American journal of pharmaceutical education · 2026
Student Performance and Instructional Complexity Across Team-Based and Mastery Learning Course Formats.
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Persky Adam M, Noel Zachary R, McLaughlin Jacqueline E
Аннотация
OBJECTIVE: To compare cumulative final examination performance between mastery learning (ML) and team-based learning (TBL) and conduct an exploratory characterization of differences in instructional complexity between the 2 models.
METHODS: This retrospective study compared 7 years of student performance data from a TBL course format (2012-2018, n = 996) with 7 years following implementation of ML (2019-2025, n = 1078). Course content, sequencing, learning objectives, and cumulative examination blueprint remained relatively stable across years. The primary outcome was student performance on a cumulative final examination. Final examination scores were compared between instructional models using Welch's independent-samples t test. Effect sizes were calculated using Cohen's d. An exploratory retrospective analysis used instructor-informed ratings to characterize relative instructional complexity between formats.
RESULTS: Students in the ML format achieved statistically significantly higher examination scores than students in the TBL format (ML = 90.0%, TBL = 88.0%, mean difference = 2.0% points, d = 0.24). Results remained consistent when COVID-19-affected cohorts were excluded from analysis. Mastery learning was also associated with lower score variability (SD = 7.9 vs 8.9), suggesting greater consistency in student performance. The exploratory instructor-informed complexity analysis suggested that TBL concentrated relative instructional complexity within synchronous classroom coordination and team management, whereas ML concentrated complexity within assessment infrastructure, reassessment processes, and individualized learner support.
CONCLUSION: Both TBL and ML were associated with high cumulative final examination performance despite substantial differences in instructional organization and operational demands. Selection of an instructional model should depend on alignment among learning goals, institutional resources, and implementation context rather than small differences in examination outcomes.
METHODS: This retrospective study compared 7 years of student performance data from a TBL course format (2012-2018, n = 996) with 7 years following implementation of ML (2019-2025, n = 1078). Course content, sequencing, learning objectives, and cumulative examination blueprint remained relatively stable across years. The primary outcome was student performance on a cumulative final examination. Final examination scores were compared between instructional models using Welch's independent-samples t test. Effect sizes were calculated using Cohen's d. An exploratory retrospective analysis used instructor-informed ratings to characterize relative instructional complexity between formats.
RESULTS: Students in the ML format achieved statistically significantly higher examination scores than students in the TBL format (ML = 90.0%, TBL = 88.0%, mean difference = 2.0% points, d = 0.24). Results remained consistent when COVID-19-affected cohorts were excluded from analysis. Mastery learning was also associated with lower score variability (SD = 7.9 vs 8.9), suggesting greater consistency in student performance. The exploratory instructor-informed complexity analysis suggested that TBL concentrated relative instructional complexity within synchronous classroom coordination and team management, whereas ML concentrated complexity within assessment infrastructure, reassessment processes, and individualized learner support.
CONCLUSION: Both TBL and ML were associated with high cumulative final examination performance despite substantial differences in instructional organization and operational demands. Selection of an instructional model should depend on alignment among learning goals, institutional resources, and implementation context rather than small differences in examination outcomes.