Supporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics

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dc.citation.issue4
dc.citation.volume6
dc.contributor.authorRamaswami G
dc.contributor.authorSusnjak T
dc.contributor.authorMathrani A
dc.date.accessioned2023-11-20T01:38:02Z
dc.date.available2022-10-08
dc.date.available2023-11-20T01:38:02Z
dc.date.issued2022-12
dc.description.abstractLearning Analytics (LA) refers to the use of students’ interaction data within educational environments for enhancing teaching and learning environments. To date, the major focus in LA has been on descriptive and predictive analytics. Nevertheless, prescriptive analytics is now seen as a future area of development. Prescriptive analytics is the next step towards increasing LA maturity, leading to proactive decision-making for improving students’ performance. This aims to provide data-driven suggestions to students who are at risk of non-completions or other sub-optimal outcomes. These suggestions are based on what-if modeling, which leverages machine learning to model what the minimal changes to the students’ behavioral and performance patterns would be required to realize a more desirable outcome. The results of the what-if modeling lead to precise suggestions that can be converted into evidence-based advice to students. All existing studies in the educational domain have, until now, predicted students’ performance and have not undertaken further steps that either explain the predictive decisions or explore the generation of prescriptive modeling. Our proposed method extends much of the work performed in this field to date. Firstly, we demonstrate the use of model explainability using anchors to provide reasons and reasoning behind predictive models to enable the transparency of predictive models. Secondly, we show how prescriptive analytics based on what-if counterfactuals can be used to automate student feedback through prescriptive analytics.
dc.description.confidentialfalse
dc.identifierhttps://www.mdpi.com/journal/bdcc
dc.identifier105
dc.identifier.citationBig Data and Cognitive Computing, 2022, 6 (4)
dc.identifier.doi10.3390/bdcc6040105
dc.identifier.elements-id457194
dc.identifier.harvestedMassey_Dark
dc.identifier.issn2504-2289
dc.identifier.urihttps://hdl.handle.net/10179/17772
dc.publisherMDPI (Basel, Switzerland)
dc.publisher.urihttps://www.mdpi.com/journal/bdcc
dc.relation.isPartOfBig Data and Cognitive Computing
dc.relation.urihttps://doi.org/10.3390/bdcc6040105
dc.rightsCC BY 4.0
dc.subjectmachine learning
dc.subjectanchors
dc.subjectcounterfactuals
dc.subjectexplainable machine learning
dc.titleSupporting Students’ Academic Performance Using Explainable Machine Learning with Automated Prescriptive Analytics
dc.typeJournal article
massey.relation.uri-descriptionPublished version
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Mathematical and Computational Sciences
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