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Towards Using Unsupervised Learning for Comparing Traditional and Synchronous Online Learning in Assessing Students' Academic Performance
2021
Mathematics
Understanding students' learning processes and education-related phenomena by extracting knowledge from educational data sets represents a continuous interest in the educational data mining domain. Due to an accelerated expansion of online learning and digitalisation in education, there is a growing interest in understanding the impact of online learning on the academic performance of students. In this study, we comparatively investigate traditional and synchronous online learning methods to
doi:10.3390/math9222870
fatcat:yvz7or5pbbhe3kdbofrf5uvrya