Unveiling Creativity in Student Code: A Gaussian Mixture Model Approach

Veronika Bogina; Arnon Hershkovitz; Noam Koenigstein

doi:10.1145/3699682.3728317

Unveiling Creativity in Student Code: A Gaussian Mixture Model Approach

Veronika Bogina
, Arnon Hershkovitz
, Noam Koenigstein

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Creativity, characterized by the capacity to generate novel and valuable ideas or solutions through imaginative thinking and unique problem-solving, differs widely between individuals. Despite its importance, this variability is often overlooked in research on personalization in education. In this study, our goal is to personalize creativity within a programming learning platform for school students. Leveraging a unique dataset of students' initial coding attempts, we employ a Gaussian Mixture Model to identify distinct creativity profiles among learners. By integrating these insights into user modeling, this work lays the foundation for developing personalized programming curricula tailored to each student's creative strengths, highlighting the potential of creativity-aware adaptive systems in education. We make our data and code publicly available at: https://github.com/sveron/Creativity.

Original language	English
Title of host publication	UMAP 2025 - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization
Publisher	Association for Computing Machinery, Inc
Pages	347-351
Number of pages	5
ISBN (Electronic)	9798400713132
DOIs	https://doi.org/10.1145/3699682.3728317
State	Published - 13 Jun 2025
Externally published	Yes
Event	33rd ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2025 - New York City, United States Duration: 16 Jun 2025 → 19 Jun 2025

Publication series

Name	UMAP 2025 - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference	33rd ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2025
Country/Territory	United States
City	New York City
Period	16/06/25 → 19/06/25

Bibliographical note

Publisher Copyright:
© 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Keywords

Creativity
Education
Gaussian Mixture Model
Sequences

ASJC Scopus subject areas

Artificial Intelligence
Human-Computer Interaction
Safety, Risk, Reliability and Quality
Media Technology
Modeling and Simulation

Access to Document

10.1145/3699682.3728317

Cite this

Bogina, V., Hershkovitz, A., & Koenigstein, N. (2025). Unveiling Creativity in Student Code: A Gaussian Mixture Model Approach. In UMAP 2025 - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization (pp. 347-351). (UMAP 2025 - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization). Association for Computing Machinery, Inc. https://doi.org/10.1145/3699682.3728317

Bogina, Veronika ; Hershkovitz, Arnon ; Koenigstein, Noam. / Unveiling Creativity in Student Code : A Gaussian Mixture Model Approach. UMAP 2025 - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, Inc, 2025. pp. 347-351 (UMAP 2025 - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization).

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Bogina, V, Hershkovitz, A & Koenigstein, N 2025, Unveiling Creativity in Student Code: A Gaussian Mixture Model Approach. in UMAP 2025 - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization. UMAP 2025 - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, Association for Computing Machinery, Inc, pp. 347-351, 33rd ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2025, New York City, United States, 16/06/25. https://doi.org/10.1145/3699682.3728317

Unveiling Creativity in Student Code: A Gaussian Mixture Model Approach. / Bogina, Veronika; Hershkovitz, Arnon; Koenigstein, Noam.
UMAP 2025 - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, Inc, 2025. p. 347-351 (UMAP 2025 - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Bogina V, Hershkovitz A, Koenigstein N. Unveiling Creativity in Student Code: A Gaussian Mixture Model Approach. In UMAP 2025 - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization. Association for Computing Machinery, Inc. 2025. p. 347-351. (UMAP 2025 - Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization). doi: 10.1145/3699682.3728317

Unveiling Creativity in Student Code: A Gaussian Mixture Model Approach

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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