Factors Impacting Satisfaction with Blended Learning Among Private College Students in Mianyang, China

Main Article Content

Wenbo Li

Abstract

Purpose: This research aimed to examine the factors of task technology fit, confirmation, cognitive presence, teaching presence, social presence, and learner-instructors interaction to impact blended learning satisfaction for two private college students in Mianyang, China. The research population targets undergraduates who majored in art and design subjects. Research design and methodology: This research applied the quantitative method and questionnaire as instruments. Before distributing the questionnaires, Item-Objective Congruence (IOC) and a pilot test of Cronbach’s Alpha were used to test validity and reliability. Data was analyzed by utilizing Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) to validate the model’s goodness of fit and confirm the causal relationship among variables for hypothesis testing. This research surveyed 500 students through an online survey and tested the hypotheses. Results: Cognitive presence has a significant impact on social presence and satisfaction. Teaching presence has a strong impact on cognitive presence and no significant impact on satisfaction. Learner-instructor interaction has a significant impact on satisfaction. Confirmation and social presence have no impact on satisfaction. Conclusions: College managers should improve the IT system to fit learning tasks and help teachers to raise their abilities to enhance teaching effectiveness. Students should obtain more training to improve their cognitive abilities using various approaches

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Li, W. (2024). Factors Impacting Satisfaction with Blended Learning Among Private College Students in Mianyang, China. AU-GSB E-JOURNAL, 17(1), 115-128. https://doi.org/10.14456/augsbejr.2024.12
Section
Articles
Author Biography

Wenbo Li

School of Art and Design, Tianfu College of Southwestern University of Finance and Economics, China.

References

Abbas, I. Z. (2018). Blended learning and student satisfaction: An investigation into an EAP writing course. Advances in Language and Literary Studies, 9(1), 102–105. https://doi.org/10.7575/aiac.alls.v.9n.1p.102

Akyol, Z., & Garrison, D. R. (2008). The development of a community of inquiry over time in an online course: Understanding the progression and integration of social, cognitive, and teaching presence. Journal of Asynchronous Learning Networks, 12(3-4), 3-22. https://doi.org/10.24059/olj.v12i3-4.1680

Allen, I. E., & Seaman, J. (2010). Learning on demand: Online education in the United States. Transformative Professional Development for Teachers, 7(5), 1-10.

Al-Mamary, Y. H., & Shamsuddin, A. (2015). Testing of the technology acceptance model in context of Yemen. Mediterranean Journal of Social Sciences, 6(4), 268-273 https://doi.org/10.5901/mjss.2015.v6n4s1p268

Arbaugh, J. B., & Duray, R. (2001). Technological and structural characteristics, student learning and satisfaction with web-based courses an exploratory study of two on-line MBA programs. Manage. Learn. 33(3), 331–347. https://doi.org/10.5465/apbpp.2001.6133570

Awang, Z. (2012). Structural equation modeling using AMOS graphic (5th ed.). Penerbit Universiti Teknologi MARA.

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238–246. https://doi.org/10.1037/0033-2909.107.2.238

Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological bulletin, 88(3), 588–606. https://doi.org/10.1037/0033-2909.88.3.588

Bhattacherjee, A. (2001). An empirical analysis of the antecedents of electronic commerce service continuance. Decis Support Syst, 32(2), 201–214. https://doi.org/10.1016/s0167-9236(01)00111-7

Bhattacherjee, A. (2001a). Understanding information systems continuance: An expectation confirmation model. MIS Quarterly, 25(3), 351. https://doi.org/10.2307/3250921

Bollen, K. A. (1989). Structural Equations with Latent Variables (1st ed.). John Wiley & Sons, Inc.

Bordoloi, R., Das, P., & Das, K. (2021). Perception towards online/blended learning at the time of Covid-19 pandemic: an academic analysis in the Indian context. Asian Association of Open Universities Journal, 16(1), 41-60. https://doi.org/10.1108/aaouj-09-2020-0079

Boud, D., & Soler, R. (2016). Sustainable assessment revisited. Assessment and Evaluation in Higher Education, 41(3), 400-413. https://doi.org/10.1080/02602938.2015.1018133

Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge Taylor & Francis Group.

