Unveiling Creativity in Student Code: A Gaussian Mixture Model Approach

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.