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256 p. 23 cm

256 p. 23 cm

Organizations are increasingly expecting their employees to engage in self-regulated learning to meet the demands of a rapidly changing work environment (Sitzmann & Ely, 2011). Self-regulated learning is an informal learning process by which learners alter the learning experience by adapting their actions (i.e., affect, cognition, behavior) to reach a desired level of achievement (Sitzmann & Ely, 2011). One can imagine that motivating employees to self-regulate their own learning paths in the first place is difficult given various demands from work, family, and other life domains. Intervening on core self-regulatory processes in the learning environment by altering training design elements could prove useful in motivating employees to develop their knowledge and skills (e.g., Bell & Kozlowski, 2008). However, over the last decade, training design research that has languished. This might be due to a lack of deep integration of motivational theory that could help inform the design of instruction content to foster successful self-regulation in the learning environment. One design framework typically grounded in motivational theory that is used to motivate learners is gamification. Gamification is a design strategy characterized by the process of adding game mechanics, features, and/or to non-game systems to alter the user (i.e., learner) experience (Arthur et al., 2017; Deterding et al., 2011; Landers et al., 2018). A common goal of gamification is to elicit a gameful experience, or the psychological state targeted in gameful design that is analogous to the experience of entertainment games (Landers et al., 2018). Gamification researchers characterize this psychological state as intrinsically motivating, where gameplay induces “senses of enjoyment, flow, autonomy, mastery” (Koivisto &Hamari, 2019, p. 193). Another characterization of a gameful experience is “the … experience of need satisfaction”, which is what defines self-regulatory behavior as being “intrinsically motivated” (Deterding, 2015, p. ). Thus, many gamification researchers describe the goal of gamification is “is aimed at invoking users’ intrinsic motivations commonly through design reminiscent from games” (Hamari & Koivisto, 2015, p. 420). That is, a popular goal of gamification is to harness motivational aspects of games and to transfer similar levels of intrinsic motivation to other contexts that are not typically thought as gameful. In doing so, applying game elements to non-game contexts sparks

Organizations are increasingly expecting their employees to engage in self-regulated learning to meet the demands of a rapidly changing work environment (Sitzmann & Ely, 2011). Self-regulated learning is an informal learning process by which learners alter the learning experience by adapting their actions (i.e., affect, cognition, behavior) to reach a desired level of achievement (Sitzmann & Ely, 2011). One can imagine that motivating employees to self-regulate their own learning paths in the first place is difficult given various demands from work, family, and other life domains. Intervening on core self-regulatory processes in the learning environment by altering training design elements could prove useful in motivating employees to develop their knowledge and skills (e.g., Bell & Kozlowski, 2008). However, over the last decade, training design research that has languished. This might be due to a lack of deep integration of motivational theory that could help inform the design of instruction content to foster successful self-regulation in the learning environment. One design framework typically grounded in motivational theory that is used to motivate learners is gamification. Gamification is a design strategy characterized by the process of adding game mechanics, features, and/or to non-game systems to alter the user (i.e., learner) experience (Arthur et al., 2017; Deterding et al., 2011; Landers et al., 2018). A common goal of gamification is to elicit a gameful experience, or the psychological state targeted in gameful design that is analogous to the experience of entertainment games (Landers et al., 2018). Gamification researchers characterize this psychological state as intrinsically motivating, where gameplay induces “senses of enjoyment, flow, autonomy, mastery” (Koivisto &Hamari, 2019, p. 193). Another characterization of a gameful experience is “the … experience of need satisfaction”, which is what defines self-regulatory behavior as being “intrinsically motivated” (Deterding, 2015, p. ). Thus, many gamification researchers describe the goal of gamification is “is aimed at invoking users’ intrinsic motivations commonly through design reminiscent from games” (Hamari & Koivisto, 2015, p. 420). That is, a popular goal of gamification is to harness motivational aspects of games and to transfer similar levels of intrinsic motivation to other contexts that are not typically thought as gameful. In doing so, applying game elements to non-game contexts sparks

Inverse Reinforcement Learning (IRL) describes the problem of learning an unknown reward function of a Markov Decision Process (MDP) from observed behavior of an agent. Since the agent's behavior originates in its policy and MDP policies depend on both the stochastic system dynamics as well as the reward function, the solution of the inverse problem is significantly influenced by both. Current IRL approaches assume that if the transition model is unknown, additional samples from the system's dynamics are accessible, or the observed behavior provides enough samples of the system's dynamics to solve the inverse problem accurately. These assumptions are often not satisfied. To overcome this, we present a gradient-based IRL approach that simultaneously estimates the system's dynamics. By solving the combined optimization problem, our approach takes into account the bias of the demonstrations, which stems from the generating policy. The evaluation on a synthetic MDP and a transfer learning task shows improvements regarding the sample efficiency as well as the accuracy of the estimated reward functions and transition models.

