TY  - CONF
N1  - 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
N2  - 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' performancesâ??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.
KW  - Classifiers; Learning algorithms; Learning systems; Machine learning; Software engineering; Students
KW  -  Academic achievements; Academic performance; Learning analytic; Learning strategy; Machine learning algorithms; Machine-learning; Naive bayes; On-machines; Source data; Student performance
KW  -  Decision trees
ID  - scholars17301
TI  - Machine Learning Strategy for Enhancing Academic Achievement in Private University
SP  - 107
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146976338&doi=10.1109%2fICDI57181.2022.10007107&partnerID=40&md5=4b2b6ba9e52c7ed980bea2da2df32e6e
A1  - Thi Cam, H.N.
A1  - Sarlan, A.
A1  - Arshad, N.I.
A1  - Thanh, V.V.T.
EP  - 110
Y1  - 2022///
SN  - 9798350397000
PB  - Institute of Electrical and Electronics Engineers Inc.
ER  -