Open Corpus Adaptive Educational Hypermedia

Brusilovsky, Peter; Henze, Nicola

doi:10.1007/978-3-540-72079-9_22

Cited by 117 publications

(87 citation statements)

References 44 publications

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“…Despite a growing body of research predicting [2], describing [3]- [5], or providing suitable guidance on patterns of behaviour to support the learning process in online learning environments (e.g. in Learning Management Systems [6] or in MOOCs [7]), little is known on how students devote their time to learn across contexts beyond the boundaries of the virtual platform.…”

Section: Introductionmentioning

confidence: 99%

Time will tell: The role of mobile learning analytics in self-regulated learning

Tabuenca

Kalz

Drachsler

et al. 2015

Computers & Education

199

102

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Abstract. This longitudinal study explores the effects of tracking and monitoring time devoted to learn with a mobile tool, on self-regulated learning. Graduate students (n=36) from three different online courses used their own mobile devices to track how much time they devoted to learn over a period of four months. Repeated measures of the Online Self-Regulated Learning Questionnaire and Validity and Reliability of Time Management Questionnaire were taken along the course. Our findings reveal positive effects of tracking time on time management skills. Variations in the channel, content and timing of the mobile notifications to foster reflective practice are investigated, and timelogging patterns are described. These results not only provide evidence of the benefits of recording learning time, but also suggest relevant cues on how mobile notifications should be designed and prompted towards self-regulated learning of students in online courses.

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Section: Introductionmentioning

confidence: 99%

Time will tell: The role of mobile learning analytics in self-regulated learning

Tabuenca

Kalz

Drachsler

et al. 2015

Computers & Education

199

102

View full text Add to dashboard Cite

show abstract

“…One research stream focused on the versatility of LMS, attempting to provide in one system as many teacher and learner support features (from content authoring to quizzes to discussion forums) as provided by a modern LMS --plus, the ability to adapt to the user (Morimoto et al, 2007;Specht et al, 2002;Ueno, 2005 (Brusilovsky, Chavan & Farzan, 2004;Brusilovsky & Henze, 2007;Henze & Nejdl, 2001). Most recent projects, however, choose not to compete with present-day LMS, but instead to focus on adaptive features of the coming generation of Web-based educational systems.…”

Section: Adaptive Educational Hypermedia: the Third Generationmentioning

confidence: 99%

Adaptive Hypermedia for Education and Training

Brusilovsky¹

2012

Adaptive Technologies for Training and Education

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“…This is important due to pervasive challenges in assembling appropriate tutorial content, as discussed by researchers [86,58]. We therefore contrast with other researchers in peer-based intelligent tutoring [12,18,66] in allowing peers to (cautiously) play an additional role in the tutoring: that of content authors.…”

Section: Mccalla's Ecological Approachmentioning

confidence: 99%

Peer-Based Intelligent Tutoring Systems: A Corpus-Oriented Approach

Champaign

Cohen

2010

Intelligent Tutoring Systems

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In this thesis, we present an artificial intelligence approach for tutoring students in environments where there is a large repository of possible learning objects (e.g. texts, videos). In particular, we advocate that students learn on the basis of past experiences of peers. This aligns with McCalla's proposed ecological approach for intelligent tutoring, where a learning object's history-of-use is retained and leveraged to instruct future students. We offer three distinct models that serve to deliver the required intelligent tutoring: (i) a curriculum sequencing algorithm selecting which learning objects to present to students based on benefits to knowledge obtained by similar peers (ii) a framework for peers to provide commentary on the learning objects they've experienced (annotations) together with an algorithm for reasoning about which annotations to present to students that incorporates modeling trust in annotators (i.e. their reputation) and ratings provided by students (votes for and against) for the annotations they have been shown (iii) an opportunity for peers to guide the growth of the corpus by proposing divisions of current objects, together with an algorithm for reasoning about which of these new objects should be offered to students in order to enhance their learning. All three components are validated as beneficial in improving the learning of students. This is first of all achieved through a novel approach of simulated student learning, designed to enable the tracking of the experiences of a very large number of peers with an extensive repository of objects, through the effective modeling of knowledge gains. This is also coupled with a preliminary study with human participants that confirms the value of our framework. In all, we offer a rich and varied role for peers in guiding the learning of students in intelligent tutoring environments, made possible by careful modeling of the students who are being taught and of the potential benefits to learning that would be derived with the selection of appropriate tutorial content.iii Acknowledgements First and foremost I would like to thank my supervisor, Professor Robin Cohen, for guiding me through the completion of this dissertation and my PhD studies. Robin overwhelmed me with the amount of time and effort she put in to helping me complete my doctoral work. My committee members, Professor Edward Lank and Professor Peter van Beek, have provided detailed and helpful feedback throughout the process, which I greatly appreciate. Thanks as well to Professor Gord McCalla for his helpful advice. My appreciation goes out to the anonymous reviewers who considered earlier versions of this work and provided feedback during conference and journal submissions over the last 4 years.I would like to acknowledge Ahmed Ataullah, Simina Brânzei, John Doucette and Lachlan Dufton for being wonderful officemates: always willing to talk through issues I was working on, go for coffee and share a laugh. I greatly enjoyed working in the AI Group at the University of ...

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Open Corpus Adaptive Educational Hypermedia

Cited by 117 publications

References 44 publications

Time will tell: The role of mobile learning analytics in self-regulated learning

Time will tell: The role of mobile learning analytics in self-regulated learning

Adaptive Hypermedia for Education and Training

Peer-Based Intelligent Tutoring Systems: A Corpus-Oriented Approach

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