%0 Conference Paper
%A Thi Cam, H.N.
%A Sarlan, A.
%A Arshad, N.I.
%A Thanh, V.V.T.
%D 2022
%F scholars:17301
%I Institute of Electrical and Electronics Engineers Inc.
%K Classifiers; Learning algorithms; Learning systems; Machine learning; Software engineering; Students, Academic achievements; Academic performance; Learning analytic; Learning strategy; Machine learning algorithms; Machine-learning; Naive bayes; On-machines; Source data; Student performance, Decision trees
%P 107-110
%R 10.1109/ICDI57181.2022.10007107
%T Machine Learning Strategy for Enhancing Academic Achievement in Private University
%U https://khub.utp.edu.my/scholars/17301/
%X Learning analytics has developed into a powerful tool for discovering unforeseen patterns in educational data and forecasting students' academic success. In order to anticipate the final exam grades of students studying software engineering, this study suggests a learning analytics framework based on machine learning algorithms, using component grades as the source data. The dataset included the academic performance grades of 1475 students who enrolled in a private university for a year to take a Java Web Programming course. To estimate the students' final test grades, the Naive Bayes and Decision Tree algorithms' performances�which are among the machine learning algorithms were calculated and compared. A multitude of features, including the assignment, practical exam, two progress test, two workshop, and final exam, were used to make the predictions. Such data-driven research is crucial for developing a framework for learning analytics in higher education and for influencing decision-making. © 2022 IEEE.
%Z cited By 0; Conference of 2022 International Conference on Digital Transformation and Intelligence, ICDI 2022 ; Conference Date: 1 December 2022 Through 2 December 2022; Conference Code:185994