Cheng, Y. M. (2013). Exploring the roles of interaction and flow in explaining nurses' e-learning acceptance. Nurse Education Today, 33(1), 73-80. https://doi.org/10.1016/j.nedt.2012.02.005

Cheng, Y. M. (2014a). Extending the expectation-confirmation model with quality and flow to explore nurses continued blended e-learning intention. Information Technology and People, 27(3), 230-258. https://doi.org/10.1108/itp-01-2013-0024

Cheng, Y. M. (2019). How does task-technology fit influence cloud-based e-learning continuance and impact? Education þ Training, 61(4), 480-499. https://doi.org/10.1108/et-09-2018-0203

Cheung, K. S., Lam, J., Lau, N., & Shim, C. (2010). A paradigm in instructional design to support blended learning. Proceedings from the International Conference on ICT in Teaching and Learning (1st ed.). Sims University.

Cooper, D. R., & Schindler, P. (2011). Business Research Methods (11th ed.). McGraw Hill/Irwin.

Delfino, M., & Manca, S. (2007). The expression of social presence using figurative language in a web-based learning environment. Computers in Human Behavior, 23, 2190-2211. https://doi.org/10.1016/j.chb.2006.03.001

DeLone, W., & McLean, E. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19(4), 9 -30. https://doi.org/10.1080/07421222.2003.11045748

DeLone, W. H., & McLean, E. R. (1992). Information Systems Success: The Quest for the Dependent Variable. Journal of Management Information Systems, 3(1), 60–95. https://doi.org/10.1287/isre.3.1.60

Dempsey, P. R., & Zhang, J. (2019). Re-Examining the Construct Validity and Causal Relationships of Teaching, Cognitive, and Social Presence in Community of Inquiry Framework. Online Learning Journal, 23(1). https://doi.org/10.24059/olj.v23i1.1419

Deng, L., & Peng, Z. M. (2017). Future-oriented Teaching Blueprint: A Review of Teaching 2030 in America, Open Education Research, 23(1). https://doi10.13966/j.cnki.kfjyyj.2017.01.005

Dikko, M. (2016). Establishing Construct Validity and Reliability: Pilot Testing of a Qualitative Interview for Research in Takaful (Islamic Insurance). Qualitative Report, 21(3), 1-10. https://doi.org/10.46743/2160-3715/2016.2243

Doll, W., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly, 18(4), 453–461. https://doi.org/10.2307/249524

Fearnley, M. R., & Amora, J. T. (2020). Learning management system adoption in higher education using the extended technology acceptance model. IAFOR Journal of Education, 8(2), 89–106. https://doi.org/10.22492/ije.8.2.05

Fornell, C. G., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 50. https://doi.org/10.2307/3150979

Freeze, R. D., Alshare, K., Lane, P., & Wen, J. (2010). IS Success Model in E-Learning Context Basedon Students' Perceptions? Journal of Information Systems Education, 21(2), 1-10.

Garrison, D. R. (2011). E-Learning in the 21st century: A framework for research and practice (1st ed.). Routedge

Garrison, D. R., Anderson, T., & Archer, W. (1999). Critical Inquiry in a Text-Based Environment: Computer Conferencing in Higher Education. The Internet and Higher Education, 2(1), 87-105. https://doi.org/10.1016/s1096-7516(00)00016-6

Garrison, D. R., & Cleveland-Innes, M. (2005). Facilitating cognitive presence in online learning: Interaction is not enough. American Journal of Distance Education, 19(3), 133–148. https://doi.org/10.1207/s15389286ajde1903_2

Garrison, D. R. M., Cleveland, I., & Fung, T. S. (2010). Exploring causal relationships among teaching, cognitive and social presence: Student perceptions of the community of inquiry framework. The Internet and Higher Education, 13, 31-36. https://doi.org/10.1016/j.iheduc.2009.10.002

Gašević, D., Adesope, O., Joksimović, S., & Kovanović, V. (2015). Externally-facilitated regulation scaffolding and role assignment to develop cognitive presence in asynchronous online discussions. The internet and higher education, 24, 53-65. https://doi.org/10.1016/j.iheduc.2014.09.006

Gefen, D., & Straub, D. W. (2003). Gender differences in the perception and use of email: An extension to the technology acceptance model. MIS Quarterly, 21(4), 389–400. https://doi.org/10.2307/249720

Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236. https://doi.org/10.2307/249689

Green, K. R., & Chewning, H. L. (2020). The fault in our systems: LMS as a vehicle for critical pedagogy. Tec Trends, 64, 423–431. https://doi.org/10.1007/s11528-020-00480-w

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed). Pearson Prentice Hall.

