Publikation
Predicting Creativity in Online Courses
Leo Sylvio Rüdian; J. Haase; Niels Pinkwart
In: International Conference on Advanced Learning Technologies (ICALT22). IEEE International Conference on Advanced Learning Technologies (ICALT-2022), July 1-4, Bucharest, Romania, Pages 164-168, Vol. 22, ISBN 978-1-6654-9519-6, IEEE, 7/2022.
Zusammenfassung
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.