Measuring students’ learning progressions in energy using cognitive diagnostic models - Zhou, Shuqi; Traynor, Anne (Frontiers in Psychology, 2022)
This study applied cognitive diagnostic models to assess students’ learning progressions in energy. A Q-matrix (i.e., an item attribute alignment table) was proposed based on existing literature about learning progressions of energy in the physical science domain and the Trends in International Mathematics and Science Study (TIMSS) assessment framework. The Q-matrix was validated by expert review and real data analysis. Then, the deterministic inputs, noisy ‘and’ gate (DINA) model with hierarchical relations was applied to data from three jurisdictions that had stable, defined science curricula (i.e., Australia, Hong Kong, and Ontario). The results suggested that the hypothesized learning progression was consistent with the observed progression in understanding the energy concept. We also found similarities in students’ attribute mastery across the three jurisdictions. In addition, we examined the instructional sensitivity of the selected item. We discuss several curriculum-related issues and student misconceptions that may affect students’ learning progressions and mastery patterns in different regions of the world.