Unlocking the predictive power of the Rasch model: A systematic literature review on educational instrument calibration and assessment accuracy

This systematic literature review explores the application of the Rasch Model in educational measurement, highlighting its role in psychometric validation, DIF detection, and multidimensional assessments. The study examines key software tools and diverse research applications, with a focus on STEM education and large-scale testing programs. It also reviews the advantages of different Rasch-based software, including Winsteps, RUMM2030, and ConQuest, in facilitating accurate measurement. The findings reveal methodological challenges, including limited cross-cultural validations, inconsistent model applications, and insufficient sample representation, which impact the reliability and generalizability of Rasch-based assessments. The review identifies gaps in scaling methodologies, response category designs, and adaptation processes across different educational contexts. Future research should prioritize AI-driven Rasch analysis, comparative model evaluations, and interdisciplinary integrations to refine educational assessments. Additionally, expanding real-time psychometric evaluations and cross-cultural validations will enhance the applicability of the Rasch Model in diverse educational settings. Strengthening methodological rigor and ensuring greater transparency in validation procedures are crucial to advancing the field. Addressing these issues will promote more equitable, reliable, and innovative measurement frameworks, ultimately improving the accuracy and fairness of educational assessments.