Preparing new nurses with complexity science and problem-based learning.

Science.gov (United States)

Hodges, Helen F

2011-01-01

Successful nurses function effectively with adaptability, improvability, and interconnectedness, and can see emerging and unpredictable complex problems. Preparing new nurses for complexity requires a significant change in prevalent but dated nursing education models for rising graduates. The science of complexity coupled with problem-based learning and peer review contributes a feasible framework for a constructivist learning environment to examine real-time systems data; explore uncertainty, inherent patterns, and ambiguity; and develop skills for unstructured problem solving. This article describes a pilot study of a problem-based learning strategy guided by principles of complexity science in a community clinical nursing course. Thirty-five senior nursing students participated during a 3-year period. Assessments included peer review, a final project paper, reflection, and a satisfaction survey. Results were higher than expected levels of student satisfaction, increased breadth and analysis of complex data, acknowledgment of community as complex adaptive systems, and overall higher level thinking skills than in previous years. 2011, SLACK Incorporated.

Study of Environmental Data Complexity using Extreme Learning Machine

Science.gov (United States)

Leuenberger, Michael; Kanevski, Mikhail

2017-04-01

The main goals of environmental data science using machine learning algorithm deal, in a broad sense, around the calibration, the prediction and the visualization of hidden relationship between input and output variables. In order to optimize the models and to understand the phenomenon under study, the characterization of the complexity (at different levels) should be taken into account. Therefore, the identification of the linear or non-linear behavior between input and output variables adds valuable information for the knowledge of the phenomenon complexity. The present research highlights and investigates the different issues that can occur when identifying the complexity (linear/non-linear) of environmental data using machine learning algorithm. In particular, the main attention is paid to the description of a self-consistent methodology for the use of Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. By applying two ELM models (with linear and non-linear activation functions) and by comparing their efficiency, quantification of the linearity can be evaluated. The considered approach is accompanied by simulated and real high dimensional and multivariate data case studies. In conclusion, the current challenges and future development in complexity quantification using environmental data mining are discussed. References - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

Learning Latent Structure in Complex Networks

DEFF Research Database (Denmark)

Mørup, Morten; Hansen, Lars Kai

such as the Modularity, it has recently been shown that latent structure in complex networks is learnable by Bayesian generative link distribution models (Airoldi et al., 2008, Hofman and Wiggins, 2008). In this paper we propose a new generative model that allows representation of latent community structure......Latent structure in complex networks, e.g., in the form of community structure, can help understand network dynamics, identify heterogeneities in network properties, and predict ‘missing’ links. While most community detection algorithms are based on optimizing heuristic clustering objectives...... as in the previous Bayesian approaches and in addition allows learning of node specific link properties similar to that in the modularity objective. We employ a new relaxation method for efficient inference in these generative models that allows us to learn the behavior of very large networks. We compare the link...

Network affordances through online learning: Increasing use and complexity.

OpenAIRE

Hajhashemi, Karim; Anderson, Neil; Jackson, Cliff; Caltabiano, Nerina

2013-01-01

Computers, mobile devices and the Internet have enabled a learning environment described as online learning or a variety of other terms such as e-learning. Researchers believe that online learning has become more complex due to learners' sharing and acquiring knowledge at a variety of remote locations, in a variety of modalities. However, advances in technology and the integration of ICT with teaching and learning settings have quickened the growth of online learning and importantly have chan...

Complexity, Training Paradigm Design, and the Contribution of Memory Subsystems to Grammar Learning

Science.gov (United States)

Ettlinger, Marc; Wong, Patrick C. M.

2016-01-01

Although there is variability in nonnative grammar learning outcomes, the contributions of training paradigm design and memory subsystems are not well understood. To examine this, we presented learners with an artificial grammar that formed words via simple and complex morphophonological rules. Across three experiments, we manipulated training paradigm design and measured subjects' declarative, procedural, and working memory subsystems. Experiment 1 demonstrated that passive, exposure-based training boosted learning of both simple and complex grammatical rules, relative to no training. Additionally, procedural memory correlated with simple rule learning, whereas declarative memory correlated with complex rule learning. Experiment 2 showed that presenting corrective feedback during the test phase did not improve learning. Experiment 3 revealed that structuring the order of training so that subjects are first exposed to the simple rule and then the complex improved learning. The cumulative findings shed light on the contributions of grammatical complexity, training paradigm design, and domain-general memory subsystems in determining grammar learning success. PMID:27391085

Complexity, Training Paradigm Design, and the Contribution of Memory Subsystems to Grammar Learning.

Science.gov (United States)

Antoniou, Mark; Ettlinger, Marc; Wong, Patrick C M

2016-01-01

Although there is variability in nonnative grammar learning outcomes, the contributions of training paradigm design and memory subsystems are not well understood. To examine this, we presented learners with an artificial grammar that formed words via simple and complex morphophonological rules. Across three experiments, we manipulated training paradigm design and measured subjects' declarative, procedural, and working memory subsystems. Experiment 1 demonstrated that passive, exposure-based training boosted learning of both simple and complex grammatical rules, relative to no training. Additionally, procedural memory correlated with simple rule learning, whereas declarative memory correlated with complex rule learning. Experiment 2 showed that presenting corrective feedback during the test phase did not improve learning. Experiment 3 revealed that structuring the order of training so that subjects are first exposed to the simple rule and then the complex improved learning. The cumulative findings shed light on the contributions of grammatical complexity, training paradigm design, and domain-general memory subsystems in determining grammar learning success.

Complexity, Training Paradigm Design, and the Contribution of Memory Subsystems to Grammar Learning.

Directory of Open Access Journals (Sweden)

Mark Antoniou

Full Text Available Although there is variability in nonnative grammar learning outcomes, the contributions of training paradigm design and memory subsystems are not well understood. To examine this, we presented learners with an artificial grammar that formed words via simple and complex morphophonological rules. Across three experiments, we manipulated training paradigm design and measured subjects' declarative, procedural, and working memory subsystems. Experiment 1 demonstrated that passive, exposure-based training boosted learning of both simple and complex grammatical rules, relative to no training. Additionally, procedural memory correlated with simple rule learning, whereas declarative memory correlated with complex rule learning. Experiment 2 showed that presenting corrective feedback during the test phase did not improve learning. Experiment 3 revealed that structuring the order of training so that subjects are first exposed to the simple rule and then the complex improved learning. The cumulative findings shed light on the contributions of grammatical complexity, training paradigm design, and domain-general memory subsystems in determining grammar learning success.

Does formal complexity reflect cognitive complexity? Investigating aspects of the Chomsky Hierarchy in an artificial language learning study.

Science.gov (United States)

Öttl, Birgit; Jäger, Gerhard; Kaup, Barbara

2015-01-01

This study investigated whether formal complexity, as described by the Chomsky Hierarchy, corresponds to cognitive complexity during language learning. According to the Chomsky Hierarchy, nested dependencies (context-free) are less complex than cross-serial dependencies (mildly context-sensitive). In two artificial grammar learning (AGL) experiments participants were presented with a language containing either nested or cross-serial dependencies. A learning effect for both types of dependencies could be observed, but no difference between dependency types emerged. These behavioral findings do not seem to reflect complexity differences as described in the Chomsky Hierarchy. This study extends previous findings in demonstrating learning effects for nested and cross-serial dependencies with more natural stimulus materials in a classical AGL paradigm after only one hour of exposure. The current findings can be taken as a starting point for further exploring the degree to which the Chomsky Hierarchy reflects cognitive processes.

Temperature profiles from mechanical bathythermograph (MBT) casts from the USS BELMONT and Other Platforms from Ocean Weather S (OWS-S) in the North Atlantic Ocean and other locations in support of the Fleet Observations of Oceanographic Data (FLOOD) project from 1961-10-14 to 1967-02-15 (NODC Accession 6900324)

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — MBT data were collected from the USS BELMONT and Other Platforms within a 1-mile radius of Ocean Weather Station S (3210N 06430W) and in transit in support of the...

Feature economy vs. logical complexity in phonological pattern learning

NARCIS (Netherlands)

Seinhorst, K.T.

Complexity has been linked to ease of learning. This article explores the roles of two measures of complexity – feature economy and logical complexity – in the acquisition of sets of signs, taken from a small sign language that serves as an analogue of plosive inventories in spoken language. In a

Improvement of Inquiry in a Complex Technology-Enhanced Learning Environment

NARCIS (Netherlands)

Pedaste, Margus; Kori, Külli; Maeots, Mario; de Jong, Anthonius J.M.; Riopel, Martin; Smyrnaiou, Zacharoula

2016-01-01

Inquiry learning is an effective approach in science education. Complex technology-enhanced learning environments are needed to apply inquiry worldwide to support knowledge gain and improvement of inquiry skills. In our study, we applied an ecology mission in the SCY-Lab learning environment and

A deep learning approach for fetal QRS complex detection.

Science.gov (United States)

Zhong, Wei; Liao, Lijuan; Guo, Xuemei; Wang, Guoli

2018-04-20

Non-invasive foetal electrocardiography (NI-FECG) has the potential to provide more additional clinical information for detecting and diagnosing fetal diseases. We propose and demonstrate a deep learning approach for fetal QRS complex detection from raw NI-FECG signals by using a convolutional neural network (CNN) model. The main objective is to investigate whether reliable fetal QRS complex detection performance can still be obtained from features of single-channel NI-FECG signals, without canceling maternal ECG (MECG) signals. A deep learning method is proposed for recognizing fetal QRS complexes. Firstly, we collect data from set-a of the PhysioNet/computing in Cardiology Challenge database. The sample entropy method is used for signal quality assessment. Part of the bad quality signals is excluded in the further analysis. Secondly, in the proposed method, the features of raw NI-FECG signals are normalized before they are fed to a CNN classifier to perform fetal QRS complex detection. We use precision, recall, F-measure and accuracy as the evaluation metrics to assess the performance of fetal QRS complex detection. The proposed deep learning method can achieve relatively high precision (75.33%), recall (80.54%), and F-measure scores (77.85%) compared with three other well-known pattern classification methods, namely KNN, naive Bayes and SVM. the proposed deep learning method can attain reliable fetal QRS complex detection performance from the raw NI-FECG signals without canceling MECG signals. In addition, the influence of different activation functions and signal quality assessment on classification performance are evaluated, and results show that Relu outperforms the Sigmoid and Tanh on this particular task, and better classification performance is obtained with the signal quality assessment step in this study.

Evolutionary and adaptive learning in complex markets: a brief summary

Science.gov (United States)

Hommes, Cars H.

2007-06-01

We briefly review some work on expectations and learning in complex markets, using the familiar demand-supply cobweb model. We discuss and combine two different approaches on learning. According to the adaptive learning approach, agents behave as econometricians using time series observations to form expectations, and update the parameters as more observations become available. This approach has become popular in macro. The second approach has an evolutionary flavor and is sometimes referred to as reinforcement learning. Agents employ different forecasting strategies and evaluate these strategies based upon a fitness measure, e.g. past realized profits. In this framework, boundedly rational agents switch between different, but fixed behavioral rules. This approach has become popular in finance. We combine evolutionary and adaptive learning to model complex markets and discuss whether this theory can match empirical facts and forecasting behavior in laboratory experiments with human subjects.

Learning to manage complexity through simulation: students' challenges and possible strategies.

Science.gov (United States)

Gormley, Gerard J; Fenwick, Tara

2016-06-01

Many have called for medical students to learn how to manage complexity in healthcare. This study examines the nuances of students' challenges in coping with a complex simulation learning activity, using concepts from complexity theory, and suggests strategies to help them better understand and manage complexity.Wearing video glasses, participants took part in a simulation ward-based exercise that incorporated characteristics of complexity. Video footage was used to elicit interviews, which were transcribed. Using complexity theory as a theoretical lens, an iterative approach was taken to identify the challenges that participants faced and possible coping strategies using both interview transcripts and video footage.Students' challenges in coping with clinical complexity included being: a) unprepared for 'diving in', b) caught in an escalating system, c) captured by the patient, and d) unable to assert boundaries of acceptable practice.Many characteristics of complexity can be recreated in a ward-based simulation learning activity, affording learners an embodied and immersive experience of these complexity challenges. Possible strategies for managing complexity themes include: a) taking time to size up the system, b) attuning to what emerges, c) reducing complexity, d) boundary practices, and e) working with uncertainty. This study signals pedagogical opportunities for recognizing and dealing with complexity.

Brain signal complexity rises with repetition suppression in visual learning.

Science.gov (United States)

Lafontaine, Marc Philippe; Lacourse, Karine; Lina, Jean-Marc; McIntosh, Anthony R; Gosselin, Frédéric; Théoret, Hugo; Lippé, Sarah

2016-06-21

Neuronal activity associated with visual processing of an unfamiliar face gradually diminishes when it is viewed repeatedly. This process, known as repetition suppression (RS), is involved in the acquisition of familiarity. Current models suggest that RS results from interactions between visual information processing areas located in the occipito-temporal cortex and higher order areas, such as the dorsolateral prefrontal cortex (DLPFC). Brain signal complexity, which reflects information dynamics of cortical networks, has been shown to increase as unfamiliar faces become familiar. However, the complementarity of RS and increases in brain signal complexity have yet to be demonstrated within the same measurements. We hypothesized that RS and brain signal complexity increase occur simultaneously during learning of unfamiliar faces. Further, we expected alteration of DLPFC function by transcranial direct current stimulation (tDCS) to modulate RS and brain signal complexity over the occipito-temporal cortex. Participants underwent three tDCS conditions in random order: right anodal/left cathodal, right cathodal/left anodal and sham. Following tDCS, participants learned unfamiliar faces, while an electroencephalogram (EEG) was recorded. Results revealed RS over occipito-temporal electrode sites during learning, reflected by a decrease in signal energy, a measure of amplitude. Simultaneously, as signal energy decreased, brain signal complexity, as estimated with multiscale entropy (MSE), increased. In addition, prefrontal tDCS modulated brain signal complexity over the right occipito-temporal cortex during the first presentation of faces. These results suggest that although RS may reflect a brain mechanism essential to learning, complementary processes reflected by increases in brain signal complexity, may be instrumental in the acquisition of novel visual information. Such processes likely involve long-range coordinated activity between prefrontal and lower order visual

Captain America, Tuskegee, Belmont, and Righteous Guinea Pigs: Considering Scientific Ethics through Official and Subaltern Perspectives

Science.gov (United States)

Weinstein, Matthew

2008-09-01

With an eye towards a potential scientific ethics curriculum, this paper examines four contrasting discourses regarding the ethics of using human subjects in science. The first two represent official statements regarding ethics. These include the U.S.’s National Science Education Standards, that identify ethics with a professional code, and the Belmont Report, that conceptualizes ethics in three principles to guide research oversight boards. Contrasting this view of ethics as decorum and practice in line with a priori principles is the conception of ethics from unofficial sources representing populations who have been human subjects. The first counter-discourse examined comes from Guinea Pig Zero, an underground magazine for professional human subjects. Here ethics emerges as a question of politics over principle. The good behavior of the doctors and researchers is an effect of the politics and agency of the communities that supply science with subjects. The second counter-discourse is a comic book called Truth, which tells the story of Black soldiers who were used as guinea pigs in World War II. Ethics is both more political and more uncertain in this narrative. Science is portrayed as complicit with the racism of NAZI Germany; at the same time, and in contrast to the professional guinea pigs, neither agency nor politics are presented as effective tools for forcing the ethical conduct of the scientific establishment. The conclusion examines the value of presenting all of these views of scientific ethics in science education.

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

Superfund Record of Decision (EPA Region 5): Buckeye Reclamation Landfill Site, Belmont County, OH. (First remedial action), August 1991. Final report

International Nuclear Information System (INIS)

1991-01-01

The 658-acre Buckeye Reclamation site contains a 50-acre former landfill in Richland Township, Belmont County, Ohio. Land use in the area is predominantly agricultural, rural residential, and strip mining. A total of 46 domestic wells and springs are located within 1 mile of the site. The original topography of the valley has been altered by coal mining and landfill operations. Solid industrial wastes also were disposed of with municipal wastes elsewhere in the landfill. In 1980, the Waste Pit was filled with sludge, mine spoil, and overburden soil; covered with soil and garbage; and seeded. Results of the RI indicate various levels of contamination in all media sampled, except air. The Record of Decision (ROD) addresses the remediation of contaminated leachate and ground water and eliminates exposure to contaminated surface soil. The primary contaminants of concern affecting the soil and ground water are VOCs including benzene, TCE, and toluene; other organics including PAHs; and metals including arsenic, chromium, beryllium, and lead. The selected remedial action for the site is included

Variable complexity online sequential extreme learning machine, with applications to streamflow prediction

Science.gov (United States)

Lima, Aranildo R.; Hsieh, William W.; Cannon, Alex J.

2017-12-01

In situations where new data arrive continually, online learning algorithms are computationally much less costly than batch learning ones in maintaining the model up-to-date. The extreme learning machine (ELM), a single hidden layer artificial neural network with random weights in the hidden layer, is solved by linear least squares, and has an online learning version, the online sequential ELM (OSELM). As more data become available during online learning, information on the longer time scale becomes available, so ideally the model complexity should be allowed to change, but the number of hidden nodes (HN) remains fixed in OSELM. A variable complexity VC-OSELM algorithm is proposed to dynamically add or remove HN in the OSELM, allowing the model complexity to vary automatically as online learning proceeds. The performance of VC-OSELM was compared with OSELM in daily streamflow predictions at two hydrological stations in British Columbia, Canada, with VC-OSELM significantly outperforming OSELM in mean absolute error, root mean squared error and Nash-Sutcliffe efficiency at both stations.

Variability in Second Language Learning: The Roles of Individual Differences, Learning Conditions, and Linguistic Complexity

Science.gov (United States)

Tagarelli, Kaitlyn M.; Ruiz, Simón; Vega, José Luis Moreno; Rebuschat, Patrick

2016-01-01

Second language learning outcomes are highly variable, due to a variety of factors, including individual differences, exposure conditions, and linguistic complexity. However, exactly how these factors interact to influence language learning is unknown. This article examines the relationship between these three variables in language learners.…

Assessing Complexity in Learning Outcomes--A Comparison between the SOLO Taxonomy and the Model of Hierarchical Complexity

Science.gov (United States)

Stålne, Kristian; Kjellström, Sofia; Utriainen, Jukka

2016-01-01

An important aspect of higher education is to educate students who can manage complex relationships and solve complex problems. Teachers need to be able to evaluate course content with regard to complexity, as well as evaluate students' ability to assimilate complex content and express it in the form of a learning outcome. One model for evaluating…

Patients with Parkinson's disease learn to control complex systems-an indication for intact implicit cognitive skill learning.

Science.gov (United States)

Witt, Karsten; Daniels, Christine; Daniel, Victoria; Schmitt-Eliassen, Julia; Volkmann, Jens; Deuschl, Günther

2006-01-01

Implicit memory and learning mechanisms are composed of multiple processes and systems. Previous studies demonstrated a basal ganglia involvement in purely cognitive tasks that form stimulus response habits by reinforcement learning such as implicit classification learning. We will test the basal ganglia influence on two cognitive implicit tasks previously described by Berry and Broadbent, the sugar production task and the personal interaction task. Furthermore, we will investigate the relationship between certain aspects of an executive dysfunction and implicit learning. To this end, we have tested 22 Parkinsonian patients and 22 age-matched controls on two implicit cognitive tasks, in which participants learned to control a complex system. They interacted with the system by choosing an input value and obtaining an output that was related in a complex manner to the input. The objective was to reach and maintain a specific target value across trials (dynamic system learning). The two tasks followed the same underlying complex rule but had different surface appearances. Subsequently, participants performed an executive test battery including the Stroop test, verbal fluency and the Wisconsin card sorting test (WCST). The results demonstrate intact implicit learning in patients, despite an executive dysfunction in the Parkinsonian group. They lead to the conclusion that the basal ganglia system affected in Parkinson's disease does not contribute to the implicit acquisition of a new cognitive skill. Furthermore, the Parkinsonian patients were able to reach a specific goal in an implicit learning context despite impaired goal directed behaviour in the WCST, a classic test of executive functions. These results demonstrate a functional independence of implicit cognitive skill learning and certain aspects of executive functions.

Task complexity, student perceptions of vocabulary learning in EFL, and task performance.

Science.gov (United States)

Wu, Xiaoli; Lowyck, Joost; Sercu, Lies; Elen, Jan

2013-03-01

The study deepened our understanding of how students' self-efficacy beliefs contribute to the context of teaching English as a foreign language in the framework of cognitive mediational paradigm at a fine-tuned task-specific level. The aim was to examine the relationship among task complexity, self-efficacy beliefs, domain-related prior knowledge, learning strategy use, and task performance as they were applied to English vocabulary learning from reading tasks. Participants were 120 second-year university students (mean age 21) from a Chinese university. This experiment had two conditions (simple/complex). A vocabulary level test was first conducted to measure participants' prior knowledge of English vocabulary. Participants were then randomly assigned to one of the learning tasks. Participants were administered task booklets together with the self-efficacy scales, measures of learning strategy use, and post-tests. Data obtained were submitted to multivariate analysis of variance (MANOVA) and path analysis. Results from the MANOVA model showed a significant effect of vocabulary level on self-efficacy beliefs, learning strategy use, and task performance. Task complexity showed no significant effect; however, an interaction effect between vocabulary level and task complexity emerged. Results from the path analysis showed self-efficacy beliefs had an indirect effect on performance. Our results highlighted the mediating role of self-efficacy beliefs and learning strategy use. Our findings indicate that students' prior knowledge plays a crucial role on both self-efficacy beliefs and task performance, and the predictive power of self-efficacy on task performance may lie in its association with learning strategy use. © 2011 The British Psychological Society.

Predicting protein complexes using a supervised learning method combined with local structural information.

Science.gov (United States)

Dong, Yadong; Sun, Yongqi; Qin, Chao

2018-01-01

The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein interaction (PPI) networks even though many true complexes are not dense subgraphs. Supervised learning methods utilize the informative properties of known complexes; they often extract features from existing complexes and then use the features to train a classification model. The trained model is used to guide the search process for new complexes. However, insufficient extracted features, noise in the PPI data and the incompleteness of complex data make the classification model imprecise. Consequently, the classification model is not sufficient for guiding the detection of complexes. Therefore, we propose a new robust score function that combines the classification model with local structural information. Based on the score function, we provide a search method that works both forwards and backwards. The results from experiments on six benchmark PPI datasets and three protein complex datasets show that our approach can achieve better performance compared with the state-of-the-art supervised, semi-supervised and unsupervised methods for protein complex detection, occasionally significantly outperforming such methods.

Formative assessment in an online learning environment to support flexible on-the-job learning in complex professional domains

NARCIS (Netherlands)

Tamara van Gog; Desirée Joosten-ten Brinke; F. J. Prins; Dominique Sluijsmans

2010-01-01

This article describes a blueprint for an online learning environment that is based on prominent instructional design and assessment theories for supporting learning in complex domains. The core of this environment consists of formative assessment tasks (i.e., assessment for learning) that center on

Representational scripting for carrying out complex learning tasks

NARCIS (Netherlands)

Slof, B.

2011-01-01

Learning to solve complex problems is important because in our rapidly changing modern society and work environments knowing the answer is often not possible. Although educators and instructional designers acknowledge the benefits of problem solving, they also realize that learners need good

Assessment for Complex Learning Resources: Development and Validation of an Integrated Model

Directory of Open Access Journals (Sweden)

Gudrun Wesiak

2013-01-01

Full Text Available Today’s e-learning systems meet the challenge to provide interactive, personalized environments that support self-regulated learning as well as social collaboration and simulation. At the same time assessment procedures have to be adapted to the new learning environments by moving from isolated summative assessments to integrated assessment forms. Therefore, learning experiences enriched with complex didactic resources - such as virtualized collaborations and serious games - have emerged. In this extension of [1] an integrated model for e-assessment (IMA is outlined, which incorporates complex learning resources and assessment forms as main components for the development of an enriched learning experience. For a validation the IMA was presented to a group of experts from the fields of cognitive science, pedagogy, and e-learning. The findings from the validation lead to several refinements of the model, which mainly concern the component forms of assessment and the integration of social aspects. Both aspects are accounted for in the revised model, the former by providing a detailed sub-model for assessment forms.

Reinforcement learning agents providing advice in complex video games

Science.gov (United States)

Taylor, Matthew E.; Carboni, Nicholas; Fachantidis, Anestis; Vlahavas, Ioannis; Torrey, Lisa

2014-01-01

This article introduces a teacher-student framework for reinforcement learning, synthesising and extending material that appeared in conference proceedings [Torrey, L., & Taylor, M. E. (2013)]. Teaching on a budget: Agents advising agents in reinforcement learning. {Proceedings of the international conference on autonomous agents and multiagent systems}] and in a non-archival workshop paper [Carboni, N., &Taylor, M. E. (2013, May)]. Preliminary results for 1 vs. 1 tactics in StarCraft. {Proceedings of the adaptive and learning agents workshop (at AAMAS-13)}]. In this framework, a teacher agent instructs a student agent by suggesting actions the student should take as it learns. However, the teacher may only give such advice a limited number of times. We present several novel algorithms that teachers can use to budget their advice effectively, and we evaluate them in two complex video games: StarCraft and Pac-Man. Our results show that the same amount of advice, given at different moments, can have different effects on student learning, and that teachers can significantly affect student learning even when students use different learning methods and state representations.

SCScore: Synthetic Complexity Learned from a Reaction Corpus.

Science.gov (United States)

Coley, Connor W; Rogers, Luke; Green, William H; Jensen, Klavs F

2018-02-26

Several definitions of molecular complexity exist to facilitate prioritization of lead compounds, to identify diversity-inducing and complexifying reactions, and to guide retrosynthetic searches. In this work, we focus on synthetic complexity and reformalize its definition to correlate with the expected number of reaction steps required to produce a target molecule, with implicit knowledge about what compounds are reasonable starting materials. We train a neural network model on 12 million reactions from the Reaxys database to impose a pairwise inequality constraint enforcing the premise of this definition: that on average, the products of published chemical reactions should be more synthetically complex than their corresponding reactants. The learned metric (SCScore) exhibits highly desirable nonlinear behavior, particularly in recognizing increases in synthetic complexity throughout a number of linear synthetic routes.

Targeted learning in data science causal inference for complex longitudinal studies

CERN Document Server

van der Laan, Mark J

2018-01-01

This textbook for graduate students in statistics, data science, and public health deals with the practical challenges that come with big, complex, and dynamic data. It presents a scientific roadmap to translate real-world data science applications into formal statistical estimation problems by using the general template of targeted maximum likelihood estimators. These targeted machine learning algorithms estimate quantities of interest while still providing valid inference. Targeted learning methods within data science area critical component for solving scientific problems in the modern age. The techniques can answer complex questions including optimal rules for assigning treatment based on longitudinal data with time-dependent confounding, as well as other estimands in dependent data structures, such as networks. Included in Targeted Learning in Data Science are demonstrations with soft ware packages and real data sets that present a case that targeted learning is crucial for the next generatio...

Fast social-like learning of complex behaviors based on motor motifs

Science.gov (United States)

Calvo Tapia, Carlos; Tyukin, Ivan Y.; Makarov, Valeri A.

2018-05-01

Social learning is widely observed in many species. Less experienced agents copy successful behaviors exhibited by more experienced individuals. Nevertheless, the dynamical mechanisms behind this process remain largely unknown. Here we assume that a complex behavior can be decomposed into a sequence of n motor motifs. Then a neural network capable of activating motor motifs in a given sequence can drive an agent. To account for (n -1 )! possible sequences of motifs in a neural network, we employ the winnerless competition approach. We then consider a teacher-learner situation: one agent exhibits a complex movement, while another one aims at mimicking the teacher's behavior. Despite the huge variety of possible motif sequences we show that the learner, equipped with the provided learning model, can rewire "on the fly" its synaptic couplings in no more than (n -1 ) learning cycles and converge exponentially to the durations of the teacher's motifs. We validate the learning model on mobile robots. Experimental results show that the learner is indeed capable of copying the teacher's behavior composed of six motor motifs in a few learning cycles. The reported mechanism of learning is general and can be used for replicating different functions, including, for example, sound patterns or speech.

Optimizing the number of steps in learning tasks for complex skills.

Science.gov (United States)

Nadolski, Rob J; Kirschner, Paul A; van Merriënboer, Jeroen J G

2005-06-01

Carrying out whole tasks is often too difficult for novice learners attempting to acquire complex skills. The common solution is to split up the tasks into a number of smaller steps. The number of steps must be optimized for efficient and effective learning. The aim of the study is to investigate the relation between the number of steps provided to learners and the quality of their learning of complex skills. It is hypothesized that students receiving an optimized number of steps will learn better than those receiving either the whole task in only one step or those receiving a large number of steps. Participants were 35 sophomore law students studying at Dutch universities, mean age=22.8 years (SD=3.5), 63% were female. Participants were randomly assigned to 1 of 3 computer-delivered versions of a multimedia programme on how to prepare and carry out a law plea. The versions differed only in the number of learning steps provided. Videotaped plea-performance results were determined, various related learning measures were acquired and all computer actions were logged and analyzed. Participants exposed to an intermediate (i.e. optimized) number of steps outperformed all others on the compulsory learning task. No differences in performance on a transfer task were found. A high number of steps proved to be less efficient for carrying out the learning task. An intermediate number of steps is the most effective, proving that the number of steps can be optimized for improving learning.

Design and Use of a Learning Object for Finding Complex Polynomial Roots

Science.gov (United States)

Benitez, Julio; Gimenez, Marcos H.; Hueso, Jose L.; Martinez, Eulalia; Riera, Jaime

2013-01-01

Complex numbers are essential in many fields of engineering, but students often fail to have a natural insight of them. We present a learning object for the study of complex polynomials that graphically shows that any complex polynomials has a root and, furthermore, is useful to find the approximate roots of a complex polynomial. Moreover, we…

Portuguese crypto-Jews: the genetic heritage of a complex history

Science.gov (United States)

Nogueiro, Inês; Teixeira, João C.; Amorim, António; Gusmão, Leonor; Alvarez, Luis

2015-01-01

The first documents mentioning Jewish people in Iberia are from the Visigothic period. It was also in this period that the first documented anti-Judaic persecution took place. Other episodes of persecution would happen again and again during the long troubled history of the Jewish people in Iberia and culminated with the Decrees of Expulsion and the establishment of the Inquisition: some Jews converted to Catholicism while others resisted and were forcedly baptized, becoming the first Iberian Crypto-Jews. In the 18th century the official discrimination and persecution carried out by the Inquisition ended and several Jewish communities emerged in Portugal. From a populational genetics point of view, the worldwide Diaspora of contemporary Jewish communities has been intensely studied. Nevertheless, very little information is available concerning Sephardic and Iberian Crypto-Jewish descendants. Data from the Iberian Peninsula, the original geographic source of Sephardic Jews, is limited to two populations in Portugal, Belmonte, and Bragança district, and the Chueta community from Mallorca. Belmonte was the first Jewish community studied for uniparental markers. The construction of a reference model for the history of the Portuguese Jewish communities, in which the genetic and classical historical data interplay dynamically, is still ongoing. Recently an enlarged sample covering a wide region in the Northeast Portugal was undertaken, allowing the genetic profiling of male and female lineages. A Jewish specific shared female lineage (HV0b) was detected between the community of Belmonte and Bragança. In contrast to what was previously described as a hallmark of the Portuguese Jews, an unexpectedly high polymorphism of lineages was found in Bragança, showing a surprising resistance to the erosion of genetic diversity typical of small-sized isolate populations, as well as signs of admixture with the Portuguese host population. PMID:25699075

Portuguese crypto-Jews: the genetic heritage of a complex history.

Science.gov (United States)

Nogueiro, Inês; Teixeira, João C; Amorim, António; Gusmão, Leonor; Alvarez, Luis

2015-01-01

The first documents mentioning Jewish people in Iberia are from the Visigothic period. It was also in this period that the first documented anti-Judaic persecution took place. Other episodes of persecution would happen again and again during the long troubled history of the Jewish people in Iberia and culminated with the Decrees of Expulsion and the establishment of the Inquisition: some Jews converted to Catholicism while others resisted and were forcedly baptized, becoming the first Iberian Crypto-Jews. In the 18th century the official discrimination and persecution carried out by the Inquisition ended and several Jewish communities emerged in Portugal. From a populational genetics point of view, the worldwide Diaspora of contemporary Jewish communities has been intensely studied. Nevertheless, very little information is available concerning Sephardic and Iberian Crypto-Jewish descendants. Data from the Iberian Peninsula, the original geographic source of Sephardic Jews, is limited to two populations in Portugal, Belmonte, and Bragança district, and the Chueta community from Mallorca. Belmonte was the first Jewish community studied for uniparental markers. The construction of a reference model for the history of the Portuguese Jewish communities, in which the genetic and classical historical data interplay dynamically, is still ongoing. Recently an enlarged sample covering a wide region in the Northeast Portugal was undertaken, allowing the genetic profiling of male and female lineages. A Jewish specific shared female lineage (HV0b) was detected between the community of Belmonte and Bragança. In contrast to what was previously described as a hallmark of the Portuguese Jews, an unexpectedly high polymorphism of lineages was found in Bragança, showing a surprising resistance to the erosion of genetic diversity typical of small-sized isolate populations, as well as signs of admixture with the Portuguese host population.

PORTUGUESE CRYPTO-JEWS: THE GENETIC HERITAGE OF A COMPLEX HISTORY

Directory of Open Access Journals (Sweden)

Inês Pires Nogueiro

2015-02-01

Full Text Available The first documents mentioning Jewish people in Iberia are from the Visigothic period. It was also in this period that the first documented anti-Judaic persecution took place. Other episodes of persecution would happen again and again during the long troubled history of the Jewish people in Iberia and culminated with the Decrees of Expulsion and the establishment of the Inquisition: some Jews converted to Catholicism while others resisted and were forcedly baptized, becoming the first Iberian Crypto-Jews. In the 18th century the official discrimination and persecution carried out by the Inquisition ended and several Jewish communities emerged in Portugal. From a populational genetics point of view, the worldwide Diaspora of contemporary Jewish communities has been intensely studied. Nevertheless, very little information is available concerning Sephardic and Iberian Crypto-Jewish descendants. Data from the Iberian Peninsula, the original geographic source of Sephardic Jews, is limited to two populations in Portugal, Belmonte and Bragança district, and the Chueta community from Mallorca. Belmonte was the first Jewish community studied for uniparental markers. The construction of a reference model for the history of the Portuguese Jewish communities, in which the genetic and classical historical data interplay dynamically, is still ongoing. Recently an enlarged sample covering a wide region in the Northeast Portugal was undertaken, allowing the genetic profiling of male and female lineages. A Jewish specific shared female lineage (HV0b was detected between the community of Belmonte and Bragança. In contrast to what was previously described as a hallmark of the Portuguese Jews, an unexpectedly high polymorphism of lineages’ was found in Bragança, showing a surprising resistance to the erosion of genetic diversity typical of small-sized isolate populations, as well as signs of admixture with the Portuguese host population.

Interaction and Technological Resources to Support Learning of Complex Numbers

Directory of Open Access Journals (Sweden)

Cassiano Scott Puhl

2016-02-01

Full Text Available This article presents a didactic proposal, a workshop for the introduction of the study of complex numbers. Unlike recurrent practices, the workshop began developing the geometric shape of the complex number, implicitly, through vectors. Eliminating student formal vision and algebraic, enriching the teaching practice. The main objective of the strategy was to build the concept of imaginary unit without causing a feeling of strangeness or insignificance of number. The theory of David Ausubel, meaningful learning, the workshop was based on a strategy developed to analyze the subsumers of students and develop a learning by subject. Combined with dynamic and interactive activities in the workshop, there is the use of a learning object (http://matematicacomplexa.meximas.com/. An environment created and basing on the theory of meaningful learning, making students reflect and interact in developed applications sometimes being challenged and other testing hypotheses and, above all, building knowledge. This proposal provided a rich environment for exchange of information between participants and deepening of ideas and concepts that served as subsumers. The result of the experience was very positive, as evidenced by the comments and data submitted by the participants, thus demonstrating that the objectives of this didactic proposal have been achieved.

ANÁLISE FAUNÍSTICA E FLUTUAÇÃO POPULACIONAL DE MOSCAS-DAS-FRUTAS (DIPTERA: TEPHRITIDAE EM BELMONTE, BAHIA

Directory of Open Access Journals (Sweden)

MÍRIAN DA SILVA SANTOS

2011-01-01

Full Text Available This study was carried out in a mixed orchard in the municipality of Belmonte, in the southernmost region of Bahia and it aimed at characterizing the fruit fly (Diptera: Tephritidae population using faunistic analysis and studying its population fluctuation. The study was conducted from August 2007 to August 2009. Fruit fly captures were carried out using McPhail traps baited with protein hydrolisate at 5%. Weekly, the captured insects found in traps were transferred to plastic vials, one vial per trap, filled with 70% ethanol and taken to the laboratory for identification. A total of 9,709 fruit flies was captured, out of which 9,477 specimens were Anastrepha (5,908 females and 3,569 males and 232 specimens were Ceratitis capitata (Wiedemann (201 females and 31 males. Nine species of Anastrepha were recorded: Anastrepha bahiensis (Lima (2.59%, Anastrepha distincta (Greene (2.71%, Anastrepha fraterculus (Wiedemann (59.37%, Anastrepha leptozona (Hendel (0.02%, Anastrepha manihoti (Lima (0.02%, Anastrepha obliqua (Macquart (2.98%, Anastrepha serpentina (Wiedemann (0.07%, Anastrepha sororcula Zucchi (29.14%, Anastrepha zenildae Zucchi (0.22%, and C. capitata (2.88%. Anastrepha fraterculus and A. sororcula were the dominant species and only A. fraterculus was constant on the orchard. The values of the Simpson (0.51 and of Shannon (01.35 indices were intermediate and the modified Hill index was 0.49, indicating a medium diversity. The high est capturevalues of Anastrepha spp. occurred from July to December 2008, with a population peak in September.

Everyday complexities and sociomaterialities of learning, technology, affects and effects

DEFF Research Database (Denmark)

Hansbøl, Mikala

design with particular intended educational purposes (e.g. educational technology and technology education), the everyday complexities and sociomaterialities of learning and technology intermingles with how students/professionals become affected by digital technology and hence also which matters......This paper starts out with the challenge of establishing and researching relationships between educational design, digital technology and professional learning. The paper is empirical and takes point of departure in case examples from two development projects with a focus on professional education....... Both projects focus on new waysto build relationships between digital technologies, professional education and learning. Each project takes a different take on how to approach and position digital technology and it’s relationships with the educational programs and students’ learning. Project Wellfare...

Multimodal versus Unimodal Instructions in a Complex Learning Context.

NARCIS (Netherlands)

Gellevij, M.R.M.; van der Meij, Hans; de Jong, Anthonius J.M.; Pieters, Julius Marie

2002-01-01

Multimodal instruction with text and pictures was compared with unimodal, text-only instruction. More specifically, 44 students used a visual or a textual manual to learn a complex software application. During 2 103–116-min training sessions, cognitive load, and time and ability to recover from

Multimodal versus Unimodal Instruction in a Complex Learning Context.

Science.gov (United States)

Gellevij, Mark; van der Meij, Hans; de Jong, Ton; Pieters, Jules

2002-01-01

Compared multimodal instruction with text and pictures with unimodal text-only instruction as 44 college students used a visual or textual manual to learn a complex software application. Results initially support dual coding theory and indicate that multimodal instruction led to better performance than unimodal instruction. (SLD)

Community Learning Campus: It Takes a Simple Message to Build a Complex Project

Science.gov (United States)

Pearson, George

2012-01-01

Education Canada asked Tom Thompson, president of Olds College and a prime mover behind the Community Learning Campus (CLC): What were the lessons learned from this unusually ambitious education project? Thompson mentions six lessons he learned from this complex project which include: (1) Dream big, build small, act now; (2) Keep a low profile at…

Clinical quality needs complex adaptive systems and machine learning.

Science.gov (United States)

Marsland, Stephen; Buchan, Iain

2004-01-01

The vast increase in clinical data has the potential to bring about large improvements in clinical quality and other aspects of healthcare delivery. However, such benefits do not come without cost. The analysis of such large datasets, particularly where the data may have to be merged from several sources and may be noisy and incomplete, is a challenging task. Furthermore, the introduction of clinical changes is a cyclical task, meaning that the processes under examination operate in an environment that is not static. We suggest that traditional methods of analysis are unsuitable for the task, and identify complexity theory and machine learning as areas that have the potential to facilitate the examination of clinical quality. By its nature the field of complex adaptive systems deals with environments that change because of the interactions that have occurred in the past. We draw parallels between health informatics and bioinformatics, which has already started to successfully use machine learning methods.

Wilting and biological additive effect on in situ degradability and chemical composition of Arachis pintoi cv Belomonte silage

OpenAIRE

Rosana Aparecida Possenti; Evaldo Ferrari Júnior; Valdinei Tadeu Paulino; Ivani Pozar Otsuk; Patrícia Brás

2010-01-01

The purpose of this work was to evaluate the effect of wilting and biological additive amendment on chemical composition, fermentation and ruminal degradability of Arachis pintoi cv Belmonte silage. The following treatments were analysed: T1- Arachis pintoi cv Belmonte fresh forage; T2 - Arachis pintoi cv Belmonte fresh forage plus bacterial additive added to the forage prior to the ensilage; T3- Arachis pintoi cv Belmonte wilted by the sun for 4 hours; T4- Arachis pintoi cv Belmonte wilted b...

Students' explanations in complex learning of disciplinary programming

Science.gov (United States)

Vieira, Camilo

Computational Science and Engineering (CSE) has been denominated as the third pillar of science and as a set of important skills to solve the problems of a global society. Along with the theoretical and the experimental approaches, computation offers a third alternative to solve complex problems that require processing large amounts of data, or representing complex phenomena that are not easy to experiment with. Despite the relevance of CSE, current professionals and scientists are not well prepared to take advantage of this set of tools and methods. Computation is usually taught in an isolated way from engineering disciplines, and therefore, engineers do not know how to exploit CSE affordances. This dissertation intends to introduce computational tools and methods contextualized within the Materials Science and Engineering curriculum. Considering that learning how to program is a complex task, the dissertation explores effective pedagogical practices that can support student disciplinary and computational learning. Two case studies will be evaluated to identify the characteristics of effective worked examples in the context of CSE. Specifically, this dissertation explores students explanations of these worked examples in two engineering courses with different levels of transparency: a programming course in materials science and engineering glass box and a thermodynamics course involving computational representations black box. Results from this study suggest that students benefit in different ways from writing in-code comments. These benefits include but are not limited to: connecting xv individual lines of code to the overall problem, getting familiar with the syntax, learning effective algorithm design strategies, and connecting computation with their discipline. Students in the glass box context generate higher quality explanations than students in the black box context. These explanations are related to students prior experiences. Specifically, students with

Using Multiple Linear Regression Techniques to Quantify Carbon ...

