Recent activities of the Seismology Division Early Career Representative(s)

Science.gov (United States)

Agius, Matthew; Van Noten, Koen; Ermert, Laura; Mai, P. Martin; Krawczyk, CharLotte

2016-04-01

The European Geosciences Union is a bottom-up-organisation, in which its members are represented by their respective scientific divisions, committees and council. In recent years, EGU has embarked on a mission to reach out for its numerous 'younger' members by giving awards to outstanding young scientists and the setting up of Early Career Scientists (ECS) representatives. The division representative's role is to engage in discussions that concern students and early career scientists. Several meetings between all the division representatives are held throughout the year to discuss ideas and Union-wide issues. One important impact ECS representatives have had on EGU is the increased number of short courses and workshops run by ECS during the annual General Assembly. Another important contribution of ECS representatives was redefining 'Young Scientist' to 'Early Career Scientist', which avoids discrimination due to age. Since 2014, the Seismology Division has its own ECS representative. In an effort to more effectively reach out for young seismologists, a blog and a social media page dedicated to seismology have been set up online. With this dedicated blog, we'd like to give more depth to the average browsing experience by enabling young researchers to explore various seismology topics in one place while making the field more exciting and accessible to the broader community. These pages are used to promote the latest research especially of young seismologists and to share interesting seismo-news. Over the months the pages proved to be popular, with hundreds of views every week and an increased number of followers. An online survey was conducted to learn more about the activities and needs of early career seismologists. We present the results from this survey, and the work that has been carried out over the last two years, including detail of what has been achieved so far, and what we would like the ECS representation for Seismology to achieve. Young seismologists are

Representing Uncertainty on Model Analysis Plots

Science.gov (United States)

Smith, Trevor I.

2016-01-01

Model analysis provides a mechanism for representing student learning as measured by standard multiple-choice surveys. The model plot contains information regarding both how likely students in a particular class are to choose the correct answer and how likely they are to choose an answer consistent with a well-documented conceptual model.…

Recently learned foreign abstract and concrete nouns are represented in distinct cortical networks similar to the native language.

Science.gov (United States)

Mayer, Katja M; Macedonia, Manuela; von Kriegstein, Katharina

2017-09-01

In the native language, abstract and concrete nouns are represented in distinct areas of the cerebral cortex. Currently, it is unknown whether this is also the case for abstract and concrete nouns of a foreign language. Here, we taught adult native speakers of German 45 abstract and 45 concrete nouns of a foreign language. After learning the nouns for 5 days, participants performed a vocabulary translation task during functional magnetic resonance imaging. Translating abstract nouns in contrast to concrete nouns elicited responses in regions that are also responsive to abstract nouns in the native language: the left inferior frontal gyrus and the left middle and superior temporal gyri. Concrete nouns elicited larger responses in the angular gyri bilaterally and the left parahippocampal gyrus than abstract nouns. The cluster in the left angular gyrus showed psychophysiological interaction (PPI) with the left lingual gyrus. The left parahippocampal gyrus showed PPI with the posterior cingulate cortex. Similar regions have been previously found for concrete nouns in the native language. The results reveal similarities in the cortical representation of foreign language nouns with the representation of native language nouns that already occur after 5 days of vocabulary learning. Furthermore, we showed that verbal and enriched learning methods were equally suitable to teach foreign abstract and concrete nouns. Hum Brain Mapp 38:4398-4412, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Learning Opportunities for Group Learning

Science.gov (United States)

Gil, Alfonso J.; Mataveli, Mara

2017-01-01

Purpose: This paper aims to analyse the impact of organizational learning culture and learning facilitators in group learning. Design/methodology/approach: This study was conducted using a survey method applied to a statistically representative sample of employees from Rioja wine companies in Spain. A model was tested using a structural equation…

Improving Collaborative Learning in the Classroom: Text Mining Based Grouping and Representing

Science.gov (United States)

Erkens, Melanie; Bodemer, Daniel; Hoppe, H. Ulrich

2016-01-01

Orchestrating collaborative learning in the classroom involves tasks such as forming learning groups with heterogeneous knowledge and making learners aware of the knowledge differences. However, gathering information on which the formation of appropriate groups and the creation of graphical knowledge representations can be based is very effortful…

Representing uncertainty on model analysis plots

Directory of Open Access Journals (Sweden)

Trevor I. Smith

2016-09-01

Full Text Available Model analysis provides a mechanism for representing student learning as measured by standard multiple-choice surveys. The model plot contains information regarding both how likely students in a particular class are to choose the correct answer and how likely they are to choose an answer consistent with a well-documented conceptual model. Unfortunately, Bao’s original presentation of the model plot did not include a way to represent uncertainty in these measurements. I present details of a method to add error bars to model plots by expanding the work of Sommer and Lindell. I also provide a template for generating model plots with error bars.

U.S. EPA, Pesticide Product Label, B-NINE SP, 10/17/1973

Science.gov (United States)

2011-04-14

l APPLICATION NOTES , I _·t .tdel dddd.tl'ldl "'\\I'UlrHI :1/"'11 " ~ ... I': 1I1~~P(! I( .tt.,.., 1 f !IHJI' dt·~ " ,I 'I"tl "f It ... I\\:I· .... I,IV rt·~,Jll. Ht···: tt· .... ...

Active Learning Using Hint Information.

Science.gov (United States)

Li, Chun-Liang; Ferng, Chun-Sung; Lin, Hsuan-Tien

2015-08-01

The abundance of real-world data and limited labeling budget calls for active learning, an important learning paradigm for reducing human labeling efforts. Many recently developed active learning algorithms consider both uncertainty and representativeness when making querying decisions. However, exploiting representativeness with uncertainty concurrently usually requires tackling sophisticated and challenging learning tasks, such as clustering. In this letter, we propose a new active learning framework, called hinted sampling, which takes both uncertainty and representativeness into account in a simpler way. We design a novel active learning algorithm within the hinted sampling framework with an extended support vector machine. Experimental results validate that the novel active learning algorithm can result in a better and more stable performance than that achieved by state-of-the-art algorithms. We also show that the hinted sampling framework allows improving another active learning algorithm designed from the transductive support vector machine.

Reference: 163 [Arabidopsis Phenome Database[Archive

Lifescience Database Archive (English)

Full Text Available 163 http://metadb.riken.jp/db/SciNetS_ria224i/cria224u4ria224u15660207i Kanter Ulr...saccharides. 2 243-54 15660207 2005 May Planta Guerineau Fran巽ois|Kanter Ulrike|Li Yong|Pauly Markus|Tenhaken Raimund|Usadel Bj旦rn

Learning

Directory of Open Access Journals (Sweden)

Mohsen Laabidi

2014-01-01

Full Text Available Nowadays learning technologies transformed educational systems with impressive progress of Information and Communication Technologies (ICT. Furthermore, when these technologies are available, affordable and accessible, they represent more than a transformation for people with disabilities. They represent real opportunities with access to an inclusive education and help to overcome the obstacles they met in classical educational systems. In this paper, we will cover basic concepts of e-accessibility, universal design and assistive technologies, with a special focus on accessible e-learning systems. Then, we will present recent research works conducted in our research Laboratory LaTICE toward the development of an accessible online learning environment for persons with disabilities from the design and specification step to the implementation. We will present, in particular, the accessible version “MoodleAcc+” of the well known e-learning platform Moodle as well as new elaborated generic models and a range of tools for authoring and evaluating accessible educational content.

Student Attitudes toward Learning Analytics in Higher Education: "The Fitbit Version of the Learning World".

Science.gov (United States)

Roberts, Lynne D; Howell, Joel A; Seaman, Kristen; Gibson, David C

2016-01-01

Increasingly, higher education institutions are exploring the potential of learning analytics to predict student retention, understand learning behaviors, and improve student learning through providing personalized feedback and support. The technical development of learning analytics has outpaced consideration of ethical issues surrounding their use. Of particular concern is the absence of the student voice in decision-making about learning analytics. We explored higher education students' knowledge, attitudes, and concerns about big data and learning analytics through four focus groups ( N = 41). Thematic analysis of the focus group transcripts identified six key themes. The first theme, "Uninformed and Uncertain," represents students' lack of knowledge about learning analytics prior to the focus groups. Following the provision of information, viewing of videos and discussion of learning analytics scenarios three further themes; "Help or Hindrance to Learning," "More than a Number," and "Impeding Independence"; represented students' perceptions of the likely impact of learning analytics on their learning. "Driving Inequality" and "Where Will it Stop?" represent ethical concerns raised by the students about the potential for inequity, bias and invasion of privacy and the need for informed consent. A key tension to emerge was how "personal" vs. "collective" purposes or principles can intersect with "uniform" vs. "autonomous" activity. The findings highlight the need the need to engage students in the decision making process about learning analytics.

What can action learning offer a beleaguered system? A narrative representing the relationship.

Science.gov (United States)

Traeger, James

2017-05-02

Purpose This is an attempt to write an account of action learning that is as close to the ground on which it was practised as the author can make it. In that sense, the reader can read what follows below as a kind of autoethnography, a "representation as relationship" as Gergen and Gergen (2002, p. 11) call it. This is because in the opportunity of telling a story about his practice as an action learning facilitator, the author hopes to evoke that which is more akin to the contactful environment of quality action learning than any amount of abstract theorising. Design/methodology/approach This is an example of "narrative inquiry", best judged, according to Sparkes (2002), in terms of the ability of such accounts to "contribute to sociological understanding in ways that, amongst others are self-knowing, self-respecting, self-sacrificing and self-luminous". Findings As the author re-tells this partial account, he has a sense of the massive wider structures around him, but all he can see in his dim lamp is the fleeting glimpse of the local strata. The author traces his hand along the seams, not intending to dig them out, but simply to witness them, or even, in a spirit of yearning, to give them a witnessing of themselves. Originality/value To the author, this is about portraying what action learning feels like, rather than thinks like, for his own and for the benefit of other practitioners.

Fusion of deep learning architectures, multilayer feedforward networks and learning vector quantizers for deep classification learning

NARCIS (Netherlands)

Villmann, T.; Biehl, M.; Villmann, A.; Saralajew, S.

2017-01-01

The advantage of prototype based learning vector quantizers are the intuitive and simple model adaptation as well as the easy interpretability of the prototypes as class representatives for the class distribution to be learned. Although they frequently yield competitive performance and show robust

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning

Directory of Open Access Journals (Sweden)

Yuntian Feng

2017-01-01

Full Text Available We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q-Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score.

Joint Extraction of Entities and Relations Using Reinforcement Learning and Deep Learning.

Science.gov (United States)

Feng, Yuntian; Zhang, Hongjun; Hao, Wenning; Chen, Gang

2017-01-01

We use both reinforcement learning and deep learning to simultaneously extract entities and relations from unstructured texts. For reinforcement learning, we model the task as a two-step decision process. Deep learning is used to automatically capture the most important information from unstructured texts, which represent the state in the decision process. By designing the reward function per step, our proposed method can pass the information of entity extraction to relation extraction and obtain feedback in order to extract entities and relations simultaneously. Firstly, we use bidirectional LSTM to model the context information, which realizes preliminary entity extraction. On the basis of the extraction results, attention based method can represent the sentences that include target entity pair to generate the initial state in the decision process. Then we use Tree-LSTM to represent relation mentions to generate the transition state in the decision process. Finally, we employ Q -Learning algorithm to get control policy π in the two-step decision process. Experiments on ACE2005 demonstrate that our method attains better performance than the state-of-the-art method and gets a 2.4% increase in recall-score.

Science Integrating Learning Objectives: A Cooperative Learning Group Process

Science.gov (United States)

Spindler, Matt

2015-01-01

The integration of agricultural and science curricular content that capitalizes on natural and inherent connections represents a challenge for secondary agricultural educators. The purpose of this case study was to create information about the employment of Cooperative Learning Groups (CLG) to enhance the science integrating learning objectives…

Student Attitudes toward Learning Analytics in Higher Education: “The Fitbit Version of the Learning World”

Science.gov (United States)

Roberts, Lynne D.; Howell, Joel A.; Seaman, Kristen; Gibson, David C.

2016-01-01

Increasingly, higher education institutions are exploring the potential of learning analytics to predict student retention, understand learning behaviors, and improve student learning through providing personalized feedback and support. The technical development of learning analytics has outpaced consideration of ethical issues surrounding their use. Of particular concern is the absence of the student voice in decision-making about learning analytics. We explored higher education students' knowledge, attitudes, and concerns about big data and learning analytics through four focus groups (N = 41). Thematic analysis of the focus group transcripts identified six key themes. The first theme, “Uninformed and Uncertain,” represents students' lack of knowledge about learning analytics prior to the focus groups. Following the provision of information, viewing of videos and discussion of learning analytics scenarios three further themes; “Help or Hindrance to Learning,” “More than a Number,” and “Impeding Independence”; represented students' perceptions of the likely impact of learning analytics on their learning. “Driving Inequality” and “Where Will it Stop?” represent ethical concerns raised by the students about the potential for inequity, bias and invasion of privacy and the need for informed consent. A key tension to emerge was how “personal” vs. “collective” purposes or principles can intersect with “uniform” vs. “autonomous” activity. The findings highlight the need the need to engage students in the decision making process about learning analytics. PMID:28066285

Machine learning in healthcare informatics

CERN Document Server

Acharya, U; Dua, Prerna

2014-01-01

The book is a unique effort to represent a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. The book provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity and the depth and breath of this multi-disciplinary area. The integrated, panoramic view of data and machine learning techniques can provide an opportunity for novel clinical insights and discoveries.

Team-Based Learning in a Pipeline Course in Medical Microbiology for Under-Represented Student Populations in Medicine Improves Learning of Microbiology Concepts.

Science.gov (United States)

Behling, K C; Murphy, M M; Mitchell-Williams, J; Rogers-McQuade, H; Lopez, O J

2016-12-01

As part of an undergraduate pipeline program at our institution for students from underrepresented minorities in medicine backgrounds, we created an intensive four-week medical microbiology course. Team-based learning (TBL) was implemented in this course to enhance student learning of course content. Three different student cohorts participated in the study, and there were no significant differences in their prior academic achievement based on their undergraduate grade point average (GPA) and pre-course examination scores. Teaching techniques included engaged lectures using an audience response system, TBL, and guided self-directed learning. We hypothesized that more active learning exercises, irrespective of the amount of lecture time, would help students master course content. In year 2 as compared with year 1, TBL exercises were decreased from six to three with a concomitant increase in lecture time, while in year 3, TBL exercises were increased from three to six while maintaining the same amount of lecture time as in year 2. As we hypothesized, there was significant ( p < 0.01) improvement in performance on the post-course examination in years 1 and 3 compared with year 2, when only three TBL exercises were used. In contrast to the students' perceptions that more lecture time enhances learning of course content, our findings suggest that active learning strategies, such as TBL, are more effective than engaged lectures in improving student understanding of course content, as measured by post-course examination performance. Introduction of TBL in pipeline program courses may help achieve better student learning outcomes.

Team-Based Learning in a Pipeline Course in Medical Microbiology for Under-Represented Student Populations in Medicine Improves Learning of Microbiology Concepts

Directory of Open Access Journals (Sweden)

Kathryn C. Behling

2016-12-01

Full Text Available As part of an undergraduate pipeline program at our institution for students from underrepresented minorities in medicine backgrounds, we created an intensive four-week medical microbiology course. Team-based learning (TBL was implemented in this course to enhance student learning of course content. Three different student cohorts participated in the study, and there were no significant differences in their prior academic achievement based on their undergraduate grade point average (GPA and pre-course examination scores. Teaching techniques included engaged lectures using an audience response system, TBL, and guided self-directed learning. We hypothesized that more active learning exercises, irrespective of the amount of lecture time, would help students master course content. In year 2 as compared with year 1, TBL exercises were decreased from six to three with a concomitant increase in lecture time, while in year 3, TBL exercises were increased from three to six while maintaining the same amount of lecture time as in year 2. As we hypothesized, there was significant (p < 0.01 improvement in performance on the post-course examination in years 1 and 3 compared with year 2, when only three TBL exercises were used. In contrast to the students’ perceptions that more lecture time enhances learning of course content, our findings suggest that active learning strategies, such as TBL, are more effective than engaged lectures in improving student understanding of course content, as measured by post-course examination performance. Introduction of TBL in pipeline program courses may help achieve better student learning outcomes.

Learning causes reorganization of neuronal firing patterns to represent related experiences within a hippocampal schema.

Science.gov (United States)

McKenzie, Sam; Robinson, Nick T M; Herrera, Lauren; Churchill, Jordana C; Eichenbaum, Howard

2013-06-19

According to schema theory as proposed by Piaget and Bartlett, learning involves the assimilation of new memories into networks of preexisting knowledge, as well as alteration of the original networks to accommodate the new information. Recent evidence has shown that rats form a schema of goal locations and that the hippocampus plays an essential role in adding new memories to the spatial schema. Here we examined the nature of hippocampal contributions to schema updating by monitoring firing patterns of multiple CA1 neurons as rats learned new goal locations in an environment in which there already were multiple goals. Before new learning, many neurons that fired on arrival at one goal location also fired at other goals, whereas ensemble activity patterns also distinguished different goal events, thus constituting a neural representation that linked distinct goals within a spatial schema. During new learning, some neurons began to fire as animals approached the new goals. These were primarily the same neurons that fired at original goals, the activity patterns at new goals were similar to those associated with the original goals, and new learning also produced changes in the preexisting goal-related firing patterns. After learning, activity patterns associated with the new and original goals gradually diverged, such that initial generalization was followed by a prolonged period in which new memories became distinguished within the ensemble representation. These findings support the view that consolidation involves assimilation of new memories into preexisting neural networks that accommodate relationships among new and existing memories.

Student Attitudes toward Learning Analytics in Higher Education: “the fitbit version of the learning world”

Directory of Open Access Journals (Sweden)

Lynne D. Roberts

2016-12-01

Full Text Available Increasingly, higher education institutions are exploring the potential of learning analytics to predict student retention, understand learning behaviours, and improve student learning through providing personalised feedback and support. The technical development of learning analytics has outpaced consideration of ethical issues surrounding their use. Of particular concern is the absence of the student voice in decision-making about learning analytics. We explored higher education students’ knowledge, attitudes and concerns about big data and learning analytics through four focus groups (N=41. Thematic analysis of the focus group transcripts identified six key themes. The first theme, ‘Uninformed and Uncertain’, represents students’ lack of knowledge about learning analytics prior to the focus groups. Following the provision of information, viewing of videos and discussion of learning analytics scenarios three further themes; ‘Help or Hindrance to Learning’, ‘More than a Number’, and ‘Impeding Independence’; represented students’ perceptions of the likely impact of learning analytics on their learning. ‘Driving Inequality’ and ‘Where Will it Stop? represent ethical concerns raised by the students about the potential for inequity, bias and invasion of privacy and the need for informed consent. A key tension to emerge was how ‘personal’ versus ‘collective’ purposes or principles can intersect with ‘uniform’ versus ‘autonomous’ activity. The findings highlight the need the need to engage students in the decision making process about learning analytics.

Comparison of combinatorial clustering methods on pharmacological data sets represented by machine learning-selected real molecular descriptors.

Science.gov (United States)

Rivera-Borroto, Oscar Miguel; Marrero-Ponce, Yovani; García-de la Vega, José Manuel; Grau-Ábalo, Ricardo del Corazón

2011-12-27

Cluster algorithms play an important role in diversity related tasks of modern chemoinformatics, with the widest applications being in pharmaceutical industry drug discovery programs. The performance of these grouping strategies depends on various factors such as molecular representation, mathematical method, algorithmical technique, and statistical distribution of data. For this reason, introduction and comparison of new methods are necessary in order to find the model that best fits the problem at hand. Earlier comparative studies report on Ward's algorithm using fingerprints for molecular description as generally superior in this field. However, problems still remain, i.e., other types of numerical descriptions have been little exploited, current descriptors selection strategy is trial and error-driven, and no previous comparative studies considering a broader domain of the combinatorial methods in grouping chemoinformatic data sets have been conducted. In this work, a comparison between combinatorial methods is performed,with five of them being novel in cheminformatics. The experiments are carried out using eight data sets that are well established and validated in the medical chemistry literature. Each drug data set was represented by real molecular descriptors selected by machine learning techniques, which are consistent with the neighborhood principle. Statistical analysis of the results demonstrates that pharmacological activities of the eight data sets can be modeled with a few of families with 2D and 3D molecular descriptors, avoiding classification problems associated with the presence of nonrelevant features. Three out of five of the proposed cluster algorithms show superior performance over most classical algorithms and are similar (or slightly superior in the most optimistic sense) to Ward's algorithm. The usefulness of these algorithms is also assessed in a comparative experiment to potent QSAR and machine learning classifiers, where they perform

Including everyone: A peer learning program that works for under-represented minorities?

Directory of Open Access Journals (Sweden)

Jacques van der Meer

2013-04-01

Full Text Available Peer learning has long been recognised as an effective way to induct first-year students into the academic skills required to succeed at university. One recognised successful model that has been extensively researched is the Supplemental Instruction (SI model; it has operated in the US since the mid-1970s. This model is commonly known in Australasia as the Peer Assisted Study Sessions (PASS program. Although there is a considerable amount of research into SI and PASS, very little has been published about the impact of peer learning on different student groups, for example indigenous and other ethnic groups. This article reports on the results from one New Zealand university of the effectiveness of PASS for Māori and Pasifika students. The questions this article seeks to address are whether attendance of the PASS program results in better final marks for these two groups of students, and whether the number of sessions attended has an impact on the final marks.

Using standardized patients versus video cases for representing clinical problems in problem-based learning.

Science.gov (United States)

Yoon, Bo Young; Choi, Ikseon; Choi, Seokjin; Kim, Tae-Hee; Roh, Hyerin; Rhee, Byoung Doo; Lee, Jong-Tae

2016-06-01

The quality of problem representation is critical for developing students' problem-solving abilities in problem-based learning (PBL). This study investigates preclinical students' experience with standardized patients (SPs) as a problem representation method compared to using video cases in PBL. A cohort of 99 second-year preclinical students from Inje University College of Medicine (IUCM) responded to a Likert scale questionnaire on their learning experiences after they had experienced both video cases and SPs in PBL. The questionnaire consisted of 14 items with eight subcategories: problem identification, hypothesis generation, motivation, collaborative learning, reflective thinking, authenticity, patient-doctor communication, and attitude toward patients. The results reveal that using SPs led to the preclinical students having significantly positive experiences in boosting patient-doctor communication skills; the perceived authenticity of their clinical situations; development of proper attitudes toward patients; and motivation, reflective thinking, and collaborative learning when compared to using video cases. The SPs also provided more challenges than the video cases during problem identification and hypotheses generation. SPs are more effective than video cases in delivering higher levels of authenticity in clinical problems for PBL. The interaction with SPs engages preclinical students in deeper thinking and discussion; growth of communication skills; development of proper attitudes toward patients; and motivation. Considering the higher cost of SPs compared with video cases, SPs could be used most advantageously during the preclinical period in the IUCM curriculum.

Social learning through prediction error in the brain

Science.gov (United States)

Joiner, Jessica; Piva, Matthew; Turrin, Courtney; Chang, Steve W. C.

2017-06-01

Learning about the world is critical to survival and success. In social animals, learning about others is a necessary component of navigating the social world, ultimately contributing to increasing evolutionary fitness. How humans and nonhuman animals represent the internal states and experiences of others has long been a subject of intense interest in the developmental psychology tradition, and, more recently, in studies of learning and decision making involving self and other. In this review, we explore how psychology conceptualizes the process of representing others, and how neuroscience has uncovered correlates of reinforcement learning signals to explore the neural mechanisms underlying social learning from the perspective of representing reward-related information about self and other. In particular, we discuss self-referenced and other-referenced types of reward prediction errors across multiple brain structures that effectively allow reinforcement learning algorithms to mediate social learning. Prediction-based computational principles in the brain may be strikingly conserved between self-referenced and other-referenced information.

Interactive learning environments to support independent learning: the impact of discernability of embedded support devices

NARCIS (Netherlands)

Martens, Rob; Valcke, Martin; Portier, Stanley

2017-01-01

In this article the effectivity of prototypes of interactive learning environments (ILE) is investigated. These computer-based environments are used for independent learning. In the learning materials, represented in the prototypes, a clear distinction is made between the basic content and embedded

Machine learning methods for planning

CERN Document Server

Minton, Steven

1993-01-01

Machine Learning Methods for Planning provides information pertinent to learning methods for planning and scheduling. This book covers a wide variety of learning methods and learning architectures, including analogical, case-based, decision-tree, explanation-based, and reinforcement learning.Organized into 15 chapters, this book begins with an overview of planning and scheduling and describes some representative learning systems that have been developed for these tasks. This text then describes a learning apprentice for calendar management. Other chapters consider the problem of temporal credi

Conservationism is not Conservatism: Do Interest Group Endorsements Help Voters Hold Representatives Accountable?

OpenAIRE

Kaufman, Aaron

2013-01-01

Much research assumes that voters know or can learn the positions their representatives take on key issue. Arthur Lupia found that voters could learn such information through advertisements and interest group endorsements. We examine whether these cues improve voters’ ability to infer their representative’s voting behavior and find that most interest groups fail to do so. In a follow-up study, we find that voters are ignorant of which positions the interest groups take on issues. Finally, we ...

Learning in Artificial Neural Systems

Science.gov (United States)

Matheus, Christopher J.; Hohensee, William E.

1987-01-01

This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

TIGA Tide Gauge Data Reprocessing at GFZ

Science.gov (United States)

Deng, Zhiguo; Schöne, Tilo; Gendt, Gerd

2014-05-01

To analyse the tide gauge measurements for the purpose of global long-term sea level change research a well-defined absolute reference frame is required by oceanographic community. To create such frame the data from a global GNSS network located at or near tide gauges are processed. For analyzing the GNSS data on a preferably continuous basis the International GNSS Service (IGS) Tide Gauge Benchmark Monitoring Working Group (TIGA-WG) is responsible. As one of the TIGA Analysis Centers the German Research Centre for Geosciences (GFZ) is contributing to the IGS TIGA Reprocessing Campaign. The solutions of the TIGA Reprocessing Campaign will also contribute to 2nd IGS Data Reprocessing Campaign with GFZ IGS reprocessing solution. After the first IGS reprocessing finished in 2010 some improvements were implemented into the latest GFZ software version EPOS.P8: reference frame IGb08 based on ITRF2008, antenna calibration igs08.atx, geopotential model (EGM2008), higher-order ionospheric effects, new a priori meteorological model (GPT2), VMF mapping function, and other minor improvements. GPS data of the globally distributed tracking network of 794 stations for the time span from 1994 until end of 2012 are used for the TIGA reprocessing. To handle such large network a new processing strategy is developed and described in detail. In the TIGA reprocessing the GPS@TIGA data are processed in precise point positioning (PPP) mode to clean data using the IGS reprocessing orbit and clock products. To validate the quality of the PPP coordinate results the rates of 80 GPS@TIGA station vertical movement are estimated from the PPP results using Maximum Likelihood Estimation (MLE) method. The rates are compared with the solution of University of LaRochelle Consortium (ULR) (named ULR5). 56 of the 80 stations have a difference of the vertical velocities below 1 mm/yr. The error bars of PPP rates are significant larger than those of ULR5, which indicates large time correlated noise in

Heuristic Evaluation of E-Learning Courses: A Comparative Analysis of Two E-Learning Heuristic Sets

Science.gov (United States)

Zaharias, Panagiotis; Koutsabasis, Panayiotis

2012-01-01

Purpose: The purpose of this paper is to discuss heuristic evaluation as a method for evaluating e-learning courses and applications and more specifically to investigate the applicability and empirical use of two customized e-learning heuristic protocols. Design/methodology/approach: Two representative e-learning heuristic protocols were chosen…

Learning representative features for facial images based on a modified principal component analysis

Science.gov (United States)

Averkin, Anton; Potapov, Alexey

2013-05-01

The paper is devoted to facial image analysis and particularly deals with the problem of automatic evaluation of the attractiveness of human faces. We propose a new approach for automatic construction of feature space based on a modified principal component analysis. Input data sets for the algorithm are the learning data sets of facial images, which are rated by one person. The proposed approach allows one to extract features of the individual subjective face beauty perception and to predict attractiveness values for new facial images, which were not included into a learning data set. The Pearson correlation coefficient between values predicted by our method for new facial images and personal attractiveness estimation values equals to 0.89. This means that the new approach proposed is promising and can be used for predicting subjective face attractiveness values in real systems of the facial images analysis.

Collective Machine Learning: Team Learning and Classification in Multi-Agent Systems

Science.gov (United States)

Gifford, Christopher M.

2009-01-01

This dissertation focuses on the collaboration of multiple heterogeneous, intelligent agents (hardware or software) which collaborate to learn a task and are capable of sharing knowledge. The concept of collaborative learning in multi-agent and multi-robot systems is largely under studied, and represents an area where further research is needed to…

Using Technology to Support Visual Learning Strategies

Science.gov (United States)

O'Bannon, Blanche; Puckett, Kathleen; Rakes, Glenda

2006-01-01

Visual learning is a strategy for visually representing the structure of information and for representing the ways in which concepts are related. Based on the work of Ausubel, these hierarchical maps facilitate student learning of unfamiliar information in the K-12 classroom. This paper presents the research base for this Type II computer tool, as…

Machine learning, social learning and the governance of self-driving cars.

Science.gov (United States)

Stilgoe, Jack

2018-02-01

Self-driving cars, a quintessentially 'smart' technology, are not born smart. The algorithms that control their movements are learning as the technology emerges. Self-driving cars represent a high-stakes test of the powers of machine learning, as well as a test case for social learning in technology governance. Society is learning about the technology while the technology learns about society. Understanding and governing the politics of this technology means asking 'Who is learning, what are they learning and how are they learning?' Focusing on the successes and failures of social learning around the much-publicized crash of a Tesla Model S in 2016, I argue that trajectories and rhetorics of machine learning in transport pose a substantial governance challenge. 'Self-driving' or 'autonomous' cars are misnamed. As with other technologies, they are shaped by assumptions about social needs, solvable problems, and economic opportunities. Governing these technologies in the public interest means improving social learning by constructively engaging with the contingencies of machine learning.

E-learning and learning-E: reflections on training

Directory of Open Access Journals (Sweden)

Chiara Panciroli

2008-07-01

Full Text Available The increase of the traditional limits of education towards dinamic teaching and learning enviroments, of a strongly constructive nature, is strictly related with an always increasing request of knowledge elements by a part of society who made the cognitive dimension one of the development challenge. Telematic technologies, in particular those of e-learning, represents one of the possible interpretation that in this paper are going to be analysed with a problematicistic approach.

A New Learning Model on Physical Education: 5E Learning Cycle

Science.gov (United States)

Senturk, Halil Evren; Camliyer, Huseyin

2016-01-01

Many fields of education at the moment, especially in physical and technological educations, use 5E learning cycle. The process is defined as five "E"s. These represent the verbs engage, explore, explain, elaborate and evaluate. The literature has been systematically reviewed and the results show that the 5E learning cycle is an untested…

Machine learning an artificial intelligence approach

CERN Document Server

Banerjee, R; Bradshaw, Gary; Carbonell, Jaime Guillermo; Mitchell, Tom Michael; Michalski, Ryszard Spencer

1983-01-01

Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs-particularly programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV a

e-Learning Resource Brokers

NARCIS (Netherlands)

Retalis, Symeon; Papasalouros, Andreas; Avgeriou, Paris; Siassiakos, Kostas

2004-01-01

There is an exponentially increasing demand for provisioning of high-quality learning resources, which is not satisfied by current web technologies and systems. E-Learning Resource Brokers are a potential solution to this problem, as they represent the state-of-the-art in facilitating the exchange

Conversational Agents in E-Learning

Science.gov (United States)

Kerry, Alice; Ellis, Richard; Bull, Susan

This paper discusses the use of natural language or 'conversational' agents in e-learning environments. We describe and contrast the various applications of conversational agent technology represented in the e-learning literature, including tutors, learning companions, language practice and systems to encourage reflection. We offer two more detailed examples of conversational agents, one which provides learning support, and the other support for self-assessment. Issues and challenges for developers of conversational agent systems for e-learning are identified and discussed.

Open University Learning Analytics dataset.

Science.gov (United States)

Kuzilek, Jakub; Hlosta, Martin; Zdrahal, Zdenek

2017-11-28

Learning Analytics focuses on the collection and analysis of learners' data to improve their learning experience by providing informed guidance and to optimise learning materials. To support the research in this area we have developed a dataset, containing data from courses presented at the Open University (OU). What makes the dataset unique is the fact that it contains demographic data together with aggregated clickstream data of students' interactions in the Virtual Learning Environment (VLE). This enables the analysis of student behaviour, represented by their actions. The dataset contains the information about 22 courses, 32,593 students, their assessment results, and logs of their interactions with the VLE represented by daily summaries of student clicks (10,655,280 entries). The dataset is freely available at https://analyse.kmi.open.ac.uk/open_dataset under a CC-BY 4.0 license.

Distance learning

Directory of Open Access Journals (Sweden)

Katarina Pucelj

2006-12-01

Full Text Available I would like to underline the role and importance of knowledge, which is acquired by individuals as a result of a learning process and experience. I have established that a form of learning, such as distance learning definitely contributes to a higher learning quality and leads to innovative, dynamic and knowledgebased society. Knowledge and skills enable individuals to cope with and manage changes, solve problems and also create new knowledge. Traditional learning practices face new circumstances, new and modern technologies appear, which enable quick and quality-oriented knowledge implementation. The centre of learning process at distance learning is to increase the quality of life of citizens, their competitiveness on the workforce market and ensure higher economic growth. Intellectual capital is the one, which represents the biggest capital of each society and knowledge is the key factor for succes of everybody, who are fully aware of this. Flexibility, openness and willingness of people to follow new IT solutions form suitable environment for developing and deciding to take up distance learning.

Perceptions of Career Development Learning and Work-Integrated Learning in Australian Higher Education

Science.gov (United States)

McIlveen, Peter; Brooks, Sally; Lichtenberg, Anna; Smith, Martin; Torjul, Peter; Tyler, Joanne

2011-01-01

This paper is a report on the perceived correspondence between career development learning and work-integrated learning programs that were delivered by career services in Australian higher education institutions. The study entailed a questionnaire survey of representatives of university career services. The questionnaire dealt with the extent to…

An active learning representative subset selection method using net analyte signal

Science.gov (United States)

He, Zhonghai; Ma, Zhenhe; Luan, Jingmin; Cai, Xi

2018-05-01

To guarantee accurate predictions, representative samples are needed when building a calibration model for spectroscopic measurements. However, in general, it is not known whether a sample is representative prior to measuring its concentration, which is both time-consuming and expensive. In this paper, a method to determine whether a sample should be selected into a calibration set is presented. The selection is based on the difference of Euclidean norm of net analyte signal (NAS) vector between the candidate and existing samples. First, the concentrations and spectra of a group of samples are used to compute the projection matrix, NAS vector, and scalar values. Next, the NAS vectors of candidate samples are computed by multiplying projection matrix with spectra of samples. Scalar value of NAS is obtained by norm computation. The distance between the candidate set and the selected set is computed, and samples with the largest distance are added to selected set sequentially. Last, the concentration of the analyte is measured such that the sample can be used as a calibration sample. Using a validation test, it is shown that the presented method is more efficient than random selection. As a result, the amount of time and money spent on reference measurements is greatly reduced.

Representing Spatial Layout According to Intrinsic Frames of Reference.

Science.gov (United States)

Xie, Chaoxiang; Li, Shiyi; Tao, Weidong; Wei, Yiping; Sun, Hong-Jin

2017-01-01

Mou and McNamara have suggested that object locations are represented according to intrinsic reference frames. In three experiments, we investigated the limitations of intrinsic reference frames as a mean to represent object locations in spatial memory. Participants learned the locations of seven or eight common objects in a rectangular room and then made judgments of relative direction based on their memory of the layout. The results of all experiments showed that when all objects were positioned regularly, judgments of relative direction were faster or more accurate for novel headings that were aligned with the primary intrinsic structure than for other novel headings; however, when one irregularly positioned object was added to the layout, this advantage was eliminated. The experiments further indicated that with a single view at study, participants could represent the layout from either an egocentric orientation or a different orientation, according to experimental instructions. Together, these results suggest that environmental reference frames and intrinsic axes can influence performance for novel headings, but their role in spatial memory depends on egocentric experience, layout regularity, and instructions.

Machine Learning

Energy Technology Data Exchange (ETDEWEB)

Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.; Carroll, Thomas E.; Muller, George

2017-04-21

The absence of a robust and unified theory of cyber dynamics presents challenges and opportunities for using machine learning based data-driven approaches to further the understanding of the behavior of such complex systems. Analysts can also use machine learning approaches to gain operational insights. In order to be operationally beneficial, cybersecurity machine learning based models need to have the ability to: (1) represent a real-world system, (2) infer system properties, and (3) learn and adapt based on expert knowledge and observations. Probabilistic models and Probabilistic graphical models provide these necessary properties and are further explored in this chapter. Bayesian Networks and Hidden Markov Models are introduced as an example of a widely used data driven classification/modeling strategy.

Self-Determination, Engagement, and Identity in Learning German: Some Directions in the Psychology of Language Learning Motivation

Science.gov (United States)

Noels, Kimberly A.; Chaffee, Kathryn; Lou, Nigel Mantou; Dincer, Ali

2016-01-01

Drawing from Self-Determination Theory and diverse theories of language learning motivation, we present a framework that (1) represents a range of orientations that students may take towards learning German, and (2) explains how these orientations are connected to language learning engagement and diverse linguistic and non-linguistic outcomes. We…

Processes of Learning with Regard to Students’ Learning Difficulties in Mathematics

Directory of Open Access Journals (Sweden)

Amalija Zakelj

2014-06-01

Full Text Available In the introduction, we write about the process of learning mathematics: the development of mathematical concepts, numerical and spatial imagery on reading and understanding of texts, etc. The central part of the paper is devoted to the study, in which we find that identifying the learning processes associated with learning difficulties of students in mathematics, is not statistically significantly different between primary school teachers and teachers of mathematics. Both groups expose the development of numerical concepts, logical reasoning, and reading and understanding the text as the ones with which difficulties in learning mathematics appear the most frequently. All the processes of learning that the teachers assessed as the ones that represent the greatest barriers to learning have a fairly uniform average estimates of the degree of complexity, ranging from 2.6 to 2.8, which is very close to the estimate makes learning very difficult.

The Development of the Dental Liquid Ration

Science.gov (United States)

1993-10-01

taste like norma components of a meal. Thirty products supporting a three-day menu cycle consist of products such as chicken barbecue, lyonnaise ... potatoes , buttered corn, and chocolate mocha cake. There are also six flavors of at I nma deiryahake. The ULR cmme packaged in either individual serving...wide variety of entrees, vegetables, Itards,, and desserts. Prodxts inlude O~d~m hBazecu, tLczmise Potatoes , Broccoli au Grat~in, aMd dcxoolate Mocha

BIG DATA AND E-LEARNING: THE IMPACT ON THE FUTURE OF LEARNING INDUSTRY

Directory of Open Access Journals (Sweden)

Valentin PAU

2015-11-01

Full Text Available In nowadays, one of the most interesting aspects of e-Learning is that he is continuously evolving, where, the big data architecture represents an important component over which the e-Learning communities has stopped more and more. In our work paper we will analyze the technological benefits of the big data concept and the impact on the future of e-Learning but also we will mention the critical aspects regarding the integrity of the data.

Collaborative Learning or Cooperative Learning? The Name Is Not Important; Flexibility Is

Directory of Open Access Journals (Sweden)

George M. Jacobs

2015-06-01

Full Text Available Abstract A great deal of theory and research, not to mention students’ and teachers’ practical experience, supports the use of group activities in education. Collaborative learning and cooperative learning are two terms commonly used in discussions of how and why to use group activities. This article looks at the issue of whether the two terms collaborative learning and cooperative learning are synonymous or whether they represent different conceptualisations of how and why students should interact as part of their learning. Those scholars who differentiate the two terms often see collaborative learning as more student centred and cooperative learning as a more teacher centred way to facilitate student-student interaction. The present article argues that collaborative and cooperative learning should be seen as synonymous student centric approaches, and that teachers and students, regardless of which of the two terms they use, should and will vary the ways they shape their learning environments in order to best facilitate the cognitive and affective benefits that student-student interaction offers. Keywords: Collaborative learning, cooperative learning, flexibility

Institutional Perspectives: The Challenges of E-Learning Diffusion

Science.gov (United States)

Nichols, Mark

2008-01-01

There has been significant recent interest in the dynamics of institutional change and e-learning. This paper reports on the findings from a series of discussions about e-learning diffusion held with institutional e-learning representatives from across the globe. In the course of discussion it became clear that in some institutions e-learning was…

Learner Open Modeling in Adaptive Mobile Learning System for Supporting Student to Learn English

Directory of Open Access Journals (Sweden)

Van Cong Pham

2011-10-01

Full Text Available This paper represents a personalized context-aware mobile learning architecture for supporting student to learn English as foreign language in order to prepare for TOEFL test. We consider how to apply open learner modeling techniques to adapt contents for different learners based on context, which includes location, amount of time to learn, the manner as well as learner's knowledge in learning progress. Through negotiation with system, the editable learner model will be updated to support adaptive engine to select adaptive contents meeting learner's demands. Empirical testing results for students who used application prototype indicate that interaction user modeling is helpful in supporting learner to learn adaptive materials.

Lifelong learning and special libraries

OpenAIRE

Marjeta Oven; Vlasta Zabukovec

2005-01-01

Lifelong learning is becoming an important part in the development of modern society.In the lifelong learning society endeavours are being made for the education of all individuals regardless of their social and/or economic background. Lifelong learning should,therefore, be regarded as a permanent activity and as such, a significant element of socialization, inspired by common values. It represents a dynamic interaction with cultural, working and social environment. In this respect, the role ...

