Mapping student engagement in creative thinking: An explainable machine learning approach to behavioral heterogeneity

Key Points

• Engagement in creative thinking varies significantly among students, highlighting interesting patterns.
• Key evidence shows distinct behavioral profiles identified through machine learning techniques, suggesting three main engagement types.
• Analysis utilizing an explainable machine learning approach facilitates understanding of behavioral heterogeneity.
• This work may enable educators to tailor approaches based on individual student's creative engagement levels.