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Assessing the Performance of Online Students – New Data, New Approaches, Improved Accuracy
[article]
2022
arXiv
pre-print
We consider the problem of assessing the changing performance levels of individual students as they go through online courses. This student performance (SP) modeling problem is a critical step for building adaptive online teaching systems. Specifically, we conduct a study of how to utilize various types and large amounts of student log data to train accurate machine learning (ML) models that predict the performance of future students. This study is the first to use four very large sets of
arXiv:2109.01753v2
fatcat:jcazup62nza5jltkjoqdx2dzj4