Social networks and cooperative learning in a MOOC

MOOCs are changing the educational landscape and gaining a lot of attention in scientific literature. However, the pedagogical design of these proposals has been called into question. It is precisely MOOCs’ social aspect, i.e. the interaction between course participants and the support for learning processes that has become one of the main topics of interest. This article presents the results of a research project carried out at the University of the Basque Country, which focused in cooperative learning and the intensive use of social networks in a MOOC. Significant data was compiled through Likert-type surveys, revealing that the use of both external and internal social networks in a massive open online course is a factor that is evaluated positively by students. We argue that the use of social networks as a learning strategy in a MOOC has an influence on academic performance and on the students' success rate. Furthermore, the participants’ age also has a bearing on the social networks they use, and we have found that the younger members tend to work with external networks such as Twitter or personal blogs, whereas the older students are more inclined to use forums from the Chamilo or Ning platforms.

Social networks and lifelong learning in older people

Various socio-demographic factors are causing our society to coexist every day with a group of elderly population that remains active and inserted into the daily dynamics. However, it is believed that there are certain barriers that make this group of people to not adequately address the technologies and even social networks. The creation of the University Programs for the Elderly (PUM), however, is leading to a new stage, since older people who participate come into contact with all kinds of content and rigor, updating own university education, thus changing the way to tackle the most innovative and different situations. In this study, we analyze what is the knowledge and use of older people, PUM, attending the University of Jaen have of the social networks and the assessment made of the need for these programs. To achieve this, we used a methodology in which qualitative and quantitative processes were articulated, through the analysis of data obtained from interviews and a focus groups with program Aquad 7. The data collected show that there is still some ignorance about social networks by older people, but everyone values their usefulness and necessity. Participants believe that they will be least affected of the risks of these technologies and demand a greater training in these contained within the PUM.

The role of working memory capacity in multiple-cue probability learning

Multiple-cue probability learning (MCPL) involves learning to predict a criterion when outcome feedback is provided for multiple cues. A great deal of research suggests that working memory capacity (WMC) is involved in a wide range of tasks that draw on higher level cognitive processes. In three experiments, we examined the role of WMC in MCPL by introducing measures of working memory capacity, as well as other task manipulations. While individual differences in WMC positively predicted performance in some kinds of multiple-cue tasks, performance on other tasks was entirely unrelated to these differences. Performance on tasks that contained negative cues was correlated with working memory capacity, as well as measures of explicit knowledge obtained in the learning process. When the relevant cues predicted positively, however, WMC became irrelevant. The results are discussed in terms of controlled and automatic processes in learning and judgement. © 2011 The Experimental Psychology Society.

Learning experience and identity development as a research assistant /

What research learning experiences do current students have as research assistants (RAs) in the Faculty of Education at Brock University? How do the experiences of research assistants contribute to the formation of a researcher identity and influence future research plans? Despite the importance of these questions, there seems to be very little research conducted or written about the experiences of research assistants as they engage in the research process. There are few resources to which research assistants or their advisors can refer regarding graduate student research learning experiences. The purpose of this study was to understand the kinds of learning experiences that 4 RAs (who are enrolled in the Faculty of Education at Brock University, St. Catharines, Ontario) have and how those experiences contribute to their identities as researchers. Through interviews with participants, observations of participants, and textual documents produced by participants, I have (a) discovered what 4 RAs have learned while engaged in one or more research assistantships and (b) explored how these 4 RAs' experiences have shaped their identities as new researchers. My research design provided a separate case study for each participant RA, including myself as a research participant. Then as a collective, I studied all 4 cases as a case study in itself in the form of a cross-analysis to identify similarities and differences between cases. Using a variety of writing forms and visual narratives, I analyzed and interpreted the experiences of my participants utilizing arts-based literature to inform my analysis and thesis format. The final presentation includes electronic diagrams, models, poetry, a newsletter, a website presentation, and other representational arts-based forms.This thesis is a resource for current and future research assistants who can learn from the research assistant experiences presented in the research. Faculty members who hire research assistants to assist them with their research will also benefit from reading about RAs' learning experiences from the RAs' perspective. The information provided in this thesis document is a resource to inform future policies and research training initiatives in faculty departments and offices at universities. Consequently, this thesis also informs researchers (experienced and inexperienced) about how to conduct research in ways that benefit all parties and provide insight into potential ways to improve research assistantship practices.

