A multi-faceted evaluation of the impact on students of an Australian university-wide academic language development program

Goldsmith, Rosalie; Havery, Caroline; Edwards, Emily; James, M.N.; Murphy, Aurora; Mort, Pamela; Nixon, Deborah; O'Donoghue, Gemma; Yang, Jin Sug; Yeo, Joseph B. W.

doi:10.1016/j.jeap.2022.101192

Cited by 2 publications

(1 citation statement)

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“…Student evaluation of teaching feedback is often collected in higher education courses at end of the semester and comprises of both quantitative and qualitative data (Goldsmith et al, 2022). The qualitative feedback in text format presents student opinions and different perspectives of their learning experience.…”

Section: Introductionmentioning

confidence: 99%

Making sense of student feedback and engagement using artificial intelligence

Dann,

Redmond,

Fanshawe

et al. 2024

AJET

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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. Implications for practice or policy: AI offers an ability to make sense of large data sets in higher education courses. Teachers can use student feedback data categorised into types of engagement by AI to support reflection on what students value in their courses. Educators and key stakeholders can use the insights AI analysed data offers for informing pedagogical practice and decision-making in higher education to enhance student experiences.

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Section: Introductionmentioning

confidence: 99%