# Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments

arXiv: https://arxiv.org/abs/2207.01504 · HTML: https://mlsanity.com/paper/2207.01504/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 22 Jun 2022.

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

- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [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)
- [Prediction and Localization of Student Engagement in the Wild](https://mlsanity.com/paper/1804.00858/) (2018)
- [TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources](https://mlsanity.com/paper/1911.09471/) (2019)
- [PEEK: A Large Dataset of Learner Engagement with Educational Videos](https://mlsanity.com/paper/2109.03154/) (2021)
- [Curriculum Learning Strategies for IR: An Empirical Study on Conversation Response Ranking](https://mlsanity.com/paper/1912.08555/) (2019)
- [Predicting Engagement in Video Lectures](https://mlsanity.com/paper/2006.00592/) (2020)
- [MUTLA: A Large-Scale Dataset for Multimodal Teaching and Learning Analytics](https://mlsanity.com/paper/1910.06078/) (2019)
- [Semantic TrueLearn: Using Semantic Knowledge Graphs in Recommendation Systems](https://mlsanity.com/paper/2112.04368/) (2021)
- [YouNiverse: Large-Scale Channel and Video Metadata from English-Speaking YouTube](https://mlsanity.com/paper/2012.10378/) (2020)
- [Investigating Correlations of Automatically Extracted Multimodal Features and Lecture Video Quality](https://mlsanity.com/paper/2005.13876/) (2020)

## Built on by (most cited first, 1 of 1)

- [TrueReason: An Exemplar Personalised Learning System Integrating Reasoning with Foundational Models](https://mlsanity.com/paper/2502.10411/) (2025)

## Abstract (arXiv)

This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. The paper introduces i) VLE, a novel dataset that consists of content and video based features extracted from publicly available scientific video lectures coupled with implicit and explicit signals related to learner engagement, ii) two standard tasks related to predicting and ranking context-agnostic engagement in video lectures with preliminary baselines and iii) a set of experiments that validate the usefulness of the proposed dataset. Our experimental results indicate that the newly proposed VLE dataset leads to building context-agnostic engagement prediction models that are significantly performant than ones based on previous datasets, mainly attributing to the increase of training examples. VLE dataset's suitability in building models towards Computer Science/ Artificial Intelligence education focused on e-learning/ MOOC use-cases is also evidenced. Further experiments in combining the built model with a personalising algorithm show promising improvements in addressing the cold-start problem encountered in educational recommenders. This is the largest and most diverse publicly available dataset to our knowledge that deals with learner engagement prediction tasks. The dataset, helper tools, descriptive statistics and example code snippets are available publicly.