Comparative of Face-to-Face, Blended and On-line scenarios in Higher EducationAnalysis of its effects on academic results considering the interaction with e-learning platforms

  1. Puche Regaliza, Julio César 1
  2. Porras Alfonso, Santiago 1
  3. Casado Yusta, Silvia 1
  4. Pacheco Bonrostro, Joaquín 1
  1. 1 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

Revista:
Revista de investigación en educación

ISSN: 1697-5200,  2172-3427

Año de publicación: 2023

Volumen: 21

Número: 2

Páginas: 295-309

Tipo: Artículo

DOI: 10.35869/REINED.V21I2.4607 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

Otras publicaciones en: Revista de investigación en educación

Resumen

El objetivo de este trabajo es analizar el efecto de los escenarios presencial, mixto y on-line en los resultados académicos de los estudiantes considerando la interacción con plataformas e-learning. Los resultados muestran que los resultados académicos no se ven afectados por el escenario de aprendizaje, mientras que el grado de interacción con las plataformas e-learning se ve afectado por el escenario de aprendizaje. Además, el modelo de Efectos de Tratamiento se ha utilizado para estudiar el escenario de aprendizaje y la interacción con las plataformas de aprendizaje de manera conjunta. En este caso, los resultados académicos se ven afectados por el escenario on-line frente al presencial, pero no se ven afectados por el escenario mixto frente al presencial. En concreto, sobre un valor medio de 4,14 puntos, obtenido de los resultados académicos de todos los alumnos, con un tratamiento on-line los resultados bajan 1,01 puntos (24,41%), mientras que con un tratamiento mixto los resultados bajan 0,38 puntos (9,13%). Finalmente, utilizando la Regresión Extendida Polinomial Fraccionaria, se proponen modelos de predicción para cada uno de los escenarios.

