Journal of Information Systems Engineering and Management

How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review
Agostinho Sousa Pinto 1, António Abreu 1, Eusébio Costa 2, Jerónimo Paiva 1 *
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1 CEOS.PP, ISCAP, Polytechnic University of Porto, Rua Jaime Lopes Amorim, 4465-004 S. Mamede de Infesta, Portugal
2 European Institute of Superior Studies, Portugal
* Corresponding Author
Literature Review

Journal of Information Systems Engineering and Management, 2023 - Volume 8 Issue 2, Article No: 21168
https://doi.org/10.55267/iadt.07.13227

Published Online: 28 Apr 2023

Views: 1050 | Downloads: 1325

How to cite this article
APA 6th edition
In-text citation: (Pinto et al., 2023)
Reference: Pinto, A. S., Abreu, A., Costa, E., & Paiva, J. (2023). How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review. Journal of Information Systems Engineering and Management, 8(2), 21168. https://doi.org/10.55267/iadt.07.13227
Vancouver
In-text citation: (1), (2), (3), etc.
Reference: Pinto AS, Abreu A, Costa E, Paiva J. How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review. J INFORM SYSTEMS ENG. 2023;8(2):21168. https://doi.org/10.55267/iadt.07.13227
AMA 10th edition
In-text citation: (1), (2), (3), etc.
Reference: Pinto AS, Abreu A, Costa E, Paiva J. How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review. J INFORM SYSTEMS ENG. 2023;8(2), 21168. https://doi.org/10.55267/iadt.07.13227
Chicago
In-text citation: (Pinto et al., 2023)
Reference: Pinto, Agostinho Sousa, António Abreu, Eusébio Costa, and Jerónimo Paiva. "How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review". Journal of Information Systems Engineering and Management 2023 8 no. 2 (2023): 21168. https://doi.org/10.55267/iadt.07.13227
Harvard
In-text citation: (Pinto et al., 2023)
Reference: Pinto, A. S., Abreu, A., Costa, E., and Paiva, J. (2023). How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review. Journal of Information Systems Engineering and Management, 8(2), 21168. https://doi.org/10.55267/iadt.07.13227
MLA
In-text citation: (Pinto et al., 2023)
Reference: Pinto, Agostinho Sousa et al. "How Machine Learning (ML) is Transforming Higher Education: A Systematic Literature Review". Journal of Information Systems Engineering and Management, vol. 8, no. 2, 2023, 21168. https://doi.org/10.55267/iadt.07.13227
ABSTRACT
In the last decade, artificial intelligence (AI), machine learning (ML) and learning data analytics have been introduced with great effect in the field of higher education. However, despite the potential benefits for higher education institutions (HIE´s) of these emerging technologies, most of them are still in the early stages of adoption of these technologies. Thus, a systematic literature review (SLR) on the literature published over the last 5 years on potential applications of machine learning in higher education is necessary. Following the PRISMA guidelines, out of the 1887 initially identified SCOPUS-indexed publications on the topic, 171 articles were selected for review. To screen the abstracts and titles of each citation, Rayyan QCRI was used. VOSViewer, a software tool for constructing and visualizing bibliometric networks, and Microsoft Excel were used to generate charts and figures. The findings show that the most widely researched application of ML in higher education is related to the prediction of academic performance and employability of students. The implications will be invaluable for researchers and practitioners to explore how ML and AI technologies ,in the era of ChatGPT, can be used in universities without jeopardizing academic integrity.
KEYWORDS
REFERENCES
  • A. L. Samuel, 1959. Some studies in machine learning using the game of checkers. IBM Journal of Research and Development 44, 206–226. https://doi.org/10.1147/rd.441.0206
