A Systematic Review of Application of Machine Learning in Curriculum Design Among Higher Education

Year 2024, Volume: 4 Issue: 1, 15 - 24

Yanyao Deng

https://doi.org/10.57020/ject.1475566

Abstract

Machine learning has become an increasingly popular area of research in the field of education, with potential applications in various aspects of higher education curriculum design. This study aims to review the current applications of AI in the curriculum design of higher education. We conducted an initial search for articles on the application of machine learning in curriculum design in higher education. This involved searching three core educational databases, including the Educational Research Resources Information Centre (ERIC), the British Education Index (BEI), and Education Research Complete, to identify relevant literature. Subsequently, this study performed network analysis on the included literature to gain a deeper understanding of the common themes and topics within the field. The results showed a growing trend in publishing research on the application of machine learning within the educational domain. Our review pinpointed merely 11 publications specifically targeting the application of machine learning in higher education course design, with only three being peer-reviewed articles. Through the word cloud visualization, we discerned the most prominent keywords to be AI, foreign countries, pedagogy, online courses, e-learning, and course design. Collectively, these keywords underscore the significance of AI in molding the educational landscape, as well as the expanding tendency to incorporate AI technologies into online and technology-enhanced learning experiences. Although there is a significant amount of research on the application of machine learning in education, the literature on its specific use in higher education course design still needs to be expanded. Our review identified only a small number of studies that directly focused on this topic, and among them. The network analysis generated from the included literature highlights important themes related to student learning and performance and the use of models and algorithms. However, there is still a need for further research in this area to fully understand the potential of machine learning in higher education course design. This study would contribute literature in this specific field. The review can update teacher’s awareness of using machine learning in teaching practice. Additionally, it implies more and more researchers conduct related research in this area. Future studies should consider the limitations of the existing literature and explore new approaches to incorporate machine learning into curriculum design to improve student learning outcomes.

