inhibitory Control

    Directory of Open Access Journals (Sweden)

    Ning eMa

    2015-08-01

    Full Text Available Response time (RT is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task, in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop, and stop-signal onset time, SSD (stop-signal delay, with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop and SSD. The human behavioral data (n=20 bear out this prediction, showing P(stop and SSD both to be significant, independent predictors of RT, with P(stop being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

  15. Statistical learning and adaptive decision-making underlie human response time variability in inhibitory control.

    Science.gov (United States)

    Ma, Ning; Yu, Angela J

    2015-01-01

    Response time (RT) is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task (SST), in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop), and stop-signal onset time, SSD (stop-signal delay), with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop) and SSD. The human behavioral data (n = 20) bear out this prediction, showing P(stop) and SSD both to be significant, independent predictors of RT, with P(stop) being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

  16. Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

    International Nuclear Information System (INIS)

    Veronesi, F; Grassi, S

    2016-01-01

    Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners. (paper)

  17. Time series classification using k-Nearest neighbours, Multilayer Perceptron and Learning Vector Quantization algorithms

    Directory of Open Access Journals (Sweden)

    Jiří Fejfar

    2012-01-01

    Full Text Available We are presenting results comparison of three artificial intelligence algorithms in a classification of time series derived from musical excerpts in this paper. Algorithms were chosen to represent different principles of classification – statistic approach, neural networks and competitive learning. The first algorithm is a classical k-Nearest neighbours algorithm, the second algorithm is Multilayer Perceptron (MPL, an example of artificial neural network and the third one is a Learning Vector Quantization (LVQ algorithm representing supervised counterpart to unsupervised Self Organizing Map (SOM.After our own former experiments with unlabelled data we moved forward to the data labels utilization, which generally led to a better accuracy of classification results. As we need huge data set of labelled time series (a priori knowledge of correct class which each time series instance belongs to, we used, with a good experience in former studies, musical excerpts as a source of real-world time series. We are using standard deviation of the sound signal as a descriptor of a musical excerpts volume level.We are describing principle of each algorithm as well as its implementation briefly, giving links for further research. Classification results of each algorithm are presented in a confusion matrix showing numbers of misclassifications and allowing to evaluate overall accuracy of the algorithm. Results are compared and particular misclassifications are discussed for each algorithm. Finally the best solution is chosen and further research goals are given.

  18. Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

    Science.gov (United States)

    Veronesi, F.; Grassi, S.

    2016-09-01

    Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners.

  19. EFL Teachers’ Perceptions of The Place of Culture in ELT: A Survey Study at Four Universities in Ankara/Turkey

    Directory of Open Access Journals (Sweden)

    Okan ÖNALAN

    2005-10-01

    Full Text Available This study aims to investigate Turkish teachers’ opinions and beliefs on the place of target cultural information in English language teaching, as well as their related practices andapplications in EFL classrooms in Turkish higher education context. Particularly, it tries toexplore three research questions: (a How do Turkish teachers of English define culture? (bWhat are the EFL teachers’ attitudes towards incorporating cultural information into theirteaching? and (c What role do they allocate to the culture of the target language in theirclassrooms? The study shows that teachers mostly define culture in the sociological sense, suchas values and beliefs. Their definition of culture in the framework of ELT slightly shifts towardsmore visible culture, such as food and clothing. The study also reveals teachers’ positive attitudestowards incorporating cultural information in their instruction.

  20. Evaluation of software and electronics technologies for the control of the E-ELT instruments: a case study

    International Nuclear Information System (INIS)

    Di Marcantonio, P.; Cirami, R.; Coretti, I.; Chiozzi, G.; Kiekebusch, M.

    2012-01-01

    In the scope of the evaluation of architecture and technologies for the control system of the E-ELT (European-Extremely Large Telescope) instruments, a collaboration has been set up between the Instrumentation and Control Group of the INAF-OATs and the ESO Directorate of Engineering. The first result of this collaboration is the design and implementation of a prototype of a small but representative control system for a kind of multi-object (optical) spectrograph. The electronics has been based on PLCs (Programmable Logical Controller) and Ethernet based field-buses from different vendors but using international standards like the IEC 61131-3 and PLCopen Motion Control. The baseline design for the control software follows the architecture of the VLT (Very Large Telescope) Instrumentation application framework but it has been implemented using the ACS (ALMA Common Software), an open source software framework developed for the ALMA project and based on CORBA middle-ware. The communication among the software components is based on two models: CORBA calls for command/reply using the client/server paradigm and CORBA notification channel for distributing the devices status using the publisher/subscriber paradigm. The communication with the PLCs is based on OPC UA, an international standard for the communication with industrial controllers. The results of this work will contribute to the definition of the architecture of the control system that will be provided to all consortia responsible for the actual implementation of the E-ELT instruments. This paper presents the prototype motivation, its architecture, design and implementation. (authors)

  1. Why are they late? Timing abilities and executive control among students with learning disabilities.

    Science.gov (United States)

    Grinblat, Nufar; Rosenblum, Sara

    2016-12-01

    While a deficient ability to perform daily tasks on time has been reported among students with learning disabilities (LD), the underlying mechanism behind their 'being late' is still unclear. This study aimed to evaluate the organization in time, time estimation abilities, actual performance time pertaining to specific daily activities, as well as the executive functions of students with LD in comparison to those of controls, and to assess the relationships between these domains among each group. The participants were 27 students with LD, aged 20-30, and 32 gender and age-matched controls who completed the Time Organization and Participation Scale (TOPS) and the Behavioral Rating Inventory of Executive Function-Adult version (BRIEF-A). In addition, their ability to estimate the time needed to complete the task of preparing a cup of coffee as well as their actual performance time were evaluated. The results indicated that in comparison to controls, students with LD showed significantly inferior organization in time (TOPS) and executive function abilities (BRIEF-A). Furthermore, their time estimation abilities were significantly inferior and they required significantly more time to prepare a cup of coffee. Regression analysis identified the variables that predicted organization in time and task performance time among each group. The significance of the results for both theoretical and clinical implications are discussed. What this paper adds? This study examines the underlying mechanism of the phenomena of being late among students with LD. Following a recent call for using ecologically valid assessments, the functional daily ability of students with LD to prepare a cup of coffee and to organize time were investigated. Furthermore, their time estimation and executive control abilities were examined as a possible underlying mechanism for their lateness. Although previous studies have indicated executive control deficits among students with LD, to our knowledge, this

  2. Using Just-in-Time Information to Support Scientific Discovery Learning in a Computer-Based Simulation

    Science.gov (United States)

    Hulshof, Casper D.; de Jong, Ton

    2006-01-01

    Students encounter many obstacles during scientific discovery learning with computer-based simulations. It is hypothesized that an effective type of support, that does not interfere with the scientific discovery learning process, should be delivered on a "just-in-time" base. This study explores the effect of facilitating access to…

  3. A Virtual Learning Environment for Part-Time MASW Students: An Evaluation of the WebCT

    Science.gov (United States)

    Chan, Charles C.; Tsui, Ming-sum; Chan, Mandy Y. C.; Hong, Joe H.

    2008-01-01

    This study aims to evaluate the perception of a cohort of social workers studying for a part-time master's program in social work in using the popular Web-based learning platform--World Wide Web Course Tools (WebCT) as a complimentary method of teaching and learning. It was noted that social work profession began incorporating computer technology…

  4. Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning

    Directory of Open Access Journals (Sweden)

    Yingfeng Cai

    2016-01-01

    Full Text Available Night vision systems get more and more attention in the field of automotive active safety field. In this area, a number of researchers have proposed far-infrared sensor based night-time vehicle detection algorithm. However, existing algorithms have low performance in some indicators such as the detection rate and processing time. To solve this problem, we propose a far-infrared image vehicle detection algorithm based on visual saliency and deep learning. Firstly, most of the nonvehicle pixels will be removed with visual saliency computation. Then, vehicle candidate will be generated by using prior information such as camera parameters and vehicle size. Finally, classifier trained with deep belief networks will be applied to verify the candidates generated in last step. The proposed algorithm is tested in around 6000 images and achieves detection rate of 92.3% and processing time of 25 Hz which is better than existing methods.

  5. Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors.

    Science.gov (United States)

    Wei, Qinglai; Li, Benkai; Song, Ruizhuo

    2018-04-01

    In this paper, a generalized policy iteration (GPI) algorithm with approximation errors is developed for solving infinite horizon optimal control problems for nonlinear systems. The developed stable GPI algorithm provides a general structure of discrete-time iterative adaptive dynamic programming algorithms, by which most of the discrete-time reinforcement learning algorithms can be described using the GPI structure. It is for the first time that approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The admissibility of the approximate iterative control law can be guaranteed if the approximation errors satisfy the admissibility criteria. The convergence of the developed algorithm is established, which shows that the iterative value function is convergent to a finite neighborhood of the optimal performance index function, if the approximate errors satisfy the convergence criterion. Finally, numerical examples and comparisons are presented.

  6. Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data.

    Science.gov (United States)

    Janik, M; Bossew, P; Kurihara, O

    2018-07-15

    Machine learning is a class of statistical techniques which has proven to be a powerful tool for modelling the behaviour of complex systems, in which response quantities depend on assumed controls or predictors in a complicated way. In this paper, as our first purpose, we propose the application of machine learning to reconstruct incomplete or irregularly sampled data of time series indoor radon ( 222 Rn). The physical assumption underlying the modelling is that Rn concentration in the air is controlled by environmental variables such as air temperature and pressure. The algorithms "learn" from complete sections of multivariate series, derive a dependence model and apply it to sections where the controls are available, but not the response (Rn), and in this way complete the Rn series. Three machine learning techniques are applied in this study, namely random forest, its extension called the gradient boosting machine and deep learning. For a comparison, we apply the classical multiple regression in a generalized linear model version. Performance of the models is evaluated through different metrics. The performance of the gradient boosting machine is found to be superior to that of the other techniques. By applying learning machines, we show, as our second purpose, that missing data or periods of Rn series data can be reconstructed and resampled on a regular grid reasonably, if data of appropriate physical controls are available. The techniques also identify to which degree the assumed controls contribute to imputing missing Rn values. Our third purpose, though no less important from the viewpoint of physics, is identifying to which degree physical, in this case environmental variables, are relevant as Rn predictors, or in other words, which predictors explain most of the temporal variability of Rn. We show that variables which contribute most to the Rn series reconstruction, are temperature, relative humidity and day of the year. The first two are physical

  7. Automated business process management – in times of digital transformation using machine learning or artificial intelligence

    Directory of Open Access Journals (Sweden)

    Paschek Daniel

    2017-01-01

    Full Text Available The continuous optimization of business processes is still a challenge for companies. In times of digital transformation, faster changing internal and external framework conditions and new customer expectations for fastest delivery and best quality of goods and many more, companies should set up their internal process at the best way. But what to do if framework conditions changed unexpectedly? The purpose of the paper is to analyse how the digital transformation will impact the Business Process Management (BPM while using methods like machine learning or artificial intelligence. Therefore, the core components will be explained, compared and set up in relation. To identify application areas interviews and analysis will be held up with digital companies. The finding of the paper will be recommendation for action in the field of BPM and process optimization through machine learning and artificial intelligence. The Approach of optimizing and management processes via machine learning and artificial intelligence will support companies to decide which tool will be the best for automated BPM.

  8. Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics

    Science.gov (United States)

    Yu, Tao; Cai, Weiwei; Liu, Yingzheng

    2018-04-01

    Optical tomography has attracted surged research efforts recently due to the progress in both the imaging concepts and the sensor and laser technologies. The high spatial and temporal resolutions achievable by these methods provide unprecedented opportunity for diagnosis of complicated turbulent combustion. However, due to the high data throughput and the inefficiency of the prevailing iterative methods, the tomographic reconstructions which are typically conducted off-line are computationally formidable. In this work, we propose an efficient inversion method based on a machine learning algorithm, which can extract useful information from the previous reconstructions and build efficient neural networks to serve as a surrogate model to rapidly predict the reconstructions. Extreme learning machine is cited here as an example for demonstrative purpose simply due to its ease of implementation, fast learning speed, and good generalization performance. Extensive numerical studies were performed, and the results show that the new method can dramatically reduce the computational time compared with the classical iterative methods. This technique is expected to be an alternative to existing methods when sufficient training data are available. Although this work is discussed under the context of tomographic absorption spectroscopy, we expect it to be useful also to other high speed tomographic modalities such as volumetric laser-induced fluorescence and tomographic laser-induced incandescence which have been demonstrated for combustion diagnostics.

  9. Optimizing Earth Data Search Ranking using Deep Learning and Real-time User Behaviour

    Science.gov (United States)

    Jiang, Y.; Yang, C. P.; Armstrong, E. M.; Huang, T.; Moroni, D. F.; McGibbney, L. J.; Greguska, F. R., III

    2017-12-01

    Finding Earth science data has been a challenging problem given both the quantity of data available and the heterogeneity of the data across a wide variety of domains. Current search engines in most geospatial data portals tend to induce end users to focus on one single data characteristic dimension (e.g., term frequency-inverse document frequency (TF-IDF) score, popularity, release date, etc.). This approach largely fails to take account of users' multidimensional preferences for geospatial data, and hence may likely result in a less than optimal user experience in discovering the most applicable dataset out of a vast range of available datasets. With users interacting with search engines, sufficient information is already hidden in the log files. Compared with explicit feedback data, information that can be derived/extracted from log files is virtually free and substantially more timely. In this dissertation, I propose an online deep learning framework that can quickly update the learning function based on real-time user clickstream data. The contributions of this framework include 1) a log processor that can ingest, process and create training data from web logs in a real-time manner; 2) a query understanding module to better interpret users' search intent using web log processing results and metadata; 3) a feature extractor that identifies ranking features representing users' multidimensional interests of geospatial data; and 4) a deep learning based ranking algorithm that can be trained incrementally using user behavior data. The search ranking results will be evaluated using precision at K and normalized discounted cumulative gain (NDCG).

