Use of Wearable Technologies with Machine Learning to Understand University Student Learning Behaviours to Predict Students at Risk of Failing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The challenges of effective teaching in mass education environments are well documented. The cohorts of large size generally means that identification of struggling students is usually only at a point when meaningful interventions are too late. This paper reports on the use of novel technologies to provide insights into areas of learner behaviour in large-scale computer programming modules. Accordingly, this paper brings together a previous series of investigative studies of student key engagement points during a typical programming module (1) seat position tracking during programming lectures, (2) Video Lecture Capture viewing behaviours and (3) Student Heart Rate monitoring during lectures. The paper combines the significant findings of each investigation to provide a variety of analysis using Machine Learning (ML) classification modeling. The purpose of the MC study is to create models that could identify students that are likely to pass and those that may be at risk of failing the module.
Original languageEnglish
Title of host publicationInternational Conference on Human Interaction and Emerging Technologies
Subtitle of host publicationIHIET 2019: Human Interaction and Emerging Technologies
PublisherSpringer
Pages325-331
Number of pages6
Volume1018
ISBN (Print)978-3-030-25629-6
DOIs
Publication statusPublished - 25 Jul 2019

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer Verlag
ISSN (Print)2194-5357

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Keywords

  • Wearable technologies
  • third level education
  • Human factors programming learning
  • Social and affective computing

Cite this

McGowan, A., Hanna, P., Greer, D., Busch, J., Anderson, N., Collins, M., Cutting, D., Stewart, D., & McDowell, A. (2019). Use of Wearable Technologies with Machine Learning to Understand University Student Learning Behaviours to Predict Students at Risk of Failing. In International Conference on Human Interaction and Emerging Technologies: IHIET 2019: Human Interaction and Emerging Technologies (Vol. 1018, pp. 325-331). (Advances in Intelligent Systems and Computing). Springer. https://doi.org/10.1007/978-3-030-25629-6_50