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Cognitive structures that promote deep learning of gross anatomy are integral to musculoskeletal physiotherapy practice yet poorly understood. This quantitative, criterion-related validation study addressed two data modeling strategies (multidimensional scaling and Pathfinder networks) as a potential visual and quantitative representation of the cognitive structures of physiotherapy students learning gross anatomy. The study was grounded in the Adaptive Control of Thought-Rational theory of cognition. The research questions addressed the agreement (reliability, accuracy, and association) between student and expert cognitive structures and included the derived quantitative parameters as predictor variables in multiple regression to examine potential relationships with unit grades. An online survey of paired comparisons of 20 anatomical concepts relevant to musculoskeletal clinical practice generated the raw data used in the data modeling strategies for cognitive structure mapping. Convenience sampling was used to recruit 31 physiotherapy students, four course instructors, and three domain experts who completed the online survey. The results indicated moderate to high effect sizes regarding the agreement between student and expert. Six predictor variables accounted for 68.9% of the variance in unit grade indicating a large effect size. Preliminary evidence of concurrent and predictive validity was reported. Positive social change is reflected in this innovative use of data modeling strategies to represent cognitive structure and potentially enhance competency-based education critical to effective musculoskeletal physiotherapy practice.
Besselink, William Allan, "Data Modeling of Cognitive Structure in Physiotherapy Students Learning Gross Anatomy" (2021). Walden Dissertations and Doctoral Studies. 10889.