Error tracing in programming: a path to personalised feedback

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dc.contributor.authorShaka, Marthaen
dc.contributor.authorCarraro, Diegoen
dc.contributor.authorBrown, Kenneth N.en
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-04-25T13:22:13Z
dc.date.available2025-04-25T13:22:13Z
dc.date.issued2024en
dc.description.abstractKnowledge tracing, the process of estimating students’ mastery over concepts from their past performance and predicting future outcomes, often relies on binary pass/fail predictions. This hinders the provision of specific feedback by failing to diagnose precise errors. We present an error-tracing model for learning programming that advances traditional knowledge tracing by employing multi-label classification to forecast exact errors students may generate. Through experiments on a real student dataset, we validate our approach and compare it to two baseline knowledge-tracing methods. We demonstrate an improved ability to predict specific errors, for first attempts and for subsequent attempts at individual problems.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationShaka, M., Carraro, D. and Brown, K. N. (2024) 'Error tracing in programming: a path to personalised feedback', Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA). pp. 330–342. Available at: https://aclanthology.org/2024.bea-1.27/en
dc.identifier.endpage342en
dc.identifier.startpage330en
dc.identifier.urihttps://hdl.handle.net/10468/17346
dc.language.isoenen
dc.publisherAssociation for Computational Linguisticsen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Centres for Research Training (CRT) Programme/18/CRT/6223/IE/SFI Centre for Research Training in Artificial Intelligence/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Research Centres Programme::Phase 2/12/RC/2289_P2/IE/INSIGHT_Phase 2 /en
dc.relation.urihttps://aclanthology.org/2024.bea-1.27/en
dc.rights© 2024, Association for Computational Linguistics.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectKnowledge tracingen
dc.subjectBinary pass/fail predictionsen
dc.subjectReal student dataseten
dc.titleError tracing in programming: a path to personalised feedbacken
dc.typeConference itemen
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