Examining the impact of student characteristics on E-learning satisfaction: A structural equation modeling approach

Dian Zhao ,Jingshu Wang ,Yun Bai ,Rui Jin ,Yuqi Zhong ,Yang Liu ,Ruikai Yuan

doi:10.24294/jipd8406

Home - JIPD - Volume 8 - Issue 16

Submit to JIPD

Journal Browser

Volume | Year

Issue

• Current lssue

View All

News and Announcements

View All

Examining the impact of student characteristics on E-learning satisfaction: A structural equation modeling approach

Dian Zhao

Jingshu Wang

Yun Bai

Rui Jin

Yuqi Zhong

Yang Liu

Ruikai Yuan

Journal of Infrastructure Policy and Development 2024, 8(16), 8406; https://doi.org/10.24294/jipd8406

Submitted：05 Aug 2024

Accepted：12 Sept 2024

Published：18 Dec 2024

Download PDF(545.11KB)

XML

Abstract

E-learning has become an integral part of higher education, significantly influencing the teaching and learning landscape. This study investigates the impact of student characteristics such as gender, grade, and major on E-learning satisfaction. Utilizing Structural Equation Modeling (SEM) and collecting data through 527 valid questionnaires from Nanjing Normal University students, this research reveals the nuanced relationships between these variables and E-learning satisfaction. The findings indicate that gender, grade, and major significantly and positively impact student satisfaction with E-learning, highlighting the need for tailored E-learning resources to meet diverse student needs. The study underscores the importance of continuous improvement in E-learning resources and platforms to enhance student satisfaction. This research contributes to the understanding of effective E-learning strategies in higher education institutions.

Keywords

References

Al-Azawei, A., Parslow, P., & Lundqvist, K. (2016). 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. https://doi.org/10.14742/ajet.2741

Al-Fraihat, D., Joy, M., Masa’deh, R., et al. (2020). Evaluating E-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/j.chb.2019.08.004

Alghabban, W. G., & Hendley, R. (2022). Perceived Level of Usability as an Evaluation Metric in Adaptive E-learning. SN Computer Science, 3(3). https://doi.org/10.1007/s42979-022-01138-5

Ali, W. G. M. (2012). Factors affecting nursing student’s satisfaction with E-learning experience in King Khalid University. Saudi Arabia. Dimension, 5, 13-18.

Aljaraideh, Y., & Al Bataineh, K. (2019). Jordanian Students’ Barriers of Utilizing Online Learning: A Survey Study. International Education Studies, 12(5), 99. https://doi.org/10.5539/ies.v12n5p99

Al‐Maskari, A., & Sanderson, M. (2010). A review of factors influencing user satisfaction in information retrieval. Journal of the American Society for Information Science and Technology, 61(5), 859–868. Portico. https://doi.org/10.1002/asi.21300

Almusharraf, N., & Khahro, S. (2020). Students Satisfaction with Online Learning Experiences during the COVID-19 Pandemic. International Journal of Emerging Technologies in Learning (IJET), 15(21), 246. https://doi.org/10.3991/ijet.v15i21.15647

Alqahtani, M. A., Alamri, M. M., Sayaf, A. M., et al. (2022). Exploring student satisfaction and acceptance of E-learning technologies in Saudi higher education. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.939336

Al-Rahmi, A. M., Shamsuddin, A., Alturki, U., et al. (2021). The Influence of Information System Success and Technology Acceptance Model on Social Media Factors in Education. Sustainability, 13(14), 7770. https://doi.org/10.3390/su13147770

Baherimoghadam, T., Hamedani, S., mehrabi, M., et al. (2021). The effect of learning style and general self-efficacy on satisfaction of e-Learning in dental students. BMC Medical Education, 21(1). https://doi.org/10.1186/s12909-021-02903-5

Benabbes, K., Housni, K., Hmedna, B., et al. (2023). A New Hybrid Approach to Detect and Track Learner’s Engagement in e-Learning. IEEE Access, 11, 70912–70929. https://doi.org/10.1109/access.2023.3293827

