Early Prediction Of Student Academic Performance Using Machine Learning Algorithms For Educational Intervention

NEVEDHA K

Early Prediction Of Student Academic Performance Using Machine Learning Algorithms For Educational Intervention

Author(s)	Ms. NEVEDHA K
Country	India
Abstract	Early identification of academically at-risk students is essential for improving learning outcomes and enabling timely educational intervention. Traditional evaluation methods detect weak students only after examinations, which limits the ability of instructors to provide corrective support. This study proposes a machine learning based prediction model to identify student academic performance at an early stage using behavioural and academic activity data. The xAPI-Edu-Data dataset was used for experimentation. Several supervised machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors and Support Vector Machine were implemented and compared. The dataset was preprocessed using label encoding and divided into training and testing subsets. Model performance was evaluated using accuracy and classification metrics. Experimental results show that the Random Forest classifier outperformed other models with an accuracy of 86.45%, followed by Logistic Regression (79.16%), Decision Tree (77.08%), Support Vector Machine (64.58%) and K-Nearest Neighbors (63.54%). The results demonstrate that ensemble learning methods provide better prediction capability for educational datasets. The proposed model can help institutions detect low-performing students early and enable instructors to provide targeted academic support, thereby improving overall student success rates.
Keywords	Machine Learning, Student Performance Prediction, Educational Data Mining, Random Forest, Learning Analytics
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-03-11

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Early Prediction Of Student Academic Performance Using Machine Learning Algorithms For Educational Intervention

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