  • Abdelkader, H.E., Gad, A.G., Abohany, A.A., Sorour, S.E., 2022. An Efficient Data Mining Technique for Assessing Satisfaction Level With Online Learning for Higher Education Students during the COVID-19. IEEE Access 10, 6286–6303. https://doi.org/10.1109/ACCESS.2022.3143035
  • Bishop, C.M., Nasrabadi, N.M., 2006. Pattern recognition and machine learning. Springer.
  • Borrella, I., Caballero-Caballero, S., Ponce-Cueto, E., 2019. Predict and intervene: Addressing the dropout problem in a MOOC-based program, in: Proc. ACM Conf. Learn. Scale, LS. Presented at the 6th ACM Conference on Learning at Scale, L@S 2019, Association for Computing Machinery, Inc. https://doi.org/10.1145/3330430.3333634
  • Brockmann, P., Schuhbauer, H., Hinze, A., 2019. Diversity as an advantage: An analysis of career competencies for it students, in: Int. Conf. Cogn. Explor. Learn. Digit. Age, CELDA. Presented at the 16th International Conference on Cognition and Exploratory Learning in Digital Age, CELDA 2019, IADIS Press, pp. 209–216. https://doi.org/10.33965/celda2019_201911l026
  • Chi, Z., Zhang, S., Shi, L., 2023. Analysis and Prediction of MOOC Learners’ Dropout Behavior. Appl. Sci. 13. https://doi.org/10.3390/app13021068
  • Deng, L., Yu, D., 2014. Deep Learning: Methods and Applications. Foundations and Trends® in Signal Processing 7, 197–387. https://doi.org/10.1561/2000000039
  • Drori, I., Zhang, S., Shuttleworth, R., Tang, L., Lu, A., Ke, E., Liu, K., Chen, L., Tran, S., Cheng, N., Wang, R., Singh, N., Patti, T.L., Lynch, J., Shporer, A., Verma, N., Wu, E., Strang, G., 2022. A neural network solves, explains, and generates university math problems by program synthesis and few-shot learning at human level. Proc. Natl. Acad. Sci. U. S. A. 119. https://doi.org/10.1073/pnas.2123433119
  • Eegdeman, I., Cornelisz, I., van Klaveren, C., Meeter, M., 2022. Computer or teacher: Who predicts dropout best? Front. Educ. 7. https://doi.org/10.3389/feduc.2022.976922
  • ElSharkawy, G., Helmy, Y., Yehia, E., 2022. Employability Prediction of Information Technology Graduates using Machine Learning Algorithms. Intl. J. Adv. Comput. Sci. Appl. 13, 359–367. https://doi.org/10.14569/IJACSA.2022.0131043
  • Gilson, A., Safranek, C.W., Huang, T., Socrates, V., Chi, L., Taylor, R.A., Chartash, D., 2023. How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment. JMIR Med. Educ. 9. https://doi.org/10.2196/45312
  • Han, X., Huwan, T., 2022. The Modular Design of an English Pronunciation Level Evaluation System Based on Machine Learning. Secur. Commun. Networks 2022. https://doi.org/10.1155/2022/6804131
  • Ho, I.M.K., Cheong, K.Y., Weldon, A., 2021. Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques. PLoS ONE 16. https://doi.org/10.1371/journal.pone.0249423
  • Januzaj, Y., Beqiri, E., Luma, A., 2022. Alignment of Higher Education Study Programs and Job Market Demand using Machine Learning Techniques – A Case Study on Balkan Countries’ Universities. Int. J. Emerg. Technol. Learn. 17, 150–158. https://doi.org/10.3991/ijet.v17i19.31825
  • Jha, N.I., Ghergulescu, I., Moldovan, A.-N., 2019. OULAD MOOC dropout and result prediction using ensemble, deep learning and regression techniques, in: Lane H., Zvacek S., Uhomoibhi J. (Eds.), CSEDU - Proc. Int. Conf. Comput. Support. Educ. Presented at the 11th International Conference on Computer Supported Education, CSEDU 2019, SciTePress, pp. 154–164. https://doi.org/10.5220/0007767901540164
  • Kamalov, F., Sulieman, H., Calonge, D.S., 2021. Machine learning based approach to exam cheating detection. PLoS ONE 16. https://doi.org/10.1371/journal.pone.0254340
  • Kučak, D., Juričić, V., Đambić, G., 2018. MACHINE LEARNING IN EDUCATION-A SURVEY OF CURRENT RESEARCH TRENDS. Annals of DAAAM & Proceedings 29.