Bhuasiri, W., Xaymoungkhoun, O., Zo, H., et al. (2012). Critical success factors for E-learning in developing countries: A comparative analysis between ICT experts and faculty. Computers & Education, 58(2), 843–855. https://doi.org/10.1016/j.compedu.2011.10.010

Cazan, A.-M., Cocoradă, E., & Maican, C. I. (2016). Computer anxiety and attitudes towards the computer and the internet with Romanian high-school and university students. Computers in Human Behavior, 55, 258–267. https://doi.org/10.1016/j.chb.2015.09.001

Chang, C.-S., Liu, E. Z.-F., Sung, H.-Y., et al. (2013). Effects of online college student’s Internet self-efficacy on learning motivation and performance. Innovations in Education and Teaching International, 51(4), 366–377. https://doi.org/10.1080/14703297.2013.771429

Chen, L.-H., & Kuo, Y.-F. (2011). Understanding E-learning service quality of a commercial bank by using Kano’s model. Total Quality Management & Business Excellence, 22(1), 99–116. https://doi.org/10.1080/14783363.2010.532345

Chen, W. S., & Tat Yao, A. Y. (2016). An Empirical Evaluation of Critical Factors Influencing Learner Satisfaction in Blended Learning: A Pilot Study. Universal Journal of Educational Research, 4(7), 1667–1671. https://doi.org/10.13189/ujer.2016.040719

Christensen, J. M. (2021). Student Preferences and Decisions for Online or In-Person Class Sessions in Blended Learning [PhD thesis]. Brigham Young University.

Chuang, S.-C., Lin, F.-M., & Tsai, C.-C. (2015). An exploration of the relationship between Internet self-efficacy and sources of Internet self-efficacy among Taiwanese university students. Computers in Human Behavior, 48, 147–155. https://doi.org/10.1016/j.chb.2015.01.044

Clemes, M. D., Gan, C. E. C., & Kao, T.-H. (2008). University Student Satisfaction: An Empirical Analysis. Journal of Marketing for Higher Education, 17(2), 292–325. https://doi.org/10.1080/08841240801912831

Closson, R. B., & Stokes, C. (n.d.). Increasing Adult Learner Engagement in E-learning Courses through Learner Case Writing. Encyclopedia of Information Communication Technologies and Adult Education Integration, 971–984. https://doi.org/10.4018/978-1-61692-906-0.ch059

Cocea, M., & Weibelzahl, S. (2009). Log file analysis for disengagement detection in e-Learning environments. User Modeling and User-Adapted Interaction, 19(4), 341–385. https://doi.org/10.1007/s11257-009-9065-5

Engelbrecht, E. (2005). Adapting to changing expectations: Post-graduate students’ experience of an E-learning tax program. Computers & Education, 45(2), 217–229. https://doi.org/10.1016/j.compedu.2004.08.001

Eze, S. C., Chinedu-Eze, V. C. A., Okike, C. K., et al. (2020). Factors influencing the use of E-learning facilities by students in a private Higher Education Institution (HEI) in a developing economy. Humanities and Social Sciences Communications, 7(1). https://doi.org/10.1057/s41599-020-00624-6

Fredricks, J. A., Blumenfeld, P. C., & Paris, A. H. (2004). School Engagement: Potential of the Concept, State of the Evidence. Review of Educational Research, 74(1), 59–109. https://doi.org/10.3102/00346543074001059

Gantasala, V. P., Gantasala, S. B., Al Tawil, T. N., et al. (2021). Quality of learning experience, student satisfaction and perceived overall experience in the COVID-19 context. Journal of Applied Research in Higher Education, 14(1), 507–520. https://doi.org/10.1108/jarhe-12-2020-0440

Gardner, J., & Brooks, C. (2018). Student success prediction in MOOCs. User Modeling and User-Adapted Interaction, 28(2), 127–203. https://doi.org/10.1007/s11257-018-9203-z

Gašević, D., Dawson, S., Rogers, T., et al. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68–84. https://doi.org/10.1016/j.iheduc.2015.10.002

Gupta, V., & Jain, N. (2017). Harnessing information and communication technologies for effective knowledge creation. Journal of Enterprise Information Management, 30(5), 831–855. https://doi.org/10.1108/jeim-10-2016-0173