Referencias bibliográficas

  • Abadie, A., Drukker, D., Herr, J.L. & Imbers, G.W. (2004). Implementing Matching Estimators for Average Treatment Effects in Stata. The Stata Journal: Promoting communications on statistics and Stata, 4(3), 290-311.
  • Alajmi, M.F., Khan, S. & Zamani, A.S. (2012). Using instructive data mining methods to revise the impact of virtual classroom in e-learning. International Journal of Advanced Science and Technology, 45, 125-133.
  • Aldhahi, M.I., Alqahtani, A.S., Baattaiah, B.A. & Al-Mohammed, H.I. (2022). Exploring the relationship between students' learning satisfaction and self-efficacy during the emergency transition to remote learning amid the coronavirus pandemic: A cross-sectional study. Education and Information Technologies, 27, 1.323-1.340.https://doi.org/10.1007/s10639-021-10644-7
  • Anthony, B., Kamaludin, A., Romli, A., Rafei, A.F.M., Abdullah, A. & Ming, G.L. (2019). Exploring the role of blended learning for teaching and learning effectiveness in institutions of higher learning: An empirical investigation. Education and Information Technologies, 24(6), 3.433-3.466.https://doi.org/10.1007/s10639-019-09941-z
  • Arbaugh, J.B., Godfrey, M.R., Johnson, M., Pollack, B.L., Niendorf, B. & Wresch, W. (2009). Research in online and blended learning in the business disciplines: Key findings and possible future directions. The Internet and Higher Education, 12(2), 71-87.https://doi.org/10.1016/j.iheduc.2009.06.006
  • Asarta, C.J. & Schmidt, J.R. (2020). The effects of online and blended experience on outcomes in a blended learning environment. The Internet and Higher Education, 44, 100708.https://doi.org/10.1016/j.iheduc.2019.100708
  • Azizan, F.Z. (2010). Blended learning in higher education institution in Malaysia. In Proceedingsof regional conference on knowledge integration in ICT,10, (pp. 454-466).
  • Bamoallem, B. & Altarteer, S. (2022). Remote emergency learning during COVID-19 and its impact on university students perception of blended learning in KSA. Education and Information Technologies, 27, 157-179.https://doi.org/10.1007/s10639-021-10660-7
  • Bazelais, P. & Doleck, T. (2018). Blended learning and traditional learning: A comparative study of college mechanics courses. Education and Information Technologies, 23(6), 2.889-2.900.https://doi.org/10.1007/s10639-018-9748-9
  • Bolumole, M. (2020). Student life in the age of COVID-19. Higher Education Research & Development, 39(7), 1.357-1.361.https://doi.org/10.1080/07294360.2020.1825345
  • Branch, R.M. & Dousay, T.A. (2015). Survey of instructional design models. Association for Educational Communications & Technology.
  • Castro, M.D.B. & Tumibay, G.M. (2021). A literature review: efficacy of online learning courses for higher education institution using meta-analysis. Education and Information Technologies, 26(2), 1.367-1.385.https://doi.org/10.1007/s10639-019-10027-z
  • Christie, M.J. (2004). Computer databases and Aboriginal knowledge. Learning communities: International journal of learning in social contexts, 1, 4-12.
  • Cobo, A., Rocha, R. & Rodríguez-Hoyos, C. (2014). Evaluation of the interactivity of students in virtual learning environments using a multicriteria approach and data mining. Behaviour & Information Technology, 33(10), 1.000-1.012.https://doi.org/10.1080/0144929X.2013.853838
  • Davies, J. & Graff, M. (2005). Performance in e-learning: online participation and student grades. British Journal of Educational Technology, 36(4), 657-663.https://doi.org/10.1111/j.1467-8535.2005.00542.x
  • Dickfos, J., Cameron, C. & Hodgson, C. (2014). Blended learning: making an impact on assessment and self-reflection in accounting education. Education and Training, 56(2/3), 190-207.https://doi.org/10.1108/ET-09-2012-0087
  • Fouche, I. & Andrews, G. (2022). Working from home is one major disaster: An analysis of student feedback at a South African university during the COVID-19 lockdown. Education and Information Technologies, 27, 133-155.https://doi.org/10.1007/s10639-021-10652-7
  • Halverson, L.R., Graham, C.R., Spring, K.J., Drysdale, J.S. & Henrie, C.R. (2014). A thematic analysis of the most highly cited scholarship in the first decade of blended learning research. The Internet and Higher Education, 20, 20-34.https://doi.org/10.1016/j.iheduc.2013.09.004
  • Hodges, C., Moore, S., Lockee, B., Trust, T. & Bond, A. (2020). The difference between emergency remote teaching and online learning. Educause Review, 27(1), 1-9.https://er.educause.edu/articles/2020/3/the-difference-between-emergency-remote-teaching-and-online-learning
  • Jacobson, N.S., Roberts, L.J., Berns, S.B. & McGlinchey, J.B. (1999). Methods for defining and determining the clinical significance of treatment effects: description, application, and alternatives.Journal of consulting and clinical psychology, 67(3), 300-307.https://doi.org/10.1037/0022-006X.67.3.300
  • Leidner, D.E. & Jarvenpaa, S.L. (1995). The use of information technology to enhance management school education: A theoretical view. MIS Quarterly, 19(3), 265-291.DOI: 10.2307/249596
  • Maki, R.H., Maki, W.S., Patterson, M. & Whittaker, P.D. (2000). Evaluation of a Web-based introductory psychology course: I. Learning and satisfaction in on-line versus lecture courses. Behavior research methods, instruments, & computers, 32(2), 230-239.https://doi.org/10.3758/BF03207788
  • Morgan, H. (2020). Best practices for implementing remote learning during a pandemic. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 93(3), 135-141.https://doi.org/10.1080/00098655.2020.1751480
  • Nguyen, T. (2015). The effectiveness of online learning: Beyond no significant difference and future horizons. MERLOT Journal of Online Learning and Teaching, 11(2), 309-319.
  • Osborne, J.W. & Waters, E. (2002). Four assumptions of multiple regression that researchers should always test. Practical Assessment, Research, and Evaluation, 8, Article 2.https://doi.org/10.7275/r222-hv23
  • Pham, H.H. & Ho, T.T.H. (2020). Toward a ‘new normal’ with e-learning in Vietnamese higher education during the post COVID-19 pandemic. Higher Education Research & Development, 39(7), 1.327-1.331.https://doi.org/10.1080/07294360.2020.1823945
  • Riffenburgh, R.H. (2012). Statistics in Medicine (3rd ed). Academic Press. DOI: 10.1016/C2010-0-64822-X
  • Royston, P. & Sauerbrei, W. (2008). Multivariable Model-Building: A Pragmatic Approach to Regression Analysis Based on Fractional Polynomials for Modelling Continuous Variables. Wiley.DOI: 10.1111/j.1541-0420.2009.01315_1.x
  • Sheard, J. (2018). Quantitative data analysis, Research Methods (Second Edition). Chandos Publishing.DOI: 10.1016/B978-0-08-102220-7.00018-2
  • Sijtsma, K. & Emons, W.H.M. (2010). Nonparametric Statistical Methods. International Encyclopedia of Education (Third Edition). Elsevier.DOI: 10.1016/b978-0-12-818630-5.10073-9
  • Singh, H. & Reed, C. (2001). A white paper: Achieving success with blended learning. Centra software, 1, 1-11.
  • Takayama, K. (2020). Japanese nightingales (uguisu) and the margins of learning: rethinking the futurity of university education in the post-pandemic epoch. Higher Education Research & Development, 39(7), 1.342-1.345.https://doi.org/10.1080/07294360.2020.1824208
  • Van Gelderen, B. & Guthadjaka, K. (2017). The Warramiri website: applying an alternative Yolŋu epistemology to digital development. Research and Practice in Technology Enhanced Learning, 12(1), 1-19.https://doi.org/10.1186/s41039-017-0052-x
  • Wai, C.C. & Seng, E.L.K. (2014). Exploring the effectiveness and efficiency of blended learning tools in a school of business. Procedia-Social and Behavioral Sciences, 123, 470-476.https://doi.org/10.1016/j.sbspro.2014.01.1446
  • Wasserman, L. (2004). All of Statistics: A concise course in Statistical Inference. Springer.DOI: 10.1007/978-0-387-21736-9
  • Weisberg, S. (2013).Applied linear regression. John Wiley & Sons.DOI: 10.1002/0471704091
  • Yang, R. (2020). China’s higher education during the COVID-19 pandemic: some preliminary observations. Higher Education Research & Development, 39(7), 1.317-1.321.https://doi.org/10.1080/07294360.2020.1824212
Fuente de los datos: Dialnet