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 |
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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 |