Educational Innovation through Jigsaw Learning: Experiences from the Course of Data Analytics and Artificial Intelligence Tools
Academic Article in Scopus
The Jigsaw learning method is recognized for promoting collaboration and active engagement; however, its effectiveness in data analytics and artificial intelligence courses remains underexplored. This study evaluates the method's impact in a computational tools course at Tecnologico de Monterrey, aligning it with human-centered engineering education in the era of artificial intelligence and digital transformation. A multi-method approach was employed, combining quantitative surveys and qualitative word cloud analysis to capture students' experiences. Twenty-seven students were organized into expert groups, each addressing specific data analytics tasks, while only 23 participated in the study. Findings indicate that teamwork, structured collaboration, and peer interaction were major benefits, while challenges included coding errors, unclear instructions, and unequal knowledge distribution. Despite these obstacles, 82.6% of participants reported improved conceptual understanding compared to traditional instruction. Although students valued peer-driven learning, some preferred more instructor guidance. The results suggest that refining implementation through clearer task design, guided facilitation, and diversified team structures can optimize outcomes. This study highlights the potential of the Jigsaw method as an adaptable and innovationoriented pedagogy for interdisciplinary STEM education, contributing to the development of leadership and innovation in artificial intelligence-driven learning environments.
