Factors Influencing Satisfaction and Perceived Learning Performance in Blended Learning Using The Chaoxing Learning Platform
Abstract
Background and Aim: Despite the success of blended learning, student satisfaction, and perceived learning performance remain key indicators of its effectiveness. While existing studies have explored these factors, most treat technology platforms generically, with limited focus on specific platforms. Therefore, this study investigates the factors influencing satisfaction (SAT) and perceived learning performance (PLP) in blended learning, specifically using the Chaoxing Learning Platform.
Materials and Methods: A quantitative research design was employed using a structured questionnaire, grounded in blended learning theories and the Technology Acceptance Model. The survey targeted students at the School of Intelligent Manufacturing, Zhanjiang University of Science and Technology. Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) were used to analyze the data from 493 valid responses.
Results: The analysis revealed significant positive relationships between the factors and satisfaction. Perceived Ease of Use (PEU), Perceived Usefulness (PU), Social Presence (SP), Teaching Presence (TP), and Cognitive Presence (CP) all contributed to higher satisfaction levels. Additionally, satisfaction was found to be a strong predictor of perceived learning performance, indicating that satisfied students believed they had performed better academically.
Conclusion: This study confirmed five key factors influencing student satisfaction in blended learning using the Chaoxing Learning Platform. It demonstrated a strong positive relationship between satisfaction and perceived learning performance, highlighting the central role of satisfaction in determining educational outcomes in blended learning environments. However, the study's focus on a single institution may limit the generalizability of the findings. Future research should explore diverse contexts and measure actual learning outcomes for a more comprehensive understanding.
Article Details
How to Cite
Section
Articles
Copyright & License
Copyright (c) 2025 International Journal of Sociologies and Anthropologies Science Reviews

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright on any article in the International Journal of Sociologies and Anthropologies Science Reviews is retained by the author(s) under the under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Permission to use text, content, images, etc. of publication. Any user to read, download, copy, distribute, print, search, or link to the full texts of articles, crawl them for indexing, pass them as data to software, or use them for any other lawful purpose. But do not use it for commercial use or with the intent to benefit any business.
References
Al-Azawei, A., Parslow, P., & Lundqvist, K. (2017). Investigating the effect of learning styles in a blended e-learning system: An extension of the technology acceptance model (TAM). Australasian Journal of Educational Technology, 33(2). https://doi.org/10.14742/ajet.2741
Allen, I., & Seaman, J. (2006). Making the Grade: Online Education in the United States Needham. MA: Sloan-C.
Aragon, S. R. (2003). Creating social presence in online environments. New directions for adult and continuing education, 2003(100), 57-68.
Armah, J. K., Bervell, B., & Bonsu, N. O. (2023). Modelling the role of learner presence within the community of inquiry framework to determine online course satisfaction in distance education. Heliyon, 9(5), 15803. doi: 10.1016/j.heliyon.2023.e15803.
Bazelais, P., Doleck, T., & Lemay, D. J. (2018). Investigating the predictive power of TAM: A case study of CEGEP students’ intentions to use online learning technologies. Education and Information Technologies, 23, 93-111.
Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological bulletin, 88(3), 588.
Bu, X. (2019). The blended teaching mode based on Chaoxing learning APP—taking Advanced English course as an example. 2019 2nd International Conference on Education, Economics and Social Science (ICEESS 2019),
Chandra, Y., & Napitupulu, T. A. (2021). Evaluation of student satisfaction in using the learning management system for online learning at XYZ University. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(6), 2810-2816.
Chaw, L. Y., & Tang, C. M. (2018). What Makes Learning Management Systems Effective for Learning? Journal of Educational Technology Systems, 47(2), 152-169. https://doi.org/10.1177/0047239518795828
Chen, Y. (2022). Exploration of Blended Teaching in Comprehensive English Course Based on Chaoxing Mobile Platform. 2022 IEEE 2nd International Conference on Educational Technology (ICET), Beijing, China.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Dziuban, C., Graham, C. R., Moskal, P. D., Norberg, A., & Sicilia, N. (2018). Blended learning: the new normal and emerging technologies. International journal of educational technology in Higher education, 15, 1-16. https://doi.org/https://doi.org/10.1186/s41239-017-0087-5
Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95-105.
Giannousi, M., & Kioumourtzoglou, E. (2016). Cognitive, social, and teaching presence as predictors of students' satisfaction in distance learning. Mediterranean Journal of Social Sciences, 7(2), 439. DOI:10.5901/mjss.2016.v7n2s1p439
Graham, C. R. (2006). Blended learning systems. The handbook of blended learning: Global perspectives, local designs, 1, 3-21.