Keywords

Machine Learning, Computer System, Curriculum Design, Review

References

• Australia, I. E. A. o. (2013). Good practice principles in practice: Teaching across cultures. A quick guide to curriculum design. http://www.ieaa.org.au/documents/item/127
• McKimm, J. (2007). Curriculum design and development. Medical Education, 1-32.
• Newell, A. D., Foldes, C. A., Haddock, A. J., Ismail, N., & Moreno, N. P. (2023). Twelve tips for using the Understanding by Design® curriculum planning framework. Medical Teacher, 46(1), 34–39. https://doi.org/10.1080/0142159X.2023.2224498
• Macalister, J., & Nation, I. P. (2019). Language curriculum design. Routledge.
• Tessmer, M. (1990). Environment analysis: A neglected stage of instructional design. Educational technology research and development, 55-64.
• Zafari, M., Bazargani, J. S., Sadeghi-Niaraki, A., & Choi, S. M. (2022). Artificial intelligence applications in K-12 education: A systematic literature review. Ieee Access, 10, 61905-61921. Doi: 10.1109/ACCESS.2022.3179356..
• Abioye, S. O., et al. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44, 103299.
• Alpaydin, E. (2021). Machine learning. MIT Press.
• Erdem, E. S. (2014). Ses sinyallerinde duygu tanıma ve geri erişimi Başkent Üniversitesi Fen Bilimleri Enstitüsü.
• Fogel, D. B. (2006). Evolutionary computation: toward a new philosophy of machine intelligence. John Wiley & Sons
• Mohammed, M., et al. (2016). Machine learning: algorithms and applications. Crc Press.
• Kaul, V., Enslin, S., & Gross, S. A. (2020). History of artificial intelligence in medicine. Gastrointestinal endoscopy, 92(4), 807-812.
• Somvanshi, M., Chavan, P., Tambade, S., & Shinde, S. V. (2016, August). A review of machine learning techniques using decision tree and support vector machine. In 2016 international conference on computing communication control and automation (ICCUBEA) (pp. 1-7). IEEE.
• Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350-361.
• França, R. P., Monteiro, A. C. B., Arthur, R., & Iano, Y. (2021). An overview of deep learning in big data, image, and signal processing in the modern digital age. Trends in deep learning methodologies, 63-87.
• Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255-260.
• Sahu, S. K., Mokhade, A., & Bokde, N. D. (2023). An overview of machine learning, deep learning, and reinforcement learning-based techniques in quantitative finance: recent progress and challenges. Applied Sciences, 13(3), 1956.
• Nasteski, V. (2017). An overview of the supervised machine learning methods. Horizons. b, 4(51-62), 56.
• Dike, H. U., Zhou, Y., Deveerasetty, K. K., & Wu, Q. (2018, October). Unsupervised learning based on artificial neural network: A review. In 2018 IEEE International Conference on Cyborg and Bionic Systems (CBS) (pp. 322-327). IEEE.
• Li, Y. (2017). Deep reinforcement learning: An overview. arXiv preprint arXiv:1701.07274.
• Reddy, Y. C. A. P., Viswanath, P., & Reddy, B. E. (2018). Semi-supervised learning: A brief review. Int. J. Eng. Technol, 7(1.8), 81.
• Zhou, Z. H. (2018). A brief introduction to weakly supervised learning. National science review, 5(1), 44-53.
• Qu, L., Liu, S., Liu, X., Wang, M., & Song, Z. (2022). Towards label-efficient automatic diagnosis and analysis: a comprehensive survey of advanced deep learning-based weakly-supervised, semi-supervised and self-supervised techniques in histopathological image analysis. Physics in Medicine & Biology, 67(20), 20TR01.
• Wang, X., Zhao, Y., & Pourpanah, F. (2020). Recent advances in deep learning. International Journal of Machine Learning and Cybernetics, 11, 747-750.
• Kolachalama, V. B., & Garg, P. S. (2018). Machine learning and medical education. NPJ digital medicine, 1(1), 54.
• Enughwure, A. A., & Ogbise, M. E. (2020). Application of machine learning methods to predict student performance: a systematic literature review. Int. Res. J. Eng. Technol, 7(05), 3405-3415.
• Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE review, 42(4), 40.
• Adams Becker, S., Cummins, M., Davis, A., Freeman, A., Hall Giesinger, C., & Ananthanarayanan, V. (2017). NMC Horizon Report: 2017 Higher Education Edition. New Media Consortium.
• Albreiki, B., Zaki, N., & Alashwal, H. (2021). A systematic literature review of student’performance prediction using machine learning techniques. Education Sciences, 11(9), 552.
• Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424.
• Wang, H. X., Mittleman, M. A., & Orth-Gomer, K. (2005). Influence of social support on progression of coronary artery disease in women. Social science & medicine, 60(3), 599-607.
