تقرير
Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments
| العنوان: | Can Population-based Engagement Improve Personalisation? A Novel Dataset and Experiments |
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| المؤلفون: | Bulathwela, Sahan, Verma, Meghana, Perez-Ortiz, Maria, Yilmaz, Emine, Shawe-Taylor, John |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Digital Libraries, Statistics - Applications, Statistics - Machine Learning, H.3.3, J.1, I.2.0 |
| الوصف: | 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. Comment: To be presented at International Conference for Educational Data Mining 2022 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2207.01504 |
| رقم الأكسشن: | edsarx.2207.01504 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |