American International Journal of Advanced Research and Development

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Learning Analytics Models for Data-Driven Educational Decisions

Author(s) Dr. Daniel K. Sørensen
Country Denmark
Abstract Learning analytics has emerged as an important approach for transforming educational data into meaningful information that can support evidence-based academic and administrative decisions. The increasing adoption of learning management systems, digital learning platforms, online assessments, mobile applications, and intelligent educational technologies has generated substantial volumes of learner-related data. Learning analytics models can process these data to examine learner engagement, academic performance, progression, participation, and learning behaviors. This study examines learning analytics models for data-driven educational decisions, focusing on descriptive, diagnostic, predictive, and prescriptive analytics, learner-performance modeling, early-warning systems, visualization dashboards, and artificial intelligence-supported decision processes.
A qualitative descriptive and conceptual research design was adopted using secondary academic literature. The analysis indicates that descriptive analytics can summarize historical learning patterns, diagnostic analytics can identify factors associated with observed outcomes, predictive models can identify potential future academic risks, and prescriptive approaches can support possible intervention strategies. Learning analytics dashboards can provide educators and administrators with accessible performance information for instructional planning, learner support, curriculum improvement, and institutional management.
However, implementation involves challenges concerning data quality, privacy, cybersecurity, algorithmic bias, transparency, interoperability, teacher and administrator preparedness, and responsible interpretation of predictive information. The study concludes that learning analytics can strengthen educational decision-making when data-driven approaches are integrated with professional judgment, pedagogical knowledge, transparent governance, learner participation, and continuous evaluation.
Keywords Learning Analytics, Educational Data, Data-Driven Decision-Making, Predictive Analytics, Learning Management Systems, Educational Data Mining, Academic Performance, Early-Warning Systems, Artificial Intelligence, Educational Technology
Field Engineering
Published In Volume 6, Issue 4, July-August 2024
Published On 2024-07-29
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