Godfrey perfectson Oise, Ejenarhome Otega PROSPER, Augustine Osazee AIRHIAVBERE, Agwam Gladys IFEOMA

Student Success Prediction in Digital Learning Environments

Introduction

Student success prediction in digital learning environments. Predict student success in digital learning with a fairness-conscious AI framework. This study balances accuracy and ethical responsibility using RF, GBM, & SVM models, ensuring inclusion & trust.

Abstract

Acknowledging the risk of perpetuating bias in AI-driven student success prediction, this study introduces a fairness-conscious machine learning framework that aims to balance predictive accuracy with ethical responsibility in digital learning settings. Using a dataset of 5,000 anonymized student records, three models, Random Forest (RF), Gradient Boosting (GBM), and Support Vector Machine (SVM), were developed to forecast academic outcomes. Model evaluation combined standard metrics (accuracy, precision, recall, and F1-score) with fairness measures such as demographic parity, equal opportunity, and disparate impact ratio to explore trade-offs between accuracy and fairness. Results indicated that while RF and GBM had slightly higher accuracy, SVM demonstrated more consistent fairness across demographic groups, emphasizing its stronger balance between predictive power and equity. A fairness-centered optimization method was applied to embed fairness constraints directly into model training, showing that both accuracy and fairness can be improved simultaneously rather than being in opposition. The framework integrates fairness throughout data preprocessing, model development, and post-prediction review, promoting transparent and responsible decision-making. By aligning with international ethical AI standards from UNESCO and the OECD, this research provides a practical pathway for creating educational prediction systems that enhance inclusion, minimize bias, and build trust in digital learning environments.

Review

This study tackles the increasingly critical and ethically charged domain of student success prediction within digital learning environments. By immediately foregrounding the risk of bias in AI-driven systems, the authors establish a vital premise: the necessity of a fairness-conscious machine learning framework that explicitly balances predictive accuracy with ethical responsibility. This commitment to integrating ethical considerations from the outset is a significant strength, signaling a mature approach to AI deployment in sensitive educational contexts and positioning the research as highly relevant to current discussions on responsible AI development. Methodologically, the paper outlines a robust approach. The use of a substantial dataset comprising 5,000 anonymized student records provides a solid foundation for training and evaluating the selected models: Random Forest, Gradient Boosting, and Support Vector Machine. A key strength lies in the comprehensive evaluation strategy, which wisely extends beyond traditional performance metrics (accuracy, precision, recall, F1-score) to include crucial fairness measures such as demographic parity, equal opportunity, and disparate impact ratio. The reported findings – that while RF and GBM achieved slightly higher accuracy, SVM demonstrated superior fairness across demographic groups – offer valuable insights into the trade-offs involved. Crucially, the application of a fairness-centered optimization method to simultaneously improve both accuracy and fairness challenges the conventional notion of an inherent trade-off, presenting a promising avenue for future ethical AI development. The broader implications of this research are substantial. By providing an integrated framework that incorporates fairness throughout the entire machine learning lifecycle – from data preprocessing and model development to post-prediction review – the study offers a practical and actionable blueprint for fostering transparent and responsible decision-making in educational settings. The explicit alignment with international ethical AI standards from UNESCO and the OECD further enhances the work's credibility and potential for widespread adoption. This research serves as a vital guide for developing educational prediction systems that not only enhance inclusion and minimize bias but also build essential trust among students and institutions, paving the way for more equitable and effective digital learning experiences.

Full Text

Comments

You need to be logged in to post a comment.