Modelo de progressão acadêmica de alunos online no Ensino Superior

Autores

DOI:

https://doi.org/10.18861/cied.2022.13.1.3181

Palavras-chave:

ensino eletrônico, educação a distância, ensino superior, deserção dos alunos, persistência acadêmica, serviços de apoio acadêmico, alunos adultos

Resumo

A oferta formativa de programas online tem aumentado, sendo uma oportunidade para quem precisa conciliar o estudo com outras atividades. As taxas de retenção neste tipo de formação são baixas. Vários fatores explicam a evasão de alunos adultos nos programas online. O desenho é misto do tipo explicativo sequencial. A fase quantitativa permitiu a identificação de diferenças significativas entre os grupos para as variáveis ​​em estudo, a partir de uma amostra de 9.405 alunos. A fase qualitativa incluiu a aplicação de entrevistas. Constatou-se que o acompanhamento do tutor, a aprovação dos cursos de habilidades básicas, a participação dos alunos nas atividades de indução e o atendimento da unidade de apoio socioafetivo influenciam diretamente na deserção dos alunos, na sua participação na plataforma online e na média das notas. Os alunos valorizam a comunicação eficaz com professores e tutores. Deve ser gerada uma rede de contenção para apoiar o processo de continuidade dos estudos com diversas ações, também, a influência de cada curso é diferente em relação às necessidades apresentadas por cada aluno, mostrando a importância de que as instituições implementem uma diversidade de apoios que serão utilizados pelos alunos de acordo com as suas necessidades.

Downloads

Não há dados estatísticos.

Referências

Arguedas, M., Daradoumis, T., & Xhafa, F. (2016). Analyzing the effects of emotion management on time and self-management in computer-based learning. Computers in Human Behavior, 63(October), 517–529. https://doi.org/10.1016/j.chb.2016.05.068

Arguedas, M., Xhafa, F., Casillas, L., Daradoumis, T., Peña, A., & Caballé, S. (2018). A model for providing emotion awareness and feedback using fuzzy logic in online learning. Soft Computing, 22(3), 963–977. https://doi.org/10.1007/s00500-016-2399-0

Armstrong, S. N., Early, J. O., Burcin, M. M., Bolin, K., Holland, N., & No, S. (2018). New Media Tools Impact on Online, Health Science Students’ Academic Persistence and Support: Lessons Learned from Two Pilot Studies. TechTrends, 62(3), 266–275. https://doi.org/10.1007/s11528-018-0261-1

Beluce, A. C., & Oliveira, K. L. de. (2015). Students’ Motivation for Learning in Virtual Learning Environments. Paidéia (Ribeirão Preto), 25(60), 105–113. https://doi.org/10.1590/1982-43272560201513

Cacciamani, S., Cesareni, D., Perrucci, V., Balboni, G., & Khanlari, A. (2019). Effects of a social tutor on participation, sense of community and learning in Online university courses. British Journal of Educational Technology, 50(4), 1771–1784. https://doi.org/10.1111/bjet.12656

Choi, H. J., & Kim, B. U. (2018). Factors Affecting Adult Student Dropout Rates in the Korean Cyber-University Degree Programs. Journal of Continuing Higher Education, 66(1), 1–12. https://doi.org/10.1080/07377363.2017.1400357

Choi, H. J., & Park, J. H. (2018). Testing a path-analytic model of adult dropout in online degree programs. Computers and Education, 116, 130–138. https://doi.org/10.1016/j.compedu.2017.09.005

Creswell, J. & Plano Clark, V. (2011). Designing and conducting mixed methods research. SAGE.

Escudero Muñoz, J. (2014). Contexto, contenidos y procesos de innovación educativa: ¿el dónde y el cómo de la tecnología educativa? Docencia e Investigación: Revista de La Escuela Universitaria de Magisterio de Toledo, 39(24), 13–36.

Firat, M., Öztürk, A., Güneş, İ., Çolak, E., Beyaz, M., & Büyük, K. (2019). How e-learning engagement time affects academic achievement in e-learning environments. A large-scale study of open and distance learners. Open Praxis, 11(2), 129–141. http://doi.org/10.5944/openpraxis.11.2.920

Flick, U. (2007). Introducción a la investigación cualitativa. Morata.

Goda, Y., Yamada, M., Kato, H., Matsuda, T., Saito, Y., & Miyagawa, H. (2015). Procrastination and other learning behavioral types in e-learning and their relationship with learning outcomes. Learning and Individual Differences, 37, 72-80. https://doi.org/10.1016/j.lindif.2014.11.001

Guetterman, T. C., Babchuk, W. A., Howell Smith, M. C., & Stevens, J. (2019). Contemporary Approaches to Mixed Methods–Grounded Theory Research: A Field-Based Analysis. Journal of Mixed Methods Research, 13(2), 179–195. https://doi.org/10.1177/1558689817710877

Heidrich, L., Victória Barbosa, J. L., Cambruzzi, W., Rigo, S. J., Martins, M. G., & dos Santos, R. B. S. (2018). Diagnosis of learner dropout based on learning styles for online distance learning. Telematics and Informatics, 35(6), 1593–1606. https://doi.org/10.1016/j.tele.2018.04.007

