Towards an Integrative Educational Recommender for Lifelong Learners
This addresses the problem of providing scalable and transparent educational recommendations for lifelong learners, though it appears incremental as a first step towards a broader integrative approach.
The paper tackled the challenge of building a recommendation system for lifelong learning by developing TrueLearn, which models content novelty and learner background knowledge, achieving promising performance with a human-interpretable model.
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.