# PEEK: A Large Dataset of Learner Engagement with Educational Videos

arXiv: https://arxiv.org/abs/2109.03154 · HTML: https://mlsanity.com/paper/2109.03154/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 63 references that are in the map.
- First posted on arXiv 3 Sep 2021.

## Builds on (most cited first, 12 of 12)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [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)
- [Instructions and Guide for Diagnostic Questions: The NeurIPS 2020 Education Challenge](https://mlsanity.com/paper/2007.12061/) (2020)
- [Probabilistic Latent Semantic Analysis](https://mlsanity.com/paper/1301.6705/) (2013)
- [Knowledge Tracing Machines: Factorization Machines for Knowledge Tracing](https://mlsanity.com/paper/1811.03388/) (2018)
- [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)
- [Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation](https://mlsanity.com/paper/1604.02336/) (2016)
- [Goal-based Course Recommendation](https://mlsanity.com/paper/1812.10078/) (2018)
- [Towards an Integrative Educational Recommender for Lifelong Learners](https://mlsanity.com/paper/1912.01592/) (2019)
- [Predicting Engagement in Video Lectures](https://mlsanity.com/paper/2006.00592/) (2020)

## Built on by (most cited first, 9 of 9)

- [Could AI Democratise Education? Socio-Technical Imaginaries of an EdTech Revolution](https://mlsanity.com/paper/2112.02034/) (2021)
- [Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems](https://mlsanity.com/paper/2112.04368/) (2021)
- [Unsupervised Audio-Visual Lecture Segmentation](https://mlsanity.com/paper/2210.16644/) (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)
- [TrueLearn: A Python Library for Personalised Informational Recommendations with (Implicit) Feedback](https://mlsanity.com/paper/2309.11527/) (2023)
- [Prompt2LVideos: Exploring Prompts for Understanding Long-Form Multimodal Videos](https://mlsanity.com/paper/2503.08335/) (2025)
- [Aspect-Aware MOOC Recommendation in a Heterogeneous Network](https://mlsanity.com/paper/2602.05297/) (2026)

## Abstract (arXiv)

Educational recommenders have received much less attention in comparison to e-commerce and entertainment-related recommenders, even though efficient intelligent tutors have great potential to improve learning gains. One of the main challenges in advancing this research direction is the scarcity of large, publicly available datasets. In this work, we release a large, novel dataset of learners engaging with educational videos in-the-wild. The dataset, named Personalised Educational Engagement with Knowledge Topics PEEK, is the first publicly available dataset of this nature. The video lectures have been associated with Wikipedia concepts related to the material of the lecture, thus providing a humanly intuitive taxonomy. We believe that granular learner engagement signals in unison with rich content representations will pave the way to building powerful personalization algorithms that will revolutionise educational and informational recommendation systems. Towards this goal, we 1) construct a novel dataset from a popular video lecture repository, 2) identify a set of benchmark algorithms to model engagement, and 3) run extensive experimentation on the PEEK dataset to demonstrate its value. Our experiments with the dataset show promise in building powerful informational recommender systems. The dataset and the support code is available publicly.