Instituto Profesional IACC (2021). Retención neta de 1er año cerrada. https://www.iacc.cl/cifras-de-iacc/#1624485041500-0df17495-5964

Johnson, R. B. (2017). Dialectical Pluralism: A Metaparadigm Whose Time Has Come. Journal of Mixed Methods Research, 11(2), 156–173. https://doi.org/10.1177/1558689815607692

Kansteiner, K., & König, S. (2020). The Role(s) of Qualitative Content Analysis in Mixed Methods Research Designs. Forum Qualitative Sozialforschung / Forum: Qualitative Social Research, 21(1). https://doi.org/10.17169/FQS-21.1.3412

Kara, M., Erdoğdu, F., Kokoç, M., & Cagiltay, K. (2019). Challenges Faced by Adult Learners in Online Distance Education: A Literature Review. Open Praxis, 11(1), 5. https://www.openpraxis.org/articles/10.5944/openpraxis.11.1.929/

Kuckartz, U. (2014). Qualitative text analysis: A guide to methods, practice and using software. London: SAGE.

Law, K. M. Y., Lee, V. C. S., & Yu, Y. T. (2010). Learning motivation in e-learning facilitated computer programming courses. Computers and Education, 55(1), 218–228. https://doi.org/10.1016/j.compedu.2010.01.007

Lee, J., Song, H. D., & Hong, A. J. (2019). Exploring factors, and indicators for measuring students’ sustainable engagement in e-learning. Sustainability (Switzerland), 11(4). https://doi.org/10.3390/su11040985

Lim, J. M. (2016). Predicting successful completion using student delay indicators in undergraduate self-paced online courses. Distance Education, 37(3), 317–332. https://doi.org/10.1080/01587919.2016.1233050

Mateo Andrés, J. (2004). La investigación ex post-facto. En R. Bisquerra (Ed.), Metodología de la investigación educativa (pp. 195–230). La Muralla.

Muljana, P. S., & Luo, T. (2019). Factors contributing to student retention in online learning and recommended strategies for improvement: A systematic literature review. Journal of Information Technology Education: Research, 18, 19–57. https://doi.org/10.28945/4182

Noboa, A., & Robaina, N. (2014). Entrevista cualitativa. En A. Lucas & A. Noboa (Eds.), Conocer lo social: Estrategias, técnicas de construcción y análisis de datos (p. 347). Fragua.

Orellana, D., Segovia, N., & Rodríguez Cánovas, B. (2020). El abandono estudiantil en programas de educación superior virtual: revisión de literatura TT - Student dropout in online higher education programs: literature review. Revista de la educación superior, 49(194), 47–64. http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0185-27602020000200047

Pluye, P., & Hong, Q. N. (2014). Combining the power of stories and the power of numbers: Mixed methods research and mixed studies reviews. Annual Review of Public Health, 35(October 2013), 29–45. https://doi.org/10.1146/annurev-publhealth-032013-182440

Rovai, A. P., & Wighting, M. J. (2005). Feelings of alienation and community among higher education students in a virtual classroom. Internet and Higher Education, 8(2), 97–110. https://doi.org/10.1016/j.iheduc.2005.03.001

Sabariego, M., Massot, I., & Dorio, I. (2004). Métodos de investigación cualitativa. En R. Bisquerra (Ed.), Metodología de la investigación educativa (pp. 293–328). La Muralla.

Saldaña, J. (2013). The Coding Manualfor Qualitative Researchers. SAGE Publications.

Schoonenboom, J., & Johnson, R. B. (2017). Wie man ein Mixed Methods-Forschungs-Design konstruiert. Kolner Zeitschrift Fur Soziologie Und Sozialpsychologie, 69, 107–131. https://doi.org/10.1007/s11577-017-0454-1

Soffer, T., & Cohen, A. (2019). Students’ engagement characteristics predict success and completion of online courses. Journal of Computer Assisted Learning, 35(3), 378–389. https://doi.org/10.1111/jcal.12340

Staller, K. M. (2015). Qualitative analysis: The art of building bridging relationships. Qualitative Social Work, 14(2), 145–153. https://doi.org/10.1177/1473325015571210

Su, J., & Waugh, M. L. (2018). Online Student Persistence or Attrition: Observations Related to Expectations, Preferences, and Outcomes. Journal of Interactive Online Learning, 16(1), 63–79.

Wladis, C., Conway, K., & Hachey, A. C. (2015). Using course-level factors as predictors of online course outcomes: a multi-level analysis at a US urban community college. Studies in Higher Education, 42(1), 184–200. https://doi.org/10.1080/03075079.2015.1045478

Publicado

2022-02-28

Como Citar

Romero Alonso, R. E., & Anzola Vera, J. J. (2022). Modelo de progressão acadêmica de alunos online no Ensino Superior. Cuadernos De Investigación Educativa, 13(1). https://doi.org/10.18861/cied.2022.13.1.3181

Edição

Seção

Tópicos de pesquisa