Critical thinking in math: 10th-grade analysis using cognitive diagnostic modeling
DOI:
https://doi.org/10.21831/reid.v11i1.88074Keywords:
mathematics education, senior high school, attribute mastery, critical thinking, G-DINA model in rjags, MCMC convergence diagnostics, Posterior predictive checks, Educational assessment in Indonesian high schools, Data-driven feedback for critical thinking, Attribute-level mastery reporting, Diagnostic measurement in mathematics education, Bayesian cognitive diagnostic modelingAbstract
Critical thinking is widely recognized as an essential competency in mathematics education, yet assessments often fail to capture its multidimensional nature. This study applied a Bayesian Cognitive Diagnostic Modeling (G-DINA) approach to identify the mastery profiles of tenth-grade students in Indonesia across four attributes: interpretation, analysis, evaluation, and inference. Data from 60 students revealed that most learners demonstrated partial rather than full mastery, with consistent challenges in evaluative reasoning and inference. These diagnostic profiles provide actionable insights for teachers, enabling more targeted instructional strategies that go beyond total test scores. The findings highlight the potential of Bayesian CDMs to enhance classroom assessment by offering fine-grained evidence of students’ reasoning patterns. This study contributes novelty by being among the first to implement Bayesian cognitive diagnosis in mathematics education within the Indonesian context, bridging methodological innovation with practical implications for teaching and assessment.
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