Profiling young learners based on their daily study hours in a supplementary e-learning platform

Date

2023-4-24

Author

Günay, Abdulkadir

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Online learning platforms (OLPs) are widely used as supplementary tools in K-12 education. Integration methods of these platforms into traditional learning environments are diverse, and they impact how students engage and learn with them. The goal of this study is to utilize cluster analysis as a learning analytics (LA) approach to reveal distinct profiles of students from grades 4 to 8 based on the hours they interact with an e-learning platform (ELP). In particular, four variables were created that indicate students’ frequency of interaction with lessons and exercises in the OLP during in-school and out-of-school time. The analysis yielded three distinct profiles: low engagers (the most prevalent profile), out-school active learners, and in-school active learners. To examine how these profiles differed from each other in terms of their engagement, the Kruskal Wallis test was applied to compare using 18 evaluative variables as measurements of student engagement. The results showed that in almost all comparisons, out-of-school and in-school active learners exhibited similar engagement levels, but these were much higher than low engagers. In addition, the implications of the findings for the enhancement and effective integration of OLPs were presented.

Subject Keywords

Learning analytics, Online learning platforms, K-Means clustering, Young learners

URI

https://hdl.handle.net/11511/103227

Collections

Graduate School of Natural and Applied Sciences, Thesis