Halverson, L. R., & Graham, C. R. (2019). Learner Engagement in Blended Learning Environments: A Conceptual Framework. Online Learning, 23(2). https://doi.org/10.24059/olj.v23i2.1481

Hoi, V. N., & Le Hang, H. (2021). The structure of student engagement in online learning: A bi‐factor exploratory structural equation modelling approach. Journal of Computer Assisted Learning, 37(4), 1141–1153. Portico. https://doi.org/10.1111/jcal.12551

Hollenbeck, C. R., Zinkhan, G. M., & French, W. (2005). Distance Learning Trends and Benchmarks: Lessons from an Online MBA Program. Marketing Education Review, 15(2), 39–52. https://doi.org/10.1080/10528008.2005.11488904

Hong, J.-C., Lee, Y.-F., & Ye, J.-H. (2021). Procrastination predicts online self-regulated learning and online learning ineffectiveness during the coronavirus lockdown. Personality and Individual Differences, 174, 110673. https://doi.org/10.1016/j.paid.2021.110673

Hu, P. J.-H., & Hui, W. (2012). Examining the role of learning engagement in technology-mediated learning and its effects on learning effectiveness and satisfaction. Decision Support Systems, 53(4), 782–792. https://doi.org/10.1016/j.dss.2012.05.014

Jafari Navimipour, N., & Zareie, B. (2015). A model for assessing the impact of E-learning systems on employees’ satisfaction. Computers in Human Behavior, 53, 475–485. https://doi.org/10.1016/j.chb.2015.07.026

Kember, D., & Ginns, P. (2012). Evaluating teaching and learning: A practical handbook for colleges, universities and the scholarship of teaching. Routledge, 66, 375-377. https:/doi.org/10.1007/s10734- 012-9557-9. https://doi.org/10.1007/s10734-012-9557-9

Kew, S. N., & Tasir, Z. (2021). Learning Analytics in Online Learning Environment: A Systematic Review on the Focuses and the Types of Student-Related Analytics Data. Technology, Knowledge and Learning, 27(2), 405–427. https://doi.org/10.1007/s10758-021-09541-2

Kılıç-Çakmak, E., Karataş, S., & Ocak, M. (2009). An analysis of factors affecting community college students' expecta-tions on E-learning. Quarterly Review of Distance Education, 10(4), 351-361.

Kizilcec, R. F., Pérez-Sanagustín, M., & Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Computers & Education, 104, 18–33. https://doi.org/10.1016/j.compedu.2016.10.001

Krithika L.B, & Lakshmi Priya GG. (2016). Student Emotion Recognition System (SERS) for E-learning Improvement Based on Learner Concentration Metric. Procedia Computer Science, 85, 767–776. https://doi.org/10.1016/j.procs.2016.05.264

Kumar, K. L., & Owston, R. (2016). Evaluating E-learning accessibility by automated and student-centered methods. Educational Technology Research and Development, 64(2), 263–283. https://doi.org/10.1007/s11423-015-9413-6

Kumar, P., Saxena, C., & Baber, H. (2021). Learner-content interaction in E-learning- the moderating role of perceived harm of COVID-19 in assessing the satisfaction of learners. Smart Learning Environments, 8(1). https://doi.org/10.1186/s40561-021-00149-8

Kumar, R. (2022). E-learning programs in executive education: effects of perceived quality and perceived value on self-regulation and motivation. Higher Education, Skills and Work-Based Learning, 12(6), 1025–1039. https://doi.org/10.1108/heswbl-07-2022-0149

Lee, B. C., Yoon, J. O., and Lee, I. (2009). Learners’ acceptance of E-learning in South Korea: Theories and results. Comput. Educ. 53, 1320-1329. https://doi.org/10.1016/j.compedu.2009.06.014

Lee, J.-W. (2010). Online support service quality, online learning acceptance, and student satisfaction. The Internet and Higher Education, 13(4), 277–283. https://doi.org/10.1016/j.iheduc.2010.08.002

Liaw, S.-S., & Huang, H.-M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in E-learning environments. Computers & Education, 60(1), 14–24. https://doi.org/10.1016/j.compedu.2012.07.015

Martin, F., & Bolliger, D. U. (2018). Engagement Matters: Student Perceptions on the Importance of Engagement Strategies in the Online Learning Environment. Online Learning, 22(1). https://doi.org/10.24059/olj.v22i1.1092

Matthew, U. O., Kazaure, J. S., & Okafor, N. U. (2021). Contemporary development in E-learning education, cloud compu-ting technology & internet of things. EAI Endorsed Transactions on Cloud Systems, 7(20), e3-e3.