  10. Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

    Directory of Open Access Journals (Sweden)

    Juan Pardo

    2015-04-01

    Full Text Available Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

  11. Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

    Science.gov (United States)

    Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

    2015-01-01

    Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources. PMID:25905698

  12. Changes in recognition memory over time: an ERP investigation into vocabulary learning.

    Directory of Open Access Journals (Sweden)

    Shekeila D Palmer

    Full Text Available Although it seems intuitive to assume that recognition memory fades over time when information is not reinforced, some aspects of word learning may benefit from a period of consolidation. In the present study, event-related potentials (ERP were used to examine changes in recognition memory responses to familiar and newly learned (novel words over time. Native English speakers were taught novel words associated with English translations, and subsequently performed a Recognition Memory task in which they made old/new decisions in response to both words (trained word vs. untrained word, and novel words (trained novel word vs. untrained novel word. The Recognition task was performed 45 minutes after training (Day 1 and then repeated the following day (Day 2 with no additional training session in between. For familiar words, the late parietal old/new effect distinguished old from new items on both Day 1 and Day 2, although response to trained items was significantly weaker on Day 2. For novel words, the LPC again distinguished old from new items on both days, but the effect became significantly larger on Day 2. These data suggest that while recognition memory for familiar items may fade over time, recognition of novel items, conscious recollection in particular may benefit from a period of consolidation.

  13. Online learning algorithm for time series forecasting suitable for low cost wireless sensor networks nodes.

    Science.gov (United States)

    Pardo, Juan; Zamora-Martínez, Francisco; Botella-Rocamora, Paloma

    2015-04-21

    Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

  14. Using machine learning to identify structural breaks in single-group interrupted time series designs.

    Science.gov (United States)

    Linden, Ariel; Yarnold, Paul R

    2016-12-01

    Single-group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the intervention is expected to 'interrupt' the level and/or trend of the time series, subsequent to its introduction. Given that the internal validity of the design rests on the premise that the interruption in the time series is associated with the introduction of the treatment, treatment effects may seem less plausible if a parallel trend already exists in the time series prior to the actual intervention. Thus, sensitivity analyses should focus on detecting structural breaks in the time series before the intervention. In this paper, we introduce a machine-learning algorithm called optimal discriminant analysis (ODA) as an approach to determine if structural breaks can be identified in years prior to the initiation of the intervention, using data from California's 1988 voter-initiated Proposition 99 to reduce smoking rates. The ODA analysis indicates that numerous structural breaks occurred prior to the actual initiation of Proposition 99 in 1989, including perfect structural breaks in 1983 and 1985, thereby casting doubt on the validity of treatment effects estimated for the actual intervention when using a single-group ITSA design. Given the widespread use of ITSA for evaluating observational data and the increasing use of machine-learning techniques in traditional research, we recommend that structural break sensitivity analysis is routinely incorporated in all research using the single-group ITSA design. © 2016 John Wiley & Sons, Ltd.

  15. On the best learning algorithm for web services response time prediction

    DEFF Research Database (Denmark)

    Madsen, Henrik; Albu, Razvan-Daniel; Popentiu-Vladicescu, Florin

    2013-01-01

    In this article we will examine the effect of different learning algorithms, while training the MLP (Multilayer Perceptron) with the intention of predicting web services response time. Web services do not necessitate a user interface. This may seem contradictory to most people's concept of what...... an application is. A Web service is better imagined as an application "segment," or better as a program enabler. Performance is an important quality aspect of Web services because of their distributed nature. Predicting the response of web services during their operation is very important....

  16. Retroperitoneal laparoscopic nephrectomy: the effect of the learning curve, and concentrating expertise, on operating times.

    Science.gov (United States)

    Skinner, Adrian; Maoate, Kiki; Beasley, Spencer

    2010-05-01

    Laparoscopic nephrectomy is an accepted alternative to open nephrectomy. We analyzed our first 80 procedures of laparoscopic nephrectomy to evaluate the effect of experience and configuration of service on operative times. A retrospective review of 80 consecutive children who underwent retroperitoneal laparoscopic nephrectomy or heminephrectomy during an 11-year period from 1997 at Christchurch Hospital (Christchurch, New Zealand) was conducted. Operative times, in relation to the experience of the surgeon for this procedure, were analyzed. Four surgeons, assisted by an annually rotating trainee registrar, performed the procedure in 26 girls and 54 boys (range, 8 months to 15 years). Operating times ranged from 38 to 225 minutes (mean, 104). The average operative time fell from 105 to 90 minutes. One surgeon performed 40% of the procedures and assisted with a further 55%. The operative times for all surgeons showed a tendency to reduce, but this was not marked. Most procedures were performed by two surgeons working together, although one surgeon was involved in the majority of cases. The lead surgeon is often assisted by a fellow consultant colleague. Operative times were influenced by experience, but not markedly so. The shorter operative times and minimal "learning curve," compared with other reported series, may, in part, be due to the involvement of two surgeons experienced in laparoscopy for the majority of cases.

  17. Scheduling with Learning Effects and/or Time-Dependent Processing Times to Minimize the Weighted Number of Tardy Jobs on a Single Machine

    Directory of Open Access Journals (Sweden)

    Jianbo Qian

    2013-01-01

    Full Text Available We consider single machine scheduling problems with learning/deterioration effects and time-dependent processing times, with due date assignment consideration, and our objective is to minimize the weighted number of tardy jobs. By reducing all versions of the problem to an assignment problem, we solve them in O(n4 time. For some important special cases, the time complexity can be improved to be O(n2 using dynamic programming techniques.

  18. Reinforcement learning design-based adaptive tracking control with less learning parameters for nonlinear discrete-time MIMO systems.

    Science.gov (United States)

    Liu, Yan-Jun; Tang, Li; Tong, Shaocheng; Chen, C L Philip; Li, Dong-Juan

    2015-01-01

    Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: 1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and 2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.

  19. Discrete-time online learning control for a class of unknown nonaffine nonlinear systems using reinforcement learning.

    Science.gov (United States)

    Yang, Xiong; Liu, Derong; Wang, Ding; Wei, Qinglai

    2014-07-01

    In this paper, a reinforcement-learning-based direct adaptive control is developed to deliver a desired tracking performance for a class of discrete-time (DT) nonlinear systems with unknown bounded disturbances. We investigate multi-input-multi-output unknown nonaffine nonlinear DT systems and employ two neural networks (NNs). By using Implicit Function Theorem, an action NN is used to generate the control signal and it is also designed to cancel the nonlinearity of unknown DT systems, for purpose of utilizing feedback linearization methods. On the other hand, a critic NN is applied to estimate the cost function, which satisfies the recursive equations derived from heuristic dynamic programming. The weights of both the action NN and the critic NN are directly updated online instead of offline training. By utilizing Lyapunov's direct method, the closed-loop tracking errors and the NN estimated weights are demonstrated to be uniformly ultimately bounded. Two numerical examples are provided to show the effectiveness of the present approach. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. A Call to Action for Research in Digital Learning: Learning without Limits of Time, Place, Path, Pace…or Evidence

    Science.gov (United States)

    Cavanaugh, Cathy; Sessums, Christopher; Drexler, Wendy

    2015-01-01

    This essay is a call for rethinking our approach to research in digital learning. It plots a path founded in social trends and advances in education. A brief review of these trends and advances is followed by discussion of what flattened research might look like at scale. Scaling research in digital learning is crucial to advancing understanding…

  1. Using American Coming-of-Age Stories in the ELT Classroom

    Directory of Open Access Journals (Sweden)

    Elena Ortells Montón

    2017-05-01

    Full Text Available Reading constitutes solid grounds for the development of basic language and critical skills as well as for the improvement of Intercultural Communicative Competence. However, in a world dominated by visual media and technology, getting young people to read becomes a challenging experience, which turns out to be even more problematic in English language teaching. Young adult literature and multicultural coming-of-age stories can offer teachers the necessary materials to foster interest in reading and to raise intercultural awareness. In spite of its limited scope, the project reported in this article proved that a conscientious choice of extracts taken from Yang’s American Born Chinese, Alexie’s The Absolutely True Diary of a Part-Time Indian, Cisneros’s The House on Mango Street and Morrison’s The Bluest Eye, among others, could contribute to improving language learners’ linguistic and sociocultural competence. The project employed an interactive methodology based on a combination of critical multicultural pedagogy and reader-response theory, centering on the students’ perspectives of their learning experience. While this experience did not answer the question whether the learners’ reading competence had in fact increased, the students themselves acknowledged a substantial increase in reading motivation and confidence as well as cultural awareness.

  2. Unsupervised Learning of Digit Recognition Using Spike-Timing-Dependent Plasticity

    Directory of Open Access Journals (Sweden)

    Peter U. Diehl

    2015-08-01

    Full Text Available In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns, since most of such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e. conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks.

  3. Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

    Science.gov (United States)

    Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

    2017-10-06

    Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

  4. Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction.

    Science.gov (United States)

    Ak, Ronay; Fink, Olga; Zio, Enrico

    2016-08-01

    The increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria.

  5. Selective visual scaling of time-scale processes facilitates broadband learning of isometric force frequency tracking.

    Science.gov (United States)

    King, Adam C; Newell, Karl M

    2015-10-01

    The experiment investigated the effect of selectively augmenting faster time scales of visual feedback information on the learning and transfer of continuous isometric force tracking tasks to test the generality of the self-organization of 1/f properties of force output. Three experimental groups tracked an irregular target pattern either under a standard fixed gain condition or with selectively enhancement in the visual feedback display of intermediate (4-8 Hz) or high (8-12 Hz) frequency components of the force output. All groups reduced tracking error over practice, with the error lowest in the intermediate scaling condition followed by the high scaling and fixed gain conditions, respectively. Selective visual scaling induced persistent changes across the frequency spectrum, with the strongest effect in the intermediate scaling condition and positive transfer to novel feedback displays. The findings reveal an interdependence of the timescales in the learning and transfer of isometric force output frequency structures consistent with 1/f process models of the time scales of motor output variability.

  6. A real-time standard parts inspection based on deep learning

    Science.gov (United States)

    Xu, Kuan; Li, XuDong; Jiang, Hongzhi; Zhao, Huijie

    2017-10-01

    Since standard parts are necessary components in mechanical structure like bogie and connector. These mechanical structures will be shattered or loosen if standard parts are lost. So real-time standard parts inspection systems are essential to guarantee their safety. Researchers would like to take inspection systems based on deep learning because it works well in image with complex backgrounds which is common in standard parts inspection situation. A typical inspection detection system contains two basic components: feature extractors and object classifiers. For the object classifier, Region Proposal Network (RPN) is one of the most essential architectures in most state-of-art object detection systems. However, in the basic RPN architecture, the proposals of Region of Interest (ROI) have fixed sizes (9 anchors for each pixel), they are effective but they waste much computing resources and time. In standard parts detection situations, standard parts have given size, thus we can manually choose sizes of anchors based on the ground-truths through machine learning. The experiments prove that we could use 2 anchors to achieve almost the same accuracy and recall rate. Basically, our standard parts detection system could reach 15fps on NVIDIA GTX1080 (GPU), while achieving detection accuracy 90.01% mAP.

  7. Hybrid and Blended Learning: Modifying Pedagogy across Path, Pace, Time, and Place

    Science.gov (United States)

    O'Byrne, W. Ian; Pytash, Kristine E.

    2015-01-01

    Hybrid or blended learning is defined as a pedagogical approach that includes a combination of face-to-face instruction with computer-mediated instruction. The terms "blended learning", "hybrid learning", and "mixed-mode learning" are used interchangeably in current research; however, in the United States,…

  8. Learning Over Time: Using Rapid Prototyping Generative Analysis Experts and Reduction of Scope to Operationalize Design

    Science.gov (United States)

    2010-05-04

    during the Vietnam Conflict. 67 David A. Kolb , Experiential Learning : Experience as the Source of Learning and Development. (Upper Saddle River, NJ...Essentials for Military Applications. Newport Paper #10. Newport: Newport War College Press. 1996. Kolb , David A. Experiential Learning : Experience... learning over analysis. A broad review of design theory suggests that four techniques - rapid prototyping, generative analysis, use of experts, and

  9. Hybrid machine learning technique for forecasting Dhaka stock market timing decisions.

    Science.gov (United States)

    Banik, Shipra; Khodadad Khan, A F M; Anwer, Mohammad

    2014-01-01

    Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

  10. Technical Note: Deep learning based MRAC using rapid ultra-short echo time imaging.

    Science.gov (United States)

    Jang, Hyungseok; Liu, Fang; Zhao, Gengyan; Bradshaw, Tyler; McMillan, Alan B

    2018-05-15

    In this study, we explore the feasibility of a novel framework for MR-based attenuation correction for PET/MR imaging based on deep learning via convolutional neural networks, which enables fully automated and robust estimation of a pseudo CT image based on ultrashort echo time (UTE), fat, and water images obtained by a rapid MR acquisition. MR images for MRAC are acquired using dual echo ramped hybrid encoding (dRHE), where both UTE and out-of-phase echo images are obtained within a short single acquisition (35 sec). Tissue labeling of air, soft tissue, and bone in the UTE image is accomplished via a deep learning network that was pre-trained with T1-weighted MR images. UTE images are used as input to the network, which was trained using labels derived from co-registered CT images. The tissue labels estimated by deep learning are refined by a conditional random field based correction. The soft tissue labels are further separated into fat and water components using the two-point Dixon method. The estimated bone, air, fat, and water images are then assigned appropriate Hounsfield units, resulting in a pseudo CT image for PET attenuation correction. To evaluate the proposed MRAC method, PET/MR imaging of the head was performed on 8 human subjects, where Dice similarity coefficients of the estimated tissue labels and relative PET errors were evaluated through comparison to a registered CT image. Dice coefficients for air (within the head), soft tissue, and bone labels were 0.76±0.03, 0.96±0.006, and 0.88±0.01. In PET quantification, the proposed MRAC method produced relative PET errors less than 1% within most brain regions. The proposed MRAC method utilizing deep learning with transfer learning and an efficient dRHE acquisition enables reliable PET quantification with accurate and rapid pseudo CT generation. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  11. Interactive Learning Modules: Enabling Near Real-Time Oceanographic Data Use In Undergraduate Education

    Science.gov (United States)

    Kilb, D. L.; Fundis, A. T.; Risien, C. M.