Storytelling: a teaching-learning technique.

Science.gov (United States)

Geanellos, R

1996-03-01

Nurses' stories, arising from the practice world, reconstruct the essence of experience as lived and provide vehicles for learning about nursing. The learning process is forwarded by combining storytelling and reflection. Reflection represents an active, purposive, contemplative and deliberative approach to learning through which learners create meaning from the learning experience. The combination of storytelling and reflection allows the creation of links between the materials at hand and prior and future learning. As a teaching-learning technique storytelling engages learners; organizes information; allows exploration of shared lived experiences without the demands, responsibilities and consequences of practice; facilitates remembering; enhances discussion, problem posing and problem solving; and aids understanding of what it is to nurse and to be a nurse.

Frequency Hopping Transceiver Multiplexer

Science.gov (United States)

1983-03-01

ATC 17 ULR IHQ OCLI CPCTR ULTRA HIGH "OQS" UP TO 4X HIGHER THAN BEST INDUS- TRY STANDARD (ATC 100). MICROWAVE POWER, CURRENT. AND 0 RATINGS5...Q"W were assigned to element (FigC-2); which will be modelled into the transformer previously ment td . The center frequencies, "Q", frequency range...of the TD 1288 system. Temperature stability, change with time or storage. Flexure Frequency, or non-linear change over bandwidth. * Humidity

Automatic Segmentation and Deep Learning of Bird Sounds

NARCIS (Netherlands)

Koops, Hendrik Vincent; Van Balen, J.M.H.; Wiering, F.

2015-01-01

We present a study on automatic birdsong recognition with deep neural networks using the BIRDCLEF2014 dataset. Through deep learning, feature hierarchies are learned that represent the data on several levels of abstraction. Deep learning has been applied with success to problems in fields such as

Ustvarjanje produktivnega geografskega učnega okolja z vidika učnih stilov, oblik in metod = Creating the productive geographical learning environment from the point of view of learning-styles and learning-methods

Directory of Open Access Journals (Sweden)

Lea Nemec

2008-01-01

Full Text Available Experiences, which we receive in space (indirectly influence on education process respectivelyon learning-environment. Because of that is the most productive learning-environmentthose witch founded on experiential-learning. In this research experience took the leadingplace in forming didactical approaches in teaching geography and to define learning-stylesand methods respectively in the direction of creating representative geographical learningenvironment.

Semantic Learning Service Personalized

Directory of Open Access Journals (Sweden)

Yibo Chen

2012-02-01

Full Text Available To provide users with more suitable and personalized service, personalization is widely used in various fields. Current e-Learning systems search for learning resources using information search technology, based on the keywords that selected or inputted by the user. Due to lack of semantic analysis for keywords and exploring the user contexts, the system cannot provide a good learning experiment. In this paper, we defined the concept and characteristic of the personalized learning service, and proposed a semantic learning service personalized framework. Moreover, we made full use of semantic technology, using ontologies to represent the learning contents and user profile, mining and utilizing the friendship and membership of the social relationship to construct the user social relationship profile, and improved the collaboration filtering algorithm to recommend personalized learning resources for users. The results of the empirical evaluation show that the approach is effectiveness in augmenting recommendation.

Feature selection for domain knowledge representation through multitask learning

CSIR Research Space (South Africa)

Rosman, Benjamin S

2014-10-01

Full Text Available represent stimuli of interest, and rich feature sets which increase the dimensionality of the space and thus the difficulty of the learning problem. We focus on a multitask reinforcement learning setting, where the agent is learning domain knowledge...

Globalisation, Consumption and the Learning Business.

Science.gov (United States)

Field, John

1995-01-01

Distance open learning represents both an outcome of and a primary factor in globalization. Despite investment in infrastructure, software, and human resources, demand for distance open learning in the European market remains constrained. The European Union's policies conceptualize a "European economic space" that ignores the real…

Do Young Learners Exploit the Same Learning Strategies as Adults?

Science.gov (United States)

Hrozková, Ivana

2015-01-01

Learning strategies are considered to be one of the key factors affecting the learning process, its effectiveness and study results. They are important for lifelong learning of foreign languages and as a learning skill they represent a priority in the process of European globalization and integration. Moreover, learning strategies as a foreign…

Learning Disabilities: Implications for Policy regarding Research and Practice--A Report by the National Joint Committee on Learning Disabilities, March 2011

Science.gov (United States)

Learning Disabilities: A Multidisciplinary Journal, 2012

2012-01-01

The National Joint Committee on Learning Disabilities (NJCLD) affirms that the construct of learning disabilities represents a valid, unique, and heterogeneous group of disorders, and that recognition of this construct is essential for sound policy and practice. An extensive body of scientific research on learning disabilities continues to support…

An agent architecture with on-line learning of both procedural and declarative knowledge

Energy Technology Data Exchange (ETDEWEB)

Sun, R.; Peterson, T.; Merrill, E. [Univ. of Alabama, Tuscaloosa, AL (United States)

1996-12-31

In order to develop versatile cognitive agents that learn in situated contexts and generalize resulting knowledge to different environments, we explore the possibility of learning both declarative and procedural knowledge in a hybrid connectionist architecture. The architecture is based on the two-level idea proposed earlier by the author. Declarative knowledge is represented symbolically, while procedural knowledge is represented subsymbolically. The architecture integrates reactive procedures, rules, learning, and decision-making in a unified framework, and structures different learning components (including Q-learning and rule induction) in a synergistic way to perform on-line and integrated learning.

Active Learning Not Associated with Student Learning in a Random Sample of College Biology Courses

Science.gov (United States)

Andrews, T. M.; Leonard, M. J.; Colgrove, C. A.; Kalinowski, S. T.

2011-01-01

Previous research has suggested that adding active learning to traditional college science lectures substantially improves student learning. However, this research predominantly studied courses taught by science education researchers, who are likely to have exceptional teaching expertise. The present study investigated introductory biology courses randomly selected from a list of prominent colleges and universities to include instructors representing a broader population. We examined the relationship between active learning and student learning in the subject area of natural selection. We found no association between student learning gains and the use of active-learning instruction. Although active learning has the potential to substantially improve student learning, this research suggests that active learning, as used by typical college biology instructors, is not associated with greater learning gains. We contend that most instructors lack the rich and nuanced understanding of teaching and learning that science education researchers have developed. Therefore, active learning as designed and implemented by typical college biology instructors may superficially resemble active learning used by education researchers, but lacks the constructivist elements necessary for improving learning. PMID:22135373

Transfer learning improves supervised image segmentation across imaging protocols.

Science.gov (United States)

van Opbroek, Annegreet; Ikram, M Arfan; Vernooij, Meike W; de Bruijne, Marleen

2015-05-01

The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%.

Java problem-based learning

Directory of Open Access Journals (Sweden)

Goran P, Šimić

2012-01-01

Full Text Available The paper describes the self-directed problem-based learning system (PBL named Java PBL. The expert module is the kernel of Java PBL. It involves a specific domain model, a problem generator and a solution generator. The overall system architecture is represented in the paper. Java PBL can act as the stand-alone system, but it is also designed to provide support to learning management systems (LMSs. This is provided by a modular design of the system. An LMS can offer the declarative knowledge only. Java PBL offers the procedural knowledge and the progress of the learner programming skills. The free navigation, unlimited numbers of problems and recommendations represent the main pedagogical strategies and tactics implemented into the system.

Sustainability of healthcare improvement: what can we learn from learning theory?

Directory of Open Access Journals (Sweden)

Hovlid Einar

2012-08-01

the clinical system represented a change in mental models of employees that influenced how the organization changed its performance. By applying the framework of organizational learning, we learned that changes originating from a new mental model represent double-loop learning. In double-loop learning, deeper system properties are changed, and consequently changes are more likely to be sustained.

Sustainability of healthcare improvement: what can we learn from learning theory?

Science.gov (United States)

Hovlid, Einar; Bukve, Oddbjørn; Haug, Kjell; Aslaksen, Aslak Bjarne; von Plessen, Christian

2012-08-03

Changes that improve the quality of health care should be sustained. Falling back to old, unsatisfactory ways of working is a waste of resources and can in the worst case increase resistance to later initiatives to improve care. Quality improvement relies on changing the clinical system yet factors that influence the sustainability of quality improvements are poorly understood. Theoretical frameworks can guide further research on the sustainability of quality improvements. Theories of organizational learning have contributed to a better understanding of organizational change in other contexts. To identify factors contributing to sustainability of improvements, we use learning theory to explore a case that had displayed sustained improvement. Førde Hospital redesigned the pathway for elective surgery and achieved sustained reduction of cancellation rates. We used a qualitative case study design informed by theory to explore factors that contributed to sustain the improvements at Førde Hospital. The model Evidence in the Learning Organization describes how organizational learning contributes to change in healthcare institutions. This model constituted the framework for data collection and analysis. We interviewed a strategic sample of 20 employees. The in-depth interviews covered themes identified through our theoretical framework. Through a process of coding and condensing, we identified common themes that were interpreted in relation to our theoretical framework. Clinicians and leaders shared information about their everyday work and related this knowledge to how the entire clinical pathway could be improved. In this way they developed a revised and deeper understanding of their clinical system and its interdependencies. They became increasingly aware of how different elements needed to interact to enhance the performance and how their own efforts could contribute. The improved understanding of the clinical system represented a change in mental models of

Early Foundations for Mathematics Learning and Their Relations to Learning Disabilities.

Science.gov (United States)

Geary, David C

2013-02-01

Children's quantitative competencies upon entry into school can have lifelong consequences. Children who start behind generally stay behind, and mathematical skills at school completion influence employment prospects and wages in adulthood. I review the current debate over whether early quantitative learning is supported by (a) an inherent system for representing approximate magnitudes, (b) an attentional-control system that enables explicit processing of quantitative symbols, such as Arabic numerals, or (c) the logical problem-solving abilities that facilitate learning of the relations among numerals. Studies of children with mathematical learning disabilities and difficulties have suggested that each of these competencies may be involved, but to different degrees and at different points in the learning process. Clarifying how and when these competencies facilitate early quantitative learning and developing interventions to address their impact on children have the potential to yield substantial benefits for individuals and for society.

Scene recognition based on integrating active learning with dictionary learning

Science.gov (United States)

Wang, Chengxi; Yin, Xueyan; Yang, Lin; Gong, Chengrong; Zheng, Caixia; Yi, Yugen

2018-04-01

Scene recognition is a significant topic in the field of computer vision. Most of the existing scene recognition models require a large amount of labeled training samples to achieve a good performance. However, labeling image manually is a time consuming task and often unrealistic in practice. In order to gain satisfying recognition results when labeled samples are insufficient, this paper proposed a scene recognition algorithm named Integrating Active Learning and Dictionary Leaning (IALDL). IALDL adopts projective dictionary pair learning (DPL) as classifier and introduces active learning mechanism into DPL for improving its performance. When constructing sampling criterion in active learning, IALDL considers both the uncertainty and representativeness as the sampling criteria to effectively select the useful unlabeled samples from a given sample set for expanding the training dataset. Experiment results on three standard databases demonstrate the feasibility and validity of the proposed IALDL.

Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Ying Yin

2016-05-01

Full Text Available Multi-instance multi-label learning is a learning framework, where every object is represented by a bag of instances and associated with multiple labels simultaneously. The existing degeneration strategy-based methods often suffer from some common drawbacks: (1 the user-specific parameter for the number of clusters may incur the effective problem; (2 SVM may bring a high computational cost when utilized as the classifier builder. In this paper, we propose an algorithm, namely multi-instance multi-label (MIML-extreme learning machine (ELM, to address the problems. To our best knowledge, we are the first to utilize ELM in the MIML problem and to conduct the comparison of ELM and SVM on MIML. Extensive experiments have been conducted on real datasets and synthetic datasets. The results show that MIMLELM tends to achieve better generalization performance at a higher learning speed.

Personality in learning and education : A review

NARCIS (Netherlands)

DeRaad, B; Schouwenburg, HC

1996-01-01

The literature relevant to the combined area of personality and education and learning is summarized, covering almost a century of research and theorizing. Different topics considered important from the aspect of education and learning or from the aspect of personality ape represented. For

Machine learning of molecular properties: Locality and active learning

Science.gov (United States)

Gubaev, Konstantin; Podryabinkin, Evgeny V.; Shapeev, Alexander V.

2018-06-01

In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of density functional theory on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers—the out-of-sample molecules, not well-represented in the training set. In the present paper, we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests.

Balancing Design Project Supervision and Learning Facilitation

DEFF Research Database (Denmark)

Nielsen, Louise Møller

2012-01-01

experiences and expertise to guide the students’ decisions in relation to the design project. This paper focuses on project supervision in the context of design education – and more specifically on how this supervision is unfolded in a Problem Based Learning culture. The paper explores the supervisor......’s balance between the roles: 1) Design Project Supervisor – and 2) Learning Facilitator – with the aim to understand when to apply the different roles, and what to be aware of when doing so. This paper represents the first pilot-study of a larger research effort. It is based on a Lego Serious Play workshop......In design there is a long tradition for apprenticeship, as well as tradition for learning through design projects. Today many design educations are positioned within the University context, and have to be aligned with the learning culture and structure, which they represent. This raises a specific...

The development of deep learning in synthetic aperture radar imagery

CSIR Research Space (South Africa)

Schwegmann, Colin P

2017-05-01

Full Text Available sensing techniques but comes at the price of additional complexities. To adequately cope with these, researchers have begun to employ advanced machine learning techniques known as deep learning to Synthetic Aperture Radar data. Deep learning represents...

A Review of Integrating Mobile Phones for Language Learning

Science.gov (United States)

Darmi, Ramiza; Albion, Peter

2014-01-01

Mobile learning (m-learning) is gradually being introduced in language classrooms. All forms of mobile technology represent portability with smarter features. Studies have proven the concomitant role of technology beneficial for language learning. Various features in the technology have been exploited and researched for acquiring and learning…

M-Learning Adoption: A Perspective from a Developing Country

Directory of Open Access Journals (Sweden)

Shakeel Iqbal

2012-06-01

Full Text Available M-learning is the style of learning for the new millennium. Decreases in cost and increases in capabilities of mobile devices have made this medium attractive for the dissemination of knowledge. Mobile engineers, software developers, and educationists represent the supply side of this technology, whereas students represent the demand side. In order to further develop and improve this medium of learning it is imperative to find out students’ perceptions about m-learning adoption. To achieve this objective a survey was conducted among the students of 10 chartered universities operating in the twin cities of Rawalpindi and Islamabad in Pakistan. The results indicate that perceived usefulness, ease of use, and facilitating conditions significantly affect the students’ intention to adopt m-learning, whereas perceived playfulness is found to have less influence. Social influence is found to have a negative impact on adoption of m-learning. The findings of this study are useful in providing guidance to developers and educators for designing m-learning courses specifically in the context of developing countries.

Learner characteristics involved in distance learning

Energy Technology Data Exchange (ETDEWEB)

Cernicek, A.T.; Hahn, H.A.

1991-01-01

Distance learning represents a strategy for leveraging resources to solve educational and training needs. Although many distance learning programs have been developed, lessons learned regarding differences between distance learning and traditional education with respect to learner characteristics have not been well documented. Therefore, we conducted a survey of 20 distance learning professionals. The questionnaire was distributed to experts attending the second Distance Learning Conference sponsored by Los Alamos National Laboratory. This survey not only acquired demographic information from each of the respondents but also identified important distance learning student characteristics. Significant distance learner characteristics, which were revealed statistically and which influence the effectiveness of distance learning, include the following: reading level, student autonomy, and self-motivation. Distance learning cannot become a more useful and effective method of instruction without identifying and recognizing learner characteristics. It will be important to consider these characteristics when designing all distance learning courses. This paper will report specific survey findings and their implications for developing distance learning courses. 9 refs., 6 tabs.

Robots Learn Writing

Directory of Open Access Journals (Sweden)

Huan Tan

2012-01-01

Full Text Available This paper proposes a general method for robots to learn motions and corresponding semantic knowledge simultaneously. A modified ISOMAP algorithm is used to convert the sampled 6D vectors of joint angles into 2D trajectories, and the required movements for writing numbers are learned from this modified ISOMAP-based model. Using this algorithm, the knowledge models are established. Learned motion and knowledge models are stored in a 2D latent space. Gaussian Process (GP method is used to model and represent these models. Practical experiments are carried out on a humanoid robot, named ISAC, to learn the semantic representations of numbers and the movements of writing numbers through imitation and to verify the effectiveness of this framework. This framework is applied into training a humanoid robot, named ISAC. At the learning stage, ISAC not only learns the dynamics of the movement required to write the numbers, but also learns the semantic meaning of the numbers which are related to the writing movements from the same data set. Given speech commands, ISAC recognizes the words and generated corresponding motion trajectories to write the numbers. This imitation learning method is implemented on a cognitive architecture to provide robust cognitive information processing.

Lifelong learning and special libraries

Directory of Open Access Journals (Sweden)

Marjeta Oven

2005-01-01

Full Text Available Lifelong learning is becoming an important part in the development of modern society.In the lifelong learning society endeavours are being made for the education of all individuals regardless of their social and/or economic background. Lifelong learning should,therefore, be regarded as a permanent activity and as such, a significant element of socialization, inspired by common values. It represents a dynamic interaction with cultural, working and social environment. In this respect, the role of motivation should be specifically emphasized, as it can explain the causes for human behaviour.

The M-Technologies in M-Learning

DEFF Research Database (Denmark)

Annan, Nana Kofi; Adjin, Daniel Michael Okwabi; Ofori-Dwumfour, George

2013-01-01

network are vivid examples of static-ICTs while smartphones, tablets and mini laptop with wireless network connectivity, represent mobile-ICTs. The purpose of this paper is to elucidate the relationship between mobile computing and communication technologies, and their implication for education delivery....... The phenomenon of using mobile-ICTs for teaching and learning as popularly refered to as m-learning and is an off-shoot of e-learning which implies the use of static-ICTs for learning. The problem however, is that m-learning has a highly fragmented meaning because most fail to understand all the constituents...... of m-learning which this paper perceives to be the interconnectivity between mobile device, mobile telecommunications and mobile applications in their entirety as inseparable elements of m-learning. The questions that this paper seeks to address are; what are the key technological components of m-learning...

14 CFR 1274.906 - Designation of New Technology Representative and Patent Representative.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Designation of New Technology... Conditions § 1274.906 Designation of New Technology Representative and Patent Representative. Designation of New Technology Representative and Patent Representative July 2002 (a) For purposes of administration...

Constructivism, the so-called semantic learning theories, and situated cognition versus the psychological learning theories.

Science.gov (United States)

Aparicio, Juan José; Rodríguez Moneo, María

2005-11-01

In this paper, the perspective of situated cognition, which gave rise both to the pragmatic theories and the so-called semantic theories of learning and has probably become the most representative standpoint of constructivism, is examined. We consider the claim of situated cognition to provide alternative explanations of the learning phenomenon to those of psychology and, especially, to those of the symbolic perspective, currently predominant in cognitive psychology. The level of analysis of situated cognition (i.e., global interactive systems) is considered an inappropriate approach to the problem of learning. From our analysis, it is concluded that the pragmatic theories and the so-called semantic theories of learning which originated in situated cognition can hardly be considered alternatives to the psychological learning theories, and they are unlikely to add anything of interest to the learning theory or to contribute to the improvement of our knowledge about the learning phenomenon.

Probabilistic machine learning and artificial intelligence.

Science.gov (United States)

Ghahramani, Zoubin

2015-05-28

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Probabilistic machine learning and artificial intelligence

Science.gov (United States)

Ghahramani, Zoubin

2015-05-01

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

A Comparative Analysis of Three Unique Theories of Organizational Learning

Science.gov (United States)

Leavitt, Carol C.

2011-01-01

The purpose of this paper is to present three classical theories on organizational learning and conduct a comparative analysis that highlights their strengths, similarities, and differences. Two of the theories -- experiential learning theory and adaptive -- generative learning theory -- represent the thinking of the cognitive perspective, while…

Performance of Blended Learning in University Teaching:

Directory of Open Access Journals (Sweden)

Michael Reiss

2010-07-01

Full Text Available Blended learning as a combination of classroom teaching and e-learning has become a widely represented standard in employee and management development of companies. The exploratory survey “Blended Learning@University” conducted in 2008 investigated the integration of blended learning in higher education. The results of the survey show that the majority of participating academic teachers use blended learning in single courses, but not as a program of study and thus do not exploit the core performance potential of blended learning. According to the study, the main driver of blended learning performance is its embeddedness in higher education. Integrated blended programs of study deliver the best results. In blended learning, learning infrastructure (in terms of software, culture, skills, funding, content providing, etc. does not play the role of a performance driver but serves as an enabler for blended learning.

Teachers' Reflective Practice in Lifelong Learning Programs

DEFF Research Database (Denmark)

Jensen, Annie Aarup; Thomassen, Anja Overgaard

2018-01-01

This chapter explores teachers' reflective practice in lifelong learning programs based on a qualitative study of five teachers representing three part-time Master's programs. The theoretical framework for analysis of the interview data is Ellström's (1996) model for categorizing levels of action......, knowledge and learning, activity theory (Engeström, 1987) and expansive learning (Engeström & Sannino, 2010). The results show a divergence between what the teachers perceive as the Master students' learning goals and the teachers' goals and objectives. This is highlighted through the teachers' experience...

Personality, Organizational Orientations and Self-Reported Learning Outcomes

Science.gov (United States)

Bamber, David; Castka, Pavel

2006-01-01

Purpose: To identify competencies connecting personality, organizational orientations and self-reported learning outcomes (as measured by concise Likert-type scales), for individuals who are learning for their organizations. Design/methodology/approach: Five concise factor scales were constructed to represent aspects of personality. Three further…

Learning Bayesian Dependence Model for Student Modelling

Directory of Open Access Journals (Sweden)

Adina COCU

2008-12-01

Full Text Available Learning a Bayesian network from a numeric set of data is a challenging task because of dual nature of learning process: initial need to learn network structure, and then to find out the distribution probability tables. In this paper, we propose a machine-learning algorithm based on hill climbing search combined with Tabu list. The aim of learning process is to discover the best network that represents dependences between nodes. Another issue in machine learning procedure is handling numeric attributes. In order to do that, we must perform an attribute discretization pre-processes. This discretization operation can influence the results of learning network structure. Therefore, we make a comparative study to find out the most suitable combination between discretization method and learning algorithm, for a specific data set.

Using the Reggio Exhibit to Enrich Teacher Candidates' Perceptions of How Children Construct and Represent Knowledge

Science.gov (United States)

Ede, Anita R.; Da Ros-Voseles, Denise A.

2010-01-01

This teacher research study explores the changes in early childhood teacher candidates' perceptions of how children construct and represent knowledge following repeated exposure to "The Wonder of Learning: the Hundred Languages of Children" exhibit. When the renowned exhibit from Reggio Emilia was housed on the study participants' campus for 6…

Enhancing Students' Language Skills through Blended Learning

Science.gov (United States)

Banditvilai, Choosri

2016-01-01

This paper presents a case study of using blended learning to enhance students' language skills and learner autonomy in an Asian university environment. Blended learning represents an educational environment for much of the world where computers and the Internet are readily available. It combines self-study with valuable face-to-face interaction…

ENGINEERING OF UNIVERSITY INTELLIGENT LEARNING SYSTEMS

Directory of Open Access Journals (Sweden)

Vasiliy M. Trembach

2016-01-01

Full Text Available In the article issues of engineering intelligent tutoring systems of University with adaptation are considered. The article also dwells on some modern approaches to engineering of information systems. It shows the role of engineering e-learning devices (systems in system engineering. The article describes the basic principles of system engineering and these principles are expanded regarding to intelligent information systems. The structure of intelligent learning systems with adaptation of the individual learning environments based on services is represented in the article.

The cerebellum: a neuronal learning machine?

Science.gov (United States)

Raymond, J. L.; Lisberger, S. G.; Mauk, M. D.

1996-01-01

Comparison of two seemingly quite different behaviors yields a surprisingly consistent picture of the role of the cerebellum in motor learning. Behavioral and physiological data about classical conditioning of the eyelid response and motor learning in the vestibulo-ocular reflex suggests that (i) plasticity is distributed between the cerebellar cortex and the deep cerebellar nuclei; (ii) the cerebellar cortex plays a special role in learning the timing of movement; and (iii) the cerebellar cortex guides learning in the deep nuclei, which may allow learning to be transferred from the cortex to the deep nuclei. Because many of the similarities in the data from the two systems typify general features of cerebellar organization, the cerebellar mechanisms of learning in these two systems may represent principles that apply to many motor systems.

Who Knows? Metacognitive Social Learning Strategies.

Science.gov (United States)

Heyes, Cecilia

2016-03-01

To make good use of learning from others (social learning), we need to learn from the right others; from agents who know better than we do. Research on social learning strategies (SLSs) has identified rules that focus social learning on the right agents, and has shown that the behaviour of many animals conforms to these rules. However, it has not asked what the rules are made of, that is, about the cognitive processes implementing SLSs. Here, I suggest that most SLSs depend on domain-general, sensorimotor processes. However, some SLSs have the characteristics tacitly ascribed to all of them. These metacognitive SLSs represent 'who knows' in a conscious, reportable way, and have the power to promote cultural evolution. Copyright © 2015 Elsevier Ltd. All rights reserved.

Adaptation Criteria for the Personalised Delivery of Learning Materials: A Multi-Stage Empirical Investigation

Science.gov (United States)

Thalmann, Stefan

2014-01-01

Personalised e-Learning represents a major step-change from the one-size-fits-all approach of traditional learning platforms to a more customised and interactive provision of learning materials. Adaptive learning can support the learning process by tailoring learning materials to individual needs. However, this requires the initial preparation of…

Learning Grasp Affordance Densities

DEFF Research Database (Denmark)

Detry, Renaud; Kraft, Dirk; Kroemer, Oliver

2011-01-01

and relies on kernel density estimation to provide a continuous model. Grasp densities are learned and refined from exploration, by letting a robot “play” with an object in a sequence of graspand-drop actions: The robot uses visual cues to generate a set of grasp hypotheses; it then executes......We address the issue of learning and representing object grasp affordance models. We model grasp affordances with continuous probability density functions (grasp densities) which link object-relative grasp poses to their success probability. The underlying function representation is nonparametric...... these and records their outcomes. When a satisfactory number of grasp data is available, an importance-sampling algorithm turns these into a grasp density. We evaluate our method in a largely autonomous learning experiment run on three objects of distinct shapes. The experiment shows how learning increases success...

Rich media content adaptation in e-learning systems

OpenAIRE

Mirri, Silvia

2007-01-01

The wide use of e-technologies represents a great opportunity for underserved segments of the population, especially with the aim of reintegrating excluded individuals back into society through education. This is particularly true for people with different types of disabilities who may have difficulties while attending traditional on-site learning programs that are typically based on printed learning resources. The creation and provision of accessible e-learning contents may therefore become ...

SUSTAIN: a network model of category learning.

Science.gov (United States)

Love, Bradley C; Medin, Douglas L; Gureckis, Todd M

2004-04-01

SUSTAIN (Supervised and Unsupervised STratified Adaptive Incremental Network) is a model of how humans learn categories from examples. SUSTAIN initially assumes a simple category structure. If simple solutions prove inadequate and SUSTAIN is confronted with a surprising event (e.g., it is told that a bat is a mammal instead of a bird), SUSTAIN recruits an additional cluster to represent the surprising event. Newly recruited clusters are available to explain future events and can themselves evolve into prototypes-attractors-rules. SUSTAIN's discovery of category substructure is affected not only by the structure of the world but by the nature of the learning task and the learner's goals. SUSTAIN successfully extends category learning models to studies of inference learning, unsupervised learning, category construction, and contexts in which identification learning is faster than classification learning.

14 CFR 1260.58 - Designation of new technology representative and patent representative.

Science.gov (United States)

2010-01-01

... 14 Aeronautics and Space 5 2010-01-01 2010-01-01 false Designation of new technology... of new technology representative and patent representative. Designation of New Technology... of this grant entitled “New Technology,” the following named representatives are hereby designated by...

Cooperative Learning as a Tool To Teach Vertebrate Anatomy.

Science.gov (United States)

Koprowski, John L.; Perigo, Nan

2000-01-01

Describes a method for teaching biology that includes more investigative exercises that foster an environment for cooperative learning in introductory laboratories that focus on vertebrates. Fosters collaborative learning by facilitating interaction between students as they become experts on their representative vertebrate structures. (SAH)

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.

Informal Learning and Non-Formal Education for Development

Science.gov (United States)

Latchem, Colin

2014-01-01

The following article examines the issues of open, distance and technology-based informal learning and non-formal education for individual and community development. It argues that these two modes of education, which are estimated to constitute 70-90% of lifelong learning, are insufficiently represented in the literature of open and distance…

Bodily Tides Near Spin-Orbit Resonances

Science.gov (United States)

2012-01-01

radial term may cause an equipotential - surface variation of about 10 cm. This magnitude is large enough to be observed by future missions and should...U (r) = ∞∑ l=2 Ul(r) = ∞∑ l=2 kl ( R r )l+1 Wl(R, r∗), (2) R now being the mean equatorial radius of the primary, R = (R, φ, λ) being a surface point...rheology. For a homogeneous incompressible spherical primary of density ρ, surface gravity g, and rigidity μ, the static Love number of degree l is

Integrating Concept Mapping into Information Systems Education for Meaningful Learning and Assessment

Science.gov (United States)

Wei, Wei; Yue, Kwok-Bun

2017-01-01

Concept map (CM) is a theoretically sound yet easy to learn tool and can be effectively used to represent knowledge. Even though many disciplines have adopted CM as a teaching and learning tool to improve learning effectiveness, its application in IS curriculum is sparse. Meaningful learning happens when one iteratively integrates new concepts and…

How Effective Is Example Generation for Learning Declarative Concepts?

Science.gov (United States)

Rawson, Katherine A.; Dunlosky, John

2016-01-01

Declarative concepts (i.e., key terms and corresponding definitions for abstract concepts) represent foundational knowledge that students learn in many content domains. Thus, investigating techniques to enhance concept learning is of critical importance. Various theoretical accounts support the expectation that example generation will serve this…

Apprenticeship Learning: Learning to Schedule from Human Experts

Science.gov (United States)

2016-06-09

identified by the heuristic . A spectrum of problems (i.e. traveling salesman, job-shop scheduling, multi-vehicle routing) was represented , as task locations...caus- ing the codification of this knowledge to become labori- ous. We propose a new approach for capturing domain- expert heuristics through a...demonstrate that this approach accu- rately learns multi-faceted heuristics on both a synthetic data set incorporating job-shop scheduling and vehicle

PERSONALIZATION SISTEM E-LEARNING BERBASIS ONTOLOGY

Directory of Open Access Journals (Sweden)

Ahmad Ashari

2010-11-01

Full Text Available Personalization of Ontology Based E-learning System. Today, a form of technology known as Web 2.0 thatthoroughly supports web-to-web interactions is present. Interactions, such as information sharing in the forms ofdocument sharing (slideshare, picture sharing (flickr, video sharing (youtube, Wikis, and online networking (weblogand web-forum are principally accomodating community empowerment services. These factors cause the appearanceof social interaction through Internet as well as learning interaction and anywhere-anytime training which is recentlycalled e-Learning. Basically, e-Learning needs a self-employed learning method and learning habits that emphasize onthe learner as the most important role. However, e-learning system which is expected to boost the intensity of selfemployedlearning is uncapable to represent the importance. This is proven with the current e-Learning system inIndonesia that only accomodates the delivery of learning materials identical to all active learners, ignores the cognitiveaspects and does not offer any approach or experience of interactive self-learning and disregards the aspect of users’ability to adapt. The proposed e-learning system which is Web 2.0-based utilizes ontology as the representation ofmeaning of knowledge formed by the learner.

Meta-learning in decision tree induction

CERN Document Server

Grąbczewski, Krzysztof

2014-01-01

The book focuses on different variants of decision tree induction but also describes  the meta-learning approach in general which is applicable to other types of machine learning algorithms. The book discusses different variants of decision tree induction and represents a useful source of information to readers wishing to review some of the techniques used in decision tree learning, as well as different ensemble methods that involve decision trees. It is shown that the knowledge of different components used within decision tree learning needs to be systematized to enable the system to generate and evaluate different variants of machine learning algorithms with the aim of identifying the top-most performers or potentially the best one. A unified view of decision tree learning enables to emulate different decision tree algorithms simply by setting certain parameters. As meta-learning requires running many different processes with the aim of obtaining performance results, a detailed description of the experimen...

Keeping Pace with K-12 Online Learning, 2016

Science.gov (United States)

Gemin, Butch; Pape, Larry

2017-01-01

"Keeping Pace with K-12 Online Learning 2016" marks the thirteenth consecutive year Evergreen has published its annual research of the K-12 education online learning market. The thirteen years of researching, writing and publishing this report represents a time of remarkable change. There has been a constant presence that has become the…

Aesthetic Learning about, in, with and through the Arts: A Curriculum Study

Science.gov (United States)

Lindstrom, Lars

2012-01-01

Aesthetic learning is a major issue in arts education. The "method of art" is often expected to facilitate in-depth learning not only in the arts but across the curriculum. This article defines aesthetic learning in terms of a conceptual framework based on two dimensions, one representing the goal and the other the means of aesthetic learning. The…

Body in Mind: How Gestures Empower Foreign Language Learning

Science.gov (United States)

Macedonia, Manuela; Knosche, Thomas R.

2011-01-01

It has previously been demonstrated that enactment (i.e., performing representative gestures during encoding) enhances memory for concrete words, in particular action words. Here, we investigate the impact of enactment on abstract word learning in a foreign language. We further ask if learning novel words with gestures facilitates sentence…

Research on English Teaching and Learning: Taiwan (2004-2009)

Science.gov (United States)

Chen, Suchiao; Tsai, Yachin

2012-01-01

This article analyzes research in second/foreign language teaching and learning conducted in Taiwan over the period 2004-2009. Representative articles published in local refereed journals and conference proceedings--not readily accessible outside Taiwan--are reviewed to reflect current trends in English teaching and learning. The main themes…

Learning and motivation in the human striatum.

Science.gov (United States)

Shohamy, Daphna

2011-06-01

The past decade has seen a dramatic change in our understanding of the role of the striatum in behavior. Early perspectives emphasized a role for the striatum in habitual learning of stimulus-response associations and sequences of actions. Recent advances from human neuroimaging research suggest a broader role for the striatum in motivated learning. New findings demonstrate that the striatum represents multiple learning signals and highlight the contribution of the striatum across many cognitive domains and contexts. Recent findings also emphasize interactions between the striatum and other specialized brain systems for learning. Together, these findings suggest that the striatum contributes to a distributed network that learns to select actions based on their predicted value in order to optimize behavior. Copyright © 2011 Elsevier Ltd. All rights reserved.

Efficient model learning methods for actor-critic control.

Science.gov (United States)

Grondman, Ivo; Vaandrager, Maarten; Buşoniu, Lucian; Babuska, Robert; Schuitema, Erik

2012-06-01

We propose two new actor-critic algorithms for reinforcement learning. Both algorithms use local linear regression (LLR) to learn approximations of the functions involved. A crucial feature of the algorithms is that they also learn a process model, and this, in combination with LLR, provides an efficient policy update for faster learning. The first algorithm uses a novel model-based update rule for the actor parameters. The second algorithm does not use an explicit actor but learns a reference model which represents a desired behavior, from which desired control actions can be calculated using the inverse of the learned process model. The two novel methods and a standard actor-critic algorithm are applied to the pendulum swing-up problem, in which the novel methods achieve faster learning than the standard algorithm.

The contribution of temporary storage and executive processes to category learning.

Science.gov (United States)

Wang, Tengfei; Ren, Xuezhu; Schweizer, Karl

2015-09-01

Three distinctly different working memory processes, temporary storage, mental shifting and inhibition, were proposed to account for individual differences in category learning. A sample of 213 participants completed a classic category learning task and two working memory tasks that were experimentally manipulated for tapping specific working memory processes. Fixed-links models were used to decompose data of the category learning task into two independent components representing basic performance and improvement in performance in category learning. Processes of working memory were also represented by fixed-links models. In a next step the three working memory processes were linked to components of category learning. Results from modeling analyses indicated that temporary storage had a significant effect on basic performance and shifting had a moderate effect on improvement in performance. In contrast, inhibition showed no effect on any component of the category learning task. These results suggest that temporary storage and the shifting process play different roles in the course of acquiring new categories. Copyright © 2015 Elsevier B.V. All rights reserved.

Outcomes of an Academic Service-Learning Project on Four Urban Community Colleges

Science.gov (United States)

Greenwood, Debra Abston

2015-01-01

Service-learning has a rich history in higher education, with a multitude of studies indicating positive learning, community engagement, and moral development outcomes of student participants. The majority of the research findings, however, have represented four-year colleges. And while there are limited outcome studies of service-learning in…

Towards AI-powered personalization in MOOC learning

Science.gov (United States)

Yu, Han; Miao, Chunyan; Leung, Cyril; White, Timothy John

2017-12-01

Massive Open Online Courses (MOOCs) represent a form of large-scale learning that is changing the landscape of higher education. In this paper, we offer a perspective on how advances in artificial intelligence (AI) may enhance learning and research on MOOCs. We focus on emerging AI techniques including how knowledge representation tools can enable students to adjust the sequence of learning to fit their own needs; how optimization techniques can efficiently match community teaching assistants to MOOC mediation tasks to offer personal attention to learners; and how virtual learning companions with human traits such as curiosity and emotions can enhance learning experience on a large scale. These new capabilities will also bring opportunities for educational researchers to analyse students' learning skills and uncover points along learning paths where students with different backgrounds may require different help. Ethical considerations related to the application of AI in MOOC education research are also discussed.

"Notice of Violation of IEEE Publication Principles" Multiobjective Reinforcement Learning: A Comprehensive Overview.

Science.gov (United States)

Liu, Chunming; Xu, Xin; Hu, Dewen

2013-04-29

Reinforcement learning is a powerful mechanism for enabling agents to learn in an unknown environment, and most reinforcement learning algorithms aim to maximize some numerical value, which represents only one long-term objective. However, multiple long-term objectives are exhibited in many real-world decision and control problems; therefore, recently, there has been growing interest in solving multiobjective reinforcement learning (MORL) problems with multiple conflicting objectives. The aim of this paper is to present a comprehensive overview of MORL. In this paper, the basic architecture, research topics, and naive solutions of MORL are introduced at first. Then, several representative MORL approaches and some important directions of recent research are reviewed. The relationships between MORL and other related research are also discussed, which include multiobjective optimization, hierarchical reinforcement learning, and multi-agent reinforcement learning. Finally, research challenges and open problems of MORL techniques are highlighted.

Using Learning Strategies to Inhibit the Nocebo Effect.

Science.gov (United States)

Quinn, Veronica F; Colagiuri, Ben

2018-01-01

Learning is a key mechanism underpinning the development of the nocebo effect. The learning literature has cataloged and explored numerous ways in which the environment can be manipulated to prevent, reduce, or eradicate learning. Knowledge of these processes could be used to both inhibit the development of nocebo effects and reduce already established nocebo learning. This review describes the available evidence on how such learning strategies have, or could be, applied to reduce the nocebo effect in both healthy participants and patients to date. These learning strategies include overshadowing, latent inhibition, extinction, and contingency degradation. These strategies represent important new avenues for investigation and should be used by researchers to design and test interventions to reduce nocebo effects. © 2018 Elsevier Inc. All rights reserved.

Machine learning with quantum relative entropy

International Nuclear Information System (INIS)

Tsuda, Koji

2009-01-01

Density matrices are a central tool in quantum physics, but it is also used in machine learning. A positive definite matrix called kernel matrix is used to represent the similarities between examples. Positive definiteness assures that the examples are embedded in an Euclidean space. When a positive definite matrix is learned from data, one has to design an update rule that maintains the positive definiteness. Our update rule, called matrix exponentiated gradient update, is motivated by the quantum relative entropy. Notably, the relative entropy is an instance of Bregman divergences, which are asymmetric distance measures specifying theoretical properties of machine learning algorithms. Using the calculus commonly used in quantum physics, we prove an upperbound of the generalization error of online learning.

Prototype-based models in machine learning.

Science.gov (United States)

Biehl, Michael; Hammer, Barbara; Villmann, Thomas

2016-01-01

An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of potentially high-dimensional, complex datasets. We discuss basic schemes of competitive vector quantization as well as the so-called neural gas approach and Kohonen's topology-preserving self-organizing map. Supervised learning in prototype systems is exemplified in terms of learning vector quantization. Most frequently, the familiar Euclidean distance serves as a dissimilarity measure. We present extensions of the framework to nonstandard measures and give an introduction to the use of adaptive distances in relevance learning. © 2016 Wiley Periodicals, Inc.

Machine learning with quantum relative entropy

Energy Technology Data Exchange (ETDEWEB)

Tsuda, Koji [Max Planck Institute for Biological Cybernetics, Spemannstr. 38, Tuebingen, 72076 (Germany)], E-mail: koji.tsuda@tuebingen.mpg.de

2009-12-01

Density matrices are a central tool in quantum physics, but it is also used in machine learning. A positive definite matrix called kernel matrix is used to represent the similarities between examples. Positive definiteness assures that the examples are embedded in an Euclidean space. When a positive definite matrix is learned from data, one has to design an update rule that maintains the positive definiteness. Our update rule, called matrix exponentiated gradient update, is motivated by the quantum relative entropy. Notably, the relative entropy is an instance of Bregman divergences, which are asymmetric distance measures specifying theoretical properties of machine learning algorithms. Using the calculus commonly used in quantum physics, we prove an upperbound of the generalization error of online learning.

Learning Styles among Students in an Advanced Soil Management Class: Impact on Students' Performance

Science.gov (United States)

Eudoxie, Gaius D.