Deep learning of representations and its application to computer vision

L’objectif de cette thèse par articles est de présenter modestement quelques étapes du parcours qui mènera (on espère) à une solution générale du problème de l’intelligence artificielle. Cette thèse contient quatre articles qui présentent chacun une différente nouvelle méthode d’inférence perceptive en utilisant l’apprentissage machine et, plus particulièrement, les réseaux neuronaux profonds. Chacun de ces documents met en évidence l’utilité de sa méthode proposée dans le cadre d’une tâche de vision par ordinateur. Ces méthodes sont applicables dans un contexte plus général, et dans certains cas elles on tété appliquées ailleurs, mais ceci ne sera pas abordé dans le contexte de cette de thèse. Dans le premier article, nous présentons deux nouveaux algorithmes d’inférence variationelle pour le modèle génératif d’images appelé codage parcimonieux “spike- and-slab” (CPSS). Ces méthodes d’inférence plus rapides nous permettent d’utiliser des modèles CPSS de tailles beaucoup plus grandes qu’auparavant. Nous démontrons qu’elles sont meilleures pour extraire des détecteur de caractéristiques quand très peu d’exemples étiquetés sont disponibles pour l’entraînement. Partant d’un modèle CPSS, nous construisons ensuite une architecture profonde, la machine de Boltzmann profonde partiellement dirigée (MBP-PD). Ce modèle a été conçu de manière à simplifier d’entraînement des machines de Boltzmann profondes qui nécessitent normalement une phase de pré-entraînement glouton pour chaque couche. Ce problème est réglé dans une certaine mesure, mais le coût d’inférence dans le nouveau modèle est relativement trop élevé pour permettre de l’utiliser de manière pratique. Dans le deuxième article, nous revenons au problème d’entraînement joint de machines de Boltzmann profondes. Cette fois, au lieu de changer de famille de modèles, nous introduisons un nouveau critère d’entraînement qui donne naissance aux machines de Boltzmann profondes à multiples prédictions (MBP-MP). Les MBP-MP sont entraînables en une seule étape et ont un meilleur taux de succès en classification que les MBP classiques. Elles s’entraînent aussi avec des méthodes variationelles standard au lieu de nécessiter un classificateur discriminant pour obtenir un bon taux de succès en classification. Par contre, un des inconvénients de tels modèles est leur incapacité de générer deséchantillons, mais ceci n’est pas trop grave puisque la performance de classification des machines de Boltzmann profondes n’est plus une priorité étant donné les dernières avancées en apprentissage supervisé. Malgré cela, les MBP-MP demeurent intéressantes parce qu’elles sont capable d’accomplir certaines tâches que des modèles purement supervisés ne peuvent pas faire, telles que celle de classifier des données incomplètes ou encore celle de combler intelligemment l’information manquante dans ces données incomplètes. Le travail présenté dans cette thèse s’est déroulé au milieu d’une période de transformations importantes du domaine de l’apprentissage à réseaux neuronaux profonds qui a été déclenchée par la découverte de l’algorithme de “dropout” par Geoffrey Hinton. Dropout rend possible un entraînement purement supervisé d’architectures de propagation unidirectionnel sans être exposé au danger de sur- entraînement. Le troisième article présenté dans cette thèse introduit une nouvelle fonction d’activation spécialement con ̧cue pour aller avec l’algorithme de Dropout. Cette fonction d’activation, appelée maxout, permet l’utilisation de aggrégation multi-canal dans un contexte d’apprentissage purement supervisé. Nous démontrons comment plusieurs tâches de reconnaissance d’objets sont mieux accomplies par l’utilisation de maxout. Pour terminer, sont présentons un vrai cas d’utilisation dans l’industrie pour la transcription d’adresses de maisons à plusieurs chiffres. En combinant maxout avec une nouvelle sorte de couche de sortie pour des réseaux neuronaux de convolution, nous démontrons qu’il est possible d’atteindre un taux de succès comparable à celui des humains sur un ensemble de données coriace constitué de photos prises par les voitures de Google. Ce système a été déployé avec succès chez Google pour lire environ cent million d’adresses de maisons.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

