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A Good Classifier is Not Enough: A XAI Approach for Urgent Instructor-Intervention Models in MOOCs

Alrajhi, Laila; Pereira, Filipe Dwan; Cristea, Alexandra I.; Aljohani, Tahani

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Authors

Filipe Dwan Pereira



Contributors

Maria Mercedes Rodrigo
Editor

Noburu Matsuda
Editor

Vania Dimitrova
Editor

Abstract

Deciding upon instructor intervention based on learners’ comments that need an urgent response in MOOC environments is a known challenge. The best solutions proposed used automatic machine learning (ML) models to predict the urgency. These are ‘black-box’-es, with results opaque to humans. EXplainable artificial intelligence (XAI) is aiming to understand these, to enhance trust in artificial intelligence (AI)-based decision-making. We propose to apply XAI techniques to interpret a MOOC intervention model, by analysing learner comments. We show how pairing a good predictor with XAI results and especially colour-coded visualisation could be used to support instructors making decisions on urgent intervention.

Citation

Alrajhi, L., Pereira, F. D., Cristea, A. I., & Aljohani, T. (2022). A Good Classifier is Not Enough: A XAI Approach for Urgent Instructor-Intervention Models in MOOCs. In M. Mercedes Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (424-427). Springer Verlag. https://doi.org/10.1007/978-3-031-11647-6_84

Online Publication Date Jul 26, 2022
Publication Date 2022
Deposit Date Sep 26, 2022
Publicly Available Date Jul 27, 2023
Publisher Springer Verlag
Pages 424-427
Series Title Lecture Notes in Computer Science
Series Number 13356
Book Title Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium
ISBN 978-3-031-11646-9
DOI https://doi.org/10.1007/978-3-031-11647-6_84
Public URL https://durham-repository.worktribe.com/output/1620668

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