Analyzing Machine Learning Algorithm Performance in Predicting Student Academic Performance in Data Structures and Algorithms Based on Lifestyles

• Published in: 2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM) pp. 1 - 4
• Main Authors: Malasaga, Elisa V., Arguson, Angelo C., Andales, John Kenneth F., Ambat, Shaneth C., Paculanan, Rhonnel S., Pablo, May Florence San
• Format: Conference Proceeding
• Language: English
• Published: IEEE 19-11-2023
• Subjects: academic performance; classification; Feature extraction; Gaussian processes; Logistic regression; machine learning; Machine learning algorithms; Prediction algorithms; student lifestyle; Support vector machines; Surveys
• ISSN: 2770-0682
• DOI: 10.1109/HNICEM60674.2023.10589068

This research study employed machine learning algorithm in This research study employed a machine learning algorithm in predicting student academic performance in the Data Structures and Algorithm (DSA) course which is based on student lifestyle to analyze the factors that affect the high or low performance result. A total number of 251 Bachelor of Science in Computer Science (BSCS) students participated in the study where 207 or 82% were male and 44 or 18% were female. A oneshot case study was conducted that led to data collection through the administration of an online survey on former enrollees of the said course. The dataset was extracted with 43 features and was analyzed using Python on Jupyter Notebook. Randomly selected 70% of these, 176 observations, are used to train the classifier models. The remaining 30%, 75 observations, were used as the test data. In order to classify academic performance students, eight machine learning algorithms were applied based on random forest (RF), decision tree (DT), support vector machines (SVM), K-nearest neighbors (KNN), logistic regression (LR), Gaussian Naive Bayes (GNB), stochastic gradient descent (SGD), and perceptron. Although SGD and Perceptron classifier models show comparably low classification performances, both random forest and decision tree classifiers provided the highest metric performance. The study indicated that the lifestyles of students contributed to whether the student performance became high or low in their grade performance.