Learning Commonsense Categorical Knowledge in a Thread Memory System

If we are to understand how we can build machines capable of broad purpose learning and reasoning, we must first aim to build systems that can represent, acquire, and reason about the kinds of commonsense knowledge that we humans have about the world. This endeavor suggests steps such as identifying the kinds of knowledge people commonly have about the world, constructing suitable knowledge representations, and exploring the mechanisms that people use to make judgments about the everyday world. In this work, I contribute to these goals by proposing an architecture for a system that can learn commonsense knowledge about the properties and behavior of objects in the world. The architecture described here augments previous machine learning systems in four ways: (1) it relies on a seven dimensional notion of context, built from information recently given to the system, to learn and reason about objects' properties; (2) it has multiple methods that it can use to reason about objects, so that when one method fails, it can fall back on others; (3) it illustrates the usefulness of reasoning about objects by thinking about their similarity to other, better known objects, and by inferring properties of objects from the categories that they belong to; and (4) it represents an attempt to build an autonomous learner and reasoner, that sets its own goals for learning about the world and deduces new facts by reflecting on its acquired knowledge. This thesis describes this architecture, as well as a first implementation, that can learn from sentences such as ``A blue bird flew to the tree'' and ``The small bird flew to the cage'' that birds can fly. One of the main contributions of this work lies in suggesting a further set of salient ideas about how we can build broader purpose commonsense artificial learners and reasoners.

The use of ICT by adults with learning disabilities in day and residential services

The use of Information and Communication Technology (ICT) by adults with learning disabilities has been positively promoted over the past decade. More recently, policy statements and guidance from the UK government have underlined the importance of ICT for adults with learning disabilities specifically, as well as for the population in general, through the potential it offers for social inclusion. The aim of the present study was to provide a picture of how ICT is currently being used within one organisation providing specialist services for adults with learning disabilities and more specifically to provide a picture of its use in promoting community participation. Nine day and 14 residential services were visited as part of a qualitative study to answer three main questions: What kinds of computer programs are being used? What are they being used for? Does this differ between day and residential services? Computers and digital cameras were used for a wide range of activities and ‘mainstream’ programs were used more widely than those developed for specific user groups. In day services, ICT was often embedded in wider projects and activities, whilst use in houses was based around leisure interests. In both contexts, ICT was being used to facilitate communication, although this was more linked to within-service activities, rather than those external to service provision.

Students’ Role in Distance Learning

1. IntroductionMuch of the support that students have in a traditional classroom is absent in a distance learning course. In the traditional classroom, the learner is together with his or her classmates and the teacher; learning is socially embedded. Students can talk to each other and may learn from each other as they go through the learning process together. They also witness the teacher’s expression of the knowledge firsthand. The class participants communicate to each other not only through their words, but also through their gestures, facial expressions and tone of voice, and the teacher can observe the students’ progress and provide guidance and feedback in an as-needed basis. Further, through the habit of meeting in a regular place at a regular time, the participants reinforce their own and each other’s commitment to the course. A distance course must somehow provide learners other kinds of supports so that the distance learner also has a sense of connection with a learning community; can benefit from interaction with peers who are going through a similar learning process; receives feedback that allows him or her to know how he or she is progressing; and is guided enough so that he or she continues to progress towards the learning objectives. This cannot be accomplished if the distance course does not simultaneously promote student autonomy, for the distance course format requires students to take greater responsibility for their own learning. This chapter presents one distance learning course that was able to address all of these goals. The English Department at Högskolan Dalarna, Sweden, participates in a distance learning program with Vietnam National University. Students enrolled in this program study half-time for two years to complete a Master’s degree in English Linguistics. The distance courses in this program all contain two types of regular class meetings: one type is student-only seminars conducted through text chat, during which students discuss and complete assignments that prepare them for the other type of class meeting, also conducted through text chat, where the teacher is present and is the one to lead the discussion of seminar issues and assignments. The inclusion of student-only seminars in the course design allows for student independence while at the same time it encourages co-operation and solidarity. The teacher-led seminars offer the advantages of a class led by an expert.In this chapter, we present chatlog data from Vietnamese students in one distance course in English linguistics, comparing the role of the student in both student-only and teacher-led seminars. We discuss how students navigate their participation roles, through computer-mediated communication (CMC), according to seminar type, and we consider the emerging role of the autonomous student in the foreign-language medium, distance learning environment. We close by considering aspects of effective design of distance learning courses from the perspective of a foreign language (FL) environment.

