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

Ihantola, P; Vihavainen, A; Ahadi, A; Butler, M; Börstler, J; Edwards, SH; Isohanni, E; Korhonen, A; Petersen, A; Rivers, K; Rubio, MÁ; Sheard, J; Skupas, B; Spacco, J; Szabo, C; Toll, D

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

Ihantola, P Vihavainen, A Ahadi, A

Butler, M Börstler, J Edwards, SH Isohanni, E Korhonen, A Petersen, A Rivers, K Rubio, MÁ Sheard, J Skupas, B Spacco, J Szabo, C Toll, D

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Publication Type:: Conference Proceeding
Citation:: ITiCSE-WGP 2015 - Proceedings of the 2015 ITiCSE Conference on Working Group Reports, 2015, pp. 41 - 63
Issue Date:: 2015-07-04

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Field	Value	Language
dc.contributor.author	Ihantola, P	en_US
dc.contributor.author	Vihavainen, A	en_US
dc.contributor.author	Ahadi, A https://orcid.org/0000-0002-2018-3065	en_US
dc.contributor.author	Butler, M	en_US
dc.contributor.author	Börstler, J	en_US
dc.contributor.author	Edwards, SH	en_US
dc.contributor.author	Isohanni, E	en_US
dc.contributor.author	Korhonen, A	en_US
dc.contributor.author	Petersen, A	en_US
dc.contributor.author	Rivers, K	en_US
dc.contributor.author	Rubio, MÁ	en_US
dc.contributor.author	Sheard, J	en_US
dc.contributor.author	Skupas, B	en_US
dc.contributor.author	Spacco, J	en_US
dc.contributor.author	Szabo, C	en_US
dc.contributor.author	Toll, D	en_US
dc.date.available	2015-12-21	en_US
dc.date.issued	2015-07-04	en_US
dc.identifier.citation	ITiCSE-WGP 2015 - Proceedings of the 2015 ITiCSE Conference on Working Group Reports, 2015, pp. 41 - 63	en_US
dc.identifier.isbn	9781450341462	en_US
dc.identifier.uri	http://hdl.handle.net/10453/41093
dc.description.abstract	© 2015 ACM. 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.	en_US
dc.relation.ispartof	ITiCSE-WGP 2015 - Proceedings of the 2015 ITiCSE Conference on Working Group Reports	en_US
dc.relation.isbasedon	10.1145/2858796.2858798	en_US
dc.title	Educational data mining and learning analytics in programming: Literature review and case studies	en_US
dc.type	Conference Proceeding
utslib.for	1301 Education Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	in_progress
pubs.publication-status	Published	en_US

Abstract:

© 2015 ACM. 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.

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http://hdl.handle.net/10453/41093