2011-01-01

Learning styles represent an integral component of the learning environment, which has been shown to differ across institutions and disciplines. To identify learner preferences within a discipline would aid in evaluating instructional resources geared toward active learning. The learning profiles of second-year soil science students (n = 62) were…

Technology for Education and Learning

CERN Document Server

2012 international conference on Technology for Education and Learning (ICTEL 2012)

2012-01-01

This volume contains 108 selected papers presented at the 2012 international conference on Technology for Education and Learning (ICTEL 2012), Macau, China, March 1-2, 2012. The conference brought together researchers working in various different areas of Technology for Education and Learning with a main emphasis on technology for business and economy in order to foster international collaborations and exchange of new ideas. This proceedings book has its focus on Technology for Economy, Finance and Education representing some of the major subareas presented at the conference.

Bridging Cognitive And Neural Aspects Of Classroom Learning

Science.gov (United States)

Posner, Michael I.

2009-11-01

A major achievement of the first twenty years of neuroimaging is to reveal the brain networks that underlie fundamental aspects of attention, memory and expertise. We examine some principles underlying the activation of these networks. These networks represent key constraints for the design of teaching. Individual differences in these networks reflect a combination of genes and experiences. While acquiring expertise is easier for some than others the importance of effort in its acquisition is a basic principle. Networks are strengthened through exercise, but maintaining interest that produces sustained attention is key to making exercises successful. The state of the brain prior to learning may also represent an important constraint on successful learning and some interventions designed to investigate the role of attention state in learning are discussed. Teaching remains a creative act between instructor and student, but an understanding of brain mechanisms might improve opportunity for success for both participants.

A machine learning model with human cognitive biases capable of learning from small and biased datasets.

Science.gov (United States)

Taniguchi, Hidetaka; Sato, Hiroshi; Shirakawa, Tomohiro

2018-05-09

Human learners can generalize a new concept from a small number of samples. In contrast, conventional machine learning methods require large amounts of data to address the same types of problems. Humans have cognitive biases that promote fast learning. Here, we developed a method to reduce the gap between human beings and machines in this type of inference by utilizing cognitive biases. We implemented a human cognitive model into machine learning algorithms and compared their performance with the currently most popular methods, naïve Bayes, support vector machine, neural networks, logistic regression and random forests. We focused on the task of spam classification, which has been studied for a long time in the field of machine learning and often requires a large amount of data to obtain high accuracy. Our models achieved superior performance with small and biased samples in comparison with other representative machine learning methods.

Learning Motivation from a Cross-Cultural Perspective: A Moving Target?

Science.gov (United States)

Täht, Karin; Must, Olev; Peets, Kätlin; Kattel, Rainer

2014-01-01

This paper investigates the relationship between educational achievement and the motivation to learn. We used the 2006 Programme for International Student Assessment (PISA) that contains representative samples from 55 nations. A strong negative correlation between educational achievement and motivation toward science learning emerged at the…

48 CFR 1852.227-72 - Designation of new technology representative and patent representative.

Science.gov (United States)

2010-10-01

... CONTRACT CLAUSES Texts of Provisions and Clauses 1852.227-72 Designation of new technology representative... of New Technology Representative and Patent Representative (JUL 1997) (a) For purposes of administration of the clause of this contract entitled “New Technology” or “Patent Rights—Retention by the...

Cross-View Action Recognition via Transferable Dictionary Learning.

Science.gov (United States)

Zheng, Jingjing; Jiang, Zhuolin; Chellappa, Rama

2016-05-01

Discriminative appearance features are effective for recognizing actions in a fixed view, but may not generalize well to a new view. In this paper, we present two effective approaches to learn dictionaries for robust action recognition across views. In the first approach, we learn a set of view-specific dictionaries where each dictionary corresponds to one camera view. These dictionaries are learned simultaneously from the sets of correspondence videos taken at different views with the aim of encouraging each video in the set to have the same sparse representation. In the second approach, we additionally learn a common dictionary shared by different views to model view-shared features. This approach represents the videos in each view using a view-specific dictionary and the common dictionary. More importantly, it encourages the set of videos taken from the different views of the same action to have the similar sparse representations. The learned common dictionary not only has the capability to represent actions from unseen views, but also makes our approach effective in a semi-supervised setting where no correspondence videos exist and only a few labeled videos exist in the target view. The extensive experiments using three public datasets demonstrate that the proposed approach outperforms recently developed approaches for cross-view action recognition.

Sleep and Learning

Science.gov (United States)

Margoliash, Daniel

2010-03-01

The neural basis of cognition represents a grand challenge problem involving multiple disciplines and approaches to the analysis of behavior. Song learning by juvenile songbirds such as zebra finches has proven to have considerable utility for exploring how behavior is represented at multiple levels of brain function. As classically described, young birds are exposed to a ``tutor'' (adult) song and commit that song to memory early in life, then engage in an extended period (weeks) of plastic singing as they slowly learn to match vocal output to the tutor song memory via auditory feedback. In recent years, the role of sleep in learning processes has been actively explored. Young birds isolated from adult songs, then suddenly given access to such songs at circa 40 days of age, show a sudden change in their singing behavior starting on the day following first exposure. Such birds sing songs that have less structure in the mornings than do the songs sung in the afternoons before or after that morning. This fluctuation is directly the result of sleep (not circadian rhythm), and the magnitude of fluctuation is positively correlated with the ultimate similarity to the tutor song. Examining spontaneous neuronal activity in certain brain structures during the night in sleeping adults shows ``replay'' of the patterns of activity the same neurons exhibit during daytime singing, and ``preplay'' of new patterns that will first be incorporated into daytime singing the following day. In experiments on juveniles, nighttime neuronal activity shows dramatic changes associated with song learning, even on the night after the first day of tutor song exposure (preceding changes in singing behavior). Offline processing, especially sleep, has been well documented to participate in memory consolidation in a very broad range of behaviors including in humans. Placing the bird song results in a theoretical framework thereby helps to inform a very broad range of phenomena.

Pre-Learning Low-Frequency Vocabulary in Second Language Television Programmes

Science.gov (United States)

Webb, Stuart

2010-01-01

This study investigated the potential of pre-learning frequently occurring low-frequency vocabulary as a means to increase comprehension of television and incidental vocabulary learning through watching television. Eight television programmes, each representing different television genres, were analysed using the RANGE program to determine the 10…

Using the Hands to Represent Objects in Space: Gesture as a Substrate for Signed Language Acquisition.

Science.gov (United States)

Janke, Vikki; Marshall, Chloë R

2017-01-01

An ongoing issue of interest in second language research concerns what transfers from a speaker's first language to their second. For learners of a sign language, gesture is a potential substrate for transfer. Our study provides a novel test of gestural production by eliciting silent gesture from novices in a controlled environment. We focus on spatial relationships, which in sign languages are represented in a very iconic way using the hands, and which one might therefore predict to be easy for adult learners to acquire. However, a previous study by Marshall and Morgan (2015) revealed that this was only partly the case: in a task that required them to express the relative locations of objects, hearing adult learners of British Sign Language (BSL) could represent objects' locations and orientations correctly, but had difficulty selecting the correct handshapes to represent the objects themselves. If hearing adults are indeed drawing upon their gestural resources when learning sign languages, then their difficulties may have stemmed from their having in manual gesture only a limited repertoire of handshapes to draw upon, or, alternatively, from having too broad a repertoire. If the first hypothesis is correct, the challenge for learners is to extend their handshape repertoire, but if the second is correct, the challenge is instead to narrow down to the handshapes appropriate for that particular sign language. 30 sign-naïve hearing adults were tested on Marshall and Morgan's task. All used some handshapes that were different from those used by native BSL signers and learners, and the set of handshapes used by the group as a whole was larger than that employed by native signers and learners. Our findings suggest that a key challenge when learning to express locative relations might be reducing from a very large set of gestural resources, rather than supplementing a restricted one, in order to converge on the conventionalized classifier system that forms part of the

Factors Contributing to Changes in a Deep Approach to Learning in Different Learning Environments

Science.gov (United States)

Postareff, Liisa; Parpala, Anna; Lindblom-Ylänne, Sari

2015-01-01

The study explored factors explaining changes in a deep approach to learning. The data consisted of interviews with 12 students from four Bachelor-level courses representing different disciplines. We analysed and compared descriptions of students whose deep approach either increased, decreased or remained relatively unchanged during their courses.…

E-learning: new strategies and trends

Directory of Open Access Journals (Sweden)

Matteo Martini

2017-01-01

Full Text Available The aim of this paper is to present a personal point of view on the possible future trends in distance learning. The starting point of this study is represented by a review of the main innovations in digital and information technologies. This step is necessary since the evolution of distance learning is strictly correlated to the evolution of the technology that can be exploited to increase learning quality. The main arguments discussed in this paper are: massive open on-line courses (MOOCs, flipped classrooms and the evolution of the learning objects based on web and on internet technology. Concerning MOOCs, a critical analysis of the status of this type of learning is necessary to understand their possible evolution and/or their substitution. A huge number of case studies demonstrated the validity of the flipped classroom and the possibility to adopt this approach into e-learning is surely interesting. The last part of the paper is instead dedicated to future technologies like: mobile learning, 3D virtual laboratories and internet of things. As discussed, this latest innovations can push the evolution of distance learning offering real student-centered solutions.

Factors associated with learning management in Mexican micro-entrepreneurs

Directory of Open Access Journals (Sweden)

Alejandro Mungaray Lagarda

2016-10-01

Full Text Available The learning capacity of social based Mexican micro-entrepreneurs to generate new knowledge and incorporate it to its products and services is evaluated. The above is done through a confirmatory factor analysis and structural linear equation system, and the presence of static and dynamic dimensions in learning capacity, which are represented by individual stocks and flows of knowledge. The positive relationship between them demonstrates the presence of learning processes that impact positively their economic performance.

Individual differences in implicit motor learning: task specificity in sensorimotor adaptation and sequence learning.

Science.gov (United States)

Stark-Inbar, Alit; Raza, Meher; Taylor, Jordan A; Ivry, Richard B

2017-01-01

In standard taxonomies, motor skills are typically treated as representative of implicit or procedural memory. We examined two emblematic tasks of implicit motor learning, sensorimotor adaptation and sequence learning, asking whether individual differences in learning are correlated between these tasks, as well as how individual differences within each task are related to different performance variables. As a prerequisite, it was essential to establish the reliability of learning measures for each task. Participants were tested twice on a visuomotor adaptation task and on a sequence learning task, either the serial reaction time task or the alternating reaction time task. Learning was evident in all tasks at the group level and reliable at the individual level in visuomotor adaptation and the alternating reaction time task but not in the serial reaction time task. Performance variability was predictive of learning in both domains, yet the relationship was in the opposite direction for adaptation and sequence learning. For the former, faster learning was associated with lower variability, consistent with models of sensorimotor adaptation in which learning rates are sensitive to noise. For the latter, greater learning was associated with higher variability and slower reaction times, factors that may facilitate the spread of activation required to form predictive, sequential associations. Interestingly, learning measures of the different tasks were not correlated. Together, these results oppose a shared process for implicit learning in sensorimotor adaptation and sequence learning and provide insight into the factors that account for individual differences in learning within each task domain. We investigated individual differences in the ability to implicitly learn motor skills. As a prerequisite, we assessed whether individual differences were reliable across test sessions. We found that two commonly used tasks of implicit learning, visuomotor adaptation and the

Group-Based Active Learning of Classification Models.

Science.gov (United States)

Luo, Zhipeng; Hauskrecht, Milos

2017-05-01

Learning of classification models from real-world data often requires additional human expert effort to annotate the data. However, this process can be rather costly and finding ways of reducing the human annotation effort is critical for this task. The objective of this paper is to develop and study new ways of providing human feedback for efficient learning of classification models by labeling groups of examples. Briefly, unlike traditional active learning methods that seek feedback on individual examples, we develop a new group-based active learning framework that solicits label information on groups of multiple examples. In order to describe groups in a user-friendly way, conjunctive patterns are used to compactly represent groups. Our empirical study on 12 UCI data sets demonstrates the advantages and superiority of our approach over both classic instance-based active learning work, as well as existing group-based active-learning methods.

Supervised Learning with Complex-valued Neural Networks

CERN Document Server

Suresh, Sundaram; Savitha, Ramasamy

2013-01-01

Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

A model for hypermedia learning environments based on electronic books

Directory of Open Access Journals (Sweden)

Ignacio Aedo

1997-12-01

Full Text Available Current hypermedia learning environments do not have a common development basis. Their designers have often used ad-hoc solutions to solve the learning problems they have encountered. However, hypermedia technology can take advantage of employing a theoretical scheme - a model - which takes into account various kinds of learning activities, and solves some of the problems associated with its use in the learning process. The model can provide designers with the tools for creating a hypermedia learning system, by allowing the elements and functions involved in the definition of a specific application to be formally represented.

Representing time

Directory of Open Access Journals (Sweden)

Luca Poncellini

2010-06-01

Full Text Available The analysis of natural phenomena applied to architectural planning and design is facing the most fascinating and elusive of the four dimensions through which man attempts to define life within the universe: time. We all know what time is, said St. Augustine, but nobody knows how to describe it. Within architectural projects and representations, time rarely appears in explicit form. This paper presents the results of a research conducted by students of NABA and of the Polytechnic of Milan with the purpose of representing time considered as a key element within architectural projects. Student investigated new approaches and methodologies to represent time using the two-dimensional support of a sheet of paper.

Exploiting the Semantic Web to Represent Information from On-line Collaborative Learning

Directory of Open Access Journals (Sweden)

Jordi Conesa

2012-08-01

Full Text Available In this paper we propose a framework for modeling, representing populating and enriching information from online collaborative sessions within Web forums. The main piece of the framework is an ontology called Collaborative Session Conceptual Schema (CS that allows for specifying collaborative sessions. The paper describes the information this ontology needs to know, the alignment of the ontology with the ontologies of relevant specifications, how the ontology can be automatically populated from the data existent in forums, and how to model such data about what is happening during the collaboration by using a dialogue-based model. This model is based on primitive exchange moves found in any forum posts, which are then categorized at different description levels with the aim to effectively collect and classify the type and intention of the forum posts. An experiment has been conducted to assess the validity and usefulness of the presented approach. The research reported in this paper is currently undertaken within a FP7 European project called ALICE.

The patient as experience broker in clinical learning.

Science.gov (United States)

Stockhausen, Lynette J

2009-05-01

A review of the literature reveals deficit information on patient's involvement in student's learning. The study presented in this paper investigates how the educationally unprepared patient engages with students and experienced clinicians to become involved in learning and teaching encounters. As a qualitative study 14 adult patients were interviewed to determine how they perceived experienced clinicians and students engage in learning and teaching moments and how the patient contributes to students learning to care. Revealed is a new and exciting dimension in learning and teaching in the clinical environment. Patients as experience brokers are positioned in a unique learning triad as they mediate and observe teaching and learning to care between students and experienced clinicians whilst also becoming participants in teaching to care. Further investigation is warranted to determine the multi-dimensional aspects of patients' involvement in student learning in various clinical environments. Future studies have the potential to represent a new educational perspective (andragogy).

A Primer on the Statistical Modelling of Learning Curves in Health Professions Education

Science.gov (United States)

Pusic, Martin V.; Boutis, Kathy; Pecaric, Martin R.; Savenkov, Oleksander; Beckstead, Jason W.; Jaber, Mohamad Y.

2017-01-01

Learning curves are a useful way of representing the rate of learning over time. Features include an index of baseline performance (y-intercept), the efficiency of learning over time (slope parameter) and the maximal theoretical performance achievable (upper asymptote). Each of these parameters can be statistically modelled on an individual and…

Active Learning Strategies for Phenotypic Profiling of High-Content Screens.

Science.gov (United States)

Smith, Kevin; Horvath, Peter

2014-06-01

High-content screening is a powerful method to discover new drugs and carry out basic biological research. Increasingly, high-content screens have come to rely on supervised machine learning (SML) to perform automatic phenotypic classification as an essential step of the analysis. However, this comes at a cost, namely, the labeled examples required to train the predictive model. Classification performance increases with the number of labeled examples, and because labeling examples demands time from an expert, the training process represents a significant time investment. Active learning strategies attempt to overcome this bottleneck by presenting the most relevant examples to the annotator, thereby achieving high accuracy while minimizing the cost of obtaining labeled data. In this article, we investigate the impact of active learning on single-cell-based phenotype recognition, using data from three large-scale RNA interference high-content screens representing diverse phenotypic profiling problems. We consider several combinations of active learning strategies and popular SML methods. Our results show that active learning significantly reduces the time cost and can be used to reveal the same phenotypic targets identified using SML. We also identify combinations of active learning strategies and SML methods which perform better than others on the phenotypic profiling problems we studied. © 2014 Society for Laboratory Automation and Screening.

Learning Points and Routes to Recommend Trajectories

OpenAIRE

Chen, Dawei; Ong, Cheng Soon; Xie, Lexing

2016-01-01

The problem of recommending tours to travellers is an important and broadly studied area. Suggested solutions include various approaches of points-of-interest (POI) recommendation and route planning. We consider the task of recommending a sequence of POIs, that simultaneously uses information about POIs and routes. Our approach unifies the treatment of various sources of information by representing them as features in machine learning algorithms, enabling us to learn from past behaviour. Info...

Common Core Science Standards: Implications for Students with Learning Disabilities

Science.gov (United States)

Scruggs, Thomas E.; Brigham, Frederick J.; Mastropieri, Margo A.

2013-01-01

The Common Core Science Standards represent a new effort to increase science learning for all students. These standards include a focus on English and language arts aspects of science learning, and three dimensions of science standards, including practices of science, crosscutting concepts of science, and disciplinary core ideas in the various…

Pointer Animation Implementation at Development of Multimedia Learning of Java Programming

Science.gov (United States)

Rusli, Muhammad; Atmojo, Yohanes Priyo

2015-01-01

This research represents the development research using the references of previous research results related to the development of interactive multimedia learning (learner controlled), specially about the effectiveness and efficiency of multimedia learning of a content that developed by pointer animation implementation showing the content in…

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning.

Science.gov (United States)

Wang, Xinggang; Yang, Wei; Weinreb, Jeffrey; Han, Juan; Li, Qiubai; Kong, Xiangchuang; Yan, Yongluan; Ke, Zan; Luo, Bo; Liu, Tao; Wang, Liang

2017-11-13

Prostate cancer (PCa) is a major cause of death since ancient time documented in Egyptian Ptolemaic mummy imaging. PCa detection is critical to personalized medicine and varies considerably under an MRI scan. 172 patients with 2,602 morphologic images (axial 2D T2-weighted imaging) of the prostate were obtained. A deep learning with deep convolutional neural network (DCNN) and a non-deep learning with SIFT image feature and bag-of-word (BoW), a representative method for image recognition and analysis, were used to distinguish pathologically confirmed PCa patients from prostate benign conditions (BCs) patients with prostatitis or prostate benign hyperplasia (BPH). In fully automated detection of PCa patients, deep learning had a statistically higher area under the receiver operating characteristics curve (AUC) than non-deep learning (P = 0.0007 deep learning method and 0.70 (95% CI 0.63-0.77) for non-deep learning method, respectively. Our results suggest that deep learning with DCNN is superior to non-deep learning with SIFT image feature and BoW model for fully automated PCa patients differentiation from prostate BCs patients. Our deep learning method is extensible to image modalities such as MR imaging, CT and PET of other organs.

Comparison of land–atmosphere interaction at different surface types in the mid- to lower reaches of the Yangtze River valley

Directory of Open Access Journals (Sweden)

W. Guo

2016-08-01

Full Text Available The mid- to lower reaches of the Yangtze River valley are located within the typical East Asian monsoon zone. Rapid urbanization, industrialization, and development of agriculture have led to fast and complicated land use and land cover change in this region. To investigate land–atmosphere interaction in this region where human activities and monsoon climate have considerable interaction with each other, micrometeorological elements over four sites with different surface types around Nanjing, including urban surface at Dangxiao (hereafter DX-urban, suburban surface at Xianling (XL-suburb, and grassland and farmland at Lishui County (LS-grass and LS-crop, are analyzed and their differences are revealed. The impacts of surface parameters of different surface types on the radiation budget and land surface–atmosphere heat, water, and mass exchanges are investigated and compared. The results indicate the following. (1 The largest differences in daily average surface air temperature (Ta, surface skin temperature (Ts, and relative humidity (RH, which are found during the dry periods between DX-urban and LS-crop, can be up to 3.21 °C, 7.26 °C, and 22.79 %, respectively. The diurnal ranges of the above three elements are the smallest at DX-urban and the largest at LS-grass, XL-suburb, and LS-crop. (2 Differences in radiative fluxes are mainly reflected in upward shortwave radiation (USR that is related to surface albedo and upward longwave radiation (ULR that is related to Ts. When comparing four sites, it can be found that both the smallest USR and the largest ULR occur at the DX-urban site. The diurnal variation in ULR is same as that of Ts at all four sites. (3 The differences in daily average sensible heat (H and latent heat (LE between DX-urban and LS-crop are larger than 45 and 95 Wm−2, respectively. The proportion of latent heat flux in the net radiation (LE/Rn keeps increasing with the change in season from the spring to summer

Correlation of Students' Brain Types to Their Conceptions of Learning Science and Approaches to Learning Science

Science.gov (United States)

Park, Jiyeon; Jeon, Dongryul

2015-01-01

The systemizing and empathizing brain type represent two contrasted students' characteristics. The present study investigated differences in the conceptions and approaches to learning science between the systemizing and empathizing brain type students. The instruments are questionnaires on the systematizing and empathizing, questionnaires on the…

Learning analytics fundaments, applications, and trends : a view of the current state of the art to enhance e-learning

CERN Document Server

2017-01-01

This book provides a conceptual and empirical perspective on learning analytics, its goal being to disseminate the core concepts, research, and outcomes of this emergent field. Divided into nine chapters, it offers reviews oriented on selected topics, recent advances, and innovative applications. It presents the broad learning analytics landscape and in-depth studies on higher education, adaptive assessment, teaching and learning. In addition, it discusses valuable approaches to coping with personalization and huge data, as well as conceptual topics and specialized applications that have shaped the current state of the art. By identifying fundamentals, highlighting applications, and pointing out current trends, the book offers an essential overview of learning analytics to enhance learning achievement in diverse educational settings. As such, it represents a valuable resource for researchers, practitioners, and students interested in updating their knowledge and finding inspirations for their future work.

Corporate Blended Learning in Portugal: Current Status and Future Directions

Science.gov (United States)

Marcal, Julia; Caetano, Antonio

2010-01-01

The aim of this study is to characterize the current status of blended learning in Portugal, given that b-learning has grown exponentially in the Portuguese market over recent years. 38 organizations (representing 68% of all institutions certified to provide distance training by the Government Labour Office--DGERT-) participated in this study. The…

Prototype-based active learning for lemmatization

CSIR Research Space (South Africa)

Daelemans, W

2009-09-01

Full Text Available ] and Word Length [Long to Short] with the prototypical curves (e.g. Word Frequency [High to Low] and [Word Length Short to Long]). (With regard to the learning curves representing word frequency, refer to 4.1 for an explanation of why [High to Low... of language usage [15]. Secondly, in memory-based language processing [16] it has been argued, on the basis of com- parative machine learning experiments on natural lan- guage processing data, that exceptions are crucial for obtaining high generalization...

Comparison as a Universal Learning Action

Science.gov (United States)

Merkulova, T. V.

2016-01-01

This article explores "comparison" as a universal metasubject learning action, a key curricular element envisaged by the Russian Federal State Educational Standards. Representing the modern learner's fundamental pragmatic skill embedding such core capacities as information processing, critical thinking, robust decision-making, and…

[Discovery-based teaching and learning strategies in health: problematization and problem-based learning].

Science.gov (United States)

Cyrino, Eliana Goldfarb; Toralles-Pereira, Maria Lúcia

2004-01-01

Considering the changes in teaching in the health field and the demand for new ways of dealing with knowledge in higher learning, the article discusses two innovative methodological approaches: problem-based learning (PBL) and problematization. Describing the two methods' theoretical roots, the article attempts to identify their main foundations. As distinct proposals, both contribute to a review of the teaching and learning process: problematization, focused on knowledge construction in the context of the formation of a critical awareness; PBL, focused on cognitive aspects in the construction of concepts and appropriation of basic mechanisms in science. Both problematization and PBL lead to breaks with the traditional way of teaching and learning, stimulating participatory management by actors in the experience and reorganization of the relationship between theory and practice. The critique of each proposal's possibilities and limits using the analysis of their theoretical and methodological foundations leads us to conclude that pedagogical experiences based on PBL and/or problematization can represent an innovative trend in the context of health education, fostering breaks and more sweeping changes.

A Visual Detection Learning Model

Science.gov (United States)

Beard, Bettina L.; Ahumada, Albert J., Jr.; Trejo, Leonard (Technical Monitor)

1998-01-01

Our learning model has memory templates representing the target-plus-noise and noise-alone stimulus sets. The best correlating template determines the response. The correlations and the feedback participate in the additive template updating rule. The model can predict the relative thresholds for detection in random, fixed and twin noise.

Graph-based semi-supervised learning

CERN Document Server

Subramanya, Amarnag

2014-01-01

While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer visi

Learning curves for solid oxide fuel cells

Energy Technology Data Exchange (ETDEWEB)

Rivera-Tinoco, R.; Schoots, K. [Energy research Centre of the Netherlands (Netherlands). Policy Studies; Zwaan, B.C.C. van der [Energy research Centre of the Netherlands (Netherlands). Policy Studies; Columbia Univ., New York City, NY (United States). Lenfest Center for Sustainable Energy

2010-07-01

We present learning curves for solid oxide fuel cells (SOFCs) and combined heat and power (CHP) SOFC systems with an electric capacity between 1 and 250 kW. On the basis of the cost breakdown of production cost data from fuel cell manufacturers, we developed a bottom-up model that allows for determining overall manufacturing costs from their respective cost components, among which material, energy, labor, and capital charges. The results obtained from our model prove to deviate by at most 13% from total cost figures quoted in the literature. For the early pilot stage of development, we find for SOFC manufacturing a learning rate between 14% and 17%, and for total SOFC system fabrication between 16% and 19%. We argue that the corresponding cost reductions result largely from learning-by-searching effects (R and D) rather than learning-by-doing. When considering a longer time frame that includes the early commercial production stage, we find learning rates between 14% and 39%, which represent a mix of phenomena such as learning-by-doing, learning-by-searching, economies-of-scale and automation. (orig.)

Application of ICT supported learning in fluid mechanics

DEFF Research Database (Denmark)

Brohus, Henrik; Svidt, Kjeld

2004-01-01

of tools for knowledge transfer facilitates deep understanding and increases learning efficiency. Air flow is by nature invisible and represents a further challenge in the effort of providing sufficient understanding of typical flow patterns and behaviour of room air flow. An example of visualisation......This paper focuses on the application of ICT, Information & Communication Technology, supported learning in the area of fluid mechanics education. Taking a starting point in a course in Ventilation Technology, including room air flow and contaminant distribution, it explains how ICT may be used...... actively in the learning environment to increase efficiency in the learning process. The paper comprises past experiences and lessons learnt as well as prospect for future development in the area. A model is presented that describes a high efficiency learning environment where ICT plays an important role...

Learning and teaching in the regional learning environment : enabling students and teachers to cross boundaries in multi-stakeholder practices

NARCIS (Netherlands)

Oonk, Carla

2016-01-01

Finding solutions for complex societal problems requires cross-boundary collaboration between multiple stakeholders who represent various practices, disciplines and perspectives. The authentic, multi-stakeholder Regional Learning Environment (RLE) is expected to develop higher education students’

Cognitive Support Embedded in Self-Regulated E-Learning Systems for Students with Special Learning Needs

Science.gov (United States)

Chatzara, K.; Karagiannidis, C.; Stamatis, D.

2016-01-01

This paper presents an anthropocentric approach in human-machine interaction in the area of self-regulated e-learning. In an attempt to enhance communication mediated through computers for pedagogical use we propose the incorporation of an intelligent emotional agent that is represented by a synthetic character with multimedia capabilities,…

Constitutive and Operational Variation of Learning in Foraging Predatory Mites.

Science.gov (United States)

Seiter, Michael; Schausberger, Peter

2016-01-01

Learning is widely documented across animal taxa but studies stringently scrutinizing the causes of constitutive or operational variation of learning among populations and individuals are scarce. The ability to learn is genetically determined and subject to constitutive variation while the performance in learning depends on the immediate circumstances and is subject to operational variation. We assessed variation in learning ability and performance of plant-inhabiting predatory mites, Amblyseius swirskii, caused by population origin, rearing diet, and type of experience. Using an early learning foraging paradigm, we determined that homogeneous single prey environments did not select for reduced learning ability, as compared to natural prey-diverse environments, whereas a multi-generational pollen diet resulted in loss of learning, as compared to a diet of live prey. Associative learning produced stronger effects than non-associative learning but both types of experience produced persistent memory. Our study represents a key example of environmentally caused variation in learning ability and performance.

Constitutive and Operational Variation of Learning in Foraging Predatory Mites.

Directory of Open Access Journals (Sweden)

Michael Seiter

Full Text Available Learning is widely documented across animal taxa but studies stringently scrutinizing the causes of constitutive or operational variation of learning among populations and individuals are scarce. The ability to learn is genetically determined and subject to constitutive variation while the performance in learning depends on the immediate circumstances and is subject to operational variation. We assessed variation in learning ability and performance of plant-inhabiting predatory mites, Amblyseius swirskii, caused by population origin, rearing diet, and type of experience. Using an early learning foraging paradigm, we determined that homogeneous single prey environments did not select for reduced learning ability, as compared to natural prey-diverse environments, whereas a multi-generational pollen diet resulted in loss of learning, as compared to a diet of live prey. Associative learning produced stronger effects than non-associative learning but both types of experience produced persistent memory. Our study represents a key example of environmentally caused variation in learning ability and performance.

The Relative Effects of Alternative Learning Structures on Attitudes and Achievements.

Science.gov (United States)

Carifio, James

This study compared two learning structures for an introductory course in tests and measurements in terms of their relative effects on attitudes and achievement. The first structure represented a functionally arranged instructional sequence (FAIS). The second represented a psychologically arranged instructional sequence (PAIS). The instructional…

Using Active-Learning Pedagogy to Develop Essay-Writing Skills in Introductory Political Theory Tutorials

Science.gov (United States)

Murphy, Michael P. A.

2017-01-01

Building on prior research into active learning pedagogy in political science, I discuss the development of a new active learning strategy called the "thesis-building carousel," designed for use in political theory tutorials. This use of active learning pedagogy in a graduate student-led political theory tutorial represents the overlap…

Learning Consumer Tastes Through Dynamic Assortments

NARCIS (Netherlands)

Ulu, C.; Honhon, D.B.L.P.; Alptekinoglu, A.

2012-01-01

How should a firm modify its product assortment over time when learning about consumer tastes? In this paper, we study dynamic assortment decisions in a horizontally differentiated product category for which consumers' diverse tastes can be represented as locations on a Hotelling line. We presume

Attention: A Machine Learning Perspective

DEFF Research Database (Denmark)

Hansen, Lars Kai

2012-01-01

We review a statistical machine learning model of top-down task driven attention based on the notion of ‘gist’. In this framework we consider the task to be represented as a classification problem with two sets of features — a gist of coarse grained global features and a larger set of low...

Visual analytics in healthcare education: exploring novel ways to analyze and represent big data in undergraduate medical education

Directory of Open Access Journals (Sweden)

Christos Vaitsis

2014-11-01

Full Text Available Introduction. The big data present in the medical curriculum that informs undergraduate medical education is beyond human abilities to perceive and analyze. The medical curriculum is the main tool used by teachers and directors to plan, design, and deliver teaching and assessment activities and student evaluations in medical education in a continuous effort to improve it. Big data remains largely unexploited for medical education improvement purposes. The emerging research field of visual analytics has the advantage of combining data analysis and manipulation techniques, information and knowledge representation, and human cognitive strength to perceive and recognize visual patterns. Nevertheless, there is a lack of research on the use and benefits of visual analytics in medical education.Methods. The present study is based on analyzing the data in the medical curriculum of an undergraduate medical program as it concerns teaching activities, assessment methods and learning outcomes in order to explore visual analytics as a tool for finding ways of representing big data from undergraduate medical education for improvement purposes. Cytoscape software was employed to build networks of the identified aspects and visualize them.Results. After the analysis of the curriculum data, eleven aspects were identified. Further analysis and visualization of the identified aspects with Cytoscape resulted in building an abstract model of the examined data that presented three different approaches; (i learning outcomes and teaching methods, (ii examination and learning outcomes, and (iii teaching methods, learning outcomes, examination results, and gap analysis.Discussion. This study identified aspects of medical curriculum that play an important role in how medical education is conducted. The implementation of visual analytics revealed three novel ways of representing big data in the undergraduate medical education context. It appears to be a useful tool to

Visual analytics in healthcare education: exploring novel ways to analyze and represent big data in undergraduate medical education.

Science.gov (United States)

Vaitsis, Christos; Nilsson, Gunnar; Zary, Nabil

2014-01-01

Introduction. The big data present in the medical curriculum that informs undergraduate medical education is beyond human abilities to perceive and analyze. The medical curriculum is the main tool used by teachers and directors to plan, design, and deliver teaching and assessment activities and student evaluations in medical education in a continuous effort to improve it. Big data remains largely unexploited for medical education improvement purposes. The emerging research field of visual analytics has the advantage of combining data analysis and manipulation techniques, information and knowledge representation, and human cognitive strength to perceive and recognize visual patterns. Nevertheless, there is a lack of research on the use and benefits of visual analytics in medical education. Methods. The present study is based on analyzing the data in the medical curriculum of an undergraduate medical program as it concerns teaching activities, assessment methods and learning outcomes in order to explore visual analytics as a tool for finding ways of representing big data from undergraduate medical education for improvement purposes. Cytoscape software was employed to build networks of the identified aspects and visualize them. Results. After the analysis of the curriculum data, eleven aspects were identified. Further analysis and visualization of the identified aspects with Cytoscape resulted in building an abstract model of the examined data that presented three different approaches; (i) learning outcomes and teaching methods, (ii) examination and learning outcomes, and (iii) teaching methods, learning outcomes, examination results, and gap analysis. Discussion. This study identified aspects of medical curriculum that play an important role in how medical education is conducted. The implementation of visual analytics revealed three novel ways of representing big data in the undergraduate medical education context. It appears to be a useful tool to explore such data

The organization of an autonomous learning system

Science.gov (United States)

Kanerva, Pentti

1988-01-01

The organization of systems that learn from experience is examined, human beings and animals being prime examples of such systems. How is their information processing organized. They build an internal model of the world and base their actions on the model. The model is dynamic and predictive, and it includes the systems' own actions and their effects. In modeling such systems, a large pattern of features represents a moment of the system's experience. Some of the features are provided by the system's senses, some control the system's motors, and the rest have no immediate external significance. A sequence of such patterns then represents the system's experience over time. By storing such sequences appropriately in memory, the system builds a world model based on experience. In addition to the essential function of memory, fundamental roles are played by a sensory system that makes raw information about the world suitable for memory storage and by a motor system that affects the world. The relation of sensory and motor systems to the memory is discussed, together with how favorable actions can be learned and unfavorable actions can be avoided. Results in classical learning theory are explained in terms of the model, more advanced forms of learning are discussed, and the relevance of the model to the frame problem of robotics is examined.

FRIST—flipping and rotation invariant sparsifying transform learning and applications

International Nuclear Information System (INIS)

Wen, Bihan; Bresler, Yoram; Ravishankar, Saiprasad

2017-01-01

Features based on sparse representation, especially using the synthesis dictionary model, have been heavily exploited in signal processing and computer vision. However, synthesis dictionary learning typically involves NP-hard sparse coding and expensive learning steps. Recently, sparsifying transform learning received interest for its cheap computation and its optimal updates in the alternating algorithms. In this work, we develop a methodology for learning flipping and rotation invariant sparsifying transforms, dubbed FRIST, to better represent natural images that contain textures with various geometrical directions. The proposed alternating FRIST learning algorithm involves efficient optimal updates. We provide a convergence guarantee, and demonstrate the empirical convergence behavior of the proposed FRIST learning approach. Preliminary experiments show the promising performance of FRIST learning for sparse image representation, segmentation, denoising, robust inpainting, and compressed sensing-based magnetic resonance image reconstruction. (paper)

Democratic Learning Processes: Conceptual and Historical Challenges

DEFF Research Database (Denmark)

Christensen, Ann-Dorte; Rasmussen, Palle

2009-01-01

In this article democratic learning is conceptualised with inspiration from two academic traditions, one being the conceptions of citizenship, political identities and deliberative democracy in political sociology; the other theories and research on social and lifelong learning. The first part......'s empowerment and inclusion in the Danish democratic model. On the background of these two analyses the authors finally discuss some current democratic problems with integrating the diversity represented by ethnic minority groups. The discussion emphasizes the learning theory perspective on the initiative...... of the article outlines the authors' understanding of the core concepts involved. In the second part these conceptual discussions are related to two themes: the question of public adaptation of historical experiences in connection with the German reunification and the learning perspectives related to women...

Learning through Experience: The Transition from Doctoral Student to Social Work Educator

Science.gov (United States)

Oktay, Julianne S.; Jacobson, Jodi M.; Fisher, Elizabeth

2013-01-01

The researchers conducted an exploratory study using grounded theory qualitative research methods to examine experiences of social work doctoral students as they learned to teach ("N"?=?14). A core category, "learning through experience," representing a basic social process, was identified. The doctoral students experienced…

Dynamics of EEG functional connectivity during statistical learning.

Science.gov (United States)

Tóth, Brigitta; Janacsek, Karolina; Takács, Ádám; Kóbor, Andrea; Zavecz, Zsófia; Nemeth, Dezso

2017-10-01

Statistical learning is a fundamental mechanism of the brain, which extracts and represents regularities of our environment. Statistical learning is crucial in predictive processing, and in the acquisition of perceptual, motor, cognitive, and social skills. Although previous studies have revealed competitive neurocognitive processes underlying statistical learning, the neural communication of the related brain regions (functional connectivity, FC) has not yet been investigated. The present study aimed to fill this gap by investigating FC networks that promote statistical learning in humans. Young adults (N=28) performed a statistical learning task while 128-channels EEG was acquired. The task involved probabilistic sequences, which enabled to measure incidental/implicit learning of conditional probabilities. Phase synchronization in seven frequency bands was used to quantify FC between cortical regions during the first, second, and third periods of the learning task, respectively. Here we show that statistical learning is negatively correlated with FC of the anterior brain regions in slow (theta) and fast (beta) oscillations. These negative correlations increased as the learning progressed. Our findings provide evidence that dynamic antagonist brain networks serve a hallmark of statistical learning. Copyright © 2017 Elsevier Inc. All rights reserved.

On the Determinants of Employment-Related Organised Education and Informal Learning

Science.gov (United States)

Nilsson, Staffan; Rubenson, Kjell

2014-01-01

This paper analyses the distribution of employment-related organised education and informal learning in the Canadian workforce. The paper draws on a large-scale survey, the Changing Nature of Work and Lifelong Learning (WALL), which was based on structured and standardised telephone interviews with a representative sample of 5783 Canadian members…

Representing Development

DEFF Research Database (Denmark)

Representing Development presents the different social representations that have formed the idea of development in Western thinking over the past three centuries. Offering an acute perspective on the current state of developmental science and providing constructive insights into future pathways, ...

Learning in Baja California micro-enterprises

Directory of Open Access Journals (Sweden)

Michelle Texis Flores

2011-01-01

Full Text Available Mexico’s business structure has been characterized by the presence of microenterprises, particularly those averaging two workers, representing 65% of establishments in 2008 and 18% of employment. This makes them important for equity and welfare improvement of their members. This paper analyzes the performance of a group of 227 microenterprises in the state of Baja California, by the use of a practical application of the concept of learning curve arranged to incorporate returns to scale. The results indicate that in 48% of cases there is evidence of learning processes and 58% exhibited increasing returns to scale. This allows evaluating the development potential of these microenterprises and the design and implementation of proactive programs that encourage their learning and consolidation in the market.

Prototype-based models in machine learning

NARCIS (Netherlands)

Biehl, Michael; Hammer, Barbara; Villmann, Thomas

2016-01-01

An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of

Learning drifting concepts with neural networks

NARCIS (Netherlands)

Biehl, Michael; Schwarze, Holm

1993-01-01

The learning of time-dependent concepts with a neural network is studied analytically and numerically. The linearly separable target rule is represented by an N-vector, whose time dependence is modelled by a random or deterministic drift process. A single-layer network is trained online using

Solar Heliospheric and INterplanetary Environment (SHINE) Students - Student Representatives' Perspectives

Science.gov (United States)

Pahud, D. M.; Niembro, T.

2014-12-01

The SHINE workshop is an annual meeting of solar and heliospheric scientists which, in addition to aiming to improve understanding of solar disturbances and their propagation to, and effect, on the Earth (shinecon.org), is dedicated to actively supporting students. This dedication is substantiated in part through the National Science Foundation (NSF) providing funding for student attendance to the workshop, which enables student participation. Another example of SHINE's commitment to its student members is the incorporation of a Student Day prior to the workshop since 2003, entirely organized and run by two student representatives. While there are variations in format from year to year, Student Day consists of tutorials and research talks exclusively by student volunteers to an audience of only students. The day is intended to provide a low-stress environment for students to learn about the various topics addressed during the workshop, to ask questions freely, and to engage in scientific discussion with other students which hopefully is a catalyst for collaboration. As a result of positive experiences, over the past decade student attendance and participation in the workshop have increased. At the SHINE 2014 workshop, nearly a third of attendees were students. SHINE student visibility has increased over the years, with student posters being advertised at breakfast, inclusion of a student day summary by the student representatives during a plenary session, and continued support from the steering committee. Students are also promoting a broader impact of SHINE sciences via increased social media presence. From a student representative's perspective, SHINE has built and fostered a healthy student community and encourages students to engage in shaping the future of the field.

