Refine
Has Fulltext
- no (23) (remove)
Year of publication
Document Type
- Article (20)
- Conference Proceeding (2)
- Review (1)
Is part of the Bibliography
- yes (23) (remove)
Keywords
- prediction (23) (remove)
Institute
- Department Psychologie (4)
- Institut für Biochemie und Biologie (4)
- Department Linguistik (2)
- Hasso-Plattner-Institut für Digital Engineering gGmbH (2)
- Institut für Mathematik (2)
- Department für Inklusionspädagogik (1)
- Fachgruppe Betriebswirtschaftslehre (1)
- Hochschulambulanz (1)
- Institut für Ernährungswissenschaft (1)
- Institut für Geowissenschaften (1)
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.