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

Author

Listed:
  • Sahan Bulathwela

    (Centre for Artificial Intelligence, University College London, London WC1V 6BH, UK)

  • María Pérez-Ortiz

    (Centre for Artificial Intelligence, University College London, London WC1V 6BH, UK)

  • Emine Yilmaz

    (Centre for Artificial Intelligence, University College London, London WC1V 6BH, UK)

  • John Shawe-Taylor

    (Centre for Artificial Intelligence, University College London, London WC1V 6BH, UK)

Abstract

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.

Suggested Citation

  • Sahan Bulathwela & María Pérez-Ortiz & Emine Yilmaz & John Shawe-Taylor, 2022. "Power to the Learner: Towards Human-Intuitive and Integrative Recommendations with Open Educational Resources," Sustainability, MDPI, vol. 14(18), pages 1-25, September.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:18:p:11682-:d:917450
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    References listed on IDEAS

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    1. Deepani B. Guruge & Rajan Kadel & Sharly J. Halder, 2021. "The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research," Data, MDPI, vol. 6(2), pages 1-30, February.
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