Hair, J. F., Celsi, M., Ortinau, D. J., & Bush, R. P. (2010). Essentials of marketing research. McGraw-Hill/Irwin.

Hair, J. F., Money, A. H., Samouel, P., & Page, M. (2007). Research Methods for Business. Journal of Education Training, 49(4), 336–337. https://doi.org/10.1108/et.2007.49.4.336.2

Hamid, M. R., Sami, W., & Sidek, M. (2017). Discriminant Validity Assessment: Use of Fornell & Larcker Criterion versus HTMT Criterion. In Journal of Physics: Conference Series, 890(1), 012163 https://doi.org/10.1088/1742-6596/890/1/012163

Hilliard, L. P., & Stewart, M. K. (2019). Time well spent: creating a community of inquiry in blended first-year writing courses. The Internet and Higher Education, 41, 11-24. https://doi.org/10.1016/j.iheduc.2018.11.002

Hinampas, R. T., Murillo, C. R., Tan, D. A., & Layosa, R. U. (2018). Blended learning approach: effect on students’ academic achievement and practical skills in science laboratories. International Journal of Scientific and Technology Research, 7(11), 63-69.

Horzum, M. B. (2017). Interaction, Structure, Social Presence, and Satisfaction in Online Learning, Eurasia Journal of Mathematics, Science & Technology Education, 11(3), 505-512. https://doi.org/10.12973/eurasia.2014.1324a

Huang, C. H. (2021). Using PLS-SEM Model to Explore the Influencing Factors of Learning Satisfaction in Blended Learning. Educ. Sci., 11(5), 249. https://doi.org/10.3390/educsci11050249

Iahad, N. A., Mirabolghasemi, M., & Huspi, S. H. (2012). A blended community of inquiry approach: the usage of social network as a support for course management system. International Conference on Computer and Information Science, IEEE, Kuala Lumpur, 1, 180-183. https://doi.org/10.1109/iccisci.2012.6297235

Janson, A., Söllner, M., & Leimeister, J. M. (2017). Individual Appropriation of Learning Management Systems—Antecedents and Consequences. AIS Transactions on Human-Computer Interaction, 9(3), 173-201. https://doi.org/10.17705/1thci.00094

Joksimović, S., Gaševic, D., Kovanović, V., Riecke, B. E., & Hatala, M. (2015). Social presence in online discussions as a process predictor of academic outcomes. Journal of Computer Assisted Learning, 31(6), 638–654. https://doi.org/10.1111/jcal.12107

Joshi, A., Vinay, M., & Bhaskar, P. (2021). Impact of corona virus pandemic on the Indian education sector: perspectives of teachers on online teaching and assessments. Interactive Technology and Smart Education, 18(2).205-226. https://doi.org/10.1108/itse-06-2020-0087

Jusoff, K., & Khodabandelou, R. (2009). Preliminary study on the role of social presence in blended learning environment in higher education. International Education Studies, 2(4), 79-83. https://doi.org/10.5539/ies.v2n4p79

Khan, I. U. (2017). Predicting the acceptance of MOOCs in a developing country: Application of Task-Technology Fit Model, Social Motivation, and Self-determination Theory (1st ed.). Telematics and Informatics. https://doi.org/10.1016/j.tele.2017.09.009

Khan, K. A., & Tariq, R. S. M. (2020). Online Education & MOOCs: Teacher Self-Disclosure in Online Education and a Mediating Role of Social Presence. South Asian Journal of Management Sciences, 14(1), 142-158. https://doi.org/10.21621/sajms.2020141.08

Kozan, K., & Richardson, J. C. (2014). Interrelationships between and among social, teaching, and cognitive presence. The Internet and Higher Education, 21, 68-73. https://doi.org/10.1016/j.iheduc.2013.10.007

Law, G., & Li, N. (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. https://doi.org/10.1016/j.compedu.2019.02.021

Lee, M. C. (2010). Explaining and Predicting Users’ Continuance Intention Toward E-learning: An Extension of the Expectation–Confirmation Model. Computers & Education 54(2), 506–16. https://doi.org/10.1016/j.compedu.2009.09.002

Leong, C. M., Goh, C. F., Ismail, F., Tan, O. K., & Ong, C. H. (2021). E-learning satisfaction: Investigating Gender Differences. International Journal of Electronic Commerce Studies, 12(1), 1-28. https://doi.org/10.7903/ijecs.1774

Lim, D. H., & Morris, M. L. (2009). Learner and instructional factors influencing learning outcomes within a blended learning environment. Educational Technology and Society, 12(4), 282-293.