Representing dispositions

Directory of Open Access Journals (Sweden)

Röhl Johannes

2011-08-01

Full Text Available Abstract Dispositions and tendencies feature significantly in the biomedical domain and therefore in representations of knowledge of that domain. They are not only important for specific applications like an infectious disease ontology, but also as part of a general strategy for modelling knowledge about molecular interactions. But the task of representing dispositions in some formal ontological systems is fraught with several problems, which are partly due to the fact that Description Logics can only deal well with binary relations. The paper will discuss some of the results of the philosophical debate about dispositions, in order to see whether the formal relations needed to represent dispositions can be broken down to binary relations. Finally, we will discuss problems arising from the possibility of the absence of realizations, of multi-track or multi-trigger dispositions and offer suggestions on how to deal with them.

Health Barriers to Learning

Directory of Open Access Journals (Sweden)

Delaney Gracy

2014-01-01

Full Text Available This article summarizes the results from a 2013 online survey with 408 principals and assistant principals in New York City public elementary and middle schools. The survey assessed three primary areas: health issues in the school, health issues perceived as barriers to learning for affected students, and resources needed to address these barriers. Eighteen of the 22 health conditions listed in the survey were considered a moderate or serious issue within their schools by at least 10% of respondents. All 22 of the health issues were perceived as a barrier to learning by between 12% and 87% of the respondents. Representatives from schools that serve a higher percentage of low-income students reported significantly higher levels of concern about the extent of health issues and their impact on learning. Respondents most often said they need linkages with organizations that can provide additional services and resources at the school, especially for mental health.

Learning the Attachment Theory with the CM-ED Concept Map Editor

Science.gov (United States)

Rueda, U.; Arruarte, A.; Elorriaga, J. A.; Herran, E.

2009-01-01

This paper presents a study carried out at the University of the Basque Country UPV/EHU with the aim of evaluating the CM-ED (concept map editor) with social education students. Concept mapping is a widely accepted technique that promotes meaningful learning. Graphically representing concepts of the learning domain and relationships between them…

Learning tactile skills through curious exploration

Directory of Open Access Journals (Sweden)

Leo ePape

2012-07-01

Full Text Available We present curiosity-driven, autonomous acquisition of tactile exploratory skills on a biomimetic robot finger equipped with an array of microelectromechanical touch sensors. Instead of building tailored algorithms for solving a specific tactile task, we employ a more general curiosity-driven reinforcement learning approach that autonomously learns a set of motor skills in absence of an explicit teacher signal. In this approach, the acquisition of skills is driven by the information content of the sensory input signals relative to a learner that aims at representing sensory inputs using fewer and fewer computational resources. We show that, from initially random exploration of its environment, the robotic system autonomously develops a small set of basic motor skills that lead to different kinds of tactile input. Next, the system learns how to exploit the learned motor skills to solve supervised texture classification tasks. Our approach demonstrates the feasibility of autonomous acquisition of tactile skills on physical robotic platforms through curiosity-driven reinforcement learning, overcomes typical difficulties of engineered solutions for active tactile exploration and underactuated control, and provides a basis for studying developmental learning through intrinsic motivation in robots.

Dynamic neuronal ensembles: Issues in representing structure change in object-oriented, biologically-based brain models

Energy Technology Data Exchange (ETDEWEB)

Vahie, S.; Zeigler, B.P.; Cho, H. [Univ. of Arizona, Tucson, AZ (United States)

1996-12-31

This paper describes the structure of dynamic neuronal ensembles (DNEs). DNEs represent a new paradigm for learning, based on biological neural networks that use variable structures. We present a computational neural element that demonstrates biological neuron functionality such as neurotransmitter feedback absolute refractory period and multiple output potentials. More specifically, we will develop a network of neural elements that have the ability to dynamically strengthen, weaken, add and remove interconnections. We demonstrate that the DNE is capable of performing dynamic modifications to neuron connections and exhibiting biological neuron functionality. In addition to its applications for learning, DNEs provide an excellent environment for testing and analysis of biological neural systems. An example of habituation and hyper-sensitization in biological systems, using a neural circuit from a snail is presented and discussed. This paper provides an insight into the DNE paradigm using models developed and simulated in DEVS.

Pedagogical Aspects of E-learning Implementation: What Have We Learned?

Directory of Open Access Journals (Sweden)

Alka Korin-Lustig

2008-10-01

Full Text Available In this paper we present our experience regarding LMS (Learning Management System platform "Moodle" used in teaching computer-assisted courses at Faculty of Civil Engineering in Rijeka. Recent student population represents first generations that have been surrounded with computers in every aspect of everyday life since their birth. Therefore teaching process had to be adjusted according to students' expectations. Because of these reasons, during past years we have been experimenting with several LMS solutions our intent being the improvement of communication and collaboration with our students. In the last academic year, we collected some new experience using Moodle. At the end of the semester we conducted a research and tried to find answers to some questions: what was successful, what new problems arose, how the students accepted this new way of learning, what should be changed in the future?

40 CFR 60.4112 - Changing Hg designated representative and alternate Hg designated representative; changes in...

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 6 2010-07-01 2010-07-01 false Changing Hg designated representative and alternate Hg designated representative; changes in owners and operators. 60.4112 Section 60.4112... Generating Units Hg Designated Representative for Hg Budget Sources § 60.4112 Changing Hg designated...

How attention can create synaptic tags for the learning of working memories in sequential tasks

OpenAIRE

Rombouts, Jaldert O; Bohte, Sander M; Roelfsema, Pieter R

2015-01-01

htmlabstractIntelligence is our ability to learn appropriate responses to new stimuli and situations. Neurons in association cortex are thought to be essential for this ability. During learning these neurons become tuned to relevant features and start to represent them with persistent activity during memory delays. This learning process is not well understood. Here we develop a biologically plausible learning scheme that explains how trial-and-error learning induces neuronal selectivity and w...

Informal Learning in a Formal Educational System

DEFF Research Database (Denmark)

Busch, Henrik

2001-01-01

This paper presents findings related to a research study which aims to describe and understand some of the essential learning processes involved in changing a first-year physics student into a research scientist. One part of this study explores a common feature of most undergraduate studies in sc....... The settings for students' learning bear much resemblance to informal learning settings reported in earlier studies related to e.g. science center visits....... in science - the pronounced border between the domains of production and acquisition of knowledge. Based on ongoing ethnographic fieldwork, certain aspects of this division between the two domains are investigated. A case study representing students' border-crossing activities is described and discussed...

Cognitive Clusters in Specific Learning Disorder

Science.gov (United States)

Poletti, Michele; Carretta, Elisa; Bonvicini, Laura; Giorgi-Rossi, Paolo

2018-01-01

The heterogeneity among children with learning disabilities still represents a barrier and a challenge in their conceptualization. Although a dimensional approach has been gaining support, the categorical approach is still the most adopted, as in the recent fifth edition of the "Diagnostic and Statistical Manual of Mental Disorders." The…

Assessing the Change Process in Comprehensive High Schools Implementing Professional Learning Communities

Science.gov (United States)

Shaner, Robert G.

2009-01-01

Professional learning communities (PLC) have been identified as scaffolds that can facilitate, support, and sustain systemic change focused on improving student achievement. PLCs represent the application of the theoretical constructs of the learning organization within the framework of schools and school systems. Little is known about the change…

Learning to Estimate Dynamical State with Probabilistic Population Codes.

Directory of Open Access Journals (Sweden)

Joseph G Makin

2015-11-01

Full Text Available Tracking moving objects, including one's own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF, the parameters of which can be learned via latent-variable density estimation (the EM algorithm. The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, "probabilistic population codes." We show that a recurrent neural network-a modified form of an exponential family harmonium (EFH-that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.

Teaching and learning in a traveling discipline

DEFF Research Database (Denmark)

Leimbach, Timo; Goodall, Julie Bladt

2018-01-01

education. As a first step in this process we will analyse the central format of education repre-sented by the formal qualifications offered by the main certification institutions. Therefore, we utilize the framework of Trowler’s “teaching and learning regime” in order to understand the di......-mensions of this dominant mode of project management education. It results in five different dimensions: 1. Knowledge as global best practices, 2. Experience as learning, 3. Associa-tion/membership as credential and access, 4. Maintenance of knowledge, and, 5. Formal courses as relevant but not necessary. The analysis...... and discussion of the dimensions with regards to po-tential impacts of a migration underline the need for emphasizing interpersonal experience in a wider sense, for incorporating experiential learning models into our educational formats, and for combining experiential learning with critical reflection in order...

Machine learning topological states

Science.gov (United States)

Deng, Dong-Ling; Li, Xiaopeng; Das Sarma, S.

2017-11-01

Artificial neural networks and machine learning have now reached a new era after several decades of improvement where applications are to explode in many fields of science, industry, and technology. Here, we use artificial neural networks to study an intriguing phenomenon in quantum physics—the topological phases of matter. We find that certain topological states, either symmetry-protected or with intrinsic topological order, can be represented with classical artificial neural networks. This is demonstrated by using three concrete spin systems, the one-dimensional (1D) symmetry-protected topological cluster state and the 2D and 3D toric code states with intrinsic topological orders. For all three cases, we show rigorously that the topological ground states can be represented by short-range neural networks in an exact and efficient fashion—the required number of hidden neurons is as small as the number of physical spins and the number of parameters scales only linearly with the system size. For the 2D toric-code model, we find that the proposed short-range neural networks can describe the excited states with Abelian anyons and their nontrivial mutual statistics as well. In addition, by using reinforcement learning we show that neural networks are capable of finding the topological ground states of nonintegrable Hamiltonians with strong interactions and studying their topological phase transitions. Our results demonstrate explicitly the exceptional power of neural networks in describing topological quantum states, and at the same time provide valuable guidance to machine learning of topological phases in generic lattice models.

"FORCE" learning in recurrent neural networks as data assimilation

Science.gov (United States)

Duane, Gregory S.

2017-12-01

It is shown that the "FORCE" algorithm for learning in arbitrarily connected networks of simple neuronal units can be cast as a Kalman Filter, with a particular state-dependent form for the background error covariances. The resulting interpretation has implications for initialization of the learning algorithm, leads to an extension to include interactions between the weight updates for different neurons, and can represent relationships within groups of multiple target output signals.

Effect of reinforcement learning on coordination of multiangent systems

Science.gov (United States)

Bukkapatnam, Satish T. S.; Gao, Greg

2000-12-01

For effective coordination of distributed environments involving multiagent systems, learning ability of each agent in the environment plays a crucial role. In this paper, we develop a simple group learning method based on reinforcement, and study its effect on coordination through application to a supply chain procurement scenario involving a computer manufacturer. Here, all parties are represented by self-interested, autonomous agents, each capable of performing specific simple tasks. They negotiate with each other to perform complex tasks and thus coordinate supply chain procurement. Reinforcement learning is intended to enable each agent to reach a best negotiable price within a shortest possible time. Our simulations of the application scenario under different learning strategies reveals the positive effects of reinforcement learning on an agent's as well as the system's performance.

Digital Skill Training Research: Preliminary Guidelines for Distributed Learning

National Research Council Canada - National Science Library

Childs, Jerry

2001-01-01

This task was aimed at the development of guidelines for distributed learning (DL). A matrix was generated to evaluate the effectiveness of various DL media for training representative knowledge/skill types...

Neural Correlates of Morphology Acquisition through a Statistical Learning Paradigm.

Science.gov (United States)

Sandoval, Michelle; Patterson, Dianne; Dai, Huanping; Vance, Christopher J; Plante, Elena

2017-01-01

The neural basis of statistical learning as it occurs over time was explored with stimuli drawn from a natural language (Russian nouns). The input reflected the "rules" for marking categories of gendered nouns, without making participants explicitly aware of the nature of what they were to learn. Participants were scanned while listening to a series of gender-marked nouns during four sequential scans, and were tested for their learning immediately after each scan. Although participants were not told the nature of the learning task, they exhibited learning after their initial exposure to the stimuli. Independent component analysis of the brain data revealed five task-related sub-networks. Unlike prior statistical learning studies of word segmentation, this morphological learning task robustly activated the inferior frontal gyrus during the learning period. This region was represented in multiple independent components, suggesting it functions as a network hub for this type of learning. Moreover, the results suggest that subnetworks activated by statistical learning are driven by the nature of the input, rather than reflecting a general statistical learning system.

Learning Commons in Academic Libraries: Discussing Themes in the Literature from 2001 to the Present

Science.gov (United States)

Blummer, Barbara; Kenton, Jeffrey M.

2017-01-01

Although the term lacks a standard definition, learning commons represent academic library spaces that provide computer and library resources as well as a range of academic services that support learners and learning. Learning commons have been equated to a laboratory for creating knowledge and staffed with librarians that serve as facilitators of…

A transfer-learning approach to image segmentation across scanners by maximizing distribution similarity

DEFF Research Database (Denmark)

van Opbroek, Annegreet; Ikram, M. Arfan; Vernooij, Meike W.

2013-01-01

Many successful methods for biomedical image segmentation are based on supervised learning, where a segmentation algorithm is trained based on manually labeled training data. For supervised-learning algorithms to perform well, this training data has to be representative for the target data. In pr...

Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.

Science.gov (United States)

Young, Jonathan D; Cai, Chunhui; Lu, Xinghua

2017-10-03

One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause cancer at the level of the individual patient. In this study, we used unsupervised deep learning to learn the hierarchical structure within cancer gene expression data. Deep learning is a group of machine learning algorithms that use multiple layers of hidden units to capture hierarchically related, alternative representations of the input data. We hypothesize that this hierarchical structure learned by deep learning will be related to the cellular signaling system. Robust deep learning model selection identified a network architecture that is biologically plausible. Our model selection results indicated that the 1st hidden layer of our deep learning model should contain about 1300 hidden units to most effectively capture the covariance structure of the input data. This agrees with the estimated number of human transcription factors, which is approximately 1400. This result lends support to our hypothesis that the 1st hidden layer of a deep learning model trained on gene expression data may represent signals related to transcription factor activation. Using the 3rd hidden layer representation of each tumor as learned by our unsupervised deep learning model, we performed consensus clustering on all tumor samples-leading to the discovery of clusters of glioblastoma multiforme with differential survival. One of these clusters contained all of the glioblastoma samples with G-CIMP, a known methylation phenotype driven by the IDH1 mutation and associated with favorable prognosis, suggesting that the hidden units in the 3rd hidden layer representations captured a methylation signal without explicitly using methylation data as input. We also found differentially expressed genes and well-known mutations (NF1, IDH1, EGFR) that were uniquely correlated with each of these clusters. Exploring these unique genes and mutations will allow us to

Parsimonious Wavelet Kernel Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Wang Qin

2015-11-01

Full Text Available In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM was introduced by combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM. In the wavelet analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet kernel extreme learning machine (WELM maximized its capability to capture the essential features in “frequency-rich” signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time performance.

Early Fractions Learning of 3rd Grade Students in SD Laboratorium Unesa

Science.gov (United States)

Sari, Elisabet Ayunika Permata; Juniati, Dwi; Patahudin, Sitti Maesuri

2012-01-01

Fractions varied meanings is one of the causes of difficulties in learning fractions. These students should be given greater opportunities to explore the meaning of fractions before they learn the relationship between fractions and operations on fractions. Although students shading an area represents a fraction, it does not mean they really…

E-learning for medical imaging specialists: introducing blended learning in a nuclear medicine specialist course.

Science.gov (United States)

Haslerud, Torjan; Tulipan, Andreas Julius; Gray, Robert M; Biermann, Martin

2017-07-01

While e-learning has become an important tool in teaching medical students, the training of specialists in medical imaging is still dominated by lecture-based courses. To assess the potential of e-learning in specialist education in medical imaging. An existing lecture-based five-day course in Clinical Nuclear Medicine (NM) was enhanced by e-learning resources and activities, including practical exercises. An anonymized survey was conducted after participants had completed and passed the multiple choice electronic course examination. Twelve out of 15 course participants (80%) responded. Overall satisfaction with the new course format was high, but 25% of the respondents wanted more interactive elements such as discussions and practical exercises. The importance of lecture handouts and supplementary online material such as selected original articles and professional guidelines was affirmed by all the respondents (92% fully, 8% partially), while 75% fully and 25% partially agreed that the lectures had been interesting and relevant. E-learning represents a hitherto unrealized potential in the education of medical specialists. It may expedite training of medical specialists while at the same time containing costs.

Continuing Professional Education of Insurance and Risk Management Practitioners: A Comparative Case Study of Customer Service Representatives, Insurance Agents and Risk Managers

Science.gov (United States)

Krauss, George E.

2009-01-01

The purpose of this study is to understand how selected insurance practitioners learn and developed in their practices setting. The selected insurance practitioners (collectively customer service representatives, insurance agents, and risk managers) are responsible for the counseling and placement of insurance products and the implementation of…

Do sophisticated epistemic beliefs predict meaningful learning? Findings from a structural equation model of undergraduate biology learning

Science.gov (United States)

Lee, Silvia Wen-Yu; Liang, Jyh-Chong; Tsai, Chin-Chung

2016-10-01

This study investigated the relationships among college students' epistemic beliefs in biology (EBB), conceptions of learning biology (COLB), and strategies of learning biology (SLB). EBB includes four dimensions, namely 'multiple-source,' 'uncertainty,' 'development,' and 'justification.' COLB is further divided into 'constructivist' and 'reproductive' conceptions, while SLB represents deep strategies and surface learning strategies. Questionnaire responses were gathered from 303 college students. The results of the confirmatory factor analysis and structural equation modelling showed acceptable model fits. Mediation testing further revealed two paths with complete mediation. In sum, students' epistemic beliefs of 'uncertainty' and 'justification' in biology were statistically significant in explaining the constructivist and reproductive COLB, respectively; and 'uncertainty' was statistically significant in explaining the deep SLB as well. The results of mediation testing further revealed that 'uncertainty' predicted surface strategies through the mediation of 'reproductive' conceptions; and the relationship between 'justification' and deep strategies was mediated by 'constructivist' COLB. This study provides evidence for the essential roles some epistemic beliefs play in predicting students' learning.

Technology transfer and technological learning through CERN's procurement activity

CERN Document Server

Autio, Erkko; Hameri, Ari-Pekka; CERN. Geneva

2003-01-01

This report analyses the technological learning and innovation benefits derived from CERN's procurement activity during the period 1997-2001. The base population of our study, the technology-intensive suppliers to CERN, consisted of 629 companies out of 6806 companies during the same period, representing 1197 MCHF in procurement. The main findings from the study can be summarized as follows: the various learning and innovation benefits (e.g., technological learning, organizational capability development, market learning) tend to occur together. Learning and innovation benefits appear to be regulated by the quality of the supplier's relationship with CERN: the greater the amount of social capital built into the relationship, the greater the learning and innovation benefits. Regardless of relationship quality, virtually all suppliers derived significant marketing reference benefits from CERN. Many corollary benefits are associated with procurement activity. As an example, as many as 38% of the respondents devel...

Teaching Parametric Urban Design in a Blended Learning Format

DEFF Research Database (Denmark)

Steinø, Nicolai

2015-01-01

On the basis of a theoretical discussion of the concept of blended learning, this paper presents the pre- paration, execution and evaluation of a 5 ECTS blended learning course on parametric urban design for a group of some 50 BSc students of architecture and design at Aalborg university...... and curriculum develop- ment, and di culties to nd time for research. Despite the general consensus that developing online/blended learning courses requires both technical support, complex software installations, and substantial preparation time, it is shown that free web ser- vices and low-tech adaptations...... of traditional teaching assets are su cient to get started with blended learning with only little extra e ort. While the pilot blended learning course which provided the insights for this paper has room for impro- vement, it represents a decent rst shot at developing blended learning courses for higher education...

Team-based learning and ethics education in nursing.

Science.gov (United States)

Hickman, Susan E; Wocial, Lucia D

2013-12-01

This report describes the use of team-based learning concepts in an undergraduate nursing applied ethics course using established reporting guidelines. Team-based learning relies on actively engaging students in the learning process through small-group activities that facilitate the development of skills, including concept analysis, critical thinking, and problem solving. Students are divided into teams of five to seven members who collaborate throughout the semester to work through activities that build on ethics concepts introduced through reading and lectures. Nurse educators are challenged to develop educational approaches that will engage students and help them to apply what they learn from the study of ethics to the lived experience of clinical practice. The ultimate goal is to help students to develop into morally sensitive and competent professionals. Team-based learning represents a novel way to teach these skills to undergraduate nursing students. Copyright 2013, SLACK Incorporated.

Learning mechanisms to limit medication administration errors.

Science.gov (United States)

Drach-Zahavy, Anat; Pud, Dorit

2010-04-01

This paper is a report of a study conducted to identify and test the effectiveness of learning mechanisms applied by the nursing staff of hospital wards as a means of limiting medication administration errors. Since the influential report ;To Err Is Human', research has emphasized the role of team learning in reducing medication administration errors. Nevertheless, little is known about the mechanisms underlying team learning. Thirty-two hospital wards were randomly recruited. Data were collected during 2006 in Israel by a multi-method (observations, interviews and administrative data), multi-source (head nurses, bedside nurses) approach. Medication administration error was defined as any deviation from procedures, policies and/or best practices for medication administration, and was identified using semi-structured observations of nurses administering medication. Organizational learning was measured using semi-structured interviews with head nurses, and the previous year's reported medication administration errors were assessed using administrative data. The interview data revealed four learning mechanism patterns employed in an attempt to learn from medication administration errors: integrated, non-integrated, supervisory and patchy learning. Regression analysis results demonstrated that whereas the integrated pattern of learning mechanisms was associated with decreased errors, the non-integrated pattern was associated with increased errors. Supervisory and patchy learning mechanisms were not associated with errors. Superior learning mechanisms are those that represent the whole cycle of team learning, are enacted by nurses who administer medications to patients, and emphasize a system approach to data analysis instead of analysis of individual cases.

Dissociable Learning Processes Underlie Human Pain Conditioning.

Science.gov (United States)

Zhang, Suyi; Mano, Hiroaki; Ganesh, Gowrishankar; Robbins, Trevor; Seymour, Ben

2016-01-11

Pavlovian conditioning underlies many aspects of pain behavior, including fear and threat detection [1], escape and avoidance learning [2], and endogenous analgesia [3]. Although a central role for the amygdala is well established [4], both human and animal studies implicate other brain regions in learning, notably ventral striatum and cerebellum [5]. It remains unclear whether these regions make different contributions to a single aversive learning process or represent independent learning mechanisms that interact to generate the expression of pain-related behavior. We designed a human parallel aversive conditioning paradigm in which different Pavlovian visual cues probabilistically predicted thermal pain primarily to either the left or right arm and studied the acquisition of conditioned Pavlovian responses using combined physiological recordings and fMRI. Using computational modeling based on reinforcement learning theory, we found that conditioning involves two distinct types of learning process. First, a non-specific "preparatory" system learns aversive facial expressions and autonomic responses such as skin conductance. The associated learning signals-the learned associability and prediction error-were correlated with fMRI brain responses in amygdala-striatal regions, corresponding to the classic aversive (fear) learning circuit. Second, a specific lateralized system learns "consummatory" limb-withdrawal responses, detectable with electromyography of the arm to which pain is predicted. Its related learned associability was correlated with responses in ipsilateral cerebellar cortex, suggesting a novel computational role for the cerebellum in pain. In conclusion, our results show that the overall phenotype of conditioned pain behavior depends on two dissociable reinforcement learning circuits. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Learning from Tractography

DEFF Research Database (Denmark)

Kasenburg, Niklas

Analysis of structural connections between brain regions enables us to gain insight into the structural architecture of the human brain and into how connections are affected by age or pathology. Tractography is the standard tool for automatic delineation of structural connections or tracts. Post......-processing of tractography results using expert prior knowledge is often performed to ensure a robust delineation. In this thesis, I present a shortest-path tractography (SPT) framework that can automatically incorporate any prior knowledge about the location of a tract. Furthermore, I show how such a prior can be learned...... of a connection and demonstrate their application in connectivity-based parcellation. Network models are a common way to represent structural connections of the whole brain. With supervised learning methods, features are extracted from these networks and are associated with a parameter of interest. Dimensionality...

Effectiveness of a Mobile Plant Learning System in a Science Curriculum in Taiwanese Elementary Education

Science.gov (United States)

Huang, Yueh-Min; Lin, Yen-Ting; Cheng, Shu-Chen

2010-01-01

This study developed a Mobile Plant Learning System (MPLS) that provides instructors with the ways and means to facilitate student learning in an elementary-school-level botany course. The MPLS represented in this study was implemented to address problems that arise with the use of a didactic approach to teaching and learning botany, as is…

Hedging Your Bets by Learning Reward Correlations in the Human Brain

Science.gov (United States)

Wunderlich, Klaus; Symmonds, Mkael; Bossaerts, Peter; Dolan, Raymond J.

2011-01-01

Summary Human subjects are proficient at tracking the mean and variance of rewards and updating these via prediction errors. Here, we addressed whether humans can also learn about higher-order relationships between distinct environmental outcomes, a defining ecological feature of contexts where multiple sources of rewards are available. By manipulating the degree to which distinct outcomes are correlated, we show that subjects implemented an explicit model-based strategy to learn the associated outcome correlations and were adept in using that information to dynamically adjust their choices in a task that required a minimization of outcome variance. Importantly, the experimentally generated outcome correlations were explicitly represented neuronally in right midinsula with a learning prediction error signal expressed in rostral anterior cingulate cortex. Thus, our data show that the human brain represents higher-order correlation structures between rewards, a core adaptive ability whose immediate benefit is optimized sampling. PMID:21943609

Professional Learning as a Predictor for Instructional Quality: A Secondary Analysis of TALIS

Science.gov (United States)

Dogan, Selçuk; Yurtseven, Nihal

2018-01-01

The purpose of this study is to examine the effect of teachers' professional learning opportunities on instructional quality, which represents a combined approach of behaviorist, cognitivist, and constructivist principles in teaching. We incorporated professional learning communities (PLCs), professional development (PD) days, as well as 3 PD…

Linking a Learning Progression for Natural Selection to Teachers' Enactment of Formative Assessment

Science.gov (United States)

Furtak, Erin Marie

2012-01-01

Learning progressions, or representations of how student ideas develop in a domain, hold promise as tools to support teachers' formative assessment practices. The ideas represented in a learning progression might help teachers to identify and make inferences about evidence collected of student thinking, necessary precursors to modifying…

Error estimation in plant growth analysis

Directory of Open Access Journals (Sweden)

Andrzej Gregorczyk

2014-01-01

Full Text Available The scheme is presented for calculation of errors of dry matter values which occur during approximation of data with growth curves, determined by the analytical method (logistic function and by the numerical method (Richards function. Further formulae are shown, which describe absolute errors of growth characteristics: Growth rate (GR, Relative growth rate (RGR, Unit leaf rate (ULR and Leaf area ratio (LAR. Calculation examples concerning the growth course of oats and maize plants are given. The critical analysis of the estimation of obtained results has been done. The purposefulness of joint application of statistical methods and error calculus in plant growth analysis has been ascertained.

Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge.

Science.gov (United States)

Hanser, Thierry; Barber, Chris; Rosser, Edward; Vessey, Jonathan D; Webb, Samuel J; Werner, Stéphane

2014-01-01

Combining different sources of knowledge to build improved structure activity relationship models is not easy owing to the variety of knowledge formats and the absence of a common framework to interoperate between learning techniques. Most of the current approaches address this problem by using consensus models that operate at the prediction level. We explore the possibility to directly combine these sources at the knowledge level, with the aim to harvest potentially increased synergy at an earlier stage. Our goal is to design a general methodology to facilitate knowledge discovery and produce accurate and interpretable models. To combine models at the knowledge level, we propose to decouple the learning phase from the knowledge application phase using a pivot representation (lingua franca) based on the concept of hypothesis. A hypothesis is a simple and interpretable knowledge unit. Regardless of its origin, knowledge is broken down into a collection of hypotheses. These hypotheses are subsequently organised into hierarchical network. This unification permits to combine different sources of knowledge into a common formalised framework. The approach allows us to create a synergistic system between different forms of knowledge and new algorithms can be applied to leverage this unified model. This first article focuses on the general principle of the Self Organising Hypothesis Network (SOHN) approach in the context of binary classification problems along with an illustrative application to the prediction of mutagenicity. It is possible to represent knowledge in the unified form of a hypothesis network allowing interpretable predictions with performances comparable to mainstream machine learning techniques. This new approach offers the potential to combine knowledge from different sources into a common framework in which high level reasoning and meta-learning can be applied; these latter perspectives will be explored in future work.

A Visual Encapsulation of Adlerian Theory: A Tool for Teaching and Learning.

Science.gov (United States)

Osborn, Cynthia J.

2001-01-01

A visual diagram is presented in this article to illustrate 6 key concepts of Adlerian theory discussed in corresponding narrative format. It is proposed that in an age of multimedia learning, a pictorial reference can enhance the teaching and learning of Adlerian theory, representing a commitment to humanistic education. (Contains 18 references.)…

Learning to care for older patients: hospitals and nursing homes as learning environments.

Science.gov (United States)

Huls, Marije; de Rooij, Sophia E; Diepstraten, Annemie; Koopmans, Raymond; Helmich, Esther

2015-03-01

A significant challenge facing health care is the ageing of the population, which calls for a major response in medical education. Most clinical learning takes place within hospitals, but nursing homes may also represent suitable learning environments in which students can gain competencies in geriatric medicine. This study explores what students perceive as the main learning outcomes of a geriatric medicine clerkship in a hospital or a nursing home, and explicitly addresses factors that may stimulate or hamper the learning process. This qualitative study falls within a constructivist paradigm: it draws on socio-cultural learning theory and is guided by the principles of constructivist grounded theory. There were two phases of data collection. Firstly, a maximum variation sample of 68 students completed a worksheet, giving brief written answers on questions regarding their geriatric medicine clerkships. Secondly, focus group discussions were conducted with 19 purposively sampled students. We used template analysis, iteratively cycling between data collection and analysis, using a constant comparative process. Students described a broad range of learning outcomes and formative experiences that were largely distinct from their learning in previous clerkships with regard to specific geriatric knowledge, deliberate decision making, end-of-life care, interprofessional collaboration and communication. According to students, the nursing home differed from the hospital in three aspects: interprofessional collaboration was more prominent; the lower resources available in nursing homes stimulated students to be creative, and students reported having greater autonomy in nursing homes compared with the more extensive educational guidance provided in hospitals. In both hospitals and nursing homes, students not only learn to care for older patients, but also describe various broader learning outcomes necessary to become good doctors. The results of our study, in particular the

The Effect of Scaffolded Strategies on Content Learning in a Designed Science Cyberlearning Environment

Science.gov (United States)

Kern, Cynthia Lee

2013-01-01

Scientific inscriptions--graphs, diagrams, and data--and argumentation are integral to generating and communicating scientific understanding. Scientific inscriptions and argumentation are also important to learning science. However, previous research has indicated that learners struggle to understand and learn science content represented in…

Assessing orientations to learning to teach.

Science.gov (United States)

Oosterheert, Ida E; Vermunt, Jan D; Denessen, E

2002-03-01

An important purpose of teacher education is that student teachers develop and change their existing knowledge on learning and teaching. Research on how student teachers variously engage in this process is scarce. In a previous study of 30 student teachers, we identified five different orientations to learning to teach. Our aim was to extend the results of the previous study by developing an instrument to assess orientations to learning to teach at a larger scale. The development and psychometric properties of the instrument are discussed. The results with respect to how student teachers learn are compared to the results of the qualitative study. Participants in this study were 169 secondary student teachers from three institutes which had all adopted an initial in-service model of learning to teach. On the basis of extensive qualitative study, a questionnaire was developed to assess individual differences in learning to teach. Factor-, reliability-, and nonparametric scalability analyses were performed to identify reliable scales. Cluster analysis was used to identify groups of students with similar orientations to learning to teach. Eight scales covering cognitive, regulative and affective aspects of student teachers' learning were identified. Cluster analysis indicates that the instrument discriminates well between student teachers. Four of the five previously found patterns were found again. The four orientations found in relatively uniform learning environments indicate that student teachers need differential support in their learning. Although the instrument measures individual differences in a reliable way, it is somewhat one-sided in the sense that items representing constructive ways of learning dominate. New items forming a broader range of scales should be created.

Explaining Differences in Learning Outcomes in Auditing Education

DEFF Research Database (Denmark)

Holm, Claus; Steenholdt, Niels

as well as the accounting profession. This paper extends prior research on the role of declarative and procedural knowledge in performing auditing tasks. Measuring learning outcomes is a complex matter requiring sensible measures for both declarative knowledge (ability to verbalize pertinent facts...... or processes) and procedural knowledge (intellectual skills). The performance of 75 graduate accounting students representing both types of schema is examined. The findings suggest that differences in learning outcomes may be attributed to differences in student background and prior knowledge (auditing...

Learning, Leading, and Letting Go of Control

DEFF Research Database (Denmark)

Iversen, Ann-Merete; Pedersen, Anni Stavnskær; Kjær-Rasmussen, Lone Krogh

2015-01-01

The article introduces a new term in higher education: learner-led approaches in education (LED). This does not represent a single approach or dogma to replace existing dogmas, but a way of approaching learning and education that mirrors the complexity of society as it develops. LED is based...... on the assumption that all students have their own unique approach to learning and therefore have the potential to design learning processes that are meaningful for them. This removes focus from the teacher and the teaching to the learner and the learning. It builds on the student’s motivation and experienced...... meaningfulness as a driving force, and hence the term learner led. The methods applied in LED change over time, as different learners and teachers together co-create and design methods and approaches appropriate at that particular time, in that particular context and for that particular student or group...

Nonparametric, Coupled ,Bayesian ,Dictionary ,and Classifier Learning for Hyperspectral Classification.

Science.gov (United States)

Akhtar, Naveed; Mian, Ajmal

2017-10-03

We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictionary and the classifier. These processes are coupled under the same sets of Bernoulli distributions. In our approach, these distributions signify the frequency of the dictionary atom usage in representing class-specific training spectra, which also makes the dictionary discriminative. Due to the coupling between the dictionary and the classifier, the popularity of the atoms for representing different classes gets encoded into the classifier. This helps in predicting the class labels of test spectra that are first represented over the dictionary by solving a simultaneous sparse optimization problem. The labels of the spectra are predicted by feeding the resulting representations to the classifier. Our approach exploits the nonparametric Bayesian framework to automatically infer the dictionary size--the key parameter in discriminative dictionary learning. Moreover, it also has the desirable property of adaptively learning the association between the dictionary atoms and the class labels by itself. We use Gibbs sampling to infer the posterior probability distributions over the dictionary and the classifier under the proposed model, for which, we derive analytical expressions. To establish the effectiveness of our approach, we test it on benchmark hyperspectral images. The classification performance is compared with the state-of-the-art dictionary learning-based classification methods.

Student’s Perceptions on Simulation as Part of Experiential Learning in Approaches, Methods, and Techniques (AMT Course

Directory of Open Access Journals (Sweden)

Marselina Karina Purnomo

2017-03-01

Full Text Available Simulation is a part of Experiential Learning which represents certain real-life events. In this study, simulation is used as a learning activity in Approaches, Methods, and Techniques (AMT course which is one of the courses in English Language Education Study Program (ELESP of Sanata Dharma University. Since simulation represents the real-life events, it encourages students to apply the approaches, methods, and techniques being studied based on the real-life classroom. Several experts state that students are able to involve their personal experiences through simulation which additionally is believed to create a meaningful learning in the class. This study aimed to discover ELESP students’ perceptions toward simulation as a part of Experiential Learning in AMT course. From the findings, it could be inferred that students agreed that simulation in class was important for students’ learning for it formed a meaningful learning in class.  DOI: https://doi.org/10.24071/llt.2017.200104

Representing vision and blindness.

Science.gov (United States)

Ray, Patrick L; Cox, Alexander P; Jensen, Mark; Allen, Travis; Duncan, William; Diehl, Alexander D

2016-01-01

There have been relatively few attempts to represent vision or blindness ontologically. This is unsurprising as the related phenomena of sight and blindness are difficult to represent ontologically for a variety of reasons. Blindness has escaped ontological capture at least in part because: blindness or the employment of the term 'blindness' seems to vary from context to context, blindness can present in a myriad of types and degrees, and there is no precedent for representing complex phenomena such as blindness. We explore current attempts to represent vision or blindness, and show how these attempts fail at representing subtypes of blindness (viz., color blindness, flash blindness, and inattentional blindness). We examine the results found through a review of current attempts and identify where they have failed. By analyzing our test cases of different types of blindness along with the strengths and weaknesses of previous attempts, we have identified the general features of blindness and vision. We propose an ontological solution to represent vision and blindness, which capitalizes on resources afforded to one who utilizes the Basic Formal Ontology as an upper-level ontology. The solution we propose here involves specifying the trigger conditions of a disposition as well as the processes that realize that disposition. Once these are specified we can characterize vision as a function that is realized by certain (in this case) biological processes under a range of triggering conditions. When the range of conditions under which the processes can be realized are reduced beyond a certain threshold, we are able to say that blindness is present. We characterize vision as a function that is realized as a seeing process and blindness as a reduction in the conditions under which the sight function is realized. This solution is desirable because it leverages current features of a major upper-level ontology, accurately captures the phenomenon of blindness, and can be

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…

Blended learning: how can we optimise undergraduate student engagement?

Science.gov (United States)

Morton, Caroline E; Saleh, Sohag N; Smith, Susan F; Hemani, Ashish; Ameen, Akram; Bennie, Taylor D; Toro-Troconis, Maria

2016-08-04

Blended learning is a combination of online and face-to-face learning and is increasingly of interest for use in undergraduate medical education. It has been used to teach clinical post-graduate students pharmacology but needs evaluation for its use in teaching pharmacology to undergraduate medical students, which represent a different group of students with different learning needs. An existing BSc-level module on neuropharmacology was redesigned using the Blended Learning Design Tool (BLEnDT), a tool which uses learning domains (psychomotor, cognitive and affective) to classify learning outcomes into those taught best by self-directed learning (online) or by collaborative learning (face-to-face). Two online courses were developed, one on Neurotransmitters and the other on Neurodegenerative Conditions. These were supported with face-to-face tutorials. Undergraduate students' engagement with blended learning was explored by the means of three focus groups, the data from which were analysed thematically. Five major themes emerged from the data 1) Purpose and Acceptability 2) Structure, Focus and Consolidation 3) Preparation and workload 4) Engagement with e-learning component 5) Future Medical Education. Blended learning was acceptable and of interest to undergraduate students learning this subject. They expressed a desire for more blended learning in their courses, but only if it was highly structured, of high quality and supported by tutorials. Students identified that the 'blend' was beneficial rather than purely online learning.

Reactive behavior, learning, and anticipation

Science.gov (United States)

Whitehead, Steven D.; Ballard, Dana H.

1989-01-01

Reactive systems always act, thinking only long enough to 'look up' the action to execute. Traditional planning systems think a lot, and act only after generating fairly precise plans. Each represents an endpoint on a spectrum. It is argued that primitive forms of reasoning, like anticipation, play an important role in reducing the cost of learning and that the decision to act or think should be based on the uncertainty associated with the utility of executing an action in a particular situation. An architecture for an adaptable reactive system is presented and it is shown how it can be augmented with a simple anticipation mechanism that can substantially reduce the cost and time of learning.

A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation

Science.gov (United States)

Cruz-Roa, Angel; Arevalo, John; Basavanhally, Ajay; Madabhushi, Anant; González, Fabio

2015-01-01

Learning data representations directly from the data itself is an approach that has shown great success in different pattern recognition problems, outperforming state-of-the-art feature extraction schemes for different tasks in computer vision, speech recognition and natural language processing. Representation learning applies unsupervised and supervised machine learning methods to large amounts of data to find building-blocks that better represent the information in it. Digitized histopathology images represents a very good testbed for representation learning since it involves large amounts of high complex, visual data. This paper presents a comparative evaluation of different supervised and unsupervised representation learning architectures to specifically address open questions on what type of learning architectures (deep or shallow), type of learning (unsupervised or supervised) is optimal. In this paper we limit ourselves to addressing these questions in the context of distinguishing between anaplastic and non-anaplastic medulloblastomas from routine haematoxylin and eosin stained images. The unsupervised approaches evaluated were sparse autoencoders and topographic reconstruct independent component analysis, and the supervised approach was convolutional neural networks. Experimental results show that shallow architectures with more neurons are better than deeper architectures without taking into account local space invariances and that topographic constraints provide useful invariant features in scale and rotations for efficient tumor differentiation.

STEM learning activity among home-educating families

Science.gov (United States)

Bachman, Jennifer

2011-12-01

Science, technology, engineering, and mathematics (STEM) learning was studied among families in a group of home-educators in the Pacific Northwest. Ethnographic methods recorded learning activity (video, audio, fieldnotes, and artifacts) which was analyzed using a unique combination of Cultural-Historical Activity Theory (CHAT) and Mediated Action (MA), enabling analysis of activity at multiple levels. Findings indicate that STEM learning activity is family-led, guided by parents' values and goals for learning, and negotiated with children to account for learner interests and differences, and available resources. Families' STEM education practice is dynamic, evolves, and influenced by larger societal STEM learning activity. Parents actively seek support and resources for STEM learning within their home-school community, working individually and collectively to share their funds of knowledge. Home-schoolers also access a wide variety of free-choice learning resources: web-based materials, museums, libraries, and community education opportunities (e.g. afterschool, weekend and summer programs, science clubs and classes, etc.). A lesson-heuristic, grounded in Mediated Action, represents and analyzes home STEM learning activity in terms of tensions between parental goals, roles, and lesson structure. One tension observed was between 'academic' goals or school-like activity and 'lifelong' goals or everyday learning activity. Theoretical and experiential learning was found in both activity, though parents with academic goals tended to focus more on theoretical learning and those with lifelong learning goals tended to be more experiential. Examples of the National Research Council's science learning strands (NRC, 2009) were observed in the STEM practices of all these families. Findings contribute to the small but growing body of empirical CHAT research in science education, specifically to the empirical base of family STEM learning practices at home. It also fills a

Chronological changes in Japanese physicians' attitude and behavior concerning relationships with pharmaceutical representatives: a qualitative study.