Mota, F. P. B., & Cilento, I. (2020). Competence for internet use: Integrating knowledge, skills, and attitudes. Computers and Education Open, 1, 100015. https://doi.org/10.1016/j.caeo.2020.100015

Namoun, A., & Alshanqiti, A. (2020). Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review. Applied Sciences, 11(1), 237. https://doi.org/10.3390/app11010237

Orvis, K. A., Fisher, S. L., & Wasserman, M. E. (2009). Power to the people: Using learner control to improve trainee reactions and learning in web-based instructional environments. Journal of Applied Psychology, 94(4), 960–971. https://doi.org/10.1037/a0014977

Panigrahi, R., Srivastava, P. R., & Panigrahi, P. K. (2020). Effectiveness of E-learning: the mediating role of student engagement on perceived learning effectiveness. Information Technology & People, 34(7), 1840–1862. https://doi.org/10.1108/itp-07-2019-0380

Ruthotto, I., Kreth, Q., Stevens, J., et al. (2020). Lurking and participation in the virtual classroom: The effects of gender, race, and age among graduate students in computer science. Computers & Education, 151, 103854. https://doi.org/10.1016/j.compedu.2020.103854

Safsouf, Y., Mansouri, K., & Poirier, F. (2020). An Analysis to Understand the Online Learners’ Success in Public Higher Education in Morocco. Journal of Information Technology Education: Research, 19, 087–112. https://doi.org/10.28945/4518

Sampson, P., Leonard, J., Ballenger, J., & Coleman, J. (2010). Student satisfaction of online courses for educational leader-ship. Available online: https://eric.ed.gov/?id=EJ914153 (accessed on 2 September 2023).

Sun, P.-C., Tsai, R. J., Finger, G., et al. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183–1202. https://doi.org/10.1016/j.compedu.2006.11.007

Tarhini, A., Hone, K., & Liu, X. (2014). The effects of individual differences on E-learning users’ behaviour in developing countries: A structural equation model. Computers in Human Behavior, 41, 153–163. https://doi.org/10.1016/j.chb.2014.09.020

Taylor, P. S. (2007). Can clickers cure crowded classes? Maclean’s, 120(26,27), 73.

Thandevaraj, E. J., Gani, N. A. N., & Nasir, M. K. M. (2021). A Review of Psychological Impact on Students Online Learning during Covid-19 in Malaysia. Creative Education, 12(06), 1296–1306. https://doi.org/10.4236/ce.2021.126097

Wisloski, J. (2011). Online education study: As enrollment rises, institutions see online education as a ‘critical part’ of growth, Online Education Information. Available online: https://www.geteducated.com/latest-onlinE-learning-news-and-research/461-online-education-study-increasing-enrollment (accessed on 2 June 2023).

Wixom, B. H., & Todd, P. A. (2005). A Theoretical Integration of User Satisfaction and Technology Acceptance. Information Systems Research, 16(1), 85–102. https://doi.org/10.1287/isre.1050.0042

Yeh, Y.C, & Chu, L.-H. (2018). The mediating role of self-regulation on harmonious passion, obsessive passion, and knowledge management in E-learning. Educational Technology Research and Development, 66(3), 615–637. https://doi.org/10.1007/s11423-017-9562-x

Zhao, Y., Wang, N., Li, Y., et al. (2021). Do cultural differences affect users’ e‐learning adoption? A meta‐analysis. British Journal of Educational Technology, 52(1), 20–41. Portico. https://doi.org/10.1111/bjet.13002

Previous
article in this issue

Next
article in this issue