Towards an understanding of the engagement and emotional behaviour of MOOC students using sentiment and semantic features
Article
Tao, Xiaohui, Shannon-Honson, Aaron, Delaney, Patrick, Dann, Christopher, Xie, Haoran, Li, Yan and O'Neill, Shirley. 2023. "Towards an understanding of the engagement and emotional behaviour of MOOC students using sentiment and semantic features." Computers and Education: Artificial Intelligence. 4. https://doi.org/10.1016/j.caeai.2022.100116
Article Title | Towards an understanding of the engagement and emotional behaviour of MOOC students using sentiment and semantic features |
---|---|
ERA Journal ID | 212149 |
Article Category | Article |
Authors | Tao, Xiaohui, Shannon-Honson, Aaron, Delaney, Patrick, Dann, Christopher, Xie, Haoran, Li, Yan and O'Neill, Shirley |
Journal Title | Computers and Education: Artificial Intelligence |
Journal Citation | 4 |
Article Number | 100116 |
Number of Pages | 9 |
Year | 2023 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 2666-920X |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.caeai.2022.100116 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S2666920X22000716 |
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. © 2023 The Author(s) |
Keywords | MOOCs; Stress; Sentiment analysis; Performance modelling |
ANZSRC Field of Research 2020 | 460501. Data engineering and data science |
Byline Affiliations | School of Mathematics, Physics and Computing |
School of Education | |
Lingnan University of Hong Kong, China |
Permalink -
https://research.usq.edu.au/item/z26z1/towards-an-understanding-of-the-engagement-and-emotional-behaviour-of-mooc-students-using-sentiment-and-semantic-features
Download files
181
total views25
total downloads3
views this month0
downloads this month