Directory of Open Access Journals (Sweden)

Sayaka Saito

Full Text Available BACKGROUND: Recent qualitative studies indicated that physicians interact with pharmaceutical representatives depending on the relative weight of the benefits to the risks and are also influenced by a variety of experiences and circumstances. However, these studies do not provide enough information about if, when, how and why their attitudes and behaviors change over time. METHODS AND FINDINGS: A qualitative study using semi-structured face-to-face individual interviews was conducted on 9 Japanese physicians who attended a symposium on conflicts of interest held in Tokyo. Interviews were designed to explore chronological changes in individual physicians' attitude and behavior concerning relationships with pharmaceutical representatives and factors affecting such changes. Their early interaction with pharmaceutical representatives was passive as physicians were not explicitly aware of the meaning of such interaction. They began to think on their own about how to interact with pharmaceutical representatives as they progressed in their careers. Their attitude toward pharmaceutical representatives changed over time. Factors affecting attitudinal change included work environment (local regulations and job position, role models, views of patients and the public, acquisition of skills in information seeking and evidence-based medicine, and learning about the concepts of professionalism and conflict of interest. However, the change in attitude was not necessarily followed by behavioral change, apparently due to rationalization and conformity to social norms. CONCLUSIONS: Physicians' attitudes toward relationships with pharmaceutical representatives changed over time and factors affecting such changes were various. Paying attention to these factors and creating new social norms may be both necessary to produce change in behavior consistent with change in attitude.

When Average Is Not Good Enough: Students with Learning Disabilities at Selective, Private Colleges

Science.gov (United States)

Weis, Robert; Erickson, Celeste P.; Till, Christina H.

2017-01-01

Adolescents with learning disabilities disproportionately come from lower socioeconomic status backgrounds, show normative deficits in academic skills, and attend 2-year, public colleges instead of 4-year institutions. However, students with learning disabilities are well represented at the United States' most expensive and selective postsecondary…

Learning With Mixed Hard/Soft Pointwise Constraints.

Science.gov (United States)

Gnecco, Giorgio; Gori, Marco; Melacci, Stefano; Sanguineti, Marcello

2015-09-01

A learning paradigm is proposed and investigated, in which the classical framework of learning from examples is enhanced by the introduction of hard pointwise constraints, i.e., constraints imposed on a finite set of examples that cannot be violated. Such constraints arise, e.g., when requiring coherent decisions of classifiers acting on different views of the same pattern. The classical examples of supervised learning, which can be violated at the cost of some penalization (quantified by the choice of a suitable loss function) play the role of soft pointwise constraints. Constrained variational calculus is exploited to derive a representer theorem that provides a description of the functional structure of the optimal solution to the proposed learning paradigm. It is shown that such an optimal solution can be represented in terms of a set of support constraints, which generalize the concept of support vectors and open the doors to a novel learning paradigm, called support constraint machines. The general theory is applied to derive the representation of the optimal solution to the problem of learning from hard linear pointwise constraints combined with soft pointwise constraints induced by supervised examples. In some cases, closed-form optimal solutions are obtained.

Active learning methods for interactive image retrieval.

Science.gov (United States)

Gosselin, Philippe Henri; Cord, Matthieu

2008-07-01

Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.

Learning Probabilistic Logic Models from Probabilistic Examples.

Science.gov (United States)

Chen, Jianzhong; Muggleton, Stephen; Santos, José

2008-10-01

We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches - abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.

Neural Behavior Chain Learning of Mobile Robot Actions

Directory of Open Access Journals (Sweden)

Lejla Banjanovic-Mehmedovic

2012-01-01

Full Text Available This paper presents a visual/motor behavior learning approach, based on neural networks. We propose Behavior Chain Model (BCM in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are derived. Second, a multilayer neural network for offline learning of the mapping relations is used. This learning structure through neural network training process represents a connection between the visual perceptions and motor sequence of actions in order to grip a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot behavior for a variety of similar tasks in similar environment. Prediction results suggest that the methodology is adequate and could be recognized as an idea for designing different mobile robot behaviour assistance.

Bayesian nonparametric dictionary learning for compressed sensing MRI.

Science.gov (United States)

Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping

2014-12-01

We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.

Learning Matlab a problem solving approach

CERN Document Server

Gander, Walter

2015-01-01

This comprehensive and stimulating introduction to Matlab, a computer language now widely used for technical computing, is based on an introductory course held at Qian Weichang College, Shanghai University, in the fall of 2014.  Teaching and learning a substantial programming language aren’t always straightforward tasks. Accordingly, this textbook is not meant to cover the whole range of this high-performance technical programming environment, but to motivate first- and second-year undergraduate students in mathematics and computer science to learn Matlab by studying representative problems, developing algorithms and programming them in Matlab. While several topics are taken from the field of scientific computing, the main emphasis is on programming. A wealth of examples are completely discussed and solved, allowing students to learn Matlab by doing: by solving problems, comparing approaches and assessing the proposed solutions.

Deep imitation learning for 3D navigation tasks.

Science.gov (United States)

Hussein, Ahmed; Elyan, Eyad; Gaber, Mohamed Medhat; Jayne, Chrisina

2018-01-01

Deep learning techniques have shown success in learning from raw high-dimensional data in various applications. While deep reinforcement learning is recently gaining popularity as a method to train intelligent agents, utilizing deep learning in imitation learning has been scarcely explored. Imitation learning can be an efficient method to teach intelligent agents by providing a set of demonstrations to learn from. However, generalizing to situations that are not represented in the demonstrations can be challenging, especially in 3D environments. In this paper, we propose a deep imitation learning method to learn navigation tasks from demonstrations in a 3D environment. The supervised policy is refined using active learning in order to generalize to unseen situations. This approach is compared to two popular deep reinforcement learning techniques: deep-Q-networks and Asynchronous actor-critic (A3C). The proposed method as well as the reinforcement learning methods employ deep convolutional neural networks and learn directly from raw visual input. Methods for combining learning from demonstrations and experience are also investigated. This combination aims to join the generalization ability of learning by experience with the efficiency of learning by imitation. The proposed methods are evaluated on 4 navigation tasks in a 3D simulated environment. Navigation tasks are a typical problem that is relevant to many real applications. They pose the challenge of requiring demonstrations of long trajectories to reach the target and only providing delayed rewards (usually terminal) to the agent. The experiments show that the proposed method can successfully learn navigation tasks from raw visual input while learning from experience methods fail to learn an effective policy. Moreover, it is shown that active learning can significantly improve the performance of the initially learned policy using a small number of active samples.

Case-based learning facilitates critical thinking in undergraduate nutrition education: students describe the big picture.

Science.gov (United States)

Harman, Tara; Bertrand, Brenda; Greer, Annette; Pettus, Arianna; Jennings, Jill; Wall-Bassett, Elizabeth; Babatunde, Oyinlola Toyin

2015-03-01

The vision of dietetics professions is based on interdependent education, credentialing, and practice. Case-based learning is a method of problem-based learning that is designed to heighten higher-order thinking. Case-based learning can assist students to connect education and specialized practice while developing professional skills for entry-level practice in nutrition and dietetics. This study examined student perspectives of their learning after immersion into case-based learning in nutrition courses. The theoretical frameworks of phenomenology and Bloom's Taxonomy of Educational Objectives triangulated the design of this qualitative study. Data were drawn from 426 written responses and three focus group discussions among 85 students from three upper-level undergraduate nutrition courses. Coding served to deconstruct the essence of respondent meaning given to case-based learning as a learning method. The analysis of the coding was the constructive stage that led to configuration of themes and theoretical practice pathways about student learning. Four leading themes emerged. Story or Scenario represents the ways that students described case-based learning, changes in student thought processes to accommodate case-based learning are illustrated in Method of Learning, higher cognitive learning that was achieved from case-based learning is represented in Problem Solving, and Future Practice details how students explained perceived professional competency gains from case-based learning. The skills that students acquired are consistent with those identified as essential to professional practice. In addition, the common concept of Big Picture was iterated throughout the themes and demonstrated that case-based learning prepares students for multifaceted problems that they are likely to encounter in professional practice. Copyright © 2015 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering

Directory of Open Access Journals (Sweden)

Xin Tian

2017-06-01

Full Text Available We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster’s sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments.

Selection of appropriates E-learning personalization strategies from ontological perspectives

Directory of Open Access Journals (Sweden)

Fathi Essalmi

2010-10-01

Full Text Available When there are several personalization strategies of E-learning, authors of courses need to be supported for deciding which strategy will be applied for personalizing each course. In fact, the time, the efforts and the learning objects needed for preparing personalized learning scenarios depend on the personalization strategy to be applied. This paper presents an approach for selecting personalization strategies according to the feasibility of generating personalized learning scenarios with minimal intervention of the author. Several metrics are proposed for putting in order and selecting useful personalization strategies. The calculus of these metrics is automated based on the analyses of the LOM (Learning Object Metadata standard according to the semantic relations between data elements and learners’ characteristics represented in the Ontology for Selection of Personalization Strategies (OSPS.

Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences among Ontologies

Science.gov (United States)

Peng, Yefei

2010-01-01

An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the…

Implications of Bandura's Observational Learning Theory for a Competency Based Teacher Education Model.

Science.gov (United States)

Hartjen, Raymond H.

Albert Bandura of Stanford University has proposed four component processes to his theory of observational learning: a) attention, b) retention, c) motor reproduction, and d) reinforcement and motivation. This study represents one phase of an effort to relate modeling and observational learning theory to teacher training. The problem of this study…

Learning how to learn using simulation: Unpacking disguised feedback using a qualitative analysis of doctors' telephone talk.

Science.gov (United States)

Eppich, Walter J; Rethans, Jan-Joost; Dornan, Timothy; Teunissen, Pim W

2018-05-04

Telephone talk between clinicians represents a substantial workplace activity in postgraduate clinical education, yet junior doctors receive little training in goal-directed, professional telephone communication. To assess educational needs for telephone talk and develop a simulation-based educational intervention. Thematic analysis of 17 semi-structured interviews with doctors-in-training from various training levels and specialties. We identified essential elements to incorporate into simulation-based telephone talk, including common challenging situations for junior doctors as well as explicit and informal aspects that promote learning. These elements have implications for both junior doctors and clinical supervisors, including: (a) explicit teaching and feedback practices and (b) informal conversational interruptions and questions. The latter serve as "disguised" feedback, which aligns with recent conceptualizations of feedback as "performance relevant information". In addition to preparing clinical supervisors to support learning through telephone talk, we propose several potential educational strategies: (a) embedding telephone communication skills throughout simulation activities and (b) developing stand-alone curricular elements to sensitize junior doctors to "disguised" feedback during telephone talk as a mechanism to augment future workplace learning, i.e. 'learning how to learn' through simulation.

Translanguaging, Learning and Teaching in Deaf Education

Science.gov (United States)

Swanwick, Ruth

2017-01-01

This paper critiques the role of translanguaging in deaf education by examining how, and under what conditions, translanguaging practices can enhance learning and teaching. The paper explores the premise that translanguaging represents an additive view of bilingualism and multilingualism for deaf learners and offers an innovative departure from,…

Problem-Based Learning in the Earth and Space Science Classroom, K-12

Science.gov (United States)

McConnell, Tom J.; Parker, Joyce; Eberhardt, Janet

2017-01-01

If you've ever asked yourself whether problem-based learning (PBL) can bring new life to both your teaching and your students' learning, here's your answer: Yes. This all-in-one guide will help you engage your students in scenarios that represent real-world science in all its messy, thought-provoking glory. The scenarios will prompt K-12 students…

INVESTIGATING THE PERCEIVED NEEDS OF INTERNATIONAL STUDENTS LEARNING EAP

OpenAIRE

Dedy Setiawan

2009-01-01

Abstract: The perceived needs of students learning EAP were analysed u-sing a questionnaire which investigated the subjects’ preference for particular topics and various modes of learning in relation to both the target and present situation. The target situation in the questionnaire was represented by items concerning study skills; while items concerning the present situation were oriented to the contents of the EAP course and its methodology and activities. The findings provide evidence fo...

Olfactory learning and memory in the bumblebee Bombus occidentalis

Science.gov (United States)

Riveros, Andre J.; Gronenberg, Wulfila

2009-07-01

In many respects, the behavior of bumblebees is similar to that of the closely related honeybees, a long-standing model system for learning and memory research. Living in smaller and less regulated colonies, bumblebees are physiologically more robust and thus have advantages in particular for indoor experiments. Here, we report results on Pavlovian odor conditioning of bumblebees using the proboscis extension reflex (PER) that has been successfully used in honeybee learning research. We examine the effect of age, body size, and experience on learning and memory performance. We find that age does not affect learning and memory ability, while body size positively correlates with memory performance. Foraging experience seems not to be necessary for learning to occur, but it may contribute to learning performance as bumblebees with more foraging experience on average were better learners. The PER represents a reliable tool for learning and memory research in bumblebees and allows examining interspecific similarities and differences of honeybee and bumblebee behavior, which we discuss in the context of social organization.

Learning and inference in a nonequilibrium Ising model with hidden nodes.

Science.gov (United States)

Dunn, Benjamin; Roudi, Yasser

2013-02-01

We study inference and reconstruction of couplings in a partially observed kinetic Ising model. With hidden spins, calculating the likelihood of a sequence of observed spin configurations requires performing a trace over the configurations of the hidden ones. This, as we show, can be represented as a path integral. Using this representation, we demonstrate that systematic approximate inference and learning rules can be derived using dynamical mean-field theory. Although naive mean-field theory leads to an unstable learning rule, taking into account Gaussian corrections allows learning the couplings involving hidden nodes. It also improves learning of the couplings between the observed nodes compared to when hidden nodes are ignored.

The Sloan-C Pillars and Boundary Objects As a Framework for Evaluating Blended Learning

Science.gov (United States)

Laumakis, Mark; Graham, Charles; Dziuban, Chuck

2009-01-01

The authors contend that blended learning represents a boundary object; a construct that brings together constituencies from a variety of backgrounds with each of these cohorts defining the object somewhat differently. The Sloan-C Pillars (learning effectiveness, access, cost effectiveness, student satisfaction, and faculty satisfaction) provide…

Development of a model for whole brain learning of physiology.

Science.gov (United States)

Eagleton, Saramarie; Muller, Anton

2011-12-01

In this report, a model was developed for whole brain learning based on Curry's onion model. Curry described the effect of personality traits as the inner layer of learning, information-processing styles as the middle layer of learning, and environmental and instructional preferences as the outer layer of learning. The model that was developed elaborates on these layers by relating the personality traits central to learning to the different quadrants of brain preference, as described by Neethling's brain profile, as the inner layer of the onion. This layer is encircled by the learning styles that describe different information-processing preferences for each brain quadrant. For the middle layer, the different stages of Kolb's learning cycle are classified into the four brain quadrants associated with the different brain processing strategies within the information processing circle. Each of the stages of Kolb's learning cycle is also associated with a specific cognitive learning strategy. These two inner circles are enclosed by the circle representing the role of the environment and instruction on learning. It relates environmental factors that affect learning and distinguishes between face-to-face and technology-assisted learning. This model informs on the design of instructional interventions for physiology to encourage whole brain learning.

Understanding Cognitive Language Learning Strategies

Directory of Open Access Journals (Sweden)

Sergio Di Carlo

2017-01-01

Full Text Available Over time, definitions and taxonomies of language learning strategies have been critically examined. This article defines and classifies cognitive language learning strategies on a more grounded basis. Language learning is a macro-process for which the general hypotheses of information processing are valid. Cognitive strategies are represented by the pillars underlying the encoding, storage and retrieval of information. In order to understand the processes taking place on these three dimensions, a functional model was elaborated from multiple theoretical contributions and previous models: the Smart Processing Model. This model operates with linguistic inputs as well as with any other kind of information. It helps to illustrate the stages, relations, modules and processes that occur during the flow of information. This theoretical advance is a core element to classify cognitive strategies. Contributions from cognitive neuroscience have also been considered to establish the proposed classification which consists of five categories. Each of these categories has a different predominant function: classification, preparation, association, elaboration and transfer-practice. This better founded taxonomy opens the doors to potential studies that would allow a better understanding of the interdisciplinary complexity of language learning. Pedagogical and methodological implications are also discussed.

Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning.

Science.gov (United States)

Li, Bing; Yuan, Chunfeng; Xiong, Weihua; Hu, Weiming; Peng, Houwen; Ding, Xinmiao; Maybank, Steve

2017-12-01

In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (MIL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse -graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the MIL. Experiments and analyses in many practical applications prove the effectiveness of the M IL.

Orbitofrontal Cortex Encodes Memories within Value-Based Schemas and Represents Contexts That Guide Memory Retrieval

Science.gov (United States)

Farovik, Anja; Place, Ryan J.; McKenzie, Sam; Porter, Blake; Munro, Catherine E.

2015-01-01

There are a substantial number of studies showing that the orbitofrontal cortex links events to reward values, whereas the hippocampus links events to the context in which they occur. Here we asked how the orbitofrontal cortex contributes to memory where context determines the reward values associated with events. After rats learned object–reward associations that differed depending on the spatial context in which the objects were presented, neuronal ensembles in orbitofrontal cortex represented distinct value-based schemas, each composed of a systematic organization of the representations of objects in the contexts and positions where they were associated with reward or nonreward. Orbitofrontal ensembles also represent the different spatial contexts that define the mappings of stimuli to actions that lead to reward or nonreward. These findings, combined with observations on complementary memory representation within the hippocampus, suggest mechanisms through which prefrontal cortex and the hippocampus interact in support of context-guided memory. PMID:26019346

The influence of serotonin on fear learning.

Directory of Open Access Journals (Sweden)

Catherine Hindi Attar

Full Text Available Learning of associations between aversive stimuli and predictive cues is the basis of Pavlovian fear conditioning and is driven by a mismatch between expectation and outcome. To investigate whether serotonin modulates the formation of such aversive cue-outcome associations, we used functional magnetic resonance imaging (fMRI and dietary tryptophan depletion to reduce brain serotonin (5-HT levels in healthy human subjects. In a Pavlovian fear conditioning paradigm, 5-HT depleted subjects compared to a non-depleted control group exhibited attenuated autonomic responses to cues indicating the upcoming of an aversive event. These results were closely paralleled by reduced aversive learning signals in the amygdala and the orbitofrontal cortex, two prominent structures of the neural fear circuit. In agreement with current theories of serotonin as a motivational opponent system to dopamine in fear learning, our data provide first empirical evidence for a role of serotonin in representing formally derived learning signals for aversive events.

Learning molecular energies using localized graph kernels

Science.gov (United States)

Ferré, Grégoire; Haut, Terry; Barros, Kipton

2017-03-01

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

The Effect of Contextualized Conversational Feedback in a Complex Open-Ended Learning Environment

Science.gov (United States)

Segedy, James R.; Kinnebrew, John S.; Biswas, Gautam

2013-01-01

Betty's Brain is an open-ended learning environment in which students learn about science topics by teaching a virtual agent named Betty through the construction of a visual causal map that represents the relevant science phenomena. The task is complex, and success requires the use of metacognitive strategies that support knowledge acquisition,…

Hypermedia-Based Problem Based Learning in the Upper Elementary Grades: A Developmental Study.

Science.gov (United States)

Brinkerhoff, Jonathan D.; Glazewski, Krista

This paper describes the application of problem-based learning (PBL) design principles and the inclusion of teacher and study scaffolds to the design and implementation of a hypermedia-based learning unit for the upper elementary/middle school grades. The study examined the following research questions: (1) Does hypermedia-based PBL represent an…

Tone of voice guides word learning in informative referential contexts.

Science.gov (United States)

Reinisch, Eva; Jesse, Alexandra; Nygaard, Lynne C

2013-06-01

Listeners infer which object in a visual scene a speaker refers to from the systematic variation of the speaker's tone of voice (ToV). We examined whether ToV also guides word learning. During exposure, participants heard novel adjectives (e.g., "daxen") spoken with a ToV representing hot, cold, strong, weak, big, or small while viewing picture pairs representing the meaning of the adjective and its antonym (e.g., elephant-ant for big-small). Eye fixations were recorded to monitor referent detection and learning. During test, participants heard the adjectives spoken with a neutral ToV, while selecting referents from familiar and unfamiliar picture pairs. Participants were able to learn the adjectives' meanings, and, even in the absence of informative ToV, generalize them to new referents. A second experiment addressed whether ToV provides sufficient information to infer the adjectival meaning or needs to operate within a referential context providing information about the relevant semantic dimension. Participants who saw printed versions of the novel words during exposure performed at chance during test. ToV, in conjunction with the referential context, thus serves as a cue to word meaning. ToV establishes relations between labels and referents for listeners to exploit in word learning.

Music as a mnemonic to learn gesture sequences in normal aging and Alzheimer’s disease

OpenAIRE

Aline eMoussard; Emmanuel eBigand; Emmanuel eBigand; Isabelle ePeretz; Isabelle ePeretz; Isabelle ePeretz; Sylvie eBelleville; Sylvie eBelleville

2014-01-01

Strong links between music and motor functions suggest that music could represent an interesting aid for motor learning. The present study aims for the first time to test the potential of music to assist in the learning of sequences of gestures in normal and pathological aging. Participants with mild Alzheimer's disease (AD) and healthy older adults (Controls) learned sequences of meaningless gestures that were either accompanied by music or a metronome. We also manipulated the learning proce...

Music as a Mnemonic to Learn Gesture Sequences in Normal Aging and Alzheimer’s Disease

OpenAIRE

Moussard, Aline; Bigand, Emmanuel; Belleville, Sylvie; Peretz, Isabelle

2014-01-01

Strong links between music and motor functions suggest that music could represent an interesting aid for motor learning. The present study aims for the first time to test the potential of music to assist in the learning of sequences of gestures in normal and pathological aging. Participants with mild Alzheimer’s disease (AD) and healthy older adults (controls) learned sequences of meaningless gestures that were either accompanied by music or a metronome. We also manipulated the learning proce...

Collaborative learning in gerontological clinical settings: The students' perspective.

Science.gov (United States)

Suikkala, Arja; Kivelä, Eeva; Käyhkö, Pirjo

2016-03-01

This study deals with student nurses' experiences of collaborative learning in gerontological clinical settings where aged people are involved as age-experts in students' learning processes. The data were collected in 2012 using the contents of students' reflective writing assignments concerning elderly persons' life history interviews and the students' own assessments of their learning experiences in authentic elder care settings. The results, analyzed using qualitative content analysis, revealed mostly positive learning experiences. Interaction and collaborative learning activities in genuine gerontological clinical settings contributed to the students' understanding of the multiple age-related and disease-specific challenges as well as the issues of functional decline that aged patients face. Three types of factors influenced the students' collaborative learning experiences in gerontological clinical settings: student-related, patient-related and learning environment-related factors. According to the results, theoretical studies in combination with collaboration, in an authentic clinical environment, by student nurses, elderly patients, representatives of the elder care staff and nurse educators provide a feasible method for helping students transform their experiences with patients into actual skills. Their awareness of and sensitivity to the needs of the elderly increase as they learn. Copyright © 2016 Elsevier Ltd. All rights reserved.

Concept Mapping Using Cmap Tools to Enhance Meaningful Learning

Science.gov (United States)

Cañas, Alberto J.; Novak, Joseph D.

Concept maps are graphical tools that have been used in all facets of education and training for organizing and representing knowledge. When learners build concept maps, meaningful learning is facilitated. Computer-based concept mapping software such as CmapTools have further extended the use of concept mapping and greatly enhanced the potential of the tool, facilitating the implementation of a concept map-centered learning environment. In this chapter, we briefly present concept mapping and its theoretical foundation, and illustrate how it can lead to an improved learning environment when it is combined with CmapTools and the Internet. We present the nationwide “Proyecto Conéctate al Conocimiento” in Panama as an example of how concept mapping, together with technology, can be adopted by hundreds of schools as a means to enhance meaningful learning.

The New Generation of Auditors Meeting Praxis: Dual Learning's Role in Audit Students' Professional Development

Science.gov (United States)

Agevall, Lena; Broberg, Pernilla; Umans, Timurs

2018-01-01

This paper explores whether and in what way "dual learning" can develop understanding of the relationship between structure/judgement and explores audit student's perceptions of the audit profession. The Work Integrated Learning (WIL) module, serving as a tool of enabling dual learning, represents the context for this exploration. The…

Designing capacity-building in e-learning expertise: Challenges and strategies

OpenAIRE

Aczel, J. C.; Peake, S. R.; Hardy, P.

2008-01-01

This research study looks at how organizations in developing countries perceive the challenge of building capacity in e-learning expertise. Data was collected on six such organizations, and a range of perceived rationales and constraints were identified. The paper hypothesizes a four-part framework to define the e-learning capacity gaps that these circumstances appear to represent: the 'instructional design capacity gap', the 'production capacity gap', the 'tutorial capacity gap' and the 'com...

E-Model for Online Learning Communities.

Science.gov (United States)

Rogo, Ellen J; Portillo, Karen M

2015-10-01

The purpose of this study was to explore the students' perspectives on the phenomenon of online learning communities while enrolled in a graduate dental hygiene program. A qualitative case study method was designed to investigate the learners' experiences with communities in an online environment. A cross-sectional purposive sampling method was used. Interviews were the data collection method. As the original data were being analyzed, the researchers noted a pattern evolved indicating the phenomenon developed in stages. The data were re-analyzed and validated by 2 member checks. The participants' experiences revealed an e-model consisting of 3 stages of formal learning community development as core courses in the curriculum were completed and 1 stage related to transmuting the community to an informal entity as students experienced the independent coursework in the program. The development of the formal learning communities followed 3 stages: Building a Foundation for the Learning Community, Building a Supportive Network within the Learning Community and Investing in the Community to Enhance Learning. The last stage, Transforming the Learning Community, signaled a transition to an informal network of learners. The e-model was represented by 3 key elements: metamorphosis of relationships, metamorphosis through the affective domain and metamorphosis through the cognitive domain, with the most influential element being the affective development. The e-model describes a 4 stage process through which learners experience a metamorphosis in their affective, relationship and cognitive development. Synergistic learning was possible based on the interaction between synergistic relationships and affective actions. Copyright © 2015 The American Dental Hygienists’ Association.

Neuronal avalanches and learning

Energy Technology Data Exchange (ETDEWEB)

Arcangelis, Lucilla de, E-mail: dearcangelis@na.infn.it [Department of Information Engineering and CNISM, Second University of Naples, 81031 Aversa (Italy)

2011-05-01

Networks of living neurons represent one of the most fascinating systems of biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behaviour of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behaviour is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. Spontaneous neuronal activity has recently shown features in common to other complex systems. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behaviour. In this contribution we discuss a statistical mechanical model for the complex activity in a neuronal network. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. Then, we discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules, in particular the exclusive OR (XOR) and a random rule with three inputs. The learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

Neuronal avalanches and learning

International Nuclear Information System (INIS)

Arcangelis, Lucilla de

2011-01-01

Networks of living neurons represent one of the most fascinating systems of biology. If the physical and chemical mechanisms at the basis of the functioning of a single neuron are quite well understood, the collective behaviour of a system of many neurons is an extremely intriguing subject. Crucial ingredient of this complex behaviour is the plasticity property of the network, namely the capacity to adapt and evolve depending on the level of activity. This plastic ability is believed, nowadays, to be at the basis of learning and memory in real brains. Spontaneous neuronal activity has recently shown features in common to other complex systems. Experimental data have, in fact, shown that electrical information propagates in a cortex slice via an avalanche mode. These avalanches are characterized by a power law distribution for the size and duration, features found in other problems in the context of the physics of complex systems and successful models have been developed to describe their behaviour. In this contribution we discuss a statistical mechanical model for the complex activity in a neuronal network. The model implements the main physiological properties of living neurons and is able to reproduce recent experimental results. Then, we discuss the learning abilities of this neuronal network. Learning occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. The system is able to learn all the tested rules, in particular the exclusive OR (XOR) and a random rule with three inputs. The learning dynamics exhibits universal features as function of the strength of plastic adaptation. Any rule could be learned provided that the plastic adaptation is sufficiently slow.

How Attention Can Create Synaptic Tags for the Learning of Working Memories in Sequential Tasks

NARCIS (Netherlands)

Rombouts, J.O.; Bohte, S.M.; Roelfsema, P.R.

2015-01-01

Intelligence is our ability to learn appropriate responses to new stimuli and situations. Neurons in association cortex are thought to be essential for this ability. During learning these neurons become tuned to relevant features and start to represent them with persistent activity during memory

2.5-year-olds use cross-situational consistency to learn verbs under referential uncertainty.

Science.gov (United States)

Scott, Rose M; Fisher, Cynthia

2012-02-01

Recent evidence shows that children can use cross-situational statistics to learn new object labels under referential ambiguity (e.g., Smith & Yu, 2008). Such evidence has been interpreted as support for proposals that statistical information about word-referent co-occurrence plays a powerful role in word learning. But object labels represent only a fraction of the vocabulary children acquire, and arguably represent the simplest case of word learning based on observations of world scenes. Here we extended the study of cross-situational word learning to a new segment of the vocabulary, action verbs, to permit a stronger test of the role of statistical information in word learning. In two experiments, on each trial 2.5-year-olds encountered two novel intransitive (e.g., "She's pimming!"; Experiment 1) or transitive verbs (e.g., "She's pimming her toy!"; Experiment 2) while viewing two action events. The consistency with which each verb accompanied each action provided the only source of information about the intended referent of each verb. The 2.5-year-olds used cross-situational consistency in verb learning, but also showed significant limits on their ability to do so as the sentences and scenes became slightly more complex. These findings help to define the role of cross-situational observation in word learning. Copyright © 2011 Elsevier B.V. All rights reserved.

Lessons learned from existing biomass power plants

Energy Technology Data Exchange (ETDEWEB)

Wiltsee, G.

2000-02-24

This report includes summary information on 20 biomass power plants, which represent some of the leaders in the industry. In each category an effort is made to identify plants that illustrate particular points. The project experiences described capture some important lessons learned that lead in the direction of an improved biomass power industry.

Neural Basis of Reinforcement Learning and Decision Making

Science.gov (United States)

Lee, Daeyeol; Seo, Hyojung; Jung, Min Whan

2012-01-01

Reinforcement learning is an adaptive process in which an animal utilizes its previous experience to improve the outcomes of future choices. Computational theories of reinforcement learning play a central role in the newly emerging areas of neuroeconomics and decision neuroscience. In this framework, actions are chosen according to their value functions, which describe how much future reward is expected from each action. Value functions can be adjusted not only through reward and penalty, but also by the animal’s knowledge of its current environment. Studies have revealed that a large proportion of the brain is involved in representing and updating value functions and using them to choose an action. However, how the nature of a behavioral task affects the neural mechanisms of reinforcement learning remains incompletely understood. Future studies should uncover the principles by which different computational elements of reinforcement learning are dynamically coordinated across the entire brain. PMID:22462543

Informed decision-making with and for people with dementia - efficacy of the PRODECIDE education program for legal representatives: protocol of a randomized controlled trial (PRODECIDE-RCT).

Science.gov (United States)

Lühnen, Julia; Haastert, Burkhard; Mühlhauser, Ingrid; Richter, Tanja

2017-09-15

In Germany, the guardianship system provides adults who are no longer able to handle their own affairs a court-appointed legal representative, for support without restriction of legal capacity. Although these representatives only rarely are qualified in healthcare, they nevertheless play decisive roles in the decision-making processes for people with dementia. Previously, we developed an education program (PRODECIDE) to address this shortcoming and tested it for feasibility. Typical, autonomy-restricting decisions in the care of people with dementia-namely, using percutaneous endoscopic gastrostomy (PEG) or physical restrains (PR), or the prescription of antipsychotic drugs (AP)-were the subject areas trained. The training course aims to enhance the competency of legal representatives in informed decision-making. In this study, we will evaluate the efficacy of the PRODECIDE education program. A randomized controlled trial with a six-month follow-up will be conducted to compare the PRODECIDE education program with standard care, enrolling legal representatives (N = 216). The education program lasts 10 h and comprises four modules: A, decision-making processes and methods; and B, C and D, evidence-based knowledge about PEG, PR and AP, respectively. The primary outcome measure is knowledge, which is operationalized as the understanding of decision-making processes in healthcare affairs and in setting realistic expectations about benefits and harms of PEG, PR and AP in people with dementia. Secondary outcomes are sufficient and sustainable knowledge and percentage of persons concerned affected by PEG, FEM or AP. A qualitative process evaluation will be performed. Additionally, to support implementation, a concept for translating the educational contents into e-learning modules will be developed. The study results will show whether the efficacy of the education program could justify its implementation into the regular training curricula for legal representatives

Specific learning disorder: prevalence and gender differences.

Directory of Open Access Journals (Sweden)

Kristina Moll

Full Text Available Comprehensive models of learning disorders have to consider both isolated learning disorders that affect one learning domain only, as well as comorbidity between learning disorders. However, empirical evidence on comorbidity rates including all three learning disorders as defined by DSM-5 (deficits in reading, writing, and mathematics is scarce. The current study assessed prevalence rates and gender ratios for isolated as well as comorbid learning disorders in a representative sample of 1633 German speaking children in 3rd and 4th Grade. Prevalence rates were analysed for isolated as well as combined learning disorders and for different deficit criteria, including a criterion for normal performance. Comorbid learning disorders occurred as frequently as isolated learning disorders, even when stricter cutoff criteria were applied. The relative proportion of isolated and combined disorders did not change when including a criterion for normal performance. Reading and spelling deficits differed with respect to their association with arithmetic problems: Deficits in arithmetic co-occurred more often with deficits in spelling than with deficits in reading. In addition, comorbidity rates for arithmetic and reading decreased when applying stricter deficit criteria, but stayed high for arithmetic and spelling irrespective of the chosen deficit criterion. These findings suggest that the processes underlying the relationship between arithmetic and reading might differ from those underlying the relationship between arithmetic and spelling. With respect to gender ratios, more boys than girls showed spelling deficits, while more girls were impaired in arithmetic. No gender differences were observed for isolated reading problems and for the combination of all three learning disorders. Implications of these findings for assessment and intervention of learning disorders are discussed.

Computations Underlying Social Hierarchy Learning: Distinct Neural Mechanisms for Updating and Representing Self-Relevant Information.

Science.gov (United States)

Kumaran, Dharshan; Banino, Andrea; Blundell, Charles; Hassabis, Demis; Dayan, Peter

2016-12-07

Knowledge about social hierarchies organizes human behavior, yet we understand little about the underlying computations. Here we show that a Bayesian inference scheme, which tracks the power of individuals, better captures behavioral and neural data compared with a reinforcement learning model inspired by rating systems used in games such as chess. We provide evidence that the medial prefrontal cortex (MPFC) selectively mediates the updating of knowledge about one's own hierarchy, as opposed to that of another individual, a process that underpinned successful performance and involved functional interactions with the amygdala and hippocampus. In contrast, we observed domain-general coding of rank in the amygdala and hippocampus, even when the task did not require it. Our findings reveal the computations underlying a core aspect of social cognition and provide new evidence that self-relevant information may indeed be afforded a unique representational status in the brain. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

A Learning Progression for Elementary Students' Functional Thinking

Science.gov (United States)

Stephens, Ana C.; Fonger, Nicole; Strachota, Susanne; Isler, Isil; Blanton, Maria; Knuth, Eric; Murphy Gardiner, Angela

2017-01-01

In this article we advance characterizations of and supports for elementary students' progress in generalizing and representing functional relationships as part of a comprehensive approach to early algebra. Our learning progressions approach to early algebra research involves the coordination of a curricular framework and progression, an…

The Influence of Multigenerational Workforce in Effective Informal Team Learning

Directory of Open Access Journals (Sweden)

Roza Marsaulina Sibarani

2015-01-01

Full Text Available The urgency of this research arises from the convergence of two dynamics that are transforming the workplace and impacting organization performance. The first is multigenerational workforce work side by side in the same organization even in the same team. The second is informal learning, a major mode of learning in an organization. Therefore, this paper aims to explore the influence of generational background of Baby Boomers, Generation X and Generation Y in relation to informal team learning in the Indonesian business environment. Both, qualitative and quantitative studies were conducted with 21 interviewees and 184 survey respondents representing a total of 191 multigenerational teams participating in this research. The findings suggest that generational background influence informal learner and effective informal team learning, but have no direct impact on team climate. Understanding generational differences will enable individuals to learn informally and create a conducive team climate that will lead to effective informal team learning.

A causal link between prediction errors, dopamine neurons and learning.

Science.gov (United States)

Steinberg, Elizabeth E; Keiflin, Ronald; Boivin, Josiah R; Witten, Ilana B; Deisseroth, Karl; Janak, Patricia H

2013-07-01

Situations in which rewards are unexpectedly obtained or withheld represent opportunities for new learning. Often, this learning includes identifying cues that predict reward availability. Unexpected rewards strongly activate midbrain dopamine neurons. This phasic signal is proposed to support learning about antecedent cues by signaling discrepancies between actual and expected outcomes, termed a reward prediction error. However, it is unknown whether dopamine neuron prediction error signaling and cue-reward learning are causally linked. To test this hypothesis, we manipulated dopamine neuron activity in rats in two behavioral procedures, associative blocking and extinction, that illustrate the essential function of prediction errors in learning. We observed that optogenetic activation of dopamine neurons concurrent with reward delivery, mimicking a prediction error, was sufficient to cause long-lasting increases in cue-elicited reward-seeking behavior. Our findings establish a causal role for temporally precise dopamine neuron signaling in cue-reward learning, bridging a critical gap between experimental evidence and influential theoretical frameworks.

Towards Machine Learning of Motor Skills

Science.gov (United States)

Peters, Jan; Schaal, Stefan; Schölkopf, Bernhard

Autonomous robots that can adapt to novel situations has been a long standing vision of robotics, artificial intelligence, and cognitive sciences. Early approaches to this goal during the heydays of artificial intelligence research in the late 1980s, however, made it clear that an approach purely based on reasoning or human insights would not be able to model all the perceptuomotor tasks that a robot should fulfill. Instead, new hope was put in the growing wake of machine learning that promised fully adaptive control algorithms which learn both by observation and trial-and-error. However, to date, learning techniques have yet to fulfill this promise as only few methods manage to scale into the high-dimensional domains of manipulator robotics, or even the new upcoming trend of humanoid robotics, and usually scaling was only achieved in precisely pre-structured domains. In this paper, we investigate the ingredients for a general approach to motor skill learning in order to get one step closer towards human-like performance. For doing so, we study two major components for such an approach, i.e., firstly, a theoretically well-founded general approach to representing the required control structures for task representation and execution and, secondly, appropriate learning algorithms which can be applied in this setting.

Optimisation of transgene action at the post-transcriptional level: high quality parthenocarpic fruits in industrial tomatoes

Directory of Open Access Journals (Sweden)

Defez Roberto

2002-01-01

Full Text Available Abstract Background Genetic engineering of parthenocarpy confers to horticultural plants the ability to produce fruits under environmental conditions that curtail fruit productivity and quality. The DefH9-iaaM transgene, whose predicted action is to confer auxin synthesis specifically in the placenta, ovules and derived tissues, has been shown to confer parthenocarpy to several plant species (tobacco, eggplant, tomato and varieties. Results UC82 tomato plants, a typical cultivar used by the processing industry, transgenic for the DefH9-iaaM gene produce parthenocarpic fruits that are malformed. UC82 plants transgenic for the DefH9-RI-iaaM, a DefH9-iaaM derivative gene modified in its 5'ULR by replacing 53 nucleotides immediately upstream of the AUG initiation codon with an 87 nucleotides-long sequence derived from the rolA intron sequence, produce parthenocarpic fruits of high quality. In an in vitro translation system, the iaaM mRNA, modified in its 5'ULR is translated 3–4 times less efficiently than the original transcript. An optimal expressivity of parthenocarpy correlates with a reduced transgene mRNA steady state level in DefH9-RI-iaaM flower buds in comparison to DefH9-iaaM flower buds. Consistent with the known function of the iaaM gene, flower buds transgenic for the DefH9-RI-iaaM gene contain ten times more IAA than control untransformed flower buds, but five times less than DefH9-iaaM flower buds. Conclusions By using an auxin biosynthesis transgene downregulated at the post-transcriptional level, an optimal expressivity of parthenocarpy has been achieved in a genetic background not suitable for the original transgene. Thus, the method allows the generation of a wider range of expressivity of the desired trait in transgenic plants.

ENERGY-NET (Energy, Environment and Society Learning Network): Best Practices to Enhance Informal Geoscience Learning

Science.gov (United States)

Rossi, R.; Elliott, E. M.; Bain, D.; Crowley, K. J.; Steiner, M. A.; Divers, M. T.; Hopkins, K. G.; Giarratani, L.; Gilmore, M. E.

2014-12-01

While energy links all living and non-living systems, the integration of energy, the environment, and society is often not clearly represented in 9 - 12 classrooms and informal learning venues. However, objective public learning that integrates these components is essential for improving public environmental literacy. ENERGY-NET (Energy, Environment and Society Learning Network) is a National Science Foundation funded initiative that uses an Earth Systems Science framework to guide experimental learning for high school students and to improve public learning opportunities regarding the energy-environment-society nexus in a Museum setting. One of the primary objectives of the ENERGY-NET project is to develop a rich set of experimental learning activities that are presented as exhibits at the Carnegie Museum of Natural History in Pittsburgh, Pennsylvania (USA). Here we detail the evolution of the ENERGY-NET exhibit building process and the subsequent evolution of exhibit content over the past three years. While preliminary plans included the development of five "exploration stations" (i.e., traveling activity carts) per calendar year, the opportunity arose to create a single, larger topical exhibit per semester, which was assumed to have a greater impact on museum visitors. Evaluative assessments conducted to date reveal important practices to be incorporated into ongoing exhibit development: 1) Undergraduate mentors and teen exhibit developers should receive additional content training to allow richer exhibit materials. 2) The development process should be distributed over as long a time period as possible and emphasize iteration. This project can serve as a model for other collaborations between geoscience departments and museums. In particular, these practices may streamline development of public presentations and increase the effectiveness of experimental learning activities.

