External and Internal Predictors of Student Satisfaction with Online Learning Achievement
DOI:
https://doi.org/10.24059/olj.v27i3.3627Keywords:
online learning, higher education, student satisfaction, Chinese college students, Covid-19, online learning modelAbstract
Building and testing a framework of interactive and indirect predictors of student satisfaction would help us understand how to improve student online learning experience. The current study proposed that external predictors such as poor technological, environmental, and pedagogical factors would be internalized as negative psychological traits and indirectly predict student satisfaction in online learning. Results of multivariate regressions with 5824 Chinese undergraduate students demonstrated that instructors’ online teaching experience and communication with students had a stronger predictive effect on student satisfaction than wireless network quality and learning environment. Providing after-class reviewing materials to students or having longer self-learning time would not buffer students from negative external factors. Structural equation modeling analysis results showed that inferior technological, environmental, and pedagogical factors would be internalized into negative attitudes and emotions toward online learning and indirectly predict student satisfaction. Our study has implications for better understanding the extensive influence of online learning barriers caused by external conditions and building preventive mechanisms through the improvement of instructors’ teaching experience and communication with students.
References
References
Aguilera-Hermida, A. P. (2020). College students’ use and acceptance of emergency online learning due to COVID-19. International Journal of Educational Research Open, 1, 100011. https://doi.org/10.1016/j.ijedro.2020.100011
Akuratiya, A., & Meddage, N. (2020). Students’ perception of online learning during COVID-19 pandemic: A survey study of IT students. International Journal of Research and Innovation in Social Science, 4(9), 755-758. Retrieved from
https://www.researchgate.net/publication/345140171
Alavudeen, S. S., Easwaran, V., Mir, J. I., Shahrani, S. M., Aseeri, A. A., Khan, N. A., Almodeer, A. M., & Asiri, A. A. (2021). The influence of COVID-19 -related psychological and demographic variables on the effectiveness of e-learning among health care students in the southern region of Saudi Arabia. Saudi Pharmaceutical Journal, 29(7), 775–780. https://doi.org/10.1016/j.jsps.2021.05.009
Al-Emran M, Mezhuyev V, Kamaludin A. (2018).Technology acceptance model in m-learning context: A systematic review. Computers & Education, 125, 389-412. https://doi.org/10.1016/j.compedu.2018.06.008
Al‐hawari, M. A., & Mouakket, S. (2010). The influence of technology acceptance model (TAM) factors on students' e‐satisfaction and e‐retention within the context of UAE e‐learning. Education, Business and Society: Contemporary Middle Eastern Issues, 3(4), 299-314. https://doi.org/10.1108/17537981011089596
Almusharraf, N., & Khahro, S. (2020). Students satisfaction with online learning experiences during the COVID-19 pandemic. International Journal of Emerging Technologies in Learning , 15(21), 246-267. Retrieved from https://www.learntechlib.org/p/218355/.
Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133-148. https://doi.org/10.1080/01587919.2018.1553562
Anderson, T. (2008). Towards a theory of online learning. In T. Anderson (Ed), The theory and practice of online learning (pp.45-74). Edmonton: AU Press.
Artino, A. R. (2009). Think, feel, act: Motivational and emotional influences on military students’ online academic success. Journal of Computing in Higher Education, 21(2), 146-166. https://doi.org/10.1007/s12528-009-9020-9
Asoodar, M., Vaezi, S., & Izanloo, B. (2016). Framework to improve e-learner satisfaction and further strengthen e-learning implementation. Computers in Human Behavior, 63, 704–716. https://doi.org/10.1016/j.chb.2016.05.060
Author.(2020). Deleted for peer review.
Bean, J. P., & Metzner, B. S. (1985). A conceptual model of nontraditional undergraduate student attrition. Journal of Review of Education Research, 55(4), 485-540. https://doi.org/10.3102/00346543055004485
Berry, S. (2022). Creating Inclusive Online Communities: Practices that Support and Engage Diverse Students. Stylus Publishing, LLC.