This collection of abstracts is part of a continuing series providing information on recent doctoral dissertations. The 47 titles deal with a variety of topics, including the following: (1) the effect of source variation upon children's reactions to television commercials; (2) covert observation as a means of measuring the affective states of television viewers; (3) viewers' responses to a film as a socially situated event; (4) children's understanding of television reality; (5) media learning and adolescent developmental tasks; (6) the dynamics of communication and information in groups; (7) an information processing approach to uses and gratifications; (8) privacy and media encroachment; (9) the changing images of females and males in television commercials; (10) commercial broadcasters' perceptions of entry-level employment requirements for college graduates in the broadcast industry; (11) transnational advertising and the mass media in Latin America; (12) longitudinal patterns of television viewing and adolescent role socialization; (13) the relationship between persistence, attention, and observational learning of television's program content; (14) the film industry and the Vietnam war; (15) the effect of televised violence; (16) the use of videotape for studying nonverbal behavior in social settings; (17) United States Supreme Court interpretations of "public interest" in decisions concerning the media from 1927 to 1979. (FL)

We study the dynamics of supervised learning in layered neural networks, in the regime where the size $p$ of the training set is proportional to the number $N$ of inputs. Here the local fields are no longer described by Gaussian probability distributions and the learning dynamics is of a spin-glass nature, with the composition of the training set playing the role of quenched disorder. We show how dynamical replica theory can be used to predict the evolution of macroscopic observables, including the two relevant performance measures (training error and generalization error), incorporating the old formalism developed for complete training sets in the limit $\alpha=p/N\to\infty$ as a special case. For simplicity we restrict ourselves in this paper to single-layer networks and realizable tasks.

Whereas Natural User Interface technological devices, such as tablets, are bringing physical interaction back into mathematics learning activities, existing educational theory is not geared to inform or interpret such learning. In particular, educational researchers investigating instructional interactions still need intellectual and methodological frameworks for conceptualizing, designing, facilitating, and analyzing how students' immersive hands-on dynamical experiences become formulated within semiotic registers typical of mathematical discourse. We present paradigmatic empirical examples of tutor-student behaviors in an embodied-interaction learning environment, the Mathematical Imagery Trainer for Proportion. Drawing on ecological dynamics--a blend of dynamical-systems theory and ecological psychology--we describe the emergence of mathematical concepts from the guided discovery of sensorimotor schemes. [For the complete proceedings, see ED583989.]

Whereas Natural User Interface technological devices, such as tablets, are bringing physical interaction back into mathematics learning activities, existing educational theory is not geared to inform or interpret such learning. In particular, educational researchers investigating instructional interactions still need intellectual and methodological frameworks for conceptualizing, designing, facilitating, and analyzing how students' immersive hands-on dynamical experiences become formulated within semiotic registers typical of mathematical discourse. We present paradigmatic empirical examples of tutor-student behaviors in an embodied-interaction learning environment, the Mathematical Imagery Trainer for Proportion. Drawing on ecological dynamics--a blend of dynamical-systems theory and ecological psychology--we describe the emergence of mathematical concepts from the guided discovery of sensorimotor schemes. [For the complete proceedings, see ED583989.]

We present an exact solution for the dynamics of on-line Hebbian learning in neural networks, with restricted and unrealizable training sets. In contrast to other studies on learning with restricted training sets, unrealizability is here caused by structural mismatch, rather than data noise: the teacher machine is a perceptron with a reversed wedge-type transfer function, while the student machine is a perceptron with a sigmoidal transfer function. We calculate the glassy dynamics of the macroscopic performance measures, training error and generalization error, and the (non-Gaussian) student field distribution. Our results, which find excellent confirmation in numerical simulations, provide a new benchmark test for general formalisms with which to study unrealizable learning processes with restricted training sets.

James P. Lantolf is George and Jane Greer Professor Emeritus of Language Acquisition and Applied Linguistics and former director of the Center for Advanced Language Proficiency Education and Research at the Pennsylvania State University, USA. He is currently Adjunct Professor of Applied Linguistics in the same academic unit at Xi'an Jiaotong University. He is founder of the Sociocultural Theory and Second Language Learning research group, which has been holding an annual meeting to discuss research in progress on second language acquisition from the perspective of sociocultural theory since 1993. He is founding editor of "Language and Sociocultural Theory" (Equinox Press, 2013 to present) and was co-editor of "Applied Linguistics" (Oxford University Press, 1993-1998). He served as President of the American Association for Applied Linguistics (AAAL) in 2004 and was recipient of the AAAL Distinguished Scholarship and Service Award in 2016. He has co-authored or co-edited nine books and has published more than 140 articles and book chapters. His seminal book entitled "Sociocultural Theory and the Genesis of Second Language Development" (2006, coauthored with Steven L. Thorne) published by Oxford University Press coupled with a co-edited volume "Vygotskian Approaches to Second Language Research" (1994, coedited with Gabriela Appel) ushered in a new upswing of research on language development and teaching from the sociocultural theory vantage point. His latest co-edited book "The Routledge Handbook of Sociocultural Theory and Second Language Development" was published in 2018. The following is a written interview with James P. Lantolf conducted by Saeed Karimi-Aghdam. Dr. Karimi Aghdam is Associate Professor of English Language and Didactics in the Faculty of Education and Arts at Nord University, Norway. He is a member of the editorial board of "Language and Sociocultural Theory" and serves on the advisory board of the "Journal of Applied Linguistics and Applied Literature: Dynamics and Advances."

