Beyond Predictive Learning Analytics Modelling and onto Explainable Artificial Intelligence with Prescriptive Analytics and ChatGPT

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dc.citation.issue2
dc.citation.volume34
dc.contributor.authorSusnjak T
dc.date.accessioned2024-08-27T23:30:53Z
dc.date.available2024-08-27T23:30:53Z
dc.date.issued2024-06
dc.description.abstractA significant body of recent research in the field of Learning Analytics has focused on leveraging machine learning approaches for predicting at-risk students in order to initiate timely interventions and thereby elevate retention and completion rates. The overarching feature of the majority of these research studies has been on the science of prediction only. The component of predictive analytics concerned with interpreting the internals of the models and explaining their predictions for individual cases to stakeholders has largely been neglected. Additionally, works that attempt to employ data-driven prescriptive analytics to automatically generate evidence-based remedial advice for at-risk learners are in their infancy. eXplainable AI is a field that has recently emerged providing cutting-edge tools which support transparent predictive analytics and techniques for generating tailored advice for at-risk students. This study proposes a novel framework that unifies both transparent machine learning as well as techniques for enabling prescriptive analytics, while integrating the latest advances in large language models for communicating the insights to learners. This work demonstrates a predictive modelling framework for identifying learners at risk of qualification non-completion based on a real-world dataset comprising ~7000 learners with their outcomes, covering 2018 - 2022. The study further demonstrates how predictive modelling can be augmented with prescriptive analytics on two case studies to generate human-readable prescriptive feedback for those who are at risk using ChatGPT.
dc.description.confidentialfalse
dc.edition.editionJune 2004
dc.format.pagination452-482
dc.identifier.citationSusnjak T. (2024). Beyond Predictive Learning Analytics Modelling and onto Explainable Artificial Intelligence with Prescriptive Analytics and ChatGPT. International Journal of Artificial Intelligence in Education. 34. 2. (pp. 452-482).
dc.identifier.doi10.1007/s40593-023-00336-3
dc.identifier.eissn1560-4306
dc.identifier.elements-typejournal-article
dc.identifier.issn1560-4292
dc.identifier.piis40593-023-00336-3
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71393
dc.languageEnglish
dc.publisherSpringer Nature in conjunction with the International Artificial Intelligence in Education Society (IAIED)
dc.publisher.urihttps://link.springer.com/article/10.1007/s40593-023-00336-3
dc.relation.isPartOfInternational Journal of Artificial Intelligence in Education
dc.rights(c) The author/sen
dc.rights.licenseCC BYen
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectLearning analytics
dc.subjectExplainable machine learning
dc.subjectCounterfactuals
dc.subjectInterpretable machine learning
dc.subjectPrescriptive analytics
dc.subjectWhat-if modelling
dc.subjectChatGPT
dc.subjectLarge language models
dc.subjectPrompt engineering
dc.subjectLearner intervention support
dc.titleBeyond Predictive Learning Analytics Modelling and onto Explainable Artificial Intelligence with Prescriptive Analytics and ChatGPT
dc.typeJournal article
pubs.elements-id462419
pubs.organisational-groupOther
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