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The relation of convergent thinking and trace data in an online course

Leo Sylvio Rüdian; J. Haase; Niels Pinkwart
In: Die 19. Fachtagung Bildungstechnologien (DELFI). DeLFI Workshops (DeLFI-WS-2021), September 13-15, Dortmund, Germany, ISBN 978-3-88579-710-4, GI, 9/2021.


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.