African Journals Online (AJOL)

komla

Process and statistical models of productivity, though useful, are often ... The carbon balance of terrestrial ecosystems is uncertain, in part due to discrepancies and errors in .... The ecological data were collected through field work to include both .... Computer-aided Multivariate Analysis, Life Learning Publications, Belmont,.

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

Directory of Open Access Journals (Sweden)

Shiva Farashahi

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

Criteria for the Development of Complex Teaching-Learning Environments.

Science.gov (United States)

Achtenhagen, Frank

2001-01-01

Relates aspects of the didactic tradition, especially the German didactic tradition, to the theory and practice of instructional design. Focuses on processes that are necessary to the modeling of reality and describes the design and development of a virtual enterprise as a complex teaching-learning environment in a German business school.…

Learning by Preparing to Teach: Fostering Self-Regulatory Processes and Achievement during Complex Mathematics Problem Solving

Science.gov (United States)

Muis, Krista R.; Psaradellis, Cynthia; Chevrier, Marianne; Di Leo, Ivana; Lajoie, Susanne P.

2016-01-01

We developed an intervention based on the learning by teaching paradigm to foster self-regulatory processes and better learning outcomes during complex mathematics problem solving in a technology-rich learning environment. Seventy-eight elementary students were randomly assigned to 1 of 2 conditions: learning by preparing to teach, or learning for…

The importance of cultivating a preference for complexity in veterinarians for effective lifelong learning.

Science.gov (United States)

Dale, Vicki H M; Pierce, Stephanie E; May, Stephen A

2010-01-01

Much attention has been paid to the link between students' approaches to study and the quality of their learning. Less attention has been paid to the lifelong learner. We conceptualized a tripartite relationship between three measures of learning preference: conceptions of knowledge (construction and use vs. intake), need for cognition (high vs. low), and approach to study (deep vs. surface) and hypothesized that an individual's profile on these three measures-reconceptualized as a preference for complexity versus simplicity-would affect their attitude toward continuing professional development (CPD). A questionnaire was mailed to 2,000 randomly selected, home-practicing UK veterinarians to quantify their learning preferences, motivation to engage in CPD, and perception of barriers to participation and to assess the relationships between these constructs. Analysis of 775 responses (a 38.8% response rate) confirmed our tripartite model of learning and showed that a preference for complexity was negatively correlated with barriers and positively correlated with intrinsic, social, and extrinsic motivating factors, suggesting that all play a role in the continuing education of this group of professionals. A preference for simplicity was negatively correlated with social motivation and positively correlated with barriers. This study demonstrates that approach not only affects the quality of learning but crucially affects motivation to engage in CPD and perception of barriers to lifelong learning. This should emphasize to veterinary educators the importance of fostering a preference for complexity from an early age, both in terms of its immediate benefits (better understanding) and longer-term benefits (continued engagement with learning).

Investing in Youth Work: Learning from Complexity

Directory of Open Access Journals (Sweden)

Kari Denissen Cunnien

2017-04-01

Full Text Available This article proposes key elements for a system of support for youth workers to develop their professional skills and capabilities by using a human development approach.  The article argues that narrowed and bureaucratic approaches to professional development can ignore the complex dynamics of human development that support engaged learning and continuous growth and improvement.  The author suggests a more dynamic system where professional development in grounded by practice; employs reflection, mentorship and coaching; and supports healthy organizational culture to foster high quality youth work.

Learning and inference using complex generative models in a spatial localization task.

Science.gov (United States)

Bejjanki, Vikranth R; Knill, David C; Aslin, Richard N

2016-01-01

A large body of research has established that, under relatively simple task conditions, human observers integrate uncertain sensory information with learned prior knowledge in an approximately Bayes-optimal manner. However, in many natural tasks, observers must perform this sensory-plus-prior integration when the underlying generative model of the environment consists of multiple causes. Here we ask if the Bayes-optimal integration seen with simple tasks also applies to such natural tasks when the generative model is more complex, or whether observers rely instead on a less efficient set of heuristics that approximate ideal performance. Participants localized a "hidden" target whose position on a touch screen was sampled from a location-contingent bimodal generative model with different variances around each mode. Over repeated exposure to this task, participants learned the a priori locations of the target (i.e., the bimodal generative model), and integrated this learned knowledge with uncertain sensory information on a trial-by-trial basis in a manner consistent with the predictions of Bayes-optimal behavior. In particular, participants rapidly learned the locations of the two modes of the generative model, but the relative variances of the modes were learned much more slowly. Taken together, our results suggest that human performance in a more complex localization task, which requires the integration of sensory information with learned knowledge of a bimodal generative model, is consistent with the predictions of Bayes-optimal behavior, but involves a much longer time-course than in simpler tasks.

Situated learning theory: adding rate and complexity effects via Kauffman's NK model.

Science.gov (United States)

Yuan, Yu; McKelvey, Bill

2004-01-01

For many firms, producing information, knowledge, and enhancing learning capability have become the primary basis of competitive advantage. A review of organizational learning theory identifies two approaches: (1) those that treat symbolic information processing as fundamental to learning, and (2) those that view the situated nature of cognition as fundamental. After noting that the former is inadequate because it focuses primarily on behavioral and cognitive aspects of individual learning, this paper argues the importance of studying learning as interactions among people in the context of their environment. It contributes to organizational learning in three ways. First, it argues that situated learning theory is to be preferred over traditional behavioral and cognitive learning theories, because it treats organizations as complex adaptive systems rather than mere information processors. Second, it adds rate and nonlinear learning effects. Third, following model-centered epistemology, it uses an agent-based computational model, in particular a "humanized" version of Kauffman's NK model, to study the situated nature of learning. Using simulation results, we test eight hypotheses extending situated learning theory in new directions. The paper ends with a discussion of possible extensions of the current study to better address key issues in situated learning.

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,…

Mixed-complexity artificial grammar learning in humans and macaque monkeys: evaluating learning strategies.

Science.gov (United States)

Wilson, Benjamin; Smith, Kenny; Petkov, Christopher I

2015-03-01

Artificial grammars (AG) can be used to generate rule-based sequences of stimuli. Some of these can be used to investigate sequence-processing computations in non-human animals that might be related to, but not unique to, human language. Previous AG learning studies in non-human animals have used different AGs to separately test for specific sequence-processing abilities. However, given that natural language and certain animal communication systems (in particular, song) have multiple levels of complexity, mixed-complexity AGs are needed to simultaneously evaluate sensitivity to the different features of the AG. Here, we tested humans and Rhesus macaques using a mixed-complexity auditory AG, containing both adjacent (local) and non-adjacent (longer-distance) relationships. Following exposure to exemplary sequences generated by the AG, humans and macaques were individually tested with sequences that were either consistent with the AG or violated specific adjacent or non-adjacent relationships. We observed a considerable level of cross-species correspondence in the sensitivity of both humans and macaques to the adjacent AG relationships and to the statistical properties of the sequences. We found no significant sensitivity to the non-adjacent AG relationships in the macaques. A subset of humans was sensitive to this non-adjacent relationship, revealing interesting between- and within-species differences in AG learning strategies. The results suggest that humans and macaques are largely comparably sensitive to the adjacent AG relationships and their statistical properties. However, in the presence of multiple cues to grammaticality, the non-adjacent relationships are less salient to the macaques and many of the humans. © 2015 The Authors. European Journal of Neuroscience published by Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Active Learning for Directed Exploration of Complex Systems

Science.gov (United States)

Burl, Michael C.; Wang, Esther

2009-01-01

Physics-based simulation codes are widely used in science and engineering to model complex systems that would be infeasible to study otherwise. Such codes provide the highest-fidelity representation of system behavior, but are often so slow to run that insight into the system is limited. For example, conducting an exhaustive sweep over a d-dimensional input parameter space with k-steps along each dimension requires k(sup d) simulation trials (translating into k(sup d) CPU-days for one of our current simulations). An alternative is directed exploration in which the next simulation trials are cleverly chosen at each step. Given the results of previous trials, supervised learning techniques (SVM, KDE, GP) are applied to build up simplified predictive models of system behavior. These models are then used within an active learning framework to identify the most valuable trials to run next. Several active learning strategies are examined including a recently-proposed information-theoretic approach. Performance is evaluated on a set of thirteen synthetic oracles, which serve as surrogates for the more expensive simulations and enable the experiments to be replicated by other researchers.

Simulation-Based Learning Environments to Teach Complexity: The Missing Link in Teaching Sustainable Public Management

Directory of Open Access Journals (Sweden)

Michael Deegan

2014-05-01

Full Text Available While public-sector management problems are steeped in positivistic and socially constructed complexity, public management education in the management of complexity lags behind that of business schools, particularly in the application of simulation-based learning. This paper describes a Simulation-Based Learning Environment for public management education that includes a coupled case study and System Dynamics simulation surrounding flood protection, a domain where stewardship decisions regarding public infrastructure and investment have direct and indirect effects on businesses and the public. The Pointe Claire case and CoastalProtectSIM simulation provide a platform for policy experimentation under conditions of exogenous uncertainty (weather and climate change as well as endogenous effects generated by structure. We discuss the model in some detail, and present teaching materials developed to date to support the use of our work in public administration curricula. Our experience with this case demonstrates the potential of this approach to motivate sustainable learning about complexity in public management settings and enhance learners’ competency to deal with complex dynamic problems.

Thrive or overload? The effect of task complexity on novices' simulation-based learning.

Science.gov (United States)

Haji, Faizal A; Cheung, Jeffrey J H; Woods, Nicole; Regehr, Glenn; de Ribaupierre, Sandrine; Dubrowski, Adam

2016-09-01

Fidelity is widely viewed as an important element of simulation instructional design based on its purported relationship with transfer of learning. However, higher levels of fidelity may increase task complexity to a point at which novices' cognitive resources become overloaded. In this experiment, we investigate the effects of variations in task complexity on novices' cognitive load and learning during simulation-based procedural skills training. Thirty-eight medical students were randomly assigned to simulation training on a simple or complex lumbar puncture (LP) task. Participants completed four practice trials on this task (skill acquisition). After 10 days of rest, all participants completed one additional trial on their assigned task (retention) and one trial on a 'very complex' simulation designed to be similar to the complex task (transfer). We assessed LP performance and cognitive load on each trial using multiple measures. In both groups, LP performance improved significantly during skill acquisition (p ≤ 0.047, f = 0.29-0.96) and was maintained at retention. The simple task group demonstrated superior performance compared with the complex task group throughout these phases (p ≤ 0.002, d = 1.13-2.31). Cognitive load declined significantly in the simple task group (p Education.

Learning about Complex Multi-Stakeholder Issues: Assessing the Visual Problem Appraisal

NARCIS (Netherlands)

Witteveen, L.M.; Put, M.; Leeuwis, C.

2010-01-01

This paper presents an evaluation of the visual problem appraisal (VPA) learning environment in higher education. The VPA has been designed for the training of competences that are required in complex stakeholder settings in relation to sustainability issues. The design of VPA incorporates a

Joined up Thinking? Evaluating the Use of Concept-Mapping to Develop Complex System Learning

Science.gov (United States)

Stewart, Martyn

2012-01-01

In the physical and natural sciences, the complexity of natural systems and their interactions is becoming better understood. With increased emphasis on learning about complex systems, students will be encountering concepts that are dynamic, ill-structured and interconnected. Concept-mapping is a method considered particularly valuable for…

Facilitating the Evaluation of Complexity in the Public Sector: Learning from the NHS in Scotland

Science.gov (United States)

Connolly, John; Reid, Garth; Mooney, Allan

2015-01-01

It is necessary for public managers to be able to evaluate programmes in the context of complexity. This article offers key learning and reflections based on the experience of facilitating the evaluation of complexity with a range of public sector partners in Scotland. There have been several articles that consider evaluating complexity and…

Task Complexity Modulates Sleep-Related Offline Learning in Sequential Motor Skills

Directory of Open Access Journals (Sweden)

Klaus Blischke

2017-07-01

Full Text Available Recently, a number of authors have advocated the introduction of gross motor tasks into research on sleep-related motor offline learning. Such tasks are often designed to be more complex than traditional key-pressing tasks. However, until now, little effort has been undertaken to scrutinize the role of task complexity in any systematic way. Therefore, the effect of task complexity on the consolidation of gross motor sequence memory was examined by our group in a series of three experiments. Criterion tasks always required participants to produce unrestrained arm movement sequences by successively fitting a small peg into target holes on a pegboard. The sequences always followed a certain spatial pattern in the horizontal plane. The targets were visualized prior to each transport movement on a computer screen. The tasks differed with respect to sequence length and structural complexity. In each experiment, half of the participants initially learned the task in the morning and were retested 12 h later following a wake retention interval. The other half of the subjects underwent practice in the evening and was retested 12 h later following a night of sleep. The dependent variables were the error rate and total sequence execution time (inverse to the sequence execution speed. Performance generally improved during acquisition. The error rate was always low and remained stable during retention. The sequence execution time significantly decreased again following sleep but not after waking when the sequence length was long and structural complexity was high. However, sleep-related offline improvements were absent when the sequence length was short or when subjects performed a highly regular movement pattern. It is assumed that the occurrence of sleep-related offline performance improvements in sequential motor tasks is associated with a sufficient amount of motor task complexity.

SOFTWARE COMPLEX FOR CREATION AND ACCUMULATION OF MODERN LEARNING MATERIALS

Directory of Open Access Journals (Sweden)

V. V. Polinovskyi

2010-08-01

Full Text Available The article analyzes weaknesses of existing tools for lecture materials creation and suggests new complex with modular architecture, which supports different types of lecture materials, templates, interactive elements, includes lecture material database with searching, sorting, and grouping capabilities and can be used for creating lectures courses for distance learning, as well as for interactive lectures for full-time courses.

Learning with Generalization Capability by Kernel Methods of Bounded Complexity

Czech Academy of Sciences Publication Activity Database

Kůrková, Věra; Sanguineti, M.

2005-01-01

Roč. 21, č. 3 (2005), s. 350-367 ISSN 0885-064X R&D Projects: GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : supervised learning * generalization * model complexity * kernel methods * minimization of regularized empirical errors * upper bounds on rates of approximate optimization Subject RIV: BA - General Mathematics Impact factor: 1.186, year: 2005

ISLAM IN THE NON-MUSLIM AREAS OF NORTHERN NIGERIA, c

African Journals Online (AJOL)

QUADRI Y A

In his ethics and metaphysics, Chuang Tzŭ28 regarded fear of death as .... engagement in the daily business of life. ... awaits us after it, and if people are expecting it, death would be like a case of an ..... Introduction to Philosophy 8th ed., (Belmont: Wadsworth Cengage Learning, ... Bhaktivedanta Book Trust,1978), 144-145.

Optimizing the number of steps in learning tasks for complex skills.

NARCIS (Netherlands)

Nadolski, Rob; Kirschner, Paul A.; Van Merriënboer, Jeroen

2007-01-01

Background. Carrying out whole tasks is often too difficult for novice learners attempting to acquire complex skills. The common solution is to split up the tasks into a number of smaller steps. The number of steps must be optimised for efficient and effective learning. Aim. The aim of the study is

Constructivist learning theories and complex learning environments

NARCIS (Netherlands)

R-J. Simons; Dr. S. Bolhuis

2004-01-01

Learning theories broadly characterised as constructivist, agree on the importance to learning of the environment, but differ on what exactly it is that constitutes this importance. Accordingly, they also differ on the educational consequences to be drawn from the theoretical perspective. Cognitive

Estimating the complexity of 3D structural models using machine learning methods

Science.gov (United States)

Mejía-Herrera, Pablo; Kakurina, Maria; Royer, Jean-Jacques

2016-04-01

Quantifying the complexity of 3D geological structural models can play a major role in natural resources exploration surveys, for predicting environmental hazards or for forecasting fossil resources. This paper proposes a structural complexity index which can be used to help in defining the degree of effort necessary to build a 3D model for a given degree of confidence, and also to identify locations where addition efforts are required to meet a given acceptable risk of uncertainty. In this work, it is considered that the structural complexity index can be estimated using machine learning methods on raw geo-data. More precisely, the metrics for measuring the complexity can be approximated as the difficulty degree associated to the prediction of the geological objects distribution calculated based on partial information on the actual structural distribution of materials. The proposed methodology is tested on a set of 3D synthetic structural models for which the degree of effort during their building is assessed using various parameters (such as number of faults, number of part in a surface object, number of borders, ...), the rank of geological elements contained in each model, and, finally, their level of deformation (folding and faulting). The results show how the estimated complexity in a 3D model can be approximated by the quantity of partial data necessaries to simulated at a given precision the actual 3D model without error using machine learning algorithms.

Examining Motivation in Online Distance Learning Environments: Complex, Multifaceted, and Situation-Dependent

Directory of Open Access Journals (Sweden)

Maggie Hartnett

2011-10-01

Full Text Available Existing research into motivation in online environments has tended to use one of two approaches. The first adopts a trait-like model that views motivation as a relatively stable, personal characteristic of the learner. Research from this perspective has contributed to the notion that online learners are, on the whole, intrinsically motivated. The alternative view concentrates on the design of online learning environments to encourage optimal learner motivation. Neither approach acknowledges a contemporary view of motivation that emphasises the situated, mutually constitutive relationship of the learner and the learning environment. Using self-determination theory (SDT as a framework, this paper explores the motivation to learn of preservice teachers in two online distance-learning contexts. In this study, learners were found to be not primarily intrinsically motivated. Instead, student motivation was found to be complex, multifaceted, and sensitive to situational conditions.

The Conceptual Mechanism for Viable Organizational Learning Based on Complex System Theory and the Viable System Model

Science.gov (United States)

Sung, Dia; You, Yeongmahn; Song, Ji Hoon

2008-01-01

The purpose of this research is to explore the possibility of viable learning organizations based on identifying viable organizational learning mechanisms. Two theoretical foundations, complex system theory and viable system theory, have been integrated to provide the rationale for building the sustainable organizational learning mechanism. The…

Achieving Complex Learning Outcomes through Adoption of a Pedagogical Perspective: A Model for Computer Technology Delivered Instruction

Science.gov (United States)

Bellard, Breshanica

2018-01-01

Professionals responsible for the delivery of education and training using technology systems and platforms can facilitate complex learning through application of relevant strategies, principles and theories that support how learners learn and that support how curriculum should be designed in a technology based learning environment. Technological…

Low Complexity Sparse Bayesian Learning for Channel Estimation Using Generalized Mean Field

DEFF Research Database (Denmark)

Pedersen, Niels Lovmand; Manchón, Carles Navarro; Fleury, Bernard Henri

2014-01-01

We derive low complexity versions of a wide range of algorithms for sparse Bayesian learning (SBL) in underdetermined linear systems. The proposed algorithms are obtained by applying the generalized mean field (GMF) inference framework to a generic SBL probabilistic model. In the GMF framework, we...

Impact of pedagogical approaches on cognitive complexity and motivation to learn: Comparing nursing and engineering undergraduate students.

Science.gov (United States)

McComb, Sara A; Kirkpatrick, Jane M

2016-01-01

The changing higher education landscape is prompting nurses to rethink educational strategies. Looking beyond traditional professional boundaries may be beneficial. We compare nursing to engineering because engineering has similar accreditation outcome goals and different pedagogical approaches. We compare students' cognitive complexity and motivation to learn to identify opportunities to share pedagogical approaches between nursing and engineering. Cross-sectional data were collected from 1,167 freshmen through super senior students. Comparisons were made across years and between majors. Overall nursing and engineering students advance in cognitive complexity while maintaining motivation for learning. Sophomores reported the lowest scores on many dimensions indicating that their experiences need review. The strong influence of the National Council Licensure Examination on nursing students may drive their classroom preferences. Increased intrinsic motivation, coupled with decreased extrinsic motivation, suggests that we are graduating burgeoning life-long learners equipped to maintain currency. The disciplines' strategies for incorporating real-world learning opportunities differ, yet the students similarly advance in cognitive complexity and maintain motivation to learn. Lessons can be exchanged across professional boundaries. Copyright © 2016 Elsevier Inc. All rights reserved.

Learning from simple ebooks, online cases or classroom teaching when acquiring complex knowledge. A randomized controlled trial in respiratory physiology and pulmonology

DEFF Research Database (Denmark)

Worm, Bjarne Skjødt

2013-01-01

E-learning is developing fast because of the rapid increased use of smartphones, tablets and portable computers. We might not think of it as e-learning, but today many new e-books are in fact very complex electronic teaching platforms. It is generally accepted that e-learning is as effective...... as classroom teaching methods, but little is known about its value in relaying contents of different levels of complexity to students. We set out to investigate e-learning effects on simple recall and complex problem-solving compared to classroom teaching....

Instructional Control of Cognitive Load in the Design of Complex Learning Environments

NARCIS (Netherlands)

Kester, Liesbeth; Paas, Fred; Van Merriënboer, Jeroen

2010-01-01

Kester, L., Paas, F., & Van Merriënboer, J. J. G. (2010). Instructional control of cognitive load in the design of complex learning environments. In J. L. Plass, R. Moreno, & Roland Brünken (Eds.), Cognitive Load Theory (pp. 109-130). New York: Cambridge University Press.

Community detection in complex networks using deep auto-encoded extreme learning machine

Science.gov (United States)

Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing

2018-06-01

Community detection has long been a fascinating topic in complex networks since the community structure usually unveils valuable information of interest. The prevalence and evolution of deep learning and neural networks have been pushing forward the advancement in various research fields and also provide us numerous useful and off the shelf techniques. In this paper, we put the cascaded stacked autoencoders and the unsupervised extreme learning machine (ELM) together in a two-level embedding process and propose a novel community detection algorithm. Extensive comparison experiments in circumstances of both synthetic and real-world networks manifest the advantages of the proposed algorithm. On one hand, it outperforms the k-means clustering in terms of the accuracy and stability thus benefiting from the determinate dimensions of the ELM block and the integration of sparsity restrictions. On the other hand, it endures smaller complexity than the spectral clustering method on account of the shrinkage in time spent on the eigenvalue decomposition procedure.

Learning Science in a Virtual Reality Application: The Impacts of Animated-Virtual Actors' Visual Complexity

Science.gov (United States)

Kartiko, Iwan; Kavakli, Manolya; Cheng, Ken

2010-01-01

As the technology in computer graphics advances, Animated-Virtual Actors (AVAs) in Virtual Reality (VR) applications become increasingly rich and complex. Cognitive Theory of Multimedia Learning (CTML) suggests that complex visual materials could hinder novice learners from attending to the lesson properly. On the other hand, previous studies have…

Adaptive learning and complex dynamics

International Nuclear Information System (INIS)

Gomes, Orlando

2009-01-01

In this paper, we explore the dynamic properties of a group of simple deterministic difference equation systems in which the conventional perfect foresight assumption gives place to a mechanism of adaptive learning. These systems have a common feature: under perfect foresight (or rational expectations) they all possess a unique fixed point steady state. This long-term outcome is obtained also under learning if the quality underlying the learning process is high. Otherwise, when the degree of inefficiency of the learning process is relatively strong, nonlinear dynamics (periodic and a-periodic cycles) arise. The specific properties of each one of the proposed systems is explored both in terms of local and global dynamics. One macroeconomic model is used to illustrate how the formation of expectations through learning may eventually lead to awkward long-term outcomes.

Are Agile and Lean Manufacturing Systems Employing Sustainability, Complexity and Organizational Learning?

Science.gov (United States)

Flumerfelt, Shannon; Siriban-Manalang, Anna Bella; Kahlen, Franz-Josef

2012-01-01

Purpose: This paper aims to peruse theories and practices of agile and lean manufacturing systems to determine whether they employ sustainability, complexity and organizational learning. Design/methodology/approach: The critical review of the comparative operational similarities and difference of the two systems was conducted while the new views…

The amygdala complex: multiple roles in associative learning and attention.

OpenAIRE

Gallagher, M; Holland, P C

1994-01-01

Although certain neurophysiological functions of the amygdala complex in learning seem well established, the purpose of this review is to propose that an additional conceptualization of amygdala function is now needed. The research we review provides evidence that a subsystem within the amygdala provides a coordinated regulation of attentional processes. An important aspect of this additional neuropsychology of the amygdala is that it may aid in understanding the importance of connections bet...

Successfully carrying out complex learning-tasks through guiding teams' qualitative and quantitative reasoning

NARCIS (Netherlands)

Slof, B.; Erkens, G.; Kirschner, P. A.; Janssen, J.; Jaspers, J. G. M.

This study investigated whether and how scripting learners' use of representational tools in a computer supported collaborative learning (CSCL)-environment fostered their collaborative performance on a complex business-economics task. Scripting the problem-solving process sequenced and made its

Sonification and haptic feedback in addition to visual feedback enhances complex motor task learning.

Science.gov (United States)

Sigrist, Roland; Rauter, Georg; Marchal-Crespo, Laura; Riener, Robert; Wolf, Peter

2015-03-01

Concurrent augmented feedback has been shown to be less effective for learning simple motor tasks than for complex tasks. However, as mostly artificial tasks have been investigated, transfer of results to tasks in sports and rehabilitation remains unknown. Therefore, in this study, the effect of different concurrent feedback was evaluated in trunk-arm rowing. It was then investigated whether multimodal audiovisual and visuohaptic feedback are more effective for learning than visual feedback only. Naïve subjects (N = 24) trained in three groups on a highly realistic virtual reality-based rowing simulator. In the visual feedback group, the subject's oar was superimposed to the target oar, which continuously became more transparent when the deviation between the oars decreased. Moreover, a trace of the subject's trajectory emerged if deviations exceeded a threshold. The audiovisual feedback group trained with oar movement sonification in addition to visual feedback to facilitate learning of the velocity profile. In the visuohaptic group, the oar movement was inhibited by path deviation-dependent braking forces to enhance learning of spatial aspects. All groups significantly decreased the spatial error (tendency in visual group) and velocity error from baseline to the retention tests. Audiovisual feedback fostered learning of the velocity profile significantly more than visuohaptic feedback. The study revealed that well-designed concurrent feedback fosters complex task learning, especially if the advantages of different modalities are exploited. Further studies should analyze the impact of within-feedback design parameters and the transferability of the results to other tasks in sports and rehabilitation.

Controlling Uncertainty Decision Making and Learning in Complex Worlds

CERN Document Server

Osman, Magda

2010-01-01

Controlling Uncertainty: Decision Making and Learning in Complex Worlds reviews and discusses the most current research relating to the ways we can control the uncertain world around us.: Features reviews and discussions of the most current research in a number of fields relevant to controlling uncertainty, such as psychology, neuroscience, computer science and engineering; Presents a new framework that is designed to integrate a variety of disparate fields of research; Represents the first book of its kind to provide a general overview of work related to understanding control

ComplexContact: a web server for inter-protein contact prediction using deep learning

KAUST Repository

Zeng, Hong; Wang, Sheng; Zhou, Tianming; Zhao, Feifeng; Li, Xiufeng; Wu, Qing; Xu, Jinbo

2018-01-01

ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

ComplexContact: a web server for inter-protein contact prediction using deep learning

KAUST Repository

Zeng, Hong

2018-05-20

ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

ComplexContact: a web server for inter-protein contact prediction using deep learning.

Science.gov (United States)

Zeng, Hong; Wang, Sheng; Zhou, Tianming; Zhao, Feifeng; Li, Xiufeng; Wu, Qing; Xu, Jinbo

2018-05-22

ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

Early motor learning changes in upper-limb dynamics and shoulder complex loading during handrim wheelchair propulsion.

Science.gov (United States)

Vegter, Riemer J K; Hartog, Johanneke; de Groot, Sonja; Lamoth, Claudine J; Bekker, Michel J; van der Scheer, Jan W; van der Woude, Lucas H V; Veeger, Dirkjan H E J

2015-03-10

To propel in an energy-efficient manner, handrim wheelchair users must learn to control the bimanually applied forces onto the rims, preserving both speed and direction of locomotion. Previous studies have found an increase in mechanical efficiency due to motor learning associated with changes in propulsion technique, but it is unclear in what way the propulsion technique impacts the load on the shoulder complex. The purpose of this study was to evaluate mechanical efficiency, propulsion technique and load on the shoulder complex during the initial stage of motor learning. 15 naive able-bodied participants received 12-minutes uninstructed wheelchair practice on a motor driven treadmill, consisting of three 4-minute blocks separated by two minutes rest. Practice was performed at a fixed belt speed (v = 1.1 m/s) and constant low-intensity power output (0.2 W/kg). Energy consumption, kinematics and kinetics of propulsion technique were continuously measured. The Delft Shoulder Model was used to calculate net joint moments, muscle activity and glenohumeral reaction force. With practice mechanical efficiency increased and propulsion technique changed, reflected by a reduced push frequency and increased work per push, performed over a larger contact angle, with more tangentially applied force and reduced power losses before and after each push. Contrary to our expectations, the above mentioned propulsion technique changes were found together with an increased load on the shoulder complex reflected by higher net moments, a higher total muscle power and higher peak and mean glenohumeral reaction forces. It appears that the early stages of motor learning in handrim wheelchair propulsion are indeed associated with improved technique and efficiency due to optimization of the kinematics and dynamics of the upper extremity. This process goes at the cost of an increased muscular effort and mechanical loading of the shoulder complex. This seems to be associated with an

Self-Efficacy, Task Complexity and Task Performance: Exploring Interactions in Two Versions of Vocabulary Learning Tasks

Science.gov (United States)

Wu, Xiaoli; Lowyck, Joost; Sercu, Lies; Elen, Jan

2012-01-01

The present study aimed for better understanding of the interactions between task complexity and students' self-efficacy beliefs and students' use of learning strategies, and finally their interacting effects on task performance. This investigation was carried out in the context of Chinese students learning English as a foreign language in a…

Collaborative Development of e-Infrastructures and Data Management Practices for Global Change Research

Science.gov (United States)

Samors, R. J.; Allison, M. L.

2016-12-01

An e-infrastructure that supports data-intensive, multidisciplinary research is being organized under the auspices of the Belmont Forum consortium of national science funding agencies to accelerate the pace of science to address 21st century global change research challenges. The pace and breadth of change in information management across the data lifecycle means that no one country or institution can unilaterally provide the leadership and resources required to use data and information effectively, or needed to support a coordinated, global e-infrastructure. The five action themes adopted by the Belmont Forum: 1. Adopt and make enforceable Data Principles that establish a global, interoperable e-infrastructure. 2. Foster communication, collaboration and coordination between the wider research community and Belmont Forum and its projects through an e-Infrastructure Coordination, Communication, & Collaboration Office. 3. Promote effective data planning and stewardship in all Belmont Forum agency-funded research with a goal to make it enforceable. 4. Determine international and community best practice to inform Belmont Forum research e-infrastructure policy through identification and analysis of cross-disciplinary research case studies. 5. Support the development of a cross-disciplinary training curriculum to expand human capacity in technology and data-intensive analysis methods. The Belmont Forum is ideally poised to play a vital and transformative leadership role in establishing a sustained human and technical international data e-infrastructure to support global change research. In 2016, members of the 23-nation Belmont Forum began a collaborative implementation phase. Four multi-national teams are undertaking Action Themes based on the recommendations above. Tasks include mapping the landscape, identifying and documenting existing data management plans, and scheduling a series of workshops that analyse trans-disciplinary applications of existing Belmont Forum

Task complexity as a driver for collaborative learning efficiency: The collective working-memory effect

NARCIS (Netherlands)

Kirschner, Femke; Paas, Fred; Kirschner, Paul A.

2010-01-01

Kirschner, F., Paas, F., & Kirschner, P. A. (2011). Task complexity as a driver for collaborative learning efficiency: The collective working-memory effect. Applied Cognitive Psychology, 25, 615–624. doi: 10.1002/acp.1730.

Designing for Discovery Learning of Complexity Principles of Congestion by Driving Together in the TrafficJams Simulation

Science.gov (United States)

Levy, Sharona T.; Peleg, Ran; Ofeck, Eyal; Tabor, Naamit; Dubovi, Ilana; Bluestein, Shiri; Ben-Zur, Hadar

2018-01-01

We propose and evaluate a framework supporting collaborative discovery learning of complex systems. The framework blends five design principles: (1) individual action: amidst (2) social interactions; challenged with (3) multiple tasks; set in (4) a constrained interactive learning environment that draws attention to (5) highlighted target…

Successfully Carrying out Complex Learning-Tasks through Guiding Teams' Qualitative and Quantitative Reasoning

Science.gov (United States)

Slof, B.; Erkens, G.; Kirschner, P. A.; Janssen, J.; Jaspers, J. G. M.

2012-01-01

This study investigated whether and how scripting learners' use of representational tools in a computer supported collaborative learning (CSCL)-environment fostered their collaborative performance on a complex business-economics task. Scripting the problem-solving process sequenced and made its phase-related part-task demands explicit, namely…

Machine learning and complex-network for personalized and systems biomedicine

KAUST Repository

Cannistraci, Carlo Vittorio

2016-01-27

The talk will begin with an introduction on using machine learning to discover hidden information and unexpected patterns in large biomedical datasets. Then, recent results on the use of complex network theory in biomedicine and neuroscience will be discussed. In particular, metagenomics and metabolomics data, approaches for drug-target repositioning, functional/structural MR connectomes and gut-brain axis data will be presented. The conclusion will outline the novel and exciting perspectives offered by the translation of these methods from systems biology to systems medicine.

Modeling Anti-Air Warfare With Discrete Event Simulation and Analyzing Naval Convoy Operations

Science.gov (United States)

2016-06-01

W., & Scheaffer, R. L. (2008). Mathematical statistics with applications . Belmont, CA: Cengage Learning. 118 THIS PAGE INTENTIONALLY LEFT BLANK...WARFARE WITH DISCRETE EVENT SIMULATION AND ANALYZING NAVAL CONVOY OPERATIONS by Ali E. Opcin June 2016 Thesis Advisor: Arnold H. Buss Co...REPORT DATE June 2016 3. REPORT TYPE AND DATES COVERED Master’s thesis 4. TITLE AND SUBTITLE MODELING ANTI-AIR WARFARE WITH DISCRETE EVENT

QUALITY ASSURANCE IN RWANDAN HIGHER LEARNING EDUCATION: IS THE SYSTEM ADAPTIVE OR COMPLEX?

Directory of Open Access Journals (Sweden)

Nathan Kanuma Taremwa

2014-01-01

Full Text Available Developing knowledge infrastructure by massive investments in education and training are taken as a benchmark in facilitating the acceleration and possible increases in skills, capacities and competences of Rwandan people has become apriority issue in the recent years. This notion is relevant to vision 2020 where human resource development and building of a knowledge based economy are fundamental pillars. In the past years, several policy reforms have taken place in education sector. However, the overarching question is if such reforms are becoming adaptive or complex and if such reforms will not compromise the quality of education in higher learning education in Rwanda? The main objective of the study was to investigate the impact of changes in Higher Learning Institutions on the quality of education in Rwanda. This research had three hypotheses, namely; there is an impact of changes in Higher Learning Institutions on quality of education in Rwanda; the current complexity in Rwandan education system is affecting the quality of education in HLIs; Tailoring education system to the regional reforms and implementation strategies is affecting the quality of education in Rwanda. This study was carried out in 10 higher learning institutions (5 public, 5 private and 2 Ministry of Education directorates (HEC and REB. Key informants were the senior management/head of institutions, experienced academic staff, and students. The parameters considered included; the learning methods, assessment styles, workloads, language of instruction, merging of public HLIs, curriculum, and the transformation of some private higher learning institutions into company forms. Main research instruments were questionnaires and interview guides. Both qualitative and quantitative research was collected. Analyses were done using SPSS and excel packages. Major findings indicate that the system is still in transition with indicative gaps. Ample time would therefore be necessary for

Action observation versus motor imagery in learning a complex motor task: a short review of literature and a kinematics study.

Science.gov (United States)

Gatti, R; Tettamanti, A; Gough, P M; Riboldi, E; Marinoni, L; Buccino, G

2013-04-12

Both motor imagery and action observation have been shown to play a role in learning or re-learning complex motor tasks. According to a well accepted view they share a common neurophysiological basis in the mirror neuron system. Neurons within this system discharge when individuals perform a specific action and when they look at another individual performing the same or a motorically related action. In the present paper, after a short review of literature on the role of action observation and motor imagery in motor learning, we report the results of a kinematics study where we directly compared motor imagery and action observation in learning a novel complex motor task. This involved movement of the right hand and foot in the same angular direction (in-phase movement), while at the same time moving the left hand and foot in an opposite angular direction (anti-phase movement), all at a frequency of 1Hz. Motor learning was assessed through kinematics recording of wrists and ankles. The results showed that action observation is better than motor imagery as a strategy for learning a novel complex motor task, at least in the fast early phase of motor learning. We forward that these results may have important implications in educational activities, sport training and neurorehabilitation. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

The search for the youngest granites in the southern part of the Natal Metamorphic Province

International Nuclear Information System (INIS)

Thomas, R.J.; Eglington, B.M.

1990-01-01

It is clear that the Belmont Pluton and the dykes are geochemically, isotopically and therefore, genetically distinct. The Belmont pluton is probably related to the garnet leucogranite phase of the Margate Complex. It is suggested that the dykes (∼ 965 Ma) are younger than the Belmont pluton (∼1055 Ma). The relatively low initial 87 Sr/ 86 Sr are typical of the granites intruded at ∼1000 Ma. The age of the dykes is comparable with the 951 ± 16 Ma (R o =.70320 ± 13) given for the Sezela pluton. The high R o (∼0.715) of the dykes is similar to other, minor granite sheets from southern Natal, and is compatible with an origin by late-stage melting of pre-existing radiogenic material. Both the dykes and the Sezela pluton are unequivocally younger than the D 3 deformation, whereas the young dates from the Oribi Gorge Suite are controversial. Thus, although it is possible that some of the minor, intrusive granitic sheets could yet be shown to be of Pan-African age, it is evident that no significant Pan-African magmatism or thermal overprinting has affected the Natal sector of the Namaqua-Natal-Maudheim belt. 1 fig., 7 refs

Embedded interruptions and task complexity influence schema-related cognitive load progression in an abstract learning task.

Science.gov (United States)

Wirzberger, Maria; Esmaeili Bijarsari, Shirin; Rey, Günter Daniel

2017-09-01

Cognitive processes related to schema acquisition comprise an essential source of demands in learning situations. Since the related amount of cognitive load is supposed to change over time, plausible temporal models of load progression based on different theoretical backgrounds are inspected in this study. A total of 116 student participants completed a basal symbol sequence learning task, which provided insights into underlying cognitive dynamics. Two levels of task complexity were determined by the amount of elements within the symbol sequence. In addition, interruptions due to an embedded secondary task occurred at five predefined stages over the task. Within the resulting 2x5-factorial mixed between-within design, the continuous monitoring of efficiency in learning performance enabled assumptions on relevant resource investment. From the obtained results, a nonlinear change of learning efficiency over time seems most plausible in terms of cognitive load progression. Moreover, different effects of the induced interruptions show up in conditions of task complexity, which indicate the activation of distinct cognitive mechanisms related to structural aspects of the task. Findings are discussed in the light of evidence from research on memory and information processing. Copyright © 2017 Elsevier B.V. All rights reserved.

Discrimination of Rock Fracture and Blast Events Based on Signal Complexity and Machine Learning

Directory of Open Access Journals (Sweden)

Zilong Zhou

2018-01-01

Full Text Available The automatic discrimination of rock fracture and blast events is complex and challenging due to the similar waveform characteristics. To solve this problem, a new method based on the signal complexity analysis and machine learning has been proposed in this paper. First, the permutation entropy values of signals at different scale factors are calculated to reflect complexity of signals and constructed into a feature vector set. Secondly, based on the feature vector set, back-propagation neural network (BPNN as a means of machine learning is applied to establish a discriminator for rock fracture and blast events. Then to evaluate the classification performances of the new method, the classifying accuracies of support vector machine (SVM, naive Bayes classifier, and the new method are compared, and the receiver operating characteristic (ROC curves are also analyzed. The results show the new method obtains the best classification performances. In addition, the influence of different scale factor q and number of training samples n on discrimination results is discussed. It is found that the classifying accuracy of the new method reaches the highest value when q = 8–15 or 8–20 and n=140.

Self-Disclosure and Adults with Learning Disabilities: Practical Ideas about a Complex Process

Science.gov (United States)

Gerber, Paul J.; Price, Lynda A.

2008-01-01

Self-disclosure for adults with learning disabilities is very complex after the beyond-school years. The issues of invisibility, risk/benefit, and the multiple contexts of adult functioning create many challenges in the process of disclosure. Moreover, self-disclosure, one element of the larger issue of self-determination, is viewed as an entry…

Force and complexity of tongue task training influences behavioral measures of motor learning

DEFF Research Database (Denmark)

Kothari, Mohit; Svensson, Peter; Huo, Xueliang

2012-01-01

Relearning of motor skills is important in neurorehabilitation. We investigated the improvement of training success during simple tongue protrusion (two force levels) and a more complex tongue-training paradigm using the Tongue Drive System (TDS). We also compared subject-based reports of fun, pain...... training influences behavioral aspects of tongue motor learning....

Organisational simplification and secondary complexity in health services for adults with learning disabilities.

Science.gov (United States)

Heyman, Bob; Swain, John; Gillman, Maureen

2004-01-01

This paper explores the role of complexity and simplification in the delivery of health care for adults with learning disabilities, drawing upon qualitative data obtained in a study carried out in NE England. It is argued that the requirement to manage complex health needs with limited resources causes service providers to simplify, standardise and routinise care. Simplified service models may work well enough for the majority of clients, but can impede recognition of the needs of those whose characteristics are not congruent with an adopted model. The data were analysed in relation to the core category, identified through thematic analysis, of secondary complexity arising from organisational simplification. Organisational simplification generates secondary complexity when operational routines designed to make health complexity manageable cannot accommodate the needs of non-standard service users. Associated themes, namely the social context of services, power and control, communication skills, expertise and service inclusiveness and evaluation are explored in relation to the core category. The concept of secondary complexity resulting from organisational simplification may partly explain seemingly irrational health service provider behaviour.

