Ai Based Structural Equation Modelling to Classify the Students’ Performance in Higher Education Institutions
Keywords:
Structural Modelling, Classification, Student Performance, EducationAbstract
When conducting classification tests, one of the most difficult challenges that can occur is ensuring that a high degree of accuracy is maintained in spite of the presence of unbalanced data sets. Achieving a high accuracy result in a classification study in which a class with a large number of samples can be better learned does not, however, provide information about the efficiency of the results of the other classes, and the accuracy provides conclusions that are misleading due to the fact that the results are so accurate. Using this strategy, it is possible to classify the great majority of students into a range of different categories (pass/fail, risky/not hazardous, etc.). When dealing with data that is not evenly distributed, the F1-score and the ROC AUC score are more accurate evaluations of the overall performance of the model compared to the other metrics. On the other hand, certain measurements, such as recall and precision, represent the level of achievement for lessons and provide direction for understanding the material covered in those classes. If the findings of the study solely depend on the accuracy metric, then it is possible that it will be challenging to integrate these findings into reality.
Downloads
References
Wu, H., Li, S., Zheng, J., & Guo, J. (2020). Medical students’ motivation and academic performance: the mediating roles of self-efficacy and learning engagement. Medical education online, 25(1), 1742964.
Leong, L. Y., Hew, T. S., Ooi, K. B., & Wei, J. (2020). Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach. International Journal of Information Management, 51, 102047.
Susilawati, E., Lubis, H., Kesuma, S., & Pratama, I. (2022). Antecedents of Student Character in Higher Education: The role of the Automated Short Essay Scoring (ASES) digital technology-based assessment model. Eurasian Journal of Educational Research, 98(98), 203-220.
Makransky, G., & Petersen, G. B. (2019). Investigating the process of learning with desktop virtual reality: A structural equation modeling approach. Computers & Education, 134, 15-30.
Budiharso, T., & Tarman, B. (2020). Improving quality education through better working conditions of academic institutes. Journal of Ethnic and Cultural Studies, 7(1), 99-115.
Salloum, S. A., AlAhbabi, N. M. N., Habes, M., Aburayya, A., & Akour, I. (2021, March). Predicting the intention to use social media sites: a hybrid SEM-machine learning approach. In International Conference on Advanced Machine Learning Technologies and Applications (pp. 324-334). Springer, Cham.
Alshurideh, M., Al Kurdi, B., Salloum, S. A., Arpaci, I., & Al-Emran, M. (2020). Predicting the actual use of m-learning systems: a comparative approach using PLS-SEM and machine learning algorithms. Interactive Learning Environments, 1-15.
Ponnuswamy, I., & Manohar, H. L. (2016). Impact of learning organization culture on performance in higher education institutions. Studies in Higher Education, 41(1), 21-36.
Arif, I., Aslam, W., & Hwang, Y. (2020). Barriers in adoption of internet banking: A structural equation modeling-Neural network approach. Technology in Society, 61, 101231.
Marlina, E., Tjahjadi, B., & Ningsih, S. (2021). Factors affecting student performance in e-learning: A case study of higher educational institutions in Indonesia. The Journal of Asian Finance, Economics and Business, 8(4), 993-1001.
Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A., Sarker, K. U., & Sattar, M. U. (2020). Predicting student performance in higher educational institutions using video learning analytics and data mining techniques. Applied Sciences, 10(11), 3894.
Qasem, Y. A., Asadi, S., Abdullah, R., Yah, Y., Atan, R., Al-Sharafi, M. A., & Yassin, A. A. (2020). A multi-analytical approach to predict the determinants of cloud computing adoption in higher education institutions. Applied Sciences, 10(14), 4905.
Nicolas, C., Kim, J., & Chi, S. (2020). Quantifying the dynamic effects of smart city development enablers using structural equation modeling. Sustainable Cities and Society, 53, 101916.
Elnadi, M., & Gheith, M. H. (2021). Entrepreneurial ecosystem, entrepreneurial self-efficacy, and entrepreneurial intention in higher education: Evidence from Saudi Arabia. The International Journal of Management Education, 19(1), 100458.
Hasan, R., Palaniappan, S., Mahmood, S., Abbas, A., Sarker, K. U., & Sattar, M. U. (2020). Predicting student performance in higher educational institutions using video learning analytics and data mining techniques. Applied Sciences, 10(11), 3894.
Valaskova, K., Throne, O., Kral, P., & Michalkova, L. (2020). Deep learning-enabled smart process planning in cyber-physical system-based manufacturing. Journal of Self-Governance and Management Economics, 8(1), 121-127.
Hamoud, A., Hashim, A. S., & Awadh, W. A. (2018). Predicting student performance in higher education institutions using decision tree analysis. International Journal of Interactive Multimedia and Artificial Intelligence, 5, 26-31.
Williamson, B., Bayne, S., & Shay, S. (2020). The datafication of teaching in Higher Education: critical issues and perspectives. Teaching in Higher Education, 25(4), 351-365.
Yakubu, M. N., & Abubakar, A. M. (2021). Applying machine learning approach to predict students’ performance in higher educational institutions. Kybernetes.
Nylund-Gibson, K., Grimm, R. P., & Masyn, K. E. (2019). Prediction from latent classes: A demonstration of different approaches to include distal outcomes in mixture models. Structural equation modeling: A multidisciplinary Journal, 26(6), 967-985.
Tjahjadi, B., Soewarno, N., Astri, E., & Hariyati, H. (2019). Does intellectual capital matter in performance management system-organizational performance relationship? Experience of higher education institutions in Indonesia. Journal of intellectual capital.
Al-Adwan, A. S. (2020). Investigating the drivers and barriers to MOOCs adoption: The perspective of TAM. Education and information technologies, 25(6), 5771-5795.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.