This paper examines the effects of schooling on political socialization and presents a theoretical perspective on the learning of civic norms which will provide insight into both the limits of schools' current efforts at instilling civic norms and the potentialities of new approaches. Norms are defined as personally binding conceptions of right conduct. Civic norms refer to norms on political as well as moral and social questions. A review of research establishes two important points. First, the social studies curriculum, especially with respect to the development of political and moral attitudes, is failing to have any noticeable impact on students' acquisition of norms. Second, the teacher and classroom climate have the potential to be significant influences in the learning of civic norms. This pattern of findings indicates that the forces which influence the learning of civic norms center around the social, interpersonal interactions in schools and the resulting school climate. An interpretation of this phenomena is offered based on the legal socialization perspective of Hogan and Mills which acknowledges the social basis of civic learning. It is suggested that current approaches to civic and moral education place too much emphasis on student reasoning. The social dynamics involved in the acquisition of social norms, which involve nonrational factors, are largely absent in social studies theory and curriculum. Suggestions for curricular reform based on the Hogan and Mills framework are presented. For example, teacher education programs should sensitize prospective teachers to the attributes to which children selectively attend which result in the perception that the teacher is worthy of respect and credible. (Author/RM)

We study opinion dynamics in a population of interacting adaptive agents voting on a set of complex multidimensional issues. We consider agents which can classify issues into for or against. The agents arrive at the opinions about each issue in question using an adaptive algorithm. Adaptation comes from learning and the information for the learning process comes from interacting with other neighboring agents and trying to change the internal state in order to concur with their opinions. The change in the internal state is driven by the information contained in the issue and in the opinion of the other agent. We present results in a simple yet rich context where each agent uses a Boolean Perceptron to state its opinion. If there is no internal clock, so the update occurs with asynchronously exchanged information among pairs of agents, then the typical case, if the number of issues is kept small, is the evolution into a society thorn by the emergence of factions with extreme opposite beliefs. This occurs even when seeking consensus with agents with opposite opinions. The curious result is that it is learning from those that hold the same opinions that drives the emergence of factions. This results follows from the fact that factions are prevented by not learning at all from those agents that hold the same opinion. If the number of issues is large, the dynamics becomes trapped and the society does not evolve into factions and a distribution of moderate opinions is observed. We also study the less realistic, but technically simpler synchronous case showing that global consensus is a fixed point. However, the approach to this consensus is glassy in the limit of large societies if agents adapt even in the case of agreement.

We study opinion dynamics in a population of interacting adaptive agents voting on a set of complex multidimensional issues. We consider agents which can classify issues into for or against. The agents arrive at the opinions about each issue in question using an adaptive algorithm. Adaptation comes from learning and the information for the learning process comes from interacting with other neighboring agents and trying to change the internal state in order to concur with their opinions. The change in the internal state is driven by the information contained in the issue and in the opinion of the other agent. We present results in a simple yet rich context where each agent uses a Boolean Perceptron to state its opinion. If there is no internal clock, so the update occurs with asynchronously exchanged information among pairs of agents, then the typical case, if the number of issues is kept small, is the evolution into a society thorn by the emergence of factions with extreme opposite beliefs. This occurs even when seeking consensus with agents with opposite opinions. The curious result is that it is learning from those that hold the same opinions that drives the emergence of factions. This results follows from the fact that factions are prevented by not learning at all from those agents that hold the same opinion. If the number of issues is large, the dynamics becomes trapped and the society does not evolve into factions and a distribution of moderate opinions is observed. We also study the less realistic, but technically simpler synchronous case showing that global consensus is a fixed point. However, the approach to this consensus is glassy in the limit of large societies if agents adapt even in the case of agreement.

We study the dynamics of supervised learning in layered neural networks, in the regime where the size $p$ of the training set is proportional to the number $N$ of inputs. Here the local fields are no longer described by Gaussian probability distributions. We show how dynamical replica theory can be used to predict the evolution of macroscopic observables, including the relevant performance measures, incorporating the old formalism in the limit $\alpha=p/N\to\infty$ as a special case. For simplicity we restrict ourselves to single-layer networks and realizable tasks.

We present a mathematical analysis of the effects of Hebbian learning in random recurrent neural networks, with a generic Hebbian learning rule including passive forgetting and different time scales for neuronal activity and learning dynamics. Previous numerical works have reported that Hebbian learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on the neural network evolution. Furthermore, we show that the sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.

