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Intervention Prediction in MOOCs Based on Learners’ Comments: A Temporal Multi-input Approach Using Deep Learning and Transformer Models

Alrajhi, Laila and Alamri, Ahmed and Cristea, Alexandra I. (2022) 'Intervention Prediction in MOOCs Based on Learners’ Comments: A Temporal Multi-input Approach Using Deep Learning and Transformer Models.', in Intelligent Tutoring Systems. , pp. 227-237. Lecture Notes in Computer Science., 13284

Abstract

High learner dropout rates in MOOC-based education contexts have encouraged researchers to explore and propose different intervention models. In discussion forums, intervention is critical, not only to identify comments that require replies but also to consider learners who may require intervention in the form of staff support. There is a lack of research on the role of intervention based on learner comments to prevent learner dropout in MOOC-based settings. To fill this research gap, we propose an intervention model that detects when staff intervention is required to prevent learner dropout using a dataset from FutureLearn. Our proposed model was based on learners’ comments history by integrating the most-recent sequence of comments written by learners to identify if an intervention was necessary to prevent dropout. We aimed to find both the proper classifier and the number of comments representing the appropriate most recent sequence of comments. We developed several intervention models by utilising two forms of supervised multi-input machine learning (ML) classification models (deep learning and transformer). For the transformer model, specifically, we propose the siamese and dual temporal multi-input, which we term the multi-siamese BERT and multiple BERT. We further experimented with clustering learners based on their respective number of comments to analyse if grouping as a pre-processing step improved the results. The results show that, whilst multi-input for deep learning can be useful, a better overall effect is achieved by using the transformer model, which has better performance in detecting learners who require intervention. Contrary to our expectations, however, clustering before prediction can have negative consequences on prediction outcomes, especially in the underrepresented class.

Item Type:Book chapter
Full text:Publisher-imposed embargo until 24 June 2023.
(AM) Accepted Manuscript
File format - PDF
(490Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1007/978-3-031-09680-8_22
Publisher statement:The final authenticated version is available online at https://doi.org/10.1007/978-3-031-09680-8_22
Date accepted:No date available
Date deposited:26 September 2022
Date of first online publication:24 June 2022
Date first made open access:24 June 2023

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