Güzer, B., & Caner, H. (2014). The past, present and future of blended learning: an in depth analysis of literature. Procedia-social and behavioral sciences, 116, 4596-4603.
Haddad, F. S. (2018). Examining the effect of learning management system quality and perceived usefulness on student’s satisfaction. Journal of Theoretical and Applied Information Technology, 96(23), 8034-8044.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2013). Multivariate Data Analysis. Pearson Education Limited.
Hair, J.F., Black, W.C., Babin, B.J., & Anderson, R.E. (2010). Multivariate Data Analysis. 7th Edition, Pearson, New York.
Horn, M. B., & Fisher, J. F. (2017). New Faces of Blended Learning. Educational Leadership, 74(6), 59-63.
Hu, L. t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55.
Inoue, Y. (2009). Cases on Online and Blended Learning Technologies in Higher Education: Concepts and Practices: Concepts and Practices. IGI Global.
Joo, Y. J., So, H.-J., & Kim, N. H. (2018). Examination of relationships among students' self-determination, technology acceptance, satisfaction, and continuance intention to use K-MOOCs. Computers & Education, 122, 260-272.
Kaffenberger, M. (2021). Modelling the long-run learning impact of the Covid-19 learning shock: Actions to (more than) mitigate loss. International Journal of Educational Development, 81, 102326.
Kasim, N. N. M., & Khalid, F. (2016). Choosing the right learning management system (LMS) for the higher education institution context: A systematic review. International Journal of Emerging Technologies in Learning, 11(6), 55-61.
Keong, T. C., & Keong, K. O. (2021). The Relationship of Teaching, Social and Cognitive Presence with Course Satisfaction in a TESL Programme Course in a Public University in Sabah, East Malaysia. Studies in English Language Teaching, 9(2). https://doi.org/10.22158/selt.v9n2p35
Khalid, M. N., & Quick, D. (2016). Teaching Presence Influencing Online Students' Course Satisfaction at an Institution of Higher Education. International Education Studies, 9(3), 62-70.
Kilag, O. K., Obaner, E., Vidal, E., Castañares, J., Dumdum, J. N., & Hermosa, T. J. (2023). Optimizing Education: Building Blended Learning Curricula with LMS. Excellencia: International Multi-disciplinary Journal of Education (2994-9521), 1(4), 238-250.
Kucuk, S., & Richardson, J. C. (2019). A Structural Equation Model of Predictors of Online Learners' Engagement and Satisfaction. Online Learning, 23(2), 196-216.
Laifa, M., Giglou, R.I. & Akhrouf, S. (2023). Blended Learning in Algeria: Assessing Students’ Satisfaction and Future Preferences Using SEM and Sentiment Analysis. Innov High Educ. 48, 879–905. https://doi.org/10.1007/s10755-023-09658-5
Law, K. M., Geng, S., & Li, T. (2019). Student enrollment, motivation and learning performance in a blended learning environment: The mediating effects of social, teaching, and cognitive presence. Computers & Education, 136, 1-12.
Lin, W.-S., & Wang, C.-H. (2012). Antecedences to continued intentions of adopting e-learning system in blended learning instruction: A contingency framework based on models of information system success and task-technology fit. Computers & Education, 58(1), 88-99.
Liu, Y. (2022). Study on the blended teaching mode of analog electronic technology based on chaoxing learning platform. Advances in Vocational and Technical Education, 4(1), 49-54.
Luo, Y. F., Kang, S., Yang, S. C., & Lu, C. M. (2023). The relationships among Taiwanese youth’s polychronicity, multitasking behavior and perceived learning performance in online learning. Frontiers in Psychology, 14, 1131765.
Ma'arop, A. H., & Embi, M. A. (2016). Implementation of blended learning in higher learning institutions: A review of the literature. International Education Studies, 9(3), 41-52.
Mailizar, M., Burg, D., & Maulina, S. (2021). Examining university students’ behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model. Education and Information Technologies, 26(6), 7057-7077.
Malik, A., & Riasat, M. (2022). Decoding Blended Learning: Historical Development, Definitions and Components. Sukkur IBA Journal of Educational Sciences and Technologies, 2(1), 19-27.
Marsh, H. W., Hau, K.-T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler's (1999) findings. Structural equation modeling, 11(3), 320-341.