This paper delves into the complexities of cultural diversity and technology-mediated collaboration within Learning Management Systems (LMS). In contemporary educational settings, LMS often struggle to effectively accommodate the varied cultural perspectives of diverse student populations, thereby limiting their potential to create truly inclusive learning experiences. This study employs a Systematic Literature Review (SLR) to explore how intercultural discourse analysis and design justice frameworks can inform the redesign of LMS to foster more culturally responsive and equitable learning environments. Key findings reveal that participatory design and intercultural communication principles can transform LMS into inclusive digital spaces that celebrate diversity. Educators, instructional designers, and technologists are provided with actionable strategies to enhance LMS design, advocating for digital learning environments that support and celebrate the diversity of global learners. These insights highlight the transformative potential of integrating these principles into educational technologies.

We present a new model for the dynamics of the presynaptic intracellular calcium concentration in neurons evoked by various stimulation protocols. The aim of the model is twofold: We want to discuss the calcium transients during and after specific stimulation protocols as they are used to induce long-term-depression and long-term-potentiation. In addition we would like to provide a general tool which allows the comparison of different calcium experiments. This may help to draw conclusions on a wider base in future.

We begin the article with a brief discussion of why groups are recommended in language teaching. After that, we describe what Cooperative Learning (CL) is. Then, we suggest ways in which CL and Information Technology (IT) fit well together. This is followed by some examples of how to combine CL with IT. [This article was published in Teaching of English Language and Literature," v13 n2 p5-8 1997.]

The aim of the present paper is to study the effects of Hebbian learning in random recurrent neural networks with biological connectivity, i.e. sparse connections and separate populations of excitatory and inhibitory neurons. We furthermore consider that the neuron dynamics may occur at a (shorter) time scale than synaptic plasticity and consider the possibility of learning rules with passive forgetting. We show that the application of such Hebbian learning leads to drastic changes in the network dynamics and structure. In particular, the learning rule contracts the norm of the weight matrix and yields a rapid decay of the dynamics complexity and entropy. In other words, the network is rewired by Hebbian learning into a new synaptic structure that emerges with learning on the basis of the correlations that progressively build up between neurons. We also observe that, within this emerging structure, the strongest synapses organize as a small-world network. The second effect of the decay of the weight matrix spectral radius consists in a rapid contraction of the spectral radius of the Jacobian matrix. This drives the system through the ``edge of chaos'' where sensitivity to the input pattern is maximal. Taken together, this scenario is remarkably predicted by theoretical arguments derived from dynamical systems and graph theory.

The aim of the present paper is to study the effects of Hebbian learning in random recurrent neural networks with biological connectivity, i.e. sparse connections and separate populations of excitatory and inhibitory neurons. We furthermore consider that the neuron dynamics may occur at a (shorter) time scale than synaptic plasticity and consider the possibility of learning rules with passive forgetting. We show that the application of such Hebbian learning leads to drastic changes in the network dynamics and structure. In particular, the learning rule contracts the norm of the weight matrix and yields a rapid decay of the dynamics complexity and entropy. In other words, the network is rewired by Hebbian learning into a new synaptic structure that emerges with learning on the basis of the correlations that progressively build up between neurons. We also observe that, within this emerging structure, the strongest synapses organize as a small-world network. The second effect of the decay of the weight matrix spectral radius consists in a rapid contraction of the spectral radius of the Jacobian matrix. This drives the system through the ``edge of chaos'' where sensitivity to the input pattern is maximal. Taken together, this scenario is remarkably predicted by theoretical arguments derived from dynamical systems and graph theory.

This study aimed to describe the dynamics of content aspects, user aspects and social aspects of mobile learning utilization (m-learning) in vocational education from the FRAME Model perspective review. This study was quantitative descriptive research. The population in this study was teachers and students of state vocational school and private school in Makassar, Indonesia. Samples of the study were 103 teachers and 320 students, determined by a proportional random sampling technique. Data collection techniques were questionnaires. Data analysis uses descriptive analysis techniques. The results showed that the available content on the internet and accessed by teachers were relevant to the major, whereas students stated them relevantly less to their majors, so the value and substance of the utilization of m-learning content were still less relevant. The user aspect showed that teachers and students utilized mobile devices for vocational learning purposes, so the value and substance of m-learning could be still maintained. The social aspect showed that teachers and students expressed sociocultural support influenced the utilization of m-learning, so that the value and substance of m-learning in vocational education could be continually maintained. So the value and substance of m-learning in these three aspects could be maintained and improved, this study showed alternative model of utilization and development of m-learning that were appropriate for vocational education.

We document the characteristics of children and young adults identified in the Panel Study of Income Dynamics as having a learning disability and study whether legislative changes in diagnosis criteria have had a noticeable effect determining who receives a diagnosis. We further document that children and young adults identified as a having a learning disability experience less desirable outcomes early in life, including trouble with the police, drug use, violent behavior, incarceration, self-reported low levels of well-being, lower educational attainment, and less favorable labor market outcomes. We also find that the mothers of children diagnosed with learning disabilities are less likely than other mothers to participate in the labor market.

