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Identifying Factors for Master Thesis Completion and Non-completion Through Learning Analytics and Machine Learning
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2019 (English)In: Transforming Learning with Meaningful Technologies: Proceedings / [ed] Maren Scheffel, Julien Broisin, Viktoria Pammer-Schindler, Andri Ioannou, Jan Schneider, Springer, 2019, p. 28-39Conference paper, Published paper (Refereed)
Abstract [en]

The master thesis is the last formal step in most universities around the world. However, all students do not finish their master thesis. Thus, it is reasonable to assume that the non-completion of the master thesis should be viewed as a substantial problem that requires serious attention and proactive planning. This learning analytics study aims to understand better factors that influence completion and non-completion of master thesis projects. More specifically, we ask: which student and supervisor factors influence completion and non-completion of master thesis? Can we predict completion and non-completion of master thesis using such variables in order to optimise the matching of supervisors and students? To answer the research questions, we extracted data about supervisors and students from two thesis management systems which record large amounts of data related to the thesis process. The sample used was 755 master thesis projects supervised by 109 teachers. By applying traditional statistical methods (descriptive statistics, correlation tests and independent sample t-tests), as well as machine learning algorithms, we identify five central factors that can accurately predict master thesis completion and non-completion. Besides the identified predictors that explain master thesis completion and non-completion, this study contributes to demonstrating how educational data and learning analytics can produce actionable data-driven insights. In this case, insights that can be utilised to inform and optimise how supervisors and students are matched and to stimulate targeted training and capacity building of supervisors.

Place, publisher, year, edition, pages
Springer, 2019. p. 28-39
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11722
Keywords [en]
Thesis, Master, Learning analytics, Completion, Dropout, Retention, Machine learning
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-177162DOI: 10.1007/978-3-030-29736-7_3ISBN: 978-3-030-29735-0 (print)ISBN: 978-3-030-29736-7 (electronic)OAI: oai:DiVA.org:su-177162DiVA, id: diva2:1379882
Conference
14th European Conference on Technology Enhanced Learning, EC-TEL 2019, Delft, The Netherlands, September 16–19, 2019
Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2019-12-27Bibliographically approved

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