2026 ASEE Annual Conference & Exposition

Grounding research in a sound theoretical framework is identified as one of the most significant challenges for engineering education PhD students (Streveler et al., 2015). Meeting this challenge requires that PhD students understand and appreciate theories related to learning and learners’ development that enables them to design theoretical frameworks leading to rigorous engineering education research. As trained engineers, many engineering education students struggle with reading and understanding educational theoretical articles and research, often finding the language, epistemological assumptions, and conceptual frameworks unfamiliar compared to their technical training. This challenge calls for pedagogical approaches that both reduce cognitive load through distributed learning and social support from peers through enabling students to deepen understanding by teaching others.

This study implements the jigsaw method (Aronson et al., 1978), where students become “experts” on specific topics and teach peers, transforming them from passive consumers to active constructors of theoretical knowledge through structured collaboration. Two jigsaw learning cycles were conducted over four weeks, with each cycle spanning two weeks. The first cycle covered the seminal works of key learning theorists, while the second addressed motivational theories. Each cycle followed a consistent structure. Students completed reading assignments and prepared individual reading notes before class. In the first session, students met in expert groups to discuss their understanding, co-create concept maps, and plan their teaching approach. Between sessions, the instructor provided feedback on concept maps. In the second session, students regrouped into home groups where each expert taught their topic to peers using visual organizers. Students then provided peer feedback and completed individual reflections.

To explore how jigsaw learning supports graduate students’ learning, multiple data sources were collected. Student-generated artifacts included individual reading notes, co-created concept maps from expert groups, and individual written reflections. Additionally, structured classroom observations documented student engagement, teaching strategies and collaborative dynamics during both expert and home group sessions. Data analysis will employ a mixed-methods approach. Concept maps will be evaluated for conceptual accuracy, complexity, and interconnectedness of ideas using rubrics whereas classroom observation notes will be analyzed to identify patterns of collaborative knowledge construction. Student reflections will be analyzed through thematic coding to identify emergent themes related to learning processes and development of theoretical understanding. Triangulation across these multiple data sources will provide comprehensive insights into how jigsaw learning facilitates graduate engineering students learning.

Preliminary observations suggest promising student engagement and creative peer teaching strategies, with evidence of collaborative knowledge construction during both expert and home group sessions. Formal data analysis is currently underway and will provide systematic evidence of how jigsaw learning scaffolds students’ learning and development of theoretical understanding. This pedagogical innovation is timely as engineering education programs seek evidence-based approaches to prepare theoretically grounded engineering education researchers capable of conducting rigorous research, and its structured design provides a replicable model for other theory-intensive graduate engineering courses.