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Towards an Integrative Educational Recommender for Lifelong Learners

Bulathwela, S; PAerez-Ortiz, M; Yilmaz, E; Shawe-Taylor, J; (2020) Towards an Integrative Educational Recommender for Lifelong Learners. In: Proceedings of the AAAI Conference on Artificial Intelligence. (pp. pp. 13759-13760). Association for the Advancement of Artificial Intelligence (AAAI) Green open access

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Abstract

One of the most ambitious use cases of computer-assisted learning is to build a recommendation system for lifelong learning. Most recommender algorithms exploit similarities between content and users, overseeing the necessity to leverage sensible learning trajectories for the learner. Lifelong learning thus presents unique challenges, requiring scalable and transparent models that can account for learner knowledge and content novelty simultaneously, while also retaining accurate learners representations for long periods of time. We attempt to build a novel educational recommender, that relies on an integrative approach combining multiple drivers of learners engagement. Our first step towards this goal is TrueLearn, which models content novelty and background knowledge of learners and achieves promising performance while retaining a human interpretable learner model.

Type: Proceedings paper
Title: Towards an Integrative Educational Recommender for Lifelong Learners
Event: Thirty-Fourth AAAI Conference on Artificial Intelligence
Location: New York (NY), USA
Dates: 7th-12th February 2020
ISBN-13: 9781577358350
Open access status: An open access version is available from UCL Discovery
DOI: 10.1609/aaai.v34i10.7151
Publisher version: https://doi.org/10.1609/aaai.v34i10.7151
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10111249
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