Predicting Students' Academic Performance Via Machine Learning Algorithms - An Empirical Review and Practical Application

Aug 4, 2026

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Predicting academic outcomes is complex and shaped by factors such as socioeconomic background, motivation, and learning style. Machine Learning (ML) algorithms support scalable analysis of large educational datasets and enable identification of predictive patterns. Evidence indicates ML can effectively predict student performance, though results differ by dataset, model choice, and selected training features. Among five evaluated algorithms, the Random Forest Classifier achieved the strongest performance with the highest G-Mean and accuracy (0.9243 and 85.42%). The review highlights challenges including non-uniform metrics, limited generalizability, and potential bias in training data.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Introduction

Motivation and educational value

Research gap and model generalizability

Study contribution and evaluation criteria

Predicting Students' Academic Performance Via Machine Learning Algorithms: An Empirical Review and Practical Application Edmund F. Agyemang 1,2*, JosephA. Mensah2, Obu-AmoahAmpomah3, Louis Agyekum4, Justice Akuoko-Frimpong5, Amma Quansah 1, Oluwaferanmi M. Akinlosotu6 1School of Mathematical and Statistical Science, University of Texas Rio Grande Valley, USA. 2Department of Computer Science, Ashesi University, Berekuso, Eastern Region-Ghana. 3Department of Statistics, Western Michigan University, Kalamazoo-USA 4Trent University, Peterborough-Canada 5Department of Biostatistics, University of Michigan, Ann Abor-USA 6Department of Business Administration, Al-Hikmah University, Ilorin-Nigeria. Corresponding author: [edmundfosu6@gmail.com](edmundfosu6@gmail.com), ORCID ID:0000-0001-8124-4493 ABSTRACT Predicting academic outcomes is complex and influenced by factors like socioeconomic background, motivation, and learning style. Machine Learning (ML) algorithms have become increasingly important due to their ability to analyze large data volumes and identify prediction patterns. Results show ML’s success in predicting academic performance, though effectiveness varies by dataset, algorithm choice, and training features. There is no consensus on the most effective ML method for predicting student performance with broad applicability. Among the five algorithms evaluated in this study, the Random Forest Classifier emerged as the best model, achieving the highest G-Mean and accuracy of 0.9243 and 85.42% respectively. This model's performance emphasizes the importance of balanced sensitivity and specificity in predicting student academic performance. The empirical review highlights several challenges, including a lack of standardization in performance metrics, limited model generalizability, and potential bias in training data. It also notes the impact of individual and environmental factors on academic performance, emphasizing the role of instructors and policymakers in improving educational outcomes. The study provides insights into current trends in using ML algorithms for academic predictions, identifying conceptual, methodological, analytical, and ethical gaps. These gaps affect the validity and reliability of research, underscoring the need to address them for informed decision-making and improved learning outcomes. Keywords: Machine Learning Algorithms, Random Forest, Naive Bayes, Logistic Regression, Academic Performance DOI: 10.7176/CEIS/15-1-09 Publication date: September 30th 2024 1 Introduction The use of machine learning (ML) algorithms in academia has gained significant attention in recent years due to the increasing availability of educational data and advancements in ML techniques (Yagcı, 2022) . Using ML algorithms to predict students’ academic performance can give valuable insights to educators, allowing them to identify at-risk students who may need additional support, modify instructional techniques, boost learning outcomes, tailor teaching approaches to specific students’ requirements, and increase student retention rates (Adnan et al., 2021) . This procedure promotes the growth of the educational system at higher institutions because educators and policymakers can intervene early to prevent students from falling behind and increase their chances of success (Pinkus, 2008) . Applying ML algorithms to predict student academic achievement can dramatically enhance educational results and give valuable insights into the aspects contributing to academic success (Alyahyan and Du¨¸steg¨or, 2020) . Therefore, it is critical to carefully assess these algorithms’ possible benefits and limitations and ensure they are appropriately utilized. Mechanisms, such as the type of ML algorithm employed, the variables analyzed, and the assessment metrics used to determine prediction accuracy, were included as part of our investigation criteria. Applying ML algorithms in education can transform how we approach te

FAQs

Why is predicting students’ academic performance considered complex?

Academic outcomes depend on multiple influences such as socioeconomic background, motivation, and learning style, making prediction difficult.

Which machine learning model performed best in the study?

The Random Forest Classifier performed best, achieving the highest G-Mean (0.9243) and accuracy (85.42%).

What key challenges does the empirical review identify for ML-based academic prediction?

It points to lack of standardization in performance metrics, limited model generalizability, and possible bias arising from training data.

Aug 4, 2026

Upload by Aria