TY  - CHAP
A1  - Rudian, Sylvio Leo
A1  - Haase, Jennifer
A1  - Pinkwart, Niels
T1  - Predicting creativity in online courses
T2  - 2022 International Conference on Advanced Learning Technologies (ICALT)
N2  - 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.
KW  - prediction
KW  - online course
KW  - trace data
KW  - creativity
Y1  - 2022
SN  - 978-1-6654-9519-6
SN  - 978-1-6654-9520-2
U6  - https://doi.org/10.1109/ICALT55010.2022.00056
SP  - 164
EP  - 168
PB  - IEEE
CY  - Piscataway, NJ
ER  -