Bolliger, D. U., & Martindale, T. (2004). Key factors for determining student satisfaction in online courses. International Journal on E-learning, 3(1), 61-67. Retrieved from https://www.learntechlib.org/p/2226/
Chan, S. (1999). The Chinese learner – a question of style. Education + Training, 41(6/7), 294–305. https://doi.org/10.1108/00400919910285345
Chetty, R., Friedman, J. N., Hendren, N., & Stepner, M. (2020). Real-time economics: A new platform to track the impacts of covid-19 on people, businesses, and communities using private sector data. NBER Working Paper, 27431. Retrieved from https://opportunityinsights.org/wp-content/uploads/2020/06/Short_Covid_Paper.pdf
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
Daniels, L. M., & Stupnisky, R. H. (2012). Not that different in theory: Discussing the control-value theory of emotions in online learning environments. The Internet and Higher Education, 15(3), 222-226. https://doi.org/10.1016/j.iheduc.2012.04.002
Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems, 49(1), 5–22. https://doi.org/10.1177/0047239520934018
Dolighan, T., & Owen, M. (2021). Teacher efficacy for online teaching during the COVID-19 pandemic. Brock Education Journal, 30(1), 95–95. https://doi.org/10.26522/brocked.v30i1.851
Drennan, J., Kennedy, J., & Pisarski, A. (2005). Factors affecting student attitudes toward flexible online learning in management education. The Journal of Educational Research, 98(6), 331-338. https://doi.org/10.3200/JOER.98.6.331-338
Flesia, L., Monaro, M., Mazza, C., Fietta, V., Colicino, E., Segatto, B., & Roma, P. (2020). Predicting perceived stress related to the Covid-19 outbreak through stable psychological traits and machine learning models. Journal of clinical medicine, 9(10), 3350. https://doi.org/10.3390/jcm9103350
Gergen, K. J. (2015). An invitation to social construction (3rd ed.). London: SAGE Publications Ltd. https://dx.doi.org/10.4135/9781473921276
Han, J. H., & Sa, H. J. (2022). Acceptance of and satisfaction with online educational classes through the technology acceptance model (TAM): The COVID-19 situation in Korea. Asia Pacific Education Review, 23(3), 403-415. https://doi.org/10.1007/s12564-021-09716-7
Herrington, J., & Oliver, R. (2000). An instructional design framework for authentic learning environments. Educational Technology Research and Development, 48(3), 23–48. https://doi.org/10.1007/BF02319856
Ives, B. (2021). University students experience the covid-19 induced shift to remote instruction. International Journal of Educational Technology in Higher Education, 18(1), 1-16. https://doi.org/10.1186/s41239-021-00296-5
Johnson, N., Veletsianos, G., & Seaman, J. (2020). U.S. faculty and administrators’ experiences and approaches in the early weeks of the covid-19 pandemic. Online Learning, 24(2). https://doi.org/10.24059/olj.v24i2.2285
Karim, N.S. & Alam, M. (2021). Struggling with digital pandemic: Students’ narratives about adapting to online learning at home during the COVID-19 Outbreak. Southeast Asia: A Multidisciplinary Journal,21(2),15-29. Retrieved from https://fass.ubd.edu.bn/SEA/vol21-2/struggling-with-digital-pandemic.pdf
Kay JB. (2020). “Stay the fuck at home!”: Feminism, family and the private home in
a time of coronavirus. Feminist Media Studies, 20(6):883-888. https://doi.org/10.1080/14680777.2020.1765293
Keengwe, J., & Kidd, T. T. (2010). Towards best practices in online learning and teaching in higher education. MERLOT Journal of Online Learning and Teaching, 6(2), 533-541. Retrieved from https://jolt.merlot.org/vol6no2/keengwe_0610.pdf