    2012-12-01

    The focus of the Education and Public Engagement (EPE) component of the NSF's Ocean Observatories Initiative (OOI) is to provide a new layer of cyber-interactivity for undergraduate educators to bring near real-time data from the global ocean into learning environments. To accomplish this, we are designing six online services including: 1) visualization tools, 2) a lesson builder, 3) a concept map builder, 4) educational web services (middleware), 5) collaboration tools and 6) an educational resource database. Here, we report on our Fall 2012 release that includes the first four of these services: 1) Interactive visualization tools allow users to interactively select data of interest, display the data in various views (e.g., maps, time-series and scatter plots) and obtain statistical measures such as mean, standard deviation and a regression line fit to select data. Specific visualization tools include a tool to compare different months of data, a time series explorer tool to investigate the temporal evolution of select data parameters (e.g., sea water temperature or salinity), a glider profile tool that displays ocean glider tracks and associated transects, and a data comparison tool that allows users to view the data either in scatter plot view comparing one parameter with another, or in time series view. 2) Our interactive lesson builder tool allows users to develop a library of online lesson units, which are collaboratively editable and sharable and provides starter templates designed from learning theory knowledge. 3) Our interactive concept map tool allows the user to build and use concept maps, a graphical interface to map the connection between concepts and ideas. This tool also provides semantic-based recommendations, and allows for embedding of associated resources such as movies, images and blogs. 4) Education web services (middleware) will provide an educational resource database API.

  12. Lessons Learned from Real-Time, Event-Based Internet Science Communications

    Science.gov (United States)

    Phillips, T.; Myszka, E.; Gallagher, D. L.; Adams, M. L.; Koczor, R. J.; Whitaker, Ann F. (Technical Monitor)

    2001-01-01

    For the last several years the Science Directorate at Marshall Space Flight Center has carried out a diverse program of Internet-based science communication. The Directorate's Science Roundtable includes active researchers, NASA public relations, educators, and administrators. The Science@NASA award-winning family of Web sites features science, mathematics, and space news. The program includes extended stories about NASA science, a curriculum resource for teachers tied to national education standards, on-line activities for students, and webcasts of real-time events. The focus of sharing science activities in real-time has been to involve and excite students and the public about science. Events have involved meteor showers, solar eclipses, natural very low frequency radio emissions, and amateur balloon flights. In some cases, broadcasts accommodate active feedback and questions from Internet participants. Through these projects a pattern has emerged in the level of interest or popularity with the public. The pattern differentiates projects that include science from those that do not, All real-time, event-based Internet activities have captured public interest at a level not achieved through science stories or educator resource material exclusively. The worst event-based activity attracted more interest than the best written science story. One truly rewarding lesson learned through these projects is that the public recognizes the importance and excitement of being part of scientific discovery. Flying a camera to 100,000 feet altitude isn't as interesting to the public as searching for viable life-forms at these oxygen-poor altitudes. The details of these real-time, event-based projects and lessons learned will be discussed.

  13. Calculation of upper esophageal sphincter restitution time from high resolution manometry data using machine learning.

    Science.gov (United States)

    Jungheim, Michael; Busche, Andre; Miller, Simone; Schilling, Nicolas; Schmidt-Thieme, Lars; Ptok, Martin

    2016-10-15

    After swallowing, the upper esophageal sphincter (UES) needs a certain amount of time to return from maximum pressure to the resting condition. Disturbances of sphincter function not only during the swallowing process but also in this phase of pressure restitution may lead to globus sensation or dysphagia. Since UES pressures do not decrease in a linear or asymptotic manner, it is difficult to determine the exact time when the resting pressure is reached, even when using high resolution manometry (HRM). To overcome this problem a Machine Learning model was established to objectively determine the UES restitution time (RT) and moreover to collect physiological data on sphincter function after swallowing. HRM-data of 15 healthy participants performing 10 swallows each were included. After manual annotation of the RT interval by two swallowing experts, data were transferred to the Machine Learning model, which applied a sequence labeling modeling approach based on logistic regression to learn and objectivize the characteristics of all swallows. Individually computed RT values were then compared with the annotated values. Estimates of the RT were generated by the Machine Learning model for all 150 swallows. When annotated by swallowing experts mean RT of 11.16s±5.7 (SD) and 10.04s±5.74 were determined respectively, compared to model-generated values from 8.91s±3.71 to 10.87s±4.68 depending on model selection. The correlation score for the annotated RT of both examiners was 0.76 and 0.63 to 0.68 for comparison of model predicted values. Restitution time represents an important physiologic swallowing parameter not previously considered in HRM-studies of the UES, especially since disturbances of UES restitution may increase the risk of aspiration. The data presented here show that it takes approximately 9 to 11s for the UES to come to rest after swallowing. Based on maximal RT values, we demonstrate that an interval of 25-30s in between swallows is necessary until the

  14. Time-place learning and memory persist in mice lacking functional Per1 and Per2 clock genes.

    Science.gov (United States)

    Mulder, C; Van Der Zee, E A; Hut, R A; Gerkema, M P

    2013-12-01

    With time-place learning, animals link a stimulus with the location and the time of day. This ability may optimize resource localization and predator avoidance in daily changing environments. Time-place learning is a suitable task to study the interaction of the circadian system and memory. Previously, we showed that time-place learning in mice depends on the circadian system and Cry1 and/or Cry2 clock genes. We questioned whether time-place learning is Cry specific or also depends on other core molecular clock genes. Here, we show that Per1/Per2 double mutant mice, despite their arrhythmic phenotype, acquire time-place learning similar to wild-type mice. As well as an established role in circadian rhythms, Per genes have also been implicated in the formation and storage of memory. We found no deficiencies in short-term spatial working memory in Per mutant mice compared to wild-type mice. Moreover, both Per mutant and wild-type mice showed similar long-term memory for contextual features of a paradigm (a mild foot shock), measured in trained mice after a 2-month nontesting interval. In contrast, time-place associations were lost in both wild-type and mutant mice after these 2 months, suggesting a lack of maintained long-term memory storage for this type of information. Taken together, Cry-dependent time-place learning does not require Per genes, and Per mutant mice showed no PER-specific short-term or long-term memory deficiencies. These results limit the functional role of Per clock genes in the circadian regulation of time-place learning and memory.

  15. Numerical and machine learning simulation of parametric distributions of groundwater residence time in streams and wells

    Science.gov (United States)

    Starn, J. J.; Belitz, K.; Carlson, C.

    2017-12-01

    Groundwater residence-time distributions (RTDs) are critical for assessing susceptibility of water resources to contamination. This novel approach for estimating regional RTDs was to first simulate groundwater flow using existing regional digital data sets in 13 intermediate size watersheds (each an average of 7,000 square kilometers) that are representative of a wide range of glacial systems. RTDs were simulated with particle tracking. We refer to these models as "general models" because they are based on regional, as opposed to site-specific, digital data. Parametric RTDs were created from particle RTDs by fitting 1- and 2-component Weibull, gamma, and inverse Gaussian distributions, thus reducing a large number of particle travel times to 3 to 7 parameters (shape, location, and scale for each component plus a mixing fraction) for each modeled area. The scale parameter of these distributions is related to the mean exponential age; the shape parameter controls departure from the ideal exponential distribution and is partly a function of interaction with bedrock and with drainage density. Given the flexible shape and mathematical similarity of these distributions, any of them are potentially a good fit to particle RTDs. The 1-component gamma distribution provided a good fit to basin-wide particle RTDs. RTDs at monitoring wells and streams often have more complicated shapes than basin-wide RTDs, caused in part by heterogeneity in the model, and generally require 2-component distributions. A machine learning model was trained on the RTD parameters using features derived from regionally available watershed characteristics such as recharge rate, material thickness, and stream density. RTDs appeared to vary systematically across the landscape in relation to watershed features. This relation was used to produce maps of useful metrics with respect to risk-based thresholds, such as the time to first exceedance, time to maximum concentration, time above the threshold

  16. Travel time tomography with local image regularization by sparsity constrained dictionary learning

    Science.gov (United States)

    Bianco, M.; Gerstoft, P.

    2017-12-01

    We propose a regularization approach for 2D seismic travel time tomography which models small rectangular groups of slowness pixels, within an overall or `global' slowness image, as sparse linear combinations of atoms from a dictionary. The groups of slowness pixels are referred to as patches and a dictionary corresponds to a collection of functions or `atoms' describing the slowness in each patch. These functions could for example be wavelets.The patch regularization is incorporated into the global slowness image. The global image models the broad features, while the local patch images incorporate prior information from the dictionary. Further, high resolution slowness within patches is permitted if the travel times from the global estimates support it. The proposed approach is formulated as an algorithm, which is repeated until convergence is achieved: 1) From travel times, find the global slowness image with a minimum energy constraint on the pixel variance relative to a reference. 2) Find the patch level solutions to fit the global estimate as a sparse linear combination of dictionary atoms.3) Update the reference as the weighted average of the patch level solutions.This approach relies on the redundancy of the patches in the seismic image. Redundancy means that the patches are repetitions of a finite number of patterns, which are described by the dictionary atoms. Redundancy in the earth's structure was demonstrated in previous works in seismics where dictionaries of wavelet functions regularized inversion. We further exploit redundancy of the patches by using dictionary learning algorithms, a form of unsupervised machine learning, to estimate optimal dictionaries from the data in parallel with the inversion. We demonstrate our approach on densely, but irregularly sampled synthetic seismic images.

  17. Executive Functions, Time Organization and Quality of Life among Adults with Learning Disabilities.

    Directory of Open Access Journals (Sweden)

    Kineret Sharfi

    Full Text Available This study compared the executive functions, organization in time and perceived quality of life (QoL of 55 adults with learning disabilities (LD with those of 55 matched controls (mean age 30 years. Furthermore, relationships and predictive relationships between these variables among the group with LD were examined.All participants completed the Behavioral Rating Inventory of Executive Functions (BRIEF-A, the Time Organization and Participation (TOPS, A-C and the World Health Organization Quality of Life (WHOQOL questionnaires. Chi-square tests, independent t-tests and MANOVA were used to examine group differences in each of the subscales scores and ratings of each instrument. Pearson correlations and regression predictive models were used to examine the relationships between the variables in the group with LD.Adults with LD had significantly poorer executive functions (BRIEF-A, deficient organization in time abilities (TOPS A-B, accompanied with negative emotional response (TOPS- C, and lower perceived QoL (physical, psychological, social and environmental in comparison to adults without LD. Regression analysis revealed that Initiation (BRIEF-A significantly predicted approximately 15% of the participants' organization in time abilities (TOPS A, B scores beyond group membership. Furthermore, initiation, emotional control (BRIEF-A subscales and emotional responses following unsuccessful organization of time (TOPS-C together accounted for 39% of the variance of psychological QoL beyond the contribution of group membership.Deficits in initiation and emotional executive functions as well as organization in time abilities and emotional responses to impairments in organizing time affect the QoL of adults with LD and thus should be considered in further research as well as in clinical applications.

  18. Executive Functions, Time Organization and Quality of Life among Adults with Learning Disabilities.

    Science.gov (United States)

    Sharfi, Kineret; Rosenblum, Sara

    2016-01-01

    This study compared the executive functions, organization in time and perceived quality of life (QoL) of 55 adults with learning disabilities (LD) with those of 55 matched controls (mean age 30 years). Furthermore, relationships and predictive relationships between these variables among the group with LD were examined. All participants completed the Behavioral Rating Inventory of Executive Functions (BRIEF-A), the Time Organization and Participation (TOPS, A-C) and the World Health Organization Quality of Life (WHOQOL) questionnaires. Chi-square tests, independent t-tests and MANOVA were used to examine group differences in each of the subscales scores and ratings of each instrument. Pearson correlations and regression predictive models were used to examine the relationships between the variables in the group with LD. Adults with LD had significantly poorer executive functions (BRIEF-A), deficient organization in time abilities (TOPS A-B), accompanied with negative emotional response (TOPS- C), and lower perceived QoL (physical, psychological, social and environmental) in comparison to adults without LD. Regression analysis revealed that Initiation (BRIEF-A) significantly predicted approximately 15% of the participants' organization in time abilities (TOPS A, B scores) beyond group membership. Furthermore, initiation, emotional control (BRIEF-A subscales) and emotional responses following unsuccessful organization of time (TOPS-C) together accounted for 39% of the variance of psychological QoL beyond the contribution of group membership. Deficits in initiation and emotional executive functions as well as organization in time abilities and emotional responses to impairments in organizing time affect the QoL of adults with LD and thus should be considered in further research as well as in clinical applications.

  19. Real-time lexical comprehension in young children learning American Sign Language.

    Science.gov (United States)

    MacDonald, Kyle; LaMarr, Todd; Corina, David; Marchman, Virginia A; Fernald, Anne

    2018-04-16

    When children interpret spoken language in real time, linguistic information drives rapid shifts in visual attention to objects in the visual world. This language-vision interaction can provide insights into children's developing efficiency in language comprehension. But how does language influence visual attention when the linguistic signal and the visual world are both processed via the visual channel? Here, we measured eye movements during real-time comprehension of a visual-manual language, American Sign Language (ASL), by 29 native ASL-learning children (16-53 mos, 16 deaf, 13 hearing) and 16 fluent deaf adult signers. All signers showed evidence of rapid, incremental language comprehension, tending to initiate an eye movement before sign offset. Deaf and hearing ASL-learners showed similar gaze patterns, suggesting that the in-the-moment dynamics of eye movements during ASL processing are shaped by the constraints of processing a visual language in real time and not by differential access to auditory information in day-to-day life. Finally, variation in children's ASL processing was positively correlated with age and vocabulary size. Thus, despite competition for attention within a single modality, the timing and accuracy of visual fixations during ASL comprehension reflect information processing skills that are important for language acquisition regardless of language modality. © 2018 John Wiley & Sons Ltd.