Machine Translation and the Disruption of Foreign Language Learning Activities

This study examines the question of how language teachers in a highly technologyfriendly university environment view machine translation and the implications that this has for the personal learning environments of students. It brings an activity-theory perspective to the question, examining the ways that the introduction of new tools can disrupt the relationship between different elements in an activity system. This perspective opens up for an investigation of the ways that new tools have the potential to fundamentally alter traditional learning activities. In questionnaires and group discussions, respondents showed general agreement that although use of machine translation by students could be considered cheating, students are bound to use it anyway, and suggested that teachers focus on the kinds of skills students would need when using machine translation and design assignments and exams to practice and assess these skills. The results of the empirical study are used to reflect upon questions of what the roles of teachers and students are in a context where many of the skills that a person needs to be able to interact in a foreign language increasingly can be outsourced to laptops and smartphones.

Machine translation and the disruption of foreign language learning activities

This study examines the question of how language teachers in a highly technology-friendly university environment view machine translation and the implications that this has for the personal learning environments of students. It brings an activity-theory perspective to the question, examining the ways that the introduction of new tools can disrupt the relationship between different elements in an activity system. This perspective opens up for an investigation of the ways that new tools have the potential to fundamentally alter traditional learning activities. In questionnaires and group discussions, respondents showed general agreement that although use of machine translation by students could be considered cheating, students are bound to use it anyway, and suggested that teachers focus on the kinds of skills students would need when using machine translation and design assignments and exams to practice and assess these skills. The results of the empirical study are used to reflect upon questions of what the roles of teachers and students are in a context where many of the skills that a person needs to be able to interact in a foreign language increasingly can be outsourced to laptops and smartphones.

Learning for well being : Studies using the International Adult Literacy Survey

This thesis is a collection of five independent but closely related studies. The overall purpose is to approach the analysis of learning outcomes from a perspective that combines three major elements, namely lifelonglifewide learning, human capital, and the benefits of learning. The approach is based on an interdisciplinary perspective of the human capital paradigm. It considers the multiple learning contexts that are responsible for the development of embodied potential – including formal, nonformal and informal learning – and the multiple outcomes – including knowledge, skills, economic, social and others– that result from learning. The studies also seek to examine the extent and relative influence of learning in different contexts on the formation of embodied potential and how in turn that affects economic and social well being. The first study combines the three major elements, lifelonglifewide learning, human capital, and the benefits of learning into one common conceptual framework. This study forms a common basis for the four empirical studies that follow. All four empirical studies use data from the International Adult Literacy Survey (IALS) to investigate the relationships among the major elements of the conceptual framework presented in the first study. Study I. A conceptual framework for the analysis of learning outcomes This study brings together some key concepts and theories that are relevant for the analysis of learning outcomes. Many of the concepts and theories have emerged from varied disciplines including economics, educational psychology, cognitive science and sociology, to name only a few. Accordingly, some of the research questions inherent in the framework relate to different disciplinary perspectives. The primary purpose is to create a common basis for formulating and testing hypotheses as well as to interpret the findings in the empirical studies that follow. In particular, the framework facilitates the process of theorizing and hypothesizing on the relationships and processes concerning lifelong learning as well as their antecedents and consequences. Study II. Determinants of literacy proficiency: A lifelong-lifewide learning perspective This study investigates lifelong and lifewide processes of skill formation. In particular, it seeks to estimate the substitutability and complementarity effects of learning in multiple settings over the lifespan on literacy skill formation. This is done by investigating the predictive capacity of major determinants of literacy proficiency that are associated with a variety of learning contexts including school, home, work, community and leisure. An identical structural model based on previous research is fitted to the IALS data for 18 countries. The results show that even after accounting for all factors, education remains the most important predictor of literacy proficiency. In all countries, however, the total effect of education is significantly mediated through further learning occurring at work, at home and in the community. Therefore, the job and other literacy related factors complement education in predicting literacy proficiency. This result points to a virtual cycle of lifelong learning, particularly to how educational attainment influences other learning behaviours throughout life. In addition, results show that home background as measured by parents’ education is also a strong predictor of literacy proficiency, but in many countries this occurs only if a favourable home background is complemented with some post-secondary education. Study III. The effect of literacy proficiency on earnings: An aggregated occupational approach using the Canadian IALS data This study uses data from the Canadian Adult Literacy Survey to estimate the earnings return to literacy skills. The approach adapts a labour segmented view of the labour market by aggregating occupations into seven types, enabling the estimation of the variable impact of literacy proficiency on earnings, both within and between different types of occupations. This is done using Hierarchical Linear Modeling (HLM). The method used to construct the aggregated occupational classification is based on analysis that considers the role of cognitive and other skills in relation to the nature of occupational tasks. Substantial premiums are found to be associated with some occupational types even after adjusting for within occupational differences in individual characteristics such as schooling, literacy proficiency, labour force experience and gender. Average years of schooling and average levels of literacy proficiency at the between level account for over two-thirds of the premiums. Within occupations, there are significant returns to schooling but they vary depending on the type of occupations. In contrast, the within occupational return of literacy proficiency is not necessarily significant. The latter depends on the type of occupation. Study IV: Determinants of economic and social outcomes from a lifewide learning perspective in Canada In this study the relationship between learning in different contexts, which span the lifewide learning dimension, and individual earnings on the one hand and community participation on the other are examined in separate but comparable models. Data from the Canadian Adult Literacy Survey are used to estimate structural models, which correspond closely to the common conceptual framework outlined in Study I. The findings suggest that the relationship between formal education and economic and social outcomes is complex with confounding effects. The results indicate that learning occurring in different contexts and for different reasons leads to different kinds of benefits. The latter finding suggests a potential trade-off between realizing economic and social benefits through learning that are taken for either job-related or personal-interest related reasons. Study V: The effects of learning on economic and social well being: A comparative analysis Using the same structural model as in Study IV, hypotheses are comparatively examined using the International Adult Literacy Survey data for Canada, Denmark, the Netherlands, Norway, the United Kingdom, and the United States. The main finding from Study IV is confirmed for an additional five countries, namely that the effect of initial schooling on well being is more complex than a direct one and it is significantly mediated by subsequent learning. Additionally, findings suggest that people who devote more time to learning for job-related reasons than learning for personal-interest related reasons experience higher levels of economic well being. Moreover, devoting too much time to learning for personal-interest related reasons has a negative effect on earnings except in Denmark. But the more time people devote to learning for personal-interest related reasons tends to contribute to higher levels of social well being. These results again suggest a trade-off in learning for different reasons and in different contexts.

