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Educational Data Mining and Learning Analytics in Programming: Literature Review and Case Studies
Tampere University of Technology, Finland.
University of Helsinki, Finland.
University of Technology, Australia.
Monash University, Australia.
Show others and affiliations
2015 (English)In: Proceedings of the 2015 ITiCSE on Working Group Reports / [ed] Program ChairsNoa Ragonis Beit Berl College and Technion-ITT, Israel Päivi Kinnunen Aalto University, Finland, New York: ACM Press, 2015, 41-63 p.Conference paper, Published paper (Refereed)
Abstract [en]

Educational data mining and learning analytics promise better understanding of student behavior and knowledge, as well as new information on the tacit factors that contribute to student actions. This knowledge can be used to inform decisions related to course and tool design and pedagogy, and to further engage students and guide those at risk of failure. This working group report provides an overview of the body of knowledge regarding the use of educational data mining and learning analytics focused on the teaching and learning of programming. In a literature survey on mining students’ programming processes for 2005–2015, we observe a significant increase in work related to the field. However, the majority of the studies focus on simplistic metric analysis and are conducted within a single institution and a single course. This indicates the existence of further avenues of research and a critical need for validation and replication to better understand the various contributing factors and the reasons why certain results occur. We introduce a novel taxonomy to analyse replicating studies and discuss the importance of replicating and reproducing previous work. We describe what is the state of the art in collecting and sharing programming data. To better understand the challenges involved in replicating or reproducing existing studies, we report our experiences from three case studies using programming data. Finally, we present a discussion of future directions for the education and research community.

Place, publisher, year, edition, pages
New York: ACM Press, 2015. 41-63 p.
Keyword [en]
educational data mining; learning analytics; programming; replication; literature review
National Category
Computer Science
Research subject
Computer and Information Sciences Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-50120DOI: 10.1145/2858796.2858798ISI: 000389809400002ISBN: 978-1-4503-4146-2 (print)OAI: oai:DiVA.org:lnu-50120DiVA: diva2:908537
Conference
ITiCSEInnovation and Technology in Computer Science Education, Vilnius, LITHUANIA, JUL 04-08, 2015
Available from: 2016-03-02 Created: 2016-03-02 Last updated: 2017-01-16Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf