# VLEngagement: A Dataset of Scientific Video Lectures for Evaluating Population-based Engagement

arXiv: https://arxiv.org/abs/2011.02273 · HTML: https://mlsanity.com/paper/2011.02273/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [Multimodal Lecture Presentations Dataset: Understanding Multimodality in Educational Slides](https://mlsanity.com/paper/2208.08080/) (2022)
- [VQualA 2025 Challenge on Engagement Prediction for Short Videos: Methods and Results](https://mlsanity.com/paper/2509.02969/) (2025)
- [An AI-based Learning Companion Promoting Lifelong Learning Opportunities for All](https://mlsanity.com/paper/2112.01242/) (2021)
- [Delving Deep into Engagement Prediction of Short Videos](https://mlsanity.com/paper/2410.00289/) (2024)
- [Engagement Prediction of Short Videos with Large Multimodal Models](https://mlsanity.com/paper/2508.02516/) (2025)
- [Unsupervised Audio-Visual Lecture Segmentation](https://mlsanity.com/paper/2210.16644/) (2022)
- [CLUE: Contextualised Unified Explainable Learning of User Engagement in Video Lectures](https://mlsanity.com/paper/2201.05651/) (2022)
- [Prompt2LVideos: Exploring Prompts for Understanding Long-Form Multimodal Videos](https://mlsanity.com/paper/2503.08335/) (2025)

## Abstract (arXiv)

With the emergence of e-learning and personalised education, the production and distribution of digital educational resources have boomed. Video lectures have now become one of the primary modalities to impart knowledge to masses in the current digital age. The rapid creation of video lecture content challenges the currently established human-centred moderation and quality assurance pipeline, demanding for more efficient, scalable and automatic solutions for managing learning resources. Although a few datasets related to engagement with educational videos exist, there is still an important need for data and research aimed at understanding learner engagement with scientific video lectures. This paper introduces VLEngagement, a novel dataset that consists of content-based and video-specific features extracted from publicly available scientific video lectures and several metrics related to user engagement. We introduce several novel tasks related to predicting and understanding context-agnostic engagement in video lectures, providing preliminary baselines. This is the largest and most diverse publicly available dataset to our knowledge that deals with such tasks. The extraction of Wikipedia topic-based features also allows associating more sophisticated Wikipedia based features to the dataset to improve the performance in these tasks. The dataset, helper tools and example code snippets are available publicly at https://github.com/sahanbull/context-agnostic-engagement