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

Alrajhi, Laila and Pereira, Filipe Dwan and Cristea, Alexandra I. and Aljohani, Tahani (2022) 'A Good Classifier is Not Enough: A XAI Approach for Urgent Instructor-Intervention Models in MOOCs.', in Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. , pp. 424-427. Lecture Notes in Computer Science., 13356

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.

Item Type:Book chapter
Full text:Publisher-imposed embargo until 26 July 2023.
(AM) Accepted Manuscript
File format - PDF
(197Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1007/978-3-031-11647-6_84
Publisher statement:The final authenticated version is available online at https://doi.org/10.1007/978-3-031-11647-6_84
Date accepted:No date available
Date deposited:26 September 2022
Date of first online publication:26 July 2022
Date first made open access:26 July 2023

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