Improving Teaching and Learning in Higher Education through Machine Learning: Proof of Concept’ of AI’s Ability to Assess the Use of Key Microskills.
Article
Article Title | Improving Teaching and Learning in Higher Education through Machine Learning: Proof of Concept’ of AI’s Ability to Assess the Use of Key Microskills. |
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ERA Journal ID | 200411 |
Article Category | Article |
Authors | Dann, Christopher, O'Neill, Shirley, Getenet, Seyum, Chakraborty, Subrata, Saleh, Khaled and Yu, Kun |
Journal Title | Education Sciences |
Journal Citation | 14 (8) |
Article Number | 886 |
Number of Pages | 17 |
Year | 2024 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2227-7102 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/educsci14080886 |
Web Address (URL) | https://www.mdpi.com/2227-7102/14/8/886 |
Abstract | Advances in artificial intelligence (AI), including intelligent machines, are opening new possibilities to support teaching and learning in higher education. This research has found a ‘proof of concept’ in the application of machine learning in the assessment of educators’ use of four key microskills, drawn from an internationally established framework. The analysis of teaching videos where these microskills were demonstrated multiple times in front of a green screen or in a space formed the data set. Multiple videos of this nature were recorded to allow for increased analysis and deconstruction of the video components to enable the application of machine learning. The results showed how AI can be used to support the collaborative and reflective practice of educators in a time when online teaching has become the norm. Having achieved a ‘proof of concept’, this research has laid the groundwork to allow for the whole framework of ten microskills to be applied in this way thus adding a new dimension to its use. Providing such critical information that is not currently available in such a systematic and personalised way to educators in the higher education sector can also support the validity of formative assessment practices. |
Keywords | higher education; artificial intelligence; reflective practice; microskills; machine learning |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 390307. Teacher education and professional development of educators |
Byline Affiliations | School of Education |
University of New England | |
University of Newcastle | |
University of Technology Sydney |
https://research.usq.edu.au/item/z8y61/improving-teaching-and-learning-in-higher-education-through-machine-learning-proof-of-concept-of-ai-s-ability-to-assess-the-use-of-key-microskills
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