Musical journey: a virtual world gamification experience for music learning

OpenAIRE

Gomes, José; Figueiredo, Mauro; Amante, Lúcia

2014-01-01

Games are an integral part of the learning process of humans, in particular for children, who exploit the imagery as an intrinsic part of their lives. Features from games have been successfully implemented as a means to captivate and motivate students to perform learning at various levels of education in traditional schools. This paper presents a virtual world – Musical Journey – representing the Aesthetic Periods of Music History. This virtual environment allows students to freely explore an...

The impact of levodopa on non-declarative and declarative learning

OpenAIRE

Fuhrer, Hannah

2015-01-01

We aim to assess the role of levodopa in non-declarative and declarative learning. Patients with Parkinson’s disease are known to suffer from striatal dopamine depletion and to be impaired in non- declarative memory tasks. We therefore hypothesized that the intake of levodopa may improve non- declarative learning. Furthermore, as declarative memory is represented in medial temporal lobe structures, we anticipated patients with Parkinson’s disease not to be impaired in declarative tests. We st...

Learning of a Formation Principle for the Secondary Phonemic Function of a Syllabic Orthography

Science.gov (United States)

Fletcher-Flinn, Claire M.; Thompson, G. Brian; Yamada, Megumi; Meissel, Kane

2014-01-01

It has been observed in Japanese children learning to read that there is an early and rapid shift from exclusive reading of hiragana as syllabograms to the dual-use convention in which some hiragana also represent phonemic elements. Such rapid initial learning appears contrary to the standard theories of reading acquisition that require…

Exemplar variability facilitates rapid learning of an otherwise unlearnable grammar by individuals with language-based learning disability.

Science.gov (United States)

von Koss Torkildsen, Janne; Dailey, Natalie S; Aguilar, Jessica M; Gómez, Rebecca; Plante, Elena

2013-04-01

Even without explicit instruction, learners are able to extract information about the form of a language simply by attending to input that reflects the underlying grammar. In this study, the authors explored the role of variability in this learning by asking whether varying the number of unique exemplars heard by the learner affects learning of an artificial syntactic form. Learners with normal language (n = 16) and language-based learning disability (LLD; n = 16) were exposed to strings of nonwords that represented an underlying grammar. Half of the learners heard 3 exemplars 16 times each (low variability group), and the other half of the learners heard 24 exemplars twice each (high variability group). Learners were then tested for recognition of items heard and generalization of the grammar with new nonword strings. Only those learners with LLD who were in the high variability group were able to demonstrate generalization of the underlying grammar. For learners with normal language, both those in the high and the low variability groups showed generalization of the grammar, but relative effect sizes suggested a larger learning effect in the high variability group. The results demonstrate that the structure of the learning context can determine the ability to generalize from specific training items to novel cases.

Place and Response Learning in the Open-field Tower Maze.

Science.gov (United States)

Lipatova, Olga; Campolattaro, Matthew M; Toufexis, Donna J; Mabry, Erin A

2015-10-28

This protocol describes how the Open-field Tower Maze (OFTM) paradigm is used to study spatial learning in rodents. This maze is especially useful for examining how rats learn to use a place- or response-learning to successfully navigate in an open-field arena. Additionally, this protocol describes how the OFTM differs from other behavioral maze paradigms that are commonly used to study spatial learning in rodents. The OFTM described in this article was adapted from the one previously described by Cole, Clipperton, and Walt (2007). Specifically, the OFTM was created to test spatial learning in rodents without the experimenter having to consider how "stress" might play a role as a confounding variable. Experiments have shown that stress-alone can significantly affect cognitive function(1). The representative results section contains data from an experiment that used the OFTM to examine the effects of estradiol treatment on place- and response-learning in adult female Sprague Dawley rats(2). Future studies will be designed to examine the role of the hippocampus and striatum in place- and response-learning in the OFTM.

Learning Digital Test and Diagnostics via Internet

Directory of Open Access Journals (Sweden)

Heinz-Dietrich Wuttke

2007-02-01

Full Text Available An environment targeted to e-learning is presented for teaching design and test of electronic systems. The environment consists of a set of Java applets, and of web based access to the hardware equipments, which can be used in the classroom, for learning at home, in laboratory research and training, or for carrying out testing of students during exams. The tools support university courses on digital electronics, computer hardware, testing and design for testability to learn by hands-on exercises how to design digital systems, how to make them testable, how to build self-testing systems, how to generate test patterns, how to analyze the quality of tests, and how to localize faults in hardware. The tasks chosen for hands-on training represent simultaneously research problems, which allow to fostering in students critical thinking, problem solving skills and creativity.

Learning situation models in a smart home.

Science.gov (United States)

Brdiczka, Oliver; Crowley, James L; Reignier, Patrick

2009-02-01

This paper addresses the problem of learning situation models for providing context-aware services. Context for modeling human behavior in a smart environment is represented by a situation model describing environment, users, and their activities. A framework for acquiring and evolving different layers of a situation model in a smart environment is proposed. Different learning methods are presented as part of this framework: role detection per entity, unsupervised extraction of situations from multimodal data, supervised learning of situation representations, and evolution of a predefined situation model with feedback. The situation model serves as frame and support for the different methods, permitting to stay in an intuitive declarative framework. The proposed methods have been integrated into a whole system for smart home environment. The implementation is detailed, and two evaluations are conducted in the smart home environment. The obtained results validate the proposed approach.

Early clinical experience: do students learn what we expect?

Science.gov (United States)

Helmich, Esther; Bolhuis, Sanneke; Laan, Roland; Koopmans, Raymond

2011-07-01

Early clinical experience is thought to contribute to the professional development of medical students, but little is known about the kind of learning processes that actually take place. Learning in practice is highly informal and may be difficult to direct by predefined learning outcomes. Learning in medical practice includes a socialisation process in which some learning outcomes may be valued, but others neglected or discouraged. This study describes students' learning goals (prior to a Year 1 nursing attachment) and learning outcomes (after the attachment) in relation to institutional educational goals, and evaluates associations between learning outcomes, student characteristics and place of attachment. A questionnaire containing open-ended questions about learning goals and learning outcomes was administered to all Year 1 medical students (n = 347) before and directly after a 4-week nursing attachment in either a hospital or a nursing home. Two confirmatory focus group interviews were conducted and data were analysed using qualitative and quantitative content analyses. Students' learning goals corresponded with educational goals with a main emphasis on communication and empathy. Other learning goals included gaining insight into the organisation of health care and learning to deal with emotions. Self-reported learning outcomes were the same, but students additionally mentioned reflection on professional behaviour and their own future development. Women and younger students mentioned communication and empathy more often than men and older students. Individual learning goals, with the exception of communicating and empathising with patients, did not predict learning outcomes. Students' learning goals closely match educational goals, which are adequately met in early nursing attachments in both hospitals and nursing homes. Learning to deal with emotions was under-represented as a learning goal and learning outcome, which may indicate that emotional aspects

Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation.

Science.gov (United States)

Kato, Ayaka; Morita, Kenji

2016-10-01

It has been suggested that dopamine (DA) represents reward-prediction-error (RPE) defined in reinforcement learning and therefore DA responds to unpredicted but not predicted reward. However, recent studies have found DA response sustained towards predictable reward in tasks involving self-paced behavior, and suggested that this response represents a motivational signal. We have previously shown that RPE can sustain if there is decay/forgetting of learned-values, which can be implemented as decay of synaptic strengths storing learned-values. This account, however, did not explain the suggested link between tonic/sustained DA and motivation. In the present work, we explored the motivational effects of the value-decay in self-paced approach behavior, modeled as a series of 'Go' or 'No-Go' selections towards a goal. Through simulations, we found that the value-decay can enhance motivation, specifically, facilitate fast goal-reaching, albeit counterintuitively. Mathematical analyses revealed that underlying potential mechanisms are twofold: (1) decay-induced sustained RPE creates a gradient of 'Go' values towards a goal, and (2) value-contrasts between 'Go' and 'No-Go' are generated because while chosen values are continually updated, unchosen values simply decay. Our model provides potential explanations for the key experimental findings that suggest DA's roles in motivation: (i) slowdown of behavior by post-training blockade of DA signaling, (ii) observations that DA blockade severely impairs effortful actions to obtain rewards while largely sparing seeking of easily obtainable rewards, and (iii) relationships between the reward amount, the level of motivation reflected in the speed of behavior, and the average level of DA. These results indicate that reinforcement learning with value-decay, or forgetting, provides a parsimonious mechanistic account for the DA's roles in value-learning and motivation. Our results also suggest that when biological systems for value-learning

Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation.

Directory of Open Access Journals (Sweden)

Ayaka Kato

2016-10-01

Full Text Available It has been suggested that dopamine (DA represents reward-prediction-error (RPE defined in reinforcement learning and therefore DA responds to unpredicted but not predicted reward. However, recent studies have found DA response sustained towards predictable reward in tasks involving self-paced behavior, and suggested that this response represents a motivational signal. We have previously shown that RPE can sustain if there is decay/forgetting of learned-values, which can be implemented as decay of synaptic strengths storing learned-values. This account, however, did not explain the suggested link between tonic/sustained DA and motivation. In the present work, we explored the motivational effects of the value-decay in self-paced approach behavior, modeled as a series of 'Go' or 'No-Go' selections towards a goal. Through simulations, we found that the value-decay can enhance motivation, specifically, facilitate fast goal-reaching, albeit counterintuitively. Mathematical analyses revealed that underlying potential mechanisms are twofold: (1 decay-induced sustained RPE creates a gradient of 'Go' values towards a goal, and (2 value-contrasts between 'Go' and 'No-Go' are generated because while chosen values are continually updated, unchosen values simply decay. Our model provides potential explanations for the key experimental findings that suggest DA's roles in motivation: (i slowdown of behavior by post-training blockade of DA signaling, (ii observations that DA blockade severely impairs effortful actions to obtain rewards while largely sparing seeking of easily obtainable rewards, and (iii relationships between the reward amount, the level of motivation reflected in the speed of behavior, and the average level of DA. These results indicate that reinforcement learning with value-decay, or forgetting, provides a parsimonious mechanistic account for the DA's roles in value-learning and motivation. Our results also suggest that when biological systems

The enigma of number: why children find the meanings of even small number words hard to learn and how we can help them do better.

Directory of Open Access Journals (Sweden)

Michael Ramscar

Full Text Available Although number words are common in everyday speech, learning their meanings is an arduous, drawn-out process for most children, and the source of this delay has long been the subject of inquiry. Children begin by identifying the few small numerosities that can be named without counting, and this has prompted further debate over whether there is a specific, capacity-limited system for representing these small sets, or whether smaller and larger sets are both represented by the same system. Here we present a formal, computational analysis of number learning that offers a possible solution to both puzzles. This analysis indicates that once the environment and the representational demands of the task of learning to identify sets are taken into consideration, a continuous system for learning, representing and discriminating set-sizes can give rise to effective discontinuities in processing. At the same time, our simulations illustrate how typical prenominal linguistic constructions ("there are three balls" structure information in a way that is largely unhelpful for discrimination learning, while suggesting that postnominal constructions ("balls, there are three" will facilitate such learning. A training-experiment with three-year olds confirms these predictions, demonstrating that rapid, significant gains in numerical understanding and competence are possible given appropriately structured postnominal input. Our simulations and results reveal how discrimination learning tunes children's systems for representing small sets, and how its capacity-limits result naturally out of a mixture of the learning environment and the increasingly complex task of discriminating and representing ever-larger number sets. They also explain why children benefit so little from the training that parents and educators usually provide. Given the efficacy of our intervention, the ease with which it can be implemented, and the large body of research showing how early

Informal Science learning in PIBID: identifying and interpreting the strands

Directory of Open Access Journals (Sweden)

Thomas Barbosa Fejolo

2013-10-01

Full Text Available This paper presents a research on informal Science learning in the context of the Institutional Scholarship Program Initiation to Teaching (PIBID. We take as reference the strands of informal Science learning (FAC, representing six dimensions of learning, they are: 1 Development of interest in Science; 2 Understanding of scientific knowledge; 3 Engaging in scientific reasoning; 4 Reflection on Science; 5 Engagement in scientific practice; 6 Identification with Science. For the lifting data, it was used the filming record of the interactions and dialogues of undergraduate students while performing activities of Optical Spectroscopy in the laboratory. Based on the procedures of content analysis and interpretations through communication, we investigate which of the six strands were present during the action of the students in activities. As a result we have drawn a learning profile for each student by distributing communications in different strands of informal Science learning.

Learning a commonsense moral theory.

Science.gov (United States)

Kleiman-Weiner, Max; Saxe, Rebecca; Tenenbaum, Joshua B

2017-10-01

We introduce a computational framework for understanding the structure and dynamics of moral learning, with a focus on how people learn to trade off the interests and welfare of different individuals in their social groups and the larger society. We posit a minimal set of cognitive capacities that together can solve this learning problem: (1) an abstract and recursive utility calculus to quantitatively represent welfare trade-offs; (2) hierarchical Bayesian inference to understand the actions and judgments of others; and (3) meta-values for learning by value alignment both externally to the values of others and internally to make moral theories consistent with one's own attachments and feelings. Our model explains how children can build from sparse noisy observations of how a small set of individuals make moral decisions to a broad moral competence, able to support an infinite range of judgments and decisions that generalizes even to people they have never met and situations they have not been in or observed. It also provides insight into the causes and dynamics of moral change across time, including cases when moral change can be rapidly progressive, changing values significantly in just a few generations, and cases when it is likely to move more slowly. Copyright © 2017 Elsevier B.V. All rights reserved.

Learning topic models by belief propagation.

Science.gov (United States)

Zeng, Jia; Cheung, William K; Liu, Jiming

2013-05-01

Latent Dirichlet allocation (LDA) is an important hierarchical Bayesian model for probabilistic topic modeling, which attracts worldwide interest and touches on many important applications in text mining, computer vision and computational biology. This paper represents the collapsed LDA as a factor graph, which enables the classic loopy belief propagation (BP) algorithm for approximate inference and parameter estimation. Although two commonly used approximate inference methods, such as variational Bayes (VB) and collapsed Gibbs sampling (GS), have gained great success in learning LDA, the proposed BP is competitive in both speed and accuracy, as validated by encouraging experimental results on four large-scale document datasets. Furthermore, the BP algorithm has the potential to become a generic scheme for learning variants of LDA-based topic models in the collapsed space. To this end, we show how to learn two typical variants of LDA-based topic models, such as author-topic models (ATM) and relational topic models (RTM), using BP based on the factor graph representations.

Attribute Learning for SAR Image Classification

Directory of Open Access Journals (Sweden)

Chu He

2017-04-01

Full Text Available This paper presents a classification approach based on attribute learning for high spatial resolution Synthetic Aperture Radar (SAR images. To explore the representative and discriminative attributes of SAR images, first, an iterative unsupervised algorithm is designed to cluster in the low-level feature space, where the maximum edge response and the ratio of mean-to-variance are included; a cross-validation step is applied to prevent overfitting. Second, the most discriminative clustering centers are sorted out to construct an attribute dictionary. By resorting to the attribute dictionary, a representation vector describing certain categories in the SAR image can be generated, which in turn is used to perform the classifying task. The experiments conducted on TerraSAR-X images indicate that those learned attributes have strong visual semantics, which are characterized by bright and dark spots, stripes, or their combinations. The classification method based on these learned attributes achieves better results.

Vocal learning in the functionally referential food grunts of chimpanzees.

Science.gov (United States)

Watson, Stuart K; Townsend, Simon W; Schel, Anne M; Wilke, Claudia; Wallace, Emma K; Cheng, Leveda; West, Victoria; Slocombe, Katie E

2015-02-16

One standout feature of human language is our ability to reference external objects and events with socially learned symbols, or words. Exploring the phylogenetic origins of this capacity is therefore key to a comprehensive understanding of the evolution of language. While non-human primates can produce vocalizations that refer to external objects in the environment, it is generally accepted that their acoustic structure is fixed and a product of arousal states. Indeed, it has been argued that the apparent lack of flexible control over the structure of referential vocalizations represents a key discontinuity with language. Here, we demonstrate vocal learning in the acoustic structure of referential food grunts in captive chimpanzees. We found that, following the integration of two groups of adult chimpanzees, the acoustic structure of referential food grunts produced for a specific food converged over 3 years. Acoustic convergence arose independently of preference for the food, and social network analyses indicated this only occurred after strong affiliative relationships were established between the original subgroups. We argue that these data represent the first evidence of non-human animals actively modifying and socially learning the structure of a meaningful referential vocalization from conspecifics. Our findings indicate that primate referential call structure is not simply determined by arousal and that the socially learned nature of referential words in humans likely has ancient evolutionary origins. Copyright © 2015 Elsevier Ltd. All rights reserved.

Games for Organizational Learning in Production Management

DEFF Research Database (Denmark)

Riis, Jens Ove; Smeds, Riitta; Johansen, John

1998-01-01

Development and implementation of new production management methods and systems represents achalIenge to the individual employee, manager, student or teacher. Understanding dynamic and complex systems is very difficult, old methods and working habits have to be unlearned, a new kind of knowledge....... Finally, the use of games will be tied to the concept of organizational learning....

Neuromorphic function learning with carbon nanotube based synapses

International Nuclear Information System (INIS)

Gacem, Karim; Filoramo, Arianna; Derycke, Vincent; Retrouvey, Jean-Marie; Chabi, Djaafar; Zhao, Weisheng; Klein, Jacques-Olivier

2013-01-01

The principle of using nanoscale memory devices as artificial synapses in neuromorphic circuits is recognized as a promising way to build ground-breaking circuit architectures tolerant to defects and variability. Yet, actual experimental demonstrations of the neural network type of circuits based on non-conventional/non-CMOS memory devices and displaying function learning capabilities remain very scarce. We show here that carbon-nanotube-based memory elements can be used as artificial synapses, combined with conventional neurons and trained to perform functions through the application of a supervised learning algorithm. The same ensemble of eight devices can notably be trained multiple times to code successively any three-input linearly separable Boolean logic function despite device-to-device variability. This work thus represents one of the very few demonstrations of actual function learning with synapses based on nanoscale building blocks. The potential of such an approach for the parallel learning of multiple and more complex functions is also evaluated. (paper)

Proceedings of IEEE Machine Learning for Signal Processing Workshop XVI

DEFF Research Database (Denmark)

Larsen, Jan

These proceedings contains refereed papers presented at the sixteenth IEEE Workshop on Machine Learning for Signal Processing (MLSP'2006), held in Maynooth, Co. Kildare, Ireland, September 6-8, 2006. This is a continuation of the IEEE Workshops on Neural Networks for Signal Processing (NNSP......). The name of the Technical Committee, hence of the Workshop, was changed to Machine Learning for Signal Processing in September 2003 to better reflect the areas represented by the Technical Committee. The conference is organized by the Machine Learning for Signal Processing Technical Committee...... the same standard as the printed version and facilitates the reading and searching of the papers. The field of machine learning has matured considerably in both methodology and real-world application domains and has become particularly important for solution of problems in signal processing. As reflected...

An Efficient Inductive Genetic Learning Algorithm for Fuzzy Relational Rules

Directory of Open Access Journals (Sweden)

Antonio

2012-04-01

Full Text Available Fuzzy modelling research has traditionally focused on certain types of fuzzy rules. However, the use of alternative rule models could improve the ability of fuzzy systems to represent a specific problem. In this proposal, an extended fuzzy rule model, that can include relations between variables in the antecedent of rules is presented. Furthermore, a learning algorithm based on the iterative genetic approach which is able to represent the knowledge using this model is proposed as well. On the other hand, potential relations among initial variables imply an exponential growth in the feasible rule search space. Consequently, two filters for detecting relevant potential relations are added to the learning algorithm. These filters allows to decrease the search space complexity and increase the algorithm efficiency. Finally, we also present an experimental study to demonstrate the benefits of using fuzzy relational rules.

A Human/Computer Learning Network to Improve Biodiversity Conservation and Research

OpenAIRE

Kelling, Steve; Gerbracht, Jeff; Fink, Daniel; Lagoze, Carl; Wong, Weng-Keen; Yu, Jun; Damoulas, Theodoros; Gomes, Carla

2012-01-01

In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a Human-Computer Learning Network,...

E-learning on the road: online learning and social media for continuing professional competency.

Directory of Open Access Journals (Sweden)

Alan M Batt

2016-06-01

Full Text Available Background The impact of social media and online learning in health professions education has previously shown generally positive results in medical, nursing and pharmacy students. To date there has not been any extensive research into social media and online learning use by prehospital health care professionals such as paramedics. Aim & Methods We sought to identify the extent to which Irish pre-hospital practitioners make use of online learning and social media for continuous professional competency (CPC, and the means by which they do so. A cross-sectional online survey of practitioners was conducted to obtain both quantitative and qualitative data. The release of the survey was in a controlled manner to PHECC registrants via various channels. Participation was voluntary and anonymous. Results A total of 248 respondents completed the survey in full by closing date of 31 March 2015, representing 5.4% of all registrants (n=4,555. 77% of respondents were male, and the majority were registered as Emergency Medical Technicians (49%, followed by Advanced Paramedics (26%. Over 78% of respondents used a mobile device in the course of their clinical duties; the majority used an iOS device. Social media and online learning were considered learning tools by over 75% of respondents, and over 74% agreed they should be further incorporated into prehospital education. The most popular platforms for CPC activities were YouTube and Facebook. The majority of respondents (88% viewed self-directed activities to constitute continuous professional development activity, but 64% felt that an activity that resulted in the awarding of a certificate was better value. Over 90% of respondents had previous experience with online learning, but only 42% indicated they had previously purchased or paid for online learning. Conclusion Prehospital practitioners in Ireland in the population studied consider online learning and social media acceptable for CPC purposes. The main

Prototypes and matrix relevance learning in complex fourier space

NARCIS (Netherlands)

Straat, M.; Kaden, M.; Gay, M.; Villmann, T.; Lampe, Alexander; Seiffert, U.; Biehl, M.; Melchert, F.

2017-01-01

In this contribution, we consider the classification of time-series and similar functional data which can be represented in complex Fourier coefficient space. We apply versions of Learning Vector Quantization (LVQ) which are suitable for complex-valued data, based on the so-called Wirtinger

Optimal Control via Reinforcement Learning with Symbolic Policy Approximation

NARCIS (Netherlands)

Kubalìk, Jiřì; Alibekov, Eduard; Babuska, R.; Dochain, Denis; Henrion, Didier; Peaucelle, Dimitri

2017-01-01

Model-based reinforcement learning (RL) algorithms can be used to derive optimal control laws for nonlinear dynamic systems. With continuous-valued state and input variables, RL algorithms have to rely on function approximators to represent the value function and policy mappings. This paper

Molecular basis sets - a general similarity-based approach for representing chemical spaces.

Science.gov (United States)

Raghavendra, Akshay S; Maggiora, Gerald M

2007-01-01

A new method, based on generalized Fourier analysis, is described that utilizes the concept of "molecular basis sets" to represent chemical space within an abstract vector space. The basis vectors in this space are abstract molecular vectors. Inner products among the basis vectors are determined using an ansatz that associates molecular similarities between pairs of molecules with their corresponding inner products. Moreover, the fact that similarities between pairs of molecules are, in essentially all cases, nonzero implies that the abstract molecular basis vectors are nonorthogonal, but since the similarity of a molecule with itself is unity, the molecular vectors are normalized to unity. A symmetric orthogonalization procedure, which optimally preserves the character of the original set of molecular basis vectors, is used to construct appropriate orthonormal basis sets. Molecules can then be represented, in general, by sets of orthonormal "molecule-like" basis vectors within a proper Euclidean vector space. However, the dimension of the space can become quite large. Thus, the work presented here assesses the effect of basis set size on a number of properties including the average squared error and average norm of molecular vectors represented in the space-the results clearly show the expected reduction in average squared error and increase in average norm as the basis set size is increased. Several distance-based statistics are also considered. These include the distribution of distances and their differences with respect to basis sets of differing size and several comparative distance measures such as Spearman rank correlation and Kruscal stress. All of the measures show that, even though the dimension can be high, the chemical spaces they represent, nonetheless, behave in a well-controlled and reasonable manner. Other abstract vector spaces analogous to that described here can also be constructed providing that the appropriate inner products can be directly

Learning about America, and about Buying from Starbucks

Science.gov (United States)

Simon, Bryant

2011-01-01

Professor Bryant Simon is the author of "Everything But the Coffee: Learning About America From Starbucks" (University of California Press, 2009). He presented his key findings to the class and summarizes them here. Studying Starbucks reveals essential truths about what its customers, who represented a large cross-section of the American…

Learning Local Components to Understand Large Bayesian Networks

DEFF Research Database (Denmark)

Zeng, Yifeng; Xiang, Yanping; Cordero, Jorge

2009-01-01

(domain experts) to extract accurate information from a large Bayesian network due to dimensional difficulty. We define a formulation of local components and propose a clustering algorithm to learn such local components given complete data. The algorithm groups together most inter-relevant attributes......Bayesian networks are known for providing an intuitive and compact representation of probabilistic information and allowing the creation of models over a large and complex domain. Bayesian learning and reasoning are nontrivial for a large Bayesian network. In parallel, it is a tough job for users...... in a domain. We evaluate its performance on three benchmark Bayesian networks and provide results in support. We further show that the learned components may represent local knowledge more precisely in comparison to the full Bayesian networks when working with a small amount of data....

Examining Collaborative Knowledge Construction in Microblogging-Based Learning Environments

Directory of Open Access Journals (Sweden)

Tian Luo

2017-09-01

Full Text Available Aim/Purpose: The purpose of the study is to provide foundational research to exemplify how knowledge construction takes place in microblogging-based learning environments, to understand learner interaction representing the knowledge construction process, and to analyze learner perception, thereby suggesting a model of delivery for microblogging. Background: Up-and-coming digital native learners crave the real-time, multimedia, global-interconnectedness of microblogging, yet there has been limited research that specifically proposes a working model of Twitter’s classroom integration for designers and practitioners without bundling it in with other social media tools. Methodology: This semester-long study utilized a case-study research design via a multi-dimensional approach in a hybrid classroom with both face-to-face and online environments. Tweets were collected from four types of activities and coded based on content within their contextual setting. Twenty-four college students participated in the study. Contribution: The findings shed light on the process of knowledge construction in mi-croblogging and reveal key types of knowledge manifested during learning activities. The study also proposes a model for delivering microblogging to formal learning environments applicable to various contexts for designers and practitioners. Findings: There are distinct learner interaction patterns representing the process of knowledge construction in microblogging activities ranging from low-order to high-order cognitive tasks. Students generally were in favor of the Twitter integration in this study. Recommendations for Practitioners: The three central activities (exploring hashtags, discussion topics, and participating in live chats along with the backchannel activity formulate a working model that represents the sequential process of Twitter integration into classrooms. Impact on Society: Microblogging allows learners omnichannel access while hashtags

Socioeconomic variation, number competence, and mathematics learning difficulties in young children.

Science.gov (United States)

Jordan, Nancy C; Levine, Susan C

2009-01-01

As a group, children from disadvantaged, low-income families perform substantially worse in mathematics than their counterparts from higher-income families. Minority children are disproportionately represented in low-income populations, resulting in significant racial and social-class disparities in mathematics learning linked to diminished learning opportunities. The consequences of poor mathematics achievement are serious for daily functioning and for career advancement. This article provides an overview of children's mathematics difficulties in relation to socioeconomic status (SES). We review foundations for early mathematics learning and key characteristics of mathematics learning difficulties. A particular focus is the delays or deficiencies in number competencies exhibited by low-income children entering school. Weaknesses in number competence can be reliably identified in early childhood, and there is good evidence that most children have the capacity to develop number competence that lays the foundation for later learning.

A Survey Of Top 10 Open Source Learning Management Systems

Directory of Open Access Journals (Sweden)

Mohamed R. Elabnody

2015-08-01

Full Text Available Open Source LMSs are fully flexible and customizable so they can be designed in line with your schoolorganizations brand image. Open Source LMSs can also be converted to social learning platforms. You can create an online community through your LMS. This paper describes the most important features in learning management systems LMS that are critical to compare and contrast depend on your system requirements. Also represents a multiple LMS providers that are designed to use in university environment.

Human infants' learning of social structures: the case of dominance hierarchy.

Science.gov (United States)

Mascaro, Olivier; Csibra, Gergely

2014-01-01

We tested 15-month-olds' capacity to represent social-dominance hierarchies with more than two agents. Our results showed that infants found it harder to memorize dominance relations that were presented in an order that hindered the incremental formation of a single structure (Study 1). These results suggest that infants attempt to build structures incrementally, relation by relation, thereby simplifying the complex problem of recognizing a social structure. Infants also found circular dominance structures harder to process than linear dominance structures (Study 2). These expectations about the shape of structures may facilitate learning. Our results suggest that infants attempt to represent social structures composed of social relations. They indicate that human infants go beyond learning about individual social partners and their respective relations and form hypotheses about how social groups are organized.

Exploring the Impact of Engaged Teachers on Implementation Fidelity and Reading Skill Gains in a Blended Learning Reading Program

Science.gov (United States)

Schechter, Rachel L.; Kazakoff, Elizabeth R.; Bundschuh, Kristine; Prescott, Jen Elise; Macaruso, Paul

2017-01-01

The number of K-12 classrooms adopting blended learning models is rapidly increasing and represents a cultural shift in teaching and learning; however, fidelity of implementation of these new blended learning programs varies widely. This study aimed to examine the role of teacher engagement in student motivation and achievement in a blended…

Learning How to Learn

DEFF Research Database (Denmark)

Lauridsen, Karen M.; Lauridsen, Ole

Ole Lauridsen, Aarhus School of Business and Social Sciences, Aarhus University, Denmark Karen M. Lauridsen, Aarhus School of Business and Social Sciences, Aarhus University, Denmark Learning Styles in Higher Education – Learning How to Learn Applying learning styles (LS) in higher education...... by Constructivist learning theory and current basic knowledge of how the brain learns. The LS concept will thus be placed in a broader learning theoretical context as a strong learning and teaching tool. Participants will be offered the opportunity to have their own LS preferences established before...... teaching leads to positive results and enhanced student learning. However, learning styles should not only be considered a didactic matter for the teacher, but also a tool for the individual students to improve their learning capabilities – not least in contexts where information is not necessarily...

Learning Read-constant Polynomials of Constant Degree modulo Composites

DEFF Research Database (Denmark)

Chattopadhyay, Arkadev; Gavaldá, Richard; Hansen, Kristoffer Arnsfelt

2011-01-01

Boolean functions that have constant degree polynomial representation over a fixed finite ring form a natural and strict subclass of the complexity class \\textACC0ACC0. They are also precisely the functions computable efficiently by programs over fixed and finite nilpotent groups. This class...... is not known to be learnable in any reasonable learning model. In this paper, we provide a deterministic polynomial time algorithm for learning Boolean functions represented by polynomials of constant degree over arbitrary finite rings from membership queries, with the additional constraint that each variable...

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...

Designing Data Visualizations Representing Informational Relationships

CERN Document Server

Steele, Julie

2011-01-01

Data visualization is an efficient and effective medium for communicating large amounts of information, but the design process can often seem like an unexplainable creative endeavor. This concise book aims to demystify the design process by showing you how to use a linear decision-making process to encode your information visually. Delve into different kinds of visualization, including infographics and visual art, and explore the influences at work in each one. Then learn how to apply these concepts to your design process. Learn data visualization classifications, including explanatory, expl

Active learning strategies for the deduplication of electronic patient data using classification trees.

Science.gov (United States)

Sariyar, M; Borg, A; Pommerening, K

2012-10-01

Supervised record linkage methods often require a clerical review to gain informative training data. Active learning means to actively prompt the user to label data with special characteristics in order to minimise the review costs. We conducted an empirical evaluation to investigate whether a simple active learning strategy using binary comparison patterns is sufficient or if string metrics together with a more sophisticated algorithm are necessary to achieve high accuracies with a small training set. Based on medical registry data with different numbers of attributes, we used active learning to acquire training sets for classification trees, which were then used to classify the remaining data. Active learning for binary patterns means that every distinct comparison pattern represents a stratum from which one item is sampled. Active learning for patterns consisting of the Levenshtein string metric values uses an iterative process where the most informative and representative examples are added to the training set. In this context, we extended the active learning strategy by Sarawagi and Bhamidipaty (2002). On the original data set, active learning based on binary comparison patterns leads to the best results. When dropping four or six attributes, using string metrics leads to better results. In both cases, not more than 200 manually reviewed training examples are necessary. In record linkage applications where only forename, name and birthday are available as attributes, we suggest the sophisticated active learning strategy based on string metrics in order to achieve highly accurate results. We recommend the simple strategy if more attributes are available, as in our study. In both cases, active learning significantly reduces the amount of manual involvement in training data selection compared to usual record linkage settings. Copyright © 2012 Elsevier Inc. All rights reserved.

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.

Adaptive nodes enrich nonlinear cooperative learning beyond traditional adaptation by links.

Science.gov (United States)

Sardi, Shira; Vardi, Roni; Goldental, Amir; Sheinin, Anton; Uzan, Herut; Kanter, Ido

2018-03-23

Physical models typically assume time-independent interactions, whereas neural networks and machine learning incorporate interactions that function as adjustable parameters. Here we demonstrate a new type of abundant cooperative nonlinear dynamics where learning is attributed solely to the nodes, instead of the network links which their number is significantly larger. The nodal, neuronal, fast adaptation follows its relative anisotropic (dendritic) input timings, as indicated experimentally, similarly to the slow learning mechanism currently attributed to the links, synapses. It represents a non-local learning rule, where effectively many incoming links to a node concurrently undergo the same adaptation. The network dynamics is now counterintuitively governed by the weak links, which previously were assumed to be insignificant. This cooperative nonlinear dynamic adaptation presents a self-controlled mechanism to prevent divergence or vanishing of the learning parameters, as opposed to learning by links, and also supports self-oscillations of the effective learning parameters. It hints on a hierarchical computational complexity of nodes, following their number of anisotropic inputs and opens new horizons for advanced deep learning algorithms and artificial intelligence based applications, as well as a new mechanism for enhanced and fast learning by neural networks.

Learning curves for solid oxide fuel cells

International Nuclear Information System (INIS)

Rivera-Tinoco, Rodrigo; Schoots, Koen; Zwaan, Bob van der

2012-01-01

Highlights: ► We present learning curves for fuel cells based on empirical data. ► We disentangle different cost reduction mechanisms for SOFCs. ► We distinguish between learning-by-doing, R and D, economies-of-scale and automation. - Abstract: In this article we present learning curves for solid oxide fuel cells (SOFCs). With data from fuel cell manufacturers we derive a detailed breakdown of their production costs. We develop a bottom-up model that allows for determining overall SOFC manufacturing costs with their respective cost components, among which material, energy, labor and capital charges. The results obtained from our model prove to deviate by at most 13% from total cost figures quoted in the literature. For the R and D stage of development and diffusion, we find local learning rates between 13% and 17% and we demonstrate that the corresponding cost reductions result essentially from learning-by-searching effects. When considering periods in time that focus on the pilot and early commercial production stages, we find regional learning rates of 27% and 1%, respectively, which we assume derive mainly from genuine learning phenomena. These figures turnout significantly higher, approximately 44% and 12% respectively, if also effects of economies-of-scale and automation are included. When combining all production stages we obtain lr = 35%, which represents a mix of cost reduction phenomena. This high learning rate value and the potential to scale up production suggest that continued efforts in the development of SOFC manufacturing processes, as well as deployment and use of SOFCs, may lead to substantial further cost reductions.

Transfer learning improves supervised image segmentation across imaging protocols

DEFF Research Database (Denmark)

van Opbroek, Annegreet; Ikram, M. Arfan; Vernooij, Meike W.

2015-01-01

with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two MRI brain-segmentation tasks with multi-site data: white matter, gray matter, and CSF segmentation; and white-matter- /MS-lesion segmentation......The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform...... well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore...

Kernel-based discriminant feature extraction using a representative dataset

Science.gov (United States)

Li, Honglin; Sancho Gomez, Jose-Luis; Ahalt, Stanley C.

2002-07-01

Discriminant Feature Extraction (DFE) is widely recognized as an important pre-processing step in classification applications. Most DFE algorithms are linear and thus can only explore the linear discriminant information among the different classes. Recently, there has been several promising attempts to develop nonlinear DFE algorithms, among which is Kernel-based Feature Extraction (KFE). The efficacy of KFE has been experimentally verified by both synthetic data and real problems. However, KFE has some known limitations. First, KFE does not work well for strongly overlapped data. Second, KFE employs all of the training set samples during the feature extraction phase, which can result in significant computation when applied to very large datasets. Finally, KFE can result in overfitting. In this paper, we propose a substantial improvement to KFE that overcomes the above limitations by using a representative dataset, which consists of critical points that are generated from data-editing techniques and centroid points that are determined by using the Frequency Sensitive Competitive Learning (FSCL) algorithm. Experiments show that this new KFE algorithm performs well on significantly overlapped datasets, and it also reduces computational complexity. Further, by controlling the number of centroids, the overfitting problem can be effectively alleviated.

A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas

OpenAIRE

Wang, Haohan; Raj, Bhiksha

2015-01-01

This report will show the history of deep learning evolves. It will trace back as far as the initial belief of connectionism modelling of brain, and come back to look at its early stage realization: neural networks. With the background of neural network, we will gradually introduce how convolutional neural network, as a representative of deep discriminative models, is developed from neural networks, together with many practical techniques that can help in optimization of neural networks. On t...

Auto-Encoder based Deep Learning for Surface Electromyography Signal Processing

Directory of Open Access Journals (Sweden)

Marwa Farouk Ibrahim Ibrahim

2018-01-01

Full Text Available Feature extraction is taking a very vital and essential part of bio-signal processing. We need to choose one of two paths to identify and select features in any system. The most popular track is engineering handcrafted, which mainly depends on the user experience and the field of application. While the other path is feature learning, which depends on training the system on recognising and picking the best features that match the application. The main concept of feature learning is to create a model that is expected to be able to learn the best features without any human intervention instead of recourse the traditional methods for feature extraction or reduction and avoid dealing with feature extraction that depends on researcher experience. In this paper, Auto-Encoder will be utilised as a feature learning algorithm to practice the recommended model to excerpt the useful features from the surface electromyography signal. Deep learning method will be suggested by using Auto-Encoder to learn features. Wavelet Packet, Spectrogram, and Wavelet will be employed to represent the surface electromyography signal in our recommended model. Then, the newly represented bio-signal will be fed to stacked autoencoder (2 stages to learn features and finally, the behaviour of the proposed algorithm will be estimated by hiring different classifiers such as Extreme Learning Machine, Support Vector Machine, and SoftMax Layer. The Rectified Linear Unit (ReLU will be created as an activation function for extreme learning machine classifier besides existing functions such as sigmoid and radial basis function. ReLU will show a better classification ability than sigmoid and Radial basis function (RBF for wavelet, Wavelet scale 5 and wavelet packet signal representations implemented techniques. ReLU will illustrate better classification ability, as an activation function, than sigmoid and poorer than RBF for spectrogram signal representation. Both confidence interval and

Enacting Conceptual Metaphor through Blending: Learning activities embodying the substance metaphor for energy

Science.gov (United States)

Close, Hunter G.; Scherr, Rachel E.

2015-04-01

We demonstrate that a particular blended learning space is especially productive in developing understanding of energy transfers and transformations. In this blended space, naturally occurring learner interactions like body movement, gesture, and metaphorical speech are blended with a conceptual metaphor of energy as a substance in a class of activities called Energy Theater. We illustrate several mechanisms by which the blended aspect of the learning environment promotes productive intellectual engagement with key conceptual issues in the learning of energy, including distinguishing among energy processes, disambiguating matter and energy, identifying energy transfer, and representing energy as a conserved quantity. Conceptual advancement appears to be promoted especially by the symbolic material and social structure of the Energy Theater environment, in which energy is represented by participants and objects are represented by areas demarcated by loops of rope, and by Energy Theater's embodied action, including body locomotion, gesture, and coordination of speech with symbolic spaces in the Energy Theater arena. Our conclusions are (1) that specific conceptual metaphors can be leveraged to benefit science instruction via the blending of an abstract space of ideas with multiple modes of concrete human action, and (2) that participants' structured improvisation plays an important role in leveraging the blend for their intellectual development.

Learning dynamics in research alliances: A panel data analysis

NARCIS (Netherlands)

Duso, T.; Pennings, E.; Seldeslachts, J.

2010-01-01

The aim of this paper is to empirically test the determinants of Research Joint Ventures’ (RJVs) group dynamics. We develop a model based on learning and transaction cost theories, which represent the benefits and costs of RJV participation, respectively. According to our framework, firms at each

Is Your Gifted Child Ready for Online Learning?

Science.gov (United States)

Potts, Jessica Alison; Potts, Skip

2017-01-01

Virtual classrooms, which have grown at an unprecedented rate in recent years, represent a unique opportunity for gifted students who do not have appropriate educational options in their brick-and-mortar schools. Students who are engaged in online learning have access to flexible, high quality curricula and can be grouped with their intellectual…

Multiple-instance learning as a classifier combining problem

DEFF Research Database (Denmark)

Li, Yan; Tax, David M. J.; Duin, Robert P. W.

2013-01-01

In multiple-instance learning (MIL), an object is represented as a bag consisting of a set of feature vectors called instances. In the training set, the labels of bags are given, while the uncertainty comes from the unknown labels of instances in the bags. In this paper, we study MIL with the ass...