Integrating Emotion and Cognition in Successful Service Learning: A Complex System Approach (Invited)

Science.gov (United States)

Raia, F.

2010-12-01

Service-learning (S-L) has evolved as valuable pedagogic concept during the last two decades, based on the hypothesis that learning can best be accomplished when placed in the context of real-life social settings, e.g. schools, production, research, healthcare etc. What students learn in the academic course/context must be elaborated in the context of the S-L experience. In return for the authentic learning experience, the learner provides the service-provider with a "free" service. This reciprocality makes service-learning an appealing concept. Because of its attractive "win-win" design, the field of service-learning is continuously expanding. At a major public university CCNY with a very diverse student population, we were interested in developing and participating in S-L experience in the field of Earth System Science. We designed an upper level undergraduate course - Environmental Soil Science for Urban Sustainability - specifically targeted to students of Earth Science, Engineering, Economics and, Political Sciences to support environmental entrepreneurship. Specifically, we integrated S-L activities in the exploration of soil studies and urban agriculture. Students worked together in small groups both in class and for their S-L experience (30 hours) with urban garden and agriculture organizations. Students were required to apply the content learned in the academic course providing soil testing and soil evaluation to the partners, generate reports through a series of homework assignments and journal entries connecting three major components: Community Service, Personal Experience and Course Content. Our experience with this course shows the following results: S-L must be considered a complex system characterized by the continually changing interactions among the above mentioned three major components and three social and academic diverse groups of people involved: Students, Service-Providers and Academic Instructors. Because experience alone does not produce

Effect of Error Augmentation on Brain Activation and Motor Learning of a Complex Locomotor Task

Directory of Open Access Journals (Sweden)

Laura Marchal-Crespo

2017-09-01

Full Text Available Up to date, the functional gains obtained after robot-aided gait rehabilitation training are limited. Error augmenting strategies have a great potential to enhance motor learning of simple motor tasks. However, little is known about the effect of these error modulating strategies on complex tasks, such as relearning to walk after a neurologic accident. Additionally, neuroimaging evaluation of brain regions involved in learning processes could provide valuable information on behavioral outcomes. We investigated the effect of robotic training strategies that augment errors—error amplification and random force disturbance—and training without perturbations on brain activation and motor learning of a complex locomotor task. Thirty-four healthy subjects performed the experiment with a robotic stepper (MARCOS in a 1.5 T MR scanner. The task consisted in tracking a Lissajous figure presented on a display by coordinating the legs in a gait-like movement pattern. Behavioral results showed that training without perturbations enhanced motor learning in initially less skilled subjects, while error amplification benefited better-skilled subjects. Training with error amplification, however, hampered transfer of learning. Randomly disturbing forces induced learning and promoted transfer in all subjects, probably because the unexpected forces increased subjects' attention. Functional MRI revealed main effects of training strategy and skill level during training. A main effect of training strategy was seen in brain regions typically associated with motor control and learning, such as, the basal ganglia, cerebellum, intraparietal sulcus, and angular gyrus. Especially, random disturbance and no perturbation lead to stronger brain activation in similar brain regions than error amplification. Skill-level related effects were observed in the IPS, in parts of the superior parietal lobe (SPL, i.e., precuneus, and temporal cortex. These neuroimaging findings

Nursing students learning the pharmacology of diabetes mellitus with complexity-based computerized models: A quasi-experimental study.

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Dubovi, Ilana; Dagan, Efrat; Sader Mazbar, Ola; Nassar, Laila; Levy, Sharona T

2018-02-01

Pharmacology is a crucial component of medications administration in nursing, yet nursing students generally find it difficult and self-rate their pharmacology skills as low. To evaluate nursing students learning pharmacology with the Pharmacology Inter-Leaved Learning-Cells environment, a novel approach to modeling biochemical interactions using a multiscale, computer-based model with a complexity perspective based on a small set of entities and simple rules. This environment represents molecules, organelles and cells to enhance the understanding of cellular processes, and combines these cells at a higher scale to obtain whole-body interactions. Sophomore nursing students who learned the pharmacology of diabetes mellitus with the Pharmacology Inter-Leaved Learning-Cells environment (experimental group; n=94) or via a lecture-based curriculum (comparison group; n=54). A quasi-experimental pre- and post-test design was conducted. The Pharmacology-Diabetes-Mellitus questionnaire and the course's final exam were used to evaluate students' knowledge of the pharmacology of diabetes mellitus. Conceptual learning was significantly higher for the experimental than for the comparison group for the course final exam scores (unpaired t=-3.8, pLearning with complexity-based computerized models is highly effective and enhances the understanding of moving between micro and macro levels of the biochemical phenomena, this is then related to better understanding of medication actions. Moreover, the Pharmacology Inter-Leaved Learning-Cells approach provides a more general reasoning scheme for biochemical processes, which enhances pharmacology learning beyond the specific topic learned. The present study implies that deeper understanding of pharmacology will support nursing students' clinical decisions and empower their proficiency in medications administration. Copyright © 2017 Elsevier Ltd. All rights reserved.

Learning from simple ebooks, online cases or classroom teaching when acquiring complex knowledge. A randomized controlled trial in respiratory physiology and pulmonology.

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Worm, Bjarne Skjødt

2013-01-01

E-learning is developing fast because of the rapid increased use of smartphones, tablets and portable computers. We might not think of it as e-learning, but today many new e-books are in fact very complex electronic teaching platforms. It is generally accepted that e-learning is as effective as classroom teaching methods, but little is known about its value in relaying contents of different levels of complexity to students. We set out to investigate e-learning effects on simple recall and complex problem-solving compared to classroom teaching. 63 nurses specializing in anesthesiology were evenly randomized into three groups. They were given internet-based knowledge tests before and after attending a teaching module about respiratory physiology and pulmonology. The three groups was either an e-learning group with eBook teaching material, an e-learning group with case-based teaching or a group with face-to-face case-based classroom teaching. After the module the students were required to answer a post-test. Time spent and the number of logged into the system was also measured. For simple recall, all methods were equally effective. For problem-solving, the eCase group achieved a comparable knowledge level to classroom teaching, while textbook learning was inferior to both (p<0.01). The textbook group also spent the least amount of time on acquiring knowledge (33 minutes, p<0.001), while the eCase group spent significantly more time on the subject (53 minutes, p<0.001) and logged into the system significantly more (2.8 vs 1.6, p<0.001). E-learning based cases are an effective tool for teaching complex knowledge and problem-solving ability, but future studies using higher-level e-learning are encouraged.Simple recall skills, however, do not require any particular learning method.

Learning from simple ebooks, online cases or classroom teaching when acquiring complex knowledge. A randomized controlled trial in respiratory physiology and pulmonology.

Directory of Open Access Journals (Sweden)

Bjarne Skjødt Worm

Full Text Available BACKGROUND AND AIMS: E-learning is developing fast because of the rapid increased use of smartphones, tablets and portable computers. We might not think of it as e-learning, but today many new e-books are in fact very complex electronic teaching platforms. It is generally accepted that e-learning is as effective as classroom teaching methods, but little is known about its value in relaying contents of different levels of complexity to students. We set out to investigate e-learning effects on simple recall and complex problem-solving compared to classroom teaching. METHODS: 63 nurses specializing in anesthesiology were evenly randomized into three groups. They were given internet-based knowledge tests before and after attending a teaching module about respiratory physiology and pulmonology. The three groups was either an e-learning group with eBook teaching material, an e-learning group with case-based teaching or a group with face-to-face case-based classroom teaching. After the module the students were required to answer a post-test. Time spent and the number of logged into the system was also measured. RESULTS: For simple recall, all methods were equally effective. For problem-solving, the eCase group achieved a comparable knowledge level to classroom teaching, while textbook learning was inferior to both (p<0.01. The textbook group also spent the least amount of time on acquiring knowledge (33 minutes, p<0.001, while the eCase group spent significantly more time on the subject (53 minutes, p<0.001 and logged into the system significantly more (2.8 vs 1.6, p<0.001. CONCLUSIONS: E-learning based cases are an effective tool for teaching complex knowledge and problem-solving ability, but future studies using higher-level e-learning are encouraged.Simple recall skills, however, do not require any particular learning method.

Examining the Potential of Web-Based Multimedia to Support Complex Fine Motor Skill Learning: An Empirical Study

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Papastergiou, Marina; Pollatou, Elisana; Theofylaktou, Ioannis; Karadimou, Konstantina

2014-01-01

Research on the utilization of the Web for complex fine motor skill learning that involves whole body movements is still scarce. The aim of this study was to evaluate the impact of the introduction of a multimedia web-based learning environment, which was targeted at a rhythmic gymnastics routine consisting of eight fine motor skills, into an…

The emergence of learning-teaching trajectories in education: a complex dynamic systems approach.

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Steenbeek, Henderien; van Geert, Paul

2013-04-01

In this article we shall focus on learning-teaching trajectories ='successful' as well as 'unsuccessful' ones - as emergent and dynamic phenomena resulting from the interactions in the entire educational context, in particular the interaction between students and teachers viewed as processes of intertwining self-, other- and co-regulation. The article provides a review of the educational research literature on action regulation in learning and teaching, and interprets this literature in light of the theory of complex dynamic systems. Based on this reinterpretation of the literature, two dynamic models are proposed, one focusing on the short-term dynamics of learning-teaching interactions as they take place in classrooms, the other focusing on the long-term dynamics of interactions in a network of variables encompassing concerns, evaluations, actions and action effects (such as learning) students and teachers. The aim of presenting these models is to demonstrate, first, the possibility of transforming existing educational theory into dynamic models and, second, to provide some suggestions as to how such models can be used to further educational theory and practice.

Visual artificial grammar learning by rhesus macaques (Macaca mulatta): exploring the role of grammar complexity and sequence length.

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Heimbauer, Lisa A; Conway, Christopher M; Christiansen, Morten H; Beran, Michael J; Owren, Michael J

2018-03-01

Humans and nonhuman primates can learn about the organization of stimuli in the environment using implicit sequential pattern learning capabilities. However, most previous artificial grammar learning studies with nonhuman primates have involved relatively simple grammars and short input sequences. The goal in the current experiments was to assess the learning capabilities of monkeys on an artificial grammar-learning task that was more complex than most others previously used with nonhumans. Three experiments were conducted using a joystick-based, symmetrical-response serial reaction time task in which two monkeys were exposed to grammar-generated sequences at sequence lengths of four in Experiment 1, six in Experiment 2, and eight in Experiment 3. Over time, the monkeys came to respond faster to the sequences generated from the artificial grammar compared to random versions. In a subsequent generalization phase, subjects generalized their knowledge to novel sequences, responding significantly faster to novel instances of sequences produced using the familiar grammar compared to those constructed using an unfamiliar grammar. These results reveal that rhesus monkeys can learn and generalize the statistical structure inherent in an artificial grammar that is as complex as some used with humans, for sequences up to eight items long. These findings are discussed in relation to whether or not rhesus macaques and other primate species possess implicit sequence learning abilities that are similar to those that humans draw upon to learn natural language grammar.

Managing Complexity

DEFF Research Database (Denmark)

Maylath, Bruce; Vandepitte, Sonia; Minacori, Patricia

2013-01-01

and into French. The complexity of the undertaking proved to be a central element in the students' learning, as the collaboration closely resembles the complexity of international documentation workplaces of language service providers. © Association of Teachers of Technical Writing.......This article discusses the largest and most complex international learning-by-doing project to date- a project involving translation from Danish and Dutch into English and editing into American English alongside a project involving writing, usability testing, and translation from English into Dutch...

Environmental Factors Affecting Computer Assisted Language Learning Success: A Complex Dynamic Systems Conceptual Model

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Marek, Michael W.; Wu, Wen-Chi Vivian

2014-01-01

This conceptual, interdisciplinary inquiry explores Complex Dynamic Systems as the concept relates to the internal and external environmental factors affecting computer assisted language learning (CALL). Based on the results obtained by de Rosnay ["World Futures: The Journal of General Evolution", 67(4/5), 304-315 (2011)], who observed…

Using virtual humans and computer animations to learn complex motor skills: a case study in karate

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Spanlang Bernhard

2011-12-01

Full Text Available Learning motor skills is a complex task involving a lot of cognitive issues. One of the main issues consists in retrieving the relevant information from the learning environment. In a traditional learning situation, a teacher gives oral explanations and performs actions to provide the learner with visual examples. Using virtual reality (VR as a tool for learning motor tasks is promising. However, it raises questions about the type of information this kind of environments can offer. In this paper, we propose to analyze the impact of virtual humans on the perception of the learners. As a case study, we propose to apply this research problem to karate gestures. The results of this study show no significant difference on the after training performance of learners confronted to three different learning environments (traditional group, video and VR.

Slowness and sparseness have diverging effects on complex cell learning.

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Jörn-Philipp Lies

2014-03-01

Full Text Available Following earlier studies which showed that a sparse coding principle may explain the receptive field properties of complex cells in primary visual cortex, it has been concluded that the same properties may be equally derived from a slowness principle. In contrast to this claim, we here show that slowness and sparsity drive the representations towards substantially different receptive field properties. To do so, we present complete sets of basis functions learned with slow subspace analysis (SSA in case of natural movies as well as translations, rotations, and scalings of natural images. SSA directly parallels independent subspace analysis (ISA with the only difference that SSA maximizes slowness instead of sparsity. We find a large discrepancy between the filter shapes learned with SSA and ISA. We argue that SSA can be understood as a generalization of the Fourier transform where the power spectrum corresponds to the maximally slow subspace energies in SSA. Finally, we investigate the trade-off between slowness and sparseness when combined in one objective function.

Using mLearning and MOOCs to Understand Chaos, Emergence, and Complexity in Education

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deWaard, Inge; Abajian, Sean; Gallagher, Michael Sean; Hogue, Rebecca; Keskin, Nilgun; Koutropoulos, Apostolos; Rodriguez, Osvaldo C.

2011-01-01

In this paper, we look at how the massive open online course (MOOC) format developed by connectivist researchers and enthusiasts can help analyze the complexity, emergence, and chaos at work in the field of education today. We do this through the prism of a MobiMOOC, a six-week course focusing on mLearning that ran from April to May 2011. MobiMOOC…

The Azteca Chess experience: learning how to share concepts of ecological complexity with small coffee farmers

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Luís García-Barrios

2017-06-01

Full Text Available Small-scale coffee farmers understand certain complex ecological processes, and successfully navigate some of the challenges emerging from the ecological complexity on their farms. It is generally thought that scientific knowledge is able to complement farmers' knowledge. However, for this collaboration to be fruitful, the gap between the knowledge frameworks of both farmers and scientists will need to be closed. We report on the learning results of 14 workshops held in Chiapas, Mexico during 2015 in which 117 small-scale coffee farmers of all genders (30% women and ages who had little schooling were exposed by researchers to a natural history narrative, a multispecies network representation, a board game, and a series of graphical quizzes, all related to a nine-species complex ecological network with potential for autonomous control of the ongoing and devastating coffee rust epidemic that was affecting them. Farmers' retention and understanding of direct and indirect bilateral interactions among organisms was assessed with different methods to elucidate the effect of adding Azteca Chess gaming sessions to a detailed and very graphical lecture. Evaluation methods that were better adapted to farmers' conditions improved learning scores and showed statistically significant age effect (players older than 40 had lower retention scores and gaming effect (lower retention of interactions included in the lecture but not in the game. The combination of lecture and game sessions helped participants better understand cascades of trait-mediated interactions. Participants' debriefings confirmed qualitatively that they learned that beneficial organisms and interactions occur on their farms, and that gaming was enjoyable, motivating, and critical to grasp complex interactions. Many of the farmers concluded that the outcome of these interactions is not unique and not always in favor of rust control but is context dependent. Many concluded that there are

Complexity explained

CERN Document Server

Erdi, Peter

2008-01-01

This book explains why complex systems research is important in understanding the structure, function and dynamics of complex natural and social phenomena. Readers will learn the basic concepts and methods of complex system research.

A Study of the Effect of Dyad Practice Versus That of Individual Practice on Simulation-Based Complex Skills Learning and of Students’ Perceptions of How and Why Dyad Practice Contributes to Learning

DEFF Research Database (Denmark)

Räder, Sune Bernd Emil Werner; Henriksen, Ann-Helen; Butrymovich, Vitalij

2014-01-01

PURPOSE: The aims of this study were (1) to explore the effectiveness of dyad practice compared with individual practice on a simulator for learning a complex clinical skill and (2) to explore medical students' perceptions of how and why dyad practice on a simulator contributes to learning...... a complex skill. METHOD: In 2011, the authors randomly assigned 84 medical students to either the dyad or the individual practice group to learn coronary angiography skills using instruction videos and a simulator. Two weeks later, participants each performed two video-recorded coronary angiographies...... of the two groups (mean±standard deviation, 68%±13% for individual versus 63%±16% for dyad practice; P=.18). Dyad practice participants noted that several key factors contributed to their learning: being equal-level novices, the quality of the cooperation between partners, observational learning and overt...

Using complexity theory to develop a student-directed interprofessional learning activity for 1220 healthcare students.

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Jorm, Christine; Nisbet, Gillian; Roberts, Chris; Gordon, Christopher; Gentilcore, Stacey; Chen, Timothy F

2016-08-08

More and better interprofessional practice is predicated to be necessary to deliver good care to the patients of the future. However, universities struggle to create authentic learning activities that enable students to experience the dynamic interprofessional interactions common in healthcare and that can accommodate large interprofessional student cohorts. We investigated a large-scale mandatory interprofessional learning (IPL) activity for health professional students designed to promote social learning. A mixed methods research approach determined feasibility, acceptability and the extent to which student IPL outcomes were met. We developed an IPL activity founded in complexity theory to prepare students for future practice by engaging them in a self-directed (self-organised) learning activity with a diverse team, whose assessable products would be emergent creations. Complicated but authentic clinical cases (n = 12) were developed to challenge student teams (n = 5 or 6). Assessment consisted of a written management plan (academically marked) and a five-minute video (peer marked) designed to assess creative collaboration as well as provide evidence of integrated collective knowledge; the cohesive patient-centred management plan. All students (including the disciplines of diagnostic radiology, exercise physiology, medicine, nursing, occupational therapy, pharmacy, physiotherapy and speech pathology), completed all tasks successfully. Of the 26 % of students who completed the evaluation survey, 70 % agreed or strongly agreed that the IPL activity was worthwhile, and 87 % agreed or strongly agreed that their case study was relevant. Thematic analysis found overarching themes of engagement and collaboration-in-action suggesting that the IPL activity enabled students to achieve the intended learning objectives. Students recognised the contribution of others and described negotiation, collaboration and creation of new collective knowledge after working

Zebrafish and relational memory: Could a simple fish be useful for the analysis of biological mechanisms of complex vertebrate learning?

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Gerlai, Robert

2017-08-01

Analysis of the zebrafish allows one to combine two distinct scientific approaches, comparative ethology and neurobehavioral genetics. Furthermore, this species arguably represents an optimal compromise between system complexity and practical simplicity. This mini-review focuses on a complex form of learning, relational learning and memory, in zebrafish. It argues that zebrafish are capable of this type of learning, and it attempts to show how this species may be useful in the analysis of the mechanisms and the evolution of this complex brain function. The review is not intended to be comprehensive. It is a short opinion piece that reflects the author's own biases, and it draws some of its examples from the work coming from his own laboratory. Nevertheless, it is written in the hope that it will persuade those who have not utilized zebrafish and who may be interested in opening their research horizon to this relatively novel but powerful vertebrate research tool. Copyright © 2017 Elsevier B.V. All rights reserved.

Cognitive Strategies and Skill Acquisition.

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1981-02-09

Behavior (Acadmic Press, N.Y., 1974). ( 9). Craik , F.I.M., 8 Lockhart , R.S., Levels of processing : A frame- work for memory research, Journal of...C.D., a Stein, B.S., Some general constraints on learning and memory research, in: F.I.M. Craik 6 L.S. Cermak.(eds.), Levels of Processing and...instructions, or instructions in the use of particular strategies. (Belmont & Butterfield, 1971; Craik & Lockhart , 1972; Weinstein, 1978) have had

Effects of cerebellar nuclear inactivation on the learning of a complex forelimb movement in cats.

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Wang, J J; Shimansky, Y; Bracha, V; Bloedel, J R

1998-05-01

The purpose of this study was to determine the effects of inactivating concurrently the cerebellar interposed and dentate nuclei on the capacity of cats to acquire and retain a complex, goal-directed forelimb movement. To assess the effects on acquisition, cats were required to learn to move a vertical manipulandum bar through a two-segment template with a shape approximating an inverted "L" after the injection of muscimol (saline for the control group) in the interposed and dentate cerebellar nuclei. During training periods, they were exposed progressively to more difficult templates, which were created by decreasing the angle between the two segments of the template. After determining the most difficult template the injected animals could learn within the specified time and performance constraints, the retraining phase of the experiment was initiated in which the cats were required to execute the same sequence of templates in the absence of any injection. This stage of the experiment assessed retention and determined the extent of any relearning required to execute the task at criterion levels. Next, the animals were overtrained without any injection on the most difficult template they could perform. Finally, to determine the effects of nuclear inactivation on retention after extensive retraining, their capacity to perform the same template was determined after muscimol injection in the interposed and dentate nuclei. The findings show that during the inactivation of the dentate and interposed nuclei the animals could learn to execute the more difficult templates. However, when required to execute the most difficult template learned under muscimol on the day after injections were discontinued, the cats had to "relearn" (reacquire) the movement. Finally, when the cerebellar nuclei were inactivated after the animals learned the task in the absence of any injections during the retraining phase, retention was not blocked. The data indicate that the intermediate and

Testing complex animal cognition: Concept learning, proactive interference, and list memory.

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Wright, Anthony A

2018-01-01

This article describes an approach for assessing and comparing complex cognition in rhesus monkeys and pigeons by training them in a sequence of synergistic tasks, each yielding a whole function for enhanced comparisons. These species were trained in similar same/different tasks with expanding training sets (8, 16, 32, 64, 128 … 1024 pictures) followed by novel-stimulus transfer eventually resulting in full abstract-concept learning. Concept-learning functions revealed better rhesus transfer throughout and full concept learning at the 128 set, versus pigeons at the 256 set. They were then tested in delayed same/different tasks for proactive interference by inserting occasional tests within trial-unique sessions where the test stimulus matched a previous sample stimulus (1, 2, 4, 8, 16 trials prior). Proactive-interference functions revealed time-based interference for pigeons (1, 10 s delays), but event-based interference for rhesus (no effect of 1, 10, 20 s delays). They were then tested in list-memory tasks by expanding the sample to four samples in trial-unique sessions (minimizing proactive interference). The four-item, list-memory functions revealed strong recency memory at short delays, gradually changing to strong primacy memory at long delays over 30 s for rhesus, and 10 s for pigeons. Other species comparisons and future directions are discussed. © 2018 Society for the Experimental Analysis of Behavior.

Impact of a Modified Jigsaw Method for Learning an Unfamiliar, Complex Topic

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Denise Kolanczyk

2017-09-01

Full Text Available Objective: The aim of this study was to use the jigsaw method with an unfamiliar, complex topic and to evaluate the effectiveness of the jigsaw teaching method on student learning of assigned material (“jigsaw expert” versus non-assigned material (“jigsaw learner”. Innovation: The innovation was implemented in an advanced cardiology elective. Forty students were assigned a pre-reading and one of four valvular heart disorders, a topic not previously taught in the curriculum. A pre-test and post-test evaluated overall student learning. Student performance on pre/post tests as the “jigsaw expert” and “jigsaw learner” was also compared. Critical Analysis: Overall, the post-test mean score of 85.75% was significantly higher than that of the pre-test score of 56.75% (p<0.05. There was significant improvement in scores regardless of whether the material was assigned (“jigsaw experts” pre=58.8% and post=82.5%; p<0.05 or not assigned (“jigsaw learners” pre= 56.25% and post= 86.56%, p<0.05 for pre-study. Next Steps: The use of the jigsaw method to teach unfamiliar, complex content helps students to become both teachers and active listeners, which are essential to the skills and professionalism of a health care provider. Further studies are needed to evaluate use of the jigsaw method to teach unfamiliar, complex content on long-term retention and to further examine the effects of expert vs. non-expert roles. Conflict of Interest We declare no conflicts of interest or financial interests that the authors or members of their immediate families have in any product or service discussed in the manuscript, including grants (pending or received, employment, gifts, stock holdings or options, honoraria, consultancies, expert testimony, patents and royalties.   Type: Note

Human algorithmic stability and human Rademacher complexity

NARCIS (Netherlands)

Vahdat, Mehrnoosh; Oneto, L.; Ghio, A; Anguita, D.; Funk, M.; Rauterberg, G.W.M.

2015-01-01

In Machine Learning (ML), the learning process of an algo- rithm given a set of evidences is studied via complexity measures. The way towards using ML complexity measures in the Human Learning (HL) domain has been paved by a previous study, which introduced Human Rademacher Complexity (HRC): in this

Analyzing discourse and text complexity for learning and collaborating a cognitive approach based on natural language processing

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Dascălu, Mihai

2014-01-01

With the advent and increasing popularity of Computer Supported Collaborative Learning (CSCL) and e-learning technologies, the need of automatic assessment and of teacher/tutor support for the two tightly intertwined activities of comprehension of reading materials and of collaboration among peers has grown significantly. In this context, a polyphonic model of discourse derived from Bakhtin’s work as a paradigm is used for analyzing both general texts and CSCL conversations in a unique framework focused on different facets of textual cohesion. As specificity of our analysis, the individual learning perspective is focused on the identification of reading strategies and on providing a multi-dimensional textual complexity model, whereas the collaborative learning dimension is centered on the evaluation of participants’ involvement, as well as on collaboration assessment. Our approach based on advanced Natural Language Processing techniques provides a qualitative estimation of the learning process and enhance...

The effects of practice schedule and critical thinking prompts on learning and transfer of complex judgment

NARCIS (Netherlands)

Helsdingen, Anne; Van Gog, Tamara; Van Merriënboer, Jeroen

2010-01-01

Helsdingen, A. S., Van Gog, T., & Van Merriënboer, J. J. G. (2011). The effects of practice schedule and critical thinking prompts on learning and transfer of complex judgment task. Journal of Educational Psychology, 103(2), 383-398. doi:10.1037/a0022370

The Dream About the Magic Silver Bullet – the Complexity of Designing for Tablet-Mediated Learning

DEFF Research Database (Denmark)

Jahnke, Isa; Svendsen, Niels Vandel; Johansen, Simon Kristoffer

2014-01-01

learning. We report the gaps and interrelations between the dreams and the practice of the teachers. They dream about an interconnected praxis – the magic silver bullet – and establish their visions of inter- connectivity because of their breakdown experiences of media tablets aiding complexity instead...

ASSESSMENT OF STUDENT LEARNING IN VIRTUAL SPACES, USING ORDERS OF COMPLEXITY IN LEVELS OF THINKING

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Jose CAPACHO

2017-04-01

Full Text Available This paper aims at showing a new methodology to assess student learning in virtual spaces supported by Information and Communications Technology-ICT. The methodology is based on the Conceptual Pedagogy Theory, and is supported both on knowledge instruments (KI and intelectual operations (IO. KI are made up of teaching materials embedded in the virtual environment. The student carries out IO in his/her virtual formation process based on KI. Both instruments of knowledge and intellectual operations can be mathematically modelled by using functions of increasing complexity order. These functions represent the student’s learning change. This paper main contribution is to show that these functions let the student go from a concrete thinking to a formal one in his/her virtual learning process. The research showed that 47% of the students moved from a concrete thinking level to the formal thinking level.

Emergence: Complexity Pedagogy in Action

Science.gov (United States)

Jonas-Simpson, Christine

2015-01-01

Many educators are looking for new ways to engage students and each other in order to enrich curriculum and the teaching-learning process. We describe an example of how we enacted teaching-learning approaches through the insights of complexity thinking, an approach that supports the emergence of new possibilities for teaching-learning in the classroom and online. Our story begins with an occasion to meet with 10 nursing colleagues in a three-hour workshop using four activities that engaged learning about complexity thinking and pedagogy. Guiding concepts for the collaborative workshop were nonlinearity, distributed decision-making, divergent thinking, self-organization, emergence, and creative exploration. The workshop approach considered critical questions to spark our collective inquiry. We asked, “What is emergent learning?” and “How do we, as educators and learners, engage a community so that new learning surfaces?” We integrated the arts, creative play, and perturbations within a complexity approach. PMID:25838945

Effect of tDCS on task relevant and irrelevant perceptual learning of complex objects.

Science.gov (United States)

Van Meel, Chayenne; Daniels, Nicky; de Beeck, Hans Op; Baeck, Annelies

2016-01-01

During perceptual learning the visual representations in the brain are altered, but these changes' causal role has not yet been fully characterized. We used transcranial direct current stimulation (tDCS) to investigate the role of higher visual regions in lateral occipital cortex (LO) in perceptual learning with complex objects. We also investigated whether object learning is dependent on the relevance of the objects for the learning task. Participants were trained in two tasks: object recognition using a backward masking paradigm and an orientation judgment task. During both tasks, an object with a red line on top of it were presented in each trial. The crucial difference between both tasks was the relevance of the object: the object was relevant for the object recognition task, but not for the orientation judgment task. During training, half of the participants received anodal tDCS stimulation targeted at the lateral occipital cortex (LO). Afterwards, participants were tested on how well they recognized the trained objects, the irrelevant objects presented during the orientation judgment task and a set of completely new objects. Participants stimulated with tDCS during training showed larger improvements of performance compared to participants in the sham condition. No learning effect was found for the objects presented during the orientation judgment task. To conclude, this study suggests a causal role of LO in relevant object learning, but given the rather low spatial resolution of tDCS, more research on the specificity of this effect is needed. Further, mere exposure is not sufficient to train object recognition in our paradigm.

Learning in Complex Environments: The Effects of Background Speech on Early Word Learning

Science.gov (United States)

McMillan, Brianna T. M.; Saffran, Jenny R.

2016-01-01

Although most studies of language learning take place in quiet laboratory settings, everyday language learning occurs under noisy conditions. The current research investigated the effects of background speech on word learning. Both younger (22- to 24-month-olds; n = 40) and older (28- to 30-month-olds; n = 40) toddlers successfully learned novel…

Scripted collaboration in serious gaming for complex learning: Effects of multiple perspectives when acquiring water management skills

NARCIS (Netherlands)

Hummel, Hans; Van Houcke, Jasper; Nadolski, Rob; Van der Hiele, Tony; Kurvers, Hub; Löhr, Ansje

2010-01-01

Hummel, H. G. K., Van Houcke, J., Nadolski, R. J., Van der Hiele, T., Kurvers, H., & Löhr, A. (2011). Scripted collaboration in gaming for complex learning: Effects of multiple perspectives when acquiring water management skills. British Journal of Educational Technology, 42(6),

Autonomy and informed consent: a mistaken association?

Science.gov (United States)

Kristinsson, Sigurdur

2007-09-01

For decades, the greater part of efforts to improve regulatory frameworks for research ethics has focused on informed consent procedures; their design, codification and regulation. Why is informed consent thought to be so important? Since the publication of the Belmont Report in 1979, the standard response has been that obtaining informed consent is a way of treating individuals as autonomous agents. Despite its political success, the philosophical validity of this Belmont view cannot be taken for granted. If the Belmont view is to be based on a conception of autonomy that generates moral justification, it will either have to be reinterpreted along Kantian lines or coupled with a something like Mill's conception of individuality. The Kantian interpretation would be a radical reinterpretation of the Belmont view, while the Millian justification is incompatible with the liberal requirement that justification for public policy should be neutral between controversial conceptions of the good. This consequence might be avoided by replacing Mill's conception of individuality with a procedural conception of autonomy, but I argue that the resulting view would in fact fail to support a non-Kantian, autonomy-based justification of informed consent. These difficulties suggest that insofar as informed consent is justified by respect for persons and considerations of autonomy, as the Belmont report maintained, the justification should be along the lines of Kantian autonomy and not individual autonomy.

Managing Contextual Complexity in an Experiential Learning Course: A Dynamic Systems Approach through the Identification of Turning Points in Students' Emotional Trajectories

Directory of Open Access Journals (Sweden)

Gloria Nogueiras

2017-05-01

Full Text Available This study adopts a dynamic systems approach to investigate how individuals successfully manage contextual complexity. To that end, we tracked individuals' emotional trajectories during a challenging training course, seeking qualitative changes–turning points—and we tested their relationship with the perceived complexity of the training. The research context was a 5-day higher education course based on process-oriented experiential learning, and the sample consisted of 17 students. The students used a five-point Likert scale to rate the intensity of 16 emotions and the complexity of the training on 8 measurement points. Monte Carlo permutation tests enabled to identify 30 turning points in the 272 emotional trajectories analyzed (17 students * 16 emotions each. 83% of the turning points indicated a change of pattern in the emotional trajectories that consisted of: (a increasingly intense positive emotions or (b decreasingly intense negative emotions. These turning points also coincided with particularly complex periods in the training as perceived by the participants (p = 0.003, and p = 0.001 respectively. The relationship between positively-trended turning points in the students' emotional trajectories and the complexity of the training may be interpreted as evidence of a successful management of the cognitive conflict arising from the clash between the students' prior ways of meaning-making and the challenging demands of the training. One of the strengths of this study is that it provides a relatively simple procedure for identifying turning points in developmental trajectories, which can be applied to various longitudinal experiences that are very common in educational and developmental contexts. Additionally, the findings contribute to sustaining that the assumption that complex contextual demands lead unfailingly to individuals' learning is incomplete. Instead, it is how individuals manage complexity which may or may not lead to

Complexity control in statistical learning

Indian Academy of Sciences (India)

complexity of the class of models from which we are to choose our model. In this ... As is explained in §2, we use the concept of covering numbers to quantify the complexity of a class of ..... called structural risk minimization (SRM). Vapnik ...

Learning-by-Concordance (LbC): introducing undergraduate students to the complexity and uncertainty of clinical practice.

Science.gov (United States)

Fernandez, Nicolas; Foucault, Amélie; Dubé, Serge; Robert, Diane; Lafond, Chantal; Vincent, Anne-Marie; Kassis, Jeannine; Kazitani, Driss; Charlin, Bernard

2016-10-01

A current challenge in medical education is the steep exposure to the complexity and uncertainty of clinical practice in early clerkship. The gap between pre-clinical courses and the reality of clinical decision-making can be overwhelming for undergraduate students. The Learning-by-Concordance (LbC) approach aims to bridge this gap by embedding complexity and uncertainty by relying on real-life situations and exposure to expert reasoning processes to support learning. LbC provides three forms of support: 1) expert responses that students compare with their own, 2) expert explanations and 3) recognized scholars' key-messages. Three different LbC inspired learning tools were used by 900 undergraduate medical students in three courses: Concordance-of-Reasoning in a 1 st -year hematology course; Concordance-of-Perception in a 2nd-year pulmonary physio-pathology course, and; Concordance-of-Professional-Judgment with 3rd-year clerkship students. Thematic analysis was conducted on freely volunteered qualitative comments provided by 404 students. Absence of a right answer was challenging for 1 st year concordance-of-reasoning group; the 2 nd year visual concordance group found radiology images initially difficult and unnerving and the 3 rd year concordance-of-judgment group recognized the importance of divergent expert opinion. Expert panel answers and explanations constitute an example of "cognitive apprenticeship" that could contribute to the development of appropriate professional reasoning processes.

Influence of Cultural Cognition, Social Aspect of Culture, and Personality on Trust

Science.gov (United States)

2013-12-31

111. doi: 10.1177/00027640021956116 Matsumoto , D., & Juang, L. (2011). Culture and psychology (5th ed.). Retrieved from http://books.google.com.my...books?id=lXFY1tziMv8C&printsec=frontcover&dq= cultural + psychology + matsumoto &hl=en&sa=X&ei=TXBvUoDnKcmUrAf- xIHoAg&redir_esc=y#v=onepage&q= cultural ...20psychology%20matsumoto&f=fals e Matsumoto , D., & Juang, L. (2008). Culture & psychology (4th ed). Belmont, C.A.: Wadsworth, Cengage Learning

Learning Analytics for Networked Learning Models

Science.gov (United States)

Joksimovic, Srecko; Hatala, Marek; Gaševic, Dragan

2014-01-01

Teaching and learning in networked settings has attracted significant attention recently. The central topic of networked learning research is human-human and human-information interactions occurring within a networked learning environment. The nature of these interactions is highly complex and usually requires a multi-dimensional approach to…

Complexity Thinking in PE: Game-Centred Approaches, Games as Complex Adaptive Systems, and Ecological Values

Science.gov (United States)

Storey, Brian; Butler, Joy

2013-01-01

Background: This article draws on the literature relating to game-centred approaches (GCAs), such as Teaching Games for Understanding, and dynamical systems views of motor learning to demonstrate a convergence of ideas around games as complex adaptive learning systems. This convergence is organized under the title "complexity thinking"…

Can motto-goals outperform learning and performance goals? Influence of goal setting on performance and affect in a complex problem solving task

Directory of Open Access Journals (Sweden)

Miriam S. Rohe

2016-09-01

Full Text Available In this paper, we bring together research on complex problem solving with that on motivational psychology about goal setting. Complex problems require motivational effort because of their inherent difficulties. Goal Setting Theory has shown with simple tasks that high, specific performance goals lead to better performance outcome than do-your-best goals. However, in complex tasks, learning goals have proven more effective than performance goals. Based on the Zurich Resource Model (Storch & Krause, 2014, so-called motto-goals (e.g., "I breathe happiness" should activate a person’s resources through positive affect. It was found that motto-goals are effective with unpleasant duties. Therefore, we tested the hypothesis that motto-goals outperform learning and performance goals in the case of complex problems. A total of N = 123 subjects participated in the experiment. In dependence of their goal condition, subjects developed a personal motto, learning, or performance goal. This goal was adapted for the computer-simulated complex scenario Tailorshop, where subjects worked as managers in a small fictional company. Other than expected, there was no main effect of goal condition for the management performance. As hypothesized, motto goals led to higher positive and lower negative affect than the other two goal types. Even though positive affect decreased and negative affect increased in all three groups during Tailorshop completion, participants with motto goals reported the lowest rates of negative affect over time. Exploratory analyses investigated the role of affect in complex problem solving via mediational analyses and the influence of goal type on perceived goal attainment.

Complex scenes and situations visualization in hierarchical learning algorithm with dynamic 3D NeoAxis engine

Science.gov (United States)

Graham, James; Ternovskiy, Igor V.

2013-06-01

We applied a two stage unsupervised hierarchical learning system to model complex dynamic surveillance and cyber space monitoring systems using a non-commercial version of the NeoAxis visualization software. The hierarchical scene learning and recognition approach is based on hierarchical expectation maximization, and was linked to a 3D graphics engine for validation of learning and classification results and understanding the human - autonomous system relationship. Scene recognition is performed by taking synthetically generated data and feeding it to a dynamic logic algorithm. The algorithm performs hierarchical recognition of the scene by first examining the features of the objects to determine which objects are present, and then determines the scene based on the objects present. This paper presents a framework within which low level data linked to higher-level visualization can provide support to a human operator and be evaluated in a detailed and systematic way.

Optimal quantum sample complexity of learning algorithms

NARCIS (Netherlands)

Arunachalam, S.; de Wolf, R.

2017-01-01

In learning theory, the VC dimension of a concept class C is the most common way to measure its "richness." A fundamental result says that the number of examples needed to learn an unknown target concept c 2 C under an unknown distribution D, is tightly determined by the VC dimension d of the

Changes in Cerebral Hemodynamics during Complex Motor Learning by Character Entry into Touch-Screen Terminals.

Directory of Open Access Journals (Sweden)

Akira Sagari

Full Text Available Studies of cerebral hemodynamics during motor learning have mostly focused on neurorehabilitation interventions and their effectiveness. However, only a few imaging studies of motor learning and the underlying complex cognitive processes have been performed.We measured cerebral hemodynamics using near-infrared spectroscopy (NIRS in relation to acquisition patterns of motor skills in healthy subjects using character entry into a touch-screen terminal. Twenty healthy, right-handed subjects who had no previous experience with character entry using a touch-screen terminal participated in this study. They were asked to enter the characters of a randomly formed Japanese syllabary into the touch-screen terminal. All subjects performed the task with their right thumb for 15 s alternating with 25 s of rest for 30 repetitions. Performance was calculated by subtracting the number of incorrect answers from the number of correct answers, and gains in motor skills were evaluated according to the changes in performance across cycles. Behavioral and oxygenated hemoglobin concentration changes across task cycles were analyzed using Spearman's rank correlations.Performance correlated positively with task cycle, thus confirming motor learning. Hemodynamic activation over the left sensorimotor cortex (SMC showed a positive correlation with task cycle, whereas activations over the right prefrontal cortex (PFC and supplementary motor area (SMA showed negative correlations.We suggest that increases in finger momentum with motor learning are reflected in the activity of the left SMC. We further speculate that the right PFC and SMA were activated during the early phases of motor learning, and that this activity was attenuated with learning progress.

Complex Constructivism: A Theoretical Model of Complexity and Cognition

Science.gov (United States)

Doolittle, Peter E.

2014-01-01

Education has long been driven by its metaphors for teaching and learning. These metaphors have influenced both educational research and educational practice. Complexity and constructivism are two theories that provide functional and robust metaphors. Complexity provides a metaphor for the structure of myriad phenomena, while constructivism…

Disrupting neural activity related to awake-state sharp wave-ripple complexes prevents hippocampal learning.

Science.gov (United States)

Nokia, Miriam S; Mikkonen, Jarno E; Penttonen, Markku; Wikgren, Jan

2012-01-01

Oscillations in hippocampal local-field potentials (LFPs) reflect the crucial involvement of the hippocampus in memory trace formation: theta (4-8 Hz) oscillations and ripples (~200 Hz) occurring during sharp waves are thought to mediate encoding and consolidation, respectively. During sharp wave-ripple complexes (SPW-Rs), hippocampal cell firing closely follows the pattern that took place during the initial experience, most likely reflecting replay of that event. Disrupting hippocampal ripples using electrical stimulation either during training in awake animals or during sleep after training retards spatial learning. Here, adult rabbits were trained in trace eyeblink conditioning, a hippocampus-dependent associative learning task. A bright light was presented to the animals during the inter-trial interval (ITI), when awake, either during SPW-Rs or irrespective of their neural state. Learning was particularly poor when the light was presented following SPW-Rs. While the light did not disrupt the ripple itself, it elicited a theta-band oscillation, a state that does not usually coincide with SPW-Rs. Thus, it seems that consolidation depends on neuronal activity within and beyond the hippocampus taking place immediately after, but by no means limited to, hippocampal SPW-Rs.

