Predicting creativity in online courses
- 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.
| Author details: | Sylvio Leo RudianORCiD, Jennifer HaaseORCiDGND, Niels PinkwartORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1109/ICALT55010.2022.00056 |
| ISBN: | 978-1-6654-9519-6 |
| ISBN: | 978-1-6654-9520-2 |
| Title of parent work (English): | 2022 International Conference on Advanced Learning Technologies (ICALT) |
| Publisher: | IEEE |
| Place of publishing: | Piscataway, NJ |
| Publication type: | Conference Proceeding |
| Language: | English |
| Date of first publication: | 2022/08/17 |
| Publication year: | 2022 |
| Release date: | 2024/05/03 |
| Tag: | creativity; online course; prediction; trace data |
| Number of pages: | 5 |
| First page: | 164 |
| Last Page: | 168 |
| Organizational units: | Wirtschafts- und Sozialwissenschaftliche Fakultät / Wirtschaftswissenschaften / Fachgruppe Betriebswirtschaftslehre |
| DDC classification: | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
| Peer review: | Nicht ermittelbar |