• Chang, J., & Lu, X. (2019, August). The study on students' participation in personalized learning under the background of artificial intelligence. In 2019 10th International Conference on Information Technology in Medicine and Education (ITME) (pp. 555-558). IEEE.
• Dishon, G. (2017). New data, old tensions: Big data, personalized learning, and the challenges of progressive education. Theory and Research in Education, 15(3), 272-289.
• Chaudhri, V. K., Lane, H. C., Gunning, D., & Roschelle, J. (2013). Applications of artificial intelligence to contemporary and emerging educational challenges. Artificial Intelligence Magazine, Intelligent Learning Technologies: Part, 2(34), 4.
• Southgate, E. (2020). Artificial intelligence, ethics, equity and higher education: A ‘beginning-of-the-discussion’paper..
• Walkington, C., & Bernacki, M. L. (2020). Appraising research on personalized learning: Definitions, theoretical alignment, advancements, and future directions. Journal of research on technology in education, 52(3), 235-252.
• Kakish, K., & Pollacia, L. (2018, August). Adaptive learning to improve student success and instructor efficiency in introductory computing course. In Proceedings of the Information Systems Education Conference (Vol. 8).
• Ouyang, F., Zheng, L., & Jiao, P. (2022). Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020. Education and Information Technologies, 27(6), 7893-7925.
• Ghorbani, R., & Ghousi, R. (2020). Comparing different resampling methods in predicting students’ performance using machine learning techniques. IEEE access, 8, 67899-67911.
• Smadi, A., Al-Qerem, A., Nabot, A., Jebreen, I., Aldweesh, A., Alauthman, M., ... & Alzghoul, M. B. (2023). Unlocking the potential of competency exam data with machine learning: improving higher education evaluation. Sustainability, 15(6), 5267.
• Krechetov, I., & Romanenko, V. (2020). Implementing the adaptive learning techniques. Вопросы образования, (2 (eng)), 252-277.
• Kashive, N., Powale, L., & Kashive, K. (2020). Understanding user perception toward artificial intelligence (AI) enabled e-learning. The International Journal of Information and Learning Technology, 38(1), 1-19.
• Lippert, A., Shubeck, K., Morgan, B., Hampton, A., & Graesser, A. (2020). Multiple agent designs in conversational intelligent tutoring systems. Technology, Knowledge and Learning, 25(3), 443-463.
• Alsobhi, A. Y., & Alyoubi, K. H. (2019). Adaptation algorithms for selecting personalised learning experience based on learning style and dyslexia type. Data Technologies and Applications, 53(2), 189-200.
• Harati, H., Sujo-Montes, L., Tu, C. H., Armfield, S. J., & Yen, C. J. (2021). Assessment and learning in knowledge spaces (ALEKS) adaptive system impact on students’ perception and self-regulated learning skills. Education Sciences, 11(10), 603.
• Kaburlasos, V. G., Marinagi, C. C., & Tsoukalas, V. T. (2004, August). PARES: A software tool for computer-based testing and evaluation used in the Greek higher education system. In IEEE International Conference on Advanced Learning Technologies, 2004. Proceedings. (pp. 771-773). IEEE.
• Ross, M., Graves, C. A., Campbell, J. W., & Kim, J. H. (2013, December). Using support vector machines to classify student attentiveness for the development of personalized learning systems. In 2013 12th international conference on machine learning and applications (Vol. 1, pp. 325-328). IEEE.
• Nguyen, A., Piech, C., Huang, J., & Guibas, L. (2014, April). Codewebs: scalable homework search for massive open online programming courses. In Proceedings of the 23rd international conference on World wide web (pp. 491-502).
• Sivakumar, M., & Reddy, U. S. (2017, November). Aspect based sentiment analysis of students opinion using machine learning techniques. In 2017 international conference on inventive computing and informatics (ICICI) (pp. 726-731). IEEE.
• Uskov, V. L., Bakken, J. P., Byerly, A., & Shah, A. (2019, April). Machine learning-based predictive analytics of student academic performance in STEM education. In 2019 IEEE Global Engineering Education Conference (EDUCON) (pp. 1370-1376). IEEE.
• Wu, M., Mosse, M., Goodman, N., & Piech, C. (2019, July). Zero shot learning for code education: Rubric sampling with deep learning inference. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 782-790)..
• Rieser, V., & Lemon, O. (2011). Reinforcement learning for adaptive dialogue systems: a data-driven methodology for dialogue management and natural language generation. Springer Science & Business Media.
