International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 16, No. 3, 8 Jun. 2024
Cover page and Table of Contents: PDF (size: 572KB)
Predictive Model for Academic Training Course Recommendations Based on Machine Learning Algorithms
PDF (572KB), PP.17-26
Views: 0 Downloads: 0
Author(s)
Karanrat Thammarak
1
Witwisit Kesornsit
2
Yaowarat Sirisathitkul
1,*
Index Terms
Recommendation system, 21st century learning, Academic training course, Machine Learning, Random Forest, Backpropagation Neural Network
Abstract
Given the significance of online education, a recommendation system provides a good opportunity to advise the most suitable courses according to their interest and preferences. This study proposes an academic training course recommendation that applies machine learning algorithms to provide the most appropriate 21st century learning based on individual preferences. To address the issue of imbalanced classification, the eight development skills are grouped into three skill categories during the preprocessing stage. In the classification step, several machine learning algorithms, including Decision Tree, Random Forest, Gradient Boosting, and Backpropagation Neural Network, are used to create a predictive model, which is then compared to the results of Logistic Regression. These machine learning algorithms predict the skill group based on the teacher preference data, which results in the suggestion of training courses that are customized to the teacher's profile. According to the experimental results, all machine learning algorithms showed superior prediction performance than Logistic Regression. The Backpropagation Neural Network exhibits high precision, reaching up to 78%, and demonstrates the best performance for the testing data. This research demonstrates that machine learning algorithms significantly improve the accuracy and efficiency of the training course recommendation. On this basis, this training course recommendation system will be advantageous to both the teachers looking for up- and reskilling training courses for 21st century learning. Additionally, it will be appropriate for training course designers to establish training courses that develop 21st-century learning in accordance with participants’ interests and professional development.
Cite This Paper
Karanrat Thammarak, Witwisit Kesornsit, Yaowarat Sirisathitkul, "Predictive Model for Academic Training Course Recommendations Based on Machine Learning Algorithms", International Journal of Modern Education and Computer Science(IJMECS), Vol.16, No.3, pp. 17-26, 2024. DOI:10.5815/ijmecs.2024.03.02
Reference
[1]Hanemann, U. and Robinson, C. 2022. Rethinking literacy from a lifelong learning perspective in the context of the sustainable development goals and the international conference on adult education. International Review of Education, 68, pp. 233–258. https://doi.org/10.1007/s11159-022-09949-7.
[2]Karaca-Atik, A., Meeuwisse, M., Gorgievski, M. and Smeets, G. 2023. Uncovering important 21st century skills for sustainable career development of social sciences graduates: A systematic review. Educational Research Review, 39, 100528. https://doi.org/10.1016/j.edurev.2023.100528.
[3]P21. 2019. Framework for 21st century learning, http://static.battelleforkids.org/documents/p21/P21_Framework_Brief.pdf.
[4]Hikmawati, E., Maulidevi, N. U. and Surendro, K. 2020. Adaptive rule: A novel framework for recommender system. ICT Express, 6(3), pp. 214-219. https://doi.org/10.1016/j.icte.2020.06.001.
[5]Inusah, F., Missah, Y. M., Najim, U. and Twum, F. 2023. Agile neural expert system for managing basic education. Intelligent Systems with Applications, 17, 200178. https://doi.org/10.1016/j.iswa.2023.200178.
[6]Li, J. and Ye, Z. 2020. Course recommendations in online education based on collaborative filtering recommendation algorithm. Complexity, 2020, 6619249. https://doi.org/10.1155/2020/6619249.
[7]Wang, X., Nguyen, M., Carr, J., Cui, L. and Lim, K. 2020. A group preference-based privacy-preserving POI recommender system. ICT Express, 6(3), pp. 204-208. https://doi.org/10.1016/j.icte.2020.05.005.
[8]George, G. and Lal, A. M. 2019. Review of ontology-based recommender systems in e-learning. Computers & Education, 142, 103642. https://doi.org/10.1016/j.compedu.2019.103642.
