Educational data mining and learning analytics in programming: literature review and case studies

Simple item page
dc.contributor.authorIhantola, P.
dc.contributor.authorVihavainen, A.
dc.contributor.authorAhadi, A.
dc.contributor.authorButler, M.
dc.contributor.authorBörstler, J.
dc.contributor.authorEdwards, S.
dc.contributor.authorIsohanni, E.
dc.contributor.authorKorhonen, A.
dc.contributor.authorPetersen, A.
dc.contributor.authorRivers, K.
dc.contributor.authorRubio, M.
dc.contributor.authorSheard, J.
dc.contributor.authorSkupas, B.
dc.contributor.authorSpacco, J.
dc.contributor.authorSzabo, C.
dc.contributor.authorToll, D.
dc.contributor.conference20th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE) (4 Jul 2015 - 8 Jul 2015 : Vilnius, Lithuania)
dc.date.issued2015
dc.description.abstractEducational 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 signifcant 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.
dc.description.statementofresponsibilityPetri Ihantola, Arto Vihavainen, Alireza Ahadi, Matthew Butler, Jürgen Börstler, Stephen H. Edwards, Essi Isohanni, Ari Korhonen, Andrew Petersen, Kelly Rivers, Miguel Ángel Rubio, Judy Sheard, Bronius Skupas, Jaime Spacco, Claudia Szabo, Daniel Toll
dc.identifier.citationProceedings of the 2015 ITiCSE Conference on Working Group Reports, 2015, pp.41-63
dc.identifier.doi10.1145/2858796.2858798
dc.identifier.isbn9781450341462
dc.identifier.orcidSzabo, C. [0000-0003-2501-1155]
dc.identifier.urihttp://hdl.handle.net/2440/107663
dc.language.isoen
dc.publisherACM
dc.rights© 2016 Copyright held by the owner/author(s).
dc.source.urihttps://doi.org/10.1145/2858796.2858798
dc.titleEducational data mining and learning analytics in programming: literature review and case studies
dc.typeConference paper
pubs.publication-statusPublished

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
RA_hdl_107663.pdf
Size:
1.37 MB
Format:
Adobe Portable Document Format
Description:
Restricted Access
Download

Collections

Computer Science publications