Understanding valve program complexity in a refurbishment environment - learning from the past

International Nuclear Information System (INIS)

Roth, H.E.

2012-01-01

The complexity of Valve Program development, planning, execution and management in a refurbishment environment is an enormous undertaking requiring the proper coordination and integration of many moving parts. As such, lack of attention and understanding of this complexity has led to significant cost and schedule overruns in past refurbishment projects in the province. OPEX indicates the challenges in completing valve scope during refurbishments are related but not limited to; lack of detailed condition assessments, improper scope development, insignificant strategic approach to work task planning, scheduling and procurement, absence of contingency planning for common ‘as found’ conditions during execution, lack of proper training requirements, etc. In addition, past contracting strategies to employ numerous companies in collaboration to complete such a complex and specialized program, has resulted in further complications surrounding the management and integration of multiple quality programs and internal company processes. Finally, the aftermath of such fragmented projects results in an absolute closeout nightmare, often times taking years to locate, sift through and re-integrate pertinent information back into customer systems. Valve Program complexity cannot be understood by just anyone, only those that have lived through a refurbishment project and experienced the challenges mentioned above have the knowledge, skill, and ability to appreciate how to tactically apply past learning to realize future improvements. Furthermore, effective contractor-customer collaboration is crucial; true and in-depth knowledge and understanding of the customer quality programs, engineering and work management processes, configuration management requirements, and most importantly the imperative significance of nuclear safety, are all essential components to ensure overall alignment and program success. (author)

Understanding valve program complexity in a refurbishment environment - learning from the past

Energy Technology Data Exchange (ETDEWEB)

Roth, H.E. [Babcock & Wilcox Canada Ltd., Cambridge, Ontario (Canada)

2012-07-01

The complexity of Valve Program development, planning, execution and management in a refurbishment environment is an enormous undertaking requiring the proper coordination and integration of many moving parts. As such, lack of attention and understanding of this complexity has led to significant cost and schedule overruns in past refurbishment projects in the province. OPEX indicates the challenges in completing valve scope during refurbishments are related but not limited to; lack of detailed condition assessments, improper scope development, insignificant strategic approach to work task planning, scheduling and procurement, absence of contingency planning for common ‘as found’ conditions during execution, lack of proper training requirements, etc. In addition, past contracting strategies to employ numerous companies in collaboration to complete such a complex and specialized program, has resulted in further complications surrounding the management and integration of multiple quality programs and internal company processes. Finally, the aftermath of such fragmented projects results in an absolute closeout nightmare, often times taking years to locate, sift through and re-integrate pertinent information back into customer systems. Valve Program complexity cannot be understood by just anyone, only those that have lived through a refurbishment project and experienced the challenges mentioned above have the knowledge, skill, and ability to appreciate how to tactically apply past learning to realize future improvements. Furthermore, effective contractor-customer collaboration is crucial; true and in-depth knowledge and understanding of the customer quality programs, engineering and work management processes, configuration management requirements, and most importantly the imperative significance of nuclear safety, are all essential components to ensure overall alignment and program success. (author)

Framework for robot skill learning using reinforcement learning

Science.gov (United States)

Wei, Yingzi; Zhao, Mingyang

2003-09-01

Robot acquiring skill is a process similar to human skill learning. Reinforcement learning (RL) is an on-line actor critic method for a robot to develop its skill. The reinforcement function has become the critical component for its effect of evaluating the action and guiding the learning process. We present an augmented reward function that provides a new way for RL controller to incorporate prior knowledge and experience into the RL controller. Also, the difference form of augmented reward function is considered carefully. The additional reward beyond conventional reward will provide more heuristic information for RL. In this paper, we present a strategy for the task of complex skill learning. Automatic robot shaping policy is to dissolve the complex skill into a hierarchical learning process. The new form of value function is introduced to attain smooth motion switching swiftly. We present a formal, but practical, framework for robot skill learning and also illustrate with an example the utility of method for learning skilled robot control on line.

United States Support Programme (USSP): Lessons Learned from the Management of Complex, Multi-Stakeholder Projects for International Safeguards

International Nuclear Information System (INIS)

Diaz, R.; Tackentien, J.

2015-01-01

This paper will review USSP experiences, lessons learned, and proposed future strategies on the management of complex projects including the Universal Non-Destructive Assay Data Acquisition Platform (UNAP) instrument development task. The focus will be on identifying lessons learned to formulate strategies to minimize risk and maximize the potential of commercial success for future complex projects. Topics planned for inclusion are: 1. Initial agreement amongst all stakeholders on the justification of the need of the development including market studies of existing/near term future COTS technology capabilities; 2. Initial confirmation that there is a market for the product other than the IAEA to reduce investment risk; 3. Agreement on an accelerated initial project schedule from request acceptance to commercial unit production including per unit cost and quantities; 4. During product development, obtaining periodic customer reaffirmation of the need and quantities for the product per the existing schedule and per unit price. (author)

Acquiring organizational learning norms: a contingency approach for understanding deutero learning

NARCIS (Netherlands)

Wijnhoven, Alphonsus B.J.M.

2001-01-01

'The Learning Organization' is a configuration of learning norms (called a learning prototype here), which is seldom related to varying levels of learning needs. This article assumes that organizational environmental complexity and dynamics define four learning needs levels. Consequently, four

Assessing Complex Learning Objectives through Analytics

Science.gov (United States)

Horodyskyj, L.; Mead, C.; Buxner, S.; Semken, S. C.; Anbar, A. D.

2016-12-01

A significant obstacle to improving the quality of education is the lack of easy-to-use assessments of higher-order thinking. Most existing assessments focus on recall and understanding questions, which demonstrate lower-order thinking. Traditionally, higher-order thinking is assessed with practical tests and written responses, which are time-consuming to analyze and are not easily scalable. Computer-based learning environments offer the possibility of assessing such learning outcomes based on analysis of students' actions within an adaptive learning environment. Our fully online introductory science course, Habitable Worlds, uses an intelligent tutoring system that collects and responds to a range of behavioral data, including actions within the keystone project. This central project is a summative, game-like experience in which students synthesize and apply what they have learned throughout the course to identify and characterize a habitable planet from among hundreds of stars. Student performance is graded based on completion and accuracy, but two additional properties can be utilized to gauge higher-order thinking: (1) how efficient a student is with the virtual currency within the project and (2) how many of the optional milestones a student reached. In the project, students can use the currency to check their work and "unlock" convenience features. High-achieving students spend close to the minimum amount required to reach these goals, indicating a high-level of concept mastery and efficient methodology. Average students spend more, indicating effort, but lower mastery. Low-achieving students were more likely to spend very little, which indicates low effort. Differences on these metrics were statistically significant between all three of these populations. We interpret this as evidence that high-achieving students develop and apply efficient problem-solving skills as compared to lower-achieving student who use more brute-force approaches.

Does supporting multiple student strategies lead to greater learning and motivation? Investigating a source of complexity in the architecture of intelligent tutoring systems

NARCIS (Netherlands)

Waalkens, Maaike; Aleven, Vincent; Taatgen, Niels

Intelligent tutoring systems (ITS) support students in learning a complex problem-solving skill. One feature that makes an ITS architecturally complex, and hard to build, is support for strategy freedom, that is, the ability to let students pursue multiple solution strategies within a given problem.

Protein complex detection in PPI networks based on data integration and supervised learning method.

Science.gov (United States)

Yu, Feng; Yang, Zhi; Hu, Xiao; Sun, Yuan; Lin, Hong; Wang, Jian

2015-01-01

Revealing protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict protein complexes from protein-protein interaction (PPI) networks. However, the small amount of known physical interactions may limit protein complex detection. The new PPI networks are constructed by integrating PPI datasets with the large and readily available PPI data from biomedical literature, and then the less reliable PPI between two proteins are filtered out based on semantic similarity and topological similarity of the two proteins. Finally, the supervised learning protein complex detection (SLPC), which can make full use of the information of available known complexes, is applied to detect protein complex on the new PPI networks. The experimental results of SLPC on two different categories yeast PPI networks demonstrate effectiveness of the approach: compared with the original PPI networks, the best average improvements of 4.76, 6.81 and 15.75 percentage units in the F-score, accuracy and maximum matching ratio (MMR) are achieved respectively; compared with the denoising PPI networks, the best average improvements of 3.91, 4.61 and 12.10 percentage units in the F-score, accuracy and MMR are achieved respectively; compared with ClusterONE, the start-of the-art complex detection method, on the denoising extended PPI networks, the average improvements of 26.02 and 22.40 percentage units in the F-score and MMR are achieved respectively. The experimental results show that the performances of SLPC have a large improvement through integration of new receivable PPI data from biomedical literature into original PPI networks and denoising PPI networks. In addition, our protein complexes detection method can achieve better performance than ClusterONE.

Building Global Support for Open Data Access

Science.gov (United States)

Key, E.; Samors, R. J.; Seltzer, C. E.; Orr, B. J.

2017-12-01

The Belmont Forum, a global partnership of funding organizations, international science councils, and regional consortia is committed to the advancement of international transdisciplinary research providing knowledge for understanding, mitigating and adapting to global environmental change. The Forum is also committed to ensuring appropriate, recognizable credit is awarded to the creators of that data, each and every time it is used. At its 2015 plenary meeting, the Belmont Forum agreed on and adopted an open data policy and principles. The principles are designed to widen access to data and promote its long-term preservation in global change research; help improve data management and exploitation; coordinate and integrate disparate organizational and technical elements; fill critical global e-infrastructure gaps; share best practices; and foster new data literacy. To help implement the policy and principles, the Belmont Forum has established the e-Infrastructures and Data Management (e-I&DM) Initiative which will leverage existing knowledge and resources to illuminate achievable, reproducible systems for effective, sustainable data management practices. The overall objective of the e-I&DM Initiative is to provide advice and recommendations to the Belmont Forum member and partner organizations regarding policies, programs, procedures that could be adopted to accelerate open data sharing, data reproducibility, data curation, and other aspects of long-term data management and access. This presentation will explore current Belmont Forum activities through the e-I&DM Initiative to develop policies and practices that could be adopted by funders, publishers and researchers alike that will lead to increased data sharing with more widespread data citation/attribution - giving credit where credit is due.

Complexity in practice: understanding primary care as a complex adaptive system

Directory of Open Access Journals (Sweden)

Beverley Ellis

2010-06-01

Conclusions The results are real-world exemplars of the emergent properties of complex adaptive systems. Improving clinical governance in primary care requires both complex social interactions and underpinning informatics. The socio-technical lessons learned from this research should inform future management approaches.

Emergent learning and learning ecologies in Web 2.0

OpenAIRE

Williams, Roy; Karousou, Regina; Mackness, J.

2011-01-01

This paper describes emergent learning and situates it within learning networks and systems and the broader learning ecology of Web 2.0. It describes the nature of emergence and emergent learning and the conditions that enable emergent, self-organised learning to occur and to flourish. Specifically, it explores whether emergent learning can be validated and self-correcting and whether it is possible to link or integrate emergent and prescribed learning. It draws on complexity theory, commu...

Structure, Behavior, Function as a Framework For Teaching and Learning about Complexity In Ecosystems: Lessons from Middle School Classrooms (Invited)

Science.gov (United States)

Hmelo-Silver, C.; Gray, S.; Jordan, R.

2010-12-01

Complex systems surround us, and as Sabelli (2006) has argued, understanding complex systems is a critical component of science literacy. Understanding natural and designed systems are also prominent in the new draft science standards (NRC, 2010) and therefore of growing importance in the science classroom. Our work has focused on promoting an understanding of one complex natural system, aquatic ecosystems, which given current events, is fast becoming a requisite for informed decision-making as citizens (Jordan et al. 2008). Learners have difficulty understanding many concepts related to complex natural systems (e.g., Hmelo-Silver, Marathe, & Liu, 2007; Jordan, Gray, Liu, Demeter, & Hmelo-Silver, 2009). Studies of how students think about complex ecological systems (e.g; Hmelo-Silver, Marathe, & Liu, 2007; Hogan, 2000, Hogan & Fisherkeller, 1996: Covitt & Gunkel, 2008) have revealed difficulties in thinking beyond linear flow, single causality, and visible structure. Helping students to learn about ecosystems is a complex task that requires providing opportunities for students to not only engage directly with ecosystems but also with resources that provide relevant background knowledge and opportunities for learners to make their thinking visible. Both tasks can be difficult given the large spatial and temporal scales on which ecosystems operate. Additionally, visible components interact with often invisible components which can obscure ecosystem processes for students. Working in the context of aquatic ecosystems, we sought to provide learners with representations and simulations that make salient the relationship between system components. In particular, we provided learners with opportunities to experience both the micro-level and macro-level phenomena that are key to understanding ecosystems (Hmelo-Silver, Liu, Gray, & Jordan, submitted; Liu & Hmelo-Silver, 2008; Jacobson & Wilensky, 2006). To accomplish this, we needed to help learners make connections across

Virtual educational complex of the course “Methods of design of experiments” in distance learning environment of Moodle

Directory of Open Access Journals (Sweden)

E. V. Guseva

2016-01-01

Full Text Available Currently the information technologies have penetrated to all spheres of human activity, including education. The main objective of the article is to show the advantages of the developed complex and to familiarize with its structure too. The article presents the arguments that the use of the distance learning tools has a significant impact on Russian education. This approach provides the conditions for the development of innovative teaching methods. The approach describes the capabilities offered by the virtual education center of distance learning Moodle too. It is attractive not only openness but because it contains a large set of libraries, classes and functions in the programming language PHP too, which makes it a convenient tool for developing various online information systems. It is shown that the effectiveness of distance learning depends on the organization of educational material. The basic modules of the course were underlined. This section provides a comprehensive understanding of material. For the verification and control of students knowledge the testing system was developed. In addition, the training package has been developed which contains the information, helping to assess the level of students knowledge. The testing system includes a list of tests divided into sections and consists of a set of questions of different complexity. The questions are stored in the single database (“The bank of questions” and can be reused in one or more courses or sections. After passing the correct answers to the test questions can be available for the student. In addition, this module includes tools for grading by the teacher. The article concludes that the virtual educational complex enables to teach students, has a friendly interface that stimulate the students to continue the work and its successful completion.

Prenatal complex rhythmic music sound stimulation facilitates postnatal spatial learning but transiently impairs memory in the domestic chick.

Science.gov (United States)

Kauser, H; Roy, S; Pal, A; Sreenivas, V; Mathur, R; Wadhwa, S; Jain, S

2011-01-01

Early experience has a profound influence on brain development, and the modulation of prenatal perceptual learning by external environmental stimuli has been shown in birds, rodents and mammals. In the present study, the effect of prenatal complex rhythmic music sound stimulation on postnatal spatial learning, memory and isolation stress was observed. Auditory stimulation with either music or species-specific sounds or no stimulation (control) was provided to separate sets of fertilized eggs from day 10 of incubation. Following hatching, the chicks at age 24, 72 and 120 h were tested on a T-maze for spatial learning and the memory of the learnt task was assessed 24 h after training. In the posthatch chicks at all ages, the plasma corticosterone levels were estimated following 10 min of isolation. The chicks of all ages in the three groups took less (p memory after 24 h of training, only the music-stimulated chicks at posthatch age 24 h took a significantly longer (p music sounds facilitates spatial learning, though the music stimulation transiently impairs postnatal memory. 2011 S. Karger AG, Basel.

Machine-Learning Research

OpenAIRE

Dietterich, Thomas G.

1997-01-01

Machine-learning research has been making great progress in many directions. This article summarizes four of these directions and discusses some current open problems. The four directions are (1) the improvement of classification accuracy by learning ensembles of classifiers, (2) methods for scaling up supervised learning algorithms, (3) reinforcement learning, and (4) the learning of complex stochastic models.

Better decision making in complex, dynamic tasks training with human-facilitated interactive learning environments

CERN Document Server

Qudrat-Ullah, Hassan

2015-01-01

This book describes interactive learning environments (ILEs) and their underlying concepts. It explains how ILEs can be used to improve the decision-making process and how these improvements can be empirically verified. The objective of this book is to enhance our understanding of and to gain insights into the process by which human facilitated ILEs are effectively designed and used in improving users’ decision making in complex, dynamic tasks. This book is divided into four major parts. Part I serves as an introduction to the importance and complexity of decision making in dynamic tasks. Part II provides background material, drawing upon relevant literature, for the development of an integrated process model on the effectiveness of human facilitated ILEs in improving decision making in dynamic tasks. Part III focuses on the design, development, and application of FishBankILE in laboratory experiments to gather empirical evidence for the validity of the process model. Finally, part IV presents a comprehensi...

Bangladesh Delta: Assessment of the Causes of Sea-level Rise Hazards and Integrated Development of Predictive Modeling Towards Mitigation and Adaptation (BanD-AID)

Science.gov (United States)

Kusche, J.; Shum, C. K.; Jenkins, C. J.; Chen, J.; Guo, J.; Hossain, F.; Braun, B.; Calmant, S.; Ballu, V.; Papa, F.; Kuhn, M.; Ahmed, R.; Khan, Z. H.; Hossain, M.; Bernzen, A.; Dai, C.; Jia, Y.; Krien, Y.; Kuo, C. Y.; Liibusk, A.; Shang, K.; Testut, L.; Tseng, K. H.; Uebbing, B.; Rietbroek, R.; Valty, P.; Wan, J.

2016-12-01

As a low-lying and the largest coastal deltaic region in the world, Bangladesh already faces tremendous vulnerability. Accelerated sea-level rise, along with tectonic, sediment load and groundwater extraction induced land uplift/subsidence, have exacerbated Bangladesh's coastal vulnerability. Climate change has further intensified these risks with increasing temperatures, greater rainfall volatility, and increased incidence of intensified cyclones, in addition to its seasonal transboundary monsoonal flooding. Our Belmont Forum/IGFA G8 project BanD-AiD, http://Belmont-BanDAiD.org, or http://Blemont-SeaLevel.org, comprises of an international cross-disciplinary team including stakeholders in Bangladesh, aims at a joint assessment of the physical and social science knowledge of the physical and social dynamics which govern coastal vulnerability and societal resilience in Bangladesh. We have built a prototype observational system, following the Belmont Challenge identified Earth System Analysis & Prediction System (ESAPS) for the Bangladesh Delta, to achieve the physical science objectives of the project. The prototype observational system is exportable to other regions of the world. We studied the physical causes of relative sea-level rise in coastal Bangladesh, with the goal to separate and quantify land subsidence and geocentric sea-level rise signals at adequate spatial scales using contemporary space geodetic and remote sensing data. We used a social and natural science integrative approach to investigate the various social and economic drivers behind land use change, population increase migration and community resilience to understand the social dynamics of this complex region and to forecast likely and alternative scenarios for maintaining the societal resilience of this vital region which currently houses a quarter of Bangladesh's 160 million people.

E-Infrastructure and Data Management for Global Change Research

Science.gov (United States)

Allison, M. L.; Gurney, R. J.; Cesar, R.; Cossu, R.; Gemeinholzer, B.; Koike, T.; Mokrane, M.; Peters, D.; Nativi, S.; Samors, R.; Treloar, A.; Vilotte, J. P.; Visbeck, M.; Waldmann, H. C.

2014-12-01

The Belmont Forum, a coalition of science funding agencies from 15 countries, is supporting an 18-month effort to assess the state of international of e-infrastructures and data management so that global change data and information can be more easily and efficiently exchanged internationally and across domains. Ultimately, this project aims to address the Belmont "Challenge" to deliver knowledge needed for action to avoid and adapt to detrimental environmental change, including extreme hazardous events. This effort emerged from conclusions by the Belmont Forum that transformative approaches and innovative technologies are needed for heterogeneous data/information to be integrated and made interoperable for researchers in disparate fields, and for myriad uses across international, institutional, disciplinary, spatial and temporal boundaries. The project will deliver a Community Strategy and Implementation Plan to prioritize international funding opportunities and long-term policy recommendations on how the Belmont Forum can implement a more coordinated, holistic, and sustainable approach to funding and supporting global change research. The Plan is expected to serve as the foundation of future Belmont Forum funding calls for proposals in support of research science goals as well as to establish long term e-infrastructure. More than 120 scientists, technologists, legal experts, social scientists, and other experts are participating in six Work Packages to develop the Plan by spring, 2015, under the broad rubrics of Architecture/Interoperability and Governance: Data Integration for Multidisciplinary Research; Improved Interface between Computation & Data Infrastructures; Harmonization of Global Data Infrastructure; Data Sharing; Open Data; and Capacity Building. Recommendations could lead to a more coordinated approach to policies, procedures and funding mechanisms to support e-infrastructures in a more sustainable way.

Multimodal Learning Analytics and Education Data Mining: Using Computational Technologies to Measure Complex Learning Tasks

Science.gov (United States)

Blikstein, Paulo; Worsley, Marcelo

2016-01-01

New high-frequency multimodal data collection technologies and machine learning analysis techniques could offer new insights into learning, especially when students have the opportunity to generate unique, personalized artifacts, such as computer programs, robots, and solutions engineering challenges. To date most of the work on learning analytics…

Coupling Visualization, Simulation, and Deep Learning for Ensemble Steering of Complex Energy Models: Preprint

Energy Technology Data Exchange (ETDEWEB)

Potter, Kristin C [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Brunhart-Lupo, Nicholas J [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Bush, Brian W [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Gruchalla, Kenny M [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Bugbee, Bruce [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Krishnan, Venkat K [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

2017-10-09

We have developed a framework for the exploration, design, and planning of energy systems that combines interactive visualization with machine-learning based approximations of simulations through a general purpose dataflow API. Our system provides a visual inter- face allowing users to explore an ensemble of energy simulations representing a subset of the complex input parameter space, and spawn new simulations to 'fill in' input regions corresponding to new enegery system scenarios. Unfortunately, many energy simula- tions are far too slow to provide interactive responses. To support interactive feedback, we are developing reduced-form models via machine learning techniques, which provide statistically sound esti- mates of the full simulations at a fraction of the computational cost and which are used as proxies for the full-form models. Fast com- putation and an agile dataflow enhance the engagement with energy simulations, and allow researchers to better allocate computational resources to capture informative relationships within the system and provide a low-cost method for validating and quality-checking large-scale modeling efforts.

Case study method and problem-based learning: utilizing the pedagogical model of progressive complexity in nursing education.

Science.gov (United States)

McMahon, Michelle A; Christopher, Kimberly A

2011-08-19

As the complexity of health care delivery continues to increase, educators are challenged to determine educational best practices to prepare BSN students for the ambiguous clinical practice setting. Integrative, active, and student-centered curricular methods are encouraged to foster student ability to use clinical judgment for problem solving and informed clinical decision making. The proposed pedagogical model of progressive complexity in nursing education suggests gradually introducing students to complex and multi-contextual clinical scenarios through the utilization of case studies and problem-based learning activities, with the intention to transition nursing students into autonomous learners and well-prepared practitioners at the culmination of a nursing program. Exemplar curricular activities are suggested to potentiate student development of a transferable problem solving skill set and a flexible knowledge base to better prepare students for practice in future novel clinical experiences, which is a mutual goal for both educators and students.

Generative Learning: Adults Learning within Ambiguity

Science.gov (United States)

Nicolaides, Aliki

2015-01-01

This study explored the extent to which ambiguity can serve as a catalyst for adult learning. The purpose of this study is to understand learning that is generated when encountering ambiguity agitated by the complexity of liquid modernity. "Ambiguity," in this study, describes an encounter with an appearance of reality that is at first…

Lessons Learned from Crowdsourcing Complex Engineering Tasks.

Science.gov (United States)

Staffelbach, Matthew; Sempolinski, Peter; Kijewski-Correa, Tracy; Thain, Douglas; Wei, Daniel; Kareem, Ahsan; Madey, Gregory

2015-01-01

Crowdsourcing is the practice of obtaining needed ideas, services, or content by requesting contributions from a large group of people. Amazon Mechanical Turk is a web marketplace for crowdsourcing microtasks, such as answering surveys and image tagging. We explored the limits of crowdsourcing by using Mechanical Turk for a more complicated task: analysis and creation of wind simulations. Our investigation examined the feasibility of using crowdsourcing for complex, highly technical tasks. This was done to determine if the benefits of crowdsourcing could be harnessed to accurately and effectively contribute to solving complex real world engineering problems. Of course, untrained crowds cannot be used as a mere substitute for trained expertise. Rather, we sought to understand how crowd workers can be used as a large pool of labor for a preliminary analysis of complex data. We compared the skill of the anonymous crowd workers from Amazon Mechanical Turk with that of civil engineering graduate students, making a first pass at analyzing wind simulation data. For the first phase, we posted analysis questions to Amazon crowd workers and to two groups of civil engineering graduate students. A second phase of our experiment instructed crowd workers and students to create simulations on our Virtual Wind Tunnel website to solve a more complex task. With a sufficiently comprehensive tutorial and compensation similar to typical crowd-sourcing wages, we were able to enlist crowd workers to effectively complete longer, more complex tasks with competence comparable to that of graduate students with more comprehensive, expert-level knowledge. Furthermore, more complex tasks require increased communication with the workers. As tasks become more complex, the employment relationship begins to become more akin to outsourcing than crowdsourcing. Through this investigation, we were able to stretch and explore the limits of crowdsourcing as a tool for solving complex problems.

VOCABULARY, TEXTUAL COMPLEXITY AND READING COMPREHENSION IN DIGITAL LEARNING ENVIRONMENTS: AN INITIAL INVESTIGATION WITH HIGH SCHOOL STUDENTS

Directory of Open Access Journals (Sweden)

Maria José Bocorny Finatto

2016-12-01

Full Text Available In this paper, we describe an initial investigation that intended to qualify the elaboration and usability of didactic resources for Distance Learning (DL in the field of Languages/Portuguese Language and Reading. We present the planning of the resource, the selection of materials and the theoretical notions involved, and the initial design of the activity, which consisted in reading and evaluating the complexity of a set of short texts. The experience was successful only for a small controlled group of students and unsuccessful for the large uncontrolled group. In order to improve the devised resource and implement it didactically, there is the need to perform previous presential learning activities with the involved groups and proceed with the student’s evaluation of the results after the task is accomplished.

Coping with complexity: machine learning optimization of cell-free protein synthesis.

Science.gov (United States)

Caschera, Filippo; Bedau, Mark A; Buchanan, Andrew; Cawse, James; de Lucrezia, Davide; Gazzola, Gianluca; Hanczyc, Martin M; Packard, Norman H

2011-09-01

Biological systems contain complex metabolic pathways with many nonlinearities and synergies that make them difficult to predict from first principles. Protein synthesis is a canonical example of such a pathway. Here we show how cell-free protein synthesis may be improved through a series of iterated high-throughput experiments guided by a machine-learning algorithm implementing a form of evolutionary design of experiments (Evo-DoE). The algorithm predicts fruitful experiments from statistical models of the previous experimental results, combined with stochastic exploration of the experimental space. The desired experimental response, or evolutionary fitness, was defined as the yield of the target product, and new experimental conditions were discovered to have ∼ 350% greater yield than the standard. An analysis of the best experimental conditions discovered indicates that there are two distinct classes of kinetics, thus showing how our evolutionary design of experiments is capable of significant innovation, as well as gradual improvement. Copyright © 2011 Wiley Periodicals, Inc.

Dissecting children's observational learning of complex actions through selective video displays.

Science.gov (United States)

Flynn, Emma; Whiten, Andrew

2013-10-01

Children can learn how to use complex objects by watching others, yet the relative importance of different elements they may observe, such as the interactions of the individual parts of the apparatus, a model's movements, and desirable outcomes, remains unclear. In total, 140 3-year-olds and 140 5-year-olds participated in a study where they observed a video showing tools being used to extract a reward item from a complex puzzle box. Conditions varied according to the elements that could be seen in the video: (a) the whole display, including the model's hands, the tools, and the box; (b) the tools and the box but not the model's hands; (c) the model's hands and the tools but not the box; (d) only the end state with the box opened; and (e) no demonstration. Children's later attempts at the task were coded to establish whether they imitated the hierarchically organized sequence of the model's actions, the action details, and/or the outcome. Children's successful retrieval of the reward from the box and the replication of hierarchical sequence information were reduced in all but the whole display condition. Only once children had attempted the task and witnessed a second demonstration did the display focused on the tools and box prove to be better for hierarchical sequence information than the display focused on the tools and hands only. Copyright © 2013 Elsevier Inc. All rights reserved.

A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods.

Science.gov (United States)

Moore, Jason H; Shestov, Maksim; Schmitt, Peter; Olson, Randal S

2018-01-01

A central challenge of developing and evaluating artificial intelligence and machine learning methods for regression and classification is access to data that illuminates the strengths and weaknesses of different methods. Open data plays an important role in this process by making it easy for computational researchers to easily access real data for this purpose. Genomics has in some examples taken a leading role in the open data effort starting with DNA microarrays. While real data from experimental and observational studies is necessary for developing computational methods it is not sufficient. This is because it is not possible to know what the ground truth is in real data. This must be accompanied by simulated data where that balance between signal and noise is known and can be directly evaluated. Unfortunately, there is a lack of methods and software for simulating data with the kind of complexity found in real biological and biomedical systems. We present here the Heuristic Identification of Biological Architectures for simulating Complex Hierarchical Interactions (HIBACHI) method and prototype software for simulating complex biological and biomedical data. Further, we introduce new methods for developing simulation models that generate data that specifically allows discrimination between different machine learning methods.

Toward Learning Teams

DEFF Research Database (Denmark)

Hoda, Rashina; Babb, Jeff; Nørbjerg, Jacob

2013-01-01

to sacrifice learning-focused practices. Effective learning under pressure involves conscious efforts to implement original agile practices such as retrospectives and adapted strategies such as learning spikes. Teams, their management, and customers must all recognize the importance of creating learning teams......Today's software development challenges require learning teams that can continuously apply new engineering and management practices, new and complex technical skills, cross-functional skills, and experiential lessons learned. The pressure of delivering working software often forces software teams...

Is a "Complex" Task Really Complex? Validating the Assumption of Cognitive Task Complexity

Science.gov (United States)

Sasayama, Shoko

2016-01-01

In research on task-based learning and teaching, it has traditionally been assumed that differing degrees of cognitive task complexity can be inferred through task design and/or observations of differing qualities in linguistic production elicited by second language (L2) communication tasks. Without validating this assumption, however, it is…

Managing the Complexity of Design Problems through Studio-Based Learning

Science.gov (United States)

Cennamo, Katherine; Brandt, Carol; Scott, Brigitte; Douglas, Sarah; McGrath, Margarita; Reimer, Yolanda; Vernon, Mitzi

2011-01-01

The ill-structured nature of design problems makes them particularly challenging for problem-based learning. Studio-based learning (SBL), however, has much in common with problem-based learning and indeed has a long history of use in teaching students to solve design problems. The purpose of this ethnographic study of an industrial design class,…

Interpretable Active Learning

OpenAIRE

Phillips, Richard L.; Chang, Kyu Hyun; Friedler, Sorelle A.

2017-01-01

Active learning has long been a topic of study in machine learning. However, as increasingly complex and opaque models have become standard practice, the process of active learning, too, has become more opaque. There has been little investigation into interpreting what specific trends and patterns an active learning strategy may be exploring. This work expands on the Local Interpretable Model-agnostic Explanations framework (LIME) to provide explanations for active learning recommendations. W...

Visualizing complex processes using a cognitive-mapping tool to support the learning of clinical reasoning.

Science.gov (United States)

Wu, Bian; Wang, Minhong; Grotzer, Tina A; Liu, Jun; Johnson, Janice M

2016-08-22

Practical experience with clinical cases has played an important role in supporting the learning of clinical reasoning. However, learning through practical experience involves complex processes difficult to be captured by students. This study aimed to examine the effects of a computer-based cognitive-mapping approach that helps students to externalize the reasoning process and the knowledge underlying the reasoning process when they work with clinical cases. A comparison between the cognitive-mapping approach and the verbal-text approach was made by analyzing their effects on learning outcomes. Fifty-two third-year or higher students from two medical schools participated in the study. Students in the experimental group used the computer-base cognitive-mapping approach, while the control group used the verbal-text approach, to make sense of their thinking and actions when they worked with four simulated cases over 4 weeks. For each case, students in both groups reported their reasoning process (involving data capture, hypotheses formulation, and reasoning with justifications) and the underlying knowledge (involving identified concepts and the relationships between the concepts) using the given approach. The learning products (cognitive maps or verbal text) revealed that students in the cognitive-mapping group outperformed those in the verbal-text group in the reasoning process, but not in making sense of the knowledge underlying the reasoning process. No significant differences were found in a knowledge posttest between the two groups. The computer-based cognitive-mapping approach has shown a promising advantage over the verbal-text approach in improving students' reasoning performance. Further studies are needed to examine the effects of the cognitive-mapping approach in improving the construction of subject-matter knowledge on the basis of practical experience.

Learning To Live with Complexity.

Science.gov (United States)

Dosa, Marta

Neither the design of information systems and networks nor the delivery of library services can claim true user centricity without an understanding of the multifaceted psychological environment of users and potential users. The complexity of the political process, social problems, challenges to scientific inquiry, entrepreneurship, and…

Self-Controlled Feedback for a Complex Motor Task

Directory of Open Access Journals (Sweden)

Wolf Peter

2011-12-01

Full Text Available Self-controlled augmented feedback enhances learning of simple motor tasks. Thereby, learners tend to request feedback after trials that were rated as good by themselves. Feedback after good trials promotes positive reinforcement, which enhances motor learning. The goal of this study was to investigate when naïve learners request terminal visual feedback in a complex motor task, as conclusions drawn on simple tasks can hardly be transferred to complex tasks. Indeed, seven of nine learners stated to have intended to request feedback predominantly after good trials, but in contrast to their intention, kinematic analysis showed that feedback was rather requested randomly (23% after good, 44% after intermediate, 33% after bad trials. Moreover, requesting feedback after good trials did not correlate with learning success. It seems that self-estimation of performance in complex tasks is challenging. As a consequence, learners might have focused on certain movement aspects rather than on the overall movement. Further studies should assess the current focus of the learner in detail to gain more insight in self-estimation capabilities during complex motor task learning.

Complex Mobile Learning That Adapts to Learners' Cognitive Load

Science.gov (United States)

Deegan, Robin

2015-01-01

Mobile learning is cognitively demanding and frequently the ubiquitous nature of mobile computing means that mobile devices are used in cognitively demanding environments. This paper examines the use of mobile devices from a Learning, Usability and Cognitive Load Theory perspective. It suggests scenarios where these fields interact and presents an…

Ways of Knowing as Learning Styles: Learning MAGIC with a Partner.

Science.gov (United States)

Galotti, Kathleen M.; Drebus, David W.; Reimer, Rebecca L.

2001-01-01

College student pairs learned a complex card game using a scripted set of turns and written explanations, played the game, rated perceptions of and reactions to the learning session and their partner, and completed the Attitudes Toward Thinking and Learning Scale. Significant differences in perceptions of partners and sessions related to…

Theoretical Foundations of Active Learning

Science.gov (United States)

2009-05-01

I study the informational complexity of active learning in a statistical learning theory framework. Specifically, I derive bounds on the rates of...convergence achievable by active learning , under various noise models and under general conditions on the hypothesis class. I also study the theoretical...advantages of active learning over passive learning, and develop procedures for transforming passive learning algorithms into active learning algorithms

Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks

Science.gov (United States)

Ubbens, Jordan R.; Stavness, Ian

2017-01-01

Plant phenomics has received increasing interest in recent years in an attempt to bridge the genotype-to-phenotype knowledge gap. There is a need for expanded high-throughput phenotyping capabilities to keep up with an increasing amount of data from high-dimensional imaging sensors and the desire to measure more complex phenotypic traits (Knecht et al., 2016). In this paper, we introduce an open-source deep learning tool called Deep Plant Phenomics. This tool provides pre-trained neural networks for several common plant phenotyping tasks, as well as an easy platform that can be used by plant scientists to train models for their own phenotyping applications. We report performance results on three plant phenotyping benchmarks from the literature, including state of the art performance on leaf counting, as well as the first published results for the mutant classification and age regression tasks for Arabidopsis thaliana. PMID:28736569

Some ideas for learning CP-theories

OpenAIRE

Fierens, Daan

2008-01-01

Causal Probabilistic logic (CP-logic) is a language for describing complex probabilistic processes. In this talk we consider the problem of learning CP-theories from data. We briefly discuss three possible approaches. First, we review the existing algorithm by Meert et al. Second, we show how simple CP-theories can be learned by using the learning algorithm for Logical Bayesian Networks and converting the result into a CP-theory. Third, we argue that for learning more complex CP-theories, an ...

Visual Complexity: A Review

Science.gov (United States)

Donderi, Don C.

2006-01-01

The idea of visual complexity, the history of its measurement, and its implications for behavior are reviewed, starting with structuralism and Gestalt psychology at the beginning of the 20th century and ending with visual complexity theory, perceptual learning theory, and neural circuit theory at the beginning of the 21st. Evidence is drawn from…

What good are actions? Accelerating learning using learned action priors

CSIR Research Space (South Africa)

Rosman, Benjamin S

2012-11-01

Full Text Available The computational complexity of learning in sequential decision problems grows exponentially with the number of actions available to the agent at each state. We present a method for accelerating this process by learning action priors that express...

Leadership Learning for Complex Organizations

Science.gov (United States)

Ng, F. S. David

2015-01-01

Many school leadership programs are set and delivered in specific modules or workshops in order to achieve a pre-determined set of competencies, knowledge, and skills. In addition, these programs are driven by the faculty member and the prescribed content. As Singapore schools become more complex in the roles and responsibilities to educate the…

Learning about “wicked” problems in the Global South. Creating a film-based learning environment with “Visual Problem Appraisal”

OpenAIRE

Loes Witteveen; Rico Lie

2012-01-01

The current complexity of sustainable development in the Global South calls for the design of learning strategies that can deal with this complexity. One such innovative learning strategy, called Visual Problem Appraisal (VPA), is highlighted in this article. The strategy is termed visual as it creates a learning environment that is film-based. VPA enhances the analysis of complex issues, and facilitates stakeholder dialogue and action planning. The strategy is used in workshops dealing with ...

Circadian modulation of complex learning in diurnal and nocturnal Aplysia

OpenAIRE

Lyons, Lisa C.; Rawashdeh, Oliver; Katzoff, Ayelet; Susswein, Abraham J.; Eskin, Arnold

2005-01-01

Understanding modulation of memory, as well as the mechanisms underlying memory formation, has become a key issue in neuroscience research. Previously, we found that the formation of long-term, but not short-term, memory for a nonassociative form of learning, sensitization, was modulated by the circadian clock in the diurnal Aplysia californica. To define the scope of circadian modulation of memory, we examined an associative operant learning paradigm, learning that food is inedible (LFI). Si...

Learning to Cook: Production Learning Environment in Kitchens

Science.gov (United States)

James, Susan

2006-01-01

Learning in workplaces is neither ad hoc nor informal. Such labels are a misnomer and do not do justice to the highly-structured nature and complexity of many workplaces where learning takes place. This article discusses the organisational and structural framework developed from a three-year doctoral study into how apprentice chefs construct their…

Adaptive Landmark-Based Navigation System Using Learning Techniques

DEFF Research Database (Denmark)

Zeidan, Bassel; Dasgupta, Sakyasingha; Wörgötter, Florentin

2014-01-01

The goal-directed navigational ability of animals is an essential prerequisite for them to survive. They can learn to navigate to a distal goal in a complex environment. During this long-distance navigation, they exploit environmental features, like landmarks, to guide them towards their goal. In...... hexapod robots. As a result, it allows the robots to successfully learn to navigate to distal goals in complex environments.......The goal-directed navigational ability of animals is an essential prerequisite for them to survive. They can learn to navigate to a distal goal in a complex environment. During this long-distance navigation, they exploit environmental features, like landmarks, to guide them towards their goal....... Inspired by this, we develop an adaptive landmark-based navigation system based on sequential reinforcement learning. In addition, correlation-based learning is also integrated into the system to improve learning performance. The proposed system has been applied to simulated simple wheeled and more complex...

[Learning how to learn for specialist further education].

Science.gov (United States)

Breuer, G; Lütcke, B; St Pierre, M; Hüttl, S

2017-02-01

The world of medicine is becoming from year to year more complex. This necessitates efficient learning processes, which incorporate the principles of adult education but with unchanged periods of further education. The subject matter must be processed, organized, visualized, networked and comprehended. The learning process should be voluntary and self-driven with the aim of learning the profession and becoming an expert in a specialist field. Learning is an individual process. Despite this, the constantly cited learning styles are nowadays more controversial. An important factor is a healthy mixture of blended learning methods, which also use new technical possibilities. These include a multitude of e‑learning options and simulations, which partly enable situative learning in a "shielded" environment. An exemplary role model of the teacher and feedback for the person in training also remain core and sustainable aspects in medical further education.

Employees' and Managers' Accounts of Interactive Workplace Learning: A Grounded Theory of "Complex Integrative Learning"

Science.gov (United States)

Armson, Genevieve; Whiteley, Alma

2010-01-01

Purpose: The purpose of this paper is to investigate employees' and managers' accounts of interactive learning and what might encourage or inhibit emergent learning. Design/methodology/approach: The approach taken was a constructivist/social constructivist ontology, interpretive epistemology and qualitative methodology, using grounded theory…

Learning Faster by Discovering and Exploiting Object Similarities

Directory of Open Access Journals (Sweden)

Tadej Janež

2013-03-01

Full Text Available In this paper we explore the question: “Is it possible to speed up the learning process of an autonomous agent by performing experiments in a more complex environment (i.e., an environment with a greater number of different objects?” To this end, we use a simple robotic domain, where the robot has to learn a qualitative model predicting the change in the robot's distance to an object. To quantify the environment's complexity, we defined cardinal complexity as the number of objects in the robot's world, and behavioural complexity as the number of objects' distinct behaviours. We propose Error reduction merging (ERM, a new learning method that automatically discovers similarities in the structure of the agent's environment. ERM identifies different types of objects solely from the data measured and merges the observations of objects that behave in the same or similar way in order to speed up the agent's learning. We performed a series of experiments in worlds of increasing complexity. The results in our simple domain indicate that ERM was capable of discovering structural similarities in the data which indeed made the learning faster, clearly superior to conventional learning. This observed trend occurred with various machine learning algorithms used inside the ERM method.

