Attitudes towards Online Learning: What Do Brazilian Students Think About?

The purpose of this study was twofold. First, it aimed to determine whether there are differences in Brazilian students’ attitudes towards online learning compared to their purpose in seeking for this modality and the localization of the pole where they received face-to-face support. Second, it aimed to identify if Brazilian students’ attitudes towards online learning predict their self-perception of performance in the course. A correlational and explanatory quantitative approach was taken through a survey. Data was collected from 593 undergraduate students enrolled in the online public administration course offered by different Brazilian universities. Descriptive statistics, Pearson correlations, group comparison (ANOVA) and multiple linear regression were employed for data analysis. Findings revealed that there are differences between students’ attitudes compared to their purpose in seeking for online courses and compared to the localization of the pole where they received face-to-face support. Statistically significant differences were found for Favorable personnel and dispositional aspects (PDA), Negative affects (NA) and Internalization and habituation of use (IHU) attitude dimensions. Furthermore, the study identified predictors of students’ self-perception of performance in online courses corresponding to how much students perceive they are accustomed to and capable of taking an online course, represented by the attitude dimension IHU. Results showed significant positive relationship between two attitude dimensions (PDA and PC) and the dimension IHU. In addition, significant negative relationship was found between the biographical variable family income and the dimension IHU. The model explains 40% of the variance of IHU in which PDA represents the strongest predictor.

___

  • ABED (2016). Censo EAD.BR: Relatorio analitico da aprendizagem a distancia no Brasil 2015. Curitiba: Ibpex. http://abed.org.br/arquivos/Censo_EAD_2015_POR.pdf. Ali, S., Uppal, M. A., & Gulliver, S. R. (2018). A conceptual framework highlighting e-learning implementation barriers. Information Technology & People, 31(1), 156-180. Alshammari, S. H., Ali, M. B., & Rosli, M. S. (2016). The influences of technical support, self efficacy and instructional design on the usage and acceptance of LMS: A comprehensive review. The Turkish Online Journal of Educational Technology, 15(2), 116-125. Beldarrain, Y. (2006). Distance education trends: Integrating new technologies to foster student interaction and collaboration. Distance Education, 27(2), 139-153. doi: 10.1080/01587910600789498 Cheawjindakarn, B., Suwannatthachote, P., & Theeraroungchaisri, A. (2012). Critical success factors for online distance learning in higher education: A review of the literature [Supplement]. Creative Education, 3, 61-66. Cigdem, H., & Ozturk, M. (2016). Critical components of online learning readiness and their relationships with learner achievement. Turkish Online Journal of Distance Education, 17(2), 98-109. Coelho Junior, F. A., Cortat, M., Flores, C. L., Santos, F. A. M., Alves, G. C., Faiad, C., ... Silva, A. R. (2018). Evidences of validity of the Brazilian scale of learner’s attitude towards distance education programs. International Journal of Information and Communication Techonology Education, 14(1), 1-16. 133 Costa, E. G. (2016). Tendencias contemporaneas em educacao superior a distancia no mundo e no Brasil. Espacio Abierto, 25(3), 265-289. Croxton, R. A. (2014). The role of interactivity in student satisfaction and persistence in online learning. MERLOT Journal of Online Learning and Teaching, 10(2), 314-324. de Barba, P. G., Kennedy, G. E., & Ainley, M. D. (2016). The role of students’ motivation and participation in predicting performance in a MOOC [Special issue]. Journal of Computer Assisted Learning, 32, 218-231. doi: 10.1111/jcal.12130 Ghazal, S., Al-Samarraie, H., & Aldowah, H. (2018). “I am still learning”: Modeling LMS critical success factors for promoting students’ experience and satisfaction in a blended learning environment. IEEE Access, 6, 77179-77201. Hew, K. F. (2015). Towards a model of engaging online students: Lessons from MOOCs and four policy documents. International Journal of Information and Education Technology, 5(6), 425-431. doi: 10.7763/IJIET.2015.V5.543 Hew, K. F., Qiao, C., & Tang, Y. (2018). Understanding student engagement in large-scale open online courses: A machine learning facilitated analysis of student’s reflections in 18 highly rated MOOCs. International Review of Research in Open and Distributed Learning 19(3), 69-93. Joksimovic, S., Gasevic, D., Kovanovic, V., Riecke, B. E., & Hatala, M. (2015). Social presence in online discussions as a process predictor of academic performance. Journal of Computer Assisted Learning, 31, 638-654. Joo, Y. J., Lim, K. Y., & Kim, J. (2013). Locus of control, self-efficacy, and task value as predictors of learning outcome in an online university context. Computers & Education, 62, 149-158. Kauffman, H. (2015). A review of predictive factors of student success in and satisfaction with online learning. Research in Learning Technology, 23. http://dx.doi.org/10.3402/rlt.v23.26507 Mill, D. (2016). Educacao a Distancia: cenarios, dilemas e perspectivas. Revista de Educacao Publica, 25(59/2), 432-454. Muilenburg, L. Y., & Berge, Z. L. (2005). Student barriers to online learning: A factor analytic study. Distance Education, 26(1), 29-48. Park, J.-H., & Choi, H. J. (2009). Factors influencing adult learners’ decision to drop out or persist in online learning. Educational Technology & Society, 12(4), 207-217. Puspitasari, K. A., & Oetoyo, B. (2018). Successful students in an open and distance learning system. Turkish Online Journal of Distance Education, 19(2), 189-200. Ribas, J. C. C., Moreira, B. C. M., Catapan, A. H. (2011). Construindo referenciais de qualidade para uma gestao eficaz no sistema Universidade Aberta do Brasil: o ambiente virtual de ensino-aprendizagem e a capacitacao dos coordenadores de polo de apoio presencial. Paper presented at the 17o Congresso Internacional ABED de Educacao a Distancia, Florianopolis. Sanchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26, 1632-1640. Schlünzen Junior, K. (2013). A institucionalizacao da educacao a distancia no Brasil: cenarios e perspectivas. Teoria e Pratica da Educacao, 16(1), 113-124. Selim, H. M. (2007). Critical success factors for e-learning acceptance: confirmatory factor models. Computers & Education, 49, 396-413. Sivo, S. A., Ku, C-H., & Acharya, P. (2018). Understanding how university student perceptions of resources affect technology acceptance in online learning courses. Australasian Journal of Educational Technology, 34(4), 72-91. 134 Valencia-Arias, A., Chalela-Naffah, S., & Bermudez-Hernandez, J. (2018). A proposed model of e-learning tools acceptance among university students in developing countries. Education and Information Technologies. https://doi.org/10.1007/s10639-018-9815-2 Weidlich, J., & Bastiaens, T. J. (2018). Technology matters - The impact of transactional distance on satisfaction in online distance learning. International Review of Research in Open and Distributed Learning, 19(3), 222-242. Willging, P. A., & Johnson, S. D. (2009). Factors that influence students’ decision to dropout of online courses. Journal of Asynchronous Learning Networks, 13(3), 115-127.