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

arXiv: https://arxiv.org/abs/1911.09471 · HTML: https://mlsanity.com/paper/1911.09471/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2019.

## Built on by (most cited first, 18 of 18)

- [Scalable Educational Question Generation with Pre-trained Language Models](https://mlsanity.com/paper/2305.07871/) (2023)
- [MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs](https://mlsanity.com/paper/2304.02205/) (2023)
- [PEEK: A Large Dataset of Learner Engagement with Educational Videos](https://mlsanity.com/paper/2109.03154/) (2021)
- [Towards Goal-oriented Intelligent Tutoring Systems in Online Education](https://mlsanity.com/paper/2312.10053/) (2023)
- [Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution](https://mlsanity.com/paper/2112.02034/) (2021)
- [An AI-based Learning Companion Promoting Lifelong Learning Opportunities for All](https://mlsanity.com/paper/2112.01242/) (2021)
- [A Review of Data Mining in Personalized Education: Current Trends and Future Prospects](https://mlsanity.com/paper/2402.17236/) (2024)
- [Next Token Knowledge Tracing: Exploiting Pretrained LLM Representations to Decode Student Behaviour](https://mlsanity.com/paper/2511.02599/) (2025)
- [Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems](https://mlsanity.com/paper/2112.04368/) (2021)
- [Towards a General Pre-training Framework for Adaptive Learning in MOOCs](https://mlsanity.com/paper/2208.04708/) (2022)
- [A Toolbox for Modelling Engagement with Educational Videos](https://mlsanity.com/paper/2401.05424/) (2023)
- [Pre-Training With Scientific Text Improves Educational Question Generation](https://mlsanity.com/paper/2212.03869/) (2022)
- [TrueReason: An Exemplar Personalised Learning System Integrating Reasoning with Foundational Models](https://mlsanity.com/paper/2502.10411/) (2025)
- [Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments](https://mlsanity.com/paper/2207.01504/) (2022)
- [Towards Proactive Information Retrieval in Noisy Text with Wikipedia Concepts](https://mlsanity.com/paper/2210.09877/) (2022)
- [CLUE: Contextualised Unified Explainable Learning of User Engagement in Video Lectures](https://mlsanity.com/paper/2201.05651/) (2022)
- [TrueLearn: A Python Library for Personalised Informational Recommendations with (Implicit) Feedback](https://mlsanity.com/paper/2309.11527/) (2023)
- [Mix and Match: Context Pairing for Scalable Topic-Controlled Educational Summarisation](https://mlsanity.com/paper/2604.18087/) (2026)

## Abstract (arXiv)

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.