Phản hồi theo thời gian thực (real-time feedback): chìa khóa nâng cao hiệu quả giảng dạy và học tập trực tuyến

Tác giả: Lê Hoài Việt

Số trang: 237-245

DOI url: 10.62831/202526043

Tóm tắt:

Bài nghiên cứu phân tích hạn chế của hệ thống phản hồi truyền thống như chậm trễ, thiếu cá nhân hóa và giảm tương tác, từ đó làm suy giảm động lực và kết quả học tập. Ngược lại, phản hồi tức thời mang lại nhiều lợi ích: giúp sinh viên điều chỉnh kịp thời chiến lược học tập, gia tăng động lực nhờ cá nhân hóa bằng trí tuệ nhân tạo (AI), đồng thời kiến tạo môi trường học tập tương tác đa chiều. Kết quả cho thấy, triển khai toàn diện real-time feedback giúp giảm 35% tỷ lệ bỏ học, nâng cao mức độ gắn kết, đồng thời tạo dựng môi trường học tập linh hoạt, cá nhân hóa. Điều này khẳng định vai trò tất yếu của phản hồi tức thì trong chiến lược phát triển giáo dục trực tuyến. Trên cơ sở đó, nghiên cứu đề xuất giải pháp đồng bộ ở 3 phương diện: (1) ứng dụng công nghệ tiên tiến như chatbot, nền tảng tương tác trực tuyến; (2) đổi mới phương pháp giảng dạy với cơ chế phản hồi liên tục; và (3) phát triển năng lực giảng viên, nâng cao kỹ năng tiếp nhận phản hồi cho sinh viên.

TÀI LIỆU THAM KHẢO:

