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A Multidimensional Deep Learner Model of Urgent Instructor Intervention Need in MOOC Forum Posts

Alrajhi, Laila; Alharbi, Khulood; Cristea, Alexandra I.

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Authors

Laila Alrajhi

Khulood Alharbi



Contributors

Christos Troussas
Editor

Vivekanandan Kumar
Editor

Abstract

In recent years, massive open online courses (MOOCs) have become one of the most exciting innovations in e-learning environments. Thousands of learners around the world enroll on these online platforms to satisfy their learning needs (mostly) free of charge. However, despite the advantages MOOCs offer learners, dropout rates are high. Struggling learners often describe their feelings of confusion and need for help via forum posts. However, the often-huge numbers of posts on forums make it unlikely that instructors can respond to all learners and many of these urgent posts are overlooked or discarded. To overcome this, mining raw data for learners’ posts may provide a helpful way of classifying posts where learners require urgent intervention from instructors, to help learners and reduce the current high dropout rates. In this paper we propose, a method based on correlations of different dimensions of learners’ posts to determine the need for urgent intervention. Our initial statistical analysis found some interesting significant correlations between posts expressing sentiment, confusion, opinion, questions, and answers and the need for urgent intervention. Thus, we have developed a multidimensional deep learner model combining these features with natural language processing (NLP). To illustrate our method, we used a benchmark dataset of 29598 posts, from three different academic subject areas. The findings highlight that the combined, multi-dimensional features model is more effective than the text-only (NLP) analysis, showing that future models need to be optimised based on all these dimensions, when classifying urgent posts.

Citation

Alrajhi, L., Alharbi, K., & Cristea, A. I. (2020). A Multidimensional Deep Learner Model of Urgent Instructor Intervention Need in MOOC Forum Posts. In C. Troussas, & V. Kumar (Eds.), ITS 2020: Intelligent Tutoring Systems (226-236). https://doi.org/10.1007/978-3-030-49663-0_27

Conference Name ITS 2020: International Conference on Intelligent Tutoring Systems
Online Publication Date Jun 3, 2020
Publication Date 2020
Deposit Date Nov 2, 2021
Publicly Available Date Nov 3, 2021
Volume 12149
Pages 226-236
Series Title Lecture Notes in Computer Science
Series ISSN 0302-9743,1611-3349
Book Title ITS 2020: Intelligent Tutoring Systems
ISBN 9783030496623
DOI https://doi.org/10.1007/978-3-030-49663-0_27
Public URL https://durham-repository.worktribe.com/output/1138708

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