Kuhfeld, M., Soland, J., Tarasawa, B., Johnson, A., Ruzek, E., & Liu, J. (2020). Projecting the potential impact of covid-19 school closures on academic achievement. Educational Researcher, 49(8), 549–565. https://doi.org/10.3102/0013189X20965918
Kuo, Y. C., Walker, A. E., Schroder, K. E. E., & Belland, B. R. (2014). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35–50. https://doi.org/10.1016/j.iheduc.2013.10.001
Lakhal, S., Khechine, H., & Mukamurera, J. (2021). Explaining persistence in online courses in higher education: A difference-in-differences analysis. International Journal of Educational Technology in Higher Education, 18(1), 1-32. https://doi.org/10.1186/s41239-021-00251-4
Lee, J., & Stankov, L. (2018). Non-cognitive predictors of academic achievement: Evidence from TIMSS and PISA. Learning and Individual Differences, 65, 50–64. https://doi.org/10.1016/j.lindif.2018.05.009
Marioulas, J. (2017). China: A world leader in graduation rates. International Higher Education, 90, 28–29. https://doi.org/10.6017/ihe.2017.90.10009
Masha'al, D., Rababa, M., & Shahrour, G. (2020). Distance learning–related stress among undergraduate nursing students during the COVID-19 pandemic. Journal of Nursing Education, 59(12), 666-674. https://doi.org/10.3928/01484834-20201118-03
McInnerney, J. M., & Roberts, T. S. (2004). Online learning: Social interaction and the creation of a sense of community. Journal of Educational Technology & Society, 7(3), 73-81. Retrieved from https://www.jstor.org/stable/pdf/jeductechsoci.7.3.73.pdf
Meishar-Tal, H., Weinblat, A., & Shapira, N. (2022). Distractions in online learning among higher education students. INTED2022 Proceedings, 2723–2730. https://doi.org/10.21125/inted.2022.0795
Moore, M. G. (1989). Editorial: Three types of interaction. The American Journal of Distance Education, 3(2), 1-6. https://doi.org/10.1080/08923648909526659
Moore, M. G. (1992). Distance Education: The foundations of effective practice. The Journal of Higher Education, 63(4), 468–472. https://doi.org/10.1080/00221546.1992.11778381
Moore, M. G., & Kearsley, G. (2011). Distance education: A systems view of online learning. Cengage Learning. Wadsworth Cengage Learning.
Nambiar, D. (2020). The impact of online learning during covid-19: Students’ and teachers’ perspective. The International Journal of Indian Psychology, 8(2).783-793. Retrieved from https://www.researchgate.net/publication/343229234
Palloff, R. M., Pratt, K., & Stockley, D. (2001). Building learning communities in cyberspace: Effective strategies for the online classroom. The Canadian Journal of Higher Education, 31(3), 175-178. Retrieved from https://www.academia.edu/2051324/
Park, J. H. (2007). Factors related to learner dropout in online learning. In F. M. Nafukho, T. H. Chermack, & C. M. Graham (Eds.), Proceedings of the 2007 Academy of Human Resource Development Annual Conference (pp. 251-258). Academy of Human Resource Development. Retrieved from https://files.eric.ed.gov/fulltext/ED504556.pdf
Park, J. H., & Choi, H. J. (2009). Factors influencing adult learners' decision to drop out or persist in online learning. Journal of Educational Technology & Society, 12(4), 207-217. Retrieved from https://www.jstor.org/stable/jeductechsoci.12.4.207
Parahoo, S. K., Santally, M. I., Rajabalee, Y., & Harvey, H. L. (2016). Designing a predictive model of student satisfaction in online learning. Journal of Marketing for Higher Education, 26(1), 1-19. https://doi.org/10.1080/08841241.2015.1083511
Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational psychology review, 18(4), 315-341. https://doi.org/10.1007/s10648-006-9029-9
Pekrun, R. (2011). Emotions as drivers of learning and cognitive development. In New perspectives on affect and learning technologies (pp. 23-39). New York: Springer.