  20. Procedural learning is impaired in dyslexia: Evidence from a meta-analysis of serial reaction time studies☆

    Science.gov (United States)

    Lum, Jarrad A.G.; Ullman, Michael T.; Conti-Ramsden, Gina

    2013-01-01

    A number of studies have investigated procedural learning in dyslexia using serial reaction time (SRT) tasks. Overall, the results have been mixed, with evidence of both impaired and intact learning reported. We undertook a systematic search of studies that examined procedural learning using SRT tasks, and synthesized the data using meta-analysis. A total of 14 studies were identified, representing data from 314 individuals with dyslexia and 317 typically developing control participants. The results indicate that, on average, individuals with dyslexia have worse procedural learning abilities than controls, as indexed by sequence learning on the SRT task. The average weighted standardized mean difference (the effect size) was found to be 0.449 (CI95: .204, .693), and was significant (p dyslexia. PMID:23920029

  1. A Novel Real-Time Speech Summarizer System for the Learning of Sustainability

    Directory of Open Access Journals (Sweden)

    Hsiu-Wen Wang

    2015-04-01

    Full Text Available As the number of speech and video documents increases on the Internet and portable devices proliferate, speech summarization becomes increasingly essential. Relevant research in this domain has typically focused on broadcasts and news; however, the automatic summarization methods used in the past may not apply to other speech domains (e.g., speech in lectures. Therefore, this study explores the lecture speech domain. The features used in previous research were analyzed and suitable features were selected following experimentation; subsequently, a three-phase real-time speech summarizer for the learning of sustainability (RTSSLS was proposed. Phase One involved selecting independent features (e.g., centrality, resemblance to the title, sentence length, term frequency, and thematic words and calculating the independent feature scores; Phase Two involved calculating the dependent features, such as the position compared with the independent feature scores; and Phase Three involved comparing these feature scores to obtain weighted averages of the function-scores, determine the highest-scoring sentence, and provide a summary. In practical results, the accuracies of macro-average and micro-average for the RTSSLS were 70% and 73%, respectively. Therefore, using a RTSSLS can enable users to acquire key speech information for the learning of sustainability.

  2. Distributed Cerebellar Motor Learning; a Spike-Timing-Dependent Plasticity Model

    Directory of Open Access Journals (Sweden)

    Niceto Rafael Luque

    2016-03-01

    Full Text Available Deep cerebellar nuclei neurons receive both inhibitory (GABAergic synaptic currents from Purkinje cells (within the cerebellar cortex and excitatory (glutamatergic synaptic currents from mossy fibres. Those two deep cerebellar nucleus inputs are thought to be also adaptive, embedding interesting properties in the framework of accurate movements. We show that distributed spike-timing-dependent plasticity mechanisms (STDP located at different cerebellar sites (parallel fibres to Purkinje cells, mossy fibres to deep cerebellar nucleus cells, and Purkinje cells to deep cerebellar nucleus cells in close-loop simulations provide an explanation for the complex learning properties of the cerebellum in motor learning. Concretely, we propose a new mechanistic cerebellar spiking model. In this new model, deep cerebellar nuclei embed a dual functionality: deep cerebellar nuclei acting as a gain adaptation mechanism and as a facilitator for the slow memory consolidation at mossy fibres to deep cerebellar nucleus synapses. Equipping the cerebellum with excitatory (e-STDP and inhibitory (i-STDP mechanisms at deep cerebellar nuclei afferents allows the accommodation of synaptic memories that were formed at parallel fibres to Purkinje cells synapses and then transferred to mossy fibres to deep cerebellar nucleus synapses. These adaptive mechanisms also contribute to modulate the deep-cerebellar-nucleus-output firing rate (output gain modulation towards optimising its working range.

  3. Real-time Stereoscopic 3D for E-Robotics Learning

    Directory of Open Access Journals (Sweden)

    Richard Y. Chiou

    2011-02-01

    Full Text Available Following the design and testing of a successful 3-Dimensional surveillance system, this 3D scheme has been implemented into online robotics learning at Drexel University. A real-time application, utilizing robot controllers, programmable logic controllers and sensors, has been developed in the “MET 205 Robotics and Mechatronics” class to provide the students with a better robotic education. The integration of the 3D system allows the students to precisely program the robot and execute functions remotely. Upon the students’ recommendation, polarization has been chosen to be the main platform behind the 3D robotic system. Stereoscopic calculations are carried out for calibration purposes to display the images with the highest possible comfort-level and 3D effect. The calculations are further validated by comparing the results with students’ evaluations. Due to the Internet-based feature, multiple clients have the opportunity to perform the online automation development. In the future, students, in different universities, will be able to cross-control robotic components of different types around the world. With the development of this 3D ERobotics interface, automation resources and robotic learning can be shared and enriched regardless of location.

  4. A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity.

    Directory of Open Access Journals (Sweden)

    Quan Wang

    2017-08-01

    Full Text Available The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP and synaptic normalization (SN. When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network's changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network's sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that

  5. Real-time learning of predictive recognition categories that chunk sequences of items stored in working memory

    Directory of Open Access Journals (Sweden)

    Stephen eGrossberg

    2014-10-01

    Full Text Available How are sequences of events that are temporarily stored in a cognitive working memory unitized, or chunked, through learning? Such sequential learning is needed by the brain in order to enable language, spatial understanding, and motor skills to develop. In particular, how does the brain learn categories, or list chunks, that become selectively tuned to different temporal sequences of items in lists of variable length as they are stored in working memory, and how does this learning process occur in real time? The present article introduces a neural model that simulates learning of such list chunks. In this model, sequences of items are temporarily stored in an Item-and-Order, or competitive queuing, working memory before learning categorizes them using a categorization network, called a Masking Field, which is a self-similar, multiple-scale, recurrent on-center off-surround network that can weigh the evidence for variable-length sequences of items as they are stored in the working memory through time. A Masking Field hereby activates the learned list chunks that represent the most predictive item groupings at any time, while suppressing less predictive chunks. In a network with a given number of input items, all possible ordered sets of these item sequences, up to a fixed length, can be learned with unsupervised or supervised learning. The self-similar multiple-scale properties of Masking Fields interacting with an Item-and-Order working memory provide a natural explanation of George Miller's Magical Number Seven and Nelson Cowan's Magical Number Four. The article explains why linguistic, spatial, and action event sequences may all be stored by Item-and-Order working memories that obey similar design principles, and thus how the current results may apply across modalities. Item-and-Order properties may readily be extended to Item-Order-Rank working memories in which the same item can be stored in multiple list positions, or ranks, as in the list

  6. Fast Mapping Across Time: Memory Processes Support Children's Retention of Learned Words

    Directory of Open Access Journals (Sweden)

    Haley eVlach

    2012-02-01

    Full Text Available Children's remarkable ability to map linguistic labels to objects in the world is referred to as fast mapping. The current study examined children's (N = 216 and adults’ (N = 54 retention of fast-mapped words over time (immediately, after a 1 week delay, and after a 1 month delay. The fast mapping literature often characterizes children's retention of words as consistently high across timescales. However, the current study demonstrates that learners forget word mappings at a rapid rate. Moreover, these patterns of forgetting parallel forgetting functions of domain general memory processes. Memory processes are critical to children's word learning and the role of one such process, forgetting, is discussed in detail—forgetting supports both word mapping and the generalization of words and categories.

  7. A combination of HARMONIE short time direct normal irradiance forecasts and machine learning: The #hashtdim procedure

    Science.gov (United States)

    Gastón, Martín; Fernández-Peruchena, Carlos; Körnich, Heiner; Landelius, Tomas

    2017-06-01

    The present work describes the first approach of a new procedure to forecast Direct Normal Irradiance (DNI): the #hashtdim that treats to combine ground information and Numerical Weather Predictions. The system is centered in generate predictions for the very short time. It combines the outputs from the Numerical Weather Prediction Model HARMONIE with an adaptive methodology based on Machine Learning. The DNI predictions are generated with 15-minute and hourly temporal resolutions and presents 3-hourly updates. Each update offers forecasts to the next 12 hours, the first nine hours are generated with 15-minute temporal resolution meanwhile the last three hours present hourly temporal resolution. The system is proved over a Spanish emplacement with BSRN operative station in south of Spain (PSA station). The #hashtdim has been implemented in the framework of the Direct Normal Irradiance Nowcasting methods for optimized operation of concentrating solar technologies (DNICast) project, under the European Union's Seventh Programme for research, technological development and demonstration framework.

  8. Surgical training and the European Working Time Directive: The role of informal workplace learning.

    Science.gov (United States)

    Giles, James A

    2010-01-01

    The introduction of European Working Time Directive, limiting doctors' working hours to 48 per week, has caused recent controversy within the profession. The Royal College of Surgeons of England in particular has been one of the loudest critics of the legislation. One of the main concerns is regarding the negative impact on training hours for those embarking on surgical careers. Simulation technology has been suggested as a method to overcome this reduction in hospital training hours, and research suggests that this is a good substitute for operative training in a theatre. However, modern educational theory emphasises the power of informal workplace learning in postgraduate education, and the essential role of experience in training future surgeons. Copyright 2010 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.

  9. Real-time dosimetry system in catheterisation laboratory: utility as a learning tool in radiation protection

    International Nuclear Information System (INIS)

    Pinto Monedero, M.; Rodriguez Cobo, C.; Pifarre Martinez, X.; Ruiz Martin, J.; Barros Candelero, J.M.; Goicolea Ruigomez, J.; Diaz Blaires, G.; Garcia Lunar, I.

    2015-01-01

    Document available in abstract form only. Full text of publication follows: Workers at the catheter laboratory are among the most exposed to ionising radiation in hospitals. However, it is difficult to be certain of the radiation doses received by the staff, as personal dosemeters are often misused, and thus, the dose history is not reliable. Moreover, the information provided by personal dosemeters corresponds to the monthly accumulated dose, so corrective actions tends to be delayed. The purpose of this work is, on the one hand, to use a real-time dosimetry system to establish the occupational doses per procedure of workers at the catheter laboratories and, on the other hand, to evaluate its utility as a learning tool for radiation protection purposes with the simultaneous video recording of the interventions. (authors)

  10. A Just-in-Time Learning based Monitoring and Classification Method for Hyper/Hypocalcemia Diagnosis.

    Science.gov (United States)

    Peng, Xin; Tang, Yang; He, Wangli; Du, Wenli; Qian, Feng

    2017-01-20

    This study focuses on the classification and pathological status monitoring of hyper/hypo-calcemia in the calcium regulatory system. By utilizing the Independent Component Analysis (ICA) mixture model, samples from healthy patients are collected, diagnosed, and subsequently classified according to their underlying behaviors, characteristics, and mechanisms. Then, a Just-in-Time Learning (JITL) has been employed in order to estimate the diseased status dynamically. In terms of JITL, for the purpose of the construction of an appropriate similarity index to identify relevant datasets, a novel similarity index based on the ICA mixture model is proposed in this paper to improve online model quality. The validity and effectiveness of the proposed approach have been demonstrated by applying it to the calcium regulatory system under various hypocalcemic and hypercalcemic diseased conditions.

  11. Fast Mapping Across Time: Memory Processes Support Children's Retention of Learned Words.

    Science.gov (United States)

    Vlach, Haley A; Sandhofer, Catherine M

    2012-01-01

    Children's remarkable ability to map linguistic labels to referents in the world is commonly called fast mapping. The current study examined children's (N = 216) and adults' (N = 54) retention of fast-mapped words over time (immediately, after a 1-week delay, and after a 1-month delay). The fast mapping literature often characterizes children's retention of words as consistently high across timescales. However, the current study demonstrates that learners forget word mappings at a rapid rate. Moreover, these patterns of forgetting parallel forgetting functions of domain-general memory processes. Memory processes are critical to children's word learning and the role of one such process, forgetting, is discussed in detail - forgetting supports extended mapping by promoting the memory and generalization of words and categories.

  12. Reinforcement learning for adaptive optimal control of unknown continuous-time nonlinear systems with input constraints

    Science.gov (United States)

    Yang, Xiong; Liu, Derong; Wang, Ding

    2014-03-01

    In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.

  13. "Our Journey through Time": An Oral History Project Carried out by Young People with Learning Disabilities

    Science.gov (United States)

    Bentley, Sarah; Nicholls, Rickie; Price, Maxine; Wilkinson, Aaron; Purcell, Matthew; Woodhall, Martin; Walmsley, Jan

    2011-01-01

    We are five young people with learning disabilities who found out about the history of hospitals for people with learning disabilities in our area, and made a film about the project. The project taught us what life had been like for some people with learning disabilities only 30 years ago. It was very different to our lives; we have more choice,…

  14. Infant Statistical-Learning Ability Is Related to Real-Time Language Processing

    Science.gov (United States)

    Lany, Jill; Shoaib, Amber; Thompson, Abbie; Estes, Katharine Graf

    2018-01-01

    Infants are adept at learning statistical regularities in artificial language materials, suggesting that the ability to learn statistical structure may support language development. Indeed, infants who perform better on statistical learning tasks tend to be more advanced in parental reports of infants' language skills. Work with adults suggests…

  15. Evaluation of Multiple Kernel Learning Algorithms for Crop Mapping Using Satellite Image Time-Series Data

    Science.gov (United States)

    Niazmardi, S.; Safari, A.; Homayouni, S.

    2017-09-01

    Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.

  16. Machine-learning-based Brokers for Real-time Classification of the LSST Alert Stream

    Science.gov (United States)

    Narayan, Gautham; Zaidi, Tayeb; Soraisam, Monika D.; Wang, Zhe; Lochner, Michelle; Matheson, Thomas; Saha, Abhijit; Yang, Shuo; Zhao, Zhenge; Kececioglu, John; Scheidegger, Carlos; Snodgrass, Richard T.; Axelrod, Tim; Jenness, Tim; Maier, Robert S.; Ridgway, Stephen T.; Seaman, Robert L.; Evans, Eric Michael; Singh, Navdeep; Taylor, Clark; Toeniskoetter, Jackson; Welch, Eric; Zhu, Songzhe; The ANTARES Collaboration

    2018-05-01

    The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demand that the astronomical community update its follow-up paradigm. Alert-brokers—automated software system to sift through, characterize, annotate, and prioritize events for follow-up—will be critical tools for managing alert streams in the LSST era. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using the available multiband optical photometry. We describe three illustrative stages of the pipeline, serving the three goals of early, intermediate, and retrospective classification of alerts. The first takes the form of variable versus transient categorization, the second a multiclass typing of the combined variable and transient data set, and the third a purity-driven subtyping of a transient class. Although several similar algorithms have proven themselves in simulations, we validate their performance on real observations for the first time. We quantitatively evaluate our pipeline on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, and demonstrate very competitive classification performance. We describe our progress toward adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.