  • McKinsey, 2022. Machine learning in higher education [WWW Document]. URL https://www.mckinsey.com/industries/education/our-insights/using-machine-learning-to-improve-student-success-in-higher-education (accessed 3.20.23).
  • Mewburn, I., Grant, W.J., Suominen, H., Kizimchuk, S., 2020. A Machine Learning Analysis of the Non-academic Employment Opportunities for Ph.D. Graduates in Australia. High. Educ. Policy 33, 799–813. https://doi.org/10.1057/s41307-018-0098-4
  • Moher, D., Liberati, A., Tetzlaff, J., Altman, D., Antes, G., Atkins, D., Barbour, V., Barrowman, N., Berlin, J., Clark, J., Clarke, M., Cook, D., D’Amico, R., Deeks, J., Devereaux, P.J., Dickersin, K., Egger, M., Ernst, E., Gøtzsche, P.C., Tugwell, P., 2014. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Revista Espanola de Nutricion Humana y Dietetica 18, 172–181.
  • Mourdi, Y., Sadgal, M., Fathi, W.B., Kabtane, H.E., 2020. A machine learning based approach to enhance MOOC users’ classification. Turk. Online J. Distance Educ. 21, 54–68. https://doi.org/10.17718/TOJDE.727976
  • Musso, M.F., Hernández, C.F.R., Cascallar, E.C., 2020. Predicting key educational outcomes in academic trajectories: a machine-learning approach. High. Educ. 80, 875–894. https://doi.org/10.1007/s10734-020-00520-7
  • Nawang, H., Makhtar, M., Hamzah, W.M.A.F.W., 2021. A systematic literature review on student performance predictions. Int. J. Adv. Technol. Eng. Explor. 8, 1441–1453. https://doi.org/10.19101/IJATEE.2021.874521
  • Rista, A., Mukli, L., 2022. Predicting and Analyzing Student Absenteeism Using Machine Learning Algorithm. Integr. Educ. 26, 216–228. https://doi.org/10.15507/1991-9468.107.026.202202.216-228
  • Saidani, O., Menzli, L.J., Ksibi, A., Alturki, N., Alluhaidan, A.S., 2022. Predicting Student Employability Through the Internship Context Using Gradient Boosting Models. IEEE Access 10, 46472–46489. https://doi.org/10.1109/ACCESS.2022.3170421
  • Sangalli, V.A., Martinez-Munoz, G., Canabate, E.P., 2020. Identifying cheating users in online courses, in: Cardoso A., Alves G.R., Restivo T. (Eds.), IEEE Global Eng. Edu. Conf., EDUCON. Presented at the 11th IEEE Global Engineering Education Conference, EDUCON 2020, IEEE Computer Society, pp. 1168–1175. https://doi.org/10.1109/EDUCON45650.2020.9125252
  • Shafiq, D.A., Marjani, M., Habeeb, R.A.A., Asirvatham, D., 2022. Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review. IEEE Access 10, 72480–72503. https://doi.org/10.1109/ACCESS.2022.3188767
  • Sobnath, D., Kaduk, T., Rehman, I.U., Isiaq, O., 2020. Feature Selection for UK Disabled Students’ Engagement Post Higher Education: A Machine Learning Approach for a Predictive Employment Model. IEEE Access 8, 159530–159541. https://doi.org/10.1109/ACCESS.2020.3018663
  • Tanuar, E., Heryadi, Y., Lukas, Abbas, B.S., Gaol, F.L., 2019. Using Machine Learning Techniques to Earlier Predict Student’s Performance, in: Indones. Assoc. Pattern Recognit. Int. Conf., INAPR - Proc. Presented at the 1st Indonesian Association for Pattern Recognition International Conference, INAPR 2018, Institute of Electrical and Electronics Engineers Inc., pp. 85–89. https://doi.org/10.1109/INAPR.2018.8626856
  • Wagstaff, B., Lu, C., Chen, X.A., 2019. Automatic exam grading by a mobile camera: Snap a picture to grade your tests, in: Int Conf Intell User Interfaces Proc IUI. Presented at the 24th International Conference on Intelligent User Interfaces, IUI 2019, Association for Computing Machinery, pp. 3–4. https://doi.org/10.1145/3308557.3308661
  • Wang, Y., 2022. Construction of Intelligent Evaluation Model of English Composition Based on Machine Learning. Mobile Information Systems 2022. https://doi.org/10.1155/2022/3499799
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