Peterson, C., & Barrett, L. C. (1987). Explanatory style and academic performance among university freshman. Journal of Personality and Social Psychology, 53(3), 603–607. https://doi.org/10.1037/0022-3514.53.3.603
Podolsky, A., Kini, T., & Darling-Hammond, L. (2019). Does teaching experience increase teacher effectiveness? A review of US research. Journal of Professional Capital and Community, 4(4), 286-308. https://doi.org/10.1108/JPCC-12-2018-0032
Putri, R. S., Purwanto, A., Pramono, R., Asbari, M., Wijayanti, L. M., & Hyun, C. C. (2020). Impact of the COVID-19 pandemic on online home learning: An explorative study of primary schools in Indonesia. International Journal of Advanced Science and Technology, 29(5), 4809-4818. Retrieved from https://www.researchgate.net/publication/341194197
Rajabalee, Y. B., & Santally, M. I. (2021). Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy. Education and Information Technologies, 26(3), 2623-2656. https://doi.org/10.1007/s10639-020-10375-1
Rahim, N. B. (2020). Improving student engagement and behavioural outcomes via persistence among distance learners. Akademika, 90(2), 91-102. Retrieved from http://journalarticle.ukm.my/16461/1/39460-132451-1-PB.pdf
Razai, M. S., Oakeshott, P., Kankam, H., Galea, S., & Stokes-Lampard, H. (2020). Mitigating the psychological effects of social isolation during the covid-19 pandemic. BMJ, 369: m1904. https://doi.org/10.1136/bmj.m1904
Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. The Internet and Higher Education, 6(1), 1–16. https://doi.org/10.1016/S1096-7516(02)00158-6
Sahin, I., & Shelley, M. (2008). Considering Students’ Perceptions: The distance education student satisfaction model. Journal of Educational Technology & Society, 11(3), 216–223. Retrieved from https://www.researchgate.net/publication/220374226
Sari, F. M., & Oktaviani, L. (2021). Undergraduate students’ views on the use of online learning platform during COVID-19 pandemic. Teknosastik, 19(1), 41-47. https://doi.org/10.33365/ts.v19i1.896
Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Education, 49(2), 396–413. https://doi.org/10.1016/j.compedu.2005.09.004
Simamora, R. M. (2020). The Challenges of online learning during the COVID-19 pandemic: An essay analysis of performing arts education students. Studies in Learning and Teaching, 1(2), 86-103. https://doi.org/10.46627/silet.v1i2.38
Sit, H. H. W. (2013). Characteristics of Chinese students' learning styles. International proceedings of economics development and research, 62(8), 36-39. Retrieved from http://www.ipedr.com/vol62/008-ICLMC2013-M10004.pdf
StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: Stata Corp LLC.
Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (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
Tempelaar, D. T., Niculescu, A., Rienties, B., Gijselaers, W. H., & Giesbers, B. (2012). How achievement emotions impact students' decisions for online learning, and what precedes those emotions. The Internet and Higher Education, 15(3), 161-169. https://doi.org/10.1016/j.iheduc.2011.10.003
Tinto, V. (1993). Leaving college: rethinking the causes and cures of student attrition. (2nd ed.). Chicago: University of Chicago Press.
Ulmer, L.W., Watson, L.W., & Derby, D. (2007). Perceptions of higher education faculty
members on the value of distance education. The Quarterly Review of Distance Education, 8(1), 59–70. Retrieved from https://www.learntechlib.org/p/106716/
Volery, T., & Lord, D. (2000). Critical success factors in online education. International Journal of Educational Management, 14(5), 216–223. https://doi.org/10.1108/09513540010344731
Vygotsky, L.S. (1978). Mind in Society: The Development of Higher Psychological Processes. Cambridge, MA: Harvard University Press.
Wan, Z., Wang, Y., & Haggerty, N. (2008). Why people benefit from e-learning differently: The effects of psychological processes on e-learning outcomes. Information & management, 45(8), 513-521. https://doi.org/10.1016/j.im.2008.08.003
Yunusa, A. A., & Umar, I. N. (2021). A scoping review of critical predictive factors (CPFs) of satisfaction and perceived learning outcomes in e-learning environments. Education and Information Technologies, 26(1), 1223–1270. https://doi.org/10.1007/s10639-020-10286-1
Zeng, X., & Wang, T. (2021). College Student Satisfaction with Online Learning during COVID-19: A review and implications. International Journal of Multidisciplinary Perspectives in Higher Education, 6(1), 182-195. https://www.ojed.org/index.php/jimphe/article/view/3502
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