  17. Machine Learning-based Transient Brokers for Real-time Classification of the LSST Alert Stream

    Science.gov (United States)

    Narayan, Gautham; Zaidi, Tayeb; Soraisam, Monika; ANTARES Collaboration

    2018-01-01

    The number of transient events discovered by wide-field time-domain surveys already far outstrips the combined followup resources of the astronomical community. This number will only increase as we progress towards the commissioning of the Large Synoptic Survey Telescope (LSST), breaking the community's current followup paradigm. Transient brokers - software to sift through, characterize, annotate and prioritize events for followup - will be a critical tool for managing alert streams in the LSST era. Developing the algorithms that underlie the brokers, and obtaining simulated LSST-like datasets prior to LSST commissioning, to train and test these algorithms are formidable, though not insurmountable challenges. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is a joint project of the National Optical Astronomy Observatory and the Department of Computer Science at the University of Arizona. We have been developing completely automated methods to characterize and classify variable and transient events from their multiband optical photometry. We describe the hierarchical ensemble machine learning algorithm we are developing, and test its performance on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, as well as our progress towards incorporating these into a real-time event broker working on live alert streams from time-domain surveys.

  18. Relations between the development of future time perspective in three life domains, investment in learning, and academic achievement

    NARCIS (Netherlands)

    Peetsma, T.; van der Veen, I.

    2011-01-01

    Relations between the development of future time perspectives in three life domains (i.e., school and professional career, social relations, and leisure time) and changes in students’ investment in learning and academic achievement were examined in this study. Participants were 584 students in the

  19. Relations between the Development of Future Time Perspective in Three Life Domains, Investment in Learning, and Academic Achievement

    Science.gov (United States)

    Peetsma, Thea; van der Veen, Ineke

    2011-01-01

    Relations between the development of future time perspectives in three life domains (i.e., school and professional career, social relations, and leisure time) and changes in students' investment in learning and academic achievement were examined in this study. Participants were 584 students in the first and 584 in the second year of the lower…

  20. Technology Applied to ELT: Reviewing Practical Uses to Enhance English Teaching Programs

    Directory of Open Access Journals (Sweden)

    Javier Rojas Serrano

    2007-12-01

    Full Text Available In this paper, the author reports on some of the areas of technology currently used in the teaching and learning of languages, and reviews some of the uses of technological tools that are present in the English Adult Program and activities carried out at The Centro Colombo Americano (CCA in Bogota, Colombia. After briefly describing what is being applied worldwide in terms of what is being done at the CCA, some suggestions are offered in order to enhance the English teaching and learning processes through the use of new technologies.

  1. Learning potentials and pitfalls working with animation aesthetics as leisure-time pedagogues

    DEFF Research Database (Denmark)

    Ringskou, Lea Thomsen; Ahm, Jacob Noer

    of ethnographic participant observations, accompanied by qualitative semi-structured focus group interviews.Findings:Aesthetic animation learning processes involve more playful and creative learning processes, acknowledging both sound, pictures, body and movement as signs of learning. Especially movement......, as a central part of animations aesthetics, offers both potentials and pittfalls when it comes to the learning processes of the children and calls for pedagogical attention. Overall, the research project constructs knowledge about the pedagogy emerging when working with the learning processes of animation...

  2. Exploring the Hidden Agenda in the Representation of Culture in International and Localised ELT Textbooks

    Science.gov (United States)

    Tajeddin, Zia; Teimournezhad, Shohreh

    2015-01-01

    The rise of English as an international language (EIL) has challenged the focus on native-speaker culture in second language teaching and learning. Exposing learners to a single culture is no longer considered sufficient as intercultural language teaching and understanding gains momentum. The aim of this study was to investigate the representation…

  3. An Evaluation of Classroom Activities and Exercises in ELT Classroom for General Purposes Course

    Science.gov (United States)

    Zohrabi, Mohammad

    2011-01-01

    It is through effective implementation of activities and exercises which students can be motivated and consequently lead to language learning. However, as an insider, the experience of teaching English for General Purposes (EGP) course indicates that it has some problems which need to be modified. In order to evaluate the EGP course,…

  4. Using Online Computer Games in the ELT Classroom: A Case Study

    Science.gov (United States)

    Vasileiadou, Ioanna; Makrina, Zafiri

    2017-01-01

    The purpose of this research was to investigate the effectiveness of computer games in learning English as a foreign language and the extent to which they increase motivation in young students. More particularly, this research investigated the validity of the hypothesis that computer games are a particularly motivating means for young students to…

  5. The Using of Casual Style in ELT for Young Learners (Sociolinguistics Perspectives)

    Science.gov (United States)

    Debora, Irene

    2013-01-01

    Young Learners have less reinforcement to speak with others. One of the causes is the trend of formal or clumsy learning setting in emphasizing the speaking proficiency. Speaking based on the culture context also contribute them in increasing their motivation to express their ideas. "Casual style" as one of the language variations gives…

  6. Call Me... Maybe: A Framework for Integrating the Internet into ELT

    Science.gov (United States)

    Chinnery, George M.

    2014-01-01

    This article outlines reasons to use (or not use) the Internet in English language teaching, exploring the Internet as tutor and tool. Discussion of Internet content includes types of content and how to select, save, and use content. Various learning tasks, appropriate even for those without Internet access, are presented and highlighted in a…

  7. Perceptions of Using Social Media as an ELT Tool among EFL Teachers in the Saudi Context

    Science.gov (United States)

    Allam, Madawi; Elyas, Tariq

    2016-01-01

    Social media technologies have undeniably become an integral part of people's lives and they have been widely used amongst the new generations, particularly, university students. This widespread of social media technologies has certainly made a huge impact on the way people learn and interact with each other resulting in the emergence of…

  8. Manifestations of Globalization and Linguistic Imperialism in English Language Teaching and Materials Preparation: Ideology in the International ELT Textbooks

    Directory of Open Access Journals (Sweden)

    Seyyed Ali Kazemi

    2017-09-01

    Full Text Available This study intended to investigate the imposition of values and ideological patterns of particular societies affecting learners' identity as a result of globalization and linguistic imperialism in the internationally distributed textbooks which are developed to meet the English language needs of international learners and are broadly used in Islamic countries like Iran. It was important to work out whether violation of standards and ideological patterns of certain societies could be detected. For that reason, critical discourse analysis (CDA with its theory and procedures, as developed by Fairclough (1989, used in conversations, illustrations and reading passages in Interchange, Four Corners, Top Notch and American English File series and three meaning dimensions– the textbooks content, the social relations of the characters in the textbooks, and their subject positions– were classified and analyzed statistically. Overall, the findings of this study represented that these ELT books are by some means unfair and inclined to signify a specific discourse type, that is, the Western culture discourse, ideological patterns, and consumer societies, which can impose the Western view and have different effects on students' identity in Islamic countries.

  9. The Role of Electronic Learning Technology in Networks Systems

    International Nuclear Information System (INIS)

    Abd ELhamid, A.; Ayad, N.M.A.; Fouad, Y.; Abdelkader, T.

    2016-01-01

    Recently, Electronic Learning Technology (ELT) has been widely spread as one of the new technologies in the world through using Information and Communication Technology (ICT). One of the strategies of ELT is Simulation, for instance Military and Medical simulations that are used to avoid risks and reduce Costs. A wireless communication network refers to any network not physically connected by cables, which enables the desired convenience and mobility for the user. Wireless communication networks have been useful in areas such as commerce, education and defense. According to the nature of a particular application, they can be used in home-based and industrial systems or in commercial and military environments. Historically, Mobile Ad-hoc Networks (MANET) have primarily been used for tactical military network related applications to improve battlefield communications/ survivability. MANET is a collection of wireless nodes that can dynamically be set up anywhere and anytime without using any pre-existing network infrastructure. Mobility in wireless networks basically refers to nodes changing its point of attachment to the network. Also, how the end terminals can move, there are many mobility models described the movement of nodes, many researchers use the Random Way point Mobility Model (RWPM). In this paper, a Graphical User Interface (GUI) for RWPM simulation is introduced as a proposal to be used through ELT Project. In the research area of computer and communications networks, simulation is a very useful technique for the behavior of networks

  10. Sparse Bayesian learning machine for real-time management of reservoir releases

    Science.gov (United States)

    Khalil, Abedalrazq; McKee, Mac; Kemblowski, Mariush; Asefa, Tirusew

    2005-11-01

    Water scarcity and uncertainties in forecasting future water availabilities present serious problems for basin-scale water management. These problems create a need for intelligent prediction models that learn and adapt to their environment in order to provide water managers with decision-relevant information related to the operation of river systems. This manuscript presents examples of state-of-the-art techniques for forecasting that combine excellent generalization properties and sparse representation within a Bayesian paradigm. The techniques are demonstrated as decision tools to enhance real-time water management. A relevance vector machine, which is a probabilistic model, has been used in an online fashion to provide confident forecasts given knowledge of some state and exogenous conditions. In practical applications, online algorithms should recognize changes in the input space and account for drift in system behavior. Support vectors machines lend themselves particularly well to the detection of drift and hence to the initiation of adaptation in response to a recognized shift in system structure. The resulting model will normally have a structure and parameterization that suits the information content of the available data. The utility and practicality of this proposed approach have been demonstrated with an application in a real case study involving real-time operation of a reservoir in a river basin in southern Utah.

  11. Big Data Toolsets to Pharmacometrics: Application of Machine Learning for Time-to-Event Analysis.

    Science.gov (United States)

    Gong, Xiajing; Hu, Meng; Zhao, Liang

    2018-05-01

    Additional value can be potentially created by applying big data tools to address pharmacometric problems. The performances of machine learning (ML) methods and the Cox regression model were evaluated based on simulated time-to-event data synthesized under various preset scenarios, i.e., with linear vs. nonlinear and dependent vs. independent predictors in the proportional hazard function, or with high-dimensional data featured by a large number of predictor variables. Our results showed that ML-based methods outperformed the Cox model in prediction performance as assessed by concordance index and in identifying the preset influential variables for high-dimensional data. The prediction performances of ML-based methods are also less sensitive to data size and censoring rates than the Cox regression model. In conclusion, ML-based methods provide a powerful tool for time-to-event analysis, with a built-in capacity for high-dimensional data and better performance when the predictor variables assume nonlinear relationships in the hazard function. © 2018 The Authors. Clinical and Translational Science published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.

  12. Upper Extremity Motor Learning among Individuals with Parkinson's Disease: A Meta-Analysis Evaluating Movement Time in Simple Tasks

    Directory of Open Access Journals (Sweden)

    K. Felix

    2012-01-01

    Full Text Available Motor learning has been found to occur in the rehabilitation of individuals with Parkinson's disease (PD. Through repetitive structured practice of motor tasks, individuals show improved performance, confirming that motor learning has probably taken place. Although a number of studies have been completed evaluating motor learning in people with PD, the sample sizes were small and the improvements were variable. The purpose of this meta-analysis was to determine the ability of people with PD to learn motor tasks. Studies which measured movement time in upper extremity reaching tasks and met the inclusion criteria were included in the analysis. Results of the meta-analysis indicated that people with PD and neurologically healthy controls both demonstrated motor learning, characterized by a decrease in movement time during upper extremity movements. Movement time improvements were greater in the control group than in individuals with PD. These results support the findings that the practice of upper extremity reaching tasks is beneficial in reducing movement time in persons with PD and has important implications for rehabilitation.

  13. The difference of delay time in monitoring system of facial acupressure learning media using bluetooth, wireless and ethernet

    Science.gov (United States)

    Agustin, Eny Widhia; Hangga, Arimaz; Fahrian, Muhammad Iqbal; Azhari, Anis Fikri

    2018-03-01

    The implementation of monitoring system in the facial acupressure learning media could increase the students' proficiency. However the common learning media still has not implemented a monitoring system in their learning process. This research was conducted to implement monitoring system in the mannequin head prototype as a learning media of facial acupressure using Bluetooth, wireless and Ethernet. The results of the implementation of monitoring system in the prototype showed that there were differences in the delay time between Bluetooth and wireless or Ethernet. The results data showed no difference in the average delay time between the use of Bluetooth with wireless and the use of Bluetooth with Ethernet in monitoring system of facial acupressure learning media. From all the facial acupressure points, the forehead facial acupressure point has the longest delay time of 11.93 seconds. The average delay time in all 3 class rooms was 1.96 seconds therefore the use of Bluetooth, wireless and Ethernet is highly recommended in the monitoring system of facial acupressure.

  14. Phase transitions between lower and higher level management learning in times of crisis: an experimental study based on synergetics.

    Science.gov (United States)

    Liening, Andreas; Strunk, Guido; Mittelstadt, Ewald

    2013-10-01

    Much has been written about the differences between single- and double-loop learning, or more general between lower level and higher level learning. Especially in times of a fundamental crisis, a transition between lower and higher level learning would be an appropriate reaction to a challenge coming entirely out of the dark. However, so far there is no quantitative method to monitor such a transition. Therefore we introduce theory and methods of synergetics and present results from an experimental study based on the simulation of a crisis within a business simulation game. Hypothesized critical fluctuations - as a marker for so-called phase transitions - have been assessed with permutation entropy. Results show evidence for a phase transition during the crisis, which can be interpreted as a transition between lower and higher level learning.

  15. The Using of Casual Style in ELT For Young Learners (Sociolinguistics Perspectives

    Directory of Open Access Journals (Sweden)

    Irene Debora

    2013-01-01

    Full Text Available Young Learners have less reinforcement to speak with others. One of the causes is the trend of formal or clumsy learning setting in emphasizing the speaking proficiency. Speaking based on the culture context also contribute them in increasing their motivation to express their ideas. Casual style as one of the language variations gives contribution in increasing students’ motivation to be more active in the class. The simply characteristics of Casual style can be memorized and applied easily. Casual style tends to adjust the culture of speaker in communication so it will be easy to be understood. Students also can use the utterances in their daily life because most of the utterances are familiar for them. The raising of  motivation from students will give positive effect for teaching and learning process, students, and also teachers.

