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

Journal:
Revista de investigación en educación

ISSN: 1697-5200,  2172-3427

Year of publication: 2023

Volume: 21

Issue: 2

Pages: 295-309

Type: Article

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

More publications in: Revista de investigación en educación

Abstract

The aim of this work is to analyze the effect of face-to-face, blended and on-line scenarios on students’ academic results considering the interaction with e-learning platforms. The results show that academic results are not affected by the learning scenario, while the degree of interaction with e-learning platforms is affected by the learning scenario. Furthermore, Treatment Effects model has been used to study the learning scenario and the interaction with e-learning platforms together. In this case, the academic results are affected by the on-line versus the face-to-face scenario but are not affected by the blended versus the face-to-face scenario. Specifically, on an average value of 4,14 points, obtained from the academic results of all students, with an on-line treatment, the results drop 1,01 points (24,41%), while with a blended treatment, the results drop 0,38 points (9,13%). Finally, utilizing Fractional Polynomial Extended Regress, prediction models are proposed for each of the scenarios.

Bibliographic References

  • 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
Data source: Dialnet