Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/107663
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Type: | Conference paper |
Title: | Educational data mining and learning analytics in programming: literature review and case studies |
Author: | Ihantola, P. Vihavainen, A. Ahadi, A. Butler, M. Börstler, J. Edwards, S. Isohanni, E. Korhonen, A. Petersen, A. Rivers, K. Rubio, M. Sheard, J. Skupas, B. Spacco, J. Szabo, C. Toll, D. |
Citation: | Proceedings of the 2015 ITiCSE Conference on Working Group Reports, 2015, pp.41-63 |
Publisher: | ACM |
Issue Date: | 2015 |
ISBN: | 9781450341462 |
Conference Name: | 20th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE) (4 Jul 2015 - 8 Jul 2015 : Vilnius, Lithuania) |
Statement of Responsibility: | Petri 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 |
Abstract: | 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 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. |
Rights: | © 2016 Copyright held by the owner/author(s). |
DOI: | 10.1145/2858796.2858798 |
Published version: | http://dx.doi.org/10.1145/2858796.2858798 |
Appears in Collections: | Aurora harvest 8 Computer Science publications |
Files in This Item:
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RA_hdl_107663.pdf Restricted Access | Restricted Access | 1.41 MB | Adobe PDF | View/Open |
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