IMPROVING INTELLECTUAL MECHANISMS FOR MONITORING STUDENTS’ EDUCATION
DOI:
https://doi.org/10.5281/zenodo.18067895Abstract
This article analyzes the issues of improving the intellectual mechanisms of monitoring student knowledge in higher education institutions. During the study, the effectiveness of educational data intelligence (EDI), deep knowledge tracking (DKT) and human-assisted machine learning (HITL-ML) approaches was studied. It was proved that the accuracy of predicting academic risks using hybrid neural network models reaches 98.8%, thereby increasing the efficiency of education by 24.6%. The ethical aspects of the system and the prospects for its integration into the HEMIS information platform of Uzbekistan are also covered.
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