E-learning Programmes and Courses Evaluation Report

DEFF Research Database (Denmark)

Badger, Merete; Prag, Sidsel-Marie Winther; Monaco, Lucio

the development, testing, and evaluation of a series of applications for training and entrepreneurship in the field of sustainable energy (project work package 2, 3, and 4). This report describes the project outcomes related to this work stream with focus on E-learning programmes and courses. It represents...... the project deliverable “D3.4 E-learning Programmes and Courses Evaluation Report”. The applications developed for entrepreneurship will be described and evaluated in the project deliverable “D4.3 e-link evaluation report”. The second work stream (work package 5) covers the development of E......-infrastructure eduGAIN2. The access point is a web portal – the VCH Portal – which is used to manage users and groups. The E-infrastructure of VCH will be described and evaluated in the project deliverable “5.4 Virtual Campus Hub Technology Evaluation Report”. The E-learning applications described in this report...

Temperature dependency in motor skill learning.

Science.gov (United States)

Immink, Maarten A; Wright, David L; Barnes, William S

2012-01-01

The present study investigated the role of temperature as a contextual condition for motor skill learning. Precision grip task training occurred while forearm cutaneous temperature was either heated (40-45 °C) or cooled (10-15 °C). At test, temperature was either reinstated or changed. Performance was comparable between training conditions while at test, temperature changes decreased accuracy, especially after hot training conditions. After cold training, temperature change deficits were only evident when concurrent force feedback was presented. These findings are the first evidence of localized temperature dependency in motor skill learning in humans. Results are not entirely accounted for by a context-dependent memory explanation and appear to represent an interaction of neuromuscular and sensory processes with the temperature present during training and test.

Distributional Language Learning: Mechanisms and Models of ategory Formation.

Science.gov (United States)

Aslin, Richard N; Newport, Elissa L

2014-09-01

In the past 15 years, a substantial body of evidence has confirmed that a powerful distributional learning mechanism is present in infants, children, adults and (at least to some degree) in nonhuman animals as well. The present article briefly reviews this literature and then examines some of the fundamental questions that must be addressed for any distributional learning mechanism to operate effectively within the linguistic domain. In particular, how does a naive learner determine the number of categories that are present in a corpus of linguistic input and what distributional cues enable the learner to assign individual lexical items to those categories? Contrary to the hypothesis that distributional learning and category (or rule) learning are separate mechanisms, the present article argues that these two seemingly different processes---acquiring specific structure from linguistic input and generalizing beyond that input to novel exemplars---actually represent a single mechanism. Evidence in support of this single-mechanism hypothesis comes from a series of artificial grammar-learning studies that not only demonstrate that adults can learn grammatical categories from distributional information alone, but that the specific patterning of distributional information among attested utterances in the learning corpus enables adults to generalize to novel utterances or to restrict generalization when unattested utterances are consistently absent from the learning corpus. Finally, a computational model of distributional learning that accounts for the presence or absence of generalization is reviewed and the implications of this model for linguistic-category learning are summarized.

OSMOSE experiment representativity studies.

Energy Technology Data Exchange (ETDEWEB)

Aliberti, G.; Klann, R.; Nuclear Engineering Division

2007-10-10

The OSMOSE program aims at improving the neutronic predictions of advanced nuclear fuels through measurements in the MINERVE facility at the CEA-Cadarache (France) on samples containing the following separated actinides: Th-232, U-233, U-234, U-235, U-236, U-238, Np-237, Pu-238, Pu-239, Pu-240, Pu-241, Pu-242, Am-241, Am-243, Cm-244 and Cm-245. The goal of the experimental measurements is to produce a database of reactivity-worth measurements in different neutron spectra for the separated heavy nuclides. This database can then be used as a benchmark for integral reactivity-worth measurements to verify and validate reactor analysis codes and integral cross-section values for the isotopes tested. In particular, the OSMOSE experimental program will produce very accurate sample reactivity-worth measurements for a series of actinides in various spectra, from very thermalized to very fast. The objective of the analytical program is to make use of the experimental data to establish deficiencies in the basic nuclear data libraries, identify their origins, and provide guidelines for nuclear data improvements in coordination with international programs. To achieve the proposed goals, seven different neutron spectra can be created in the MINERVE facility: UO2 dissolved in water (representative of over-moderated LWR systems), UO2 matrix in water (representative of LWRs), a mixed oxide fuel matrix, two thermal spectra containing large epithermal components (representative of under-moderated reactors), a moderated fast spectrum (representative of fast reactors which have some slowing down in moderators such as lead-bismuth or sodium), and a very hard spectrum (representative of fast reactors with little moderation from reactor coolant). The different spectra are achieved by changing the experimental lattice within the MINERVE reactor. The experimental lattice is the replaceable central part of MINERVE, which establishes the spectrum at the sample location. This configuration

Machine Learning Techniques in Clinical Vision Sciences.

Science.gov (United States)

Caixinha, Miguel; Nunes, Sandrina

2017-01-01

This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration

Our (Represented) World: A Quantum-Like Object

Science.gov (United States)

Lambert-Mogiliansky, Ariane; Dubois, François

It has been suggested that observed cognitive limitations may be an expression of the quantum-like structure of the mind. In this chapter we explore some implications of this hypothesis for learning i.e., for the construction of a representation of the world. For a quantum-like individual, there exists a multiplicity of mentally incompatible (Bohr complementary) but equally valid and complete representations (mental pictures) of the world. The process of learning i.e., of constructing a representation, involves two kinds of operations on the mental picture. The acquisition of new data which is modelled as a preparation procedure and the processing of data which is modelled as an introspective measurement operation. This process is shown not to converge to a single mental picture. Rather, it can evolve forever. We define a concept of entropy to capture relative intrinsic uncertainty. The analysis suggests a new perspective on learning. First, it implies that we must turn to double objectification as in Quantum Mechanics: the cognitive process is the primary object of learning. Second, it suggests that a representation of the world arises as the result of creative interplay between the mind and the environment.

Amplifying human ability through autonomics and machine learning in IMPACT

Science.gov (United States)

Dzieciuch, Iryna; Reeder, John; Gutzwiller, Robert; Gustafson, Eric; Coronado, Braulio; Martinez, Luis; Croft, Bryan; Lange, Douglas S.

2017-05-01

Amplifying human ability for controlling complex environments featuring autonomous units can be aided by learned models of human and system performance. In developing a command and control system that allows a small number of people to control a large number of autonomous teams, we employ an autonomics framework to manage the networks that represent mission plans and the networks that are composed of human controllers and their autonomous assistants. Machine learning allows us to build models of human and system performance useful for monitoring plans and managing human attention and task loads. Machine learning also aids in the development of tactics that human supervisors can successfully monitor through the command and control system.

Prototype-based Models for the Supervised Learning of Classification Schemes

Science.gov (United States)

Biehl, Michael; Hammer, Barbara; Villmann, Thomas

2017-06-01

An introduction is given to the use of prototype-based models in supervised machine learning. The main concept of the framework is to represent previously observed data in terms of so-called prototypes, which reflect typical properties of the data. Together with a suitable, discriminative distance or dissimilarity measure, prototypes can be used for the classification of complex, possibly high-dimensional data. We illustrate the framework in terms of the popular Learning Vector Quantization (LVQ). Most frequently, standard Euclidean distance is employed as a distance measure. We discuss how LVQ can be equipped with more general dissimilarites. Moreover, we introduce relevance learning as a tool for the data-driven optimization of parameterized distances.

ASSOCIATION BETWEEN AFFECTS AND REPRESENTATIONS INVOLVED IN THE SCHOOL LEARNING ENVIRONMENT

OpenAIRE

Andreia Osti; Ana Paula Porto Noronha

2017-01-01

This study assumes that the affective dimensions involves the process of planning and developing pedagogical practices and are an important factor in determining the nature of relations between the students and the various objects of knowledge. In this sense, the study aimed to analyze how students represent the affective aspects of both the teaching and learning process and what are their perceptions of the learning environment. The participants were 120 students of the 5th year of elementar...

Learning to Learn.

Science.gov (United States)

Weiss, Helen; Weiss, Martin

1988-01-01

The article reviews theories of learning (e.g., stimulus-response, trial and error, operant conditioning, cognitive), considers the role of motivation, and summarizes nine research-supported rules of effective learning. Suggestions are applied to teaching learning strategies to learning-disabled students. (DB)

Transient Response Analysis of Metropolis Learning in Games

KAUST Repository

Jaleel, Hassan

2017-10-19

The objective of this work is to provide a qualitative description of the transient properties of stochastic learning dynamics like adaptive play, log-linear learning, and Metropolis learning. The solution concept used in these learning dynamics for potential games is that of stochastic stability, which is based on the stationary distribution of the reversible Markov chain representing the learning process. However, time to converge to a stochastically stable state is exponential in the inverse of noise, which limits the use of stochastic stability as an effective solution concept for these dynamics. We propose a complete solution concept that qualitatively describes the state of the system at all times. The proposed concept is prevalent in control systems literature where a solution to a linear or a non-linear system has two parts, transient response and steady state response. Stochastic stability provides the steady state response of stochastic learning rules. In this work, we study its transient properties. Starting from an initial condition, we identify the subsets of the state space called cycles that have small hitting times and long exit times. Over the long time scales, we provide a description of how the distributions over joint action profiles transition from one cycle to another till it reaches the globally optimal state.

Transient Response Analysis of Metropolis Learning in Games

KAUST Repository

Jaleel, Hassan; Shamma, Jeff S.

2017-01-01

The objective of this work is to provide a qualitative description of the transient properties of stochastic learning dynamics like adaptive play, log-linear learning, and Metropolis learning. The solution concept used in these learning dynamics for potential games is that of stochastic stability, which is based on the stationary distribution of the reversible Markov chain representing the learning process. However, time to converge to a stochastically stable state is exponential in the inverse of noise, which limits the use of stochastic stability as an effective solution concept for these dynamics. We propose a complete solution concept that qualitatively describes the state of the system at all times. The proposed concept is prevalent in control systems literature where a solution to a linear or a non-linear system has two parts, transient response and steady state response. Stochastic stability provides the steady state response of stochastic learning rules. In this work, we study its transient properties. Starting from an initial condition, we identify the subsets of the state space called cycles that have small hitting times and long exit times. Over the long time scales, we provide a description of how the distributions over joint action profiles transition from one cycle to another till it reaches the globally optimal state.

Enhancing Diversity in Undergraduate Science: Self-Efficacy Drives Performance Gains with Active Learning

OpenAIRE

Ballen, Cissy J.; Wieman, Carl; Salehi, Shima; Searle, Jeremy B.; Zamudio, Kelly R.

2017-01-01

Efforts to retain underrepresented minority (URM) students in science, technology, engineering, and mathematics (STEM) have shown only limited success in higher education, due in part to a persistent achievement gap between students from historically underrepresented and well-represented backgrounds. To test the hypothesis that active learning disproportionately benefits URM students, we quantified the effects of traditional versus active learning on student academic performance, science self...

Representing Uncertainty by Probability and Possibility

DEFF Research Database (Denmark)

of uncertain parameters. Monte Carlo simulation is readily used for practical calculations. However, an alternative approach is offered by possibility theory making use of possibility distributions such as intervals and fuzzy intervals. This approach is well suited to represent lack of knowledge or imprecision......Uncertain parameters in modeling are usually represented by probability distributions reflecting either the objective uncertainty of the parameters or the subjective belief held by the model builder. This approach is particularly suited for representing the statistical nature or variance...

Malnutrition, C0gnitive Development, and Learning.

Science.gov (United States)

Thomas, Susan B., Comp.

This bibliography is designed to be as comprehensive as possible on the effects of nutrition on learning. While a few of the citations are relatively old, they represent the beginning of research interest in the area. Most of the citations are from the late 1960's or early 1970's. Much of the research in the area uses animals as subjects, rather…

An efficient flow-based botnet detection using supervised machine learning

DEFF Research Database (Denmark)

Stevanovic, Matija; Pedersen, Jens Myrup

2014-01-01

Botnet detection represents one of the most crucial prerequisites of successful botnet neutralization. This paper explores how accurate and timely detection can be achieved by using supervised machine learning as the tool of inferring about malicious botnet traffic. In order to do so, the paper...... introduces a novel flow-based detection system that relies on supervised machine learning for identifying botnet network traffic. For use in the system we consider eight highly regarded machine learning algorithms, indicating the best performing one. Furthermore, the paper evaluates how much traffic needs...... to accurately and timely detect botnet traffic using purely flow-based traffic analysis and supervised machine learning. Additionally, the results show that in order to achieve accurate detection traffic flows need to be monitored for only a limited time period and number of packets per flow. This indicates...

Using e-learning to support the Basic Medical Training programme ...

African Journals Online (AJOL)

learning, the author (LA) agreed to convene a meeting of individuals who could further this project and eight people met in London on 5th June 2015. The individuals included Royal College representatives as well as people who had been involved in ...

Self-supervised learning as an enabling technology for future space exploration robots: ISS experiments on monocular distance learning

Science.gov (United States)

van Hecke, Kevin; de Croon, Guido C. H. E.; Hennes, Daniel; Setterfield, Timothy P.; Saenz-Otero, Alvar; Izzo, Dario

2017-11-01

Although machine learning holds an enormous promise for autonomous space robots, it is currently not employed because of the inherent uncertain outcome of learning processes. In this article we investigate a learning mechanism, Self-Supervised Learning (SSL), which is very reliable and hence an important candidate for real-world deployment even on safety-critical systems such as space robots. To demonstrate this reliability, we introduce a novel SSL setup that allows a stereo vision equipped robot to cope with the failure of one of its cameras. The setup learns to estimate average depth using a monocular image, by using the stereo vision depths from the past as trusted ground truth. We present preliminary results from an experiment on the International Space Station (ISS) performed with the MIT/NASA SPHERES VERTIGO satellite. The presented experiments were performed on October 8th, 2015 on board the ISS. The main goals were (1) data gathering, and (2) navigation based on stereo vision. First the astronaut Kimiya Yui moved the satellite around the Japanese Experiment Module to gather stereo vision data for learning. Subsequently, the satellite freely explored the space in the module based on its (trusted) stereo vision system and a pre-programmed exploration behavior, while simultaneously performing the self-supervised learning of monocular depth estimation on board. The two main goals were successfully achieved, representing the first online learning robotic experiments in space. These results lay the groundwork for a follow-up experiment in which the satellite will use the learned single-camera depth estimation for autonomous exploration in the ISS, and are an advancement towards future space robots that continuously improve their navigation capabilities over time, even in harsh and completely unknown space environments.

Implicit sequence learning in deaf children with cochlear implants.

Science.gov (United States)

Conway, Christopher M; Pisoni, David B; Anaya, Esperanza M; Karpicke, Jennifer; Henning, Shirley C

2011-01-01

Deaf children with cochlear implants (CIs) represent an intriguing opportunity to study neurocognitive plasticity and reorganization when sound is introduced following a period of auditory deprivation early in development. Although it is common to consider deafness as affecting hearing alone, it may be the case that auditory deprivation leads to more global changes in neurocognitive function. In this paper, we investigate implicit sequence learning abilities in deaf children with CIs using a novel task that measured learning through improvement to immediate serial recall for statistically consistent visual sequences. The results demonstrated two key findings. First, the deaf children with CIs showed disturbances in their visual sequence learning abilities relative to the typically developing normal-hearing children. Second, sequence learning was significantly correlated with a standardized measure of language outcome in the CI children. These findings suggest that a period of auditory deprivation has secondary effects related to general sequencing deficits, and that disturbances in sequence learning may at least partially explain why some deaf children still struggle with language following cochlear implantation. © 2010 Blackwell Publishing Ltd.

Infants learn better from left to right: a directional bias in infants' sequence learning.

Science.gov (United States)

Bulf, Hermann; de Hevia, Maria Dolores; Gariboldi, Valeria; Macchi Cassia, Viola

2017-05-26

A wealth of studies show that human adults map ordered information onto a directional spatial continuum. We asked whether mapping ordinal information into a directional space constitutes an early predisposition, already functional prior to the acquisition of symbolic knowledge and language. While it is known that preverbal infants represent numerical order along a left-to-right spatial continuum, no studies have investigated yet whether infants, like adults, organize any kind of ordinal information onto a directional space. We investigated whether 7-month-olds' ability to learn high-order rule-like patterns from visual sequences of geometric shapes was affected by the spatial orientation of the sequences (left-to-right vs. right-to-left). Results showed that infants readily learn rule-like patterns when visual sequences were presented from left to right, but not when presented from right to left. This result provides evidence that spatial orientation critically determines preverbal infants' ability to perceive and learn ordered information in visual sequences, opening to the idea that a left-to-right spatially organized mental representation of ordered dimensions might be rooted in biologically-determined constraints on human brain development.

Physiology Learning for Veterinary Students: Impact of Guided Practices on Students' Opinion and Physiological Parameters

Science.gov (United States)

García-Vázquez, Francisco A.; Romar, Raquel; Gadea, Joaquín; Matás, Carmen; Coy, Pilar; Ruiz, Salvador

2018-01-01

Over recent decades, education has increasingly focused on student-centered learning. Guided practices represent a new way of learning for undergraduate students of physiology, whereby the students turn into teacher-students and become more deeply involved in the subject by preparing and teaching a practical (laboratory) class to their peers. The…

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

Energy Technology Data Exchange (ETDEWEB)

Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K. [Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT (United Kingdom); McEwen, Jason D., E-mail: dr.michelle.lochner@gmail.com [Mullard Space Science Laboratory, University College London, Surrey RH5 6NT (United Kingdom)

2016-08-01

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

International Nuclear Information System (INIS)

Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K.; McEwen, Jason D.

2016-01-01

Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

Learning of N-layers neural network

Directory of Open Access Journals (Sweden)

Vladimír Konečný

2005-01-01

Full Text Available In the last decade we can observe increasing number of applications based on the Artificial Intelligence that are designed to solve problems from different areas of human activity. The reason why there is so much interest in these technologies is that the classical way of solutions does not exist or these technologies are not suitable because of their robustness. They are often used in applications like Business Intelligence that enable to obtain useful information for high-quality decision-making and to increase competitive advantage.One of the most widespread tools for the Artificial Intelligence are the artificial neural networks. Their high advantage is relative simplicity and the possibility of self-learning based on set of pattern situations.For the learning phase is the most commonly used algorithm back-propagation error (BPE. The base of BPE is the method minima of error function representing the sum of squared errors on outputs of neural net, for all patterns of the learning set. However, while performing BPE and in the first usage, we can find out that it is necessary to complete the handling of the learning factor by suitable method. The stability of the learning process and the rate of convergence depend on the selected method. In the article there are derived two functions: one function for the learning process management by the relative great error function value and the second function when the value of error function approximates to global minimum.The aim of the article is to introduce the BPE algorithm in compact matrix form for multilayer neural networks, the derivation of the learning factor handling method and the presentation of the results.

Reinforcement and inference in cross-situational word learning.

Science.gov (United States)

Tilles, Paulo F C; Fontanari, José F

2013-01-01

Cross-situational word learning is based on the notion that a learner can determine the referent of a word by finding something in common across many observed uses of that word. Here we propose an adaptive learning algorithm that contains a parameter that controls the strength of the reinforcement applied to associations between concurrent words and referents, and a parameter that regulates inference, which includes built-in biases, such as mutual exclusivity, and information of past learning events. By adjusting these parameters so that the model predictions agree with data from representative experiments on cross-situational word learning, we were able to explain the learning strategies adopted by the participants of those experiments in terms of a trade-off between reinforcement and inference. These strategies can vary wildly depending on the conditions of the experiments. For instance, for fast mapping experiments (i.e., the correct referent could, in principle, be inferred in a single observation) inference is prevalent, whereas for segregated contextual diversity experiments (i.e., the referents are separated in groups and are exhibited with members of their groups only) reinforcement is predominant. Other experiments are explained with more balanced doses of reinforcement and inference.

An Interactive Graphics Program for Assistance in Learning Convolution.

Science.gov (United States)

Frederick, Dean K.; Waag, Gary L.

1980-01-01

A program has been written for the interactive computer graphics facility at Rensselaer Polytechnic Institute that is designed to assist the user in learning the mathematical technique of convolving two functions. Because convolution can be represented graphically by a sequence of steps involving folding, shifting, multiplying, and integration, it…

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets.

Science.gov (United States)

Korotcov, Alexandru; Tkachenko, Valery; Russo, Daniel P; Ekins, Sean

2017-12-04

Machine learning methods have been applied to many data sets in pharmaceutical research for several decades. The relative ease and availability of fingerprint type molecular descriptors paired with Bayesian methods resulted in the widespread use of this approach for a diverse array of end points relevant to drug discovery. Deep learning is the latest machine learning algorithm attracting attention for many of pharmaceutical applications from docking to virtual screening. Deep learning is based on an artificial neural network with multiple hidden layers and has found considerable traction for many artificial intelligence applications. We have previously suggested the need for a comparison of different machine learning methods with deep learning across an array of varying data sets that is applicable to pharmaceutical research. End points relevant to pharmaceutical research include absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties, as well as activity against pathogens and drug discovery data sets. In this study, we have used data sets for solubility, probe-likeness, hERG, KCNQ1, bubonic plague, Chagas, tuberculosis, and malaria to compare different machine learning methods using FCFP6 fingerprints. These data sets represent whole cell screens, individual proteins, physicochemical properties as well as a data set with a complex end point. Our aim was to assess whether deep learning offered any improvement in testing when assessed using an array of metrics including AUC, F1 score, Cohen's kappa, Matthews correlation coefficient and others. Based on ranked normalized scores for the metrics or data sets Deep Neural Networks (DNN) ranked higher than SVM, which in turn was ranked higher than all the other machine learning methods. Visualizing these properties for training and test sets using radar type plots indicates when models are inferior or perhaps over trained. These results also suggest the need for assessing deep learning further

Collaborative Game-based Learning - Automatized Adaptation Mechanics for Game-based Collaborative Learning using Game Mastering Concepts

OpenAIRE

Wendel, Viktor Matthias

2015-01-01

Learning and playing represent two core aspects of the information and communication society nowadays. Both issues are subsumed in Digital Education Games, one major field of Serious Games. Serious Games combine concepts of gaming with a broad range of application fields: among others, educational sectors and training or health and sports, but also marketing, advertisement, political education, and other societally relevant areas such as climate, energy, and safety. This work focuses on colla...

Representing high-dimensional data to intelligent prostheses and other wearable assistive robots: A first comparison of tile coding and selective Kanerva coding.

Science.gov (United States)

Travnik, Jaden B; Pilarski, Patrick M

2017-07-01

Prosthetic devices have advanced in their capabilities and in the number and type of sensors included in their design. As the space of sensorimotor data available to a conventional or machine learning prosthetic control system increases in dimensionality and complexity, it becomes increasingly important that this data be represented in a useful and computationally efficient way. Well structured sensory data allows prosthetic control systems to make informed, appropriate control decisions. In this study, we explore the impact that increased sensorimotor information has on current machine learning prosthetic control approaches. Specifically, we examine the effect that high-dimensional sensory data has on the computation time and prediction performance of a true-online temporal-difference learning prediction method as embedded within a resource-limited upper-limb prosthesis control system. We present results comparing tile coding, the dominant linear representation for real-time prosthetic machine learning, with a newly proposed modification to Kanerva coding that we call selective Kanerva coding. In addition to showing promising results for selective Kanerva coding, our results confirm potential limitations to tile coding as the number of sensory input dimensions increases. To our knowledge, this study is the first to explicitly examine representations for realtime machine learning prosthetic devices in general terms. This work therefore provides an important step towards forming an efficient prosthesis-eye view of the world, wherein prompt and accurate representations of high-dimensional data may be provided to machine learning control systems within artificial limbs and other assistive rehabilitation technologies.

Early Fractions Learning of 3rd Grade Students in SD Laboratorium Unesa

OpenAIRE

Elisabet Ayunika Permata Sari; Dwi Juniati; Sitti Maesuri Patahudin

2012-01-01

Fractions varied meanings is one of the causes of difficulties in learning fractions. These students  should be given greater opportunities to explore the meaning of fractions before they learn the relationship between fractions and operations on fractions. Although students can shading area represents a fraction, does not mean they really understand the meaning of fractions as a whole. With a realistic approach to mathematics, students are given the contextual issues of equitable distributio...

Applications of Speech-to-Text Recognition and Computer-Aided Translation for Facilitating Cross-Cultural Learning through a Learning Activity: Issues and Their Solutions

Science.gov (United States)

Shadiev, Rustam; Wu, Ting-Ting; Sun, Ai; Huang, Yueh-Min

2018-01-01

In this study, 21 university students, who represented thirteen nationalities, participated in an online cross-cultural learning activity. The participants were engaged in interactions and exchanges carried out on Facebook® and Skype® platforms, and their multilingual communications were supported by speech-to-text recognition (STR) and…

Autonomous learning derived from experimental modeling of physical laws.

Science.gov (United States)

Grabec, Igor

2013-05-01

This article deals with experimental description of physical laws by probability density function of measured data. The Gaussian mixture model specified by representative data and related probabilities is utilized for this purpose. The information cost function of the model is described in terms of information entropy by the sum of the estimation error and redundancy. A new method is proposed for searching the minimum of the cost function. The number of the resulting prototype data depends on the accuracy of measurement. Their adaptation resembles a self-organized, highly non-linear cooperation between neurons in an artificial NN. A prototype datum corresponds to the memorized content, while the related probability corresponds to the excitability of the neuron. The method does not include any free parameters except objectively determined accuracy of the measurement system and is therefore convenient for autonomous execution. Since representative data are generally less numerous than the measured ones, the method is applicable for a rather general and objective compression of overwhelming experimental data in automatic data-acquisition systems. Such compression is demonstrated on analytically determined random noise and measured traffic flow data. The flow over a day is described by a vector of 24 components. The set of 365 vectors measured over one year is compressed by autonomous learning to just 4 representative vectors and related probabilities. These vectors represent the flow in normal working days and weekends or holidays, while the related probabilities correspond to relative frequencies of these days. This example reveals that autonomous learning yields a new basis for interpretation of representative data and the optimal model structure. Copyright © 2012 Elsevier Ltd. All rights reserved.

Fantastic Learning Moments and Where to Find Them

Directory of Open Access Journals (Sweden)

Alexander Y. Sheng

2017-12-01

-based tool to fulfill and optimize the experiential learning cycle for our learners. In our environment, patient rooms represented the most frequent location of “learning moments,” followed by physician workstations. EM-bound students were considerably more likely to document “learning moments” occurring at the workstation and less likely in patient rooms than their non EM-bound colleagues.

Online Library of Scientific Models, A New Way to Teach, Learn, and Share Learning Experience

Directory of Open Access Journals (Sweden)

Hatem H. Elrefaei

2008-05-01

Full Text Available While scientific models are usually communicated in paper format, the need to reprogram every model by every user results in a huge loss of efforts, time and money, hence lengthening the educational and research developing cycle and loosing the learning experience and expertise gained by every user. We demonstrate a new portal www.imodelit.com that hosts a library of scientific models for electrical engineers in the form of java applets. They are all conformal, informative, with strong input and output filing system. The software design allows a fast developing cycle and it represents a strong infrastructure that can be shared by researchers to develop their own applets to be posted on the library. We aim for a community based library of scientific models that enhances the e-learning process for engineering students.

Influences of Formal Learning, Personal Learning Orientation, and Supportive Learning Environment on Informal Learning

Science.gov (United States)

Choi, Woojae; Jacobs, Ronald L.

2011-01-01

While workplace learning includes formal and informal learning, the relationship between the two has been overlooked, because they have been viewed as separate entities. This study investigated the effects of formal learning, personal learning orientation, and supportive learning environment on informal learning among 203 middle managers in Korean…

A Bayesian Model of Biases in Artificial Language Learning: The Case of a Word-Order Universal

Science.gov (United States)

Culbertson, Jennifer; Smolensky, Paul

2012-01-01

In this article, we develop a hierarchical Bayesian model of learning in a general type of artificial language-learning experiment in which learners are exposed to a mixture of grammars representing the variation present in real learners' input, particularly at times of language change. The modeling goal is to formalize and quantify hypothesized…

Detection of player learning curve in a car driving game

NARCIS (Netherlands)

Bontchev, Boyan; Vassileva, Dessislava

2018-01-01

Detection of learning curves of player metrics is very important for the serious (or so called applied) games, because it provides an indicator representing how players master the game tasks by acquiring cognitive abilities, knowledge, and necessary skills for solving the game challenges. Real

Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis

Energy Technology Data Exchange (ETDEWEB)

Stevens, Andrew J.; Pu, Yunchen; Sun, Yannan; Spell, Gregory; Carin, Lawrence

2017-04-20

We introduce new dictionary learning methods for tensor-variate data of any order. We represent each data item as a sum of Kruskal decomposed dictionary atoms within the framework of beta-process factor analysis (BPFA). Our model is nonparametric and can infer the tensor-rank of each dictionary atom. This Kruskal-Factor Analysis (KFA) is a natural generalization of BPFA. We also extend KFA to a deep convolutional setting and develop online learning methods. We test our approach on image processing and classification tasks achieving state of the art results for 2D & 3D inpainting and Caltech 101. The experiments also show that atom-rank impacts both overcompleteness and sparsity.

IMPROVING TRUST THROUGH ETHICAL LEADERSHIP: MOVING BEYOND THE SOCIAL LEARNING THEORY TO A HISTORICAL LEARNING APPROACH

Directory of Open Access Journals (Sweden)

Omoregie Charles Osifo

2016-12-01

Full Text Available The complex nature of trust and its evolving relative concepts require a more idealistic and simpler review. Ethical leadership is related to trust, honesty, transparency, compassion, empathy, results-orientedness, and many other behavioral attributes. Ethical leadership and good leadership are the same, because they represent practicing what one preaches or showing a way to the accomplishment of set goals. The outcomes and findings of many research papers on trust and ethical leadership report positive correlations between ethical leadership and trust. Improving trust from different rational standpoints requires moving and looking beyond the popular theoretical framework through which most results are derived in order to create a new thinking perspective. Social learning theory strongly emphasizes modelling while the new historical learning approach, proposed by the author, is defined as an approach that creates unique historical awareness among individuals, groups, institutions, societies, and nations to use previous experience(s or occurrence(s as a guide in developing positive opinion(s and framework(s in order to tackle the problems and issues of today and tomorrow. Social learning theory is seen as limited from the perspectives of balancing the equation between leadership and trust, the non-compatibility of the values of different generations at work, and other approaches and methods that support the historical approach. This paper is argumentative, adopts a writer´s perspective, and employs a logical analysis of the literature. The main contention is that a historical learning approach can inform an independent-learning to improve trust and its relatives (e.g. motivation and performance, because independent learning can positively shape the value of integrity, which is an integral part of ethical leadership. Historical learning can positively shape leadership in every perspective, because good leadership can develop based on history and

[Critical care nurse learning of continuous renal replacement therapy: the efficacy of a self-learning manual].

Science.gov (United States)

Huang, Yi-Chen; Hsu, Li-Ling

2011-02-01

Many nurses have difficulty learning to use the complex, non-traditional, and regularly-updated critical care equipment. Failure to use such equipment properly can seriously compromise treatment and endanger patient health and lives. New self-learning materials for novice nurses are necessary to provide essential and effective guidance as a part of formal nursing training. Such materials can enhance the capabilities of critical care nurses and, thus, improve the general quality of critical care. The purpose of this research was to develop a continuous renal replacement therapy (CRRT)-themed self-learning manual that would provide easily absorbed and understood knowledge in an easy-to-carry format for ICU nursing staff. This study also investigated CCRT skill learning efficacy. This study adopted a quasi-experimental design with pretests and posttests. Purposive sampling generated a sample of 66 critical care nurses currently working at one hospital in Taipei City. Participants submitted a completed self-assessment survey that rated their command of continuous renal replacement therapy before and after the self-learning manual intervention. Survey data were analyzed using SPSS Version 17.0 for Windows. The two major findings derived from the study included: (1) The mean response score from the self-assessment survey filled out after the intervention was 91.06 and 79.75 (SD = 9.49 and 11.65), respectively, for experimental and control groups. Such demonstrated significant difference. (2) The mean posttest score after the intervention for the experimental group was 91.06 ± 9.49. This represents a significant increase of 10.35 ± 10.35 over their mean pretest score (80.71 ± 11.82). The experimental group showed other significant differences in terms of the CRRT self-assessment survey posttest. Self-learning manuals may be introduced in nursing education as useful aids and catalysts to achieve more effective and satisfying learning experiences.

40 CFR 72.22 - Alternate designated representative.

Science.gov (United States)

2010-07-01

... designated representative is selected shall include a procedure for the owners and operators of the source and affected units at the source to authorize the alternate designated representative to act in lieu...) In the event of a conflict, any action taken by the designated representative shall take precedence...

Learning, Realizability and Games in Classical Arithmetic

Science.gov (United States)

Aschieri, Federico

2010-12-01

In this dissertation we provide mathematical evidence that the concept of learning can be used to give a new and intuitive computational semantics of classical proofs in various fragments of Predicative Arithmetic. First, we extend Kreisel modified realizability to a classical fragment of first order Arithmetic, Heyting Arithmetic plus EM1 (Excluded middle axiom restricted to Sigma^0_1 formulas). We introduce a new realizability semantics we call "Interactive Learning-Based Realizability". Our realizers are self-correcting programs, which learn from their errors and evolve through time. Secondly, we extend the class of learning based realizers to a classical version PCFclass of PCF and, then, compare the resulting notion of realizability with Coquand game semantics and prove a full soundness and completeness result. In particular, we show there is a one-to-one correspondence between realizers and recursive winning strategies in the 1-Backtracking version of Tarski games. Third, we provide a complete and fully detailed constructive analysis of learning as it arises in learning based realizability for HA+EM1, Avigad's update procedures and epsilon substitution method for Peano Arithmetic PA. We present new constructive techniques to bound the length of learning processes and we apply them to reprove - by means of our theory - the classic result of Godel that provably total functions of PA can be represented in Godel's system T. Last, we give an axiomatization of the kind of learning that is needed to computationally interpret Predicative classical second order Arithmetic. Our work is an extension of Avigad's and generalizes the concept of update procedure to the transfinite case. Transfinite update procedures have to learn values of transfinite sequences of non computable functions in order to extract witnesses from classical proofs.

The experience of applying academic service learning within the ...

African Journals Online (AJOL)

The experience of applying academic service learning within the discipline of speech pathology and audiology at a South African university. ... The argument put forward is that this type of pedagogy would appear to be applicable across a broad range of disciplines and represents one strategy for assisting higher education ...

Non-monotonic Pre-fixed Points and Learning

Directory of Open Access Journals (Sweden)

Stefano Berardi

2013-08-01

Full Text Available We consider the problem of finding pre-fixed points of interactive realizers over arbitrary knowledge spaces, obtaining a relative recursive procedure. Knowledge spaces and interactive realizers are an abstract setting to represent learning processes, that can interpret non-constructive proofs. Atomic pieces of information of a knowledge space are stratified into levels, and evaluated into truth values depending on knowledge states. Realizers are then used to define operators that extend a given state by adding and possibly removing atoms: in a learning process states of knowledge change nonmonotonically. Existence of a pre-fixed point of a realizer is equivalent to the termination of the learning process with some state of knowledge which is free of patent contradictions and such that there is nothing to add. In this paper we generalize our previous results in the case of level 2 knowledge spaces and deterministic operators to the case of omega-level knowledge spaces and of non-deterministic operators.

Metadata and Ontologies in Learning Resources Design

Science.gov (United States)

Vidal C., Christian; Segura Navarrete, Alejandra; Menéndez D., Víctor; Zapata Gonzalez, Alfredo; Prieto M., Manuel

Resource design and development requires knowledge about educational goals, instructional context and information about learner's characteristics among other. An important information source about this knowledge are metadata. However, metadata by themselves do not foresee all necessary information related to resource design. Here we argue the need to use different data and knowledge models to improve understanding the complex processes related to e-learning resources and their management. This paper presents the use of semantic web technologies, as ontologies, supporting the search and selection of resources used in design. Classification is done, based on instructional criteria derived from a knowledge acquisition process, using information provided by IEEE-LOM metadata standard. The knowledge obtained is represented in an ontology using OWL and SWRL. In this work we give evidence of the implementation of a Learning Object Classifier based on ontology. We demonstrate that the use of ontologies can support the design activities in e-learning.

Representative Sampling for reliable data analysis

DEFF Research Database (Denmark)

Petersen, Lars; Esbensen, Kim Harry

2005-01-01

regime in order to secure the necessary reliability of: samples (which must be representative, from the primary sampling onwards), analysis (which will not mean anything outside the miniscule analytical volume without representativity ruling all mass reductions involved, also in the laboratory) and data...

Improving the Understanding of Research Methodology and Self-Regulated Learning Through Blog Project

OpenAIRE

Retnawati, Heri

2017-01-01

: This classroom action research seeks to improve self-regulated learning (SRL) and understanding of research methodology at the graduate school. Nineteen graduate school students were involved. Using project-based learning (PjBL), students were assigned to create online blogs as the main project. The blog was intended for representing their understanding of research methodology by writing review of research articles and submitting a research proposal. The classroom action research was based ...

OAS :: Authorities : Permanent Representatives to the OAS

Science.gov (United States)

Rights Actions against Corruption C Children Civil Registry Civil Society Contact Us Culture Cyber Representative of Belize Diego Pary Rodríguez Bolivia Diego Pary Rodríguez Ambassador, Permanent Representative of Bolivia José Luiz Machado Brazil José Luiz Machado e Costa Ambassador, Permanent Representative

Starting Point on the Development of Environemental Risk Management Compe-tences: experiential learning

Directory of Open Access Journals (Sweden)

Irene Martín Rubio

2017-06-01

Full Text Available One of the characteristics of the European Space of Higher Education  is to consider university degrees in terms of learning outcomes, and essentially expressed in forms of competence. Competencies represent a dynamic combination of attributes such as knowledge and its application, attitudes and responsibilities that describe the learning outcomes of a particular program. Transversal competences, such as competences towards environmental  risk management, are part of the general characteristics of human action in economic and technical environments. The training, evaluation and development of professional competences can present different approaches and teaching-learning methodologies. In our study, we focus on learning from experience. With the evaluation of students' learning styles, we can begin to know how our students begin to develop their skills towards environmental risk management.

List of Accredited Representatives

Data.gov (United States)

Department of Veterans Affairs — VA accreditation is for the sole purpose of providing representation services to claimants before VA and does not imply that a representative is qualified to provide...

A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system.

Science.gov (United States)

Chrysafiadi, Konstantina; Virvou, Maria

2013-12-01

In this paper a knowledge representation approach of an adaptive and/or personalized tutoring system is presented. The domain knowledge should be represented in a more realistic way in order to allow the adaptive and/or personalized tutoring system to deliver the learning material to each individual learner dynamically taking into account her/his learning needs and her/his different learning pace. To succeed this, the domain knowledge representation has to depict the possible increase or decrease of the learner's knowledge. Considering that the domain concepts that constitute the learning material are not independent from each other, the knowledge representation approach has to allow the system to recognize either the domain concepts that are already partly or completely known for a learner, or the domain concepts that s/he has forgotten, taking into account the learner's knowledge level of the related concepts. In other words, the system should be informed about the knowledge dependencies that exist among the domain concepts of the learning material, as well as the strength on impact of each domain concept on others. Fuzzy Cognitive Maps (FCMs) seem to be an ideal way for representing graphically this kind of information. The suggested knowledge representation approach has been implemented in an e-learning adaptive system for teaching computer programming. The particular system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus and was compared with a corresponding system, in which the domain knowledge was represented using the most common used technique of network of concepts. The results of the evaluation were very encouraging.

Visible learning a synthesis of over 800 meta-analyses relating to achievement

CERN Document Server

Hattie, John A C

2009-01-01

This unique and ground-breaking book is the result of 15 years research and synthesises over 800 meta-analyses on the influences on achievement in school-aged students. It builds a story about the power of teachers, feedback, and a model of learning and understanding. The research involves many millions of students and represents the largest ever evidence based research into what actually works in schools to improve learning. Areas covered include the influence of the student, home, school, curricula, teacher, and teaching strategies. A model of teaching and learning is developed based on the notion of visible teaching and visible learning. A major message is that what works best for students is similar to what works best for teachers - an attention to setting challenging learning intentions, being clear about what success means, and an attention to learning strategies for developing conceptual understanding about what teachers and students know and understand. Although the current evidence based fad has turn...

Co-Labeling for Multi-View Weakly Labeled Learning.

Science.gov (United States)

Xu, Xinxing; Li, Wen; Xu, Dong; Tsang, Ivor W

2016-06-01

It is often expensive and time consuming to collect labeled training samples in many real-world applications. To reduce human effort on annotating training samples, many machine learning techniques (e.g., semi-supervised learning (SSL), multi-instance learning (MIL), etc.) have been studied to exploit weakly labeled training samples. Meanwhile, when the training data is represented with multiple types of features, many multi-view learning methods have shown that classifiers trained on different views can help each other to better utilize the unlabeled training samples for the SSL task. In this paper, we study a new learning problem called multi-view weakly labeled learning, in which we aim to develop a unified approach to learn robust classifiers by effectively utilizing different types of weakly labeled multi-view data from a broad range of tasks including SSL, MIL and relative outlier detection (ROD). We propose an effective approach called co-labeling to solve the multi-view weakly labeled learning problem. Specifically, we model the learning problem on each view as a weakly labeled learning problem, which aims to learn an optimal classifier from a set of pseudo-label vectors generated by using the classifiers trained from other views. Unlike traditional co-training approaches using a single pseudo-label vector for training each classifier, our co-labeling approach explores different strategies to utilize the predictions from different views, biases and iterations for generating the pseudo-label vectors, making our approach more robust for real-world applications. Moreover, to further improve the weakly labeled learning on each view, we also exploit the inherent group structure in the pseudo-label vectors generated from different strategies, which leads to a new multi-layer multiple kernel learning problem. Promising results for text-based image retrieval on the NUS-WIDE dataset as well as news classification and text categorization on several real-world multi

Visual Learning Induces Changes in Resting-State fMRI Multivariate Pattern of Information.

