@inproceedings{scholars17301,
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {2022 International Conference on Digital Transformation and Intelligence, ICDI 2022 - Proceedings},
           title = {Machine Learning Strategy for Enhancing Academic Achievement in Private University},
           pages = {107--110},
            note = {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},
            year = {2022},
             doi = {10.1109/ICDI57181.2022.10007107},
          author = {Thi Cam, H. N. and Sarlan, A. and Arshad, N. I. and Thanh, V. V. T.},
            isbn = {9798350397000},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146976338&doi=10.1109\%2fICDI57181.2022.10007107&partnerID=40&md5=4b2b6ba9e52c7ed980bea2da2df32e6e},
        keywords = {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},
        abstract = {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{\^a}??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. {\^A}{\copyright} 2022 IEEE.}
}