Analysis of Online Classroom Education on the Learning Patterns for College Students Using Human-Computer Interaction

References

• Abdel‐Basset, M., Manogaran, G., Mohamed, M., & Rushdy, E. (2019). Internet of things in smart education environment: Supportive framework in the decision‐making process. Concurrency and Computation: Practice and Experience, 31(10), e4515. https://doi.org/10.1002/cpe.4515
   Web of Science ®Google Scholar
• Acevedo, Y. V. N., Marín, C. E. M., Garcia, P. A. G., & Crespo, R. G. (2018, April). A proposal to a decision support system with learning analytics [Paper presentation]. 2018 IEEE Global Engineering Education Conference (EDUCON) (pp. 161–168). IEEE. https://doi.org/10.1109/EDUCON.2018.8363223
   Google Scholar
• Al Mahdi, Z., Rao Naidu, V., & Kurian, P. (2019). Analyzing the role of human-computer interaction principles for E-learning solution design. In A. Al-Masri & K. Curran (Eds.), Smart technologies and innovation for a sustainable future (pp. 41–44). Springer.
   Google Scholar
• Al Mamun, M. A., Lawrie, G., & Wright, T. (2020). Instructional design of scaffolded online learning modules for self-directed and inquiry-based learning environments. Computers & Education, 144, 103695. https://doi.org/10.1016/j.compedu.2019.103695
   Web of Science ®Google Scholar
• Al-Taie, M. Z., Kadry, S., & Lucas, J. P. (2019). Online data preprocessing: A case study approach. International Journal of Electrical & Computer Engineering, 9(4), 2620–2626. https://doi.org/10.11591/ijece.v9i4.pp2620-2626
   Google Scholar
• Bouilheres, F., McDonald, S., Nkhoma, C., & Jandug-Montera, L. (2020). Defining student learning experience through blended learning. Education and Information Technologies, 150(1), 1–21.
   Google Scholar
• Douglas, K. A., Merzdorf, H. E., Hicks, N. M., Sarfraz, M. I., & Bermel, P. (2020). Challenges to assessing motivation in MOOC learners: An application of an argument-based approach. Computers & Education, 150, 103829. https://doi.org/10.1016/j.compedu.2020.103829
   Google Scholar
• Ghafari, S. M., Beheshti, A., Joshi, A., Paris, C., Yakhchi, S., Jolfaei, A., & Orgun, M. A. (2020, November). A dynamic deep trust prediction approach for online social networks [Paper presentation]. Proceedings of the 18th International Conference on Advances in Mobile Computing & Multimedia (pp. 11–19). https://doi.org/10.1145/3428690.3429167
   Google Scholar
• Ghosh, S., Chatterjee, A., Sen, S., Kumar, N., & Sarkar, R. (2020). CTRL–CapTuRedLight: A novel feature descriptor for online Assamese numeral recognition. Multimedia Tools and Applications, 80, 30033–30056. https://doi.org/10.1007/s11042-020-10081-7
   Google Scholar
• Hwang, G. J., Wang, S. Y., & Lai, C. L. (2021). Effects of a social regulation-based online learning framework on students’ learning achievements and behaviors in mathematics. Computers & Education, 160(1), 104031. https://doi.org/10.1016/j.compedu.2020.104031
   Web of Science ®Google Scholar
• Joo, H., Ahmed, S. H., & Lim, Y. (2020). Traffic signal control for smart cities using reinforcement learning. Computer Communications, 154, 324–330. https://doi.org/10.1016/j.comcom.2020.03.005
   Web of Science ®Google Scholar
• Kbar, G., Alazab, A., & Agbinya, J. (2019, February). Multi-factor-based enhancing students’ motivations [Paper presentation]. 2019 IEEE International Conference on Industrial Technology (ICIT) (pp. 1054–1059). IEEE. https://doi.org/10.1109/ICIT.2019.8754982
   Google Scholar
• Littenberg-Tobias, J., & Reich, J. (2020). Evaluating access, quality, and equity in online learning: A case study of a MOOC-based blended professional degree program. The Internet and Higher Education, 47, 100759. https://doi.org/10.1016/j.iheduc.2020.100759
   Web of Science ®Google Scholar
• Liu, Y., Nie, J., Li, X., Ahmed, S. H., Lim, W. Y. B., & Miao, C. (2021). Federated learning in the sky: Aerial-ground air quality sensing framework with UAV swarms. IEEE Internet of Things Journal, 8(12), 9827–9837. https://doi.org/10.1109/JIOT.2020.3021006
   Google Scholar
• Mubarak, A. A., Cao, H., & Zhang, W. (2020). Prediction of students’ early dropout based on their interaction logs in online learning environment. Interactive Learning Environments, 1–20. https://doi.org/10.1080/10494820.2020.1727529
   Web of Science ®Google Scholar
• Mulenga, E. M., & Marbán, J. M. (2020). Prospective teachers’ online learning mathematics activities in the age of COVID-19: A cluster analysis approach. Eurasia Journal of Mathematics, Science and Technology Education, 16(9), em1872. https://doi.org/10.29333/ejmste/8345
   Google Scholar
• Niet, Y. V., Díaz, V. G., Montenegro, C. E. (2016, September). Academic decision making model for higher education institutions using learning analytics [Paper presentation]. 2016 4th International Symposium on Computational and Business Intelligence (ISCBI) (pp. 27–32). IEEE.