  16. The role of timing in the induction of neuromodulation in perceptual learning by transcranial electric stimulation.

    Science.gov (United States)

    Pirulli, Cornelia; Fertonani, Anna; Miniussi, Carlo

    2013-07-01

    Transcranial electric stimulation (tES) protocols are able to induce neuromodulation, offering important insights to focus and constrain theories of the relationship between brain and behavior. Previous studies have shown that different types of tES (i.e., direct current stimulation - tDCS, and random noise stimulation - tRNS) induce different facilitatory behavioral effects. However to date is not clear which is the optimal timing to apply tES in relation to the induction of robust facilitatory effects. The goal of this work was to investigate how different types of tES (tDCS and tRNS) can modulate behavioral performance in the healthy adult brain in relation to their timing of application. We applied tES protocols before (offline) or during (online) the execution of a visual perceptual learning (PL) task. PL is a form of implicit memory that is characterized by an improvement in sensory discrimination after repeated exposure to a particular type of stimulus and is considered a manifestation of neural plasticity. Our aim was to understand if the timing of tES is critical for the induction of differential neuromodulatory effects in the primary visual cortex (V1). We applied high-frequency tRNS, anodal tDCS and sham tDCS on V1 before or during the execution of an orientation discrimination task. The experimental design was between subjects and performance was measured in terms of d' values. The ideal timing of application varied depending on the stimulation type. tRNS facilitated task performance only when it was applied during task execution, whereas anodal tDCS induced a larger facilitation if it was applied before task execution. The main result of this study is the finding that the timing of identical tES protocols yields opposite effects on performance. These results provide important guidelines for designing neuromodulation induction protocols and highlight the different optimal timing of the two excitatory techniques. Copyright © 2013 Elsevier Inc. All

  17. TIME-ON-TASK IN PRIMARY CLASSROOMS, DURING DIFFERENT TEACHING-LEARNING APPROACHES

    OpenAIRE

    Sachin Mohite; Meenal Dashputre

    2017-01-01

    The entire education system is moving from the teacher-centered teaching-learning approaches towards student-centered teaching-learning approaches, with anticipation that it would increase the learning outcomes. This empirical study was carried out to compare the traditional and non-traditional classrooms. It also tried to understand the effectiveness of the Alternate Instructions in the Mathematics and Primary Language (Marathi) classrooms. This study collected about 8000 snapshots from the ...

  18. A New Tool for CME Arrival Time Prediction using Machine Learning Algorithms: CAT-PUMA

    Science.gov (United States)

    Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

    2018-03-01

    Coronal mass ejections (CMEs) are arguably the most violent eruptions in the solar system. CMEs can cause severe disturbances in interplanetary space and can even affect human activities in many aspects, causing damage to infrastructure and loss of revenue. Fast and accurate prediction of CME arrival time is vital to minimize the disruption that CMEs may cause when interacting with geospace. In this paper, we propose a new approach for partial-/full halo CME Arrival Time Prediction Using Machine learning Algorithms (CAT-PUMA). Via detailed analysis of the CME features and solar-wind parameters, we build a prediction engine taking advantage of 182 previously observed geo-effective partial-/full halo CMEs and using algorithms of the Support Vector Machine. We demonstrate that CAT-PUMA is accurate and fast. In particular, predictions made after applying CAT-PUMA to a test set unknown to the engine show a mean absolute prediction error of ∼5.9 hr within the CME arrival time, with 54% of the predictions having absolute errors less than 5.9 hr. Comparisons with other models reveal that CAT-PUMA has a more accurate prediction for 77% of the events investigated that can be carried out very quickly, i.e., within minutes of providing the necessary input parameters of a CME. A practical guide containing the CAT-PUMA engine and the source code of two examples are available in the Appendix, allowing the community to perform their own applications for prediction using CAT-PUMA.

  19. Recognition of Time Stamps on Full-Disk Hα Images Using Machine Learning Methods

    Science.gov (United States)

    Xu, Y.; Huang, N.; Jing, J.; Liu, C.; Wang, H.; Fu, G.

    2016-12-01

    Observation and understanding of the physics of the 11-year solar activity cycle and 22-year magnetic cycle are among the most important research topics in solar physics. The solar cycle is responsible for magnetic field and particle fluctuation in the near-earth environment that have been found increasingly important in affecting the living of human beings in the modern era. A systematic study of large-scale solar activities, as made possible by our rich data archive, will further help us to understand the global-scale magnetic fields that are closely related to solar cycles. The long-time-span data archive includes both full-disk and high-resolution Hα images. Prior to the widely use of CCD cameras in 1990s, 35-mm films were the major media to store images. The research group at NJIT recently finished the digitization of film data obtained by the National Solar Observatory (NSO) and Big Bear Solar Observatory (BBSO) covering the period of 1953 to 2000. The total volume of data exceeds 60 TB. To make this huge database scientific valuable, some processing and calibration are required. One of the most important steps is to read the time stamps on all of the 14 million images, which is almost impossible to be done manually. We implemented three different methods to recognize the time stamps automatically, including Optical Character Recognition (OCR), Classification Tree and TensorFlow. The latter two are known as machine learning algorithms which are very popular now a day in pattern recognition area. We will present some sample images and the results of clock recognition from all three methods.

  20. Real-Time Strategy Video Game Experience and Visual Perceptual Learning.

    Science.gov (United States)

    Kim, Yong-Hwan; Kang, Dong-Wha; Kim, Dongho; Kim, Hye-Jin; Sasaki, Yuka; Watanabe, Takeo

    2015-07-22

    Visual perceptual learning (VPL) is defined as long-term improvement in performance on a visual-perception task after visual experiences or training. Early studies have found that VPL is highly specific for the trained feature and location, suggesting that VPL is associated with changes in the early visual cortex. However, the generality of visual skills enhancement attributable to action video-game experience suggests that VPL can result from improvement in higher cognitive skills. If so, experience in real-time strategy (RTS) video-game play, which may heavily involve cognitive skills, may also facilitate VPL. To test this hypothesis, we compared VPL between RTS video-game players (VGPs) and non-VGPs (NVGPs) and elucidated underlying structural and functional neural mechanisms. Healthy young human subjects underwent six training sessions on a texture discrimination task. Diffusion-tensor and functional magnetic resonance imaging were performed before and after training. VGPs performed better than NVGPs in the early phase of training. White-matter connectivity between the right external capsule and visual cortex and neuronal activity in the right inferior frontal gyrus (IFG) and anterior cingulate cortex (ACC) were greater in VGPs than NVGPs and were significantly correlated with RTS video-game experience. In both VGPs and NVGPs, there was task-related neuronal activity in the right IFG, ACC, and striatum, which was strengthened after training. These results indicate that RTS video-game experience, associated with changes in higher-order cognitive functions and connectivity between visual and cognitive areas, facilitates VPL in early phases of training. The results support the hypothesis that VPL can occur without involvement of only visual areas. Significance statement: Although early studies found that visual perceptual learning (VPL) is associated with involvement of the visual cortex, generality of visual skills enhancement by action video-game experience

  1. Just-in-Time Teaching in undergraduate physics courses: Implementation, learning, and perceptions

    Science.gov (United States)

    Dwyer, Jessica Hewitt

    Regardless of discipline, a decades-long battle has ensued within nearly every classroom in higher education: instructors getting students to come to class prepared to learn. In response to this clash between teacher expectations and frequent student neglect, a group of four physics education researchers developed a reformed instructional strategy called Just-in-Time Teaching (JiTT). This dissertation investigates the following three areas: 1) the fidelity with which undergraduate physics instructors implement JiTT, 2) whether student performance predicts student perception of their instructor's fidelity of JiTT implementation, and 3) whether student perception of their instructor's fidelity of JiTT implementation correlates with student views of their physics course. A blend of quantitative data (e.g., students grades, inventory scores, and questionnaire responses) are integrated with qualitative data (e.g., individual faculty interviews, student focus group discussions, and classroom observations). This study revealed no statistically significant relationship between instructors who spent time on a predefined JiTT critical component and their designation as a JiTT user or non-user. While JiTT users implemented the pedagogy in accordance with the creators' intended ideal vision, many also had trouble reconciling personal concerns about their role as a JiTT adopter and the anticipated demand of the innovation. I recommend that this population of faculty members can serve as a JiTT model for other courses, disciplines, and/or institutions. Student performance was not a predictor of student perception instructor fidelity of JiTT implementation. Additionally, the majority of students in this study reported they read their textbook prior to class and that JiTT assignments helped them prepare for in-class learning. I found evidence that exposure to the JiTT strategy may correlate with a more favorable student view of their physics course. Finally, according to students

  2. Europe: Strategies and agendas for lifelong learning at time of crisis

    DEFF Research Database (Denmark)

    Milana, Marcella

    2014-01-01

    A complete overview of lifelong learning strategies in Europe, at both international and national levels, calls for understanding the processes through which these strategies take shape. Accordingly, in this contribution, lifelong learning strategies are analyzed through a critical lens...... and the OECD, with important consequences for lifelong learning policy. Evidence is found, for instance, in the formation of a reductionist skills agenda, joint between the EU and the OECD; an agenda capable of influencing future governmental thinking about lifelong learning and adult education in Europe....

  3. Training haptic stiffness discrimination: time course of learning with or without visual information and knowledge of results.

    Science.gov (United States)

    Teodorescu, Kinneret; Bouchigny, Sylvain; Korman, Maria

    2013-08-01

    In this study, we explored the time course of haptic stiffness discrimination learning and how it was affected by two experimental factors, the addition of visual information and/or knowledge of results (KR) during training. Stiffness perception may integrate both haptic and visual modalities. However, in many tasks, the visual field is typically occluded, forcing stiffness perception to be dependent exclusively on haptic information. No studies to date addressed the time course of haptic stiffness perceptual learning. Using a virtual environment (VE) haptic interface and a two-alternative forced-choice discrimination task, the haptic stiffness discrimination ability of 48 participants was tested across 2 days. Each day included two haptic test blocks separated by a training block Additional visual information and/or KR were manipulated between participants during training blocks. Practice repetitions alone induced significant improvement in haptic stiffness discrimination. Between days, accuracy was slightly improved, but decision time performance was deteriorated. The addition of visual information and/or KR had only temporary effects on decision time, without affecting the time course of haptic discrimination learning. Learning in haptic stiffness discrimination appears to evolve through at least two distinctive phases: A single training session resulted in both immediate and latent learning. This learning was not affected by the training manipulations inspected. Training skills in VE in spaced sessions can be beneficial for tasks in which haptic perception is critical, such as surgery procedures, when the visual field is occluded. However, training protocols for such tasks should account for low impact of multisensory information and KR.

  4. Sci-Fri AM: Quality, Safety, and Professional Issues 04: Predicting waiting times in Radiation Oncology using machine learning

    International Nuclear Information System (INIS)

    Joseph, Ackeem; Herrera, David; Hijal, Tarek; Hendren, Laurie; Leung, Alvin; Wainberg, Justin; Sawaf, Marya; Maxim, Gorshkov; Maglieri, Robert; Keshavarz, Mehryar; Kildea, John

    2016-01-01

    We describe a method for predicting waiting times in radiation oncology. Machine learning is a powerful predictive modelling tool that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The patient waiting experience remains one of the most vexing challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick and in pain, to worry about when they will receive the care they need. In radiation oncology, patients typically experience three types of waiting: Waiting at home for their treatment plan to be prepared Waiting in the waiting room for daily radiotherapy Waiting in the waiting room to see a physician in consultation or follow-up These waiting periods are difficult for staff to predict and only rough estimates are typically provided, based on personal experience. In the present era of electronic health records, waiting times need not be so uncertain. At our centre, we have incorporated the electronic treatment records of all previously-treated patients into our machine learning model. We found that the Random Forest Regression model provides the best predictions for daily radiotherapy treatment waiting times (type 2). Using this model, we achieved a median residual (actual minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes. The main features that generated the best fit model (from most to least significant) are: Allocated time, median past duration, fraction number and the number of treatment fields.

  5. Sci-Fri AM: Quality, Safety, and Professional Issues 04: Predicting waiting times in Radiation Oncology using machine learning

    Energy Technology Data Exchange (ETDEWEB)

    Joseph, Ackeem; Herrera, David; Hijal, Tarek; Hendren, Laurie; Leung, Alvin; Wainberg, Justin; Sawaf, Marya; Maxim, Gorshkov; Maglieri, Robert; Keshavarz, Mehryar; Kildea, John [McGill University Health Centre (Canada)

    2016-08-15

    We describe a method for predicting waiting times in radiation oncology. Machine learning is a powerful predictive modelling tool that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The patient waiting experience remains one of the most vexing challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick and in pain, to worry about when they will receive the care they need. In radiation oncology, patients typically experience three types of waiting: Waiting at home for their treatment plan to be prepared Waiting in the waiting room for daily radiotherapy Waiting in the waiting room to see a physician in consultation or follow-up These waiting periods are difficult for staff to predict and only rough estimates are typically provided, based on personal experience. In the present era of electronic health records, waiting times need not be so uncertain. At our centre, we have incorporated the electronic treatment records of all previously-treated patients into our machine learning model. We found that the Random Forest Regression model provides the best predictions for daily radiotherapy treatment waiting times (type 2). Using this model, we achieved a median residual (actual minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes. The main features that generated the best fit model (from most to least significant) are: Allocated time, median past duration, fraction number and the number of treatment fields.

