Analyzing and Predicting Student Performance in Discrete Mathematics Using Supervised Learning Algorithms

Discrete Mathematics is an important and challenging course for computer science and engineering students. It includes topics, such as logic, sets, proofs, number theory, graphs, trees, computation, relations, functions, and basic algorithmic concepts. These topics require strong analytical reasonin...

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Bibliographic Details
Published in:Computer applications in engineering education Vol. 33; no. 6
Main Author: Uddin, Mohammad Salah
Format: Journal Article
Language:English
Published: Hoboken Wiley Subscription Services, Inc 01.11.2025
Subjects:
Accuracy
Artificial intelligence
Clustering
Colleges & universities
Combinatorics
Datasets
Decision trees
Learning
Machine learning
Mathematics
Number theory
Students
Supervised learning
Support vector machines
Teachers
ISSN:1061-3773, 1099-0542
Online Access:Get full text
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Summary:Discrete Mathematics is an important and challenging course for computer science and engineering students. It includes topics, such as logic, sets, proofs, number theory, graphs, trees, computation, relations, functions, and basic algorithmic concepts. These topics require strong analytical reasoning and consistent effort. As a result, many students find this course challenging to perform well. The aim of this study is to predict student performance in a Discrete Mathematics course at a reputed private university located in Bangladesh. Data were collected from both course instructors and students during the spring and summer semester of 2025. Instructors provided academic records, such as attendance, quizzes, assignments, and midterm scores. Students provided additional information, which included daily study time, subject interests, and use of learning platforms. The final data set included records for 240 students. K ‐means clustering with the Davies–Bouldin method was used to group similar students. Then, four machine learning (ML) models were trained and tested: Support Vector Machine (SVM), Decision Tree, K ‐Nearest Neighbors, and Naïve Bayes. The models were implemented using Python's scikit‐learn library, with stratified sampling and fivefold cross‐validation. Among the models, SVM achieved the highest accuracy of 96% after parameter tuning. Naïve Bayes had the lowest accuracy due to the assumption of feature independence. Key predictors of performance included mean score, attendance, and daily study hours. Findings show that ML can help instructors identify at‐risk students early, provide focused academic support, and improve learning outcomes. While the results are promising, the study is limited by sample size and does not include psychological or emotional factors. Future work will explore larger data sets and apply interpretable Artificial Intelligence techniques for better model transparency.
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ISSN:1061-3773
1099-0542
DOI:10.1002/cae.70108