Improving e-learning by Emotive Feedback

DEFF Research Database (Denmark)

Sharp, Robin; Gjedde, Lisa

2011-01-01

This paper considers the use of feedback with emotive elements in order to improve the efficiency of e-learning for teaching complex technical subjects to the general public by stimulation of implicit learning. An example is presented, based on an effort to investigate the current level of IT sec......This paper considers the use of feedback with emotive elements in order to improve the efficiency of e-learning for teaching complex technical subjects to the general public by stimulation of implicit learning. An example is presented, based on an effort to investigate the current level...

Whose University is it anyway? The complex world(s) of lifelong (higher) learning, government policy and institutional habitus

OpenAIRE

Marr, Liz; Harvey, Morag

2012-01-01

At a time of worldwide economic recession, policy decisions at governmental and institutional level have to balance the basic human rights of access to education with the skills needs for economic competitiveness. This is playing out across Europe in a myriad of ways, as social problems exacerbated by lack of opportunity, add to the complexity of funding decisions.\\ud As part of the OPULL (Opening up Universities to Lifelong Learning) project, four European universities have been conducting r...

ARCHITECTURES FOR DISTRIBUTED AND COMPLEX M-LEARNING SYSTEMS: Applying Intelligent Technologies.

Directory of Open Access Journals (Sweden)

Ozlem OZAN,

2010-04-01

Full Text Available Today mobile technologies have become an integral part of the learning activities. With mobile technologies ―Any time, anywhere, any device‖ promise of e-learning is going to become actually applicable and mobile technologies are going to provide opportunities to be ―always on‖ and connected for twenty-first century learners and to get information on demand with ―just enough, just in time, and just for me‖ approach (Yamamoto, Ozan, & Demiray, 2010. Mobile technology includes both hardware and networking applications; hence both of them are necessary for the existence of m-Learning. Today one of the big challenges of mobile learning is technical issues. This book provides case studies and solution about technical applications of mobile learning.The book's broader audience is anyone who is interested in mobile learning systems‘ architecture. Beside this, it gives valuable information for mobile learning designers.The book is edited by The book is edited by Angel Juan , Thanasis Daradoumis, Fatos Xhafa and Santi Caballé. Angel A. Juan is an associate professor of simulation and data analysis in the computer sciences department at the Open University of Catalonia (Spain.Thanasis Daradoumis is an associate professor

Learning to Learn: towards a Relational and Transformational Model of Learning for Improved Integrated Care Delivery

Directory of Open Access Journals (Sweden)

John Diamond

2013-06-01

Full Text Available Health and social care systems are implementing fundamental changes to organizational structures and work practices in an effort to achieve integrated care. While some integration initiatives have produced positive outcomes, many have not. We reframe the concept of integration as a learning process fueled by knowledge exchange across diverse professional and organizational communities. We thus focus on the cognitive and social dynamics of learning in complex adaptive systems, and on learning behaviours and conditions that foster collective learning and improved collaboration. We suggest that the capacity to learn how to learn shapes the extent to which diverse professional groups effectively exchange knowledge and self-organize for integrated care delivery.

Fostering Complexity Thinking in Action Research for Change in Social-Ecological Systems

Directory of Open Access Journals (Sweden)

Kevin H. Rogers

2013-06-01

Full Text Available Complexity thinking is increasingly being embraced by a wide range of academics and professionals as imperative for dealing with today's pressing social-ecological challenges. In this context, action researchers partner directly with stakeholders (communities, governance institutions, and work resource managers, etc. to embed a complexity frame of reference for decision making. In doing so, both researchers and stakeholders must strive to internalize not only "intellectual complexity" (knowing but also "lived complexity" (being and practicing. Four common conceptualizations of learning (explicit/tacit knowledge framework; unlearning selective exposure; conscious/competence learning matrix; and model of learning loops are integrated to provide a new framework that describes how learning takes place in complex systems. Deep reflection leading to transformational learning is required to foster the changes in mindset and behaviors needed to adopt a complexity frame of reference. We then present three broad frames of mind (openness, situational awareness, and a healthy respect for the restraint/action paradox, which each encompass a set of habits of mind, to create a useful framework that allows one to unlearn reductionist habits while adopting and embedding those more conducive to working in complex systems. Habits of mind provide useful heuristic tools to guide researchers and stakeholders through processes of participative planning and adaptive decision making in complex social-ecological systems.

Preference Learning and Ranking by Pairwise Comparison

Science.gov (United States)

Fürnkranz, Johannes; Hüllermeier, Eyke

This chapter provides an overview of recent work on preference learning and ranking via pairwise classification. The learning by pairwise comparison (LPC) paradigm is the natural machine learning counterpart to the relational approach to preference modeling and decision making. From a machine learning point of view, LPC is especially appealing as it decomposes a possibly complex prediction problem into a certain number of learning problems of the simplest type, namely binary classification. We explain how to approach different preference learning problems, such as label and instance ranking, within the framework of LPC. We primarily focus on methodological aspects, but also address theoretical questions as well as algorithmic and complexity issues.

Learning about “wicked” problems in the Global South. Creating a film-based learning environment with “Visual Problem Appraisal”

Directory of Open Access Journals (Sweden)

Loes Witteveen

2012-03-01

Full Text Available The current complexity of sustainable development in the Global South calls for the design of learning strategies that can deal with this complexity. One such innovative learning strategy, called Visual Problem Appraisal (VPA, is highlighted in this article. The strategy is termed visual as it creates a learning environment that is film-based. VPA enhances the analysis of complex issues, and facilitates stakeholder dialogue and action planning. The strategy is used in workshops dealing with problem analysis and policy design, and involves the participants “meeting” stakeholders through filmed narratives. The article demonstrates the value of using film in multi stakeholder learning environments addressing issues concerning sustainable development.

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.

Early Language Learning: Complexity and Mixed Methods

Science.gov (United States)

Enever, Janet, Ed.; Lindgren, Eva, Ed.

2017-01-01

This is the first collection of research studies to explore the potential for mixed methods to shed light on foreign or second language learning by young learners in instructed contexts. It brings together recent studies undertaken in Cameroon, China, Croatia, Ethiopia, France, Germany, Italy, Kenya, Mexico, Slovenia, Spain, Sweden, Tanzania and…

Training complex judgment: The effects of critical thinking and complex judgment

NARCIS (Netherlands)

Helsdingen, Anne; Van Gog, Tamara; Van Merriënboer, Jeroen

2010-01-01

Helsdingen, A. S., Van Gog, T., & Van Merrienboer, J. J. G. (2009). Training complex judgment: The effects of critical thinking and contextual interference. Paper presented at the International Center for Learning, Education and Performance Systems (ICLEPS). Talahassee, Florida: Florida State

Complexity in language acquisition.

Science.gov (United States)

Clark, Alexander; Lappin, Shalom

2013-01-01

Learning theory has frequently been applied to language acquisition, but discussion has largely focused on information theoretic problems-in particular on the absence of direct negative evidence. Such arguments typically neglect the probabilistic nature of cognition and learning in general. We argue first that these arguments, and analyses based on them, suffer from a major flaw: they systematically conflate the hypothesis class and the learnable concept class. As a result, they do not allow one to draw significant conclusions about the learner. Second, we claim that the real problem for language learning is the computational complexity of constructing a hypothesis from input data. Studying this problem allows for a more direct approach to the object of study--the language acquisition device-rather than the learnable class of languages, which is epiphenomenal and possibly hard to characterize. The learnability results informed by complexity studies are much more insightful. They strongly suggest that target grammars need to be objective, in the sense that the primitive elements of these grammars are based on objectively definable properties of the language itself. These considerations support the view that language acquisition proceeds primarily through data-driven learning of some form. Copyright © 2013 Cognitive Science Society, Inc.

Revisiting the Blended Learning Literature: Using a Complex Adaptive Systems Framework

Science.gov (United States)

Wang, Yuping; Han, Xibin; Yang, Juan

2015-01-01

This research has two aims: (1) to bridge a gap in blended learning research--the lack of a systems approach to the understanding of blended learning research and practice, and (2) to promote a more comprehensive understanding of what has been achieved and what needs to be achieved in blended learning research and practice. To achieve these aims,…

THE DESIGNING OF ELECTRONIC TEACHING-METHODS COMPLEX «GRAPHICS» FOR REALIZATION OF COMPUTER-BASED LEARNING OF ENGINEERING-GRAPHIC DISCIPLINES

Directory of Open Access Journals (Sweden)

Іван Нищак

2015-12-01

Full Text Available The article contains Theoretical Foundations of designing of author’s electronic educational-methodical complex (EEMC «Graphics», intended to implement the engineering-graphic preparation of future teachers of technology in terms of computer-based learning. The process of designing of electronic educational-methodical complex “Graphics” includes the following successive stages: 1 identification of didactic goals and objectives; 2the designing of patterns of EEMC; 3 the selection of contents and systematization of educational material; 4 the program-technical implementation of EEMC; 5 interface design; 6 expert assessment of quality of EEMC; 7 testing of EEMC; 8 adjusting the software; 9 the development of guidelines and instructions for the use of EEMC.

Global learning for local solutions: Reducing vulnerability of marine-dependent coastal communities

Science.gov (United States)

Salim, S. S.; Paytan, A.

2016-12-01

The project `Global learning for local solutions: Reducing vulnerability of marine-dependent coastal communities' (GULLS) falls within the Belmont Forum and G8 Research Councils Initiative on Multilateral Research Funding. Participants include teams from nine countries: Australia, Brazil, India, Madagascar, Mozambique, New Zealand, South Africa, the United Kingdom and the United States of America. The project focuses on five regional `hotspots' of climate and social change, defined as fast-warming marine areas and areas experiencing social tensions as a result of change: south-east Australia, Brazil, India, South Africa, and the Mozambique Channel and adjacent countries of Mozambique and Madagascar. These areas require most urgent attention and serve as valuable case studies for wider applications. The project aims to assist coastal communities and other stakeholders dependent on marine resources to adapt to climate change and variability through an integrated and trans-disciplinary approach. Combining best available global knowledge with local knowledge and conditions, it is exploring adaptation options and approaches to strengthen resilience at local and community levels, with a focus on options for reconciling the needs for food security with long-term sustainability and conservation. The project will also contribute to capacity development and empowering fishing communities and other fisheries-dependent stakeholders.A standardized vulnerability assessment framework is being developed that will be used to integrate results from natural, social and economic studies in order to identify needs and options for strengthening management and existing policies. Structured comparisons between the hot-spots will assist global efforts for adaptation and strengthening resilience in marine and coastal social-ecological systems.

Unified Computational Intelligence for Complex Systems

CERN Document Server

Seiffertt, John

2010-01-01

Computational intelligence encompasses a wide variety of techniques that allow computation to learn, to adapt, and to seek. That is, they may be designed to learn information without explicit programming regarding the nature of the content to be retained, they may be imbued with the functionality to adapt to maintain their course within a complex and unpredictably changing environment, and they may help us seek out truths about our own dynamics and lives through their inclusion in complex system modeling. These capabilities place our ability to compute in a category apart from our ability to e

Emergent Learning and Learning Ecologies in Web 2.0

Directory of Open Access Journals (Sweden)

Roy Williams

2011-03-01

Full Text Available This paper describes emergent learning and situates it within learning networks and systems and the broader learning ecology of Web 2.0. It describes the nature of emergence and emergent learning and the conditions that enable emergent, self-organised learning to occur and to flourish. Specifically, it explores whether emergent learning can be validated and self-correcting and whether it is possible to link or integrate emergent and prescribed learning. It draws on complexity theory, communities of practice, and the notion of connectivism to develop some of the foundations for an analytic framework, for enabling and managing emergent learning and networks in which agents and systems co-evolve. It then examines specific cases of learning to test and further develop the analytic framework.The paper argues that although social networking media increase the potential range and scope for emergent learning exponentially, considerable effort is required to ensure an effective balance between openness and constraint. It is possible to manage the relationship between prescriptive and emergent learning, both of which need to be part of an integrated learning ecology.

A Holistic Approach to Scoring in Complex Mobile Learning Scenarios

Science.gov (United States)

Gebbe, Marcel; Teine, Matthias; Beutner, Marc

2016-01-01

Interactive dialogues are key elements for designing authentic and motivating learning situations, and in combination with learning analysis they provide educators and users with the opportunity to track information related to professional competences, but mind-sets as well. This paper offers exemplary insights into the project NetEnquiry that is…

Learning, working memory, and intelligence revisited.

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Tamez, Elaine; Myerson, Joel; Hale, Sandra

2008-06-01

Based on early findings showing low correlations between intelligence test scores and learning on laboratory tasks, psychologists typically have dismissed the role of learning in intelligence and emphasized the role of working memory instead. In 2006, however, B.A. Williams developed a verbal learning task inspired by three-term reinforcement contingencies and reported unexpectedly high correlations between this task and Raven's Advanced Progressive Matrices (RAPM) scores [Williams, B.A., Pearlberg, S.L., 2006. Learning of three-term contingencies correlates with Raven scores, but not with measures of cognitive processing. Intelligence 34, 177-191]. The present study replicated this finding: Performance on the three-term learning task explained almost 25% of the variance in RAPM scores. Adding complex verbal working memory span, measured using the operation span task, did not improve prediction. Notably, this was not due to a lack of correlation between complex working memory span and RAPM scores. Rather, it occurred because most of the variance captured by the complex working memory span was already accounted for by the three-term learning task. Taken together with the findings of Williams and Pearlberg, the present results make a strong case for the role of learning in performance on intelligence tests.

Convergence of Batch Split-Complex Backpropagation Algorithm for Complex-Valued Neural Networks

Directory of Open Access Journals (Sweden)

Huisheng Zhang

2009-01-01

Full Text Available The batch split-complex backpropagation (BSCBP algorithm for training complex-valued neural networks is considered. For constant learning rate, it is proved that the error function of BSCBP algorithm is monotone during the training iteration process, and the gradient of the error function tends to zero. By adding a moderate condition, the weights sequence itself is also proved to be convergent. A numerical example is given to support the theoretical analysis.

Learning to live with complexity.

Science.gov (United States)

Sargut, Gökçe; McGrath, Rita Gunther

2011-09-01

Business life has always featured the unpredictable, the surprising, and the unexpected. But in today's hyperconnected world, complexity is the norm. Systems that used to be separate are now intertwined and interdependent, and knowing the starting conditions is no guide to predicting outcomes; too many continuously changing interactive elements are in play. Managers looking to navigate these difficulties need to adopt new approaches. They should drop outmoded forecasting tools-for example, ones that rely on averages, which are often less important than outliers. Instead, they should use models that simulate the behavior of the system. They should also make sure that their data include a good amount of future-oriented information. Risk mitigation is crucial as well. Managers should minimize the need to rely on predictions-for instance, they can give users a say in product design. They can decouple elements in a system and build in redundancy to minimize the consequences of a partial system failure, and turn to outside partners to extend their own company's capabilities. They can complement hard analysis with "soft" methods such as storytelling to make potentially important future possibilities more real. And they can make trade-offs that keep early failures small and provide the diversity of thought needed in a nimble organization faced with complexity on virtually every front.

Pilot Skill Development with Implicit and Explicit Learning: Considerations for Task Complexity

National Research Council Canada - National Science Library

Sullivan, Ryan

2000-01-01

.... Research in learning strategies has recently focused on implicit and explicit learning to determine if it is more important to focus on conscious facts or unconscious procedural performance during the learning process...

Exploring the application of an evolutionary educational complex systems framework to teaching and learning about issues in the science and technology classroom

Science.gov (United States)

Yoon, Susan Anne

Understanding the world through a complex systems lens has recently garnered a great deal of interest in many knowledge disciplines. In the educational arena, interactional studies, through their focus on understanding patterns of system behaviour including the dynamical processes and trajectories of learning, lend support for investigating how a complex systems approach can inform educational research. This study uses previously existing literature and tools for complex systems applications and seeks to extend this research base by exploring learning outcomes of a complex systems framework when applied to curriculum and instruction. It is argued that by applying the evolutionary dynamics of variation, interaction and selection, complexity may be harnessed to achieve growth in both the social and cognitive systems of the classroom. Furthermore, if the goal of education, i.e., the social system under investigation, is to teach for understanding, conceptual knowledge of the kind described in Popper's (1972; 1976) World 3, needs to evolve. Both the study of memetic processes and knowledge building pioneered by Bereiter (cf. Bereiter, 2002) draw on the World 3 notion of ideas existing as conceptual artifacts that can be investigated as products outside of the individual mind providing an educational lens from which to proceed. The curricular topic addressed is the development of an ethical understanding of the scientific and technological issues of genetic engineering. 11 grade 8 students are studied as they proceed through 40 hours of curricular instruction based on the complex systems evolutionary framework. Results demonstrate growth in both complex systems thinking and content knowledge of the topic of genetic engineering. Several memetic processes are hypothesized to have influenced how and why ideas change. Categorized by factors influencing either reflective or non-reflective selection, these processes appear to have exerted differential effects on students

Measuring unintended effects in peacebuilding: What the field of international cooperation can learn from innovative approaches shaped by complex contexts.

Science.gov (United States)

Lemon, Adrienne; Pinet, Mélanie

2018-06-01

Capturing unintended impacts has been a persistent struggle in all fields of international development, and the field of peacebuilding is no exception. However, because peacebuilding focuses on relationships in complex contexts, the field of peacebuilding has, by necessity, made efforts towards finding practical ways to reflect upon both the intended and unintended effects of this work. To explore what lessons can be learned from the peacebuilding field, this study examines the evaluations of Search for Common Ground, a peacebuilding organisation working in over 35 countries across the world. Analysis focuses on 96 evaluations completed between 2013 and 2016 in 24 countries across Africa, Asia, and the MENA regions that found unintended effects. Programmes focusing on women, youth, and radio were most effective at identifying and explaining unintended effects, likely because the project design guided broader lines of questioning from the beginning. The paper argues that OECD-DAC guidelines are not enough on their own to guide evaluators into exploration of unintended effects, and teams instead need to work together to decide where, when and how they will look for them. Different approaches were also used to capture positive and negative outcomes, suggesting that evaluators need to decide at what level they are evaluating and how to tie effects back to the project's contribution. This study explores evaluation techniques and approaches used to understand impact in complex contexts in the peacebuilding field, and draws on lessons learned for the benefit of other fields dealing with similar complexities in international development and cooperation among actors. Copyright © 2017 Elsevier Ltd. All rights reserved.

Teachability in Computational Learning

OpenAIRE

Shinohara, Ayumi; Miyano, Satoru

1990-01-01

This paper considers computationai learning from the viewpoint of teaching. We introduce a notion of teachability with which we establish a relationship between the learnability and teachability. We also discuss the complexity issues of a teacher in relation to learning.

Implicit learning as an ability.

Science.gov (United States)

Kaufman, Scott Barry; Deyoung, Colin G; Gray, Jeremy R; Jiménez, Luis; Brown, Jamie; Mackintosh, Nicholas

2010-09-01

The ability to automatically and implicitly detect complex and noisy regularities in the environment is a fundamental aspect of human cognition. Despite considerable interest in implicit processes, few researchers have conceptualized implicit learning as an ability with meaningful individual differences. Instead, various researchers (e.g., Reber, 1993; Stanovich, 2009) have suggested that individual differences in implicit learning are minimal relative to individual differences in explicit learning. In the current study of English 16-17year old students, we investigated the association of individual differences in implicit learning with a variety of cognitive and personality variables. Consistent with prior research and theorizing, implicit learning, as measured by a probabilistic sequence learning task, was more weakly related to psychometric intelligence than was explicit associative learning, and was unrelated to working memory. Structural equation modeling revealed that implicit learning was independently related to two components of psychometric intelligence: verbal analogical reasoning and processing speed. Implicit learning was also independently related to academic performance on two foreign language exams (French, German). Further, implicit learning was significantly associated with aspects of self-reported personality, including intuition, Openness to Experience, and impulsivity. We discuss the implications of implicit learning as an ability for dual-process theories of cognition, intelligence, personality, skill learning, complex cognition, and language acquisition. 2010 Elsevier B.V. All rights reserved.

Grasping the Dynamic Complexity of Team Learning: An Integrative Model for Effective Team Learning in Organisations

Science.gov (United States)

Decuyper, Stefan; Dochy, Filip; Van den Bossche, Piet

2010-01-01

In this article we present an integrative model of team learning. Literature shows that effective team learning requires the establishment of a dialogical space amongst team members, in which communicative behaviours such as "sharing", "co-construction" and "constructive conflict" are balanced. However, finding this balance is not enough.…

Complex Dynamic Systems View on Conceptual Change: How a Picture of Students' Intuitive Conceptions Accrue from Dynamically Robust Task Dependent Learning Outcomes

Science.gov (United States)

Koponen, Ismo T.; Kokkonen, Tommi; Nousiainen, Maiji

2017-01-01

We discuss here conceptual change and the formation of robust learning outcomes from the viewpoint of complex dynamic systems (CDS). The CDS view considers students' conceptions as context dependent and multifaceted structures which depend on the context of their application. In the CDS view the conceptual patterns (i.e. intuitive conceptions…

Leading healthcare in complexity.

Science.gov (United States)

Cohn, Jeffrey

2014-12-01

Healthcare institutions and providers are in complexity. Networks of interconnections from relationships and technology create conditions in which interdependencies and non-linear dynamics lead to surprising, unpredictable outcomes. Previous effective approaches to leadership, focusing on top-down bureaucratic methods, are no longer effective. Leading in complexity requires leaders to accept the complexity, create an adaptive space in which innovation and creativity can flourish and then integrate the successful practices that emerge into the formal organizational structure. Several methods for doing adaptive space work will be discussed. Readers will be able to contrast traditional leadership approaches with leading in complexity. They will learn new behaviours that are required of complexity leaders, along with challenges they will face, often from other leaders within the organization.

Learning about “wicked” problems in the Global South. Creating a film-based learning environment with “Visual Problem Appraisal”

NARCIS (Netherlands)

Witteveen, L.M.; Lie, R.

2012-01-01

The current complexity of sustainable development in the Global South calls for the design of learning strategies that can deal with this complexity. One such innovative learning strategy, called Visual Problem Appraisal (VPA), is highlighted in this article. The strategy is termed visual as it

Visual Short-Term Memory Complexity

DEFF Research Database (Denmark)

Sørensen, Thomas Alrik

of objective complexity, it seems that subjective complexity - which is dependent on the familiarity of the stimulus - plays a more important role than the objective visual complexity of the objects stored. In two studies, we explored how familiarity influences the capacity of VSTM. 1) In children learning...... and Cavanagh (2004) have raised the question that the capacity of VSTM is dependent on visual complexity rather than the number of objects. We hypothesise that VSTM capacity is dependent on both the objective and subjective complexity of visual stimuli. Contrary to Alvarez and Cavanagh, who argue for the role...... for letters and pictures remained similar. Our results indicate that VSTM capacity for familiar items is larger irrespective of visual complexity....

Machine Learning

CERN Multimedia

CERN. Geneva

2017-01-01

Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

Learning reduced kinetic Monte Carlo models of complex chemistry from molecular dynamics.

Science.gov (United States)

Yang, Qian; Sing-Long, Carlos A; Reed, Evan J

2017-08-01

We propose a novel statistical learning framework for automatically and efficiently building reduced kinetic Monte Carlo (KMC) models of large-scale elementary reaction networks from data generated by a single or few molecular dynamics simulations (MD). Existing approaches for identifying species and reactions from molecular dynamics typically use bond length and duration criteria, where bond duration is a fixed parameter motivated by an understanding of bond vibrational frequencies. In contrast, we show that for highly reactive systems, bond duration should be a model parameter that is chosen to maximize the predictive power of the resulting statistical model. We demonstrate our method on a high temperature, high pressure system of reacting liquid methane, and show that the learned KMC model is able to extrapolate more than an order of magnitude in time for key molecules. Additionally, our KMC model of elementary reactions enables us to isolate the most important set of reactions governing the behavior of key molecules found in the MD simulation. We develop a new data-driven algorithm to reduce the chemical reaction network which can be solved either as an integer program or efficiently using L1 regularization, and compare our results with simple count-based reduction. For our liquid methane system, we discover that rare reactions do not play a significant role in the system, and find that less than 7% of the approximately 2000 reactions observed from molecular dynamics are necessary to reproduce the molecular concentration over time of methane. The framework described in this work paves the way towards a genomic approach to studying complex chemical systems, where expensive MD simulation data can be reused to contribute to an increasingly large and accurate genome of elementary reactions and rates.

Quantum Speedup for Active Learning Agents

Directory of Open Access Journals (Sweden)

Giuseppe Davide Paparo

2014-07-01

Full Text Available Can quantum mechanics help us build intelligent learning agents? A defining signature of intelligent behavior is the capacity to learn from experience. However, a major bottleneck for agents to learn in real-life situations is the size and complexity of the corresponding task environment. Even in a moderately realistic environment, it may simply take too long to rationally respond to a given situation. If the environment is impatient, allowing only a certain time for a response, an agent may then be unable to cope with the situation and to learn at all. Here, we show that quantum physics can help and provide a quadratic speedup for active learning as a genuine problem of artificial intelligence. This result will be particularly relevant for applications involving complex task environments.

Study of the convergence behavior of the complex kernel least mean square algorithm.

Science.gov (United States)

Paul, Thomas K; Ogunfunmi, Tokunbo

2013-09-01

The complex kernel least mean square (CKLMS) algorithm is recently derived and allows for online kernel adaptive learning for complex data. Kernel adaptive methods can be used in finding solutions for neural network and machine learning applications. The derivation of CKLMS involved the development of a modified Wirtinger calculus for Hilbert spaces to obtain the cost function gradient. We analyze the convergence of the CKLMS with different kernel forms for complex data. The expressions obtained enable us to generate theory-predicted mean-square error curves considering the circularity of the complex input signals and their effect on nonlinear learning. Simulations are used for verifying the analysis results.

Learning and Motivational Processes When Students Design Curriculum-Based Digital Learning Games

DEFF Research Database (Denmark)

Weitze, Charlotte Lærke

2015-01-01

This design-based research (DBR) project has developed an overall gamified learning design (big Game) to facilitate the learning process for adult students by inviting them to be their own learning designers through designing digital learning games (small games) in cross-disciplinary subject...... matters. The DBR project has investigated and experimented with which elements, methods, and processes are important when aiming at creating a cognitive complex (Anderson and Krathwohl, 2001) and motivating learning process within a reusable game-based learning design. This project took place in a co......, or programming provide a rich context for learning, since the construction of artefacts, in this case learning games, enables reflection and new ways of thinking. The students learned from reflection and interaction with the tools alone as well as in collaboration with peers. After analysing the students...

Machine Learning in Medicine.

Science.gov (United States)

Deo, Rahul C

2015-11-17

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome. © 2015 American Heart Association, Inc.

Machine Learning in Medicine

Science.gov (United States)

Deo, Rahul C.

2015-01-01

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games – tasks which would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in healthcare. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades – and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome. PMID:26572668

Learning with incomplete information in the committee machine.

Science.gov (United States)

Bergmann, Urs M; Kühn, Reimer; Stamatescu, Ion-Olimpiu

2009-12-01

We study the problem of learning with incomplete information in a student-teacher setup for the committee machine. The learning algorithm combines unsupervised Hebbian learning of a series of associations with a delayed reinforcement step, in which the set of previously learnt associations is partly and indiscriminately unlearnt, to an extent that depends on the success rate of the student on these previously learnt associations. The relevant learning parameter lambda represents the strength of Hebbian learning. A coarse-grained analysis of the system yields a set of differential equations for overlaps of student and teacher weight vectors, whose solutions provide a complete description of the learning behavior. It reveals complicated dynamics showing that perfect generalization can be obtained if the learning parameter exceeds a threshold lambda ( c ), and if the initial value of the overlap between student and teacher weights is non-zero. In case of convergence, the generalization error exhibits a power law decay as a function of the number of examples used in training, with an exponent that depends on the parameter lambda. An investigation of the system flow in a subspace with broken permutation symmetry between hidden units reveals a bifurcation point lambda* above which perfect generalization does not depend on initial conditions. Finally, we demonstrate that cases of a complexity mismatch between student and teacher are optimally resolved in the sense that an over-complex student can emulate a less complex teacher rule, while an under-complex student reaches a state which realizes the minimal generalization error compatible with the complexity mismatch.

Multimodal sequence learning.

Science.gov (United States)

Kemény, Ferenc; Meier, Beat

2016-02-01

While sequence learning research models complex phenomena, previous studies have mostly focused on unimodal sequences. The goal of the current experiment is to put implicit sequence learning into a multimodal context: to test whether it can operate across different modalities. We used the Task Sequence Learning paradigm to test whether sequence learning varies across modalities, and whether participants are able to learn multimodal sequences. Our results show that implicit sequence learning is very similar regardless of the source modality. However, the presence of correlated task and response sequences was required for learning to take place. The experiment provides new evidence for implicit sequence learning of abstract conceptual representations. In general, the results suggest that correlated sequences are necessary for implicit sequence learning to occur. Moreover, they show that elements from different modalities can be automatically integrated into one unitary multimodal sequence. Copyright © 2015 Elsevier B.V. All rights reserved.

Theorizing Learning Process: An Experiential, Constructivist Approach to Young People's Learning about Global Poverty and Development

Science.gov (United States)

Brown, Kate

2015-01-01

Learning processes in global education have not been significantly theorized, with the notable exception of the application of transformative learning theory. No theory of learning is complete, and to understand the complexity of learning, multiple theoretical lenses must be applied. This article looks at Jarvis's (2006) model of lifelong learning…

Towards effective learning strategies

NARCIS (Netherlands)

Bergstra, Anouk Simone

2015-01-01

To become self-regulative in learning, students should be able to deploy various learning strategies in a flexible way. For this, they require specific knowledge and skills, referred to as metacognition. Metacognition is a complex concept that is difficult for teachers to teach to their students.

Building flexibility and managing complexity in community mental health: lessons learned in a large urban centre.

Science.gov (United States)

Stergiopoulos, Vicky; Saab, Dima; Francombe Pridham, Kate; Aery, Anjana; Nakhost, Arash

2018-01-24

Across many jurisdictions, adults with complex mental health and social needs face challenges accessing appropriate supports due to system fragmentation and strict eligibility criteria of existing services. To support this underserviced population, Toronto's local health authority launched two novel community mental health models in 2014, inspired by Flexible Assertive Community Team principles. This study explores service user and provider perspectives on the acceptability of these services, and lessons learned during early implementation. We purposively sampled 49 stakeholders (staff, physicians, service users, health systems stakeholders) and conducted 17 semi-structured qualitative interviews and 5 focus groups between October 23, 2014 and March 2, 2015, exploring stakeholder perspectives on the newly launched team based models, as well as activities and strategies employed to support early implementation. Interviews and focus groups were audio recorded, transcribed verbatim and analyzed using thematic analysis. Findings revealed wide-ranging endorsement for the two team-based models' success in engaging the target population of adults with complex service needs. Implementation strengths included the broad recognition of existing service gaps, the use of interdisciplinary teams and experienced service providers, broad partnerships and collaboration among various service sectors, training and team building activities. Emerging challenges included lack of complementary support services such as suitable housing, organizational contexts reluctant to embrace change and risk associated with complexity, as well as limited service provider and organizational capacity to deliver evidence-based interventions. Findings identified implementation drivers at the practitioner, program, and system levels, specific to the implementation of community mental health interventions for adults with complex health and social needs. These can inform future efforts to address the health

Knowledge Visualization for Self-Regulated Learning

Science.gov (United States)

Wang, Minhong; Peng, Jun; Cheng, Bo; Zhou, Hance; Liu, Jie

2011-01-01

The Web allows self-regulated learning through interaction with large amounts of learning resources. While enjoying the flexibility of learning, learners may suffer from cognitive overload and conceptual and navigational disorientation when faced with various information resources under disparate topics and complex knowledge structures. This study…

Factors affecting learning of vector math from computer-based practice: Feedback complexity and prior knowledge

Directory of Open Access Journals (Sweden)

Andrew F. Heckler

2016-06-01

Full Text Available In experiments including over 450 university-level students, we studied the effectiveness and time efficiency of several levels of feedback complexity in simple, computer-based training utilizing static question sequences. The learning domain was simple vector math, an essential skill in introductory physics. In a unique full factorial design, we studied the relative effects of “knowledge of correct response” feedback and “elaborated feedback” (i.e., a general explanation both separately and together. A number of other factors were analyzed, including training time, physics course grade, prior knowledge of vector math, and student beliefs about both their proficiency in and the importance of vector math. We hypothesize a simple model predicting how the effectiveness of feedback depends on prior knowledge, and the results confirm this knowledge-by-treatment interaction. Most notably, elaborated feedback is the most effective feedback, especially for students with low prior knowledge and low course grade. In contrast, knowledge of correct response feedback was less effective for low-performing students, and including both kinds of feedback did not significantly improve performance compared to elaborated feedback alone. Further, while elaborated feedback resulted in higher scores, the learning rate was at best only marginally higher because the training time was slightly longer. Training time data revealed that students spent significantly more time on the elaborated feedback after answering a training question incorrectly. Finally, we found that training improved student self-reported proficiency and that belief in the importance of the learned domain improved the effectiveness of training. Overall, we found that computer based training with static question sequences and immediate elaborated feedback in the form of simple and general explanations can be an effective way to improve student performance on a physics essential skill

Self-Paced Prioritized Curriculum Learning With Coverage Penalty in Deep Reinforcement Learning.

Science.gov (United States)

Ren, Zhipeng; Dong, Daoyi; Li, Huaxiong; Chen, Chunlin; Zhipeng Ren; Daoyi Dong; Huaxiong Li; Chunlin Chen; Dong, Daoyi; Li, Huaxiong; Chen, Chunlin; Ren, Zhipeng

2018-06-01

In this paper, a new training paradigm is proposed for deep reinforcement learning using self-paced prioritized curriculum learning with coverage penalty. The proposed deep curriculum reinforcement learning (DCRL) takes the most advantage of experience replay by adaptively selecting appropriate transitions from replay memory based on the complexity of each transition. The criteria of complexity in DCRL consist of self-paced priority as well as coverage penalty. The self-paced priority reflects the relationship between the temporal-difference error and the difficulty of the current curriculum for sample efficiency. The coverage penalty is taken into account for sample diversity. With comparison to deep Q network (DQN) and prioritized experience replay (PER) methods, the DCRL algorithm is evaluated on Atari 2600 games, and the experimental results show that DCRL outperforms DQN and PER on most of these games. More results further show that the proposed curriculum training paradigm of DCRL is also applicable and effective for other memory-based deep reinforcement learning approaches, such as double DQN and dueling network. All the experimental results demonstrate that DCRL can achieve improved training efficiency and robustness for deep reinforcement learning.

Learning and Motivational Processes When Students Design Curriculum‐Based Digital Learning Games

DEFF Research Database (Denmark)

Weitze, Charlotte Lærke

2016-01-01

This design‐based research (DBR) project has developed an overall gamified learning design (big Game) to facilitate the learning process for adult students by inviting them to be their own learning designers through designing digital learning games (small games) in cross‐disciplinary subject...... matters. The DBR project has investigated and experimented with which elements, methods, and processes are important when aiming at creating a cognitive complex (Anderson and Krathwohl, 2001) and motivating learning process within a reusable game‐based learning design. This project took place in a co......, or programming provide a rich context for learning, since the construction of artefacts, in this case learning games, enables reflection and new ways of thinking. The students learned from reflection and interaction with the tools alone as well as in collaboration with peers. After analysing the students...

Block Play and Mathematics Learning in Preschool: The Effects of Building Complexity, Peer and Teacher Interactions in the Block Area, and Replica Play Materials

Science.gov (United States)

Trawick-Smith, Jeffrey; Swaminathan, Sudha; Baton, Brooke; Danieluk, Courtney; Marsh, Samantha; Szarwacki, Monika

2017-01-01

Block play has been included in early childhood classrooms for over a century, yet few studies have examined its effects on learning. Several previous investigations indicate that the complexity of block building is associated with math ability, but these studies were often conducted in adult-guided, laboratory settings. In the present…

System complexity and (im)possible sound changes

NARCIS (Netherlands)

Seinhorst, K.T.

2016-01-01

In the acquisition of phonological patterns, learners tend to considerably reduce the complexity of their input. This learning bias may also constrain the set of possible sound changes, which might be expected to contain only those changes that do not increase the complexity of the system. However,

Transformative learning spaces

DEFF Research Database (Denmark)

Maslo, Elina

Despite rapid development of learning theory in general and language learning theory in particular in the last years, we still cannot provide an unequivocal answer on the question “why do individuals who presumably possess similar cognitive capacities for second language learning achieve such var......, Leo (2010). The ecology of language learning: Practice to theory, theory to practice. Procedia – Social and Behavioral Sciences. Elsevier......., social, personal, cultural, and historical world they live in (van Lier, 2000). People can learn when they discover possibilities for learning, which appear in this complex world – so called affordances (Gibson, 1979). This happens in the interaction between people and their environment on the basis...... to the different ways of interaction of cognitive, affective and social factors by different individuals. Learning stories, where multilingual individuals are telling about their subjective experiences in language learning in particular and learning in general, are constructed by using a special developed...

Capturing the complexity: Content, type, and amount of instruction and quality of the classroom learning environment synergistically predict third graders' vocabulary and reading comprehension outcomes.

Science.gov (United States)

Connor, Carol McDonald; Spencer, Mercedes; Day, Stephanie L; Giuliani, Sarah; Ingebrand, Sarah W; McLean, Leigh; Morrison, Frederick J

2014-08-01

We examined classrooms as complex systems that affect students' literacy learning through interacting effects of content and amount of time individual students spent in literacy instruction along with the global quality of the classroom-learning environment. We observed 27 third grade classrooms serving 315 target students using two different observation systems. The first assessed instruction at a more micro-level; specifically, the amount of time individual students spent in literacy instruction defined by the type of instruction, role of the teacher, and content. The second assessed the quality of the classroom-learning environment at a more macro level focusing on classroom organization, teacher responsiveness, and support for vocabulary and language. Results revealed that both global quality of the classroom learning environment and time individual students spent in specific types of literacy instruction covering specific content interacted to predict students' comprehension and vocabulary gains whereas neither system alone did. These findings support a dynamic systems model of how individual children learn in the context of classroom literacy instruction and the classroom-learning environment, which can help to improve observations systems, advance research, elevate teacher evaluation and professional development, and enhance student achievement.

Capturing the complexity: Content, type, and amount of instruction and quality of the classroom learning environment synergistically predict third graders’ vocabulary and reading comprehension outcomes

Science.gov (United States)

Connor, Carol McDonald; Spencer, Mercedes; Day, Stephanie L.; Giuliani, Sarah; Ingebrand, Sarah W.; McLean, Leigh; Morrison, Frederick J.

2014-01-01

We examined classrooms as complex systems that affect students’ literacy learning through interacting effects of content and amount of time individual students spent in literacy instruction along with the global quality of the classroom-learning environment. We observed 27 third grade classrooms serving 315 target students using two different observation systems. The first assessed instruction at a more micro-level; specifically, the amount of time individual students spent in literacy instruction defined by the type of instruction, role of the teacher, and content. The second assessed the quality of the classroom-learning environment at a more macro level focusing on classroom organization, teacher responsiveness, and support for vocabulary and language. Results revealed that both global quality of the classroom learning environment and time individual students spent in specific types of literacy instruction covering specific content interacted to predict students’ comprehension and vocabulary gains whereas neither system alone did. These findings support a dynamic systems model of how individual children learn in the context of classroom literacy instruction and the classroom-learning environment, which can help to improve observations systems, advance research, elevate teacher evaluation and professional development, and enhance student achievement. PMID:25400293

Developmental song learning as a model to understand neural mechanisms that limit and promote the ability to learn.

Science.gov (United States)

London, Sarah E

2017-11-20

Songbirds famously learn their vocalizations. Some species can learn continuously, others seasonally, and still others just once. The zebra finch (Taeniopygia guttata) learns to sing during a single developmental "Critical Period," a restricted phase during which a specific experience has profound and permanent effects on brain function and behavioral patterns. The zebra finch can therefore provide fundamental insight into features that promote and limit the ability to acquire complex learned behaviors. For example, what properties permit the brain to come "on-line" for learning? How does experience become encoded to prevent future learning? What features define the brain in receptive compared to closed learning states? This piece will focus on epigenomic, genomic, and molecular levels of analysis that operate on the timescales of development and complex behavioral learning. Existing data will be discussed as they relate to Critical Period learning, and strategies for future studies to more directly address these questions will be considered. Birdsong learning is a powerful model for advancing knowledge of the biological intersections of maturation and experience. Lessons from its study not only have implications for understanding developmental song learning, but also broader questions of learning potential and the enduring effects of early life experience on neural systems and behavior. Copyright © 2017. Published by Elsevier B.V.

Satlc model lesson for teaching and learning complex ...

African Journals Online (AJOL)

Environmental chemistry is one of the disciplines of Science. For the goal of the deep learning of the subject, it is indispensable to present perception and models of chemical behaviour explicitly. This can be accomplished by giving careful consideration to the development of concepts such that newer approaches are given ...

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.

Perspectives on competency-based medical education from the learning sciences.

Science.gov (United States)

Swing, Susan R

2010-01-01

A central component of competency-based medical education is a framework of higher-order and more fundamental competencies whose purpose is to focus instruction and learning. In the language of the learning sciences, many of these competencies are complex cognitive-perceptual or cognitive-motor skills. Competency-based medical education has been criticized for being reductionistic, that is, for focusing on atomistic skills and failing to capture the essence of professional activities as manifested by complex, integrated capabilities. The value of identifying fundamental skill components is supported by theory and evidence from the learning sciences, however. Complex skills are constructed from fundamental, component skills. Proficient performance of the former is achieved as components are refined and integrated during repeated performance of the skill in a realistic context and as feedback on performance is provided. Competency-based medical education does not propose specific methods for teaching competencies. The learning and instructional sciences, however, posit a number of conditions for learning that support the acquisition of simple skills and their flexible integration into complex capabilities. Learners' motivation and self-regulation skills will also have an impact on the extent to which they engage in learning processes that result in the integration of knowledge and skills into complex competencies.

