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Physics phenomena are widely viewed in daily life, and the technical nature of physics makes modern life seem to be simpler than it was many years ago. Physics teaching and learning, on the other hand, has not always been done effectively, especially in developing countries. This study aimed measured the effectiveness using mobile physics on students' reinvigorating active engagement and learning dynamics on teaching uniform accelerated motion. The study employed the one-group pretest-posttest design. The design compared the result obtained from researcher -- made -- pretest and posttest and adapted science engagement scales. The study revealed that mobile physics as innovation had a positive effect on the learning dynamic of the learners, as evidenced by the significantly greater mean in the posttest. The data showed that there is significant difference in the pretest and posttest score of students in the utilization of mobile physics on teaching uniform accelerated motion. It is also evident that learners highly have engagement during the innovation on teaching uniform accelerated motion. Future utilization of this mobile physics as an innovation would raise learners' active engagement and learning dynamics in teaching physics concepts.

Physics phenomena are widely viewed in daily life, and the technical nature of physics makes modern life seem to be simpler than it was many years ago. Physics teaching and learning, on the other hand, has not always been done effectively, especially in developing countries. This study aimed measured the effectiveness using mobile physics on students' reinvigorating active engagement and learning dynamics on teaching uniform accelerated motion. The study employed the one-group pretest-posttest design. The design compared the result obtained from researcher -- made -- pretest and posttest and adapted science engagement scales. The study revealed that mobile physics as innovation had a positive effect on the learning dynamic of the learners, as evidenced by the significantly greater mean in the posttest. The data showed that there is significant difference in the pretest and posttest score of students in the utilization of mobile physics on teaching uniform accelerated motion. It is also evident that learners highly have engagement during the innovation on teaching uniform accelerated motion. Future utilization of this mobile physics as an innovation would raise learners' active engagement and learning dynamics in teaching physics concepts.

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

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)

There is a general Mathematics phobia among the young school students. In this paper, we made an effort to find ways to motivate and create interest among students. We organized a training programme and introduced an open source software, namely, GeoGebra to a group of senior secondary students of Synod Higher Secondary School, Aizawl, Mizoram, India. We discussed some of the problems from their text book. We analyzed the impact of the training programme through the pre and post questionnaires. The analysis showed that the training programme was successful in motivating and generating more interest among the students. We conclude that in classroom teaching, the teacher should put more emphasis in motivating the students and use software based methods as far as possible.

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.]

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.]

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.

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.

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.

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.

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.

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.)

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.

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.]

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.

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.

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.

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)

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)

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)

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.

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."

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."

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.

Learning and memory are acquired through long-lasting changes in synapses. In the simplest models, such synaptic potentiation typically leads to runaway excitation, but in reality there must exist processes that robustly preserve overall stability of the neural system dynamics. How is this accomplished? Various approaches to this basic question have been considered. Here we propose a particularly compelling and natural mechanism for preserving stability of learning neural systems. This mechanism is based on the global processes by which metabolic resources are distributed to the neurons by glial cells. Specifically, we introduce and study a model comprised of two interacting networks: a model neural network interconnected by synapses which undergo spike-timing dependent plasticity (STDP); and a model glial network interconnected by gap junctions which diffusively transport metabolic resources among the glia and, ultimately, to neural synapses where they are consumed. Our main result is that the biophysical constraints imposed by diffusive transport of metabolic resources through the glial network can prevent runaway growth of synaptic strength, both during ongoing activity and during learning. Our findings suggest a previously unappreciated role for glial transport of metabolites in the feedback control stabilization of neural network dynamics during learning.

