A deep learning framework with visualisation for uncovering students’ learning progression and learning bottlenecks

Chun Yan Enoch SIT, Siu Cheung KONG

Research output: Contribution to journalArticlespeer-review

1 Citation (Scopus)

Abstract

Educational process mining aims (EPM) to help teachers understand the overall learning process of their students. Although deep learning models have shown promising results in many domains, the event log dataset in many online courses may not be large enough for deep learning models to approximate the probability distribution of students’ learning sequence due to a lack of participants. This study proposes a deep learning framework to help uncover the learning progression of learners. It aims to produce a graphical representation of the overall educational process from event logs. Our framework adopts the Smith–Waterman algorithm from the bioinformatics field to evaluate general learning sequences generated from deep learning models. Using our framework, we compare the performance of a deep learning model with the modified cross-attention layer and a model without modification and find that the modified model outperforms the other. The contribution of this framework is that it enables the use of neural architecture search techniques to uncover students’ general learning sequence irrespective of the dataset’s size. The framework also helps educators identify education materials that present as learning bottlenecks, enabling them to improve the materials and their respective layout order, thus facilitating student learning. Copyright © 2023 The Author(s).

Original languageEnglish
Pages (from-to)223-249
JournalJournal of Educational Computing Research
Volume62
Issue number1
Early online dateSept 2023
DOIs
Publication statusPublished - Mar 2024

Citation

Sit, C. Y. E., & Kong, S.-C. (2024). A deep learning framework with visualisation for uncovering students’ learning progression and learning bottlenecks. Journal of Educational Computing Research. 62(1), 223-249. https://doi.org/10.1177/07356331231200600

Keywords

  • Deep learning
  • Students’ learning progression
  • Sequence alignment algorithm
  • Transformers
  • Learning bottlenecks
  • Educational process mining

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