AI Framework for Academic Progress Monitoring and Personalized Assignment Recommendation in Higher Education
Keywords:
Artificial Intelligence, Decision Tree, Educational Data Mining, Learning Analytics, Academic Progress Monitoring, Personalized LearningAbstract
The rapid development of Artificial Intelligence (AI) has significantly expanded its applications in higher education, particularly in the fields of learning analytics and personalized learning. Continuous monitoring of students' academic progress and providing personalized learning recommendations remain major challenges in higher education. This paper proposes an AI-assisted framework for academic progress monitoring and personalized assignment recommendation based on a Decision Tree classification model for predicting students' expected academic performance. The proposed approach integrates students' learning activities, formal assessment results, and historical instructor decisions to predict the expected grade category and automatically generate personalized assignments with an appropriate level of difficulty. A prototype of the framework was implemented in Python using the scikit-learn library and evaluated on a dataset of 50 students. Experimental results demonstrate that the proposed framework provides transparent and interpretable predictions, reduces the effort required for continuous monitoring of student progress, and supports the effective implementation of personalized learning. Rather than replacing instructor judgment, the proposed approach serves as an intelligent decision-support tool that can be integrated into existing learning management systems and adapted to a wide range of higher education courses.
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