Abu-Ghuwaleh, M., & Saffaf, R. (2023). Integrating AI and NLP with project-based learning in STEAM education. [Online] Available at https://doi.org/10.20944/preprints202306.0848.v1 AL-DAREÝ,, I., & Elhag, A. (2022). The effect of feedback type in the e-learning environment on students’ achievement and motivation. Journal of Educational Technology and Online Learning, 5(3), 694-705. Baloran, E., Hernan, J., & Taoy, J. (2021). Course satisfaction and student engagement in online learning amid COVID-19 pandemic: A structural equation model. Turkish Online Journal of Distance Education, 1-12. Bariham, I. (2022). Senior high school teachers' and students' perception about the integration of online learning and its impact on their application of technology in teaching and learning of social studies in northern region, Ghana. Social Education Research, 161-174. Camilleri, M., & Camilleri, A. (2022). Remote learning via video conferencing technologies: Implications for research and practice. Technology in Society, 68, 101881. Chen, K., Ko, Y., Hsieh, M., Chiang, W., & Huei-Ming, M. (2019). Interventions to improve the quality of bystander cardiopulmonary resuscitation: A systematic review. PLOS ONE, 14(2), e0211792. Chen, Y. (2025). Evaluation of the application effect of intelligent teaching systems in mathematics education. International Journal of Web-Based Learning and Teaching Technologies, 20(1), 1-19. Chukwu, J. (2024). The effectiveness of gamification in online learning. Journal of Online and Distance Learning, 3(1), 53-65. Eck, S., Hapfelmeier, A., Linde, K., Schultz, K., Gensichen, J., Sanftenberg, L., … & Schneider, A. (2022). Effectiveness of an online education program for asthma patients in general practice: Study protocol for a cluster randomized controlled trial. BMC Pulmonary Medicine, 22(1). ElSayad, G., Saad, N., & Ramayah, T. (2021). How higher education students in Egypt perceived online learning engagement and satisfaction during the COVID-19 pandemic. Journal of Computers in Education, 8(4), 527-550. Fazlollahi, A., Bakhaidar, M., Alsayegh, A., Yilmaz, R., Winkler-Schwartz, A., Mirchi, N.,… & Maestro, R. (2022). Effect of artificial intelligence tutoring vs expert instruction on learning simulated surgical skills among medical students. JAMA Network Open, 5(2), e2149008. Gan, Z., An, Z., & Liu, F. (2021). Teacher feedback practices, student feedback motivation, and feedback behavior: How are they associated with learning outcomes? Frontiers in Psychology, 12. Halpin, P., & Crowther, G. (2021). Tunes in the zoom room: Remote learning via videoconference discussions of physiology songs. Journal of Microbiology and Biology Education, 22(1). Hariadi, M., Suryati, N., Kuswandi, D., & Wedi, A. (2023). Students’ engagement in EFL online learning (pp. 432-442). Hidayah, N., & Indriani, L. (2021). Real time feedback in English microteaching practice: A case study on online learning. Metathesis: Journal of English Language, Literature, and Teaching, 5(2), 155. Huang, J. (2020). Successes and challenges: Online teaching and learning of chemistry in higher education in China in the time of COVID-19. Journal of Chemical Education, 97(9), 2810-2814. Hu, Y., Yin, J., Xu, H., & Pan, Y. (2024). Effects of different AI-driven chatbot feedback on learning outcomes and brain activity. Iqbal, S., Ahmad, S., Akkour, K., AlHadab, F., AlHuwaiji, S., & Alghamadi, M. (2021). Audience response system (ARS); A way to foster formative assessment and motivation among medical students. MedEdPublish, 10(1). Islam, M., Kim, D., & Kwon, M. (2020). A comparison of two forms of instruction: Pre-recorded video lectures vs. live Zoom lectures for education in the business management field. Sustainability, 12(19), 8149. Jiranantanagorn, P., Shen, H., Goodwin, R., & Teoh, K. (2015). Classense: A mobile digital backchannel system for monitoring class morale. International Journal of Learning and Teaching, 1(2), 161-167. Kamruzzaman, M., Alanazi, S., Alruwaili, M., Alshammari, N., Elaiwat, S., Abu-Zanona, M., … & Alanazi, B. (2023). AI- and IoT-assisted sustainable education systems during pandemics, such as COVID-19, for smart cities. Sustainability, 15(10), 8354. Kçk, S., & Richardson, J. (2019). A structural equation model of predictors of online learners’ engagement and satisfaction. Online Learning, 23(2). Lin, C., Zhang, Y., & Zheng, B. (2017). The roles of learning strategies and motivation in online language learning: A structural equation modeling analysis. Computers & Education, 113, 75-85. Malekjafarian, A., & Gordan, M. (2024). On the use of an online polling platform for enhancing student engagement in an engineering module. Education Sciences, 14(5), 536. Maraza-Quispe, B. (2024). Impact of the use of gamified online tools: A study with Kahoot and Quizizz in the educational context. International Journal of Information and Education Technology, 14(1), 132-140. Naz, I., & Robertson, R. (2024). Exploring the feasibility and efficacy of ChatGPT3 for personalized feedback in teaching. The Electronic Journal of E-Learning, 22(2), 98-111. Nieboer, M., Jie, L., Willemse, L., Peek, S., Braun, S., & Wouters, E. (2021). Attitudes