<![CDATA[Adaptive testing systems require accurate and timely estimation of students' skill mastery levels to support decision-making in question selection and assessment flow determination. However, uncertainty in students' knowledge levels and variations in learning behavior often limit the performance of conventional predictive models. This study proposes an optimized predictive modeling framework for skill mastery to enhance decision-making quality in adaptive testing. The proposed framework integrates a machine learning-based skill mastery prediction model with an optimization mechanism to improve model accuracy and stability, while accommodating the sequential nature and uncertainty of student responses. Learning interaction data is used to dynamically model the development of skill mastery levels, which are then utilized as decision-support input in the adaptive testing system. The proposed predictive skill mastery model shows strong and consistent performance with an AUC value of 0.822, Average Precision of 0.868, accuracy of 0.757, and a precision balance of 0.834, recall of 0.788, and F1-score of 0.810, supported by well-calibrated probabilities and the ability to respond adaptively to student learning dynamics, making it suitable for use to support decision-making in adaptive test decision-making systems. The results of this study confirm the potential of integrating predictive analytics and optimization techniques in developing intelligent adaptive assessment systems.]]>
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