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

Aljohani, Tahani and Cristea, Alexandra (2021) 'Training Temporal and NLP Features via Extremely Randomised Trees for Educational Level Classification.', ITS World Congress Hamburg, Germany.


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.

Item Type:Conference item (Paper)
Full text:Publisher-imposed embargo
(AM) Accepted Manuscript
File format - PDF
Publisher Web site:
Date accepted:13 March 2021
Date deposited:13 April 2021
Date of first online publication:2021
Date first made open access:No date available

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