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Article

Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources

Centre for Artificial Intelligence, University College London, London WC1V 6BH, UK
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Academic Editor: Eddie W.L. Cheng
Sustainability 2022, 14(18), 11682; https://doi.org/10.3390/su141811682
Received: 15 June 2022 / Revised: 11 August 2022 / Accepted: 9 September 2022 / Published: 17 September 2022
(This article belongs to the Special Issue AI and Interaction Technologies for Social Sustainability)
Educational recommenders have received much less attention in comparison with e-commerce- and entertainment-related recommenders, even though efficient intelligent tutors could have potential to improve learning gains and enable advances in education that are essential to achieving the world’s sustainability agenda. Through this work, we make foundational advances towards building a state-aware, integrative educational recommender. The proposed recommender accounts for the learners’ interests and knowledge at the same time as content novelty and popularity, with the end goal of improving predictions of learner engagement in a lifelong-learning educational video platform. Towards achieving this goal, we (i) formulate and evaluate multiple probabilistic graphical models to capture learner interest; (ii) identify and experiment with multiple probabilistic and ensemble approaches to combine interest, novelty, and knowledge representations together; and (iii) identify and experiment with different hybrid recommender approaches to fuse population-based engagement prediction to address the cold-start problem, i.e., the scarcity of data in the early stages of a user session, a common challenge in recommendation systems. Our experiments with an in-the-wild interaction dataset of more than 20,000 learners show clear performance advantages by integrating content popularity, learner interest, novelty, and knowledge aspects in an informational recommender system, while preserving scalability. Our recommendation system integrates a human-intuitive representation at its core, and we argue that this transparency will prove important in efforts to give agency to the learner in interacting, collaborating, and governing their own educational algorithms. View Full-Text
Keywords: open education; recommendation systems; lifelong e-learning; state-based learner modelling; Sustainable Development Goal 4 open education; recommendation systems; lifelong e-learning; state-based learner modelling; Sustainable Development Goal 4
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MDPI and ACS Style

Bulathwela, S.; Pérez-Ortiz, M.; Yilmaz, E.; Shawe-Taylor, J. Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources. Sustainability 2022, 14, 11682. https://doi.org/10.3390/su141811682

AMA Style

Bulathwela S, Pérez-Ortiz M, Yilmaz E, Shawe-Taylor J. Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources. Sustainability. 2022; 14(18):11682. https://doi.org/10.3390/su141811682

Chicago/Turabian Style

Bulathwela, Sahan, María Pérez-Ortiz, Emine Yilmaz, and John Shawe-Taylor. 2022. "Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources" Sustainability 14, no. 18: 11682. https://doi.org/10.3390/su141811682

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