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Computer Science — 2025, Undergraduate
This study developed an Intelligent Adaptive E-Learning System that adjusts learning content and difficulty to the performance of each learner. Static online platforms present the same material to all learners regardless of ability, limiting engagement and outcomes. The system tracks learner progress, adapts lesson sequencing, and provides targeted remedial content based on performance data. Built with Node.js, Express, and MongoDB, the system was evaluated with students and lecturers. Findings indicated improved engagement, better mastery of concepts, and higher completion rates. The study recommends integration of the system into blended learning programmes.
For reference and learning only. Do not submit this work as your own.

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