Bulathwela, Sahan;
Verma, Meghana;
Perez-Ortiz, Maria;
Yilmaz, Emine;
Shawe-Taylor, John;
(2022)
Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments.
In:
Proceedings of the 15th International Conference on Educational Data Mining, July 2022.
(pp. pp. 414-421).
International Educational Data Mining Society
Preview |
Text
2022.EDM-short-papers.40.pdf Download (537kB) | Preview |
Abstract
This work explores how population-based engagement prediction can address cold-start at scale in large learning resource collections. This 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.
| Type: | Proceedings paper |
|---|---|
| Title: | Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments |
| Event: | EDM 2022: 15th International Conference on Educational Data Mining, 24-27 July 2022, Durham, UK |
| ISBN-13: | 9781733673631 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.5281/zenodo.6853185 |
| Publisher version: | https://educationaldatamining.org/edm2022/proceedi... |
| Language: | English |
| Additional information: | Copyright © 2022 The author(s). This work is distributed under the Creative Commons Attribution NonCommercial NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license. |
| Keywords: | Population-based Engagement, Cold-start, Educational Recommender, Personalised Education, AI in Education |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10160388 |
Archive Staff Only
 |
View Item |
