Most existing diagnostic models are developed to detect whether students have mastered a set of skills of interest, but few have focused on identifying what scientific misconceptions students possess. This article developed a general dual-purpose model for simultaneously estimating students' overall ability and the presence and absence of misconceptions. The expectation-maximization algorithm was developed to estimate the model parameters. A simulation study was conducted to evaluate to what extent the parameters can be accurately recovered under varied conditions. A set of real data in science education was also analyzed to examine the viability of the proposed model in practice.
CITATION STYLE
Ma, W., Sorrel, M. A., Zhai, X., & Ge, Y. (2024). A Dual-Purpose Model for Binary Data: Estimating Ability and Misconceptions. Journal of Educational Measurement. https://doi.org/10.1111/jedm.12383
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