  6. Role of aging and hippocampus in Time-Place Learning: link to episodic-like memory?

    Directory of Open Access Journals (Sweden)

    Cornelis Kees Mulder

    2016-01-01

    Full Text Available Introduction: with time-place learning (TPL, animals link an event with the spatial location and the time of day. The what-where-when TPL components make the task putatively episodic-like in nature. Animals use an internal sense of time to master TPL, which is circadian system based. Finding indications for a role of the hippocampus and (early aging-sensitivity in TPL would strengthen the episodic-like memory nature of the paradigm. Methods: previously, we used C57Bl/6 mice for our TPL research. Here, we used CD1 mice which are less hippocampal-driven and age faster compared to C57Bl/6 mice. To demonstrate the low degree of hippocampal-driven performance in CD1 mice, a cross maze was used. The spontaneous alternation test was used to score spatial working memory in CD1 mice at four different age categories (young (3-6 months, middle-aged (7-11 months, aged (12-18 months and old (>19 months. TPL performance of middle-aged and aged CD1 mice was tested in a setup with either two or three time points per day (2-arm or 3-arm TPL task. Immunostainings was applied on brains of young and middle-aged C57Bl/6 mice that had successfully mastered the 3-arm TPL task. Results: in contrast to C57Bl/6 mice, middle-aged and aged CD1 mice were less hippocampus-driven and failed to master the 3-arm TPL task. They could, however, master the 2-arm TPL task primarily via an ordinal (non-circadian timing system. c-Fos, CRY2, vasopressin (AVP, and pCREB were investigated. We found no differences at the level of the suprachiasmatic nucleus (SCN; circadian master clock, whereas CRY2 expression was increased in the hippocampal dentate gyrus. The most pronounced difference between TPL trained and control mice was found in c-Fos expression in the paraventricular thalamic nucleus, a circadian system relay station. Conclusions: These results further indicate a key role of CRY proteins in TPL and confirm the limited role of the SCN in TPL. Based on the poor TPL performance of

  7. `Teaching What I Learned': Exploring students' Earth and Space Science learning experiences in secondary school with a particular focus on their comprehension of the concept of `geologic time'

    Science.gov (United States)

    Yoon, Sae Yeol; Peate, David W.

    2015-06-01

    According to the national survey of science education, science educators in the USA currently face many challenges such as lack of qualified secondary Earth and Space Science (ESS) teachers. Less qualified teachers may have difficulty teaching ESS because of a lack of conceptual understanding, which leads to diminished confidence in content knowledge. More importantly, teachers' limited conceptual understanding of the core ideas automatically leads to a lack of pedagogical content knowledge. This mixed methods study aims to explore the ways in which current secondary schooling, especially the small numbers of highly qualified ESS teachers in the USA, might influence students' learning of the discipline. To gain a better understanding of the current conditions of ESS education in secondary schools, in the first phase, we qualitatively examined a sample middle and high school ESS textbook to explore how the big ideas of ESS, particularly geological time, are represented. In the second phase, we quantitatively analyzed the participating college students' conceptual understanding of geological time by comparing those who had said they had had secondary school ESS learning experience with those who did not. Additionally, college students' perceptions on learning and teaching ESS are discussed. Findings from both the qualitative and quantitative phases indicate participating students' ESS learning experience in their secondary schools seemed to have limited or little influence on their conceptual understandings of the discipline. We believe that these results reflect the current ESS education status, connected with the declining numbers of highly qualified ESS teachers in secondary schools.

  8. A deep learning framework for financial time series using stacked autoencoders and long-short term memory.

    Science.gov (United States)

    Bao, Wei; Yue, Jun; Rao, Yulei

    2017-01-01

    The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.

  9. A deep learning framework for financial time series using stacked autoencoders and long-short term memory

    Science.gov (United States)

    Bao, Wei; Rao, Yulei

    2017-01-01

    The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day’s closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance. PMID:28708865

  10. A deep learning framework for financial time series using stacked autoencoders and long-short term memory.

    Directory of Open Access Journals (Sweden)

    Wei Bao

    Full Text Available The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT, stacked autoencoders (SAEs and long-short term memory (LSTM are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.

  11. Aircraft Control Using Engine Thrust: A History of Learning TOC Real-Time

    Science.gov (United States)

    Cole, Jennifer H.; Batteas, Frank; Fullerton, Gordon

    2006-01-01

    A history of learning the operation of Throttles Only Control (TOC) to control an aircraft in real time using engine thrust is shown. The topics include: 1) Past TOC Accidents/Incidents; 2) 1972: DC-10 American Airlines; 3) May 1974: USAF B-52H; 4) April 1975: USAF C-5A; 5) April 1975: USAF C-5A; 6) 1981: USAF B-52G; 7) August 1985: JAL 123 B-747; 8) JAL 123 Survivor Story; 9) JAL 123 Investigation Findings; 10) July 1989: UAL 232 DC-10; 11) UAL 232 DC-10; 12) Eastwind 517 B-737; 13) November 2003: DHL A-300; 14) Historically, TOC has saved lives; 15) Automated Throttles-Only Control; 16) PCA Project; 17) Propulsion-Controlled Aircraft; 18) MD-11 PCA System and Flight Test Envelope; 19) MD-11 Simulation, PCA ILS-Soupled Landing Dispersion; 20) Throttles-Only Pitch and Roll Control Power; 21) PCA in Commercial Fleet; 22) Fall 2005: PCAR Project; 23) PCAR Background - TOC; and 24) PCAR Background - TOC.

  12. Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity.

    Science.gov (United States)

    D'Souza, Prashanth; Liu, Shih-Chii; Hahnloser, Richard H R

    2010-03-09

    It is widely believed that sensory and motor processing in the brain is based on simple computational primitives rooted in cellular and synaptic physiology. However, many gaps remain in our understanding of the connections between neural computations and biophysical properties of neurons. Here, we show that synaptic spike-time-dependent plasticity (STDP) combined with spike-frequency adaptation (SFA) in a single neuron together approximate the well-known perceptron learning rule. Our calculations and integrate-and-fire simulations reveal that delayed inputs to a neuron endowed with STDP and SFA precisely instruct neural responses to earlier arriving inputs. We demonstrate this mechanism on a developmental example of auditory map formation guided by visual inputs, as observed in the external nucleus of the inferior colliculus (ICX) of barn owls. The interplay of SFA and STDP in model ICX neurons precisely transfers the tuning curve from the visual modality onto the auditory modality, demonstrating a useful computation for multimodal and sensory-guided processing.

  13. Reduction in training time of a deep learning model in detection of lesions in CT

    Science.gov (United States)

    Makkinejad, Nazanin; Tajbakhsh, Nima; Zarshenas, Amin; Khokhar, Ashfaq; Suzuki, Kenji

    2018-02-01

    Deep learning (DL) emerged as a powerful tool for object detection and classification in medical images. Building a well-performing DL model, however, requires a huge number of images for training, and it takes days to train a DL model even on a cutting edge high-performance computing platform. This study is aimed at developing a method for selecting a "small" number of representative samples from a large collection of training samples to train a DL model for the could be used to detect polyps in CT colonography (CTC), without compromising the classification performance. Our proposed method for representative sample selection (RSS) consists of a K-means clustering algorithm. For the performance evaluation, we applied the proposed method to select samples for the training of a massive training artificial neural network based DL model, to be used for the classification of polyps and non-polyps in CTC. Our results show that the proposed method reduce the training time by a factor of 15, while maintaining the classification performance equivalent to the model trained using the full training set. We compare the performance using area under the receiveroperating- characteristic curve (AUC).

  14. Dynamic time warping and machine learning for signal quality assessment of pulsatile signals

    International Nuclear Information System (INIS)

    Li, Q; Clifford, G D

    2012-01-01

    In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6 s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal. (paper)

  15. Dynamic time warping and machine learning for signal quality assessment of pulsatile signals.

    Science.gov (United States)

    Li, Q; Clifford, G D

    2012-09-01

    In this work, we describe a beat-by-beat method for assessing the clinical utility of pulsatile waveforms, primarily recorded from cardiovascular blood volume or pressure changes, concentrating on the photoplethysmogram (PPG). Physiological blood flow is nonstationary, with pulses changing in height, width and morphology due to changes in heart rate, cardiac output, sensor type and hardware or software pre-processing requirements. Moreover, considerable inter-individual and sensor-location variability exists. Simple template matching methods are therefore inappropriate, and a patient-specific adaptive initialization is therefore required. We introduce dynamic time warping to stretch each beat to match a running template and combine it with several other features related to signal quality, including correlation and the percentage of the beat that appeared to be clipped. The features were then presented to a multi-layer perceptron neural network to learn the relationships between the parameters in the presence of good- and bad-quality pulses. An expert-labeled database of 1055 segments of PPG, each 6 s long, recorded from 104 separate critical care admissions during both normal and verified arrhythmic events, was used to train and test our algorithms. An accuracy of 97.5% on the training set and 95.2% on test set was found. The algorithm could be deployed as a stand-alone signal quality assessment algorithm for vetting the clinical utility of PPG traces or any similar quasi-periodic signal.

  16. 2D co-ordinate transformation based on a spike timing-dependent plasticity learning mechanism.

    Science.gov (United States)

    Wu, QingXiang; McGinnity, Thomas Martin; Maguire, Liam; Belatreche, Ammar; Glackin, Brendan

    2008-11-01

    In order to plan accurate motor actions, the brain needs to build an integrated spatial representation associated with visual stimuli and haptic stimuli. Since visual stimuli are represented in retina-centered co-ordinates and haptic stimuli are represented in body-centered co-ordinates, co-ordinate transformations must occur between the retina-centered co-ordinates and body-centered co-ordinates. A spiking neural network (SNN) model, which is trained with spike-timing-dependent-plasticity (STDP), is proposed to perform a 2D co-ordinate transformation of the polar representation of an arm position to a Cartesian representation, to create a virtual image map of a haptic input. Through the visual pathway, a position signal corresponding to the haptic input is used to train the SNN with STDP synapses such that after learning the SNN can perform the co-ordinate transformation to generate a representation of the haptic input with the same co-ordinates as a visual image. The model can be applied to explain co-ordinate transformation in spiking neuron based systems. The principle can be used in artificial intelligent systems to process complex co-ordinate transformations represented by biological stimuli.

  17. Time, Space and Structure in an E-Learning and E-Mentoring Project

    Science.gov (United States)

    Loureiro-Koechlin, Cecilia; Allan, Barbara

    2010-01-01

    This study focuses on a project, "EMPATHY Net-Works," which developed a learning community as a means of encouraging women to progress into employment and management positions in the logistics and supply chain industries (LaSCI). Learning activities were organised in the form of a taught module containing face-to-face and online elements and…

  18. Pink Time: Evidence of Self-Regulated Learning and Academic Motivation among Undergraduate Students

    Science.gov (United States)

    Baird, Timothy D.; Kniola, David J.; Lewis, Ashley L.; Fowler, Shelli B.

    2015-01-01

    This article describes and analyzes a classroom assignment to promote intrinsic motivation for learning in college students. Here, grades and instructor expectations for content are viewed as students' primary motivations for learning, and correspondingly present obstacles for improved critical thinking skills, student autonomy, and engagement.…

  19. Experience the city : analysis of space-time behavior and spatial learning

    NARCIS (Netherlands)

    Moiseeva, A.

    2013-01-01

    Learning plays an important role by coding information into individual cognitive maps that can be used to make decisions concerning individual behavior in space. Through traveling people learn about the urban environment and update their knowledge. In this regard, the growing concern in the field of

  20. Baby FaceTime: Can Toddlers Learn from Online Video Chat?

    Science.gov (United States)

    Myers, Lauren J.; LeWitt, Rachel B.; Gallo, Renee E.; Maselli, Nicole M.

    2017-01-01

    There is abundant evidence for the "video deficit": children under 2 years old learn better in person than from video. We evaluated whether these findings applied to video chat by testing whether children aged 12-25 months could form relationships with and learn from on-screen partners. We manipulated social contingency: children…

  1. Real time reinforcement learning control of dynamic systems applied to an inverted pendulum

    NARCIS (Netherlands)

    van Luenen, W.T.C.; van Luenen, W.T.C.; Stender, J.; Addis, T.

    1990-01-01

    Describes work started in order to investigate the use of neural networks for application in adaptive or learning control systems. Neural networks have learning capabilities and they can be used to realize non-linear mappings. These are attractive features which could make them useful building

  2. Pastors and the "Perpetuum Mobile": The Dynamics of Professional Learning in Times of Reform

    Science.gov (United States)

    Reite, Ingrid Chr.

    2015-01-01

    In a changing knowledge society, many workplaces experience a great number of reforms, implying improvement, new ways of working and professional learning. When a reform is introduced, however, does a professional act as an ever-moving machine--a "perpetuum mobile"--always learning with full energy? In this article, I ask the following:…

  3. Continuous-time on-policy neural reinforcement learning of working memory tasks

    NARCIS (Netherlands)

    D. Zambrano (Davide); P.R. Roelfsema; S.M. Bohte (Sander)

    2015-01-01

    htmlabstractAs living organisms, one of our primary characteristics is the ability to rapidly process and react to unknown and unexpected events. To this end, we are able to recognize an event or a sequence of events and learn to respond properly. Despite advances in machine learning, current

  4. Digital tomosynthesis for evaluating metastatic lung nodules: nodule visibility, learning curves, and reading times.

    Science.gov (United States)

    Lee, Kyung Hee; Goo, Jin Mo; Lee, Sang Min; Park, Chang Min; Bahn, Young Eun; Kim, Hyungjin; Song, Yong Sub; Hwang, Eui Jin

    2015-01-01

    To evaluate nodule visibility, learning curves, and reading times for digital tomosynthesis (DT). We included 80 patients who underwent computed tomography (CT) and DT before pulmonary metastasectomy. One experienced chest radiologist annotated all visible nodules on thin-section CT scans using computer-aided detection software. Two radiologists used CT as the reference standard and retrospectively graded the visibility of nodules on DT. Nodule detection performance was evaluated in four sessions of 20 cases each by six readers. After each session, readers were unblinded to the DT images by revealing the true-positive markings and were instructed to self-analyze their own misreads. Receiver-operating-characteristic curves were determined. Among 414 nodules on CT, 53.3% (221/414) were visible on DT. The main reason for not seeing a nodule on DT was small size (93.3%, ≤ 5 mm). DT revealed a substantial number of malignant nodules (84.1%, 143/170). The proportion of malignant nodules among visible nodules on DT was significantly higher (64.7%, 143/221) than that on CT (41.1%, 170/414) (p 0.8, and the average detection rate for malignant nodules was 85% (210/246). The inter-session analysis of the AUC showed no significant differences among the readers, and the detection rate for malignant nodules did not differ across sessions. A slight improvement in reading times was observed. Most malignant nodules > 5 mm were visible on DT. As nodule detection performance was high from the initial session, DT may be readily applicable for radiology residents and board-certified radiologists.