Imbalanced learning foundations, algorithms, and applications

CERN Document Server

He, Haibo

2013-01-01

The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles,

Learning Vue.js 2 learn how to build amazing and complex reactive web applications easily with Vue.js

CERN Document Server

Filipova, Olga

2016-01-01

About This Book Learn how to propagate DOM changes across the website without writing extensive jQuery callbacks code. Learn how to achieve reactivity and easily compose views with Vue.js and understand what it does behind the scenes. Explore the core features of Vue.js with small examples, learn how to build dynamic content into preexisting web applications, and build Vue.js applications from scratch. Who This Book Is For This book is perfect for novice web developer seeking to learn new technologies or frameworks and also for webdev gurus eager to enrich their experience. Whatever your level of expertise, this book is a great introduction to the wonderful world of reactive web apps. What You Will Learn Build a fully functioning reactive web application in Vue.js from scratch. The importance of the MVVM architecture and how Vue.js compares with other frameworks such as Angular.js and React.js. How to bring reactivity to an existing static application using Vue.js. How to use p...

Social learning for solving complex problems: a promising solution or wishful thinking? A case study of multi-actor negotiation for the integrated management and sustainable use of the Drentsche Aa area in the Netherlands

NARCIS (Netherlands)

Bommel, van S.; Roling, N.G.; Aarts, M.N.C.; Turnhout, E.

2009-01-01

Social learning has been championed as a promising approach to address complex resource problems. According to theory, social learning requires several pre-conditions to be met, including (1) a divergence of interests, (2) mutual interdependence and (3) the ability to communicate. This article

Organizational Learning in Health Care Organizations

Directory of Open Access Journals (Sweden)

Savithiri Ratnapalan

2014-02-01

Full Text Available The process of collective education in an organization that has the capacity to impact an organization’s operations, performance and outcomes is called organizational learning. In health care organizations, patient care is provided through one or more visible and invisible teams. These teams are composed of experts and novices from diverse backgrounds working together to provide coordinated care. The number of teams involved in providing care and the possibility of breakdowns in communication and coordinated care increases in direct proportion to sophisticated technology and treatment strategies of complex disease processes. Safe patient care is facilitated by individual professional learning; inter-professional team learning and system based organizational learning, which encompass modified context specific learning by multiple teams and team members in a health care organization. Organizational learning in health care systems is central to managing the learning requirements in complex interconnected dynamic systems where all have to know common background knowledge along with shared meta-knowledge of roles and responsibilities to execute their assigned functions, communicate and transfer the flow of pertinent information and collectively provide safe patient care. Organizational learning in health care is not a onetime intervention, but a continuing organizational phenomenon that occurs through formal and informal learning which has reciprocal association with organizational change. As such, organizational changes elicit organizational learning and organizational learning implements new knowledge and practices to create organizational changes.

Modelling unsupervised online-learning of artificial grammars: linking implicit and statistical learning.

Science.gov (United States)

Rohrmeier, Martin A; Cross, Ian

2014-07-01

Humans rapidly learn complex structures in various domains. Findings of above-chance performance of some untrained control groups in artificial grammar learning studies raise questions about the extent to which learning can occur in an untrained, unsupervised testing situation with both correct and incorrect structures. The plausibility of unsupervised online-learning effects was modelled with n-gram, chunking and simple recurrent network models. A novel evaluation framework was applied, which alternates forced binary grammaticality judgments and subsequent learning of the same stimulus. Our results indicate a strong online learning effect for n-gram and chunking models and a weaker effect for simple recurrent network models. Such findings suggest that online learning is a plausible effect of statistical chunk learning that is possible when ungrammatical sequences contain a large proportion of grammatical chunks. Such common effects of continuous statistical learning may underlie statistical and implicit learning paradigms and raise implications for study design and testing methodologies. Copyright © 2014 Elsevier Inc. All rights reserved.

Lessons learned bulletin

International Nuclear Information System (INIS)

1994-05-01

During the past four years, the Department of Energy -- Savannah River Operations Office and the Westinghouse Savannah River Company (WSRC) Environmental Restoration (ER) Program completed various activities ranging from waste site investigations to closure and post closure projects. Critiques for lessons learned regarding project activities are performed at the completion of each project milestone, and this critique interval allows for frequent recognition of lessons learned. In addition to project related lessons learned, ER also performs lessons learned critiques. T'he Savannah River Site (SRS) also obtains lessons learned information from general industry, commercial nuclear industry, naval nuclear programs, and other DOE sites within the complex. Procedures are approved to administer the lessons learned program, and a database is available to catalog applicable lessons learned regarding environmental remediation, restoration, and administrative activities. ER will continue to use this database as a source of information available to SRS personnel

A novel multi-agent decentralized win or learn fast policy hill-climbing with eligibility trace algorithm for smart generation control of interconnected complex power grids

International Nuclear Information System (INIS)

Xi, Lei; Yu, Tao; Yang, Bo; Zhang, Xiaoshun

2015-01-01

Highlights: • Proposing a decentralized smart generation control scheme for the automatic generation control coordination. • A novel multi-agent learning algorithm is developed to resolve stochastic control problems in power systems. • A variable learning rate are introduced base on the framework of stochastic games. • A simulation platform is developed to test the performance of different algorithms. - Abstract: This paper proposes a multi-agent smart generation control scheme for the automatic generation control coordination in interconnected complex power systems. A novel multi-agent decentralized win or learn fast policy hill-climbing with eligibility trace algorithm is developed, which can effectively identify the optimal average policies via a variable learning rate under various operation conditions. Based on control performance standards, the proposed approach is implemented in a flexible multi-agent stochastic dynamic game-based smart generation control simulation platform. Based on the mixed strategy and average policy, it is highly adaptive in stochastic non-Markov environments and large time-delay systems, which can fulfill automatic generation control coordination in interconnected complex power systems in the presence of increasing penetration of decentralized renewable energy. Two case studies on both a two-area load–frequency control power system and the China Southern Power Grid model have been done. Simulation results verify that multi-agent smart generation control scheme based on the proposed approach can obtain optimal average policies thus improve the closed-loop system performances, and can achieve a fast convergence rate with significant robustness compared with other methods

Deep Learning through Concept-Based Inquiry

Science.gov (United States)

Donham, Jean

2010-01-01

Learning in the library should present opportunities to enrich student learning activities to address concerns of interest and cognitive complexity, but these must be tasks that call for in-depth analysis--not merely gathering facts. Library learning experiences need to demand enough of students to keep them interested and also need to be…

Statistical and machine learning approaches for network analysis

CERN Document Server

Dehmer, Matthias

2012-01-01

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation

Sustained Professional Development on Cooperative Learning: Impact on Six Teachers' Practices and Students' Learning.

Science.gov (United States)

Goodyear, Victoria A

2017-03-01

It has been argued, extensively and internationally, that sustained school-based continuous professional development (CPD) has the potential to overcome some of the shortcomings of traditional one-off CPD programs. Yet, the evidence base on more effective or less effective forms of CPD is contradictory. The mechanisms by which sustained support should be offered are unclear, and the impacts on teachers' and students' learning are complex and difficult to track. The purpose of this study was to examine the impact of a sustained school-based, tailored, and supported CPD program on teachers' practices and students' learning. Data are reported from 6 case studies of individual teachers engaged in a yearlong CPD program focused on cooperative learning. The CPD program involved participatory action research and frequent interaction/support from a boundary spanner (researcher/facilitator). Data were gathered from 29 video-recorded lessons, 108 interviews, and 35 field journal entries. (a) Individualized (external) support, (b) departmental (internal) support, and (c) sustained support impacted teachers' practices of cooperative learning. The teachers adapted their practices of cooperative learning in response to their students' learning needs. Teachers began to develop a level of pedagogical fluency, and in doing so, teachers advanced students' learning. Because this study demonstrates impact, it contributes to international literature on effective CPD. The key contribution is the detailed evidence about how and why CPD supported 6 individual teachers to learn-differently-and the complexity of the learning support required to engage in ongoing curriculum development to positively impact student learning.

Dynamic Learning Objects to Teach Java Programming Language

Science.gov (United States)

Narasimhamurthy, Uma; Al Shawkani, Khuloud

2010-01-01

This article describes a model for teaching Java Programming Language through Dynamic Learning Objects. The design of the learning objects was based on effective learning design principles to help students learn the complex topic of Java Programming. Visualization was also used to facilitate the learning of the concepts. (Contains 1 figure and 2…

[Anaesthetists learn--do institutions also learn? Importance of institutional learning and corporate culture in clinics].

Science.gov (United States)

Schüpfer, G; Gfrörer, R; Schleppers, A

2007-10-01

In only a few contexts is the need for substantial learning more pronounced than in health care. For a health care provider, the ability to learn is essential in a changing environment. Although individual humans are programmed to learn naturally, organisations are not. Learning that is limited to individual professions and traditional approaches to continuing medical education is not sufficient to bring about substantial changes in the learning capacity of an institution. Also, organisational learning is an important issue for anaesthesia departments. Future success of an organisation often depends on new capabilities and competencies. Organisational learning is the capacity or processes within an organisation to maintain or improve performance based on experience. Learning is seen as a system-level phenomenon as it stays in the organisation regardless of the players involved. Experience from other industries shows that learning strategies tend to focus on single loop learning, with relatively little double loop learning and virtually no meta-learning or non-learning. The emphasis on team delivery of health care reinforces the need for team learning. Learning organisations make learning an intrinsic part of their organisations and are a place where people continually learn how to learn together. Organisational learning practice can help to improve existing skills and competencies and to change outdated assumptions, procedures and structures. So far, learning theory has been ignored in medicine, due to a wide variety of complex political, economic, social, organisational culture and medical factors that prevent innovation and resist change. The organisational culture is central to every stage of the learning process. Learning organisations move beyond simple employee training into organisational problem solving, innovation and learning. Therefore, teamwork and leadership are necessary. Successful organisations change the competencies of individuals, the systems

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.

Can a multimedia tool help students' learning performance in complex biology subjects?

Directory of Open Access Journals (Sweden)

Pinar Koseoglu

2015-11-01

Full Text Available The aim of the present study was to determine the effects of multimedia-based biology teaching (Mbio and teacher-centered biology (TCbio instruction approaches on learners' biology achievements, as well as their views towards learning approaches. During the research process, an experimental design with two groups, TCbio (n = 22 and Mbio (n = 26, were used. The results of the study proved that the Mbio approach was more effective than the TCbio approach with regard to supporting meaningful learning, academic achievement, enjoyment and motivation. Moreover, the TCbio approach is ineffective in terms of time management, engaging attention, and the need for repetition of subjects. Additionally, the results were discussed in terms of teaching, learning, multimedia design as well as biology teaching/learning.

Changes in Search Path Complexity and Length During Learning of a Virtual Water Maze: Age Differences and Differential Associations with Hippocampal Subfield Volumes.

Science.gov (United States)

Daugherty, Ana M; Bender, Andrew R; Yuan, Peng; Raz, Naftali

2016-06-01

Impairment of hippocampus-dependent cognitive processes has been proposed to underlie age-related deficits in navigation. Animal studies suggest a differential role of hippocampal subfields in various aspects of navigation, but that hypothesis has not been tested in humans. In this study, we examined the association between volume of hippocampal subfields and age differences in virtual spatial navigation. In a sample of 65 healthy adults (age 19-75 years), advanced age was associated with a slower rate of improvement operationalized as shortening of the search path over 25 learning trials on a virtual Morris water maze task. The deficits were partially explained by greater complexity of older adults' search paths. Larger subiculum and entorhinal cortex volumes were associated with a faster decrease in search path complexity, which in turn explained faster shortening of search distance. Larger Cornu Ammonis (CA)1-2 volume was associated with faster distance shortening, but not in path complexity reduction. Age differences in regional volumes collectively accounted for 23% of the age-related variance in navigation learning. Independent of subfield volumes, advanced age was associated with poorer performance across all trials, even after reaching the asymptote. Thus, subiculum and CA1-2 volumes were associated with speed of acquisition, but not magnitude of gains in virtual maze navigation. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

FILE: a tool for the study of inquiry learning.

NARCIS (Netherlands)

Hulshof, C.D.; Wilhelm, P.; Beishuizen, J.J.; Beishuizen, J.J.; van Rijn, H.

2005-01-01

A computerized learning environment (Flexible Inquiry Learning Environment; FILE) is discussed. FILE allows researchers in inquiry learning to design, administer, and analyze learning tasks in which content domain and task complexity can be configured independently, while other factors (e.g., the

Complex-valued neural networks advances and applications

CERN Document Server

Hirose, Akira

2013-01-01

Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and

Complexity in physics and technology

CERN Document Server

Garrido, Manuel S

1992-01-01

A system is loosely defined as complex if it is composed of a large number of elements, interacting with each other, and the emergent global dynamics is qualitatively different from the dynamics of each one of the parts. The global dynamics may be either ordered or chaotic and among the most interesting emergent global properties are those of learning and adaptation.Complex systems, in the above sense, appear in many fields ranging from physics and technology to life and social sciences. Research in complex systems involves therefore a wide range of topics, studied in seemingly disparate field

Social learning for solving complex problems: a promising solution or wishful thinking?: a case-study of multi-actor negotiation for the integrated management and the sustainable use of the Drentsche Aa area in the Netherlands

NARCIS (Netherlands)

van Bommel, S.; Röling, N.; Aarts, N.; Turnhout, E.

2009-01-01

Social learning has been championed as a promising approach to address complex resource problems. According to theory, social learning requires several pre-conditions to be met, including (1) a divergence of interests, (2) mutual interdependence and (3) the ability to communicate. This article

On Emotional Barriers to Second Language Learning

Institute of Scientific and Technical Information of China (English)

Chen Qin

2012-01-01

Language learning is a very complex process, which is related to many factors, either internal or external. Affective factors plays an important role in a second language learning. If only we realize such affective factors, we can overcome the emotional barriers effectively and have a successful learning.

Learning Analytics for 21st Century Competencies

Science.gov (United States)

Buckingham Shum, Simon; Crick, Ruth Deakin

2016-01-01

Many educational institutions are shifting their teaching and learning towards equipping students with knowledge, skills, and dispositions that prepare them for lifelong learning, in a complex and uncertain world. These have been termed "21st century competencies." Learning analytics (LA) approaches in general offer different kinds of…

Broadening conceptions of learning in medical education: the message from teamworking.

Science.gov (United States)

Bleakley, Alan

2006-02-01

There is a mismatch between the broad range of learning theories offered in the wider education literature and a relatively narrow range of theories privileged in the medical education literature. The latter are usually described under the heading of 'adult learning theory'. This paper critically addresses the limitations of the current dominant learning theories informing medical education. An argument is made that such theories, which address how an individual learns, fail to explain how learning occurs in dynamic, complex and unstable systems such as fluid clinical teams. Models of learning that take into account distributed knowing, learning through time as well as space, and the complexity of a learning environment including relationships between persons and artefacts, are more powerful in explaining and predicting how learning occurs in clinical teams. Learning theories may be privileged for ideological reasons, such as medicine's concern with autonomy. Where an increasing amount of medical education occurs in workplace contexts, sociocultural learning theories offer a best-fit exploration and explanation of such learning. We need to continue to develop testable models of learning that inform safe work practice. One type of learning theory will not inform all practice contexts and we need to think about a range of fit-for-purpose theories that are testable in practice. Exciting current developments include dynamicist models of learning drawing on complexity theory.

On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions.

Science.gov (United States)

Schmitt, Michael

2004-09-01

We study networks of spiking neurons that use the timing of pulses to encode information. Nonlinear interactions model the spatial groupings of synapses on the neural dendrites and describe the computations performed at local branches. Within a theoretical framework of learning we analyze the question of how many training examples these networks must receive to be able to generalize well. Bounds for this sample complexity of learning can be obtained in terms of a combinatorial parameter known as the pseudodimension. This dimension characterizes the computational richness of a neural network and is given in terms of the number of network parameters. Two types of feedforward architectures are considered: constant-depth networks and networks of unconstrained depth. We derive asymptotically tight bounds for each of these network types. Constant depth networks are shown to have an almost linear pseudodimension, whereas the pseudodimension of general networks is quadratic. Networks of spiking neurons that use temporal coding are becoming increasingly more important in practical tasks such as computer vision, speech recognition, and motor control. The question of how well these networks generalize from a given set of training examples is a central issue for their successful application as adaptive systems. The results show that, although coding and computation in these networks is quite different and in many cases more powerful, their generalization capabilities are at least as good as those of traditional neural network models.

Computer-based learning in neuroanatomy: A longitudinal study of learning, transfer, and retention

Science.gov (United States)

Chariker, Julia H.

A longitudinal experiment was conducted to explore computer-based learning of neuroanatomy. Using a realistic 3D graphical model of neuroanatomy, and sections derived from the model, exploratory graphical tools were integrated into interactive computer programs so as to allow adaptive exploration. 72 participants learned either sectional anatomy alone or learned whole anatomy followed by sectional anatomy. Sectional anatomy was explored either in perceptually continuous animation or discretely, as in the use of an anatomical atlas. Learning was measured longitudinally to a high performance criterion. After learning, transfer to biomedical images and long-term retention was tested. Learning whole anatomy prior to learning sectional anatomy led to a more efficient learning experience. Learners demonstrated high levels of transfer from whole anatomy to sectional anatomy and from sectional anatomy to complex biomedical images. All learning groups demonstrated high levels of retention at 2--3 weeks.

Sport and Exercise Pedagogy and Questions about Learning

Science.gov (United States)

Quennerstedt, Mikael; Öhman, Marie; Armour, Kathleen

2014-01-01

One important challenge ahead for sport and exercise pedagogy (SEP) researchers is to consider afresh questions about learning. Learning in the fields of sport, physical activity and physical education (PE) is a particularly complex business. Most existing theories of learning are defined cognitively, yet learning in sport and physical activity…

Cross-Disciplinary Collaboration and Learning

Directory of Open Access Journals (Sweden)

Deana D. Pennington

2008-12-01

Full Text Available Complex environmental problem solving depends on cross-disciplinary collaboration among scientists. Collaborative research must be preceded by an exploratory phase of collective thinking that creates shared conceptual frameworks. Collective thinking, in a cross-disciplinary setting, depends on the facility with which collaborators are able to learn and understand each others' perspectives. This paper applies three perspectives on learning to the problem of enabling cross-disciplinary collaboration: Maslow's hierarchy of needs, constructivism, and organizational learning. Application of learning frameworks to collaboration provides insights regarding receptive environments for collaboration, and processes that facilitate cross-disciplinary interactions. These environments and interactions need time to develop and require a long phase of idea generation preceding any focused research effort. The findings highlight that collaboration is itself a complex system of people, scientific theory, and tools that must be intentionally managed. Effective management of the system requires leaders who are facilitators and are capable of orchestrating effective environments and interactions.

Embracing Complexity: Using Technology to Develop a Life-Long Learning Model for Non-Working Time in the Interdependent Homes for Adults with Autism Spectrum Disorders

Science.gov (United States)

Chiang, I-Tsun; Chen, Mei-Li

2011-01-01

The purpose of this study was to employ complexity theory as a theoretical framework and technology to facilitate the development of a life-long learning model for non-working time in the interdependent homes for adults with Autism Spectrum Disorders (ASD). A "Shining Star Sustainable Action Project" of the ROC Foundation for Autistic…

A strategy learning model for autonomous agents based on classification

Directory of Open Access Journals (Sweden)

Śnieżyński Bartłomiej

2015-09-01

Full Text Available In this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process

Computer Assisted Language Learning (CALL) Software: Evaluation ...

African Journals Online (AJOL)

Evaluating the nature and extent of the influence of Computer Assisted Language Learning (CALL) on the quality of language learning is highly problematic. This is owing to the number and complexity of interacting variables involved in setting the items for teaching and learning languages. This paper identified and ...

Learning JavaScript

CERN Document Server

Powers, Shelley

2008-01-01

Packed with best practices and examples of JavaScript use, Learning JavaScript provides complete, no-nonsense coverage of this quirky yet essential language for web development. You'll learn everything from primitive data types to complex features, including JavaScript elements involved with Ajax and dynamic page effects. By the end of the book, you'll be able to work with even the most sophisticated libraries and web applications.

Learning Low-Dimensional Metrics

OpenAIRE

Jain, Lalit; Mason, Blake; Nowak, Robert

2017-01-01

This paper investigates the theoretical foundations of metric learning, focused on three key questions that are not fully addressed in prior work: 1) we consider learning general low-dimensional (low-rank) metrics as well as sparse metrics; 2) we develop upper and lower (minimax)bounds on the generalization error; 3) we quantify the sample complexity of metric learning in terms of the dimension of the feature space and the dimension/rank of the underlying metric;4) we also bound the accuracy ...

Learning radiological appearances of diseases: Does comparison help?

NARCIS (Netherlands)

Kok, Ellen M.; de Bruin, Anique B H; Robben, Simon C. F.; van Merrienboer, Jeroen J. G.

Comparison learning is a promising approach for learning complex real-life visual tasks. When medical students study radiological appearances of diseases, comparison of images showing diseases with images showing no abnormalities could help them learn to discriminate relevant, disease-related

Generative Learning Objects Instantiated with Random Numbers Based Expressions

Directory of Open Access Journals (Sweden)

Ciprian Bogdan Chirila

2015-12-01

Full Text Available The development of interactive e-learning content requires special skills like programming techniques, web integration, graphic design etc. Generally, online educators do not possess such skills and their e-learning products tend to be static like presentation slides and textbooks. In this paper we propose a new interactive model of generative learning objects as a compromise betweenstatic, dull materials and dynamic, complex software e-learning materials developed by specialized teams. We find that random numbers based automatic initialization learning objects increases content diversity, interactivity thus enabling learners’ engagement. The resulted learning object model is at a limited level of complexity related to special e-learning software, intuitive and capable of increasing learners’ interactivity, engagement and motivation through dynamic content. The approach was applied successfully on several computer programing disciplines.

Complex population response of dorsal putamen neurons predicts the ability to learn.

Science.gov (United States)

Laquitaine, Steeve; Piron, Camille; Abellanas, David; Loewenstein, Yonatan; Boraud, Thomas

2013-01-01

Day-to-day variability in performance is a common experience. We investigated its neural correlate by studying learning behavior of monkeys in a two-alternative forced choice task, the two-armed bandit task. We found substantial session-to-session variability in the monkeys' learning behavior. Recording the activity of single dorsal putamen neurons we uncovered a dual function of this structure. It has been previously shown that a population of neurons in the DLP exhibits firing activity sensitive to the reward value of chosen actions. Here, we identify putative medium spiny neurons in the dorsal putamen that are cue-selective and whose activity builds up with learning. Remarkably we show that session-to-session changes in the size of this population and in the intensity with which this population encodes cue-selectivity is correlated with session-to-session changes in the ability to learn the task. Moreover, at the population level, dorsal putamen activity in the very beginning of the session is correlated with the performance at the end of the session, thus predicting whether the monkey will have a "good" or "bad" learning day. These results provide important insights on the neural basis of inter-temporal performance variability.

Epistemic complexity and the journeyman-expert transition

Directory of Open Access Journals (Sweden)

Thomas J. Bing

2012-02-01

Full Text Available Physics students can encounter difficulties in physics problem solving as a result of failing to use knowledge that they have but do not perceive as relevant or appropriate. In previous work we have demonstrated that some of these difficulties may be epistemological. Students may limit the kinds of knowledge that they use. For example, they may use formal manipulations and ignore physical sense making or vice versa. Both beginning (novice and intermediate (journeymen students demonstrate these difficulties. Learning both to switch one’s epistemological lens on a problem and to integrate different kinds of knowledge is a critical component of learning to solve problems in physics effectively. In this paper, we present two case studies in which journeyman students (upper-division physics majors demonstrate switching between epistemological resources in approaching a complex problem. We conjecture that mastering these epistemological skills is an essential component of learning complex problem solving in physics.

Epistemic complexity and the journeyman-expert transition

Directory of Open Access Journals (Sweden)

Thomas J. Bing

2012-02-01

Full Text Available Physics students can encounter difficulties in physics problem solving as a result of failing to use knowledge that they have but do not perceive as relevant or appropriate. In previous work we have demonstrated that some of these difficulties may be epistemological. Students may limit the kinds of knowledge that they use. For example, they may use formal manipulations and ignore physical sense making or vice versa. Both beginning (novice and intermediate (journeymen students demonstrate these difficulties. Learning both to switch one’s epistemological lens on a problem and to integrate different kinds of knowledge is a critical component of learning to solve problems in physics effectively. In this paper, we present two case studies in which journeyman students (upper-division physics majors demonstrate switching between epistemological resources in approaching a complex problem. We conjecture that mastering these epistemological skills is an essential component of learning complex problem solving in physics.

Training Project Management Complexity in Postgraduate And Continuing Education Programs: A Learning Strategy in The Eshe (European Space of Higher Education) Framework

OpenAIRE

Mtnez-Almela, Jesús; de los Rios, Ignacio

2011-01-01

The objective of this paper is to address the methodological process of a teaching strategy for training project managment complexity in postgraduate programs. The proposal is made up of different methods —intuitive, comparative, deductive, case study, problem-solving Project-Based Learning— and different activities inside and outside the classroom. This integration of methods motivated the current use of the concept of “learning strategy”. The strategy has two phases: firstly, the integra...

The Managers’ Experiential Learning of Program Planning in Active Ageing Learning Centers

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Chun-Ting Yeh

2016-12-01

Full Text Available Planning older adult learning programs is really a complex work. Program planners go through different learning stages and accumulate experiences to be able to undertake the task alone. This study aimed to explore the experiential learning process of older adult learning program planners who work in the Active Ageing Learning Centers (AALCs. Semi-structure interviews were conducted with seven program planners. The findings of this study were identified as follows. 1 Before being a program planner, the participants’ knowledge results from grasping and transforming experience gained from their family, their daily lives and past learning experiences; 2 after being a program planner, the participants’ experiential learning focused on leadership, training in the institute, professional development, as well as involvement in organizations for elderly people; and 3 the participants’ experiential learning outcomes in the older adult learning program planning include: their ability to reflect on the appropriateness and fulfillment of program planning, to apply theoretical knowledge and professional background in the field, and to make plans for future learning and business strategies.

Design of digital learning material for bioprocess-engineering-education

NARCIS (Netherlands)

Schaaf, van der H.

2007-01-01

With the advance of computers and the internet, new types of learning material can be developed: web-based digital learning material. Because many complex learning objectives in the food- and bioprocess technology domain are difficult to achieve in a traditional learning environment, a project was

Information maximization explains the emergence of complex cell-like neurons

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Takuma eTanaka

2013-11-01

Full Text Available We propose models and a method to qualitatively explain the receptive field properties of complex cells in the primary visual cortex. We apply a learning method based on the information maximization principle in a feedforward network, which comprises an input layer of image patches, simple cell-like first-output-layer neurons, and second-output-layer neurons (Model 1. The information maximization results in the emergence of the complex cell-like receptive field properties in the second-output-layer neurons. After learning, second-output-layer neurons receive connection weights having the same size from two first-output-layer neurons with sign-inverted receptive fields. The second-output-layer neurons replicate the phase invariance and iso-orientation suppression. Furthermore, on the basis of these results, we examine a simplified model showing the emergence of complex cell-like receptive fields (Model 2. We show that after learning, the output neurons of this model exhibit iso-orientation suppression, cross-orientation facilitation, and end stopping, which are similar to those found in complex cells. These properties of model neurons suggest that complex cells in the primary visual cortex become selective to features composed of edges to increase the variability of the output.

Fast and Epsilon-Optimal Discretized Pursuit Learning Automata.

Science.gov (United States)

Zhang, JunQi; Wang, Cheng; Zhou, MengChu

2015-10-01

Learning automata (LA) are powerful tools for reinforcement learning. A discretized pursuit LA is the most popular one among them. During an iteration its operation consists of three basic phases: 1) selecting the next action; 2) finding the optimal estimated action; and 3) updating the state probability. However, when the number of actions is large, the learning becomes extremely slow because there are too many updates to be made at each iteration. The increased updates are mostly from phases 1 and 3. A new fast discretized pursuit LA with assured ε -optimality is proposed to perform both phases 1 and 3 with the computational complexity independent of the number of actions. Apart from its low computational complexity, it achieves faster convergence speed than the classical one when operating in stationary environments. This paper can promote the applications of LA toward the large-scale-action oriented area that requires efficient reinforcement learning tools with assured ε -optimality, fast convergence speed, and low computational complexity for each iteration.

Using Video Game Telemetry Data to Research Motor Chunking, Action Latencies, and Complex Cognitive-Motor Skill Learning.

Science.gov (United States)

Thompson, Joseph J; McColeman, C M; Stepanova, Ekaterina R; Blair, Mark R

2017-04-01

Many theories of complex cognitive-motor skill learning are built on the notion that basic cognitive processes group actions into easy-to-perform sequences. The present work examines predictions derived from laboratory-based studies of motor chunking and motor preparation using data collected from the real-time strategy video game StarCraft 2. We examined 996,163 action sequences in the telemetry data of 3,317 players across seven levels of skill. As predicted, the latency to the first action (thought to be the beginning of a chunked sequence) is delayed relative to the other actions in the group. Other predictions, inspired by the memory drum theory of Henry and Rogers, received only weak support. Copyright © 2017 Cognitive Science Society, Inc.

HSTLBO: A hybrid algorithm based on Harmony Search and Teaching-Learning-Based Optimization for complex high-dimensional optimization problems.

Directory of Open Access Journals (Sweden)

Shouheng Tuo

Full Text Available Harmony Search (HS and Teaching-Learning-Based Optimization (TLBO as new swarm intelligent optimization algorithms have received much attention in recent years. Both of them have shown outstanding performance for solving NP-Hard optimization problems. However, they also suffer dramatic performance degradation for some complex high-dimensional optimization problems. Through a lot of experiments, we find that the HS and TLBO have strong complementarity each other. The HS has strong global exploration power but low convergence speed. Reversely, the TLBO has much fast convergence speed but it is easily trapped into local search. In this work, we propose a hybrid search algorithm named HSTLBO that merges the two algorithms together for synergistically solving complex optimization problems using a self-adaptive selection strategy. In the HSTLBO, both HS and TLBO are modified with the aim of balancing the global exploration and exploitation abilities, where the HS aims mainly to explore the unknown regions and the TLBO aims to rapidly exploit high-precision solutions in the known regions. Our experimental results demonstrate better performance and faster speed than five state-of-the-art HS variants and show better exploration power than five good TLBO variants with similar run time, which illustrates that our method is promising in solving complex high-dimensional optimization problems. The experiment on portfolio optimization problems also demonstrate that the HSTLBO is effective in solving complex read-world application.

EDUCATEE'S THESAURUS AS AN OBJECT OF MEASURING LEARNED MATERIAL OF THE DISTANCE LEARNING COURSE

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Alexander Aleksandrovich RYBANOV

2013-10-01

Full Text Available Monitoring and control over the process of studying the distance learning course are based on solving the problem of making out an adequate integral mark to the educatee for mastering entire study course, by testing results. It is suggested to use the degree of correspondence between educatee's thesaurus and the study course thesaurus as an integral mark for the degree of mastering the distance learning course. Study course thesaurus is a set of the course objects with relations between them specified. The article considers metrics of the study course thesaurus complexity, made on the basis of the graph theory and the information theory. It is suggested to use the amount of information contained in the study course thesaurus graph as the metrics of the study course thesaurus complexity. Educatee's thesaurus is considered as an object of measuring educational material learned at the semantic level and is assessed on the basis of amount of information contained in its graph, taking into account the factors of learning the thesaurus objects.

Team learning : New insights through a temporal lens

NARCIS (Netherlands)

Lehmann-Willenbrock, N.

2017-01-01

Team learning is a complex social phenomenon that develops and changes over time. Hence, to promote understanding of the fine-grained dynamics of team learning, research should account for the temporal patterns of team learning behavior. Taking important steps in this direction, this special issue

Selecting Learning Tasks: Effects of Adaptation and Shared Control on Learning Efficiency and Task Involvement

Science.gov (United States)

Corbalan, Gemma; Kester, Liesbeth; van Merrienboer, Jeroen J. G.

2008-01-01

Complex skill acquisition by performing authentic learning tasks is constrained by limited working memory capacity [Baddeley, A. D. (1992). Working memory. "Science, 255", 556-559]. To prevent cognitive overload, task difficulty and support of each newly selected learning task can be adapted to the learner's competence level and perceived task…

Strategies and Rubrics for Teaching Complex Systems Theory to Novices (Invited)

Science.gov (United States)

Fichter, L. S.

2010-12-01

Bifurcation. Self-similarity. Fractal. Sensitive dependent. Agents. Self-organized criticality. Avalanche behavior. Power laws. Strange attractors. Emergence. The language of complexity is fundamentally different from the language of equilibrium. If students do not know these phenomena, and what they tell us about the pulse of dynamic systems, complex systems will be opaque. A complex system is a group of agents. (individual interacting units, like birds in a flock, sand grains in a ripple, or individual friction units along a fault zone), existing far from equilibrium, interacting through positive and negative feedbacks, following simple rules, forming interdependent, dynamic, evolutionary networks. Complex systems produce behaviors that cannot be predicted deductively from knowledge of the behaviors of the individual components themselves; they must be experienced. What complexity theory demonstrates is that, by following simple rules, all the agents end up coordinating their behavior—self organizing—so that what emerges is not chaos, but meaningful patterns. How can we introduce Freshman, non-science, general education students to complex systems theories, in 3 to 5 classes; in a way they really get it, and can use the principles to understand real systems? Complex systems theories are not a series of unconnected or disconnected equations or models; they are developed as narratives that makes sense of how all the pieces and properties are interrelated. The principles of complex systems must be taught as deliberately and systematically as the equilibrium principles normally taught; as, say, the systematic training from pre-algebra and geometry to algebra. We have developed a sequence of logically connected narratives (strategies and rubrics) that introduce complex systems principles using models that can be simulated in a computer, in class, in real time. The learning progression has a series of 12 models (e.g. logistic system, bifurcation diagrams, genetic

Maintaining collaborative, democratic and dialogue-based learning processes in virtual and game-based learning environments

DEFF Research Database (Denmark)

Gyldendahl Jensen, Camilla; Sorensen, Elsebeth Korsgaard

2017-01-01

The incorporation and use of virtual learning platforms, including computer games, in the education sector, challenge these years the complexity of the learning environment regarding maintaining collaborative, democratic and dialogue-based learning processes that support a high degree of reflection....... When virtual learning platforms are used in an educational context, a fundamental paradox appears as the student needs an active and practice-oriented participation identity to learn while at the same time needing to learn to acquire a participation identity. This identity is raised and trained...... by being a continuous part of a community that recalls the scenarios of reality. It is therefore crucial that the learning environment reflects the reality of which the students' professionalism is unfolded. Learning is, therefore, something more and not just the acquisition of knowledge and past actions...

A Study on Information Technology Integrated Guided Iscovery Instruction towards Students' Learning Achievement and Learning Retention

Science.gov (United States)

Shieh, Chich-Jen; Yu, Lean

2016-01-01

In the information explosion era with constant changes of information, educators have promoted various effective learning strategies for students adapting to the complex modern society. The impact and influence of traditional teaching method have information technology integrated modern instruction and science concept learning play an important…

Language Disorders Are Learning Disabilities: Challenges on the Divergent and Diverse Paths to Language Learning Disability

Science.gov (United States)

Sun, Lei; Wallach, Geraldine P.

2014-01-01

This article takes readers along the pathway of language learning and disorders across childhood and adolescence, highlighting the complex relationship between early (preschool) language disorders and later (school age) learning disabilities. The discussion starts with a review of diagnostic labels widely used in schools and other professional…

Web based Interactive 3D Learning Objects for Learning Management Systems

Directory of Open Access Journals (Sweden)

Stefan Hesse

2012-02-01

Full Text Available In this paper, we present an approach to create and integrate interactive 3D learning objects of high quality for higher education into a learning management system. The use of these resources allows to visualize topics, such as electro-technical and physical processes in the interior of complex devices. This paper addresses the challenge of combining rich interactivity and adequate realism with 3D exercise material for distance elearning.

Automatic Emergence Detection in Complex Systems

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Eugene Santos

2017-01-01

Full Text Available Complex systems consist of multiple interacting subsystems, whose nonlinear interactions can result in unanticipated (emergent system events. Extant systems analysis approaches fail to detect such emergent properties, since they analyze each subsystem separately and arrive at decisions typically through linear aggregations of individual analysis results. In this paper, we propose a quantitative definition of emergence for complex systems. We also propose a framework to detect emergent properties given observations of its subsystems. This framework, based on a probabilistic graphical model called Bayesian Knowledge Bases (BKBs, learns individual subsystem dynamics from data, probabilistically and structurally fuses said dynamics into a single complex system dynamics, and detects emergent properties. Fusion is the central element of our approach to account for situations when a common variable may have different probabilistic distributions in different subsystems. We evaluate our detection performance against a baseline approach (Bayesian Network ensemble on synthetic testbeds from UCI datasets. To do so, we also introduce a method to simulate and a metric to measure discrepancies that occur with shared/common variables. Experiments demonstrate that our framework outperforms the baseline. In addition, we demonstrate that this framework has uniform polynomial time complexity across all three learning, fusion, and reasoning procedures.

A Judgement-Based Model of Workplace Learning

Science.gov (United States)

Athanasou, James A.

2004-01-01

The purpose of this paper is to outline a judgement-based model of adult learning. This approach is set out as a Perceptual-Judgemental-Reinforcement approach to social learning under conditions of complexity and where there is no single, clearly identified correct response. The model builds upon the Hager-Halliday thesis of workplace learning and…

Open Data in Global Environmental Research: Findings from the Community

Energy Technology Data Exchange (ETDEWEB)

Van Honk, J.; Calero-Medina, C.; Costas, R.

2016-07-01

This paper presents findings from the Belmont Forum’s survey on Open Data which targeted the global environmental research and data infrastructure community (Schmidt, Gemeinholzer & Treloar, 2016). It highlights users’ perceptions of the term “open data”, expectations of infrastructure functionalities, and barriers and enablers for the sharing of data. A wide range of good practice examples was pointed out by the respondents which demonstrates a substantial uptake of data sharing through e-infrastructures and a further need for enhancement and consolidation. Among all policy responses, funder policies seem to be the most important motivator. This supports the conclusion that stronger mandates will strengthen the case for data sharing. The Belmont Forum, a group of high-level representatives from major funding agencies across the globe, coordinates funding for collaborative research to address the challenges and opportunities of global environmental change. In particular, the E-Infrastructure and Data Management Collaborative Research Action has brought together domain scientists, computer and information scientists, legal scholars, social scientists, and other experts from more than 14 countries to establish recommendations on how the Belmont Forum can implement a more coordinated, holistic, and sustainable approach to the funding and support of global environmental change research. (Author)

Complexity and Control: Towards a Rigorous Behavioral Theory of Complex Dynamical Systems

Science.gov (United States)

Ivancevic, Vladimir G.; Reid, Darryn J.

We introduce our motive for writing this book on complexity and control with a popular "complexity myth," which seems to be quite wide spread among chaos and complexity theory fashionistas: quote>Low-dimensional systems usually exhibit complex behaviours (which we know fromMay's studies of the Logisticmap), while high-dimensional systems usually exhibit simple behaviours (which we know from synchronisation studies of the Kuramoto model)...quote> We admit that this naive view on complex (e.g., human) systems versus simple (e.g., physical) systems might seem compelling to various technocratic managers and politicians; indeed, the idea makes for appealing sound-bites. However, it is enough to see both in the equations and computer simulations of pendula of various degree - (i) a single pendulum, (ii) a double pendulum, and (iii) a triple pendulum - that this popular myth is plain nonsense. The only thing that we can learn from it is what every tyrant already knows: by using force as a strong means of control, it is possible to effectively synchronise even hundreds of millions of people, at least for a while.

Challenges in the Verification of Reinforcement Learning Algorithms

Science.gov (United States)

Van Wesel, Perry; Goodloe, Alwyn E.

2017-01-01

Machine learning (ML) is increasingly being applied to a wide array of domains from search engines to autonomous vehicles. These algorithms, however, are notoriously complex and hard to verify. This work looks at the assumptions underlying machine learning algorithms as well as some of the challenges in trying to verify ML algorithms. Furthermore, we focus on the specific challenges of verifying reinforcement learning algorithms. These are highlighted using a specific example. Ultimately, we do not offer a solution to the complex problem of ML verification, but point out possible approaches for verification and interesting research opportunities.

Application of Ausubel's Theory of Meaningful Verbal Learning to Curriculum, Teaching and Learning of Deaf Students.

Science.gov (United States)

Biser, Eileen

Implications of D. Ausubel's Theory of Meaningful Verbal Learning and its derivative, the Advance Organizer Model of Teaching, for deaf students are examined. Ausubel believes that complex intellectual processes (thinking, language, problem-solving, concept formation) are the major aspects of learning, and that primary emphasis should be placed on…

Learning from others mistakes: how social media etiquette distorts informal learning online.

OpenAIRE

Osborne, Nicola

2014-01-01

Informal learning and information exchange form an important part of interactions between professionals in social media spaces but these spaces also trigger complex performances of self (Goffman 1959, Barbour and Marshall 2012). This paper, drawing upon research investigating the nature and efficacy of collaborative learning between professional participants within social media spaces, expands upon key findings on the roles of self-presentation, and emerging etiquette practices around peer co...

Deep Learning for Hate Speech Detection in Tweets

OpenAIRE

Badjatiya, Pinkesh; Gupta, Shashank; Gupta, Manish; Varma, Vasudeva

2017-01-01

Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a ...

Multilingual and social semiotic perspectives on literacy learning and teaching

DEFF Research Database (Denmark)

Laursen, Helle Pia

to the complex processes involved in biliterate meaning making and script learning. Multilingual and social semiotic perspectives on literacy learning and teaching – summaryOn the basis of data from the longitudinal study Signs of Language, I focus on how a social semiotic perspective on literacy learning...... and teaching can contribute to expanding the conceptualization of literacy to be more sensitive to the complex processes involved in biliterate meaning making and script learning.......Multilingual and social semiotic perspectives on literacy learning and teaching – abstract In the context of an increasing multilingualism, literacy teaching has become a central and contested issue in public and political debate. International comparisons of levels of literacy have been...

The Role of Celestial Compass Information in Cataglyphis Ants during Learning Walks and for Neuroplasticity in the Central Complex and Mushroom Bodies.