The Andy Holt Virtual Library, with a focus on the Humanities and Fine Arts, is free and open to the public, though designed to serve the learning communities within the College of Humanities and Fine Arts at the University of Tennessee-Martin (UT). It also plays a resource role in UT's New College and the Tennessee Governors School for the Humanities. This independent information source is linked to in key places to the Paul Meek Library on the UT campus. This presentation describes the development of the library and illustrates many of its links. (Contains a 21-item bibliography for digital and virtual libraries.) (Author/MES)

This project developed fundamental theory and novel algorithms for adaptive learning in autonomous collective-agent systems. The first goal was to develop a new mathematical framework for analyzing the dynamical mechanisms that support learning in a range of novel information processing substrates. The second goal was to adapt this single-agent learning theory and associated learning algorithms to the distributed setting in which a population of autonomous agents communicate to achieve a desired group task. The results provide a mathematically sound basis for quantitatively measuring 1) the degree of individual-agent intelligence; 2) the significance of agent-environment interaction; and 3) the emergence of cooperation in agent collectives. A novel feature was that the theoretical approach synthesized recent results in the areas of machine learning, statistical inference, statistical mechanics, nonlinear dynamics, and pattern formation theory and then applied them to single-agent learning and adaptive learning in agent collectives. The central impact of the project will be its systematic and quantitative approach to predicting behavioral complexity in agents and in their environments. This will provide a foundation for measuring agent and agent-collective intelligence which, in turn, should allow for systematic engineering and monitoring of these systems.

We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework, we propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent Neural Network based Encoder-Decoder framework to predict these sequences of flows. We argue that in order for the decoder to reconstruct these sequences, the encoder must learn a robust video representation that captures long-term motion dependencies and spatial-temporal relations. We demonstrate the effectiveness of our learned temporal representations on activity classification across multiple modalities and datasets such as NTU RGB+D and MSR Daily Activity 3D. Our framework is generic to any input modality, i.e., RGB, Depth, and RGB-D videos.

In this case study we discuss the dynamics that drive a free-of-charge project-based learning extension course. We discuss the lessons learned in the course, "Laboratory of Social Projects." The course aimed to teach project management skills to the participants. It was conducted from August to November of 2015, at Federal University of São Paulo (Unifesp), Osasco Campus, Brazil. The course had 72 participants (41 community members and 31 university students). The participants worked in teams of four members (on average) and developed 13 projects on behalf of eight NGOs that help people in need. In our research, we followed a mixed methods approach, using project reports, project blogs and the course management database as sources of information. We made use of system thinking analysis to reveal the dynamics that unfolded during the course. Our main findings are the following: 1) free-of-charge extension courses can be much more challenging to manage than traditional courses once the workload of the professors involved can be substantially higher than in similar regular courses; 2) the use of project-based learning techniques in extension courses can be very effective; 3) problems are created when the students drop out of the course.

A formalism for describing the dynamics of Genetic Algorithms (GAs) using methods from statistical mechanics is applied to the problem of generalization in a perceptron with binary weights. The dynamics are solved for the case where a new batch of training patterns is presented to each population member each generation, which considerably simplifies the calculation. The theory is shown to agree closely to simulations of a real GA averaged over many runs, accurately predicting the mean best solution found. For weak selection and large problem size the difference equations describing the dynamics can be expressed analytically and we find that the effects of noise due to the finite size of each training batch can be removed by increasing the population size appropriately. If this population resizing is used, one can deduce the most computationally efficient size of training batch each generation. For independent patterns this choice also gives the minimum total number of training patterns used. Although using independent patterns is a very inefficient use of training patterns in general, this work may also prove useful for determining the optimum batch size in the case where patterns are recycled.

A formalism for describing the dynamics of Genetic Algorithms (GAs) using methods from statistical mechanics is applied to the problem of generalization in a perceptron with binary weights. The dynamics are solved for the case where a new batch of training patterns is presented to each population member each generation, which considerably simplifies the calculation. The theory is shown to agree closely to simulations of a real GA averaged over many runs, accurately predicting the mean best solution found. For weak selection and large problem size the difference equations describing the dynamics can be expressed analytically and we find that the effects of noise due to the finite size of each training batch can be removed by increasing the population size appropriately. If this population resizing is used, one can deduce the most computationally efficient size of training batch each generation. For independent patterns this choice also gives the minimum total number of training patterns used. Although using independent patterns is a very inefficient use of training patterns in general, this work may also prove useful for determining the optimum batch size in the case where patterns are recycled.

