Training Temporal and NLP Features via Extremely Randomised Trees for Educational Level Classification

Aljohani, Tahani; Cristea, Alexandra I.

Abstract

Massive Open Online Courses (MOOCs) have become universal learning resources, and the COVID-19 pandemic is rendering these platforms even more necessary. These platforms also bring incredible diversity of learners in terms of their traits. A research area called Author Profiling (AP in general; here, Learner Profiling (LP)), is to identify such traits about learners, which is vital in MOOCs for, e.g., preventing plagiarism, or eligibility for course certification. Identifying a learner’s trait in a MOOC is notoriously hard to do from textual content alone. We argue that to predict a learner’s academic level, we need to also be using other features stemming from MOOC platforms, such as derived from learners’ actions on the platform. In this study, we specifically examine time stamps, quizzes, and discussions. Our novel approach for the task achieves a high accuracy (90% in average) even with a simple shallow classifier, irrespective of data size, outperforming the state of the art.

Citation

Aljohani, T., & Cristea, A. I. (2021). Training Temporal and NLP Features via Extremely Randomised Trees for Educational Level Classification. In A. I. Cristea, & C. Troussas (Eds.), Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings (136-147). Springer Verlag. https://doi.org/10.1007/978-3-030-80421-3_17

• Online Publication Date: Jul 9, 2021
• Publication Date: 2021
• Deposit Date: Apr 12, 2021
• Publicly Available Date: Apr 13, 2021
• Publisher: Springer Verlag
• Pages: 136-147
• Series Title: Lecture Notes in Computer Science
• Series Number: 12677
• Book Title: Intelligent Tutoring Systems: 17th International Conference, ITS 2021, Virtual Event, June 7–11, 2021, Proceedings
• ISBN: 9783030804206
• DOI: https://doi.org/10.1007/978-3-030-80421-3_17
• Public URL: https://durham-repository.worktribe.com/output/1625799
• Contract Date: Mar 13, 2021