Exploring student course transition prediction in online schools using positive and unlabeled data
Miki Katsuragi, Kenji Tanaka
Abstract
In this study, we analyzed the daily learning data of students enrolled in an online school to predict school withdrawal using machine learning techniques. Specifically, we focused on predicting student withdrawal one month in advance based on their attribute data, class enrollment data, and communication records between teachers and students over the preceding three months. Unlike traditional binary classification methods that simply categorize outcomes as Positive or Negative, we employed a Positive and Unlabeled (PU) Learning approach to consider not only the timing of withdrawal but also the potential for future withdrawals. This approach resulted in an improved recall rate, increasing the accuracy of our predictions of student course changes from 63% to 72%
Keywords
References
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Submitted date:
01/06/2024
Accepted date:
08/31/2024
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