# A Toolbox for Modelling Engagement with Educational Videos

arXiv: https://arxiv.org/abs/2401.05424 · HTML: https://mlsanity.com/paper/2401.05424/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 30 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [EdNet: A Large-Scale Hierarchical Dataset in Education](https://mlsanity.com/paper/1912.03072/) (2019)
- [ORCAS: 18 Million Clicked Query-Document Pairs for Analyzing Search](https://mlsanity.com/paper/2006.05324/) (2020)
- [TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources](https://mlsanity.com/paper/1911.09471/) (2019)
- [Combating the Filter Bubble: Designing for Serendipity in a University Course Recommendation System](https://mlsanity.com/paper/1907.01591/) (2019)
- [Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution](https://mlsanity.com/paper/2112.02034/) (2021)
- [Assessing the Performance of Online Students -- New Data, New Approaches, Improved Accuracy](https://mlsanity.com/paper/2109.01753/) (2021)

## Built on by (most cited first, 3 of 3)

- [A Novel Approach to Scalable and Automatic Topic-Controlled Question Generation in Education](https://mlsanity.com/paper/2501.05220/) (2025)
- [Next Token Knowledge Tracing: Exploiting Pretrained LLM Representations to Decode Student Behaviour](https://mlsanity.com/paper/2511.02599/) (2025)
- [TrueReason: An Exemplar Personalised Learning System Integrating Reasoning with Foundational Models](https://mlsanity.com/paper/2502.10411/) (2025)

## Abstract (arXiv)

With the advancement and utility of Artificial Intelligence (AI), personalising education to a global population could be a cornerstone of new educational systems in the future. This work presents the PEEKC dataset and the TrueLearn Python library, which contains a dataset and a series of online learner state models that are essential to facilitate research on learner engagement modelling.TrueLearn family of models was designed following the "open learner" concept, using humanly-intuitive user representations. This family of scalable, online models also help end-users visualise the learner models, which may in the future facilitate user interaction with their models/recommenders. The extensive documentation and coding examples make the library highly accessible to both machine learning developers and educational data mining and learning analytics practitioners. The experiments show the utility of both the dataset and the library with predictive performance significantly exceeding comparative baseline models. The dataset contains a large amount of AI-related educational videos, which are of interest for building and validating AI-specific educational recommenders.