ScholarWorks@숙명여자대학교

초록

As the educational paradigm shifts toward learner-centered instruction, demand continues to grow for personalized teaching that reflects each learner’s needs, goals, and preferences. This dynamic is particularly evident in mathematics education, where the cumulative and hierarchical structure of content can easily result in learning gaps and notable disparities in achievement levels. Recent national assessments in Korea have revealed that the proportion of underperforming students in mathematics is significantly higher than in other subjects. However, practical constraints, including large class sizes and administrative burdens, hinder the implementation of personalized instruction. This study addresses this challenge by supporting data-driven personalized instruction in secondary mathematics; specifically, it designs and validates a teacher dashboard based on the MathCoDi cognitive diagnostic assessment platform. The dashboard was developed according to the design principles of Lim et al. (2024) and refined through two rounds of expert validation and three months of field application with mathematics teachers. The final dashboard includes task-level and group-level views, cognitive mastery statistics, and user guidance. This study provides a practical and systematic tool to aid mathematics teachers in interpreting diagnostic assessment results and effectively incorporating them into instructional planning.

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