Science.gov (United States)

Grob, Robin; Fleischmann, Pauline N; Grübel, Kornelia; Wehner, Rüdiger; Rössler, Wolfgang

2017-01-01

Central place foragers are faced with the challenge to learn the position of their nest entrance in its surroundings, in order to find their way back home every time they go out to search for food. To acquire navigational information at the beginning of their foraging career, Cataglyphis noda performs learning walks during the transition from interior worker to forager. These small loops around the nest entrance are repeatedly interrupted by strikingly accurate back turns during which the ants stop and precisely gaze back to the nest entrance-presumably to learn the landmark panorama of the nest surroundings. However, as at this point the complete navigational toolkit is not yet available, the ants are in need of a reference system for the compass component of the path integrator to align their nest entrance-directed gazes. In order to find this directional reference system, we systematically manipulated the skylight information received by ants during learning walks in their natural habitat, as it has been previously suggested that the celestial compass, as part of the path integrator, might provide such a reference system. High-speed video analyses of distinct learning walk elements revealed that even exclusion from the skylight polarization pattern, UV-light spectrum and the position of the sun did not alter the accuracy of the look back to the nest behavior. We therefore conclude that C. noda uses a different reference system to initially align their gaze directions. However, a comparison of neuroanatomical changes in the central complex and the mushroom bodies before and after learning walks revealed that exposure to UV light together with a naturally changing polarization pattern was essential to induce neuroplasticity in these high-order sensory integration centers of the ant brain. This suggests a crucial role of celestial information, in particular a changing polarization pattern, in initially calibrating the celestial compass system.

The Role of Celestial Compass Information in Cataglyphis Ants during Learning Walks and for Neuroplasticity in the Central Complex and Mushroom Bodies

Directory of Open Access Journals (Sweden)

Robin Grob

2017-11-01

Full Text Available Central place foragers are faced with the challenge to learn the position of their nest entrance in its surroundings, in order to find their way back home every time they go out to search for food. To acquire navigational information at the beginning of their foraging career, Cataglyphis noda performs learning walks during the transition from interior worker to forager. These small loops around the nest entrance are repeatedly interrupted by strikingly accurate back turns during which the ants stop and precisely gaze back to the nest entrance—presumably to learn the landmark panorama of the nest surroundings. However, as at this point the complete navigational toolkit is not yet available, the ants are in need of a reference system for the compass component of the path integrator to align their nest entrance-directed gazes. In order to find this directional reference system, we systematically manipulated the skylight information received by ants during learning walks in their natural habitat, as it has been previously suggested that the celestial compass, as part of the path integrator, might provide such a reference system. High-speed video analyses of distinct learning walk elements revealed that even exclusion from the skylight polarization pattern, UV-light spectrum and the position of the sun did not alter the accuracy of the look back to the nest behavior. We therefore conclude that C. noda uses a different reference system to initially align their gaze directions. However, a comparison of neuroanatomical changes in the central complex and the mushroom bodies before and after learning walks revealed that exposure to UV light together with a naturally changing polarization pattern was essential to induce neuroplasticity in these high-order sensory integration centers of the ant brain. This suggests a crucial role of celestial information, in particular a changing polarization pattern, in initially calibrating the celestial compass system.

Complex Adaptive Schools: Educational Leadership and School Change

Science.gov (United States)

Kershner, Brad; McQuillan, Patrick

2016-01-01

This paper utilizes the theoretical framework of complexity theory to compare and contrast leadership and educational change in two urban schools. Drawing on the notion of a complex adaptive system--an interdependent network of interacting elements that learns and evolves in adapting to an ever-shifting context--our case studies seek to reveal the…

Ethics and Justice in Learning Analytics

Science.gov (United States)

Johnson, Jeffrey Alan

2017-01-01

The many complex challenges posed by learning analytics can best be understood within a framework of structural justice, which focuses on the ways in which the informational, operational, and organizational structures of learning analytics influence students' capacities for self-development and self-determination. This places primary…

The effect of haptic guidance and visual feedback on learning a complex tennis task.

Science.gov (United States)

Marchal-Crespo, Laura; van Raai, Mark; Rauter, Georg; Wolf, Peter; Riener, Robert

2013-11-01

While haptic guidance can improve ongoing performance of a motor task, several studies have found that it ultimately impairs motor learning. However, some recent studies suggest that the haptic demonstration of optimal timing, rather than movement magnitude, enhances learning in subjects trained with haptic guidance. Timing of an action plays a crucial role in the proper accomplishment of many motor skills, such as hitting a moving object (discrete timing task) or learning a velocity profile (time-critical tracking task). The aim of the present study is to evaluate which feedback conditions-visual or haptic guidance-optimize learning of the discrete and continuous elements of a timing task. The experiment consisted in performing a fast tennis forehand stroke in a virtual environment. A tendon-based parallel robot connected to the end of a racket was used to apply haptic guidance during training. In two different experiments, we evaluated which feedback condition was more adequate for learning: (1) a time-dependent discrete task-learning to start a tennis stroke and (2) a tracking task-learning to follow a velocity profile. The effect that the task difficulty and subject's initial skill level have on the selection of the optimal training condition was further evaluated. Results showed that the training condition that maximizes learning of the discrete time-dependent motor task depends on the subjects' initial skill level. Haptic guidance was especially suitable for less-skilled subjects and in especially difficult discrete tasks, while visual feedback seems to benefit more skilled subjects. Additionally, haptic guidance seemed to promote learning in a time-critical tracking task, while visual feedback tended to deteriorate the performance independently of the task difficulty and subjects' initial skill level. Haptic guidance outperformed visual feedback, although additional studies are needed to further analyze the effect of other types of feedback visualization on

Foreign language learning as a complex dynamic process: A microgenetic case study of a Chinese child's English learning trajectory

NARCIS (Netherlands)

Sun, He; Steinkrauss, Rasmus; van der Steen, Steffie; Cox, Ralf; de Bot, Kees

2016-01-01

The current study focuses on one child's (male, 3 years old) learning behaviors in an English as a Foreign Language classroom, and explores the coordination and developmental patterns of his nonverbal (gestures and body language) and verbal (verbal repetition and verbal responses) learning behaviors

Overcoming complexities: Damage detection using dictionary learning framework

Science.gov (United States)

Alguri, K. Supreet; Melville, Joseph; Deemer, Chris; Harley, Joel B.

2018-04-01

For in situ damage detection, guided wave structural health monitoring systems have been widely researched due to their ability to evaluate large areas and their ability detect many types of damage. These systems often evaluate structural health by recording initial baseline measurements from a pristine (i.e., undamaged) test structure and then comparing later measurements with that baseline. Yet, it is not always feasible to have a pristine baseline. As an alternative, substituting the baseline with data from a surrogate (nearly identical and pristine) structure is a logical option. While effective in some circumstance, surrogate data is often still a poor substitute for pristine baseline measurements due to minor differences between the structures. To overcome this challenge, we present a dictionary learning framework to adapt surrogate baseline data to better represent an undamaged test structure. We compare the performance of our framework with two other surrogate-based damage detection strategies: (1) using raw surrogate data for comparison and (2) using sparse wavenumber analysis, a precursor to our framework for improving the surrogate data. We apply our framework to guided wave data from two 108 mm by 108 mm aluminum plates. With 20 measurements, we show that our dictionary learning framework achieves a 98% accuracy, raw surrogate data achieves a 92% accuracy, and sparse wavenumber analysis achieves a 57% accuracy.

The learning potentials and challenges when integrating Web 2.0 in a problem-based learning approach

DEFF Research Database (Denmark)

Buus, Lillian

The research makes a triple co-construction between problem-based learning (PBL), learning design and action research within the area of networked learning. The complexity this creates in the research can lead it in many different directions, because it builds on collaboration, interaction...... and “elements” in motion. The authors' assumption is built on the perspective that knowledge is constructed in social collaborative interactions between people. Furthermore, she claims that the ideology of Web 2.0 provides research opportunities to study phenomena also found in PBL and networked learning...

Efficacy of Two Different Instructional Methods Involving Complex Ecological Content

Science.gov (United States)

Randler, Christoph; Bogner, Franz X.

2009-01-01

Teaching and learning approaches in ecology very often follow linear conceptions of ecosystems. Empirical studies with an ecological focus consistent with existing syllabi and focusing on cognitive achievement are scarce. Consequently, we concentrated on a classroom unit that offers learning materials and highlights the existing complexity rather…

Balancing exploration and exploitation in learning to rank online

NARCIS (Netherlands)

Hofmann, K.; Whiteson, S.; de Rijke, M.

2011-01-01

As retrieval systems become more complex, learning to rank approaches are being developed to automatically tune their parameters. Using online learning to rank approaches, retrieval systems can learn directly from implicit feedback, while they are running. In such an online setting, algorithms need

Improving the Design of Workplace E-Learning Research

Science.gov (United States)

Dubois, Cathy; Long, Lori

2012-01-01

E-learning researchers face considerable challenges in creating meaningful and generalizable studies due to the complex nature of this dynamic training medium. Our experience in conducting workplace e-learning research led us to create this guide for planning research on e-learning. We share the unanticipated complications we encountered in our…

The Materiality of Learning

DEFF Research Database (Denmark)

Sørensen, Estrid

or postgraduate students interested in a variety of fields, including educational studies, educational psychology, social anthropology, and STS. Original ethnographic descriptions showing the fine details of how materials influence the learning process Introduces the advanced and complex Actor-Network Theory......The field of educational research lacks a methodology for the study of learning that does not begin with humans, their aims, and their interests. The Materiality of Learning seeks to overcome this human-centered mentality by developing a novel spatial approach to the materiality of learning....... Drawing on science and technology studies (STS), Estrid Sørensen compares an Internet-based 3D virtual environment project in a fourth-grade class with the class's work with traditional learning materials, including blackboards, textbooks, notebooks, pencils, and rulers. Taking into account pupils...

Designing for Learning and Play - The Smiley Model as a Framework

DEFF Research Database (Denmark)

Weitze, Charlotte Lærke

is relevant to the professional creation of small digital learning games as well as the big Game [3], that is, the learning and play situations that exist surrounding the use of small learning games, when students discuss, negotiate, develop, and decide what to do next inside the learning games. The desired...... balance is lost if the learning processes become shallow – at a low level of cognitive complexity – though it may be great fun [4]. Conversely, a game may facilitate good learning processes and many learning activities but result in low motivation among students because it is considered boring.......When seeking to create ideal learning environments for students and teachers, it can be a challenge to find a balance between facilitating learning processes at high levels of cognitive complexity [1] and creating playful and engaging experiences for students and teachers [2]. This challenge...

Preschoolers' Preference for Syntactic Complexity Varies by Socioeconomic Status

Science.gov (United States)

Corriveau, Kathleen H.; Kurkul, Katelyn; Arunachalam, Sudha

2016-01-01

Two experiments investigated whether 4- and 5-year-old children choose to learn from informants who use more complex syntax (passive voice) over informants using more simple syntax (active voice). In Experiment 1 (N = 30), children viewed one informant who consistently used the passive voice and another who used active voice. When learning novel…

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.

78 FR 21515 - Prevailing Rate Systems; Redefinition of the St. Louis, MO; Southern Missouri; Cleveland, OH; and...

Science.gov (United States)

2013-04-11

... have a significant economic impact on a substantial number of small entities because they will affect... Beaver Butler Washington Westmoreland Area of Application. Survey area plus: Ohio: Belmont Harrison...

Variable learning performance: the levels of behaviour organization.

Science.gov (United States)

Csányi, V; Altbäcker, V

1990-01-01

Our experiments were focused on some special aspects of learning in the paradise fish. Passive avoidance conditioning method was used with different success depending on the complexity of the learning tasks. In the case of simple behavioural elements various "constrains" on avoidance learning were found. In a small, covered place the fish were ready to perform freezing reaction and mild punishment increased the frequency and duration of the freezing bouts very substantially. However, it was very difficult to enhance the frequency of freezing by punishment in a tank with transparent walls, where the main response to punishment was escape. The most easily learned tasks were the complex ones which had several different solutions. The fish learned to avoid either side of an aquarium very easily because they could use various behavioural elements to solve the problem. These findings could be interpreted within the framework of different organizational levels of behaviour.

Mere exposure alters category learning of novel objects

Directory of Open Access Journals (Sweden)

Jonathan R Folstein

2010-08-01

Full Text Available We investigated how mere exposure to complex objects with correlated or uncorrelated object features affects later category learning of new objects not seen during exposure. Correlations among pre-exposed object dimensions influenced later category learning. Unlike other published studies, the collection of pre-exposed objects provided no information regarding the categories to be learned, ruling out unsupervised or incidental category learning during pre-exposure. Instead, results are interpreted with respect to statistical learning mechanisms, providing one of the first demonstrations of how statistical learning can influence visual object learning.

Mere exposure alters category learning of novel objects.

Science.gov (United States)

Folstein, Jonathan R; Gauthier, Isabel; Palmeri, Thomas J

2010-01-01

We investigated how mere exposure to complex objects with correlated or uncorrelated object features affects later category learning of new objects not seen during exposure. Correlations among pre-exposed object dimensions influenced later category learning. Unlike other published studies, the collection of pre-exposed objects provided no information regarding the categories to be learned, ruling out unsupervised or incidental category learning during pre-exposure. Instead, results are interpreted with respect to statistical learning mechanisms, providing one of the first demonstrations of how statistical learning can influence visual object learning.

Incremental learning of skill collections based on intrinsic motivation

Science.gov (United States)

Metzen, Jan H.; Kirchner, Frank

2013-01-01

Life-long learning of reusable, versatile skills is a key prerequisite for embodied agents that act in a complex, dynamic environment and are faced with different tasks over their lifetime. We address the question of how an agent can learn useful skills efficiently during a developmental period, i.e., when no task is imposed on him and no external reward signal is provided. Learning of skills in a developmental period needs to be incremental and self-motivated. We propose a new incremental, task-independent skill discovery approach that is suited for continuous domains. Furthermore, the agent learns specific skills based on intrinsic motivation mechanisms that determine on which skills learning is focused at a given point in time. We evaluate the approach in a reinforcement learning setup in two continuous domains with complex dynamics. We show that an intrinsically motivated, skill learning agent outperforms an agent which learns task solutions from scratch. Furthermore, we compare different intrinsic motivation mechanisms and how efficiently they make use of the agent's developmental period. PMID:23898265

E-learning implementation from strategic perspective

DEFF Research Database (Denmark)

Lin, Chih-Cheng; Ma, Zheng; Chang, Chi-Cheng

2012-01-01

are now facing the challenges of selecting and implementing the right e-learning solutions. In order to understand the entire process associated with e-learning implementation in higher institutes which has not yet been a linear process but came probably with top-down, bottom-up, or flowers blooming...... approach. However, the transform process is extremely complex. To make sense of this complexity, the authors adopted strategic IS management profile (Sabherwal et al., 2001) into the research. To explore this speculation, the research uses a qualitative constructivist approach. Based on an exhaustive case...... study of one higher institute's experience, the paper shows that maintaining the alignment is still a crucial issue but hard to achieve. The pressure of achieving alignment may be even more considerable with the implementation of e-learning systems....

Strategies for Better Learning of English Grammar: Chinese vs. Thais

Science.gov (United States)

Supakorn, Patnarin; Feng, Min; Limmun, Wanida

2018-01-01

The success of language learning significantly depends on multiple sets of complex factors; among these are language-learning strategies of which learners in different countries may show different preferences. Needed areas of language learning strategy research include, among others, the strategy of grammar learning and the context-based approach…

Building Critical Capacities for Leadership Learning.

Science.gov (United States)

Torrez, Mark Anthony; Rocco, Melissa L

2015-01-01

Cognitive elements of transformational learning, particularly metacognition and critical self-reflection, are discussed as essential considerations for leadership development in the 21st century. The importance of developmentally sequencing leadership-learning experiences and addressing evolving complexities of leadership identity are also explored. © 2015 Wiley Periodicals, Inc., A Wiley Company.

Reconceptualising Learning in Transdisciplinary Languages Education

Science.gov (United States)

Scarino, Angela; Liddicoat, Anthony J.

2016-01-01

Understanding and working with the complexity of second language learning and use in an intercultural orientation necessitates a re-examination of the different theories of learning that inform the different schools of second language acquisition (SLA). This re-examination takes place in a context where explicitly conceptualizing the nature of…

Machine learning in genetics and genomics

Science.gov (United States)

Libbrecht, Maxwell W.; Noble, William Stafford

2016-01-01

The field of machine learning promises to enable computers to assist humans in making sense of large, complex data sets. In this review, we outline some of the main applications of machine learning to genetic and genomic data. In the process, we identify some recurrent challenges associated with this type of analysis and provide general guidelines to assist in the practical application of machine learning to real genetic and genomic data. PMID:25948244

Non-linguistic learning in aphasia: Effects of training method and stimulus characteristics

Science.gov (United States)

Vallila-Rohter, Sofia; Kiran, Swathi

2013-01-01

Purpose The purpose of the current study was to explore non-linguistic learning ability in patients with aphasia, examining the impact of stimulus typicality and feedback on success with learning. Method Eighteen patients with aphasia and eight healthy controls participated in this study. All participants completed four computerized, non-linguistic category-learning tasks. We probed learning ability under two methods of instruction: feedback-based (FB) and paired-associate (PA). We also examined the impact of task complexity on learning ability, comparing two stimulus conditions: typical (Typ) and atypical (Atyp). Performance was compared between groups and across conditions. Results Results demonstrated that healthy controls were able to successfully learn categories under all conditions. For our patients with aphasia, two patterns of performance arose. One subgroup of patients was able to maintain learning across task manipulations and conditions. The other subgroup of patients demonstrated a sensitivity to task complexity, learning successfully only in the typical training conditions. Conclusions Results support the hypothesis that impairments of general learning are present in aphasia. Some patients demonstrated the ability to extract category information under complex training conditions, while others learned only under conditions that were simplified and emphasized salient category features. Overall, the typical training condition facilitated learning for all participants. Findings have implications for therapy, which are discussed. PMID:23695914

Non-Technical Skills Bingo-a game to facilitate the learning of complex concepts.

Science.gov (United States)

Dieckmann, Peter; Glavin, Ronnie; Hartvigsen Grønholm Jepsen, Rikke Malene; Krage, Ralf

2016-01-01

Acquiring the concepts of non-technical skills (NTS) beyond a superficial level is a challenge for healthcare professionals and simulation faculty. Current simulation-based approaches to teach NTS are challenged when learners have to master NTS concepts, clinically challenging situations, and simulation as a complex technique. The combination of all three aspects might overwhelm learners. To facilitate the deeper comprehension of NTS concepts, we describe an innovative video-based game, the Non-Technical Skills (NTS) Bingo. Participants get NTS Bingo cards that show five NTS elements each. While observing (non-medical) video clips, they try to find examples for the elements on their cards, typically observable behaviours that match a given element. After the video, participants "defend" their solution in a discussion with the game leader and other players. This discussion and the reflection aim to deepen the processing of the NTS concepts. We provide practical guidance for the conduct of NTS Bingo, including a selection of usable video clips and tips for the facilitated discussion after a clip. We use NTS in anaesthesia as example and provide guidance on how to adapt NTS Bingo to other disciplines. NTS Bingo is based on theoretical considerations on concept learning, which we describe to support the rationale for its conduct.

Adult Learning and Communicative Rationality

DEFF Research Database (Denmark)

Rasmussen, Palle Damkjær

2017-01-01

In recent years the concept of learning has been used widely in education policy discourse, often replacing the concept of education. This has provoked criticism and attempts to restate the humanist ideas of the concept of education. This paper discusses the relations between the two concepts of ...... approach from two critical social theorists, Jürgen Habermas and Oskar Negt. These contributions substantiate the interactive dimension of learning, the links between individual learning processes and the complex forms and conditions of life in contemporary societies....

Building Better Drought Resilience Through Improved Monitoring and Early Warning: Learning From Stakeholders in Europe, the USA, and Australia

Science.gov (United States)

Stahl, K.; Hannaford, J.; Bachmair, S.; Tijdeman, E.; Collins, K.; Svoboda, M.; Knutson, C. L.; Wall, N.; Smith, K. H.; Bernadt, T.; Crossman, N. D.; Overton, I. C.; Barker, L. J.; Acreman, M. C.

2016-12-01

With climate projections suggesting that droughts will intensify in many regions in future, improved drought risk management may reduce potential threats to freshwater security across the globe. One aspect that has been called for in this respect is an improvement of the linkage of drought monitoring and early warning, which currently focuses largely on indicators from meteorology and hydrology, to drought impacts on environment and society. However, a survey of existing monitoring and early warning systems globally, that we report on in this contribution, demonstrates that although impacts are being monitored, there is limited work, and certainly little consensus, on how to best achieve this linkage. The Belmont Forum project DrIVER (Drought impacts: Vulnerability thresholds in monitoring and early-warning research) carried out a number of stakeholder workshops in North America, Europe and Australia to elaborate on options for such improvements. A first round of workshops explored current drought management practices among a very diverse range of stakeholders, and their expectations from monitoring and early warning systems (particularly regarding impact characterization). The workshops revealed some disconnects between the indices used in the public early warning systems and those used by local decision-makers, e.g. to trigger drought measures. Follow-up workshops then explored how the links between information at these different scales can be bridged and applied. Impact information plays a key role in this task. This contribution draws on the lessons learned from the transdisciplinary interactions in DrIVER, to enhance the usability of drought monitoring and early-warning systems and other risk management strategies.

Workplace Learning - How We Keep Track of Relevant Information

NARCIS (Netherlands)

Bischoff, Kerstin; Herder, Eelco; Nejdl, Wolfgang

2007-01-01

At the workplace, learning is often a by-product of working on complex projects, requiring self-steered, need-driven and goal-oriented retrieval of information just in time from documents or peers. The personal desktop provides one rich source for learning material and for adaptation of learning

From Teaching Assistant (TA) Training to Workplace Learning

Science.gov (United States)

Korpan, Cynthia

2014-01-01

In this paper, I propose a renewed look at how teaching assistants (TAs) are being prepared to fulfill their duties in higher education. I argue that the apprenticeship model of learning that is currently in use be replaced by the more holistic workplace learning approach. Workplace learning theories take into consideration the complexity of the…

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...

Formal Framework to improve the reliability of concurrent and collaborative learning games

Directory of Open Access Journals (Sweden)

Mounier

2014-05-01

Full Text Available Multi-player learning games are complex software applications resulting from a costly and complex engineering process, and involving multiple stakeholders (domain experts, teachers, game designers, programmers, testers, etc.. Moreover, they are dynamic systems that evolve over time and implement complex interactions between objects and players. Usually, once a learning game is developed, testing activities are conducted by humans who explore the possible executions of the game’s scenario to detect bugs. The complexity and the dynamic nature of multiplayer learning games enforces the complexity of testing activities. Indeed, it is impracticable to explore manually all possible executions due to their huge number. Moreover, the test cannot verify some properties on multi-player and collaborative scenarios, such as paths leading to deadlock between learners or prevent learners to meet all objectives and win the game. This type of properties should be verified at the design stage. We propose a framework enabling a formal modeling of game scenarios and an associated automatic verification of learning game’s scenario at the design stage of the development process.We use Symmetric Petri nets as a modeling language and choose to verify properties by means of model checkers. This paper discusses the possibilities offered by this framework to verify learning game’s properties before the programming stage.

Systems for Teaching Complex Texts: A Proof-of-Concept Investigation

Science.gov (United States)

Fisher, Douglas; Frey, Nancy

2016-01-01

In this article we investigate the systems that need to be in place for students to learn from increasingly complex texts. Our concept, drawn from past research, includes clear learning targets, teacher modeling, collaborative conversations, close reading, small group reading, and wide reading. Using a "proof of concept" model, we follow…

Incremental Learning of Skill Collections based on Intrinsic Motivation

Directory of Open Access Journals (Sweden)

Jan Hendrik Metzen

2013-07-01

Full Text Available Life-long learning of reusable, versatile skills is a key prerequisite forembodied agents that act in a complex, dynamic environment and are faced withdifferent tasks over their lifetime. We address the question of how an agentcan learn useful skills efficiently during a developmental period,i.e., when no task is imposed on him and no external reward signal is provided.Learning of skills in a developmental period needs to be incremental andself-motivated. We propose a new incremental, task-independent skill discoveryapproach that is suited for continuous domains. Furthermore, the agent learnsspecific skills based on intrinsic motivation mechanisms thatdetermine on which skills learning is focused at a given point in time. Weevaluate the approach in a reinforcement learning setup in two continuousdomains with complex dynamics. We show that an intrinsically motivated, skilllearning agent outperforms an agent which learns task solutions from scratch.Furthermore, we compare different intrinsic motivation mechanisms and howefficiently they make use of the agent's developmental period.

Transforming Low Socioeconomic Status Schools to Learning for Well-Being Schools

DEFF Research Database (Denmark)

Nunez, Heilyn Camacho

2016-01-01

This article presents the initial finding about the complexity of dealing with a transformation of a low socioeconomic school into a learning for well-being school. The article looks at the problem through the lens of complexity theory to discuss the different components, subsystems and the diffe......This article presents the initial finding about the complexity of dealing with a transformation of a low socioeconomic school into a learning for well-being school. The article looks at the problem through the lens of complexity theory to discuss the different components, subsystems...

Toward A Dual-Learning Systems Model of Speech Category Learning

Directory of Open Access Journals (Sweden)

Bharath eChandrasekaran

2014-07-01

Full Text Available More than two decades of work in vision posits the existence of dual-learning systems of category learning. The reflective system uses working memory to develop and test rules for classifying in an explicit fashion, while the reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-learning systems models hypothesize that in learning natural categories, learners initially use the reflective system and, with practice, transfer control to the reflexive system. The role of reflective and reflexive systems in auditory category learning and more specifically in speech category learning has not been systematically examined. In this article we describe a neurobiologically-constrained dual-learning systems theoretical framework that is currently being developed in speech category learning and review recent applications of this framework. Using behavioral and computational modeling approaches, we provide evidence that speech category learning is predominantly mediated by the reflexive learning system. In one application, we explore the effects of normal aging on non-speech and speech category learning. We find an age related deficit in reflective-optimal but not reflexive-optimal auditory category learning. Prominently, we find a large age-related deficit in speech learning. The computational modeling suggests that older adults are less likely to transition from simple, reflective, uni-dimensional rules to more complex, reflexive, multi-dimensional rules. In a second application we summarize a recent study examining auditory category learning in individuals with elevated depressive symptoms. We find a deficit in reflective-optimal and an enhancement in reflexive-optimal auditory category learning. Interestingly, individuals with elevated depressive symptoms also show an advantage in learning speech categories. We end with a brief summary and description of a number of future directions.

Collaborative DFA learning applied to Grid administration

NARCIS (Netherlands)

Mulder, W.; Jacobs, C.J.H.; van Someren, M.; van Erp, M.; Stehouwer, H.; van Zaanen, M.

2009-01-01

This paper proposes a distributed learning mechanism that learns patterns from distributed datasets. The complex and dynamic settings of grid environments requires supporting systems to be of a more sophisticated level. Contemporary tools lack the ability to relate and infer events. We developed an

Outsmarting neural networks: an alternative paradigm for machine learning

Energy Technology Data Exchange (ETDEWEB)

Protopopescu, V.; Rao, N.S.V.

1996-10-01

We address three problems in machine learning, namely: (i) function learning, (ii) regression estimation, and (iii) sensor fusion, in the Probably and Approximately Correct (PAC) framework. We show that, under certain conditions, one can reduce the three problems above to the regression estimation. The latter is usually tackled with artificial neural networks (ANNs) that satisfy the PAC criteria, but have high computational complexity. We propose several computationally efficient PAC alternatives to ANNs to solve the regression estimation. Thereby we also provide efficient PAC solutions to the function learning and sensor fusion problems. The approach is based on cross-fertilizing concepts and methods from statistical estimation, nonlinear algorithms, and the theory of computational complexity, and is designed as part of a new, coherent paradigm for machine learning.

Social cognitive theory, metacognition, and simulation learning in nursing education.

Science.gov (United States)

Burke, Helen; Mancuso, Lorraine

2012-10-01

Simulation learning encompasses simple, introductory scenarios requiring response to patients' needs during basic hygienic care and during situations demanding complex decision making. Simulation integrates principles of social cognitive theory (SCT) into an interactive approach to learning that encompasses the core principles of intentionality, forethought, self-reactiveness, and self-reflectiveness. Effective simulation requires an environment conducive to learning and introduces activities that foster symbolic coding operations and mastery of new skills; debriefing builds self-efficacy and supports self-regulation of behavior. Tailoring the level of difficulty to students' mastery level supports successful outcomes and motivation to set higher standards. Mindful selection of simulation complexity and structure matches course learning objectives and supports progressive development of metacognition. Theory-based facilitation of simulated learning optimizes efficacy of this learning method to foster maturation of cognitive processes of SCT, metacognition, and self-directedness. Examples of metacognition that are supported through mindful, theory-based implementation of simulation learning are provided. Copyright 2012, SLACK Incorporated.

Into the Weeds: A Critical Analysis of Game Mechanics and Learning Goals in Games for Learning

Science.gov (United States)

Horstman, Theresa

2013-01-01

In the broadest scope, the purpose of this research is to expose the range and complexity of how educational games support learning. In a more narrowed scope, the purpose is to develop a method to help identify the qualities of educational video games that support learning. This is accomplished by analyzing the design of the game and the…

Towards an agential realist concept of learning

DEFF Research Database (Denmark)

Plauborg, Helle

2018-01-01

Drawing on agential realism, this article explores how learning can be understood. An agential realist way of thinking about learning is sensitive to the complexity that characterises learning as a phenomenon. Thus, learning is seen as a dynamic and emergent phenomenon, constantly undergoing...... processes of becoming and expanding the range of components involved in such constitutive processes. With inspiration from Barad’s theorisation of spatiality, temporality and the interdependence of discourse and materiality, this article focuses on timespacemattering and material-discursivity. Concepts...

Reconceptualizing Design Research in the Age of Mobile Learning

Science.gov (United States)

Bannan, Brenda; Cook, John; Pachler, Norbert

2016-01-01

The purpose of this paper is to begin to examine how the intersection of mobile learning and design research prompts the reconceptualization of research and design individually as well as their integration appropriate for current, complex learning environments. To fully conceptualize and reconceptualize design research in mobile learning, the…

Work Placements as Learning Environments for Patient Safety: Finnish and British Preregistration Nursing Students' Important Learning Events

Science.gov (United States)

Tella, Susanna; Smith, Nancy-Jane; Partanen, Pirjo; Turunen, Hannele

2016-01-01

Learning to ensure patient safety in complex health care environments is an internationally recognised concern. This article explores and compares Finnish (n = 22) and British (n = 32) pre-registration nursing students' important learning events about patient safety from their work placements in health care organisations. Written descriptions were…

Addressing complex design problems through inductive learning

OpenAIRE

Hanna, S.

2012-01-01

Optimisation and related techniques are well suited to clearly defined problems involving systems that can be accurately simulated, but not to tasks in which the phenomena in question are highly complex or the problem ill-defined. These latter are typical of architecture and particularly creative design tasks, which therefore currently lack viable computational tools. It is argued that as design teams and construction projects of unprecedented scale are increasingly frequent, this is just whe...

Deep learning for healthcare: review, opportunities and challenges.

Science.gov (United States)

Miotto, Riccardo; Wang, Fei; Wang, Shuang; Jiang, Xiaoqian; Dudley, Joel T

2017-05-06

Gaining knowledge and actionable insights from complex, high-dimensional and heterogeneous biomedical data remains a key challenge in transforming health care. Various types of data have been emerging in modern biomedical research, including electronic health records, imaging, -omics, sensor data and text, which are complex, heterogeneous, poorly annotated and generally unstructured. Traditional data mining and statistical learning approaches typically need to first perform feature engineering to obtain effective and more robust features from those data, and then build prediction or clustering models on top of them. There are lots of challenges on both steps in a scenario of complicated data and lacking of sufficient domain knowledge. The latest advances in deep learning technologies provide new effective paradigms to obtain end-to-end learning models from complex data. In this article, we review the recent literature on applying deep learning technologies to advance the health care domain. Based on the analyzed work, we suggest that deep learning approaches could be the vehicle for translating big biomedical data into improved human health. However, we also note limitations and needs for improved methods development and applications, especially in terms of ease-of-understanding for domain experts and citizen scientists. We discuss such challenges and suggest developing holistic and meaningful interpretable architectures to bridge deep learning models and human interpretability. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Complex multicellular functions at a unicellular eukaryote level: Learning, memory, and immunity.

Science.gov (United States)

Csaba, György

2017-06-01

According to experimental data, eukaryote unicellulars are able to learn, have immunity and memory. Learning is carried out in a very primitive form, and the memory is not neural but an epigenetic one. However, this epigenetic memory, which is well justified by the presence and manifestation of hormonal imprinting, is strong and permanent in the life of cell and also in its progenies. This memory is epigenetically executed by the alteration and fixation of methylation pattern of genes without changes in base sequences. The immunity of unicellulars is based on self/non-self discrimination, which leads to the destruction of non-self invaders and utilization of them as nourishment (by phagocytosis). The tools of learning, memory, and immunity of unicellulars are uniformly found in plasma membrane receptors, which formed under the effect of dynamic receptor pattern generation, suggested by Koch et al., and this is the basis of hormonal imprinting, by which the encounter between a chemical substance and the cell is specifically memorized. The receptors and imprinting are also used in the later steps of evolution up to mammals (including man) in each mentioned functions. This means that learning, memory, and immunity can be deduced to a unicellular eukaryote level.

Risk management integration into complex project organizations

Science.gov (United States)

Fisher, K.; Greanias, G.; Rose, J.; Dumas, R.

2002-01-01

This paper describes the approach used in designing and adapting the SIRTF prototype, discusses some of the lessons learned in developing the SIRTF prototype, and explains the adaptability of the risk management database to varying levels project complexity.

Unicorn: Continual Learning with a Universal, Off-policy Agent

OpenAIRE

Mankowitz, Daniel J.; Žídek, Augustin; Barreto, André; Horgan, Dan; Hessel, Matteo; Quan, John; Oh, Junhyuk; van Hasselt, Hado; Silver, David; Schaul, Tom

2018-01-01

Some real-world domains are best characterized as a single task, but for others this perspective is limiting. Instead, some tasks continually grow in complexity, in tandem with the agent's competence. In continual learning, also referred to as lifelong learning, there are no explicit task boundaries or curricula. As learning agents have become more powerful, continual learning remains one of the frontiers that has resisted quick progress. To test continual learning capabilities we consider a ...

'n voorgestelde raamwerk vir die literatuurstudie as kwalitatiewe ...

African Journals Online (AJOL)

admin

Wellington: Lux. Verbi BM. WHITE, J.E.. 2003. Contemporary moral problems. Belmont, CA: Wadsworth. Sleutelwoorde. Keywords. Kwalitatiewe navorsing. Qualitative research. Literatuurstudie. Literature study. Genadedood. Euthanasia. Lategan. Literatuurstudie as kwalitatiewe navorsingsmetodologiese tegniek. 142.

Implementation of a school-based social and emotional learning intervention: understanding diffusion processes within complex systems.

Science.gov (United States)

Evans, Rhiannon; Murphy, Simon; Scourfield, Jonathan

2015-07-01

Sporadic and inconsistent implementation remains a significant challenge for social and emotional learning (SEL) interventions. This may be partly explained by the dearth of flexible, causative models that capture the multifarious determinants of implementation practices within complex systems. This paper draws upon Rogers (2003) Diffusion of Innovations Theory to explain the adoption, implementation and discontinuance of a SEL intervention. A pragmatic, formative process evaluation was conducted in alignment with phase 1 of the UK Medical Research Council's framework for Developing and Evaluating Complex Interventions. Employing case-study methodology, qualitative data were generated with four socio-economically and academically contrasting secondary schools in Wales implementing the Student Assistance Programme. Semi-structured interviews were conducted with 15 programme stakeholders. Data suggested that variation in implementation activity could be largely attributed to four key intervention reinvention points, which contributed to the transformation of the programme as it interacted with contextual features and individual needs. These reinvention points comprise the following: intervention training, which captures the process through which adopters acquire knowledge about a programme and delivery expertise; intervention assessment, which reflects adopters' evaluation of an intervention in relation to contextual needs; intervention clarification, which comprises the cascading of knowledge through an organisation in order to secure support in delivery; and intervention responsibility, which refers to the process of assigning accountability for sustainable delivery. Taken together, these points identify opportunities to predict and intervene with potential implementation problems. Further research would benefit from exploring additional reinvention activity.

MLnet report: training in Europe on machine learning

OpenAIRE

Ellebrecht, Mario; Morik, Katharina

1999-01-01

Machine learning techniques offer opportunities for a variety of applications and the theory of machine learning investigates problems that are of interest for other fields of computer science (e.g., complexity theory, logic programming, pattern recognition). However, the impacts of machine learning can only be recognized by those who know the techniques and are able to apply them. Hence, teaching machine learning is necessary before this field can diversify computer science. In order ...

Abstraction in artificial intelligence and complex systems

CERN Document Server

Saitta, Lorenza

2013-01-01

Abstraction is a fundamental mechanism underlying both human and artificial perception, representation of knowledge, reasoning and learning. This mechanism plays a crucial role in many disciplines, notably Computer Programming, Natural and Artificial Vision, Complex Systems, Artificial Intelligence and Machine Learning, Art, and Cognitive Sciences. This book first provides the reader with an overview of the notions of abstraction proposed in various disciplines by comparing both commonalities and differences.  After discussing the characterizing properties of abstraction, a formal model, the K

Intercultural challenge to language learning

Directory of Open Access Journals (Sweden)

Luz María Muñoz de Cote

2012-10-01

Full Text Available This paper presents the findings of a qualitative research project set to investigate the piloting process of an innovative language program for university students. It challenges traditional English language teaching courses celebrating a view centered on learning; classes become spaces for students to understand the language they are learning through the development of small projects. The approach moves from a teaching transmission paradigm to one where the most important agent is each student who has to engage with a topic of his or her interest. Students are seen as individuals whose knowledge and understanding of the world is valued and not as people whose lack of language skills prevents themfrom engaging in discussions of complex topics. The objective of this innovation is to enhance students’ understanding and use of academic English in their field of interest. In this project, we argue that knowledge and understanding of the mother tongue and culture play key roles in the development of a second language. A number of studies suggest that students who had strong first language literacy skills achieved higher proficiency levels in their second language. Based on this argument and Vygotsky’s sociocultural learning theory, we designed disciplinary content language learning workshops for first-degree students. The main tenet is that students can develop academic English given that they know about their discipline. Findings so far reveal the difficulty of students to take distance from their previous learning experiences. They also show that students’ ideas expressed in English are far more complex than what would be expected of them given their second language skills. The complexity is not only related to thecontent, but to the way they construct their paragraphs and the understanding of how the register of their field  may be used.

The Application of the Complex Field of Geodesy to an Entrance Level College Course using Cognitive Learning Techniques.

Science.gov (United States)

Menard, J.; Beall King, A.; Larson, P. B.

2017-12-01

The study of the shape of the Earth is called geodesy. It is a complex and rich field, encompassing GPS, the development of satellites to measure Earth, and the many applications of these measurements to better understand our planet. What is the best way to explain complex concepts to an entry-level college student, such as geodesy or gravitation? What is the most efficient way to peek a student's interest in an abstract field? Two people are walking side by side on a crowded street. Do they talk? Do they look at each other? Do they laugh together? Do they touch? Even though the bond between these two people cannot necessarily be physically seen, it is possible, by looking at their behavior towards each other, to determine whether or not they know each other. If they do, they are attracted to one another, walking together in the same direction, exchanging ideas or laughs. The Moon attracts the Earth's oceans, forming tides. The Earth attracts the Moon into staying in orbit. They are attracted to each other by the invisible yet quantifiable force of gravitation. In order to ensure that first year college students understand the concept and applications of geodesy, and find interest in the field, several teaching and learning techniques must be used. These techniques are compared to one another in terms of efficiency both by comparing the students' success through quizzes and discussions, and by comparing the students' enjoyment of and interest in the class through evaluations at the beginning and end of each class in order to assess how much material was learned, understood, and retained. This study is conducted via a short course with volunteer students. The course is a combination of lecture, discussion, experiments, and field work. Quizzes are used to evaluate not the students, but their improvement as a result of the efficacy of the teaching method. In class group and one on one discussions are used as the main part of the final grade.

The effects of cholesterol on learning and memory.

Science.gov (United States)

Schreurs, Bernard G

2010-07-01

Cholesterol is vital to normal brain function including learning and memory but that involvement is as complex as the synthesis, metabolism and excretion of cholesterol itself. Dietary cholesterol influences learning tasks from water maze to fear conditioning even though cholesterol does not cross the blood brain barrier. Excess cholesterol has many consequences including peripheral pathology that can signal brain via cholesterol metabolites, pro-inflammatory mediators and antioxidant processes. Manipulations of cholesterol within the central nervous system through genetic, pharmacological, or metabolic means circumvent the blood brain barrier and affect learning and memory but often in animals already otherwise compromised. The human literature is no less complex. Cholesterol reduction using statins improves memory in some cases but not others. There is also controversy over statin use to alleviate memory problems in Alzheimer's disease. Correlations of cholesterol and cognitive function are mixed and association studies find some genetic polymorphisms are related to cognitive function but others are not. In sum, the field is in flux with a number of seemingly contradictory results and many complexities. Nevertheless, understanding cholesterol effects on learning and memory is too important to ignore.

Complexity-aware high efficiency video coding

CERN Document Server

Correa, Guilherme; Agostini, Luciano; Cruz, Luis A da Silva

2016-01-01

This book discusses computational complexity of High Efficiency Video Coding (HEVC) encoders with coverage extending from the analysis of HEVC compression efficiency and computational complexity to the reduction and scaling of its encoding complexity. After an introduction to the topic and a review of the state-of-the-art research in the field, the authors provide a detailed analysis of the HEVC encoding tools compression efficiency and computational complexity.  Readers will benefit from a set of algorithms for scaling the computational complexity of HEVC encoders, all of which take advantage from the flexibility of the frame partitioning structures allowed by the standard.  The authors also provide a set of early termination methods based on data mining and machine learning techniques, which are able to reduce the computational complexity required to find the best frame partitioning structures. The applicability of the proposed methods is finally exemplified with an encoding time control system that emplo...

Foundations for a new science of learning.