• Iqbal, Z., Qadir, J., Mian, A. N., & Kamiran, F. (2017). Machine learning based student grade prediction: A case study. arXiv preprint arXiv:1708.08744.
• Adnan, M., Habib, A., Ashraf, J., Mussadiq, S., Raza, A. A., Abid, M., ... & Khan, S. U. (2021). Predicting at-risk students at different percentages of course length for early intervention using machine learning models. Ieee Access, 9, 7519-7539. doi: 10.1109/ACCESS.2021.3049446
• Song, C. (2022). Educational Information Refinement with Application Using Massive‐Scale Data Mining. Mathematical Problems in Engineering, 2022(1), 2372723.
• Vrakas, D., Tsoumakas, G., Kokkoras, F., Bassiliades, N., Vlahavas, I., & Anagnostopoulos, D. (2007). PASER: a curricula synthesis system based on automated problem solving. International Journal of Teaching and Case Studies, 1(1-2), 159-170
• Wallace, S. A., McCartney, R., & Russell, I. (2010). Games and machine learning: a powerful combination in an artificial intelligence course. Computer Science Education, 20(1), 17-36.
• Wang, D., Ram, P., Weidele, D. K. I., Liu, S., Muller, M., Weisz, J. D., ... & Amini, L. (2020, March). Autoai: Automating the end-to-end ai lifecycle with humans-in-the-loop. In Companion Proceedings of the 25th International Conference on Intelligent User Interfaces (pp. 77-78).
• Chamunyonga, C., Edwards, C., Caldwell, P., Rutledge, P., & Burbery, J. (2020). The impact of artificial intelligence and machine learning in radiation therapy: considerations for future curriculum enhancement. Journal of Medical Imaging and Radiation Sciences, 51(2), 214-220.
• Musso, M. F., Kyndt, E., Cascallar, E. C., & Dochy, F. (2013). Predicting general academic performance and identifying the differential contribution of participating variables using artificial neural networks. Frontline Learning Research, 1(1), 42-71.
• Balcioğlu, Y. S., & Artar, M. (2023). Predicting academic performance of students with machine learning. Information Development. https://doi.org/10.1177/02666669231213023
• Nawaz, R., Sun, Q., Shardlow, M., Kontonatsios, G., Aljohani, N. R., Visvizi, A., & Hassan, S. U. (2022). Leveraging AI and machine learning for national student survey: actionable insights from textual feedback to enhance quality of teaching and learning in UK’s higher education. Applied Sciences, 12(1), 514. https://doi.org/10.3390/app12010514
• Dennehy, D., Conboy, K., & Babu, J. (2023). Adopting learning analytics to inform postgraduate curriculum design: Recommendations and research agenda. Information Systems Frontiers, 25(4), 1315-1331. https://doi.org/10.1007/s10796-021-10183-z
• Supraja, S., Hartman, K., Tatinati, S., & Khong, A. W. (2017). Toward the Automatic Labeling of Course Questions for Ensuring Their Alignment with Learning Outcomes. International Educational Data Mining Society.
• Kowalska, A., Banasiak, R., Stańdo, J., Wróbel-Lachowska, M., Kozłowska, A., & Romanowski, A. (2022). Study on Using Machine Learning-Driven Classification for Analysis of the Disparities between Categorized Learning Outcomes. Electronics, 11(22), 3652.
• Karalar, H., Kapucu, C., & Gürüler, H. (2021). Predicting students at risk of academic failure using ensemble model during pandemic in a distance learning system. International Journal of Educational Technology in Higher Education, 18(1), 63. https://doi.org/10.1186/s41239-021-00300-y
• Chang, Q., Pan, X., Manikandan, N., & Ramesh, S. (2022). Artificial intelligence technologies for teaching and learning in higher education. International Journal of Reliability, Quality and Safety Engineering, 29(05), 2240006. https://doi.org/10.1142/S021853932240006X
• Stadelmann, T., Keuzenkamp, J., Grabner, H., & Würsch, C. (2021). The AI-atlas: didactics for teaching AI and machine learning on-site, online, and hybrid. Education Sciences, 11(7), 318.
• Lee, C. A., Tzeng, J. W., Huang, N. F., & Su, Y. S. (2021). Prediction of student performance in massive open online courses using deep learning system based on learning behaviors. Educational Technology & Society, 24(3), 130-146.
• Souri, A., Ghafour, M. Y., Ahmed, A. M., Safara, F., Yamini, A., & Hoseyninezhad, M. (2020). A new machine learning-based healthcare monitoring model for student’s condition diagnosis in Internet of Things environment. Soft Computing, 24(22), 17111-17121.
• Mansur, A. B. F., Yusof, N., & Basori, A. H. (2019). Personalized learning model based on deep learning algorithm for student behaviour analytic. Procedia Computer Science, 163, 125-133.