[9]Khanal, S. S., Prasad, P., Alsadoon, A. and Maag, A. 2020. A systematic review: Machine learning based recommendation systems for e-learning. Education and Information Technologies, 25, pp. 2635–2664. https://doi.org/10.1007/s10639-019-10063-9.
[10]Lee, H. and Lee, J. 2019. Scalable deep learning-based recommendation systems. ICT Express, 5(2), pp. 84-88. https://doi.org/10.1016/j.icte.2018.05.003.
[11]Velankar, M. and Kulkarni, P. 2023. Music recommendation systems: Overview and challenges. In: Biswas, A., Wennekes, E., Wieczorkowska, A., Laskar, R.H. (eds) Advances in Speech and Music Technology. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-18444-4_3.
[12]Alsayed, A. O., Rahim, M. S. M., Albidewi, I., Hussain, M., Jabeen, H. M., Alromema, N., Hussain, S. and Jibril, M. L. 2021. Selection of the right undergraduate major by students using supervised learning techniques. Applied Sciences, 11(22), 10639. https://doi.org/10.3390/app112210639.
[13]Dirin, A. and Saballe, C. A. 2022. Machine learning models to predict students’ study path selection. International Journal of Interactive Mobile Technologies, 16(1), pp. 158-183. https://doi.org/10.3991/ijim.v16i01.20121.
[14]Chen, R., Hua, Q., Chang, Y. S., Bo, W, Zhang, L. and Kong, X. 2018. A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks. IEEE Access, 6, pp. 64301-64320. https://doi.org/10.1109/ACCESS.2018.2877208.
[15]Nilashi, M., Minaei-Bidgoli, B., Alghamdi, A., Alrizq, M., Alghamdi, O., Nayer, F. K., Aljehane, N. O., Khosravi, A. and Mohd, S. 2022. Knowledge discovery for course choice decision in massive open online courses using machine learning approaches. Expert Systems with Applications, 199, 117092. https://doi.org/10.1016/j.eswa.2022.117092.
[16]Wu, B. and Liu, L. 2023. Personalized hybrid recommendation algorithm for MOOCs based on learners’ dynamic preferences and multidimensional capabilities. Applied Science, 13, 5548. https://doi.org/10.3390/app13095548.
[17]Chang H. T., Lin, C. Y., Wang, L. C. and Tseng, F. C. 2022. How students can effectively choose the right courses: Building a recommendation system to assist students in choosing courses adaptively. Educational Technology and Society, 25(1), pp. 61-74. https://www.jstor.org/stable/48647030.
[18]Chang, H. T., Lin, C. Y., Jheng, W. B., Chen, S. H., Wu, H. H., Tseng, F. C. and Wang, L. C. 2023. AI, please help me choose a course: Building a personalized hybrid course recommendation system to assist students in choosing courses adaptively. Educational Technology and Society, 26(1), pp. 203-217. https://doi.org/10.30191/ETS.202301_26(1).0015.
[19]Yilmaz, F. G. K. and Yilmaz, R. 2020. Student opinions about personalized recommendation and feedback based on learning analytics. Technology, Knowledge and Learning, 25, pp. 753-768. https://doi.org/10.1007/s10758-020-09460-8.
[20]Esteban, A., Zafra, A. and Romero, C. 2020. Helping university students to choose elective courses by using a hybrid multi-criteria recommendation system with genetic optimization. Knowledge-Based Systems, 194, 105385. https://doi.org/10.1016/j.knosys.2019.105385.
[21]Tahir, S., Hafeez, Y., Abbas, M.A. Nawaz, A. and Hamid, B. 2022. Smart learning objects retrieval for e-learning with contextual recommendation based on collaborative filtering. Education and Information Technologies, 27, pp. 8631–8668. https://doi.org/10.1007/s10639-022-10966-0.
[22]Gomez-Uribe, C. A. and Hunt, N. 2016. The netflix recommender system: Algorithms, business value, and innovation. ACM Transactions on Management Information Systems, 6(4), 13. https://doi.org/10.1145/2843948.
