Adaptive Learning Analytics Driven by AI for Distance Learning
Rakibul Haque
Ladoke Akintola University of Technology
Abstract
With more and more people learning from home, new methods are required to support diverse learners effectively. This study investigates the integration of AI-driven adaptive learning analytics in remote learning environments. The proposed framework uses machine learning and data analytics to analyze various forms of learner interaction data and dynamically update learning pathways and interventions in real time, thereby improving engagement and performance. The system leverages predictive analytics to monitor individual learning patterns and provide personalized feedback and learning materials tailored to each learner. Case studies demonstrate that adaptive approaches significantly increase student satisfaction and sustain their engagement compared to traditional distance learning methods. The findings suggest that AI-driven adaptive learning analytics can greatly enhance the effectiveness and accessibility of distance education.
Keywords
AI-powered adaptive learning, Learning analytics, Remote education, Personalized learning, Machine learning in education, Educational data mining, Real-time analytics, Distance learning, Learner engagement, Intelligent tutoring systems.
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