Educational Data Analytics for Intelligent Learning Management

Rawad Chaker

Educational Data Analytics for Intelligent Learning Management

Author(s)	Rawad Chaker
Country	France
Abstract	Educational data analytics has emerged as an important approach for improving learning management through the systematic collection, processing, interpretation, and application of educational data. The integration of learning analytics, artificial intelligence, machine learning, predictive modeling, data visualization, and intelligent decision-support technologies enables educational institutions to understand learner behavior, monitor academic progression, identify potential learning difficulties, and support evidence-informed instructional planning. This study examines educational data analytics for intelligent learning management, focusing on learner performance analysis, predictive analytics, personalized learning, academic monitoring, intelligent intervention, and institutional decision-making. A qualitative descriptive and conceptual research design was adopted using secondary academic literature. The analysis indicates that educational data analytics can improve the visibility of learning processes by transforming learner-generated data into actionable information. Predictive models can assist with early identification of academic difficulties, while dashboards and visualization tools can support educators in monitoring learner progression. However, challenges involving data quality, privacy, cybersecurity, algorithmic bias, interoperability, analytical expertise, and responsible interpretation may influence successful implementation. Effective educational data analytics requires reliable data infrastructures, transparent analytical processes, appropriate institutional governance, and collaboration between educators and technology specialists. The study concludes that educational data analytics can strengthen intelligent learning management when analytical technologies are integrated with sound pedagogy, ethical data practices, teacher expertise, and learner-centered educational objectives.
Keywords	Educational Data Analytics, Learning Management, Learning Analytics, Artificial Intelligence, Machine Learning, Predictive Analytics, Student Performance, Intelligent Education, Data Visualization, Educational Technology
Field	Engineering
Published In	Volume 6, Issue 2, March-April 2024
Published On	2024-03-25

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American International Journal of Advanced Research and Development

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Educational Data Analytics for Intelligent Learning Management

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