Communication Dans Un Congrès Année : 2025

Teaching Tools to improve student engagement in higher education

Résumé

Over the past two decades, a wide range of teaching strategies have been developed to enhance student engagement in higher education, including gamified learning, educational videos, and flipped classrooms. This study aims to go beyond evaluating these methods by applying machine learning techniques to identify distinct student engagement profiles. Using principal component analysis (PCA) adapted for binary variables, combined with hierarchical clustering algorithms, we classify students into engagement typologies—ranging from highly engaged to "at-risk" students with consistently low engagement scores. The dataset includes survey responses from students in Europe, the United States, and other global regions, allowing for comparative insights across cultural and institutional contexts. Our analysis highlights that, while teaching tools matter, the educator's teaching style remains the most influential factor in driving engagement. These findings emphasize the value of using data-driven approaches to better understand and support diverse student learning behaviors.

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Dates et versions

hal-05118356 , version 1 (18-06-2025)

Identifiants

  • HAL Id : hal-05118356 , version 1

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Habib Chamoun-Nicolas, María Victoria Ramírez-Muñoz, Francisco Rabadán Peréz. Teaching Tools to improve student engagement in higher education. XXXIII Jornadas ASEPUMA XXI Encuentro Internacional, Universidad de Castilla-La Mancha, Jun 2025, Toledo (ESP), España. ⟨hal-05118356⟩
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