Identification and Minimization of Students Academics Failure Using Machine Learning Techniques

Abstract

Early identification of students at risk of academic failure is critical for ensuring timely support and improving educational outcomes. Despite the availability of extra assignments and projects, many students struggle academically without early intervention. This project aims to address this issue by developing a predictive model that identifies high school students at risk of failing before the end of the academic period. By accurately predicting these students, teachers can provide additional assistance and resources to prevent failure and enhance overall student performance.The core objective of this project is to increase student success by offering targeted support to those identified as at risk. We will analyze various factors influencing student performance, such as academic history, behavior, and engagement, and use this data to develop a model that can predict potential failures early. By providing insights into students’ risk levels, educators will be equipped to take timely actions, such as offering extra learning materials and personalized interventions, to improve academic outcomes. This project emphasizes the importance of early intervention in education and aims to reduce student failure rates.

Introduction

The project aims to predict high school students at risk of academic failure early, enabling educators to provide timely and targeted support. Using student data (academic history, behavior, engagement), a machine learning-based model will be developed to reduce failure rates and improve educational outcomes.

Literature Survey

Three prior studies are reviewed:

1. Ahmed Malik et al. (2021): Compared ML algorithms (KNN, Decision Trees, Naive Bayes) for predicting at-risk students; noted scalability and feature independence challenges.

2. Sarah Brown et al. (2022): Used SVM and Logistic Regression for dropout prediction; faced data quality and non-academic factor issues.

3. Rahul Patel et al. (2023): Integrated ML with adaptive platforms; highlighted cost and system integration challenges.

Methodology

• Random Forest Algorithm is used for prediction. It builds multiple decision trees on random data subsets and features, reducing overfitting and improving prediction accuracy.

System Overview

Hardware Requirements:

• Mid-range PC/laptop with 8GB RAM, modern CPU, internet access.

Software Requirements:

• OS: Windows/macOS/Linux

• Tools: Python, R, IDEs (e.g., Jupyter, VS Code)

• Libraries: scikit-learn, TensorFlow, Pandas, etc.

Module Description

Hybrid Ensemble Model Architecture:

1. Base Learners:

   • Logistic Regression, Naive Bayes, Random Forest, Decision Tree, AdaBoost, K-NN.

2. Meta-Learner:

   • SVM aggregates base learner predictions to produce the final output.

3. Stacking Process:

   • Training Phase: Train base learners; generate meta-dataset (D’) from validation predictions; train SVM meta-learner.

   • Prediction Phase: Base learners predict; meta-learner combines outputs for final prediction.

   • Validation: 10-Fold Cross-Validation ensures robust performance.

Outcome:
A robust ensemble-based predictive system capable of early identification of students at academic risk, enabling proactive educational interventions.

Conclusion

This project aims to develop a machine learning-based system to identify and minimize students’ academic failure. By leveraging educational data and machine learning techniques, we can predict academic failure and provide early interventions to support students. The expected outcomes include improved educational outcomes, reduced academic failure rates, and enhanced student success. By leveraging machine learning, this project aims to create a system that predicts academic failure and minimizes it through early identification and targeted interventions. This approach not only improves student performance but also enhances the overall educational experience

References

[1] S. Lee and J. Y. Chung, ‘‘The machine learning-based dropout early warning system for improving the performance of dropout prediction,’’ , Jul. 2019. [2] K. T. Chui, R. W. Liu, M. Zhao, and P. O. D. Pablos, ‘‘Predicting students’ performance with school and family tutoring using generative adversarial network-based deep support vector machine , Feb 2020. [3] M. Adnan, A. Habib, J. Ashraf, S. Mussadiq, A. A. Raza, M. Abid, M. Bashir, and S. U. Khan, “predicting at risk students at different percentages of course length for early intervention using machine learning models , March 2021 [4] J. Berens, K. Schneider, S. Görtz, S. Oster, and J. Burghoff, ‘‘Early detection of students at risk—Predicting student dropouts using administrative student data and machine learning methods,’’ CESifo Group Munich, Dec. 30, 2022.

Copyright

Copyright © 2025 Kirti Ambekar , Snehal Matade , Shruti Deshmukh , Arohi Birari , Prof. K. B. Dhomse . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.