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Edit, Run, Error, Repeat: Learning Analytics to Find Struggling Students in Upper Secondary Programming Classes
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Education Research. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. MINT. (UpCERG)ORCID iD: 0000-0003-4250-1201
2023 (English)In: ITiCSE 2023: Proceedings of the 2023 Conference on Innovation and Technology in Computer Science Education / [ed] Mikko-Jussi Laakso; Mattia Monga; Simon; Judithe Sheard, Association for Computing Machinery (ACM), 2023, Vol. 2, p. 629-630Conference paper, Published paper (Refereed)
Abstract [en]

This dissertation research explores the potential of using learning analytics to improve programming education. The research goals include replicating previous research through studying heterogeneous groups of students at upper secondary schools over several months. The expected contribution of this dissertation is to provide insights into how learning analytics can identify struggling students.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2023. Vol. 2, p. 629-630
Keywords [en]
learning analytics, programming, educational data mining, upper secondary
National Category
Pedagogy Didactics
Identifiers
URN: urn:nbn:se:uu:diva-514791DOI: 10.1145/3587103.3594144ISI: 001054840400035ISBN: 979-8-4007-0139-9 (print)OAI: oai:DiVA.org:uu-514791DiVA, id: diva2:1806887
Conference
28th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE), July 8-12, 2023, Univ Turku, Turku, Finland
Available from: 2023-10-24 Created: 2023-10-24 Last updated: 2026-01-09Bibliographically approved

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Snider, Johan

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