TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources

TL;DR

This paper introduces TrueLearn, a set of Bayesian algorithms designed to personalize lifelong learning recommendations by modeling learner knowledge and engagement with open educational resources, aiming for scalable, transparent, and effective educational systems.

Contribution

It proposes novel online Bayesian strategies inspired by item response theory and knowledge tracing, utilizing Wikipedia-based knowledge components for resource understanding.

Findings

Algorithms show promising performance in educational recommendation tasks.

Constructed a large dataset of open educational video lectures for testing.

Demonstrated the potential of Bayesian methods for lifelong learning personalization.

Abstract

The recent advances in computer-assisted learning systems and the availability of open educational resources today promise a pathway to providing cost-efficient, high-quality education to large masses of learners. One of the most ambitious use cases of computer-assisted learning is to build a lifelong learning recommendation system. Unlike short-term courses, lifelong learning presents unique challenges, requiring sophisticated recommendation models that account for a wide range of factors such as background knowledge of learners or novelty of the material while effectively maintaining knowledge states of masses of learners for significantly longer periods of time (ideally, a lifetime). This work presents the foundations towards building a dynamic, scalable and transparent recommendation system for education, modelling learner's knowledge from implicit data in the form of engagement with open educational resources. We i) use a text ontology based on Wikipedia to automatically extract knowledge components of educational resources and, ii) propose a set of online Bayesian strategies inspired by the well-known areas of item response theory and knowledge tracing. Our proposal, TrueLearn, focuses on recommendations for which the learner has enough background knowledge (so they are able to understand and learn from the material), and the material has enough novelty that would help the learner improve their knowledge about the subject and keep them engaged. We further construct a large open educational video lectures dataset and test the performance of the proposed algorithms, which show clear promise towards building an effective educational recommendation system.