Work

Unveiling Creativity in Student Code: A Gaussian Mixture Model Approach

Year: 2025

Type: conference-paper

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.

Source: ACM Conference on User Modeling, Adaptation and Personalization (UMAP)

DOI: 10.1145/3699682.3728317

Authors: Veronika Bogina, Arnon Hershkovitz, Noam Koenigstein

Institution: Tel Aviv University

Language: English

Topic: Teaching and Learning Programming

Subfield: Computer Science Applications

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: bronze

Funder: Tel Aviv University