• Ofori, F., Maina, E., & Gitonga, R. (2020). Using machine learning algorithms to predict students’ performance and improve learning outcome: A literature based review. Journal of Information and Technology, 4(1), 33-55.
• Almufarreh, A., Noaman, K. M., & Saeed, M. N. (2023). Academic teaching quality framework and performance evaluation using machine learning. Applied Sciences, 13(5), 3121. https://doi.org/10.3390/app13053121
• Çağataylı, M., & Çelebi, E. (2022). Estimating academic success in higher education using big five personality traits, a machine learning approach. Arabian Journal for Science and Engineering, 47(2), 1289-1298. doi: 10.1007/s13369-021-05873-4.
• Chang, S. H., Yao, K. C., Chung, C. Y., Nien, S. C., Chen, Y. T., Ho, W. S., Lin, T. C., Shih, F. C., & Chung, T. C. (2023). Integration of Artificial Intelligence and Machine Learning Content in Technology and Science Curriculum. International Journal of Engineering Education, 39(6), 1343–1357.
• Villegas-Ch, W., Govea, J., & Revelo-Tapia, S. (2023). Improving student retention in institutions of higher education through machine learning: A sustainable approach. Sustainability, 15(19), 14512. doi: 10.3390/su151914512.
• Ilic, M. P., Paun, D., Popovic Ševic, N., Hadžic, A., & Jianu, A. (2021). Needs and Performance Analysis for Changes in Higher Education and Implementation of Artificial Intelligence, Machine Learning, and Extended Reality. Education Sciences, 11, 568. doi: 10.3390/educsci11100568.
• Go, M. B., Junior, R. A. G., Velos, S. P., Dayupay, J. P., Cababat, F. G., Baird, J. C. C., & Quiñanola, H. (2023). A data mining approach to classifying e-learning satisfaction of higher education students: a Philippine case. International Journal of Innovation and Learning, 33(3), 314-329. doi: 10.1504/IJIL.2023.130103.
• ElSharkawy, G., Helmy, Y., & Yehia, E. (2022). Employability prediction of information technology graduates using machine learning algorithms. International Journal of Advanced Computer Science and Applications, 13(10), 359–367.
• Dipierro, A. R., & De Witte, K. (2024). The underlying signals of efficiency in European universities: a combined efficiency and machine learning approach. Studies in Higher Education, 1-20. doi: 10.1080/03075079.2024.2370948.
• Sghir, N., Adadi, A., & Lahmer, M. (2023). Recent advances in Predictive Learning Analytics: A decade systematic review (2012–2022). Education and information technologies, 28(7), 8299-8333. doi: 10.1007/s10639-022-11536-0.
• Sanchez, D. T., Peconcillo Jr, L. B., De Vera, J. V., Mahajan, R., Kumar, T., & Bhosle, A. A. (2022). Machine Learning Techniques for Quality Management in Teaching Learning Process in Higher Education by Predicting the Student's Academic Performance. International Journal of Next-Generation Computing, 13(3), 678–689.
• Ureel, Y., Dobbelaere, M. R., Ouyang, Y., De Ras, K., Sabbe, M. K., Marin, G. B., & Van Geem, K. M. (2023). Active machine learning for chemical engineers: a bright future lies ahead!. Engineering, 27, 23-30. doi: 10.1016/j.eng.2023.02.019.
• Ibarra-Vazquez, G., Ramírez-Montoya, M. S., & Buenestado-Fernández, M. (2023). Forecasting Gender in Open Education Competencies: A Machine Learning Approach. IEEE Transactions on Learning Technologies, 17, 1236–1247. doi: 10.1109/TLT.2023.3336541.
• Pelzer, E., & Turner, B. O. (2022). Interactions with a machine teacher: Effects of Wiley’s Daila on student learning outcomes and teaching effectiveness. Communication Teacher, 36(4), 314-329. doi: 10.1080/17404622.2021.2001551.
• Bruno, G., Diglio, A., Kalinowski, T. B., Piccolo, C., & Rippa, P. (2021). University-Business Cooperation to design a transnational curriculum for energy efficiency operations. Studies in Higher Education, 46(4), 763-781. doi: 10.1080/03075079.2019.1652810.
• Shang, H., & Sivaparthipan, C. B. (2022). Interactive teaching using human-machine interaction for higher education systems. Computers and Electrical Engineering, 100, 107811. doi: 10.1016/j.compeleceng.2022.107811.
• Xiao, W., & Hu, J. (2023). Analyzing Effective Factors of Online Learning Performance by Interpreting Machine Learning Models. IEEE Access, 11, 132435-132447. doi: 10.1109/ACCESS.2023.3334915.
• Kondoyanni, M., Loukatos, D., Arvanitis, K. G., Lygkoura, K. A., Symeonaki, E., & Maraveas, C. (2024). Adding Machine-Learning Functionality to Real Equipment for Water Preservation: An Evaluation Case Study in Higher Education. Sustainability, 16(8), 3261. doi: 10.3390/su16083261.
• Sheridan, L., & Gigliotti, A. (2023). Designing online teaching curriculum to optimise learning for all students in higher education. The Curriculum Journal, 34(4), 651-673, doi: 10.1002/curj.208.