Optimizing Case-based Instruction in Teacher Education: Generating versus Modeling Solutions for Improvement Over Time
Citation (APA 7th): Carbonneau, K. J., & Jernigan, M. A. (2025). Optimizing case-based instruction in teacher education: Generating versus modeling solutions for improvement over time. The Teacher Educators’ Journal, 18, 32-58. https://doi.org/10.66196/QFGT6468
Kira J. Carbonneau, Morgan A. Jernigan
Washington State University
Abstract
In this study, we compared the effectiveness of two instructional approaches—modeling and generative tasks—within case-based instruction (CBI) in developing preservice teachers’ classroom case analysis skills. Forty preservice teachers were randomly assigned to engage in CBI with either a modeling or generative task, working through three classroom cases over a four-week period. Results from mixed factorial ANOVA showed that while both approaches initially provided similar benefits, the generative task yielded progressively higher gains in participants' demonstration of learning theories over time, as evidenced by significant time-based improvements. In contrast, those in the modeling condition did not show growth, suggesting limited cognitive engagement when simply observing expert examples. These findings highlight the potential of generative tasks to deepen cognitive processing, promote independent analysis, and encourage critical reflection. Our results underscore the need for exploration into instructional methods in CBI to prepare teachers more effectively for complex classroom scenarios. Limitations and directions for future research are discussed.
