Data Analytics on Online Student Engagement Data for Academic Performance Modeling
Article
Article Title | Data Analytics on Online Student Engagement Data for Academic Performance Modeling |
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ERA Journal ID | 210567 |
Article Category | Article |
Authors | Tao, Xiaohui (Author), Shannon-Honson, Aaron (Author), Delaney, Patrick (Author), Li, Lin (Author), Dann, Christopher (Author), Li, Yan (Author) and Xie, Haoran (Author) |
Journal Title | IEEE Access |
Journal Citation | 10, pp. 103176-103186 |
Number of Pages | 11 |
Year | 2022 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Place of Publication | United States |
ISSN | 2169-3536 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2022.3208953 |
Web Address (URL) | https://ieeexplore.ieee.org/document/9900318 |
Abstract | In large MOOC cohorts, the sheer variance and volume of discussion forum posts can make it difficult to for instructors to distinguish nuanced behaviour in students such as positive engagement or stress. Sentiment analysis has been used to build student behavioural models, however, more recent research suggests that separating sentiment and stress into different measures could improve text analysis in this domain. Detecting stress in a MOOC corpus is challenging as students may use language that does not conform to standard definitions, but new techniques like TensiStrength provide more nuanced measures of stress. In this work, we introduce an ensemble method that extracts features of engagement, semantics and sentiment and stress from an AdelaideX student dataset. Stacked and voting methods are used to compare performance measures on how accurately these features can predict student grades. The stacked method performed best across all measures, with our Random Forest baseline further demonstrating that negative sentiment and stress had little impact on academic results. As a secondary analysis, we explored whether stress among student posts increased in 2020 compared to 2019 due to COVID-19 to understand the impact of major events on online learners, but found no significant change. Importantly, our model indicates that there may be a relationship between features, which warrants future research. |
Keywords | Electronic learning; Computer aided instruction; Feature extraction; Semantics; Anxiety disorders; Stress measurement; Analytical models; Natural language processing; Education; Online services; Ensemble method; natural language processing; MOOC; academic performance modeling |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 390405. Educational technology and computing |
460507. Information extraction and fusion | |
460208. Natural language processing | |
Institution of Origin | University of Southern Queensland |
Byline Affiliations | School of Mathematics, Physics and Computing |
Wuhan University of Technology, China | |
School of Education | |
Lingnan University of Hong Kong, China |
https://research.usq.edu.au/item/q7v87/data-analytics-on-online-student-engagement-data-for-academic-performance-modeling
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