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  - 
TY  - CHAP
A1  - Rüdian, Sylvio Leo
A1  - Haase, Jennifer
A1  - Pinkwart, Niels
T1  - The relation of convergent thinking and trace data in an online course
T2  - Die 19. Fachtagung Bildungstechnologien (DELFI) / 
Lecture Notes in Informatics (LNI)
N2  - Many prediction tasks can be done based on users’ trace data. In this paper, we explored convergent thinking as a personality-related attribute and its relation to features gathered in interactive and non-interactive tasks of an online course. This is an under-utilized attribute that could be used for adapting online courses according to the creativity level to enhance the motivation of learners. Therefore, we used the logfile data of a 60 minutes Moodle course with N=128 learners, combined with the Remote Associates Test (RAT). We explored the trace data and found a weak correlation between interactive tasks and the RAT score, which was the highest considering the overall dataset. We trained a Random Forest Regressor to predict convergent thinking based on the trace data and analyzed the feature importance. The result has shown that the interactive tasks have the highest importance in prediction, but the accuracy is very low. We discuss the potential for personalizing online courses and address further steps to improve the applicability.
KW  - Convergent thinking
KW  - creativity
KW  - online course
KW  - MOOC
KW  - prediction
Y1  - 2021
UR  - https://dl.gi.de/bitstream/handle/20.500.12116/37008/DELFI_2021_181-186.pdf?sequence=1
SP  - 181
EP  - 186
PB  - Gesellschaft für Informatik
CY  - Bonn
ER  -