iCreate: Mining creative thinking patterns from contextualized educational data
Abstract
Creativity can be defined as the process of having original ideas that have value. The use of educational technology to promote creativity has attracted a great deal of attention. However, mining creative thinking patterns from educational data remains challenging. In this paper, we introduce a pipeline to contextualize the raw educational data, such as assessments and class activities. We also evaluate our approach with a real-world dataset and highlight how the proposed pipeline can help instructors understand creative thinking patterns from students’ activities and assessment tasks.
Document Type
Conference Proceeding
Date of Publication
1-1-2022
Volume
13356 LNCS
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher
Springer
School
School of Arts and Humanities
RAS ID
52053
Copyright
subscription content
First Page
352
Last Page
356
Comments
Shabani, N., Beheshti, A., Farhood, H., Bower, M., Garrett, M., & Rokny, H. A. (2022). iCreate: Mining creative thinking patterns from contextualized educational data. In International Conference on Artificial Intelligence in Education (pp. 352-356). Springer. https://doi.org/10.1007/978-3-031-11647-6_68