relation: https://khub.utp.edu.my/scholars/17301/
title: Machine Learning Strategy for Enhancing Academic Achievement in Private University
creator: Thi Cam, H.N.
creator: Sarlan, A.
creator: Arshad, N.I.
creator: Thanh, V.V.T.
description: 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.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2022
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Thi Cam, H.N. and Sarlan, A. and Arshad, N.I. and Thanh, V.V.T.  (2022) Machine Learning Strategy for Enhancing Academic Achievement in Private University.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146976338&doi=10.1109%2fICDI57181.2022.10007107&partnerID=40&md5=4b2b6ba9e52c7ed980bea2da2df32e6e
relation: 10.1109/ICDI57181.2022.10007107
identifier: 10.1109/ICDI57181.2022.10007107