Lin, C. H., Zheng, B., & Zhang, Y. (2017). Interactions and learning outcomes in online language courses. British Journal of Educational Technology, 48(3), 730-748. https://doi.org/10.1111/bjet.12457

Liu, P. (2021). Empirical Research of Complex Adaptive Blended Learning in English Intensive Reading Teaching. Overseas English, 11, 1-10.

Lwoga, E. T., & Komba, M. (2015). Antecedents of continued usage intentions of web-based learning management system in Tanzania. Education þ Training, 57(7), 738-756. https://doi.org/10.1108/et-02-2014-0014

MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure models. Psychological Methods, 1, 130-149. https://doi.org/10.1037/1082-989x.1.2.130

Maddrell, J. A., Morrison, G. R., & Watson, G. S. (2017). Presence and learning in a community of inquiry. Distance Education, 38(2), 245-258. https://doi.org/10.1080/01587919.2017.1322062

McGill, J., & Klobas, J. E. (2009). A task-technology fit view of learning management system impact. Computers & Education, 52, 496508.

Mirabolghasemi, M., Choshaly, S. H., & Iahad, N. A. (2019). Using the HOT-fit model to predict the determinants of E-learning readiness in higher education: a developing country’s perspective. Education and Information Technologies, 24(6), 3555-3576. https://doi.org/10.1007/s10639-019-09945-9

Mirabolghasemi, M., & Iahad, N. A. (2016). Evaluating learning experience through educational social network support in blended learning, Mobile and Blended Learning Innovations for Improved Learning Outcomes (1st ed.) IGI Global.

Mirabolghasemi, M., Reyhaneh, S., & Choshaly, H. (2021). An investigation into the determinants of blended leaning satisfaction from EFL learners’ perspective Interactive. Technology and Smart Education, 18(1), 69-84.

Moore, M. G. (1989). Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1-7. https://doi.org/10.1080/08923648909526659

Moore, M. G., & Kearsley, G. (Ed.). (2012). Distance education: A systematic view of online learning (1st ed.). Wadsworth Cengage Learning.

Murphy, E., & Rodríguez-Manzanares, M. A. (2008). Revisiting transactional distance theory in a context of web-based high-school distance education. Journal of Distance Education, 22(2), 1-14.

Ngan, L. (2011). Effective student project management with peer interaction. In V. Lee, F. L. Wang, S. Cheung & A. Hung (Eds.), Blended learning: Maximization of teaching and learning effectiveness (pp. 178-180). City University of Hong Kong.

Paechter, M., & Maier, B. (2010). Online or face-to-face? Students' experiences and preferences in e-learning. The Internet and Higher Education, 13(4), 292-297. https://doi.org/10.1016/j.iheduc.2010.09.004

Palmer, S. R., & Holt, D. M. (2009). Examining student satisfaction with wholly online learning. Journal of Computer Assisted Learning, 25(2), 101-113. https://doi.org/10.1111/j.1365-2729.2008.00294.x

Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40. https://doi.org/10.1590/0101-60830000000081

Rhode, J., Richter, S., Gowen, P., Miller, T., & Wills, C. (2017). Understanding faculty use of the learning management system. Online Learning, 21(3), 68–86. https://doi.org/10.24059/olj.v21i3.1217

Sambell, K., & Brown, S. (2021). Changing assessment for good: building on the emergency switch to promote future-oriented assessment and feedback designs. in Baughan, P. (Ed.), Assessment and Feedback in a Post-Pandemic Era: A Time for Learning and Inclusion. Advance HE.

