Making Sense of Student Feedback and Engagement Using Artificial Intelligence

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  • Additional Information
    • Availability:
      Australasian Society for Computers in Learning in Tertiary Education. Ascilite Secretariat, P.O. Box 44, Figtree, NSW, Australia. Tel: +61-8-9367-1133; e-mail: [email protected]; Web site: https://ajet.org.au/index.php/AJET
    • Peer Reviewed:
      Y
    • Source:
      19
    • Education Level:
      Higher Education
      Postsecondary Education
    • Subject Terms:
      Artificial Intelligence; Learner Engagement; Feedback (Response); Decision Making; Educational Strategies; School Holding Power; Higher Education; Success; Courses; Data; Prediction; Classification; Technology Uses in Education; Natural Language Processing; Foreign Countries; College Students; Student Evaluation of Teacher Performance
    • Subject Terms:
      Australia
    • Accession Number:
      10.14742/ajet.8903
    • ISSN:
      1449-3098
      1449-5554
    • 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.
    • Abstract:
      As Provided
    • Publication Date:
      2024
    • Accession Number:
      EJ1447223