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Assessing the Influence of Time on Features for the Prediction of User Dropout

Parisa Shayan; Martin Atzmueller; Menno van Zaanen
In: Proc. IEEE International Conference on Tools with Artificial Intelligence. International Conference on Tools with Artificial Intelligence (AAAI SSS), IEEE, 2022.


This article investigates the influence of time on features for the prediction of user dropout in an online training platform. Specifically, we target a comparison between the two time measurements: activity-based versus duration-based. Considering time, we utilize features in different groups: either activity-based features for activities or duration-based features for duration, as well as the start-based, action-type-based, and course- based features for both time measurements. The most surprising aspect of the results is the high accuracy of the classifiers from the tenth activity (which corresponds to almost half a day on average) onward. While the action-type- based and the course-based features have a major influence on dropout, the start-based features are only influential in the classifiers that use information of activities at beginning. In addition, the activity-based features have only a minor impact in the middle of the course whilst the duration-based features have a major influence throughout the course.