Using Similarity Metrics for Matching Lifelong Learners

Labeke, Nicolas Van; Poulovassilis, Alexandra; Magoulas, George D.

doi:10.1007/978-3-540-69132-7_19

Cited by 6 publications

(7 citation statements)

References 6 publications

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“…Some research on collaborative filtering recommender systems that may be of value for us to explore in the future includes that of Herlocker et al [11] which explores what not to recommend (i.e. removing irrelevant items) and that of Labeke et al [12] which is directly applied to educational applications and suggests a kind of string-based coding of the learning achieved by students, to pattern match with similar students in order to suggest appropriate avenues for educating these new students.…”

Section: Discussionmentioning

confidence: 99%

Coping with Poor Advice from Peers in Peer-Based Intelligent Tutoring: The Case of Avoiding Bad Annotations of Learning Objects

Champaign¹,

Zhang²,

Cohen³

2011

User Modeling, Adaption and Personalization

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Abstract. In this paper, we examine a challenge that arises in the application of peer-based tutoring: coping with inappropriate advice from peers. We examine an environment where students are presented with those learning objects predicted to improve their learning (on the basis of the success of previous, like-minded students) but where peers can additionally inject annotations. To avoid presenting annotations that would detract from student learning (e.g. those found confusing by other students) we integrate trust modeling, to detect over time the reputation of the annotation (as voted by previous students) and the reputability of the annotator. We empirically demonstrate, through simulation, that even when the environment is populated with a large number of poor annotations, our algorithm for directing the learning of the students is effective, confirming the value of our proposed approach for student modeling. In addition, the research introduces a valuable integration of trust modeling into educational applications.

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Section: Discussionmentioning

confidence: 99%

Coping with Poor Advice from Peers in Peer-Based Intelligent Tutoring: The Case of Avoiding Bad Annotations of Learning Objects

Champaign¹,

Zhang²,

Cohen³

2011

User Modeling, Adaption and Personalization

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“…Specifically within the ITS domain, there has been work done [46] on similarity matching in lifelong learners. In this work the experiences a learner has had are codified as strings.…”

Section: Recommender Systemsmentioning

confidence: 99%

“…In contrast, our approach to curriculum sequencing uses typical ITS interactions and does not elicit anything specific from the user. Our annotations approach uses elicit ratings, but will make recommendations with an incomplete set of ratings, whereas Lebeke et al's system [46] requires full information to function properly. In their system user histories must be continually updated, with the ongoing issue of out-of-date user profiles.…”

Section: Recommender Systemsmentioning

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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“…In the context of L4All, our main requirement for using similarity metrics is to encode the episodes of a timeline into a token-based string. Our encoding makes four simplifying assumptions (discussed in detail in [10]):…”

Section: Comparing Different Users' Timelinesmentioning

confidence: 99%

“…We refer the reader to [10] for a comparison of ten different similarity metrics that we considered for trialling in the system. These were all part of the SimMetrics Java package (see www.dcs.shef.ac.uk/~sam/stringmetrics.html).…”

Section: Comparing Different Users' Timelinesmentioning

confidence: 99%

Searching for “People Like Me” in a Lifelong Learning System

Labeke

Magoulas

Poulovassilis

2009

Lecture Notes in Computer Science

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Abstract. The L4All system allows learners to record and share learning pathways through educational offerings, with the aim of facilitating progression from Secondary Education through to Further Education and on to Higher Education. It allows users to create and maintain their timeline (a chronological record of their learning, work and personal episodes) and share it with other users, aiming to foster collaborative elaboration of future goals and aspirations. This paper describes the design of the system's facility for searching for "people like me", presents the results of an evaluation session with a group of mature learners, and discusses outcomes arising from this evaluation.

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Using Similarity Metrics for Matching Lifelong Learners

Cited by 6 publications

References 6 publications

Coping with Poor Advice from Peers in Peer-Based Intelligent Tutoring: The Case of Avoiding Bad Annotations of Learning Objects

Coping with Poor Advice from Peers in Peer-Based Intelligent Tutoring: The Case of Avoiding Bad Annotations of Learning Objects

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

Searching for “People Like Me” in a Lifelong Learning System

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