Innovative Models for Evaluating Student Achievement: A Comparative Study of Local and Global Practices
DOI:
https://doi.org/10.55640/eijp-06-02-35Keywords:
Student achievement, innovative assessment, formative assessmentAbstract
Educational systems increasingly face a dual pressure: to preserve the reliability and comparability of achievement measurement while also capturing complex competencies that traditional grading and examinations often miss. This article analyzes innovative models for evaluating student achievement through a comparative lens that juxtaposes “local” assessment traditions—typically centered on teacher-assigned grades, end-of-term examinations, and standardized tests within national curricula—with “global” practices developed in international assessment programs and transnational curricula. Using documentary analysis of authoritative frameworks and research literature, the study synthesizes five methodological directions shaping contemporary assessment: formative assessment and assessment for learning, standards-based and mastery-oriented grading, performance assessment with rubrics and moderation, digital and adaptive testing grounded in item response theory, and learning-analytics–supported evidence systems. The results propose an integrated comparative model that maps these approaches to validity goals, equity considerations, feedback cycles, and implementation conditions in schools and higher education. Special attention is given to the global influence of OECD assessment innovation and PISA frameworks, as well as to current reforms in Uzbekistan aimed at aligning curricula and assessment with international standards and strengthening fairness and quality assurance. The discussion highlights the main trade-offs between innovation and comparability, the risks of superficial adoption, and strategies to ensure that new assessment models improve learning rather than merely increasing measurement. The paper concludes that the most sustainable innovations are those that connect assessment to instruction through clear criteria, calibrated judgment, and data-informed support—while keeping systems accountable to transparency, fairness, and educational purpose.
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