Towards an Understanding of the Engagement and Emotional Behaviour of MOOC Students using Sentiment and Semantic Features

Xiaohui TAO*, Aaron SHANNON-HONSON, Patrick DELANEY, Christopher DANN, Haoran XIE, Yan LI, Shirley O'NEILL

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

10 Citations (Scopus)

Abstract

Online learning and teaching increased in 2020, driven by the COVID-19 pandemic. As many researchers attempted to understand the impact stress had on the emotional behaviours and academic performance of students, most studies explored these pre- and during-COVID behaviours in the context of brick and mortar institutions transitioning to online delivery. There is an opportunity to compare the experiences of students in the MOOC environment in this period, particularly in terms of the difference of engagement, semantics and sentiment/stress behaviours in 2019 and 2020. In this study, we use a dataset from AdelaideX between this time period to identify the most significant features that impact student outcomes. Where previous machine learning approaches used singular features such as student interaction or sentiment in discussion forum posts, we incorporate three feature categories of engagement, semantics and sentiment/stress in an ensemble model is based on voting and stacked methods to determining the relationship between them and academic performance. From our results, we discover that sentiment/stress played little part in academic performance and was relatively unchanged in online courses in this dataset between 2019 and 2020. We present two individual student cases to further contextualise our findings.
Original languageEnglish
Article number100116
JournalComputers and Education: Artificial Intelligence
Volume4
Early online date20 Jan 2023
DOIs
Publication statusPublished - Jan 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s)

Keywords

  • MOOCs
  • Performance modelling
  • Sentiment analysis
  • Stress

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