A Good Classifier is Not Enough: A XAI Approach for Urgent Instructor-Intervention Models in MOOCs
| Autor: | Laila Alrajhi, Filipe Dwan Pereira, Alexandra I. Cristea, Tahani Aljohani |
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| Přispěvatelé: | Mercedes Rodrigo, Maria, Matsuda, Noburu, Cristea, Alexandra I., Dimitrova, Vania |
| Rok vydání: | 2022 |
| Zdroj: | Mercedes Rodrigo, Maria & Matsuda, Noburu & Cristea, Alexandra I. & Dimitrova, Vania (Eds.). Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. : Springer, pp. 424-427, Lecture Notes in Computer Science, Vol.13356 Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium ISBN: 9783031116469 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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