Rich media content adaptation in e-learning systems

The wide use of e-technologies represents a great opportunity for underserved segments of the population, especially with the aim of reintegrating excluded individuals back into society through education. This is particularly true for people with different types of disabilities who may have difficulties while attending traditional on-site learning programs that are typically based on printed learning resources. The creation and provision of accessible e-learning contents may therefore become a key factor in enabling people with different access needs to enjoy quality learning experiences and services. Another e-learning challenge is represented by m-learning (which stands for mobile learning), which is emerging as a consequence of mobile terminals diffusion and provides the opportunity to browse didactical materials everywhere, outside places that are traditionally devoted to education. Both such situations share the need to access materials in limited conditions and collide with the growing use of rich media in didactical contents, which are designed to be enjoyed without any restriction. Nowadays, Web-based teaching makes great use of multimedia technologies, ranging from Flash animations to prerecorded video-lectures. Rich media in e-learning can offer significant potential in enhancing the learning environment, through helping to increase access to education, enhance the learning experience and support multiple learning styles. Moreover, they can often be used to improve the structure of Web-based courses. These highly variegated and structured contents may significantly improve the quality and the effectiveness of educational activities for learners. For example, rich media contents allow us to describe complex concepts and process flows. Audio and video elements may be utilized to add a “human touch” to distance-learning courses. Finally, real lectures may be recorded and distributed to integrate or enrich on line materials. A confirmation of the advantages of these approaches can be seen in the exponential growth of video-lecture availability on the net, due to the ease of recording and delivering activities which take place in a traditional classroom. Furthermore, the wide use of assistive technologies for learners with disabilities injects new life into e-learning systems. E-learning allows distance and flexible educational activities, thus helping disabled learners to access resources which would otherwise present significant barriers for them. For instance, students with visual impairments have difficulties in reading traditional visual materials, deaf learners have trouble in following traditional (spoken) lectures, people with motion disabilities have problems in attending on-site programs. As already mentioned, the use of wireless technologies and pervasive computing may really enhance the educational learner experience by offering mobile e-learning services that can be accessed by handheld devices. This new paradigm of educational content distribution maximizes the benefits for learners since it enables users to overcome constraints imposed by the surrounding environment. While certainly helpful for users without disabilities, we believe that the use of newmobile technologies may also become a fundamental tool for impaired learners, since it frees them from sitting in front of a PC. In this way, educational activities can be enjoyed by all the users, without hindrance, thus increasing the social inclusion of non-typical learners. While the provision of fully accessible and portable video-lectures may be extremely useful for students, it is widely recognized that structuring and managing rich media contents for mobile learning services are complex and expensive tasks. Indeed, major difficulties originate from the basic need to provide a textual equivalent for each media resource composing a rich media Learning Object (LO). Moreover, tests need to be carried out to establish whether a given LO is fully accessible to all kinds of learners. Unfortunately, both these tasks are truly time-consuming processes, depending on the type of contents the teacher is writing and on the authoring tool he/she is using. Due to these difficulties, online LOs are often distributed as partially accessible or totally inaccessible content. Bearing this in mind, this thesis aims to discuss the key issues of a system we have developed to deliver accessible, customized or nomadic learning experiences to learners with different access needs and skills. To reduce the risk of excluding users with particular access capabilities, our system exploits Learning Objects (LOs) which are dynamically adapted and transcoded based on the specific needs of non-typical users and on the barriers that they can encounter in the environment. The basic idea is to dynamically adapt contents, by selecting them from a set of media resources packaged in SCORM-compliant LOs and stored in a self-adapting format. The system schedules and orchestrates a set of transcoding processes based on specific learner needs, so as to produce a customized LO that can be fully enjoyed by any (impaired or mobile) student.