   Google Scholar
• Ouyang, F., Chang, Y. H., Scharber, C., Jiao, P., & Huang, T. (2020). Examining the instructor-student collaborative partnership in an online learning community course. Instructional Science, 48, 183–204. https://doi.org/10.1007/s11251-020-09507-4
   Google Scholar
• Paulsen, J., & McCormick, A. C. (2020). Reassessing disparities in online learner student engagement in higher education. Educational Researcher, 49(1), 20–29. https://doi.org/10.3102/0013189X19898690
   Web of Science ®Google Scholar
• Pham, D. V., Nguyen, G. L., Nguyen, T. N., Pham, C. V., & Nguyen, A. V. (2020). Multi-topic misinformation blocking with budget constraint on online social networks. IEEE Access, 8(1), 78879–78889. https://doi.org/10.1109/ACCESS.2020.2989140
   Web of Science ®Google Scholar
• Rakic, S., Tasic, N., Marjanovic, U., Softic, S., Lüftenegger, E., & Turcin, I. (2020). Student performance on an E-learning platform: Mixed method approach. International Journal of Emerging Technologies in Learning, 15(2), 187–203. https://doi.org/10.3991/ijet.v15i02.11646
   Google Scholar
• Sahoo, S. R., Gupta, B. B., Choi, C., Hsu, C. H., & Chui, K. T. (2020, December). Behavioral Analysis to Detect Social Spammer in Online Social Networks (OSNs) [Paper presentation]. International Conference on Computational Data and Social Networks (pp. 321–332). Springer.
   Google Scholar
• Senthilkumar, K., & Easwaramoorthy, S. (2017). A survey on cyber security awareness among college students in Tamil Nadu. IOP Conference Series: Materials Science and Engineering, 263(4), 042043. IOP Publishing. https://doi.org/10.1088/1757-899X/263/4/042043
   Google Scholar
• Shakeel, P. M., Baskar, S., Dhulipala, V. S., Mishra, S., & Jaber, M. M. (2018). Maintaining security and privacy in health care system using learning-based deep-Q-networks. Journal of Medical Systems, 42(10), 1–10. https://doi.org/10.1007/s10916-018-1045-z
   Google Scholar
• Shakeel, P. M., Tolba, A., Al-Makhadmeh, Z., & Jaber, M. M. (2020). Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networks. Neural Computing and Applications, 32(3), 777–790. https://doi.org/10.1007/s00521-018-03972-2
   Google Scholar
• Shearer, R. L., Aldemir, T., Hitchcock, J., Resig, J., Driver, J., & Kohler, M. (2020). What students want: A vision of a future online learning experience grounded in distance education theory. American Journal of Distance Education, 34(1), 36–52. https://doi.org/10.1080/08923647.2019.1706019
   Web of Science ®Google Scholar
• Tanwar, S., Obaidat, M. S., Tyagi, S., & Kumar, N. (2019). Online signature-based biometric recognition. In M. S. Obaidat, I. Traore, & I. Woungang (Eds.), Biometric-based physical and cybersecurity systems (pp. 255–285). Springer.
   Google Scholar
• van Halem, N., van Klaveren, C., Drachsler, H., Schmitz, M., & Cornelisz, I. (2020). Tracking patterns in self-regulated learning using students' self-reports and online trace data. Frontline Learning Research, 8(3), 140–163. https://doi.org/10.14786/flr.v8i3.497
   Google Scholar
• Yuan, X., Abouelenien, M., & Elhoseny, M. (2018). A boosting-based decision fusion method for learning from large, imbalanced face data set. In A. E. Hassanien, M. Elhoseny, & J. Kacprzyk (Eds.), Quantum computing: An environment for intelligent large scale real application (pp. 433–448). Springer.
   Google Scholar