This article is from PLoS Computational Biology , volume 10 . Abstract Learning arises through the activity of large ensembles of cells, yet most of the data neuroscientists accumulate is at the level of individual neurons; we need models that can bridge this gap. We have taken spatial learning as our starting point, computationally modeling the activity of place cells using methods derived from algebraic topology, especially persistent homology. We previously showed that ensembles of hundreds of place cells could accurately encode topological information about different environments (“learn” the space) within certain values of place cell firing rate, place field size, and cell population; we called this parameter space the learning region. Here we advance the model both technically and conceptually. To make the model more physiological, we explored the effects of theta precession on spatial learning in our virtual ensembles. Theta precession, which is believed to influence learning and memory, did in fact enhance learning in our model, increasing both speed and the size of the learning region. Interestingly, theta precession also increased the number of spurious loops during simplicial complex formation. We next explored how downstream readout neurons might define co-firing by grouping together cells within different windows of time and thereby capturing different degrees of temporal overlap between spike trains. Our model's optimum coactivity window correlates well with experimental data, ranging from ∼150–200 msec. We further studied the relationship between learning time, window width, and theta precession. Our results validate our topological model for spatial learning and open new avenues for connecting data at the level of individual neurons to behavioral outcomes at the neuronal ensemble level. Finally, we analyzed the dynamics of simplicial complex formation and loop transience to propose that the simplicial complex provides a useful working description of the spatial learning process.

The beliefs of job seekers about their own future prospects are important, but despite being a long-standing topic of interest, relatively little is known about how unemployed job seekers themselves think their job-finding probability will evolve if they remain unemployed for longer. In this study, we conduct a survey to explore this question.

We examine the effects of memory and different updating paradigms in a game-theoretic model of competitive learning, where agents are influenced in their choice of strategy by both the choices made by, and the consequent success rates of, their immediate neighbours. We apply parallel and sequential updates in all possible combinations to the two competing rules, and find, typically, that the phase diagram of the model consists of a disordered phase separating two ordered phases at coexistence. A major result is that the corresponding critical exponents belong to the generalised universality class of the voter model. When the two strategies are distinct but not too different, we find the expected linear response behaviour as a function of their difference.Finally, we look at the extreme situation when a superior strategy, accompanied by a short memory of earlier outcomes, is pitted against its inverse; interestingly, we find that a long memory of earlier outcomes can occasionally compensate for the choice of a globally inferior strategy.

We examine the effects of memory and different updating paradigms in a game-theoretic model of competitive learning, where agents are influenced in their choice of strategy by both the choices made by, and the consequent success rates of, their immediate neighbours. We apply parallel and sequential updates in all possible combinations to the two competing rules, and find, typically, that the phase diagram of the model consists of a disordered phase separating two ordered phases at coexistence. A major result is that the corresponding critical exponents belong to the generalised universality class of the voter model. When the two strategies are distinct but not too different, we find the expected linear response behaviour as a function of their difference.Finally, we look at the extreme situation when a superior strategy, accompanied by a short memory of earlier outcomes, is pitted against its inverse; interestingly, we find that a long memory of earlier outcomes can occasionally compensate for the choice of a globally inferior strategy.

Using the cavity method and diagrammatic methods, we model the dynamics of batch learning of restricted sets of examples. Simulations of the Green's function and the cavity activation distributions support the theory well. The learning dynamics approaches a steady state in agreement with the static version of the cavity method. The picture of the rough energy landscape is reviewed.

Using the cavity method and diagrammatic methods, we model the dynamics of batch learning of restricted sets of examples. Simulations of the Green's function and the cavity activation distributions support the theory well. The learning dynamics approaches a steady state in agreement with the static version of the cavity method. The picture of the rough energy landscape is reviewed.

For early language development, it is critical for young infants to be able to appropriately associate an auditory word with a certain object. Research has suggested that social contexts facilitate infants’ word learning despite the complexity of social information (e.g. Kuhl et al., 2003). While a body of studies have demonstrated infants’ cognition and learning are facilitated by social environmental factors such as joint attention (e.g. Striano, Reid & Hoehl, 2006; Parise et al., 2008), to our knowledge, no studies thus far have directly explored the temporal dynamics of infant’s word learning emerging in a social interactional setting. Hence, the effect of social interaction on the process of infants’ word learning developing through social interactions is yet to be fully understood. The use of neural data can address this question by providing insights into what cognitive processes are involved when infants form the link between a word and an object during a live situation. Using an electroencephalogram (EEG), the proposed study aims to explore neural correlates of 10- to 11-month-old infants while they interact with an experimenter who demonstrates the name and the function of novel objects in a live setting. The specific goal of the study is to investigate how infant’s brain activity as an index of infant word learning develops over the course of a social interaction. The study will be amongst the first to demonstrate the temporal dynamics of infant’s word learning emerging in live interactional contexts. This study is expected to advance our understanding on how infants’ language development is mediated by their social environment. The results of this study are expected to help deepen our knowledge on what social environment we could offer infants to facilitate their language development.

This study investigates the effectiveness of the live interactive broadcast method of delivering engineering content into a standard university classroom. The Dynamics Concepts Inventory, a standardized quantitative assessment, has been administered as a pretest and final assessment for the broadcast section of dynamics for four semesters. Student attitudes toward the non-standard learning environment were assessed through an instructor-developed survey, third party interviews, and anecdotal evidence. Results of the DCI indicate that outcomes from the broadcast section are similar to national average for lecture style classes. Attitude surveys revealed a persistent sense of student isolation and frustration with lack of personal contact with the instructor, but little difficulty with the broadcast and technology assisted class room environment.

