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Predicting Preferences in Online Courses

Online courses have very high dropout rates worldwide. Learners are demotivated based on bad learning experiences, lack of time, or motivation. While some factors of online courses could be optimized for all learners, e.g. the quality, it is essential to note that a one-size-fits-all solution is not sufficient. Learners have different preferences. They feel well with some methods while others may not. In this paper, we predict five learning preferences based on trace and performance data. The promising result shows that we can predict the values of our five preferences with acceptable accuracy up to.75, which can be used for further adaptions.