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

Aljohani, Tahani; Cristea, Alexandra I.

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Contributors

Christos Troussas
Editor

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

Acceptance Date Mar 13, 2021
Online Publication Date Jul 9, 2021
Publication Date 2021
Deposit Date Apr 12, 2021
Publicly Available Date Mar 29, 2024
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

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