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Predicting performance of students in a flipped classroom using machine learning: towards automated data-driven formative feedback
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
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: Journal of Systemics, Cybernetics and Informatics, ISSN 1690-4524, Vol. 17, no 2, p. 17-21Article in journal (Refereed) Published
Abstract [en]

Learning analytics (LA) is a relatively new research discipline that uses data to try to improve learning, optimizing the learning process and develop the environment in which learning occurs. One of the objectives of LA is to monitor students activities and early predict performance to improve retention, offer personalized feedback and facilitate the provision of support to the students. Flipped classroom is one of the pedagogical methods that find strength in the combination of physical and digital environments i.e. blended learning environments. Flipped classroom often make use of learning management systems in which video-recorded lectures and digital material is made available, which thus generates data about students interactions with these materials. In this paper, we report on a study conducted with focus on a flipped learning course in research methodology. Based on data regarding how students interact with course material (video recorded lectures and reading material), how they interact with teachers and other peers in discussion forums, and how they perform on a digital assessment (digital quiz), we apply machine learning methods (i.e. Neural Networks, Nave Bayes, Random Forest, kNN, and Logistic regression) in order to predict students overall performance on the course. The final predictive model that we present in this paper could with fairly high accuracy predict low- and high achievers in the course based on activity and early assessment data. Using this approach, we are given opportunities to develop learning management systems that provide automatic datadriven formative feedback that can help students to selfregulate as well as inform teachers where and how to intervene and scaffold students.

Place, publisher, year, edition, pages
2019. Vol. 17, no 2, p. 17-21
National Category
Human Aspects of ICT
Research subject
Information Society
Identifiers
URN: urn:nbn:se:su:diva-177172OAI: oai:DiVA.org:su-177172DiVA, id: diva2:1379892
Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2020-01-07Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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  • en-US
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