Making sense of student feedback and engagement using artificial intelligence
Article
Article Title | Making sense of student feedback and engagement using artificial intelligence |
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ERA Journal ID | 20265 |
Article Category | Article |
Authors | Dann, Christopher, Redmond, Petrea, Fanshawe, Melissa, Brown, Alice, Getenet, Seyum, Shaik, Thanveer, Tao, Xiaohui, Galligan, Linda and Li, Yan |
Journal Title | Australasian Journal of Educational Technology |
Journal Citation | 40 (3) |
Number of Pages | 19 |
Year | 2024 |
Publisher | Australasian Society for Computers in Learning in Tertiary Education (ASCILITE) |
Place of Publication | Australia |
ISSN | 1449-5554 |
Digital Object Identifier (DOI) | https://doi.org/10.14742/ajet.8903 |
Web Address (URL) | https://ajet.org.au/index.php/AJET/article/view/8903 |
Abstract | Making sense of student feedback and engagement is important for informing pedagogical decision-making and broader strategies related to student retention and success in higher education courses. Although learning analytics and other strategies are employed within courses to understand student engagement, the interpretation of data for larger data sets is more challenging and rarely pursued. This is concerning as data offers the potential for critical insights into engagement behaviour and the value students place on engagement. Artificial intelligence (AI) offers a revolutionary ability to make sense of data, with capacity for prediction and classification, by consuming vast amounts of structured and unstructured data sets. This paper reports on how AI methodologies (specifically, deep learning and natural language processing) were used to leverage labelled student feedback in terms of online engagement in five courses in a regional Australian university. This paper reinforces the value of AI as a viable and scalable multilayered analysis tool for analysing and interpreting student feedback, particularly for categorising student responses as to the types of engagement that they most valued to support their learning. The paper concludes with a discussion of suggested further refinement, including how the AI-derived data may add insights for informing pedagogical practice. |
Keywords | student engagement, student experience, online engagement, artificial intelligence, natural language processing, higher education, regional university |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 390303. Higher education |
460208. Natural language processing | |
Byline Affiliations | School of Education |
School of Mathematics, Physics and Computing |
https://research.usq.edu.au/item/z7971/making-sense-of-student-feedback-and-engagement-using-artificial-intelligence
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