Martin, F., Wu, T., Wan, L., & Xie, K. (2022). A Meta-Analysis on the Community of Inquiry Presences and Learning Outcomes in Online and Blended Learning Environments. Online Learning, 26(1), 325-359.
Mirabolghasemi, M., Shasti, R., & Hosseinikhah Choshaly, S. (2021). An investigation into the determinants of blended leaning satisfaction from EFL learners’ perspective. Interactive Technology and Smart Education, 18(1), 69-84.
Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological bulletin, 105(3), 430.
Ofosu-Ampong, K., Boateng, R., Kolog, E. A., & Anning-Dorson, T. (2020). Examining Information Quality and Perceived Learning Performance in a Gamified Environment. 2020 IEEE 22nd Conference on Business Informatics (CBI),
Pikhart, M., & Klímová, B. (2020). eLearning 4.0 as a sustainability strategy for generation Z language learners: Applied linguistics of second language acquisition in younger adults. Societies, 10(2), 38. https://doi.org/10.3390/soc10020038
Rasheed, R. A., Kamsin, A., & Abdullah, N. A. (2020). Challenges in the online component of blended learning: A systematic review. Computers & Education, 144, 103701. https://doi.org/10.1016/j.compedu.2019.103701
Richardson, J. C., Maeda, Y., Lv, J., & Caskurlu, S. (2017). Social presence in relation to students' satisfaction and learning in the online environment: A meta-analysis. Computers in Human Behavior, 71, 402-417.
Salimon, M. G., Sanuri, S. M. M., Aliyu, O. A., Perumal, S., & Yusr, M. M. (2021). E-learning satisfaction and retention: A concurrent perspective of cognitive absorption, perceived social presence and technology acceptance model. Journal of Systems and Information Technology, 23(1), 109-129.
Shah, H. J., & Attiq, S. (2016). Impact of technology quality, perceived ease of use and perceived usefulness in the formation of consumer’s satisfaction in the context of e-learning. Abasyn J. Soc. Sci, 9(1), 124-140.
Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. Leading-edge psychological tests and testing research, 27-50.
Szeto, E. (2015). Community of Inquiry as an instructional approach: What effects of teaching, social and cognitive presences are there in blended synchronous learning and teaching? Computers & Education, 81, 191-201.
Szymkowiak, A., Melović, B., Dabić, M., Jeganathan, K., & Kundi, G. S. (2021). Information technology and Gen Z: The role of teachers, the internet, and technology in the education of young people. Technology in Society, 65, 101565.
Teo, S. C., Lilian, A., & Koo, A. C. (2023). Examining the effects of academic motivation and online learning on Malaysian tertiary students’ psychological well-being and perceived learning performance. Cogent Education, 10(1), 2186025.
Thomas, P. Y. (2010). Towards developing a web-based blended learning environment at the University of Botswana. University of South Africa.
Wijaya, M. I., Suzanna, S., Utomo, D., & Adnizio, K. (2021). Analysing The Impact of Social Presence on Student Satisfaction Through Small Group Discussion in A Synchronous Online Learning. 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM),
Wu, J., & Liu, W. (2013). An Empirical Investigation of the Critical Factors Affecting Students’ Satisfaction in EFL Blended Learning. Journal of Language Teaching and Research, 4, 176-185.
https://doi.org/10.4304/jltr.4.1.176-185
Yan, J., Yang, H., Niu, J., & Chen, Y. (2022). Smart Teaching Reform and Practice of Flipped Classroom in Culture Geography Course Based on Chaoxing Learning Platform. Journal of Education and Learning, 11(6). https://doi.org/10.5539/jel.v11n6p103
Yoo, L., & Jung, D. (2022). Teaching Presence, Self-Regulated Learning and Learning Satisfaction on Distance Learning for Students in a Nursing Education Program. Int J Environ Res Public Health, 19(7). https://doi.org/10.3390/ijerph19074160
Zhu, Z. (2016). New developments of smarter education: from flipped classroom to smart classroom and smart learning space. Open education research, 22(1), 18-26.
Zou, C., Li, P., & Jin, L. (2022). Integrating smartphones in EFL classrooms: Students’ satisfaction and perceived learning performance. Education and Information Technologies, 27(9), 12667-12688.
Zviran, M., Pliskin, N., & Levin, R. (2005). Measuring user satisfaction and perceived usefulness in the ERP context. Journal of computer information systems, 45(3), 43-52.