[23]Renz, A. and Vladova, G. 2021. Reinvigorating the discourse on human-centered artificial intelligence in educational technologies. Technology Innovation Management Review, 11(5), pp. 5-16. http://doi.org/10.22215/timreview/1438.
[24]Chung, D., Jeong, P., Kwon, D. and Han, H. 2023. Technology acceptance prediction of robo-advisors by machine learning. Intelligent Systems with Applications, 18, 200197. https://doi.org/10.1016/j.iswa.2023.200197.
[25]Urdaneta-Ponte, M. C., Mendez-Zorrilla, A. and Oleagordia-Ruiz, I. 2021. Recommendation systems for education: Systematic review. Electronics, 10(14), 1611. https://doi.org/10.3390/electronics10141611.
[26]Dake, D. K. and Gyimah, E. 2023. Using sentiment analysis to evaluate qualitative students’ responses. Education and Information Technologies, 28, pp. 4629–4647. https://doi.org/10.1007/s10639-022-11349-1.
[27]Kesornsit, W. and Sirisathitkul, Y. 2022. Hybrid machine learning model for electricity consumption prediction using random forest and artificial neural networks. Applied Computational Intelligence and Soft Computing, 2022, 1562942. https://doi.org/10.1155/2022/1562942.
[28]Orji, F. A. and Vassileva, J. 2023. Predicting the persuasiveness of influence strategies from student online learning behaviour using machine learning methods. Journal of Educational Computing Research, 61(7), pp. 1410-1429. https://doi.org/10.1177/07356331231178873.
[29]Zhang, C., Liu, C., Zhang, X. and Almpanidis, G. 2017. An up-to-date comparison of state-of-the-art classification algorithms. Expert Systems with Applications, 82, pp. 128-150. https://doi.org/10.1016/j.eswa.2017.04.003.
[30]Rani, S., Kaur, M., Kumar, M., Ravi, V., Ghosh, U. and Mohanty, J. R. 2023. Detection of shilling attack in recommender system for YouTube video statistics using machine learning techniques. Soft Computing, 27(1), pp. 377-389. https://doi.org/10.1007/s00500-021-05586-8.
[31]Jena, K. K., Bhoi, S. K., Malik, T. K., Sahoo, K. S., Jhanjhi, N. Z., Bhatia, S. and Amsaad, F. 2023. E-learning course recommender system using collaborative filtering models. Electronics, 12, 157. https:// doi.org/10.3390/electronics12010157.
[32]Ling, H. C. and Chiang, H. S. 2022. Learning performance in adaptive learning systems: A case study of web programming learning recommendations. Frontiers in Psychology, 13, 770637. https://doi.org/10.3389/fpsyg.2022.770637.
[33]Parvez, S. J., Arulprakasam, A., Ahamed, I. T. and Carolyn, J. J. 2022. Engineering a novel recruitment system using gradient boosted decision tree algorithm and business intelligence principles. Journal of Pharmaceutical Negative Results, 13, pp. 448-459. https://doi.org/10.47750/pnr.2022.13.S03.074.
[34]Choi, Y. H., Lee, J. and Yang, J. 2022. Development of a service parts recommendation system using clustering and classification of machine learning. Expert Systems with Applications, 188, 116084. https://doi.org/10.1016/j.eswa.2021.116084.
[35]Zayed, Y., Salman, Y. and Hasasneh, A. 2022. A recommendation system for selecting the appropriate undergraduate program at higher education institutions using graduate student data. Applied Sciences, 12(24), 12525. https://doi.org/10.3390/app122412525.
[36]Shen, R. 2022. Prediction method of user behavior label based on the BP neural network. Scientific Programming, 2022, 7241956. https://doi.org/10.1155/2022/7241956.
[37]Bashar, M. A., Nayak, R., Walmsley, K. A. and Heath, K. 2023. Machine learning for predicting propensity-to-pay energy bills. Intelligent Systems with Applications, 17, 200176, https://doi.org/10.1016/j.iswa.2023.200176.