THE ROLE OF SIMULATION IN SUPPORTING LONGER-TERM LEARNING AND MENTORING WITH TECHNOLOGY

Mentoring is an important part of professional development and longer-term learning. The nature of longer-term mentoring contexts means that designing, developing, and testing adaptive learning sys-tems for use in this kind of context would be very costly as it would require substantial amounts of fi-nancial, human, and time resources. Simulation is a cheaper and quicker approach for evaluating the impact of various design and development decisions. Within the Artificial Intelligence in Education (AIED) research community, however, surprisingly little attention has been paid to how to design, de-velop, and use simulations in longer-term learning contexts. The central challenge is that adaptive learning system designers and educational practitioners have limited guidance on what steps to consider when designing simulations for supporting longer-term mentoring system design and development deci-sions. My research work takes as a starting point VanLehn et al.’s [1] introduction to applications of simulated students and Erickson et al.’s [2] suggested approach to creating simulated learning envi-ronments. My dissertation presents four research directions using a real-world longer-term mentoring context, a doctoral program, for illustrative purposes. The first direction outlines a framework for guid-ing system designers as to what factors to consider when building pedagogical simulations, fundamen-tally to answer the question: how can a system designer capture a representation of a target learning context in a pedagogical simulation model? To illustrate the feasibility of this framework, this disserta-tion describes how to build, the SimDoc model, a pedagogical model of a longer-term mentoring learn-ing environment – a doctoral program. The second direction builds on the first, and considers the issue of model fidelity, essentially to answer the question: how can a system designer determine a simulation model’s fidelity to the desired granularity level? This dissertation shows how data from a target learning environment, the research literature, and common sense are combined to achieve SimDoc’s medium fidelity model. The third research direction explores calibration and validation issues to answer the question: how many simulation runs does it take for a practitioner to have confidence in the simulation model’s output? This dissertation describes the steps taken to calibrate and validate the SimDoc model, so its output statistically matches data from the target doctoral program, the one at the university of Saskatchewan. The fourth direction is to demonstrate the applicability of the resulting pedagogical model. This dissertation presents two experiments using SimDoc to illustrate how to explore pedagogi-cal questions concerning personalization strategies and to determine the effectiveness of different men-toring strategies in a target learning context. Overall, this dissertation shows that simulation is an important tool in the AIED system design-ers’ toolkit as AIED moves towards designing, building, and evaluating AIED systems meant to support learners in longer-term learning and mentoring contexts. Simulation allows a system designer to exper-iment with various design and implementation decisions in a cost-effective and timely manner before committing to these decisions in the real world

Personalized Recommender Systems for Resource-based Learning - Hybrid Graph-based Recommender Systems for Folksonomies

As the Web increasingly pervades our everyday lives, we are faced with an overload of information. We often learn on-the-job without a teacher and without didactically prepared learning resources. We not only learn on our own but also collaboratively on social platforms where we discuss issues, exchange information and share knowledge with others. We actively learn with resources we find on the Web such as videos, blogs, forums or wikis. This form of self-regulated learning is called resource-based learning. An ongoing challenge in technology enhanced learning (TEL) and in particular in resource-based learning, is supporting learners in finding learning resources relevant to their current needs and learning goals. In social tagging systems, users collaboratively attach keywords called tags to resources thereby forming a network-like structure called a folksonomy. Additional semantic information gained for example from activity hierarchies or semantic tags, form an extended folksonomy and provide valuable information about the context of the resources the learner has tagged, the related activities the resources could be relevant for, and the learning task the learner is currently working on. This additional semantic information could be exploited by recommender systems to generate personalized recommendations of learning resources. Thus, the first research goal of this thesis is to develop and evaluate personalized recommender algorithms for a resource-based learning scenario. To this end, the resource-based learning application scenario is analysed, taking an existing learning platform as a concrete example, in order to determine which additional semantic information could be exploited for the recommendation of learning resources. Several new hybrid graph-based recommender approaches are implemented and evaluated. Additional semantic information gained from activities, activity hierarchies, semantic tag types, the semantic relatedness between tags and the context-specific information found in a folksonomy are thereby exploited. The proposed recommender algorithms are evaluated in offline experiments on different datasets representing diverse evaluation scenarios. The evaluation results show that incorporating additional semantic information is advantageous for providing relevant recommendations. The second goal of this thesis is to investigate alternative evaluation approaches for recommender algorithms for resource-based learning. Offline experiments are fast to conduct and easy to repeat, however they face the so called incompleteness problem as datasets are limited to the historical interactions of the users. Thus newly recommended resources, in which the user had not shown an interest in the past, cannot be evaluated. The recommendation of novel and diverse learning resources is however a requirement for TEL and needs to be evaluated. User studies complement offline experiments as the users themselves judge the relevance or novelty of the recommendations. But user studies are expensive to conduct and it is often difficult to recruit a large number of participants. Therefore a gap exists between the fast, easy to repeat offline experiments and the more expensive user studies. Crowdsourcing is an alternative as it offers the advantages of offline experiments, whilst still retaining the advantages of a user-centric evaluation. In this thesis, a crowdsourcing evaluation approach for recommender algorithms for TEL is proposed and a repeated evaluation of one of the proposed recommender algorithms is conducted as a proof-of-concept. The results of both runs of the experiment show that crowdsourcing can be used as an alternative approach to evaluate graph-based recommender algorithms for TEL