Science.gov (United States)

Meltzoff, Andrew N; Kuhl, Patricia K; Movellan, Javier; Sejnowski, Terrence J

2009-07-17

Human learning is distinguished by the range and complexity of skills that can be learned and the degree of abstraction that can be achieved compared with those of other species. Homo sapiens is also the only species that has developed formal ways to enhance learning: teachers, schools, and curricula. Human infants have an intense interest in people and their behavior and possess powerful implicit learning mechanisms that are affected by social interaction. Neuroscientists are beginning to understand the brain mechanisms underlying learning and how shared brain systems for perception and action support social learning. Machine learning algorithms are being developed that allow robots and computers to learn autonomously. New insights from many different fields are converging to create a new science of learning that may transform educational practices.

Play, learn, explore: grasping complexity through gaming and ...

African Journals Online (AJOL)

Increased demand for agricultural products, the aspirations of rural communities and a growing recognition of planetary boundaries outline the complex trade-offs resource users are facing on a daily basis. Management problems typically involve multiple stakeholders with diverse and often conflicting worldviews, needs ...

Complexity for survival of livings

Energy Technology Data Exchange (ETDEWEB)

Zak, Michail [Jet Propulsion Laboratory, California Institute of Technology, Advance Computing Algorithms and IVHM Group, Pasadena, CA 91109 (United States)]. E-mail: Michail.Zak@jpl.nasa.gov

2007-05-15

A connection between survivability of livings and complexity of their behavior is established. New physical paradigms-exchange of information via reflections, and chain of abstractions-explaining and describing progressive evolution of complexity in living (active) systems are introduced. A biological origin of these paradigms is associated with a recently discovered mirror neuron that is able to learn by imitation. As a result, an active element possesses the self-nonself images and interacts with them creating the world of mental dynamics. Three fundamental types of complexity of mental dynamics that contribute to survivability are identified. Mathematical model of the corresponding active systems is described by coupled motor-mental dynamics represented by Langevin and Fokker-Planck equations, respectively, while the progressive evolution of complexity is provided by nonlinear evolution of probability density. Application of the proposed formalism to modeling common-sense-based decision-making process is discussed.

Complexity for survival of livings

International Nuclear Information System (INIS)

Zak, Michail

2007-01-01

A connection between survivability of livings and complexity of their behavior is established. New physical paradigms-exchange of information via reflections, and chain of abstractions-explaining and describing progressive evolution of complexity in living (active) systems are introduced. A biological origin of these paradigms is associated with a recently discovered mirror neuron that is able to learn by imitation. As a result, an active element possesses the self-nonself images and interacts with them creating the world of mental dynamics. Three fundamental types of complexity of mental dynamics that contribute to survivability are identified. Mathematical model of the corresponding active systems is described by coupled motor-mental dynamics represented by Langevin and Fokker-Planck equations, respectively, while the progressive evolution of complexity is provided by nonlinear evolution of probability density. Application of the proposed formalism to modeling common-sense-based decision-making process is discussed

Complexity and competition in appetitive and aversive neural circuits

Directory of Open Access Journals (Sweden)

Crista L. Barberini

2012-11-01

Full Text Available Decision-making often involves using sensory cues to predict possible rewarding or punishing reinforcement outcomes before selecting a course of action. Recent work has revealed complexity in how the brain learns to predict rewards and punishments. Analysis of neural signaling during and after learning in the amygdala and orbitofrontal cortex, two brain areas that process appetitive and aversive stimuli, reveals a dynamic relationship between appetitive and aversive circuits. Specifically, the relationship between signaling in appetitive and aversive circuits in these areas shifts as a function of learning. Furthermore, although appetitive and aversive circuits may often drive opposite behaviors – approaching or avoiding reinforcement depending upon its valence – these circuits can also drive similar behaviors, such as enhanced arousal or attention; these processes also may influence choice behavior. These data highlight the formidable challenges ahead in dissecting how appetitive and aversive neural circuits interact to produce a complex and nuanced range of behaviors.

Transforming Leadership Development for Significant Learning.

Science.gov (United States)

Owen, Julie E

2015-01-01

Leadership education is undergoing a transformation where powerful pedagogies and emerging knowledge about the scholarship of teaching and learning supplant long held and often-outmoded practices of leadership education. This transformation requires new commitments to evidence-based practice, critical consciousness, and more complex understanding of the levers of leadership learning. © 2015 Wiley Periodicals, Inc., A Wiley Company.

KNOWLEDGE IN LEARNING COMPANIES

Directory of Open Access Journals (Sweden)

Alexandrina Cristina VASILE

2016-05-01

Full Text Available Changes are the only constant value in the current unpredictable economy. Under these circumstances leaders and employees must manage the external and internal environment and bring profitability for their companies. This paper gives an introductory approach to different perspective over learning companies in international literature. Different theoretical aspects, models and theories are taken into account for having a higher visibility to the complex concept of learning companies from leadership side to multiculturalism as the firm profitability should be the final goal of each economic system. The article concludes that not the process of learning is important but the adaptability to every different environment must be seen as vital.

Complexity of Economical Systems

Directory of Open Access Journals (Sweden)

G. P. Pavlos

2015-01-01

Full Text Available In this study new theoretical concepts are described concerning the interpretation of economical complex dynamics. In addition a summary of an extended algorithm of nonlinear time series analysis is provided which is applied not only in economical time series but also in other physical complex systems (e.g. [22, 24]. In general, Economy is a vast and complicated set of arrangements and actions wherein agents—consumers, firms, banks, investors, government agencies—buy and sell, speculate, trade, oversee, bring products into being, offer services, invest in companies, strategize, explore, forecast, compete, learn, innovate, and adapt. As a result the economic and financial variables such as foreign exchange rates, gross domestic product, interest rates, production, stock market prices and unemployment exhibit large-amplitude and aperiodic fluctuations evident in complex systems. Thus, the Economics can be considered as spatially distributed non-equilibrium complex system, for which new theoretical concepts, such as Tsallis non extensive statistical mechanics and strange dynamics, percolation, nonGaussian, multifractal and multiscale dynamics related to fractional Langevin equations can be used for modeling and understanding of the economical complexity locally or globally.

Trends in e-learning and factors for successful and effective introduction of e-learning in adult education in Slovenia

OpenAIRE

Lea Bregar

2011-01-01

E-learning provides a number of advantages for adult education, which derive from its greater flexibility regarding time, space, pace, content and learning methods. Today e-learning manifests itself in a great variety of implementation program- mes, which are based on various teaching models, employ a wide range of technological tools, reflect different roles of educational institutions management and different institutional frameworks. The versatility and the dynamic and complex character of...

Learning predictive statistics from temporal sequences: Dynamics and strategies.

Science.gov (United States)

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

2017-10-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics-that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.

Automated design of complex dynamic systems.

Directory of Open Access Journals (Sweden)

Michiel Hermans

Full Text Available Several fields of study are concerned with uniting the concept of computation with that of the design of physical systems. For example, a recent trend in robotics is to design robots in such a way that they require a minimal control effort. Another example is found in the domain of photonics, where recent efforts try to benefit directly from the complex nonlinear dynamics to achieve more efficient signal processing. The underlying goal of these and similar research efforts is to internalize a large part of the necessary computations within the physical system itself by exploiting its inherent non-linear dynamics. This, however, often requires the optimization of large numbers of system parameters, related to both the system's structure as well as its material properties. In addition, many of these parameters are subject to fabrication variability or to variations through time. In this paper we apply a machine learning algorithm to optimize physical dynamic systems. We show that such algorithms, which are normally applied on abstract computational entities, can be extended to the field of differential equations and used to optimize an associated set of parameters which determine their behavior. We show that machine learning training methodologies are highly useful in designing robust systems, and we provide a set of both simple and complex examples using models of physical dynamical systems. Interestingly, the derived optimization method is intimately related to direct collocation a method known in the field of optimal control. Our work suggests that the application domains of both machine learning and optimal control have a largely unexplored overlapping area which envelopes a novel design methodology of smart and highly complex physical systems.

A Case of Generativity in a Culturally and Linguistically Complex English Language Arts Classroom

Science.gov (United States)

Skerrett, Allison

2011-01-01

This article examines an ESL English language arts teacher's conceptions of linguistic diversity, literacy learning and her role as teacher in a culturally and linguistically complex classroom. It further examines her processes of learning about, and developing curricular and pedagogical innovations to meet, her students' learning needs. The…

A Jigsaw Lesson for Operations of Complex Numbers.

Science.gov (United States)

Lucas, Carol A.

2000-01-01

Explains the cooperative learning technique of jigsaw. Details the use of a jigsaw lesson for explaining complex numbers to intermediate algebra students. Includes copies of the handouts given to the expert groups. (Author/ASK)

Motivating the interest in Danish literature with Mobile Persuasive Learning

DEFF Research Database (Denmark)

Gram-Hansen, Sandra Burri; Kristensen, Karina Dyrby; Gram-Hansen, Lasse Burri

2013-01-01

This paper analyses and discusses the potential of Mobile Persuasive Learning (MPL) in relation to learning scenarios that involve complex and interdisciplinary learning material. A specific example of MPL is presented, which has been developed with the intent to motivate the interest of the life...... and works of Danish author and playwright Kaj Munk. A Persuasive Learning Design (PLD) is tried in a specific learning scenario that aims to introduce the history of Kaj Munk to students in lower secondary education in Vester Hassing in Northern Jutland. The methodological background for the chosen scenario...... is described and evaluation activities are presented and discussed and it is argued that while the topic is possibly too complex for the particular age group of students chosen, evaluation feedback and researcher observations point towards a significant potential in further developing MPL-designs in a school...

Approximate kernel competitive learning.

Science.gov (United States)

Wu, Jian-Sheng; Zheng, Wei-Shi; Lai, Jian-Huang

2015-03-01

Kernel competitive learning has been successfully used to achieve robust clustering. However, kernel competitive learning (KCL) is not scalable for large scale data processing, because (1) it has to calculate and store the full kernel matrix that is too large to be calculated and kept in the memory and (2) it cannot be computed in parallel. In this paper we develop a framework of approximate kernel competitive learning for processing large scale dataset. The proposed framework consists of two parts. First, it derives an approximate kernel competitive learning (AKCL), which learns kernel competitive learning in a subspace via sampling. We provide solid theoretical analysis on why the proposed approximation modelling would work for kernel competitive learning, and furthermore, we show that the computational complexity of AKCL is largely reduced. Second, we propose a pseudo-parallelled approximate kernel competitive learning (PAKCL) based on a set-based kernel competitive learning strategy, which overcomes the obstacle of using parallel programming in kernel competitive learning and significantly accelerates the approximate kernel competitive learning for large scale clustering. The empirical evaluation on publicly available datasets shows that the proposed AKCL and PAKCL can perform comparably as KCL, with a large reduction on computational cost. Also, the proposed methods achieve more effective clustering performance in terms of clustering precision against related approximate clustering approaches. Copyright © 2014 Elsevier Ltd. All rights reserved.

Persuasive Designs for Learning - Learning in Persuasive Design

DEFF Research Database (Denmark)

Hansen, Sandra Burri Gram

and evaluate persuasive learning designs for energy and environment education. Through an exploratory mixed methods approach, I extend my understanding of persuasive design and strive to establish its claim in relation to other more established research areas. I contribute to the field by arguing towards......In this dissertation, the potential of applying persuasive design principles to the development of learning designs in complex organizations is explored, analysed and developed. My research is conducted in collaboration with the Danish Ministry of Defence and the Danish army, where I design, test...... the potential of constructive ethics applied in practice, and with initial steps towards a methodological framework for persuasive design, which bridges between system-oriented and user-centred approaches to design....

Learning induces the translin/trax RNase complex to express activin receptors for persistent memory.

Science.gov (United States)

Park, Alan Jung; Havekes, Robbert; Fu, Xiuping; Hansen, Rolf; Tudor, Jennifer C; Peixoto, Lucia; Li, Zhi; Wu, Yen-Ching; Poplawski, Shane G; Baraban, Jay M; Abel, Ted

2017-09-20

Long-lasting forms of synaptic plasticity and memory require de novo protein synthesis. Yet, how learning triggers this process to form memory is unclear. Translin/trax is a candidate to drive this learning-induced memory mechanism by suppressing microRNA-mediated translational silencing at activated synapses. We find that mice lacking translin/trax display defects in synaptic tagging, which requires protein synthesis at activated synapses, and long-term memory. Hippocampal samples harvested from these mice following learning show increases in several disease-related microRNAs targeting the activin A receptor type 1C (ACVR1C), a component of the transforming growth factor-β receptor superfamily. Furthermore, the absence of translin/trax abolishes synaptic upregulation of ACVR1C protein after learning. Finally, synaptic tagging and long-term memory deficits in mice lacking translin/trax are mimicked by ACVR1C inhibition. Thus, we define a new memory mechanism by which learning reverses microRNA-mediated silencing of the novel plasticity protein ACVR1C via translin/trax.

Role of amylase, mucin, IgA and albumin on salivary protein ...

Indian Academy of Sciences (India)

2013-03-15

Mar 15, 2013 ... important and widely operating buffers in body fluids are proteins ... Protein buffer systems include basic and acidic groups, which act as hydrogen ion ..... Sherwood L 2006 Fundamentals of physiology (Belmont: Thomson.

ReactionPredictor: prediction of complex chemical reactions at the mechanistic level using machine learning.

Science.gov (United States)

Kayala, Matthew A; Baldi, Pierre

2012-10-22

Proposing reasonable mechanisms and predicting the course of chemical reactions is important to the practice of organic chemistry. Approaches to reaction prediction have historically used obfuscating representations and manually encoded patterns or rules. Here we present ReactionPredictor, a machine learning approach to reaction prediction that models elementary, mechanistic reactions as interactions between approximate molecular orbitals (MOs). A training data set of productive reactions known to occur at reasonable rates and yields and verified by inclusion in the literature or textbooks is derived from an existing rule-based system and expanded upon with manual curation from graduate level textbooks. Using this training data set of complex polar, hypervalent, radical, and pericyclic reactions, a two-stage machine learning prediction framework is trained and validated. In the first stage, filtering models trained at the level of individual MOs are used to reduce the space of possible reactions to consider. In the second stage, ranking models over the filtered space of possible reactions are used to order the reactions such that the productive reactions are the top ranked. The resulting model, ReactionPredictor, perfectly ranks polar reactions 78.1% of the time and recovers all productive reactions 95.7% of the time when allowing for small numbers of errors. Pericyclic and radical reactions are perfectly ranked 85.8% and 77.0% of the time, respectively, rising to >93% recovery for both reaction types with a small number of allowed errors. Decisions about which of the polar, pericyclic, or radical reaction type ranking models to use can be made with >99% accuracy. Finally, for multistep reaction pathways, we implement the first mechanistic pathway predictor using constrained tree-search to discover a set of reasonable mechanistic steps from given reactants to given products. Webserver implementations of both the single step and pathway versions of Reaction

Learning Predictive Statistics: Strategies and Brain Mechanisms.

Science.gov (United States)

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

2017-08-30

When immersed in a new environment, we are challenged to decipher initially incomprehensible streams of sensory information. However, quite rapidly, the brain finds structure and meaning in these incoming signals, helping us to predict and prepare ourselves for future actions. This skill relies on extracting the statistics of event streams in the environment that contain regularities of variable complexity from simple repetitive patterns to complex probabilistic combinations. Here, we test the brain mechanisms that mediate our ability to adapt to the environment's statistics and predict upcoming events. By combining behavioral training and multisession fMRI in human participants (male and female), we track the corticostriatal mechanisms that mediate learning of temporal sequences as they change in structure complexity. We show that learning of predictive structures relates to individual decision strategy; that is, selecting the most probable outcome in a given context (maximizing) versus matching the exact sequence statistics. These strategies engage distinct human brain regions: maximizing engages dorsolateral prefrontal, cingulate, sensory-motor regions, and basal ganglia (dorsal caudate, putamen), whereas matching engages occipitotemporal regions (including the hippocampus) and basal ganglia (ventral caudate). Our findings provide evidence for distinct corticostriatal mechanisms that facilitate our ability to extract behaviorally relevant statistics to make predictions. SIGNIFICANCE STATEMENT Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. Past work has studied how humans identify repetitive patterns and associative pairings. However, the natural environment contains regularities that vary in complexity from simple repetition to complex probabilistic combinations. Here, we combine behavior and multisession fMRI to track the brain mechanisms that mediate our ability to adapt to

Identifying Students learning Styles as a Way to Promote Learning Quality

Directory of Open Access Journals (Sweden)

Jafar Sadegh Tabrizi

2013-05-01

Full Text Available Introduction: The major part of peoples knowledge, skills and abilities are achieved during the complex process called learning. Learning is not simply the product of mere intelligence and capabilities of individual; it also depends on other factors such as personality traits, personal interests, and t ype of duty and di fferent methods and st yles. The understanding of each individual fits with his/her learning style. The aim of this study was to determine the learning st yles of Health Care Management students in Tabriz University of Medical Sciences. Methods: Learning styles of 55 Health Services Management students in Tabriz Health and Nutrition Faculty were evaluated in 2009 using a twelve-question Kolb questionnaire in a descriptive study. The data was anal yzed using SPSS. And the frequency of students learning styles was identified by their ages and averages. Results: In this study, 69% of the students were female and the dominant learning method was Assimilator (42%. Other styles with a regard to their frequency were Diverge (24%, Coverage (22%and Accommodator (12%. In the present study,no statistically significant relationship was found in learning styles between the gender (p= 0.644and average (p = 0.676of the students. Conclusion: Assimilator and Diverge methods were the most common ones among the management students. Hence, to improve the quality of learning in this group of students, it is proposed that the teachers use interactive and creative teaching methods such as small and la rge group discussion,brain storming, problem solving, debate-based learning, self-learning and lecturing.

Designing an Interactive Multimedia Environment for Learning and Aiding Troubleshooting

National Research Council Canada - National Science Library

Kolodner, Janet

1997-01-01

.... However troubleshooting is a complex process both to learn and perform. This report examines the prospects for designing an interactive learning environment that helps users acquire and engage in effective troubleshooting...

Performance in Physiology Evaluation: Possible Improvement by Active Learning Strategies

Science.gov (United States)

Montrezor, Luís H.

2016-01-01

The evaluation process is complex and extremely important in the teaching/learning process. Evaluations are constantly employed in the classroom to assist students in the learning process and to help teachers improve the teaching process. The use of active methodologies encourages students to participate in the learning process, encourages…

Human demonstrations for fast and safe exploration in reinforcement learning

NARCIS (Netherlands)

Schonebaum, G.K.; Junell, J.L.; van Kampen, E.

2017-01-01

Reinforcement learning is a promising framework for controlling complex vehicles with a high level of autonomy, since it does not need a dynamic model of the vehicle, and it is able to adapt to changing conditions. When learning from scratch, the performance of a reinforcement learning controller

Civil Society, Adult Learning and Action in India.

Science.gov (United States)

Tandon, Rajesh

2000-01-01

Five case studies of individual and collective learning projects in India demonstrate that (1) the impetus for civic action arises from local conditions; (2) transformative action requires sustained adult learning; and (3) civil society is a complex concept reflecting diverse priorities and perspectives. (SK)

Learning automata theory and applications

CERN Document Server

Najim, K

1994-01-01

Learning systems have made a significant impact on all areas of engineering problems. They are attractive methods for solving many problems which are too complex, highly non-linear, uncertain, incomplete or non-stationary, and have subtle and interactive exchanges with the environment where they operate. The main aim of the book is to give a systematic treatment of learning automata and to produce a guide to a wide variety of ideas and methods that can be used in learning systems, including enough theoretical material to enable the user of the relevant techniques and concepts to understand why

It helps to untangle really complicated situations: 'AS IF' supervision for working with complexity

OpenAIRE

Haydon-Laurelut, Mark; Millett, E.; Bissmire, D.; Doswell, S.; Heneage, C.

2012-01-01

Working effectively with people with learning disabilities may well involve negotiating complex systems of relationships. Negotiating a network - particularly in the context of risk, anxiety, and conflict - is a common task for clinical psychologists and systemic psychotherapists in Community Learning Disability Teams (CLDT's). In this paper we describe our use of the 'AS IF' consultation exercise (Anderson, 1987) as a tool for addressing complexity and stuck-ness. We have employed 'AS IF' in...

Implications of Complexity and Chaos Theories for Organizations that Learn

Science.gov (United States)

Smith, Peter A. C.

2003-01-01

In 1996 Hubert Saint-Onge and Smith published an article ("The evolutionary organization: avoiding a Titanic fate", in The Learning Organization, Vol. 3 No. 4), based on their experience at the Canadian Imperial Bank of Commerce (CIBC). It was established at CIBC that change could be successfully facilitated through blended application…

Applied complex variables for scientists and engineers

CERN Document Server

Kwok, Yue Kuen

2010-01-01

This introduction to complex variable methods begins by carefully defining complex numbers and analytic functions, and proceeds to give accounts of complex integration, Taylor series, singularities, residues and mappings. Both algebraic and geometric tools are employed to provide the greatest understanding, with many diagrams illustrating the concepts introduced. The emphasis is laid on understanding the use of methods, rather than on rigorous proofs. Throughout the text, many of the important theoretical results in complex function theory are followed by relevant and vivid examples in physical sciences. This second edition now contains 350 stimulating exercises of high quality, with solutions given to many of them. Material has been updated and additional proofs on some of the important theorems in complex function theory are now included, e.g. the Weierstrass–Casorati theorem. The book is highly suitable for students wishing to learn the elements of complex analysis in an applied context.

Using Ontologies for the E-learning System in Healthcare Human Resources Management

Directory of Open Access Journals (Sweden)

Lidia BAJENARU

2015-01-01

Full Text Available This paper provides a model for the use of ontology in e-learning systems for structuring educational content in the domain of healthcare human resources management (HHRM in Romania. In this respect we propose an effective method to improve the learning system by providing personalized learning paths created using ontology and advanced educational strategies to provide a personalized learning content for the medical staff. Personalization of e-learning process for the chosen target group will be achieved by setting up learning path for each user according to his profile. This will become possible using: domain ontology, learning objects, modeling student knowledge. Developing an ontology-based system for competence management allows complex interactions, providing intelligent interfacing. This is a new approach for the healthcare system managers in permanent training based on e-learning technologies and specific ontologies in a complex area that needs urgent modernization and efficiency to meet the public health economic, social and political context of Romania.

Efficacy of Simulation-Based Learning of Electronics Using Visualization and Manipulation

Science.gov (United States)

Chen, Yu-Lung; Hong, Yu-Ru; Sung, Yao-Ting; Chang, Kuo-En

2011-01-01

Software for simulation-based learning of electronics was implemented to help learners understand complex and abstract concepts through observing external representations and exploring concept models. The software comprises modules for visualization and simulative manipulation. Differences in learning performance of using the learning software…

Embracing Big Data in Complex Educational Systems: The Learning Analytics Imperative and the Policy Challenge

Science.gov (United States)

Macfadyen, Leah P.; Dawson, Shane; Pardo, Abelardo; Gaševic, Dragan

2014-01-01

In the new era of big educational data, learning analytics (LA) offer the possibility of implementing real-time assessment and feedback systems and processes at scale that are focused on improvement of learning, development of self-regulated learning skills, and student success. However, to realize this promise, the necessary shifts in the…

Toward a Learning Science for Complex Crowdsourcing Tasks

Science.gov (United States)

Doroudi, Shayan; Kamar, Ece; Brunskill, Emma; Horvitz, Eric

2016-01-01

We explore how crowdworkers can be trained to tackle complex crowdsourcing tasks. We are particularly interested in training novice workers to perform well on solving tasks in situations where the space of strategies is large and workers need to discover and try different strategies to be successful. In a first experiment, we perform a comparison…

Genetics Home Reference: tarsal-carpal coalition syndrome

Science.gov (United States)

... Belmonte JC, Choe S. Structural basis of BMP signalling inhibition by the cystine knot protein Noggin. Nature. 2002 ... Links Data Files & API Site Map Subscribe Customer Support USA.gov Copyright Privacy Accessibility FOIA Viewers & Players ...

Integrating e-Learning and Classroom Learning; Four Years of Asynchronous Learning to Improve Academic Competences

Directory of Open Access Journals (Sweden)

Bart Rienties

2008-06-01

Full Text Available In an ever-changing world, competencies to process information efficiently are essential. However, several researchers indicate that graduates have limited abilities to solve complex problems in reality. In this paper, a possible solution to increase competences in effective searching, analysing and comparing information is provided. In a blended-learning environment, students had to share information before coming to class. The results of an analysis of four consecutive years of computersupported learning in a master-course indicate that students are willing to share information when conditions are favourable. In addition, a specific redesign of the task, control and social dimension let to increased knowledge sharing. Future research is necessary to assess whether this also has increased performance.

The effects of age, rank and neophobia on social learning in horses.

Science.gov (United States)

Krueger, Konstanze; Farmer, Kate; Heinze, Jürgen

2014-05-01

Social learning is said to meet the demands of complex environments in which individuals compete over resources and cooperate to share resources. Horses (Equus caballus) were thought to lack social learning skills because they feed on homogenously distributed resources with few reasons for conflict. However, the horse's social environment is complex, which raises the possibility that its capacity for social transfer of feeding behaviour has been underestimated. We conducted a social learning experiment using 30 socially kept horses of different ages. Five horses, one from each group, were chosen as demonstrators, and the remaining 25 horses were designated observers. Observers from each group were allowed to watch their group demonstrator opening a feeding apparatus. We found that young, low-ranking and more exploratory horses learned by observing older members of their own group, and the older the horse, the more slowly it appeared to learn. Social learning may be an adaptive specialisation to the social environment. Older animals may avoid the potential costs of acquiring complex and potentially disadvantageous feeding behaviours from younger group members. We argue that horses show social learning in the context of their social ecology and that research procedures must take such contexts into account. Misconceptions about the horse's sociality may have hampered earlier studies.

Machine Learning and Radiology

Science.gov (United States)

Wang, Shijun; Summers, Ronald M.

2012-01-01

In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077

The effects of utilizing a near-patient e-learning tool on medical student learning.

Science.gov (United States)

Selzer, Rob; Tallentire, Victoria R; Foley, Fiona

2015-01-01

This study aimed to develop a near-patient, e-learning tool and explore student views on how utilization of such a tool influenced their learning. Third year medical students from Monash University in Melbourne, Australia were invited to trial a novel, near-patient, e-learning tool in two separate pilots within the ward environment. All participating students were invited to contribute to focus groups which were audio-recorded, transcribed verbatim and thematically analyzed. Four focus groups were conducted with a total of 17 participants. The emerging themes revealed influences on the students' learning both prior to and during a clinical encounter, as well as following completion of an e-learning module. The unifying concept which linked all six themes and formed the central feature of the experience was patient-centered learning. This occurred through the acquisition of contextualized knowledge and the facilitation of workplace integration. Utilization of a near-patient e-learning tool influences medical student learning in a number of complex, inter-related ways. Clinical e-learning tools are poised to become more commonplace and provide many potential benefits to student learning. However, incorporation of technology into clinical encounters requires specific skills which should form an integral part of primary medical training.

Modeling Learning in Doubly Multilevel Binary Longitudinal Data Using Generalized Linear Mixed Models: An Application to Measuring and Explaining Word Learning.

Science.gov (United States)

Cho, Sun-Joo; Goodwin, Amanda P

2016-04-01

When word learning is supported by instruction in experimental studies for adolescents, word knowledge outcomes tend to be collected from complex data structure, such as multiple aspects of word knowledge, multilevel reader data, multilevel item data, longitudinal design, and multiple groups. This study illustrates how generalized linear mixed models can be used to measure and explain word learning for data having such complexity. Results from this application provide deeper understanding of word knowledge than could be attained from simpler models and show that word knowledge is multidimensional and depends on word characteristics and instructional contexts.

Using a computer learning environment for initial training in dealing with social - communicative problems.

NARCIS (Netherlands)

Holsbrink-Engels, G.A.

2000-01-01

The most widely practised instructional method for the development of interpersonal skills is role-play. Role-play is supposed to be a complex learning environment for novices to develop interpersonal skills. The learning environment is complex because of two factors. Firstly, the cognitive load is

Learning Type Extension Trees for Metal Bonding State Prediction

DEFF Research Database (Denmark)

Frasconi, Paolo; Jaeger, Manfred; Passerini, Andrea

2008-01-01

Type Extension Trees (TET) have been recently introduced as an expressive representation language allowing to encode complex combinatorial features of relational entities. They can be efficiently learned with a greedy search strategy driven by a generalized relational information gain and a discr......Type Extension Trees (TET) have been recently introduced as an expressive representation language allowing to encode complex combinatorial features of relational entities. They can be efficiently learned with a greedy search strategy driven by a generalized relational information gain...

Workplace Learning - How We Keep Track of Relevant Information

OpenAIRE

Bischoff, Kerstin; Herder, Eelco; Nejdl, Wolfgang

2007-01-01

At the workplace, learning is often a by-product of working on complex projects, requiring self-steered, need-driven and goal-oriented retrieval of information just in time from documents or peers. The personal desktop provides one rich source for learning material and for adaptation of learning resources. Data within that personal information space enables learning from previous experience, sharing tacit and explicit knowledge, and allows for establishing context and context-aware delivery o...

How Do Korsakoff Patients Learn New Concepts?

Science.gov (United States)

Pitel, Anne Lise; Beaunieux, Helene; Guillery-Girard, Berengere; Witkowski, Thomas; de la Sayette, Vincent; Viader, Fausto; Desgranges, Beatrice; Eustache, Francis

2009-01-01

The goal of the present investigation was to assess semantic learning in Korsakoff patients (KS), compared with uncomplicated alcoholics (AL) and control subjects (CS), taking the nature of the information to-be-learned and the episodic memory profiles of the three groups into account. Ten new complex concepts, each illustrated by a photo and…

Learning expressive ontologies

CERN Document Server

Völker, J

2009-01-01

This publication advances the state-of-the-art in ontology learning by presenting a set of novel approaches to the semi-automatic acquisition, refinement and evaluation of logically complex axiomatizations. It has been motivated by the fact that the realization of the semantic web envisioned by Tim Berners-Lee is still hampered by the lack of ontological resources, while at the same time more and more applications of semantic technologies emerge from fast-growing areas such as e-business or life sciences. Such knowledge-intensive applications, requiring large scale reasoning over complex domai

How Do Hunter-Gatherer Children Learn Subsistence Skills? : A Meta-Ethnographic Review.

Science.gov (United States)

Lew-Levy, Sheina; Reckin, Rachel; Lavi, Noa; Cristóbal-Azkarate, Jurgi; Ellis-Davies, Kate

2017-12-01

Hunting and gathering is, evolutionarily, the defining subsistence strategy of our species. Studying how children learn foraging skills can, therefore, provide us with key data to test theories about the evolution of human life history, cognition, and social behavior. Modern foragers, with their vast cultural and environmental diversity, have mostly been studied individually. However, cross-cultural studies allow us to extrapolate forager-wide trends in how, when, and from whom hunter-gatherer children learn their subsistence skills. We perform a meta-ethnography, which allows us to systematically extract, summarize, and compare both quantitative and qualitative literature. We found 58 publications focusing on learning subsistence skills. Learning begins early in infancy, when parents take children on foraging expeditions and give them toy versions of tools. In early and middle childhood, children transition into the multi-age playgroup, where they learn skills through play, observation, and participation. By the end of middle childhood, most children are proficient food collectors. However, it is not until adolescence that adults (not necessarily parents) begin directly teaching children complex skills such as hunting and complex tool manufacture. Adolescents seek to learn innovations from adults, but they themselves do not innovate. These findings support predictive models that find social learning should occur before individual learning. Furthermore, these results show that teaching does indeed exist in hunter-gatherer societies. And, finally, though children are competent foragers by late childhood, learning to extract more complex resources, such as hunting large game, takes a lifetime.

PENGEMBANGAN CASE BASE LEARNING PADA MATA KULIAH PEREKONOMIAN INDONESIA

Directory of Open Access Journals (Sweden)

Hastarini Dwi Atmani

2011-05-01

Full Text Available In this time, teacher centered learning is a methods in part of higher education in Indonsia. This method, students passively receive information.Case base learning is an instructional design model that is a variant of project oriented learning. Cases are factually-based, complex problems written to stimulate classroom discussion and collaborative analysis. This one, students construct knowledge through gathering and synthesizing information and integrating it with the general skills of inquiry, communication, critical thinking, and problem solving. Key words : active learning, case base learning.

Adjusted Framework of M-Learning in Blended Learning System for Mathematics Study Field of Junior High School Level VII

Science.gov (United States)

Sugiyanta, Lipur; Sukardjo, Moch.

2018-04-01

The 2013 curriculum requires teachers to be more productive, creative, and innovative in encouraging students to be more independent by strengthening attitudes, skills and knowledge. Teachers are given the options to create lesson plan according to the environment and conditions of their students. At the junior level, Core Competence (KI) and Basic Competence (KD) have been completely designed. In addition, there had already guidebooks, both for teacher manuals (Master’s Books) and for learners (Student Books). The lesson plan and guidebooks which already exist are intended only for learning in the classroom/in-school. Many alternative classrooms and alternatives learning models opened up using educational technology. The advance of educational technology opened opportunity for combination of class interaction using mobile learning applications. Mobile learning has rapidly evolved in education for the last ten years and many initiatives have been conducted worldwide. However, few of these efforts have produced any lasting outcomes. It is evident that mobile education applications are complex and hence, will not become sustainable. Long-term sustainability remains a risk. Long-term sustainability usually was resulted from continuous adaptation to changing conditions [4]. Frameworks are therefore required to avoid sustainability pitfalls. The implementation should start from simple environment then gradually become complex through adaptation steps. Therefore, our paper developed the framework of mobile learning (m-learning) adaptation for grade 7th (junior high school). The environment setup was blended mobile learning (not full mobile learning) and emphasize on Algebra. The research is done by R&D method (research and development). Results of the framework includes requirements and adaptation steps. The adjusted m-learning framework is designed to be a guidance for teachers to adopt m-learning to support blended learning environments. During mock-up prototype, the

How can we cope with the complexity of the environment? A "Learning by modelling" approach using qualitative reasoning for developing causal models and simulations with focus on Sustainable River Catchment Management

Science.gov (United States)

Poppe, Michaela; Zitek, Andreas; Salles, Paulo; Bredeweg, Bert; Muhar, Susanne

2010-05-01

The education system needs strategies to attract future scientists and practitioners. There is an alarming decline in the number of students choosing science subjects. Reasons for this include the perceived complexity and the lack of effective cognitive tools that enable learners to acquire the expertise in a way that fits its qualitative nature. The DynaLearn project utilises a "Learning by modelling" approach to deliver an individualised and engaging cognitive tool for acquiring conceptual knowledge. The modelling approach is based on qualitative reasoning, a research area within artificial intelligence, and allows for capturing and simulating qualitative systems knowledge. Educational activities within the DynaLearn software address topics at different levels of complexity, depending on the educational goals and settings. DynaLearn uses virtual characters in the learning environment as agents for engaging and motivating the students during their modelling exercise. The DynaLearn software represents an interactive learning environment in which learners are in control of their learning activities. The software is able to coach them individually based on their current progress, their knowledge needs and learning goals. Within the project 70 expert models on different environmental issues covering seven core topics (Earth Systems and Resources, The Living World, Human population, Land and Water Use, Energy Resources and Consumption, Pollution, and Global Changes) will be delivered. In the context of the core topic "Land and Water Use" the Institute of Hydrobiology and Aquatic Ecosystem Management has developed a model on Sustainable River Catchment Management. River systems with their catchments have been tremendously altered due to human pressures with serious consequences for the ecological integrity of riverine landscapes. The operation of hydropower plants, the implementation of flood protection measures, the regulation of flow and sediment regime and intensive

Improving the Spatial Prediction of Soil Organic Carbon Stocks in a Complex Tropical Mountain Landscape by Methodological Specifications in Machine Learning Approaches.

Science.gov (United States)

Ließ, Mareike; Schmidt, Johannes; Glaser, Bruno

2016-01-01

Tropical forests are significant carbon sinks and their soils' carbon storage potential is immense. However, little is known about the soil organic carbon (SOC) stocks of tropical mountain areas whose complex soil-landscape and difficult accessibility pose a challenge to spatial analysis. The choice of methodology for spatial prediction is of high importance to improve the expected poor model results in case of low predictor-response correlations. Four aspects were considered to improve model performance in predicting SOC stocks of the organic layer of a tropical mountain forest landscape: Different spatial predictor settings, predictor selection strategies, various machine learning algorithms and model tuning. Five machine learning algorithms: random forests, artificial neural networks, multivariate adaptive regression splines, boosted regression trees and support vector machines were trained and tuned to predict SOC stocks from predictors derived from a digital elevation model and satellite image. Topographical predictors were calculated with a GIS search radius of 45 to 615 m. Finally, three predictor selection strategies were applied to the total set of 236 predictors. All machine learning algorithms-including the model tuning and predictor selection-were compared via five repetitions of a tenfold cross-validation. The boosted regression tree algorithm resulted in the overall best model. SOC stocks ranged between 0.2 to 17.7 kg m-2, displaying a huge variability with diffuse insolation and curvatures of different scale guiding the spatial pattern. Predictor selection and model tuning improved the models' predictive performance in all five machine learning algorithms. The rather low number of selected predictors favours forward compared to backward selection procedures. Choosing predictors due to their indiviual performance was vanquished by the two procedures which accounted for predictor interaction.

Do yogis have "Learning Styles"? (A somatic solution).

Science.gov (United States)

Strean, William Ben

2017-01-01

Learning styles has captivated a great deal of attention in yoga teacher training. The triad of visual, auditory, and kinesthetic learning styles has been particularly popular; yet as Sharp et al . asserted, such an approach trivializes the complexity of learning and compromises scholarship at all levels of the education community. This paper addresses that although there is great merit in recognizing yoga students' differences and preferences, many uses of learning styles in yoga teacher training are superficial and promote self-handicapping. A somatic perspective (from soma, the body in its wholeness) offers a framework to reconsider the depth of effective learning.

Engineering education as a complex system

Science.gov (United States)

Gattie, David K.; Kellam, Nadia N.; Schramski, John R.; Walther, Joachim

2011-12-01

This paper presents a theoretical basis for cultivating engineering education as a complex system that will prepare students to think critically and make decisions with regard to poorly understood, ill-structured issues. Integral to this theoretical basis is a solution space construct developed and presented as a benchmark for evaluating problem-solving orientations that emerge within students' thinking as they progress through an engineering curriculum. It is proposed that the traditional engineering education model, while analytically rigorous, is characterised by properties that, although necessary, are insufficient for preparing students to address complex issues of the twenty-first century. A Synthesis and Design Studio model for engineering education is proposed, which maintains the necessary rigor of analysis within a uniquely complex yet sufficiently structured learning environment.

Emergence of a learning community: a transforming experience at the boundaries

Science.gov (United States)

Raia, Federica

2013-03-01

I narrate a process of transformation, a professional and personal journey framed by an experience that captured my attention shaping my interpretation and reflections. From a critical complexity framework I discuss the emergence of a learning community from the cooperation among individuals of diverse social and cultural worlds sharing the need to change a traditional professional development program structure and develop a new science education Masters Degree/Certification program. I zoom into the continual redefinition of the community, its evolution and complex interrelations among its participants and the emergence of a learning community as a boundary space having an emancipatory role and allowing growth and learning. I analyze the dialectical relationship between agents' behavior either impeding growth or having an emancipatory function of a mindful RelationalAct in a complex adaptive system framework.

Researching Lifelong Learning Participation through an Interdisciplinary Lens

Science.gov (United States)

Boeren, Ellen

2017-01-01

This paper explores the interdisciplinary nature of studies in the field of lifelong learning participation. Until recently, participation studies have been presented in a rather fragmented way, often drawing on insights from separate disciplines such as sociology or psychology. The complex nature of lifelong learning participation, however, urges…

Integrating Experiential Learning and Cases in International Business

Science.gov (United States)

Ramburuth, Prem; Daniel, Shirley

2011-01-01

In no other discipline is experiential learning more important than in the complex field of International Business (IB), which aims to prepare students to work and manage across political, economic, national, and sociocultural boundaries. This paper discusses various types of experiential learning activities and approaches to IB teaching, and…

WHY CANT WE LEARN FROM OUR MISTAKES LEARN THE LESSON TELL THE STORY

International Nuclear Information System (INIS)

LANGSTAFF, D.C.

2005-01-01

Tell the story well and people can learn from the lesson. The United States Department of Energy (DOE) Office of Environmental Management (EM) and its contractors are pursuing environmental remediation at the Hanford Site. This endeavor has been underway for a number of years, both at Hanford and at other sites across the DOE complex. Independently, the occurrence of two fatalities on two Sites at opposite ends of the country within two weeks raised the question, ''What is going on in the Field?'' Corporate EM management communicated directly with Field Office Managers to answer the question. As a result of this intense interest and focused communication, EM identified four areas that need additional exploration. One of those is, ''EM's ability to learn from its mistakes.'' The need to cultivate the ability to learn from our mistakes is not unique to DOE. A quick review of EM Lessons Learned reports shows that most of the reports in the EM system originate at the sites with the largest budgets doing the most work. Not surprising. A second look, however, reveals that many reports are repetitive, that many people might consider many reports trivial, and that reports on some of the more significant events sometimes take a long time to get distributed across the DOE Complex. Spot checks of event reports revealed frequent identification of symptoms rather than root causes. With a high percentage of identified root causes in the questionable category, it is highly unlikely that the real root causes of many events are being corrected, thus leading to recurrences of events. To learn the lesson from an event, people need to be aware of the root causes of the event. Someone has to tell a story the reader can learn from, i.e., include all the information needed to understand what happened and why it happened. Most importantly, they need to understand the lesson to be learned

Authentic school science knowing and learning in open-inquiry science laboratories

CERN Document Server

Roth, Wolff-Michael

1995-01-01

According to John Dewey, Seymour Papert, Donald Schon, and Allan Collins, school activities, to be authentic, need to share key features with those worlds about which they teach. This book documents learning and teaching in open-inquiry learning environments, designed with the precepts of these educational thinkers in mind. The book is thus a first-hand report of knowing and learning by individuals and groups in complex open-inquiry learning environments in science. As such, it contributes to the emerging literature in this field. Secondly, it exemplifies research methods for studying such complex learning environments. The reader is thus encouraged not only to take the research findings as such, but to reflect on the process of arriving at these findings. Finally, the book is also an example of knowledge constructed by a teacher-researcher, and thus a model for teacher-researcher activity.