@inproceedings{RudianHaasePinkwart2022,
  author    = {Rudian, Sylvio Leo and Haase, Jennifer and Pinkwart, Niels},
  title     = {Predicting creativity in online courses},
  series = {2022 International Conference on Advanced Learning Technologies (ICALT)},
  booktitle = {2022 International Conference on Advanced Learning Technologies (ICALT)},
  publisher = {IEEE},
  address   = {Piscataway, NJ},
  isbn      = {978-1-6654-9519-6},
  doi       = {10.1109/ICALT55010.2022.00056},
  pages     = {164 -- 168},
  year      = {2022},
  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.},
  language  = {en}
}