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Measuring Students' Knowledge Mastery Patterns in Energy Using Cognitive Diagnostic Models

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posted on 10.07.2021, 21:43 by Shuqi ZhouShuqi Zhou

Cognitive diagnostic models can uncover students’ mastery of multiple fine-grained skill attributes or problem-solving processes. A number of studies have applied cognitive diagnostic models to detect students’ knowledge mastery in mathematics and language testing. However, few studies focus on cognitive diagnostic assessment in K-12 science education, and no studies on the energy topic specifically. This study applied cognitive diagnostic models to Trends in International Mathematics and Science Study (TIMSS) science achievement data to assess students’ knowledge mastery in energy. Three TIMSS participating jurisdictions, i.e., Australia, Hong Kong, and Ontario were compared. 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 TIMSS assessment framework. The Q matrix was validated through expert review and real data analysis. Then, one of the cognitive diagnostic models, i.e., the deterministic inputs, noisy and-gate (DINA) model was applied to each jurisdiction’s data.


Results suggested that the hypothesized learning progression was consistent with Australian and Ontario students’ but not Hong Kong students’ observed progression in understanding the energy concept. According to overall attribute mastery probabilities and the latent class pattern, most students failed to explain simple electrical systems. Students also performed poorly in recognizing that heating an object can increase its temperature, and that hot objects can heat up cold objects. Identifying sources of energy was found to be easiest to be mastered. I discuss several potential curriculum-related issues that may affect students’ mastery patterns in different jurisdictions.

History

Degree Type

Doctor of Philosophy

Department

Educational Studies

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

Anne Traynor

Additional Committee Member 2

Hua Hua Chang

Additional Committee Member 3

Kerrie A. Douglas

Additional Committee Member 4

Yue Yin

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