Seta, H. B., Wati, T., Muliawati, A., & Hidayanto, A. N. (2018). E-learning success model: An Extention of Delone&McLean is' success model. Indonesian Journal of Electrical Engineering and Informatics, 6(3),281-291. https://doi.org/10.11591/ijeei.v6i3.505

Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282–286. https://doi.org/10.1016/j.jfoodeng.2005.02.010

Shea, P., & Bidjerano, T. (2009). Community of inquiry as a theoretical framework to foster “epistemic engagement and cognitive presence in online education. Computers & Education, 52(3), 543–553. https://doi.org/10.1016/j.compedu.2008.10.007

Shea, P., & Bidjerano, T. (2012). Learning presence as a moderator in the Community of Inquiry model. Computers & Education, 59, 316–326. https://doi.org/10.1016/j.compedu.2012.01.011

Shea, P., Pickett, J. A. M., & Pelz, W. E. (2003). A follow-up investigation of “teaching presence in the SUNY Learning Network. Journal of Asynchronous Learning Networks, 7(2), 61-80. https://doi.org/10.24059/olj.v7i2.1856

Sica, C., & Ghisi, M. (2007). The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power. In M.A. Lange (Ed.), Leading - Edge psychological tests and testing research (pp. 27-50). Nova.

Spreng, R., & Chiou, J. (2002). A cross-cultural assessment of the satisfaction formation process. Euro J Mark, 36(7-8), 829–839. https://doi.org/10.1108/03090560210430827

Swan, K., Shea, P., Richardson, J., Ice, P., Garrison, D. R., & Cleveland-Innes, M. (2008). Validating a measurement tool of presence in online communities of inquiry. E-Mentor, 2(24), 1–12.

Swan, K., & Shih, L. F. (2019). On the nature and development of social presence in online course discussions. Journal of Asynchronous Learning Networks, 9(3), 115–136. https://doi.org/10.24059/olj.v9i3.1788

Tan, X., & Kim, Y. (2015). User acceptance of SaaS-based collaboration tools: a case of Google Docs. Journal of Enterprise Information Management, 28(3), 423-442. https://doi.org/10.1108/jeim-04-2014-0039

Tesar, M. (2020). Towards a post-Covid-19 new normality? Physical and social distancing, the move to online and higher education. Policy Futures in Education, 18(5), 556-559. https://doi.org/10.1177/1478210320935671

Thurmond, V. A. (2003). Examination of interaction variables as predictors of students’ satisfaction and willingness to enroll in future web-based courses while controlling for student characteristics [Doctoral dissertation]. University. https://www.bookpump.com/dps/pdf-b/1121814b.pdf

United Nations. (2020, December 2020). Policy Brief: Education During COVID-19 and beyond. www.un.org/development/desa/dspd/wp-content/uploads/sites/22/2020/08/sg_policy_brief_covid-19_and_education_august_2020.pdf (accessed 27 December 2020).

Vaughan, N. D. (2004). Technology in support of faculty learning communities. In M. D. Cox & L. Richlin (Eds.), Building faculty learning communities: New directions for teaching and learning (pp. 101–109). Publish.

Verawardina, U., Asnur, L., Lubis, A. L., Hendriyani, Y., Ramadhani, D., Dewi, I. P., & Sriwahyuni, T. (2020). Reviewing online learning facing the covid-19 outbreak. Talent Development and Excellence, 12, 385-392.

Wichadee, S. (2015). Factors related to faculty members’ attitude and adoption of a learning management system. The Turkish Online Journal of Educational Technology, 14(4), 53–61.

Wieland, N., & Kollias, L. (2020). Online learning before, during and after COVID-19: Observations over 20 years. International Journal of Advanced Corporate Learning, 13(2), 84. https://doi.org/10.3991/ijac.v13i2.16779

Wise, E., Chang, J., Duffy, T., & Del Vale, R. (2004). The effects of teacher social presence on student satisfaction, engagement, and learning. Journal of Educational Computing Research, 31(3), 247-271. https://doi.org/10.2190/v0lb-1m37-rnr8-y2u1

Wu, B., & Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221-232. https://doi.org/10.1016/j.chb.2016.10.028

Wu, J. H., & Wang, Y. M. (2006). Measuring KMS success: A respecification of the DeLone and McLean’s model. Information and Management, 43(6), 728–739. https://doi.org/10.1016/j.im.2006.05.002

Xu, F., Tian, M., Xu, G., Ayala, B. R., & Shen, W. (2017). Understanding Chinese users’ switching behavior of cloud storage services. The Electronic Library, 35(2), 214-232. https://doi.org/10.1108/el-04-2016-0080

Yapici, I. Ü., & Akbayin, H. (2012). The effect of blended learning model on high school students’ biology achievement and on their attitudes towards the internet. The Turkish Online Journal of Educational Technology, 11(2), 228-237.

Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767. https://doi.org/10.1016/j.chb.2010.01.013