  5. Digital tomosynthesis for evaluating metastatic lung nodules: Nodule visibility, learning curves, and reading times

    International Nuclear Information System (INIS)

    Lee, Kyung Hee; Goo, Jin Mo; Lee, Sang Min; Park, Chang Min; Bahn, Young Eun; Kim, Hyung Jin; Song, Yong Sub; Hwang, Eui Jin

    2015-01-01

    To evaluate nodule visibility, learning curves, and reading times for digital tomosynthesis (DT). We included 80 patients who underwent computed tomography (CT) and DT before pulmonary metastasectomy. One experienced chest radiologist annotated all visible nodules on thin-section CT scans using computer-aided detection software. Two radiologists used CT as the reference standard and retrospectively graded the visibility of nodules on DT. Nodule detection performance was evaluated in four sessions of 20 cases each by six readers. After each session, readers were unblinded to the DT images by revealing the true-positive markings and were instructed to self-analyze their own misreads. Receiver-operating-characteristic curves were determined. Among 414 nodules on CT, 53.3% (221/414) were visible on DT. The main reason for not seeing a nodule on DT was small size (93.3%, < or = 5 mm). DT revealed a substantial number of malignant nodules (84.1%, 143/170). The proportion of malignant nodules among visible nodules on DT was significantly higher (64.7%, 143/221) than that on CT (41.1%, 170/414) (p < 0.001). Area under the curve (AUC) values at the initial session were > 0.8, and the average detection rate for malignant nodules was 85% (210/246). The inter-session analysis of the AUC showed no significant differences among the readers, and the detection rate for malignant nodules did not differ across sessions. A slight improvement in reading times was observed. Most malignant nodules > 5 mm were visible on DT. As nodule detection performance was high from the initial session, DT may be readily applicable for radiology residents and board-certified radiologists.

  6. Digital tomosynthesis for evaluating metastatic lung nodules: Nodule visibility, learning curves, and reading times

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Kyung Hee; Goo, Jin Mo; Lee, Sang Min; Park, Chang Min; Bahn, Young Eun; Kim, Hyung Jin; Song, Yong Sub; Hwang, Eui Jin [Dept. of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul (Korea, Republic of)

    2015-04-15

    To evaluate nodule visibility, learning curves, and reading times for digital tomosynthesis (DT). We included 80 patients who underwent computed tomography (CT) and DT before pulmonary metastasectomy. One experienced chest radiologist annotated all visible nodules on thin-section CT scans using computer-aided detection software. Two radiologists used CT as the reference standard and retrospectively graded the visibility of nodules on DT. Nodule detection performance was evaluated in four sessions of 20 cases each by six readers. After each session, readers were unblinded to the DT images by revealing the true-positive markings and were instructed to self-analyze their own misreads. Receiver-operating-characteristic curves were determined. Among 414 nodules on CT, 53.3% (221/414) were visible on DT. The main reason for not seeing a nodule on DT was small size (93.3%, < or = 5 mm). DT revealed a substantial number of malignant nodules (84.1%, 143/170). The proportion of malignant nodules among visible nodules on DT was significantly higher (64.7%, 143/221) than that on CT (41.1%, 170/414) (p < 0.001). Area under the curve (AUC) values at the initial session were > 0.8, and the average detection rate for malignant nodules was 85% (210/246). The inter-session analysis of the AUC showed no significant differences among the readers, and the detection rate for malignant nodules did not differ across sessions. A slight improvement in reading times was observed. Most malignant nodules > 5 mm were visible on DT. As nodule detection performance was high from the initial session, DT may be readily applicable for radiology residents and board-certified radiologists.

  7. C. elegans GATA factors EGL-18 and ELT-6 function downstream of Wnt signaling to maintain the progenitor fate during larval asymmetric divisions of the seam cells.

    Science.gov (United States)

    Gorrepati, Lakshmi; Thompson, Kenneth W; Eisenmann, David M

    2013-05-01

    The C. elegans seam cells are lateral epithelial cells arrayed in a single line from anterior to posterior that divide in an asymmetric, stem cell-like manner during larval development. These asymmetric divisions are regulated by Wnt signaling; in most divisions, the posterior daughter in which the Wnt pathway is activated maintains the progenitor seam fate, while the anterior daughter in which the Wnt pathway is not activated adopts a differentiated hypodermal fate. Using mRNA tagging and microarray analysis, we identified the functionally redundant GATA factor genes egl-18 and elt-6 as Wnt pathway targets in the larval seam cells. EGL-18 and ELT-6 have previously been shown to be required for initial seam cell specification in the embryo. We show that in larval seam cell asymmetric divisions, EGL-18 is expressed strongly in the posterior seam-fated daughter. egl-18 and elt-6 are necessary for larval seam cell specification, and for hypodermal to seam cell fate transformations induced by ectopic Wnt pathway overactivation. The TCF homolog POP-1 binds a site in the egl-18 promoter in vitro, and this site is necessary for robust seam cell expression in vivo. Finally, larval overexpression of EGL-18 is sufficient to drive expression of a seam marker in other hypodermal cells in wild-type animals, and in anterior hypodermal-fated daughters in a Wnt pathway-sensitized background. These data suggest that two GATA factors that are required for seam cell specification in the embryo independently of Wnt signaling are reused downstream of Wnt signaling to maintain the progenitor fate during stem cell-like divisions in larval development.

  8. Enhancing Student Engagement and Active Learning through Just-in-Time Teaching and the Use of Powerpoint

    Science.gov (United States)

    Wanner, Thomas

    2015-01-01

    This instructional article is about an innovative teaching approach for enhancing student engagement and active learning in higher education through a combination of just-in-time teaching and the use of PowerPoint technology. The central component of this approach was students' pre-lecture preparation of a short PowerPoint presentation in which…

  9. Connections between Future Time Perspectives and Self-Regulated Learning for Mid-Year Engineering Students: A Multiple Case Study

    Science.gov (United States)

    Chasmar, Justine

    2017-01-01

    This dissertation presents multiple studies with the purpose of understanding the connections between undergraduate engineering students' motivations, specifically students' Future Time Perspectives (FTPs) and Self-Regulated Learning (SRL). FTP refers to the views students hold about the future and how their perceptions of current tasks are…

  10. Understanding the Association between Future Time Perspective and Self-Regulated Learning through the Lens of Self-Determination Theory

    Science.gov (United States)

    de Bilde, Jerissa; Vansteenkiste, Maarten; Lens, Willy

    2011-01-01

    The present cross-sectional research examined a process underlying the positive association between holding an extended future time perspective (FTP) and learning outcomes through the lens of self-determination theory. High school students and university students (N = 275) participated in the study. It was found that students with an extended FTP…

  11. Comprehension and Time Expended for a Doctoral Student with a Learning Disability when Reading with and without an Accommodation

    Science.gov (United States)

    Tanners, Adam; McDougall, Dennis; Skouge, Jim; Narkon, Drue

    2012-01-01

    The purpose of this alternating treatment, single-case research study was to compare reading comprehension and time expended reading, of a doctoral student with learning disabilities, under two reading conditions. In condition one, the student used a self-discovered accommodation, that is, listening, on an iPod, to an audiobook version…

  12. Reducing the Boundaries between the Community and the Academy with a Full-Time Service Learning Capstone

    Science.gov (United States)

    Ballard, Andy

    2013-01-01

    The purpose of this paper is to share my experiences as the instructor of a full-time, single semester, service-learning capstone course. In this innovative course students already volunteering in the Students in Free Enterprise (SIFE) organization work in teams to identify community needs and address them using their business skills and knowledge…

  13. The Effects of Textisms on Learning, Study Time, and Instructional Perceptions in an Online Artificial Intelligence Instructional Module

    Science.gov (United States)

    Beasley, Robert; Bryant, Nathan L.; Dodson, Phillip T.; Entwistle, Kevin C.

    2013-01-01

    The purpose of this study was to investigate the effects of textisms (i.e., abbreviated spellings, acronyms, and other shorthand notations) on learning, study time, and instructional perceptions in an online artificial intelligence instructional module. The independent variable in this investigation was experimental condition. For the control…

  14. Real-time tumor motion estimation using respiratory surrogate via memory-based learning

    Science.gov (United States)

    Li, Ruijiang; Lewis, John H.; Berbeco, Ross I.; Xing, Lei

    2012-08-01

    Respiratory tumor motion is a major challenge in radiation therapy for thoracic and abdominal cancers. Effective motion management requires an accurate knowledge of the real-time tumor motion. External respiration monitoring devices (optical, etc) provide a noninvasive, non-ionizing, low-cost and practical approach to obtain the respiratory signal. Due to the highly complex and nonlinear relations between tumor and surrogate motion, its ultimate success hinges on the ability to accurately infer the tumor motion from respiratory surrogates. Given their widespread use in the clinic, such a method is critically needed. We propose to use a powerful memory-based learning method to find the complex relations between tumor motion and respiratory surrogates. The method first stores the training data in memory and then finds relevant data to answer a particular query. Nearby data points are assigned high relevance (or weights) and conversely distant data are assigned low relevance. By fitting relatively simple models to local patches instead of fitting one single global model, it is able to capture highly nonlinear and complex relations between the internal tumor motion and external surrogates accurately. Due to the local nature of weighting functions, the method is inherently robust to outliers in the training data. Moreover, both training and adapting to new data are performed almost instantaneously with memory-based learning, making it suitable for dynamically following variable internal/external relations. We evaluated the method using respiratory motion data from 11 patients. The data set consists of simultaneous measurement of 3D tumor motion and 1D abdominal surface (used as the surrogate signal in this study). There are a total of 171 respiratory traces, with an average peak-to-peak amplitude of ∼15 mm and average duration of ∼115 s per trace. Given only 5 s (roughly one breath) pretreatment training data, the method achieved an average 3D error of 1.5 mm and 95

  15. Real-time tumor motion estimation using respiratory surrogate via memory-based learning

    International Nuclear Information System (INIS)

    Li Ruijiang; Xing Lei; Lewis, John H; Berbeco, Ross I

    2012-01-01

    Respiratory tumor motion is a major challenge in radiation therapy for thoracic and abdominal cancers. Effective motion management requires an accurate knowledge of the real-time tumor motion. External respiration monitoring devices (optical, etc) provide a noninvasive, non-ionizing, low-cost and practical approach to obtain the respiratory signal. Due to the highly complex and nonlinear relations between tumor and surrogate motion, its ultimate success hinges on the ability to accurately infer the tumor motion from respiratory surrogates. Given their widespread use in the clinic, such a method is critically needed. We propose to use a powerful memory-based learning method to find the complex relations between tumor motion and respiratory surrogates. The method first stores the training data in memory and then finds relevant data to answer a particular query. Nearby data points are assigned high relevance (or weights) and conversely distant data are assigned low relevance. By fitting relatively simple models to local patches instead of fitting one single global model, it is able to capture highly nonlinear and complex relations between the internal tumor motion and external surrogates accurately. Due to the local nature of weighting functions, the method is inherently robust to outliers in the training data. Moreover, both training and adapting to new data are performed almost instantaneously with memory-based learning, making it suitable for dynamically following variable internal/external relations. We evaluated the method using respiratory motion data from 11 patients. The data set consists of simultaneous measurement of 3D tumor motion and 1D abdominal surface (used as the surrogate signal in this study). There are a total of 171 respiratory traces, with an average peak-to-peak amplitude of ∼15 mm and average duration of ∼115 s per trace. Given only 5 s (roughly one breath) pretreatment training data, the method achieved an average 3D error of 1.5 mm and 95

  16. Ecologia: The Assumptions, Expectations, and Strategies of Modern Language Students Working in a Self-Access Learning Environment for the First Time.

    Science.gov (United States)

    Piper, Alison

    1994-01-01

    This study examined 29 second-year undergraduate students of Spanish using a self-access learning environment for the first time, focusing on their language attitudes and learning strategies. The results show that, even as modern languages majors, the students possessed a model of language and strategies for learning that were significantly…

  17. Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

    Directory of Open Access Journals (Sweden)

    Zehui Kong

    Full Text Available To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM of power-request is derived. The reinforcement learning (RL is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

  18. Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

    Science.gov (United States)

    Kong, Zehui; Zou, Yuan; Liu, Teng

    2017-01-01

    To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

  19. Mastery learning: it is time for medical education to join the 21st century.

    Science.gov (United States)

    McGaghie, William C

    2015-11-01

    Clinical medical education in the 21st century is grounded in a 19th-century model that relies on longitudinal exposure to patients as the curriculum focus. The assumption is that medical students and postgraduate residents will learn from experience, that vicarious or direct involvement in patient care is the best teacher. The weight of evidence shows, however, that results from such traditional clinical education are uneven at best. Educational inertia endorsed until recently by medical school accreditation policies has maintained the clinical medical education status quo for decades.Mastery learning is a new paradigm for medical education. Basic principles of mastery learning are that educational excellence is expected and can be achieved by all learners and that little or no variation in measured outcomes will result. This Commentary describes the origins of mastery learning and presents its essential features. The Commentary then introduces the eight reports that comprise the mastery learning cluster for this issue of Academic Medicine. The reports are intended to help medical educators recognize advantages of the mastery model and begin to implement mastery learning at their own institutions. The Commentary concludes with brief statements about future directions for mastery learning program development and research in medical education.

  20. Timing matters: The impact of immediate and delayed feedback on artificial language learning

    Directory of Open Access Journals (Sweden)

    Bertram Opitz

    2011-02-01

    Full Text Available In the present experiment, we used event-related potentials (ERP to investigate the role of immediate and delayed feedback in an artificial grammar learning task. Two groups of participants were engaged in classifying non-word strings according to an underlying rule system, not known to the participants. Visual feedback was provided after each classification either immediately or with a short delay of one second. Both groups were able to learn the artificial grammar system as indicated by an increase in classification performance. However, the gain in performance was significantly larger for the group receiving immediate feedback as compared to the group receiving delayed feedback. Learning was accompanied by an increase in P300 activity in the ERP for delayed as compared to immediate feedback. Irrespective of feedback delay, both groups exhibited learning related decreases in the feedback-related positivity (FRP elicited by positive feedback only. The feedback-related negativity (FRN, however, remained constant over the course of learning. These results suggest, first, that delayed feedback is less effective for artificial grammar learning as task requirements are very demanding, and second, that the FRP elicited by positive prediction errors decreases with learning while the FRN to negative prediction errors is elicited in an all-or-none fashion by negative feedback throughout the entire experiment.