We study the time taken by a language learner to correctly identify the meaning of all words in a lexicon under conditions where many plausible meanings can be inferred whenever a word is uttered. We show that the most basic form of cross-situational learning - whereby information from multiple episodes is combined to eliminate incorrect meanings - can perform badly when words are learned independently and meanings are drawn from a nonuniform distribution. If learners further assume that no two words share a common meaning, we find a phase transition between a maximally-efficient learning regime, where the learning time is reduced to the shortest it can possibly be, and a partially-efficient regime where incorrect candidate meanings for words persist at late times. We obtain exact results for the word-learning process through an equivalence to a statistical mechanical problem of enumerating loops in the space of word-meaning mappings.

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.

Starting with a group of reinforcement-learning agents we derive coupled replicator equations that describe the dynamics of collective learning in multiagent systems. We show that, although agents model their environment in a self-interested way without sharing knowledge, a game dynamics emerges naturally through environment-mediated interactions. An application to rock-scissors-paper game interactions shows that the collective learning dynamics exhibits a diversity of competitive and cooperative behaviors. These include quasiperiodicity, stable limit cycles, intermittency, and deterministic chaos--behaviors that should be expected in heterogeneous multiagent systems described by the general replicator equations we derive.

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.

This article concentrates on the question what kind of model--conceptual and statistical--can serve as a good working model for the study of learning and teaching processes qua processes. We claim that a good way of answering this question is to begin by observing a teaching and learning process as, where, and when it occurs. In addition, a conceptual model of intertwined learning-teaching processes is discussed, and dynamic modeling as an approach to theory formation about teaching-learning processes. The focus lies on the evolution term, the timescale of interaction processes, state space as a perspective on teacher-student interaction dynamics, and the principle of agency. Finally, an empirical approach to studying teaching-learning processes is illustrated by means of a case study, focusing on the use of cluster analyses techniques. In the Conclusion and Discussion section, further perspectives on theory building and empirical research are discussed.

This article concentrates on the question what kind of model--conceptual and statistical--can serve as a good working model for the study of learning and teaching processes qua processes. We claim that a good way of answering this question is to begin by observing a teaching and learning process as, where, and when it occurs. In addition, a conceptual model of intertwined learning-teaching processes is discussed, and dynamic modeling as an approach to theory formation about teaching-learning processes. The focus lies on the evolution term, the timescale of interaction processes, state space as a perspective on teacher-student interaction dynamics, and the principle of agency. Finally, an empirical approach to studying teaching-learning processes is illustrated by means of a case study, focusing on the use of cluster analyses techniques. In the Conclusion and Discussion section, further perspectives on theory building and empirical research are discussed.

This article concentrates on the question what kind of model--conceptual and statistical--can serve as a good working model for the study of learning and teaching processes qua processes. We claim that a good way of answering this question is to begin by observing a teaching and learning process as, where, and when it occurs. In addition, a conceptual model of intertwined learning-teaching processes is discussed, and dynamic modeling as an approach to theory formation about teaching-learning processes. The focus lies on the evolution term, the timescale of interaction processes, state space as a perspective on teacher-student interaction dynamics, and the principle of agency. Finally, an empirical approach to studying teaching-learning processes is illustrated by means of a case study, focusing on the use of cluster analyses techniques. In the Conclusion and Discussion section, further perspectives on theory building and empirical research are discussed.

The paper's primary focus revolves around the pivotal role of effective classroom management in creating an optimal learning environment for students within the English department of an education faculty. Employing field research and a questionnaire, the researchers sought to gauge the significance of classroom management, drawing insights from the experiences and background knowledge of 105 students in their third and fourth years. The study identified key components for successful classroom management, encompassing curriculum and instructional aspects, teacher behavior, and the integration of management technology. The findings underscore the students' recognition of the critical importance of managing the classroom to ensure the smooth execution of learning activities and foster positive learning outcomes. The research advocates for teachers to prioritize various elements such as rule enforcement, strategic lesson planning, and active involvement in assessment processes. The paper concludes by asserting that language teachers must be attentive to the nuances of effective classroom management in their teaching practices, with the hope that the research's outcomes will serve as a valuable resource for language educators seeking to enhance their teaching methodologies.