iCreate: Mining creative thinking patterns from contextualized educational data

Document Type

Conference Proceeding

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

13356 LNCS

First Page

352

Last Page

356

Publisher

Springer

School

School of Arts and Humanities

RAS ID

52053

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

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.

DOI

10.1007/978-3-031-11647-6_68

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Link to publisher version (DOI)

10.1007/978-3-031-11647-6_68