Prediction factors of student satisfaction in online courses
DOI:
https://doi.org/10.5944/ried.19.2.15112Keywords:
Student satisfaction, computer-based learning, distance teaching, educational administration.Abstract
The easy access and daily use of recent information and communication technologies has led to an impressive development of university offers completely in online modality. These developments have also raised important questions about the determining factors that affect learning, performance and retention of the learners of these academic programs. One of these determinants is the degree to which virtual courses or programs satisfy the learners’ expectations. Specifically, this study investigated the predictor factors of learner satisfaction identified by Sun and colleagues (2008) among Hispanic learners. The questionnaire was translated into Spanish and filled out by 102 participants. The internal consistency analysis resulted in high reliability. Correlation analysis showed that all the factors studied, except computer anxiety, are significantly correlated with learner satisfaction. The stepwise regression analysis found that course flexibility, instructor attitude towards e-learning, student Internet self-efficacy, and perception of the interaction factors determine almost 47.2% of student satisfaction. Based on these results, guidance is offered for managers of higher education virtual programs.
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Akyol, Z. & Garrison, D. R. (2010). Community of inquiry in adult online learning: Collaborative-constructivist approaches. In T. T. Kidd & J. Keengwe (Eds.), Adult learning in the digital age: Perspectives on online technologies and outcomes (pp. 52-66). Hershey, PA: Information Science Reference.
Allen, M., Bourhis, J., Burrell, N. & Mabry, E. (2002). Comparing student satisfaction with distance education to traditional classrooms in higher education: A meta-analysis. American Journal of Distance Education, 16(2), 83-97. doi: 10.1207/S15389286AJDE1602_3
Allen, M., Omori, K., Burrell, N., Mabry, E. & Timmerman, E. (2013). Satisfaction with distance education. In M. G. Moore (Ed.), Handbook of distance education (3th ed., pp. 143-154). New York: Routledge.
Belsley, D. A., Kuh, E. & Welsch, R. E. (2005). Regression diagnostics: Identifying influential data and sources of collinearity (Vol. 571): John Wiley & Sons.
Bolliger, D. U. & Martindale, T. (2004). Key factors for determining student satisfaction in online courses. International Journal on E-Learning, 3(1), 61-67.
Bray, E., Aoki, K. & Dlugosh, L. (2008). Predictors of learning satisfaction in Japanese online distance learners. The International Review of Research in Open and Distributed Learning, 9(3).
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, N.J.: L. Erlbaum Associates.
Drouin, M. A. (2008). The relationship between students’ perceived sense of community and satisfaction, achievement, and retention in an online course. Quarterly Review of Distance Education, 9(3), 267-284.
Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Los Angeles: Sage.
García Aretio, L. (2001). La educación a distancia: De la teoría a la práctica. (1a. ed.). Barcelona: Editorial Ariel.
García Aretio, L., Ruíz, M. & Domínguez, D. (2007). De la educación a distancia a la educación virtual. Barcelona: Editorial Ariel.
Geisinger, K. F. (1994). Cross-cultural normative assessment: Translation and adaptation issues influencing the normative interpretation of assessment instruments. Psychological assessment, 6(4), 304. doi: 10.1037/1040-3590.6.4.304
Jung, I. (2011). The dimensions of e-learning quality: From the learner’s perspective. Educational Technology Research and Development, 59(4), 445-464. doi: 10.1007/s11423-010-9171-4
Kirschner, P. A., Kreijns, K., Phielix, C. & Fransen, J. (2014). Awareness of cognitive and social behaviour in a CSCL environment. Journal of Computer Assisted Learning, 59-77. doi: 10.1111/jcal.12084
Kline, P. (1999). The handbook of psychological testing (2nd ed.). London; New York: Routledge.
Moore, J. C. & Shelton, K. (2014). The Sloan Consortium pillars and quality scorecard. In K. Shattuck (Ed.), Assuring quality in online education: Practices and processes at the teaching, resource, and program levels (pp. 40-49). Sterling, Virginia: Stylus Publishing, LLC.
Moore, M. G. (Ed.). (2013). Handbook of distance education (3th ed.). New York: Routledge.
Peralta Castro, R., Escobar Jurado, S. I., Mora Rodríguez, J. R., Martínez González, C. & Rocío Velandia, L. S. (2014). Caracterización de los factores de la deserción en la UNAD. Informe final de investigación. Universidad Nacional Abierta y a Distancia, Asociación Panamericana de Instituciones de Crédito Educativo. Bogotá. Recuperado de http://www.investigacion.apice.org.co/pdf/Caracterizacion-de-los-factores-de-la-desercion-en-la-UNAD-Informe-final-de-investigacion-Rafael-Peralta-y-Javier-Mora.pdf
Rubio Gómez, M. J. (2003). Memoria. Proyecto: Centro Virtual para el Desarrollo de Estándares de Calidad para la Educación Superior a Distancia en América Latina y el Caribe. Instituto Latinoamericano y del Caribe de Calidad en Educación Superior a Distancia. Loja, Ecuador. Recuperado de http://gdr1.utpl.edu.ec/centrovirtual/documentos/memorias.pdf
Rudestam, K. E. & Schoenholtz-Read, J. (2010). Handbook of online learning (2nd ed.). Thousand Oaks, Calif.: SAGE Publications.
Sanjuán Gómez, G., Gómez Martínez, M., Rabell Piera, O., Arcia Arcia, L. & Morales Velázquez, I. C. (2011). Resultados preliminares del grado de satisfacción con el empleo del aula virtual de la Facultad de Ciencias Médicas General Calixto García. Revista Habanera de Ciencias Médicas, 10(1), 114-125.
Shin, N. (2003). Transactional presence as a critical predictor of success in distance learning. Distance Education, 24(1), 69-86. doi: 10.1080/01587910303048
Simpson, O. (2003). Student retention in online, open, and distance learning. London; Sterling VA: Kogan Page.
Song, L., Singleton, E. S., Hill, J. R. & Koh, M. H. (2004). Improving online learning: Student perceptions of useful and challenging characteristics. The Internet and Higher Education, 7(1), 59-70. doi: 10.1016/j.iheduc.2003.11.003
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. doi: doi:10.1016/j.compedu.2006.11.007
Swan, K. (2001). Virtual interaction: Design factors affecting student satisfaction and perceived learning in asynchronous online courses. Distance Education, 22(2), 306-331. doi: 10.1080/0158791010220208
Tabachnick, B. G. & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Boston: Pearson Education.
Van Mierlo, C. M., Jarodzka, H., Kirschner, F. & Kirschner, P. A. (2012). Cognitive load theory in e-learning. In Z. Yan (Ed.), Encyclopedia of Cyber Behavior (Vol. 3, pp. 1178-1211): IGI Global.
Williams, S. L. (2006). The effectiveness of distance education in allied health science programs: A meta-analysis of outcomes. American Journal of Distance Education, 20(3), 127-141. doi: 10.1207/s15389286ajde2003_2
Zambrano R., J. (2012). La docencia en la sociedad red: Apuntes para la formación de docencia virtual (Vol. I). Quito: Corporación para el Desarrollo de la Educación Universitaria.
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