References

1. Aydogdu S, "predicting student final performance using artificial neural networks in online learning environments", Edu Info Tech, 2(5), pp.1913-1927, 2020.
2. Bernard J., Chang TW., Popescu E., Graf S, "Using Artificial Neural Networks to Identify Learning" Styles. In: Conati C., Heffernan N., Mitrovic A., Verdejo M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science, vol 9112, 2015, Springer, Cham. DOI: 10.1007/978-3-319-19773-9_57
3. Bokoro P., B. Paul and W. Doorsamy, "Towards smart teaching and learning: a study in a South African institution of higher learning," 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE), Vancouver, BC, Canada, pp. 1595-1598, 2019.
4. Chandrasekaran M., M. Muralidhar, C. M. Krishna, and U. S. Dixit, "Application of soft computing techniques in machining performance prediction and optimization: a literature review," Int J Adv Manuf Technol, vol. 46(5), pp. 445-464, 2010.
5. Chandwani V., S. K. Vyas, V. Agrawal, and G. Sharma, "Soft computing approach for rainfall-runoff modelling: A review," Aquatic Procedia, vol. 4, pp. 1054-1061, 2015.
6. Dahl G. E., D. Yu, L. Deng and A. Acero, "Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition," IEEE Transactions on Audio, Speech, and Language Processing, vol. 20(1), pp. 30-42, 2012, DOI: 10.1109/TASL.2011.2134090
7. Engle R.A., "Framing interactions to foster generative learning: a situative explanation of transfer in a community of learner's classroom", J. Learn. Sci, vol. 15(4), pp. 451-498, 2006.
8. Han, C. and Y. Niu, "Multi-regional Anti-jamming Communication Scheme Based on Transfer Learning and Q Learning," KSII Transactions on Internet and Information Systems, vol. 13(7), pp. 3333-3350, 2019. http://doi.org/10.3837/tiis.2019.07.001
9. Idris N., N. Yusof and P. Saad, "Concept-Based Classification for Adaptive Course Sequencing Using Artificial Neural Network," 2009 Ninth International Conference on Intelligent Systems Design and Applications, Pisa, pp. 956-960, 2009.
10. Kebritchi M, Lipschuetz A, Santiague L, "Issues and Challenges for Teaching Successful Online Courses in Higher Education: A Literature Review", Journal of Educational Technology Systems, vol. 46(1), pp. 4-29, 2017.
11. Khan, Z. F., S. R. Alotaibi, "Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective", Journal of Healthcare Engineering, vol. 2020, Article ID 8894694, 2020, DOI: 10.1155/2020/8894694
12. Khandelwal, R. and Gupta, U. K., "Applications of Artificial Neural Networks in E-Learning Personalization" 2nd International Conference on Communication & Information Processing, 2020. https://dx.doi.org/10.2139/ssrn.3648825
13. Osman A, Khan F. Novel methodology for arbitration of talented students using an electronic system: A higher education perspective. International Journal of Emerging Technologies in Learning. 2019; 14(21):250-7. doi.org: 10.3991/Fijet.v14i21.10916
14. Peters MA, Araya D, "Transforming American Education: Learning Powered by Technology", E-Learning and Digital Media, vol. 8(2), pp. 102-105, 2011.
15. Rikkert M. van der Lans, Wim J. C. M. van de Grift & K. van Veen, "Developing an Instrument for Teacher Feedback: Using the Rasch Model to Explore Teachers' Development of Effective Teaching Strategies and Behaviors", The Journal of Experimental Education, vol. 86(2), pp. 247-264, 2018, DOI: 10.1080/00220973.2016.1268086
16. Seridi H., T. Sari, T. Khadir and M. Sellami, "Adaptive Instructional Planning in Intelligent Learning Systems," Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06), Kerkrade, pp. 133-135, 2006.
17. Shen Y., X. He, J. Gao, L. Deng, and G. Mesnil, "Learning semantic representations using convolutional neural networks for web search", Proceedings of the companion publication of the 23rd international conference on World wide web companion, pp.373- 374, 2014.
18. Sun A., Li YJ., Huang YM., Li Q, "Using Facial Expression to Detect Emotion in E-learning System: A Deep Learning Method", In: Huang TC., Lau R., Huang YM., Spaniol M., Yuen CH. (eds) Emerging Technologies for Education. SETE 2017. Lecture Notes in Computer Science, vol 10676. Springer, Cham, 2017.
19. Susnea E, "Using Artificial Neural Networks in e-Learning Systems", UPB Scientific Bulletin, Series C: Electrical Engineering, vol. 72(4), pp.91-100, 2010. http://dx.doi.org/10.11648/j.ijiis.20160506.14
20. Tanner, T., & Toivonen, H, P, "redicting and preventing student failure using the k-nearest neighbor method to predict student performance in an online course environment", International Journal of Learning Technology, vol. 5(4), pp. 356-377, 2010.
21. Taylan O. and B. Karagozo, "An adaptive neurofuzzy model for prediction of student's academic performance," Computers & Industrial Engineering, vol. 57, no. 3, pp. 732-741, 2009.
22. Zantalis F, G. Koulouras, S. Karabetsos, D. Kandris, A Review of Machine Learning and IoT in Smart Transportation, Future Internet, vol.11(94), pp. 1-23, 2019. DOI: 10.3390/fi11040094
23. Xenos M., "Prediction and assessment of student behavior in open and distance education in computers using Bayesian networks," Computers & Education, vol. 43(4), pp. 345-359, 2004.