Science.gov (United States)

Guidotti, Roberto; Del Gratta, Cosimo; Baldassarre, Antonello; Romani, Gian Luca; Corbetta, Maurizio

2015-07-08

When measured with functional magnetic resonance imaging (fMRI) in the resting state (R-fMRI), spontaneous activity is correlated between brain regions that are anatomically and functionally related. Learning and/or task performance can induce modulation of the resting synchronization between brain regions. Moreover, at the neuronal level spontaneous brain activity can replay patterns evoked by a previously presented stimulus. Here we test whether visual learning/task performance can induce a change in the patterns of coded information in R-fMRI signals consistent with a role of spontaneous activity in representing task-relevant information. Human subjects underwent R-fMRI before and after perceptual learning on a novel visual shape orientation discrimination task. Task-evoked fMRI patterns to trained versus novel stimuli were recorded after learning was completed, and before the second R-fMRI session. Using multivariate pattern analysis on task-evoked signals, we found patterns in several cortical regions, as follows: visual cortex, V3/V3A/V7; within the default mode network, precuneus, and inferior parietal lobule; and, within the dorsal attention network, intraparietal sulcus, which discriminated between trained and novel visual stimuli. The accuracy of classification was strongly correlated with behavioral performance. Next, we measured multivariate patterns in R-fMRI signals before and after learning. The frequency and similarity of resting states representing the task/visual stimuli states increased post-learning in the same cortical regions recruited by the task. These findings support a representational role of spontaneous brain activity. Copyright © 2015 the authors 0270-6474/15/359786-13$15.00/0.

Projective Simulation compared to reinforcement learning

OpenAIRE

Bjerland, Øystein Førsund

2015-01-01

This thesis explores the model of projective simulation (PS), a novel approach for an artificial intelligence (AI) agent. The model of PS learns by interacting with the environment it is situated in, and allows for simulating actions before real action is taken. The action selection is based on a random walk through the episodic & compositional memory (ECM), which is a network of clips that represent previous experienced percepts. The network takes percepts as inpu...

Technology Learning Ratios in Global Energy Models

International Nuclear Information System (INIS)

Varela, M.

2001-01-01

The process of introduction of a new technology supposes that while its production and utilisation increases, also its operation improves and its investment costs and production decreases. The accumulation of experience and learning of a new technology increase in parallel with the increase of its market share. This process is represented by the technological learning curves and the energy sector is not detached from this process of substitution of old technologies by new ones. The present paper carries out a brief revision of the main energy models that include the technology dynamics (learning). The energy scenarios, developed by global energy models, assume that the characteristics of the technologies are variables with time. But this trend is incorporated in a exogenous way in these energy models, that is to say, it is only a time function. This practice is applied to the cost indicators of the technology such as the specific investment costs or to the efficiency of the energy technologies. In the last years, the new concept of endogenous technological learning has been integrated within these global energy models. This paper examines the concept of technological learning in global energy models. It also analyses the technological dynamics of the energy system including the endogenous modelling of the process of technological progress. Finally, it makes a comparison of several of the most used global energy models (MARKAL, MESSAGE and ERIS) and, more concretely, about the use these models make of the concept of technological learning. (Author) 17 refs

Alternatively Constrained Dictionary Learning For Image Superresolution.

Science.gov (United States)

Lu, Xiaoqiang; Yuan, Yuan; Yan, Pingkun

2014-03-01

Dictionaries are crucial in sparse coding-based algorithm for image superresolution. Sparse coding is a typical unsupervised learning method to study the relationship between the patches of high-and low-resolution images. However, most of the sparse coding methods for image superresolution fail to simultaneously consider the geometrical structure of the dictionary and the corresponding coefficients, which may result in noticeable superresolution reconstruction artifacts. In other words, when a low-resolution image and its corresponding high-resolution image are represented in their feature spaces, the two sets of dictionaries and the obtained coefficients have intrinsic links, which has not yet been well studied. Motivated by the development on nonlocal self-similarity and manifold learning, a novel sparse coding method is reported to preserve the geometrical structure of the dictionary and the sparse coefficients of the data. Moreover, the proposed method can preserve the incoherence of dictionary entries and provide the sparse coefficients and learned dictionary from a new perspective, which have both reconstruction and discrimination properties to enhance the learning performance. Furthermore, to utilize the model of the proposed method more effectively for single-image superresolution, this paper also proposes a novel dictionary-pair learning method, which is named as two-stage dictionary training. Extensive experiments are carried out on a large set of images comparing with other popular algorithms for the same purpose, and the results clearly demonstrate the effectiveness of the proposed sparse representation model and the corresponding dictionary learning algorithm.

SU-D-BRB-05: Quantum Learning for Knowledge-Based Response-Adaptive Radiotherapy

Energy Technology Data Exchange (ETDEWEB)

El Naqa, I; Ten, R [Haken University of Michigan, Ann Arbor, MI (United States)

2016-06-15

Purpose: There is tremendous excitement in radiotherapy about applying data-driven methods to develop personalized clinical decisions for real-time response-based adaptation. However, classical statistical learning methods lack in terms of efficiency and ability to predict outcomes under conditions of uncertainty and incomplete information. Therefore, we are investigating physics-inspired machine learning approaches by utilizing quantum principles for developing a robust framework to dynamically adapt treatments to individual patient’s characteristics and optimize outcomes. Methods: We studied 88 liver SBRT patients with 35 on non-adaptive and 53 on adaptive protocols. Adaptation was based on liver function using a split-course of 3+2 fractions with a month break. The radiotherapy environment was modeled as a Markov decision process (MDP) of baseline and one month into treatment states. The patient environment was modeled by a 5-variable state represented by patient’s clinical and dosimetric covariates. For comparison of classical and quantum learning methods, decision-making to adapt at one month was considered. The MDP objective was defined by the complication-free tumor control (P{sup +}=TCPx(1-NTCP)). A simple regression model represented state-action mapping. Single bit in classical MDP and a qubit of 2-superimposed states in quantum MDP represented the decision actions. Classical decision selection was done using reinforcement Q-learning and quantum searching was performed using Grover’s algorithm, which applies uniform superposition over possible states and yields quadratic speed-up. Results: Classical/quantum MDPs suggested adaptation (probability amplitude ≥0.5) 79% of the time for splitcourses and 100% for continuous-courses. However, the classical MDP had an average adaptation probability of 0.5±0.22 while the quantum algorithm reached 0.76±0.28. In cases where adaptation failed, classical MDP yielded 0.31±0.26 average amplitude while the

Moving Apart and Coming Together: Discourse, Engagement, and Deep Learning

Science.gov (United States)

Gomoll, Andrea S.; Hmelo-Silver, Cindy E.; Tolar, Erin; Šabanovic, Selma; Francisco, Matthew

2017-01-01

An important part of "doing" science is engaging in collaborative science practices. To better understand how to support these practices, we need to consider how students collaboratively construct and represent shared understanding in complex, problem-oriented, and authentic learning environments. This research presents a case study…

Multinomial Bayesian learning for modeling classical and nonclassical receptive field properties.

Science.gov (United States)

Hosoya, Haruo

2012-08-01

We study the interplay of Bayesian inference and natural image learning in a hierarchical vision system, in relation to the response properties of early visual cortex. We particularly focus on a Bayesian network with multinomial variables that can represent discrete feature spaces similar to hypercolumns combining minicolumns, enforce sparsity of activation to learn efficient representations, and explain divisive normalization. We demonstrate that maximal-likelihood learning using sampling-based Bayesian inference gives rise to classical receptive field properties similar to V1 simple cells and V2 cells, while inference performed on the trained network yields nonclassical context-dependent response properties such as cross-orientation suppression and filling in. Comparison with known physiological properties reveals some qualitative and quantitative similarities.

Fine Arts Standards of Learning for Virginia Public Schools

Science.gov (United States)

Virginia Department of Education, 2006

2006-01-01

The Fine Arts Standards of Learning in this publication represent a major development in public education in Virginia, emphasizing the importance of instruction in the fine arts (dance arts, music, theatre arts, and visual arts) as an important part of Virginia's efforts to provide challenging educational programs in the public schools. Knowledge…

Mechanisms underlying the social enhancement of vocal learning in songbirds.

Science.gov (United States)

Chen, Yining; Matheson, Laura E; Sakata, Jon T

2016-06-14

Social processes profoundly influence speech and language acquisition. Despite the importance of social influences, little is known about how social interactions modulate vocal learning. Like humans, songbirds learn their vocalizations during development, and they provide an excellent opportunity to reveal mechanisms of social influences on vocal learning. Using yoked experimental designs, we demonstrate that social interactions with adult tutors for as little as 1 d significantly enhanced vocal learning. Social influences on attention to song seemed central to the social enhancement of learning because socially tutored birds were more attentive to the tutor's songs than passively tutored birds, and because variation in attentiveness and in the social modulation of attention significantly predicted variation in vocal learning. Attention to song was influenced by both the nature and amount of tutor song: Pupils paid more attention to songs that tutors directed at them and to tutors that produced fewer songs. Tutors altered their song structure when directing songs at pupils in a manner that resembled how humans alter their vocalizations when speaking to infants, that was distinct from how tutors changed their songs when singing to females, and that could influence attention and learning. Furthermore, social interactions that rapidly enhanced learning increased the activity of noradrenergic and dopaminergic midbrain neurons. These data highlight striking parallels between humans and songbirds in the social modulation of vocal learning and suggest that social influences on attention and midbrain circuitry could represent shared mechanisms underlying the social modulation of vocal learning.

Indexing Density Models for Incremental Learning and Anytime Classification on Data Streams

DEFF Research Database (Denmark)

Seidl, Thomas; Assent, Ira; Kranen, Philipp

2009-01-01

Classification of streaming data faces three basic challenges: it has to deal with huge amounts of data, the varying time between two stream data items must be used best possible (anytime classification) and additional training data must be incrementally learned (anytime learning) for applying...... to the individual object to be classified) a hierarchy of mixture densities that represent kernel density estimators at successively coarser levels. Our probability density queries together with novel classification improvement strategies provide the necessary information for very effective classification at any...... point of interruption. Moreover, we propose a novel evaluation method for anytime classification using Poisson streams and demonstrate the anytime learning performance of the Bayes tree....

Mixed-reality Learning Environments: What Happens When You Move from a Laboratory to a Classroom?

OpenAIRE

King, Barbara; Smith, Carmen Petrick

2018-01-01

The advent ofmotion-controlled technologies has unlocked new possibilities for body-basedlearning in the mathematics classroom. For example, mixed-reality learning environments allow students theopportunity to embody a mathematical concept while simultaneously beingprovided a visual interface that represents their movement.  In the current study, we created amixed-reality environment to help children learn about angle measurement, andwe investigated similarities and differen...

D.3.2 PLOT Persuasive Learning Design Framework

DEFF Research Database (Denmark)

Gram-Hansen, Sandra Burri; Schärfe, Henrik; Winther-Nielsen, Nicolai

2011-01-01

of the technological learning tools and products which are currently related to the PLOT project, namely the GLOMaker and the 3ET tool, and a selection of GLOs and learning exercises. The primary focus of the analysis is to explore how the theoretical perspectives presented in D.3.1 are represented in these tools...... as an: ‚Internal report for project use containing an empirically-based assessment of existing systems and their potential in terms of learning and persuasion. This will be used as a discussion document by the consortium.‛ To meet the requirements of this deliverable, this documents presents analysis......, in particular the notions of persuasive design and constructive alignment. Whilst the report provides a persuasive design perspective on the technologies related to Euro PLOT, it must be stressed that if the document is to function as a basis for further discussion within the consortium, the partners...

The value teleradiology represents for Europe: A study of lessons learned in the U.S

International Nuclear Information System (INIS)

Pechet, Tiron C.M.; Girard, Greg; Walsh, Brent

2010-01-01

Pathology and demography have combined to fuel exponential demand for advanced medical imaging. To support this demand, radiology must move beyond traditional department or modality-based picture archiving and communication systems (PACS) to solutions that ensure access regardless of location. This article delineates underlying reasons for the growth in demand for access to medical imaging in both Europe and the United States. It explains why teleradiology/PACS is critical to support this growth in Europe. It discusses the benefits of and barriers to its widespread implementation as discovered in Canada and the U.S. and how these lessons learned relate to Europe. The article establishes the technological imperatives for teleradiology/PACS and presents three real-world case studies of successful data sharing and shared workflow models via single imaging implementations. CML HealthCare: Geographically spanning Canada and the United States with 129 sites performing nearly 5 million plus annual exams. Shields MRI: 29 facilities, including 3 Radiation Oncology centers, across an area 4 times the size of Switzerland. MRA/Novant: 40 radiologists working in a complete subspecialty reporting environment. Finally, it provides a high-level list of selection criteria for teleradiology/PACS and examines how industry trends affecting the U.S. are important baseline considerations to the success of teleradiology/PACS in Europe.

Verification of Representative Sampling in RI waste

International Nuclear Information System (INIS)

Ahn, Hong Joo; Song, Byung Cheul; Sohn, Se Cheul; Song, Kyu Seok; Jee, Kwang Yong; Choi, Kwang Seop

2009-01-01

For evaluating the radionuclide inventories for RI wastes, representative sampling is one of the most important parts in the process of radiochemical assay. Sampling to characterized RI waste conditions typically has been based on judgment or convenience sampling of individual or groups. However, it is difficult to get a sample representatively among the numerous drums. In addition, RI waste drums might be classified into heterogeneous wastes because they have a content of cotton, glass, vinyl, gloves, etc. In order to get the representative samples, the sample to be analyzed must be collected from selected every drum. Considering the expense and time of analysis, however, the number of sample has to be minimized. In this study, RI waste drums were classified by the various conditions of the half-life, surface dose, acceptance date, waste form, generator, etc. A sample for radiochemical assay was obtained through mixing samples of each drum. The sample has to be prepared for radiochemical assay and although the sample should be reasonably uniform, it is rare that a completely homogeneous material is received. Every sample is shredded by a 1 ∼ 2 cm 2 diameter and a representative aliquot taken for the required analysis. For verification of representative sampling, classified every group is tested for evaluation of 'selection of representative drum in a group' and 'representative sampling in a drum'

Low-rank sparse learning for robust visual tracking

KAUST Repository

Zhang, Tianzhu

2012-01-01

In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive properties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumination, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time complexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of challenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1]. © 2012 Springer-Verlag.

Simulated interprofessional education: an analysis of teaching and learning processes.

Science.gov (United States)

van Soeren, Mary; Devlin-Cop, Sandra; Macmillan, Kathleen; Baker, Lindsay; Egan-Lee, Eileen; Reeves, Scott

2011-11-01

Simulated learning activities are increasingly being used in health professions and interprofessional education (IPE). Specifically, IPE programs are frequently adopting role-play simulations as a key learning approach. Despite this widespread adoption, there is little empirical evidence exploring the teaching and learning processes embedded within this type of simulation. This exploratory study provides insight into the nature of these processes through the use of qualitative methods. A total of 152 clinicians, 101 students and 9 facilitators representing a range of health professions, participated in video-recorded role-plays and debrief sessions. Videotapes were analyzed to explore emerging issues and themes related to teaching and learning processes related to this type of interprofessional simulated learning experience. In addition, three focus groups were conducted with a subset of participants to explore perceptions of their educational experiences. Five key themes emerged from the data analysis: enthusiasm and motivation, professional role assignment, scenario realism, facilitator style and background and team facilitation. Our findings suggest that program developers need to be mindful of these five themes when using role-plays in an interprofessional context and point to the importance of deliberate and skilled facilitation in meeting desired learning outcomes.

STUDENT ATTITUDE IDENTIFICATION TOWARDS E-LEARNING COURSE BASED ON BIOSENSOR INFORMATION

Directory of Open Access Journals (Sweden)

Santoso Handri

2011-12-01

Full Text Available Providing attractive and interesting e-learning course materials as well as delivering normal lectures in class are important issues in an e-learning society. Knowing about this issue, first, it is essential to investigate student interest about topics and types of e-learning courses delivered by measuring the students‘ responses. Thus, this study engages in evaluation of students‘ responses initiated by two distinct e-learning materials; one is characterized by interactive material and the other by non-interactive material based on biosignals—i.e., Blood Pressure (BP, Galvanic Skin Response (GSR, and Electrocardiogram (ECG signals. Use of a multilinear principal component analysis (MPCA was proposed in this study to extract the representative features of student responses. Finally, the classification was performed by a support vector machine (SVM to discriminate between responses of students.

Iranian undergraduate nursing student perceptions of informal learning: A qualitative research.

Science.gov (United States)

Seylani, Khatereh; Negarandeh, Reza; Mohammadi, Easa

2012-11-01

Nursing education is both formal and informal. Formal education represents only a small part of all the learning involved; and many students learn more effectively through informal processes. There is little information about nursing student informal education and how it affects their character and practice. This qualitative study explores undergraduate nursing student perceptions of informal learning during nursing studies. Data were gathered through semi-structured interviews with a sample of undergraduate nursing students (n = 14). Strauss and Corbin's constant comparison analysis approach was used for data analysis. The categories that emerged included personal maturity and emotional development, social development, closeness to God, alterations in value systems, and ethical and professional commitment. Findings reveal that nursing education could take advantage of informal learning opportunities to develop students' nontechnical skills and produce more competent students. Implications for nursing education are discussed.

Does Transformational Leadership Encourage Teacher’s Use of Digital Learning Materials?

NARCIS (Netherlands)

Vermeulen, Marjan; Van Acker, Frederik; Kreijns, Karel; Van Buuren, Hans

2018-01-01

To gain insight into how to promote teachers’ use of digital learning materials (DLMs) in their pedagogical practices we adopted the Integrated Model of Behavior Prediction to investigate the relationships between organizational and teacher related variables. A representative sample of 772 teachers

A Bayesian foundation for individual learning under uncertainty

Directory of Open Access Journals (Sweden)

Christoph eMathys

2011-05-01

Full Text Available Computational learning models are critical for understanding mechanisms of adaptive behavior. However, the two major current frameworks, reinforcement learning (RL and Bayesian learning, both have certain limitations. For example, many Bayesian models are agnostic of inter-individual variability and involve complicated integrals, making online learning difficult. Here, we introduce a generic hierarchical Bayesian framework for individual learning under multiple forms of uncertainty (e.g., environmental volatility and perceptual uncertainty. The model assumes Gaussian random walks of states at all but the first level, with the step size determined by the next higher level. The coupling between levels is controlled by parameters that shape the influence of uncertainty on learning in a subject-specific fashion. Using variational Bayes under a mean field approximation and a novel approximation to the posterior energy function, we derive trial-by-trial update equations which (i are analytical and extremely efficient, enabling real-time learning, (ii have a natural interpretation in terms of RL, and (iii contain parameters representing processes which play a key role in current theories of learning, e.g., precision-weighting of prediction error. These parameters allow for the expression of individual differences in learning and may relate to specific neuromodulatory mechanisms in the brain. Our model is very general: it can deal with both discrete and continuous states and equally accounts for deterministic and probabilistic relations between environmental events and perceptual states (i.e., situations with and without perceptual uncertainty. These properties are illustrated by simulations and analyses of empirical time series. Overall, our framework provides a novel foundation for understanding normal and pathological learning that contextualizes RL within a generic Bayesian scheme and thus connects it to principles of optimality from probability

A bayesian foundation for individual learning under uncertainty.

Science.gov (United States)

Mathys, Christoph; Daunizeau, Jean; Friston, Karl J; Stephan, Klaas E

2011-01-01

Computational learning models are critical for understanding mechanisms of adaptive behavior. However, the two major current frameworks, reinforcement learning (RL) and Bayesian learning, both have certain limitations. For example, many Bayesian models are agnostic of inter-individual variability and involve complicated integrals, making online learning difficult. Here, we introduce a generic hierarchical Bayesian framework for individual learning under multiple forms of uncertainty (e.g., environmental volatility and perceptual uncertainty). The model assumes Gaussian random walks of states at all but the first level, with the step size determined by the next highest level. The coupling between levels is controlled by parameters that shape the influence of uncertainty on learning in a subject-specific fashion. Using variational Bayes under a mean-field approximation and a novel approximation to the posterior energy function, we derive trial-by-trial update equations which (i) are analytical and extremely efficient, enabling real-time learning, (ii) have a natural interpretation in terms of RL, and (iii) contain parameters representing processes which play a key role in current theories of learning, e.g., precision-weighting of prediction error. These parameters allow for the expression of individual differences in learning and may relate to specific neuromodulatory mechanisms in the brain. Our model is very general: it can deal with both discrete and continuous states and equally accounts for deterministic and probabilistic relations between environmental events and perceptual states (i.e., situations with and without perceptual uncertainty). These properties are illustrated by simulations and analyses of empirical time series. Overall, our framework provides a novel foundation for understanding normal and pathological learning that contextualizes RL within a generic Bayesian scheme and thus connects it to principles of optimality from probability theory.

SchNet - A deep learning architecture for molecules and materials

Science.gov (United States)

Schütt, K. T.; Sauceda, H. E.; Kindermans, P.-J.; Tkatchenko, A.; Müller, K.-R.

2018-06-01

Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C20-fullerene that would have been infeasible with regular ab initio molecular dynamics.

Virtual water maze learning in human increases functional connectivity between posterior hippocampus and dorsal caudate.

Science.gov (United States)

Woolley, Daniel G; Mantini, Dante; Coxon, James P; D'Hooge, Rudi; Swinnen, Stephan P; Wenderoth, Nicole

2015-04-01

Recent work has demonstrated that functional connectivity between remote brain regions can be modulated by task learning or the performance of an already well-learned task. Here, we investigated the extent to which initial learning and stable performance of a spatial navigation task modulates functional connectivity between subregions of hippocampus and striatum. Subjects actively navigated through a virtual water maze environment and used visual cues to learn the position of a fixed spatial location. Resting-state functional magnetic resonance imaging scans were collected before and after virtual water maze navigation in two scan sessions conducted 1 week apart, with a behavior-only training session in between. There was a large significant reduction in the time taken to intercept the target location during scan session 1 and a small significant reduction during the behavior-only training session. No further reduction was observed during scan session 2. This indicates that scan session 1 represented initial learning and scan session 2 represented stable performance. We observed an increase in functional connectivity between left posterior hippocampus and left dorsal caudate that was specific to scan session 1. Importantly, the magnitude of the increase in functional connectivity was correlated with offline gains in task performance. Our findings suggest cooperative interaction occurs between posterior hippocampus and dorsal caudate during awake rest following the initial phase of spatial navigation learning. Furthermore, we speculate that the increase in functional connectivity observed during awake rest after initial learning might reflect consolidation-related processing. © 2014 Wiley Periodicals, Inc.

Representative process sampling - in practice

DEFF Research Database (Denmark)

Esbensen, Kim; Friis-Pedersen, Hans Henrik; Julius, Lars Petersen

2007-01-01

Didactic data sets representing a range of real-world processes are used to illustrate "how to do" representative process sampling and process characterisation. The selected process data lead to diverse variogram expressions with different systematics (no range vs. important ranges; trends and....../or periodicity; different nugget effects and process variations ranging from less than one lag to full variogram lag). Variogram data analysis leads to a fundamental decomposition into 0-D sampling vs. 1-D process variances, based on the three principal variogram parameters: range, sill and nugget effect...

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…

Pedagogical Dramas and Transformational Play: Narratively Rich Games for Learning

Science.gov (United States)

Barab, Sasha A.; Dodge, Tyler; Ingram-Goble, Adam; Pettyjohn, Patrick; Peppler, Kylie; Volk, Charlene; Solomou, Maria

2010-01-01

Although every era is met with the introduction of powerful technologies for entertainment and learning, videogames represent a new contribution binding the two and bearing the potential to create sustained engagement in a curricular drama where the player's knowledgeable actions shape an unfolding fiction within a designed world. Although…

Children's Play and Culture Learning in an Egalitarian Foraging Society

Science.gov (United States)

Boyette, Adam H.

2016-01-01

Few systematic studies of play in foragers exist despite their significance for understanding the breadth of contexts for human development and the ontogeny of cultural learning. Forager societies lack complex social hierarchies, avenues for prestige or wealth accumulation, and formal educational institutions, and thereby represent a contrast to…

[The advantages of implementing an e-learning platform for laparoscopic liver surgery].

Science.gov (United States)

Furcea, L; Graur, F; Scurtu, L; Plitea, N; Pîslă, D; Vaida, C; Deteşan, O; Szilaghy, A; Neagoş, H; Mureşan, A; Vlad, L

2011-01-01

The rapid expansion of laparoscopic surgery has led to the development of training methods for acquiring technical skills. The importance and complexity of laparoscopic liver surgery are arguments for developing a new integrated system of teaching, learning and evaluation, based on modern educational principles, on flexibility allowing wide accessibility among surgeons. This paper presents the development of e-learning platform designed for training in laparoscopic liver surgery and pre-planning of the operation in a virtual environment. E-learning platform makes it possible to simulate laparoscopic liver surgery remotely via internet connection. The addressability of this e-learning platform is large, being represented by young surgeons who are mainly preoccupied by laparoscopic liver surgery, as well as experienced surgeons interested in obtaining a competence in the hepatic minimally invasive surgery.

Instance annotation for multi-instance multi-label learning

Science.gov (United States)

F. Briggs; X.Z. Fern; R. Raich; Q. Lou

2013-01-01

Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels. For example, an image can be represented as a bag of segments and associated with a list of objects it contains. Prior work on MIML has focused on predicting label sets for previously unseen...

Archetypal Analysis for Machine Learning

DEFF Research Database (Denmark)

Mørup, Morten; Hansen, Lars Kai

2010-01-01

Archetypal analysis (AA) proposed by Cutler and Breiman in [1] estimates the principal convex hull of a data set. As such AA favors features that constitute representative ’corners’ of the data, i.e. distinct aspects or archetypes. We will show that AA enjoys the interpretability of clustering - ...... for K-means [2]. We demonstrate that the AA model is relevant for feature extraction and dimensional reduction for a large variety of machine learning problems taken from computer vision, neuroimaging, text mining and collaborative filtering....

Higher Education Outcomes at the National Level on the Example of the Project “Collegiate Learning Assessment”

Directory of Open Access Journals (Sweden)

Sabelnikova E. V.

2015-08-01

Full Text Available We discuss the interpretation of the concept of “learning outcomes”. Theoretical analysis widely represents the interpretations of the learning outcomes of a high school student: academic skills: understanding, application of knowledge to solve problems, synthesis, analysis and evaluation; basic skills and basic knowledge, and skills of a higher order and advanced knowledge; skills of a higher order represented as a system of critical thinking, analytic reasoning, problem solving and written communication; wide abilities interpreted as verbal, quantitative and spatial thinking, understanding, problem solving and decision making. We conclude that each considered approach distinguishes meta-subjective skills, i.e. skills to interact with the quality of information regardless of the context. The ability to measure the meta-skills is discussed on an example of the “Collegiate learning assessment”, realized in the United States

Occupational therapy students in the process of interprofessional collaborative learning: a grounded theory study.

Science.gov (United States)

Howell, Dana

2009-01-01

The purpose of this grounded theory study was to generate a theory of the interprofessional collaborative learning process of occupational therapy (OT) students who were engaged in a collaborative learning experience with students from other allied health disciplines. Data consisted of semi-structured interviews with nine OT students from four different interprofessional collaborative learning experiences at three universities. The emergent theory explained OT students' need to build a culture of mutual respect among disciplines in order to facilitate interprofessional collaborative learning. Occupational therapy students went through a progression of learned skills that included learning how to represent the profession of OT, hold their weight within a team situation, solve problems collaboratively, work as a team, and ultimately, to work in an actual team in practice. This learning process occurred simultaneously as students also learned course content. The students had to contend with barriers and facilitators that influenced their participation and the success of their collaboration. Understanding the interprofessional learning process of OT students will help allied health faculty to design more effective, inclusive interprofessional courses.

Online multi-modal robust non-negative dictionary learning for visual tracking.

Science.gov (United States)

Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang

2015-01-01

Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality.

Are baboons learning "orthographic" representations? Probably not.

Directory of Open Access Journals (Sweden)

Maja Linke

Full Text Available The ability of Baboons (papio papio to distinguish between English words and nonwords has been modeled using a deep learning convolutional network model that simulates a ventral pathway in which lexical representations of different granularity develop. However, given that pigeons (columba livia, whose brain morphology is drastically different, can also be trained to distinguish between English words and nonwords, it appears that a less species-specific learning algorithm may be required to explain this behavior. Accordingly, we examined whether the learning model of Rescorla and Wagner, which has proved to be amazingly fruitful in understanding animal and human learning could account for these data. We show that a discrimination learning network using gradient orientation features as input units and word and nonword units as outputs succeeds in predicting baboon lexical decision behavior-including key lexical similarity effects and the ups and downs in accuracy as learning unfolds-with surprising precision. The models performance, in which words are not explicitly represented, is remarkable because it is usually assumed that lexicality decisions, including the decisions made by baboons and pigeons, are mediated by explicit lexical representations. By contrast, our results suggest that in learning to perform lexical decision tasks, baboons and pigeons do not construct a hierarchy of lexical units. Rather, they make optimal use of low-level information obtained through the massively parallel processing of gradient orientation features. Accordingly, we suggest that reading in humans first involves initially learning a high-level system building on letter representations acquired from explicit instruction in literacy, which is then integrated into a conventionalized oral communication system, and that like the latter, fluent reading involves the massively parallel processing of the low-level features encoding semantic contrasts.

Adaptive memory: animacy effects persist in paired-associate learning.

Science.gov (United States)

VanArsdall, Joshua E; Nairne, James S; Pandeirada, Josefa N S; Cogdill, Mindi

2015-01-01

Recent evidence suggests that animate stimuli are remembered better than matched inanimate stimuli. Two experiments tested whether this animacy effect persists in paired-associate learning of foreign words. Experiment 1 randomly paired Swahili words with matched animate and inanimate English words. Participants were told simply to learn the English "translations" for a later test. Replicating earlier findings using free recall, a strong animacy advantage was found in this cued-recall task. Concerned that the effect might be due to enhanced accessibility of the individual responses (e.g., animates represent a more accessible category), Experiment 2 selected animate and inanimate English words from two more constrained categories (four-legged animals and furniture). Once again, an advantage was found for pairs using animate targets. These results argue against organisational accounts of the animacy effect and potentially have implications for foreign language vocabulary learning.

Readiness of Adults to Learn Using E-Learning, M-Learning and T-Learning Technologies

Science.gov (United States)

Vilkonis, Rytis; Bakanoviene, Tatjana; Turskiene, Sigita

2013-01-01

The article presents results of the empirical research revealing readiness of adults to participate in the lifelong learning process using e-learning, m-learning and t-learning technologies. The research has been carried out in the framework of the international project eBig3 aiming at development a new distance learning platform blending virtual…

Improvement of Word Problem Solving and Basic Mathematics Competencies in Students with Attention Deficit/Hyperactivity Disorder and Mathematical Learning Difficulties

Science.gov (United States)

González-Castro, Paloma; Cueli, Marisol; Areces, Débora; Rodríguez, Celestino; Sideridis, Georgios

2016-01-01

Problem solving represents a salient deficit in students with mathematical learning difficulties (MLD) primarily caused by difficulties with informal and formal mathematical competencies. This study proposes a computerized intervention tool, the integrated dynamic representation (IDR), for enhancing the early learning of basic mathematical…

Development and Validation of the Motivation for Tutoring Questionnaire in Problem-Based Learning Programs

OpenAIRE

Kassab, Salah Eldin; Hassan, Nahla; El-Araby, Shimaa; Salem, Abdel Halim; Alrebish, Saleh Ali; Al-Amro, Ahmed S.; Al-Shobaili, Hani A.; Hamdy, Hossam

2017-01-01

Purpose: There are no published instruments, which measure tutor motivation for conducting small group tutorials in problem-based learning programs. Therefore, we aimed to develop a motivation for tutoring questionnaire in problem-based learning (MTQ-PBL) and evaluate its construct validity. Methods: The questionnaire included 28 items representing four constructs: tutoring self-efficacy (15 items), tutoring interest (6 items), tutoring value (4 items), and tutoring effort (3 items). Tutor...

Early Fractions Learning of 3rd Grade Students in SD Laboratorium Unesa

Directory of Open Access Journals (Sweden)

Elisabet Ayunika Permata Sari

2012-01-01

Full Text Available Fractions varied meanings is one of the causes of difficulties in learning fractions. These students should be given greater opportunities to explore the meaning of fractions before they learn the relationship between fractions and operations on fractions. Although students can shading area represents a fraction, does not mean they really understand the meaning of fractions as a whole. With a realistic approach to mathematics, students are given the contextual issues of equitable distribution and measurements that involve fractions

The value of online learning and MRI: finding a niche for expensive technologies.

Science.gov (United States)

Cook, David A

2014-11-01

The benefits of online learning come at a price. How can we optimize the overall value? Critically appraise the value of online learning. Narrative review. Several prevalent myths overinflate the value of online learning. These include that online learning is cheap and easy (it is usually more expensive), that it is more efficient (efficiency depends on the instructional design, not the modality), that it will transform education (fundamental learning principles have not changed), and that the Net Generation expects it (there is no evidence of pent-up demand). However, online learning does add real value by enhancing flexibility, control and analytics. Costs may also go down if disruptive innovations (e.g. low-cost, low-tech, but instructionally sound "good enough" online learning) supplant technically superior but more expensive online learning products. Cost-lowering strategies include focusing on core principles of learning rather than technologies, using easy-to-learn authoring tools, repurposing content (organizing and sequencing existing resources rather than creating new content) and using course templates. Online learning represents just one tool in an educator's toolbox, as does the MRI for clinicians. We need to use the right tool(s) for the right learner at the right dose, time and route.

Associative learning changes cross-modal representations in the gustatory cortex.

Science.gov (United States)

Vincis, Roberto; Fontanini, Alfredo

2016-08-30

A growing body of literature has demonstrated that primary sensory cortices are not exclusively unimodal, but can respond to stimuli of different sensory modalities. However, several questions concerning the neural representation of cross-modal stimuli remain open. Indeed, it is poorly understood if cross-modal stimuli evoke unique or overlapping representations in a primary sensory cortex and whether learning can modulate these representations. Here we recorded single unit responses to auditory, visual, somatosensory, and olfactory stimuli in the gustatory cortex (GC) of alert rats before and after associative learning. We found that, in untrained rats, the majority of GC neurons were modulated by a single modality. Upon learning, both prevalence of cross-modal responsive neurons and their breadth of tuning increased, leading to a greater overlap of representations. Altogether, our results show that the gustatory cortex represents cross-modal stimuli according to their sensory identity, and that learning changes the overlap of cross-modal representations.

The endocannabinoid system and associative learning and memory in zebrafish.

Science.gov (United States)

Ruhl, Tim; Moesbauer, Kirstin; Oellers, Nadine; von der Emde, Gerhard

2015-09-01

In zebrafish the medial pallium of the dorsal telencephalon represents an amygdala homolog structure, which is crucially involved in emotional associative learning and memory. Similar to the mammalian amygdala, the medial pallium contains a high density of endocannabinoid receptor CB1. To elucidate the role of the zebrafish endocannabinoid system in associative learning, we tested the influence of acute and chronic administration of receptor agonists (THC, WIN55,212-2) and antagonists (Rimonabant, AM-281) on two different learning paradigms. In an appetitively motivated two-alternative choice paradigm, animals learned to associate a certain color with a food reward. In a second set-up, a fish shuttle-box, animals associated the onset of a light stimulus with the occurrence of a subsequent electric shock (avoidance conditioning). Once fish successfully had learned to solve these behavioral tasks, acute receptor activation or inactivation had no effect on memory retrieval, suggesting that established associative memories were stable and not alterable by the endocannabinoid system. In both learning tasks, chronic treatment with receptor antagonists improved acquisition learning, and additionally facilitated reversal learning during color discrimination. In contrast, chronic CB1 activation prevented aversively motivated acquisition learning, while different effects were found on appetitively motivated acquisition learning. While THC significantly improved behavioral performance, WIN55,212-2 significantly impaired color association. Our findings suggest that the zebrafish endocannabinoid system can modulate associative learning and memory. Stimulation of the CB1 receptor might play a more specific role in acquisition and storage of aversive learning and memory, while CB1 blocking induces general enhancement of cognitive functions. Copyright © 2015 Elsevier B.V. All rights reserved.

Mentorship, learning curves, and balance.

Science.gov (United States)

Cohen, Meryl S; Jacobs, Jeffrey P; Quintessenza, James A; Chai, Paul J; Lindberg, Harald L; Dickey, Jamie; Ungerleider, Ross M

2007-09-01

meet this challenge without a painful learning curve belongs to both the younger professionals, who must progress through the learning curve, and the more mature professionals who must create an appropriate environment for learning. In addition to mentorship, the detailed tracking of outcomes is an essential tool for mastering any learning curve. It is crucial to utilize a detailed database to track outcomes, to learn, and to protect both yourself and your patients. It is our professional responsibility to engage in self-evaluation, in part employing voluntary sharing of data. For cardiac surgical subspecialties, the databases now existing for The European Association for CardioThoracic Surgery and The Society of Thoracic Surgeons represent the ideal tool for monitoring outcomes. Evolving initiatives in the fields of paediatric cardiology, paediatric critical care, and paediatric cardiac anaesthesia will play similar roles.A variety of professional and personal challenges must be met by all those working in health care. The acquisition of learned skills, and the use of special tools, will facilitate the process of conquering these challenges. Choosing appropriate role models and mentors can help progression through any learning curve in a controlled and protected fashion. Professional and personal satisfaction are both necessities. Finding the satisfactory balance between work and home life is difficult, but possible with the right tools, organization skills, and support system at work and at home. The concepts of mentorship, learning curves and balance cannot be underappreciated.

A learning-style theory for understanding autistic behaviors

Directory of Open Access Journals (Sweden)

Ning eQian

2011-08-01

Full Text Available Understanding autism’s ever-expanding array of behaviors, from sensation to cognition, is a major challenge. We posit that autistic and typically-developing brains implement different algorithms that are better suited to learn, represent, and process different tasks; consequently, they develop different interests and behaviors. Computationally, a continuum of algorithms exists, from lookup-table (LUT learning, which aims to store experiences precisely, to interpolation (INT learning, which focuses on extracting underlying statistical structure (regularities from experiences. We hypothesize that autistic and typical brains, respectively, are biased toward LUT and INT learning, in low and high dimensional feature spaces, possibly because of their narrow and broad tuning functions. The LUT style is good at learning relationships that are local, precise, rigid, and contain little regularity for generalization (e.g., the name-number association in a phonebook. However, it is poor at learning relationships that are context dependent, noisy, flexible, and do contain regularities for generalization (e.g., associations between gaze direction and intention, language and meaning, sensory input and interpretation, motor-control signal and movement, and social situation and proper response. The LUT style poorly compresses information, resulting in inefficiency, sensory overload (overwhelm, restricted interests, and resistance to change. It also leads to poor prediction and anticipation, frequent surprises and over-reaction (hyper-sensitivity, impaired attentional selection and switching, concreteness, strong local focus, weak adaptation, and superior and inferior performances on simple and complex tasks. The spectrum nature of autism can be explained by different degrees of LUT learning among different individuals, and in different systems of the same individual. Our theory suggests that therapy should focus on training autistic LUT algorithm to learn

A Learning-Style Theory for Understanding Autistic Behaviors

Science.gov (United States)

Qian, Ning; Lipkin, Richard M.

2011-01-01

Understanding autism's ever-expanding array of behaviors, from sensation to cognition, is a major challenge. We posit that autistic and typically developing brains implement different algorithms that are better suited to learn, represent, and process different tasks; consequently, they develop different interests and behaviors. Computationally, a continuum of algorithms exists, from lookup table (LUT) learning, which aims to store experiences precisely, to interpolation (INT) learning, which focuses on extracting underlying statistical structure (regularities) from experiences. We hypothesize that autistic and typical brains, respectively, are biased toward LUT and INT learning, in low- and high-dimensional feature spaces, possibly because of their narrow and broad tuning functions. The LUT style is good at learning relationships that are local, precise, rigid, and contain little regularity for generalization (e.g., the name–number association in a phonebook). However, it is poor at learning relationships that are context dependent, noisy, flexible, and do contain regularities for generalization (e.g., associations between gaze direction and intention, language and meaning, sensory input and interpretation, motor-control signal and movement, and social situation and proper response). The LUT style poorly compresses information, resulting in inefficiency, sensory overload (overwhelm), restricted interests, and resistance to change. It also leads to poor prediction and anticipation, frequent surprises and over-reaction (hyper-sensitivity), impaired attentional selection and switching, concreteness, strong local focus, weak adaptation, and superior and inferior performances on simple and complex tasks. The spectrum nature of autism can be explained by different degrees of LUT learning among different individuals, and in different systems of the same individual. Our theory suggests that therapy should focus on training autistic LUT algorithm to learn regularities

From learning objects to learning activities

DEFF Research Database (Denmark)

Dalsgaard, Christian

2005-01-01

This paper discusses and questions the current metadata standards for learning objects from a pedagogical point of view. From a social constructivist approach, the paper discusses how learning objects can support problem based, self-governed learning activities. In order to support this approach......, it is argued that it is necessary to focus on learning activities rather than on learning objects. Further, it is argued that descriptions of learning objectives and learning activities should be separated from learning objects. The paper presents a new conception of learning objects which supports problem...... based, self-governed activities. Further, a new way of thinking pedagogy into learning objects is introduced. It is argued that a lack of pedagogical thinking in learning objects is not solved through pedagogical metadata. Instead, the paper suggests the concept of references as an alternative...

Using a model of human visual perception to improve deep learning.