towards a sensor-feedback technology in gait rehabilitation of patients after stroke. Disability and Rehabilitation: Assistive Technology, 18(6), 889-895. Onesi-Ozigagun, O., Ololade, Y., Eyo-Udo, N., & Ogundipe, D. (2024). Revolutionizing education through AI: A comprehensive review of enhancing learning experiences. International Journal of Applied Research in Social Sciences, 6(4), 589-607. Palvia, S., Aeron, P., Gupta, P., Mahapatra, D., Parida, R., Rosner, R., … & Sindhi, S. (2018). Online education: Worldwide status, challenges, trends, and implications. Journal of Global Information Technology Management, 21(4), 233-241. Pang, S., Zhang, Y., Su, Q., & Wang, Y. (2024). Innovation in spectral analysis education: Integration of OBE, SPOC, and ideological elements for practical exploration. Advances in Educational Technology and Psychology, 8(1). Pardo, A., Jovanović, J., Dawson, S., Gašević, D., & Mirriahi, N. (2017). Using learning analytics to scale the provision of personalised feedback. British Journal of Educational Technology, 50(1), 128-138. Peculea, L. (2023). Online engagement and satisfaction of undergraduate engineering students during the COVID-19 pandemic. European Proceedings of Educational Sciences. Pereira, Í., Fernandes, E., & Flores, M. (2021). Teacher education during the COVID-19 lockdown: Insights from a formative intervention approach involving online feedback. Education Sciences, 11(8), 400. Popa, D., Repanovici, A., Lupu, D., Norel, M., & Coman, C. (2020). Using mixed methods to understand teaching and learning in COVID-19 times. Sustainability, 12(20), 8726. Raju, D., Murthy, G., Khade, S., Padmaja, B., Yashavanth, B., Kumar, S., … & Srinivasarao, C. (2021). Understanding learner behaviour in online courses through learning analytics. Asian Journal of Agricultural Extension, Economics & Sociology, 381-390. Ranganathan, H., Singh, D., Kumar, S., Sharma, S., Chua, S., Ahmad, N., … & Harikrishnan, K. (2021). Readiness towards online learning among physiotherapy undergraduates. BMC Medical Education, 21(1). Redmond, P., Heffernan, A., Abawi, L., Brown, A., & Henderson, R. (2018). An online engagement framework for higher education. Online Learning, 22(1). Seo, S., Kim, M., & Kim, Y. (2024). A design and implementation of an online video lecture system based on facial expression recognition. International Journal on Advanced Science, Engineering and Information Technology, 14(3), 866-872. Shannon, C., & Clarke, D. (2022). How teacher presence engages and supports online female postgraduate students at an Australian regional university. ASCILITE Publications, e22098. Soffer, T., & Cohen, A. (2019). Students' engagement characteristics predict success and completion of online courses. Journal of Computer Assisted Learning, 35(3), 378-389. Song, C., Shin, S., & Shin, K. (2024). Implementing the dynamic feedback-driven learning optimization framework: A machine learning approach to personalize educational pathways. Applied Sciences, 14(2), 916. Suhadi, A., & Mustaffa, N. (2023). Online learning makes student perform better: A quantitative study of interactivity during class and academic performance among USIM students. Al-I’lam - Journal of Contemporary Islamic Communication and Media, 3(1). Suharti, D., Suherdi, D., & Setyarini, S. (2021). Exploring students’ learning engagement in EFL online classroom. Advances in Social Science, Education and Humanities Research, 533, 112-118. Sziegat, H. (2024). Virtual simulation games in entrepreneurship education: Status quo and prospects. European Conference on Games Based Learning, 18(1), 1099-1106. Vistorte, A., Deroncele-Acosta, Á., Ayala, J., Barrasa, Á., López-Granero, C., & Martí-González, M. (2024). Integrating artificial intelligence to assess emotions in learning environments: A systematic literature review. Frontiers in Psychology, 15. Wise, A., Zhao, Y., & Hausknecht, S. (2014). Learning analytics for online discussions: Embedded and extracted approaches. Journal of Learning Analytics, 1(2), 48-71. Wong, B., & Li, K. (2019). A review of learning analytics intervention in higher education (2011-2018). Journal of Computers in Education, 7(1), 7-28. Yeung, M., & Yau, A. (2021). A thematic analysis of higher education students’ perceptions of online learning in Hong Kong under COVID-19: Challenges, strategies and support. Education and Information Technologies, 27(1), 181-208. Yilmaz, R., Bakhaidar, M., Alsayegh, A., Hamdan, N., Fazlollahi, A., Tee, T., … & Maestro, R. (2024). Real-time multifaceted artificial intelligence vs in-person instruction in teaching surgical technical skills: A randomized controlled trial. Scientific Reports, 14(1). Zheng, L., Niu, J., & Zhong, L. (2021). Effects of a learning analytics-based real-time feedback approach on knowledge elaboration, knowledge convergence, interactive relationships and group performance in CSCL. British Journal of Educational Technology, 53(1), 130-149.

Từ khóa:

phản hồi thời gian thực, giáo dục trực tuyến, AI trong giáo dục, tương tác học tập, động lực sinh viên, cá nhân hóa.

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