Gradient estimation in dendritic reinforcement learning

We study synaptic plasticity in a complex neuronal cell model where NMDA-spikes can arise in certain dendritic zones. In the context of reinforcement learning, two kinds of plasticity rules are derived, zone reinforcement (ZR) and cell reinforcement (CR), which both optimize the expected reward by stochastic gradient ascent. For ZR, the synaptic plasticity response to the external reward signal is modulated exclusively by quantities which are local to the NMDA-spike initiation zone in which the synapse is situated. CR, in addition, uses nonlocal feedback from the soma of the cell, provided by mechanisms such as the backpropagating action potential. Simulation results show that, compared to ZR, the use of nonlocal feedback in CR can drastically enhance learning performance. We suggest that the availability of nonlocal feedback for learning is a key advantage of complex neurons over networks of simple point neurons, which have previously been found to be largely equivalent with regard to computational capability.

Place for video games : a theoretical and pedagogical framework for multiliteracies learning in English studies

Students are now involved in a vastly different textual landscape than many English scholars, one that relies on the “reading” and interpretation of multiple channels of simultaneous information. As a response to these new kinds of literate practices, my dissertation adds to the growing body of research on multimodal literacies, narratology in new media, and rhetoric through an examination of the place of video games in English teaching and research. I describe in this dissertation a hybridized theoretical basis for incorporating video games in English classrooms. This framework for textual analysis includes elements from narrative theory in literary study, rhetorical theory, and literacy theory, and when combined to account for the multiple modalities and complexities of gaming, can provide new insights about those theories and practices across all kinds of media, whether in written texts, films, or video games. In creating this framework, I hope to encourage students to view texts from a meta-level perspective, encompassing textual construction, use, and interpretation. In order to foster meta-level learning in an English course, I use specific theoretical frameworks from the fields of literary studies, narratology, film theory, aural theory, reader-response criticism, game studies, and multiliteracies theory to analyze a particular video game: World of Goo. These theoretical frameworks inform pedagogical practices used in the classroom for textual analysis of multiple media. Examining a video game from these perspectives, I use analytical methods from each, including close reading, explication, textual analysis, and individual elements of multiliteracies theory and pedagogy. In undertaking an in-depth analysis of World of Goo, I demonstrate the possibilities for classroom instruction with a complex blend of theories and pedagogies in English courses. This blend of theories and practices is meant to foster literacy learning across media, helping students develop metaknowledge of their own literate practices in multiple modes. Finally, I outline a design for a multiliteracies course that would allow English scholars to use video games along with other texts to interrogate texts as systems of information. In doing so, students can hopefully view and transform systems in their own lives as audiences, citizens, and workers.