The purpose of the study was to model the processes involved in the development of creativity and discuss how motivation and teaching strategies figure into a creativity-supportive learning environment. The subjects included 28 college students enrolled in an Introduction to Instructional Media course. A questionnaire and interviews were used for the collection of data. Over the course of the semester, the students were tasked with making a "creative multimedia video website". The results of the study reveal that the motivation of students in areas such as task value and self-efficacy were enhanced by this exercise. Moreover, this study developed effective strategies for the promotion of creativity and the enhancement of high-level meta-cognitive processes. (Contains 4 tables and 1 figure.)

Research suggests that working memory has an important role in instrumental learning when reinforcement histories of multiple options must be tracked (Collins et al., 2017; Collins & Frank, 2012, 2018). Working memory capacity not only reflects the ability to maintain items, but also to update and shield items against interference in a context-dependent manner; functions conceivably also essential to instrumental learning in an ever-changing environment. To address the relationship of working memory capacity and the dynamics of instrumental learning in a volatile environment in a previous unpublished study, we studied choice behavior and EEG of healthy young people who performed a probabilistic reversal learning task with sudden changes in reward probability (20, 50, 80%)(Humann et al., 2020). Working memory capacity, as assessed with the automated Operation Span (AOSPAN) task, positively correlated with learning performance. A hybrid Rescorla-Wagner/Pearce-Hall model was implemented to capture dynamic changes in people’s learning rate around changes in reward probability. Low-capacity participants modulated learning rates less dynamically around such changes. Their choices were more stochastic and less guided by learnt values, resulting in less stable performance and higher susceptibility to misleading probabilistic feedback. Single-trial model-based EEG analysis furthermore revealed that reward prediction errors (RPE) and learning rates were less strongly represented in cortical activity of low-capacity participants, while the centroparietal positivity, a general correlate of behavioural adaptation, was independent of working memory capacity. Whether working memory capacity modulated learning performance in this task (causation), or performance on both tasks relied on a common underlying mechanism (correlation), is an open question that we aim to address in this online behavioural study. By experimentally manipulating the load on working memory in the original task, its causal role in learning performance and dynamics can be investigated. Our second aim is to conceptually replicate our previous correlations between working memory capacity and learning performance as well as dynamic learning rate, under the load manipulation that is most similar to the load in the original task.

This study examined how middle school teachers co-constructed norms and practices related to classroom use of laptop computers, focusing on a school that gave laptop computers to teachers, provided appropriate professional development, and offered students access to laptops. Eleven teachers were eligible to be study cases. During the first year, they were interviewed and observed. Researchers also interviewed administrators, conducted student focus groups, and observed faculty and board meetings. Teacher interviews examined beliefs about teaching and learning and teachers' instructional planning. In the second year, over 100 hours of participant observation occurred. Three teacher cases were shadowed throughout the school day, and teacher-researcher discussions were recorded. The resulting case studies described developmental changes associated with teachers' learning to use laptops in class. Conversations flowed through the school's network of settings that came to define the normative playing field for teachers' curricular decisions. Teachers' participation in these settings depended upon access to the activity and access to the social language used in the setting. Consistent teacher descriptions of peer choices to use or not use laptops indicated that access to the social language led to their understanding of the various roles laptops played in the school. (Contains 51 references.) (SM)

This review assembles what is known from studies and analyses of large-scale systemic reform initiatives aimed at mathematics and science education, especially those undertaken by state governments and the National Science Foundation. The review concentrates on qualitative investigations, which reveal whether and how these initiatives converge on the improvement of instruction. The evidence to date, though inherently incomplete, has much to say about the logic of systemic reforms, the avenues by which these reforms may reach the classroom, and the kinds of classroom-level impacts that are apparent so far, or are likely to occur in the near term. The implementation and effects of these reforms can be understood productively in the theoretical terms applied to the study of innovation and change, implementation of governmental policies, and professional and organizational learning. (Author)

The study will examine the learning rates and discount factors of the semantic and affective representations of valence during reversal learning with two states.

The study will examine the learning rates and discount factors of the semantic and affective representations of valence during reversal learning with two states.

Using the cavity method and diagrammatic methods, we model the dynamics of batch learning of restricted sets of examples, widely applicable to general learning cost functions, and fully taking into account the temporal correlations introduced by the recycling of the examples.

The complexity of chess matches has attracted broad interest since its invention. This complexity and the availability of large number of recorded matches make chess an ideal model systems for the study of population-level learning of a complex system. We systematically investigate the move-by-move dynamics of the white player's advantage from over seventy thousand high level chess matches spanning over 150 years. We find that the average advantage of the white player is positive and that it has been increasing over time. Currently, the average advantage of the white player is ~0.17 pawns but it is exponentially approaching a value of 0.23 pawns with a characteristic time scale of 67 years. We also study the diffusion of the move dependence of the white player's advantage and find that it is non-Gaussian, has long-ranged anti-correlations and that after an initial period with no diffusion it becomes super-diffusive. We find that the duration of the non-diffusive period, corresponding to the opening stage of a match, is increasing in length and exponentially approaching a value of 15.6 moves with a characteristic time scale of 130 years. We interpret these two trends as a resulting from learning of the features of the game. Additionally, we find that the exponent {\alpha} characterizing the super-diffusive regime is increasing toward a value of 1.9, close to the ballistic regime. We suggest that this trend is due to the increased broadening of the range of abilities of chess players participating in major tournaments.