DeLFI 2011 - Die 9. e-Learning Fachtagung Informatik: Poster | Workshops | Kurzbeiträge

Die 9. Tagung der Fachgruppe „E-Learning“ in der Gesellschaft für Informatik e.V. vom 5. - 8. September 2011 in Dresden setzt eine inzwischen gute Tradition wissenschaftlicher Diskussionen in diesem Fachgebiet fort. Erneut konnten interdisziplinäre Partner gewonnen werden, um unter dem Dach einer Veranstaltung mit dem Titel „Wissensgemeinschaften 2011“ unterschiedliche Facetten des Lernens mit elektronischen Medien gemeinsam zu diskutieren. Das betrifft Themenbereiche wie Wissensmanagement, Werkzeuge und Technologien für e-Learning, didaktische und technische Aspekte des Einsatzes elektronischer Hilfsmittel oder auch kooperatives Wirken in verschiedenen Arbeitsfeldern. Diese Teiltagungen - 16. Europäische Jahrestagung der Gesellschaft für Medien in der Wissenschaft „GMW 2011“, - 9. E-Learning Fachtagung Informatik der Gesellschaft für Informatik „DeLFI 2011“ und - 14. Tagung Gemeinschaften in Neuen Medien: Virtual Enterprises, Communities & Social Netorks „GeNeMe 2011“ haben mit jeweils eigenen Experten aus einer großen Zahl von Angeboten zu wissenschaftlichen Fachbeiträgen die wertvollsten ausgewählt und präsentieren diese in eigenen Tagungsbänden. Der vorliegende Band enthält darüber hinaus gehende Arbeiten, die der Teiltagung „DeLFI“ zuzuordnen sind. Dies sind vor allem Beiträge aus den Workshops: - Mobile Learning: Einsatz mobiler Endgeräte im Lernen, Wissenserwerb sowie der Lehr-/Lernorganisation - Lerninfrastruktur in Schulen: 1:1-Computing - Web 2.0 in der beruflichen Bildung aber auch die angenommenen Short Papers, Demonstrationen und Poster. Mit der Tagung „Wissensgemeinschaften 2011“ in Dresden wurde ein Ort gewählt, der in einer wachsenden Region ein Zentrum für Wissenschaft, Wirtschaft und Kultur bildet und dieser Tagung das nötige Ambiente verleiht, an dem die Zusammenarbeit zwischen Wissenschaft und Forschung auf einem hohen Niveau stattfindet und die Technische Universität eine Vorstufe zur Anerkennung auf Förderung im Rahmen der Exzellenzinitiative erreicht hat. Der besondere Dank gilt den Autoren für die eingereichten Beiträge sowie dem Programmausschuss für deren Begutachtung. Natürlich gilt dieser Dank auch den Sponsoren, Ausstellern und Gestaltern der Pre-Konferenz-Aktivitäten. Ferner möchten wir allen danken, die die Vorbereitung und Durchführung unterstützt haben, besonders den Studierenden der Fakultät Informatik der TU Dresden und Schülern der Europäische Wirtschafts- und Sprachenakademie (EWS) Dresden. Dresden, September 2011 Holger Rohland, Andrea Kienle, Steffen Friedric

Основи ресурсно-орієнтованого навчання дисциплін комп’ю-терного циклу (з досвіду аграрних коледжів)

У монографії розглянуто теоретичні та практичні аспекти ресурсно-орієнто-ваного навчання дисциплін комп’ютерного циклу студентів аграрних коледжів. Запропоновано концепцію ресурсно-орієнтованого навчання дисциплін комп’ю-терного циклу, розглянуто засоби, методи, форми ресурсно-орієнтованого навчання дисциплін комп’ютерного циклу. Подано практичні рекомендації щодо організації ресурсно-орієнтованого навчання дисциплін комп’ютерного циклу, навчально-методичної роботи в коледжі, методичні рекомендації щодо створення електронних освітніх ресурсів