Leveraging Machine Learning and AI to Enhance Educational Learning Analytics

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The application of machine learning identifies at-risk students in higher education, suggesting timely interventions.

The study found the K-means model effectively identified at-risk students with an average recall of 89%.
Data-driven insights from 1,017 students over three years highlighted attendance and interaction as key predictors.
Employing unsupervised machine learning techniques led to automated, personalised feedback that received a 93% usefulness rating.
Timely identification of at-risk students enables interventions to prevent potential dropout issues.

Ikechukwu Ogbuchi (2025) studied this question.

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View Full Paper

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The application of machine learning identifies at-risk students in higher education, suggesting timely interventions.

The study found the K-means model effectively identified at-risk students with an average recall of 89%.
Data-driven insights from 1,017 students over three years highlighted attendance and interaction as key predictors.
Employing unsupervised machine learning techniques led to automated, personalised feedback that received a 93% usefulness rating.
Timely identification of at-risk students enables interventions to prevent potential dropout issues.

Ikechukwu Ogbuchi (2025) studied this question.

View Full Paper

Ask AI

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