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- W4292360422 abstract "Many prediction tasks can be done based on users’ trace data. This paper explores divergent and convergent thinking as person-related attributes and predicts them based on features gathered in an online course. We use the logfile data of a short Moodle course, combined with an image test (IMT), the Alternate Uses Task (AUT), the Remote Associates Test (RAT), and creative self-efficacy (CSE). Our results show that originality and elaboration metrics can be predicted with an accuracy of ~.7 in cross-validation, whereby predicting fluency and RAT scores perform worst. CSE items can be predicted with an accuracy of ~.45. The best performing model is a Random Forest Tree, where the features were reduced using a Linear Discriminant Analysis in advance. The promising results can help to adjust online courses to the learners’ needs based on their creative performances." @default.
- W4292360422 created "2022-08-20" @default.
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- W4292360422 date "2022-07-01" @default.
- W4292360422 modified "2023-09-28" @default.
- W4292360422 title "Predicting Creativity in Online Courses" @default.
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- W4292360422 doi "https://doi.org/10.1109/icalt55010.2022.00056" @default.
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