The complexity of chess matches has attracted broad interest since its invention. This complexity and the availability of large number of recorded matches make chess an ideal model systems for the study of population-level learning of a complex system. We systematically investigate the move-by-move dynamics of the white player's advantage from over seventy thousand high level chess matches spanning over 150 years. We find that the average advantage of the white player is positive and that it has been increasing over time. Currently, the average advantage of the white player is ~0.17 pawns but it is exponentially approaching a value of 0.23 pawns with a characteristic time scale of 67 years. We also study the diffusion of the move dependence of the white player's advantage and find that it is non-Gaussian, has long-ranged anti-correlations and that after an initial period with no diffusion it becomes super-diffusive. We find that the duration of the non-diffusive period, corresponding to the opening stage of a match, is increasing in length and exponentially approaching a value of 15.6 moves with a characteristic time scale of 130 years. We interpret these two trends as a resulting from learning of the features of the game. Additionally, we find that the exponent {\alpha} characterizing the super-diffusive regime is increasing toward a value of 1.9, close to the ballistic regime. We suggest that this trend is due to the increased broadening of the range of abilities of chess players participating in major tournaments.

We present two models of competitive learning, which are respectively interfacial and cooperative learning. This learning is outcome-related, so that spatially and temporally local environments influence the conversion of a given site between one of two different types. We focus here on the behavior of the models at coexistence, which yields new critical behavior and the existence of a phase involving a novel type of coarsening which is oscillatory in nature.

Herding defines a deterministic dynamical system at the edge of chaos. It generates a sequence of model states and parameters by alternating parameter perturbations with state maximizations, where the sequence of states can be interpreted as "samples" from an associated MRF model. Herding differs from maximum likelihood estimation in that the sequence of parameters does not converge to a fixed point and differs from an MCMC posterior sampling approach in that the sequence of states is generated deterministically. Herding may be interpreted as a"perturb and map" method where the parameter perturbations are generated using a deterministic nonlinear dynamical system rather than randomly from a Gumbel distribution. This chapter studies the distinct statistical characteristics of the herding algorithm and shows that the fast convergence rate of the controlled moments may be attributed to edge of chaos dynamics. The herding algorithm can also be generalized to models with latent variables and to a discriminative learning setting. The perceptron cycling theorem ensures that the fast moment matching property is preserved in the more general framework.

Herding defines a deterministic dynamical system at the edge of chaos. It generates a sequence of model states and parameters by alternating parameter perturbations with state maximizations, where the sequence of states can be interpreted as "samples" from an associated MRF model. Herding differs from maximum likelihood estimation in that the sequence of parameters does not converge to a fixed point and differs from an MCMC posterior sampling approach in that the sequence of states is generated deterministically. Herding may be interpreted as a"perturb and map" method where the parameter perturbations are generated using a deterministic nonlinear dynamical system rather than randomly from a Gumbel distribution. This chapter studies the distinct statistical characteristics of the herding algorithm and shows that the fast convergence rate of the controlled moments may be attributed to edge of chaos dynamics. The herding algorithm can also be generalized to models with latent variables and to a discriminative learning setting. The perceptron cycling theorem ensures that the fast moment matching property is preserved in the more general framework.

Learning would be a convincing method to achieve coordination on an equilibrium. But does learning converge, and to what? We answer this question in generic 2-player, 2-strategy games, using Experience-Weighted Attraction (EWA), which encompasses many extensively studied learning algorithms. We exhaustively characterize the parameter space of EWA learning, for any payoff matrix, and we understand the generic properties that imply convergent or non-convergent behaviour in 2 x 2 games. Irrational choice and lack of incentives imply convergence to a mixed strategy in the centre of the strategy simplex, possibly far from the Nash Equilibrium (NE). In the opposite limit, in which the players quickly modify their strategies, the behaviour depends on the payoff matrix: (i) a strong discrepancy between the pure strategies describes dominance-solvable games, which show convergence to a unique fixed point close to the NE; (ii) a preference towards profiles of strategies along the main diagonal describes coordination games, with multiple stable fixed points corresponding to the NE; (iii) a cycle of best responses defines discoordination games, which commonly yield limit cycles or low-dimensional chaos. While it is well known that mixed strategy equilibria may be unstable, our approach is novel from several perspectives: we fully analyse EWA and provide explicit thresholds that define the onset of instability; we find an emerging taxonomy of the learning dynamics, without focusing on specific classes of games ex-ante; we show that chaos can occur even in the simplest games; we make a precise theoretical prediction that can be tested against data on experimental learning of discoordination games.