Student interactivity and academic engagement on virtual campuses of higher education institutions
DOI:
https://doi.org/10.24054/rcta.v1i47.4130Keywords:
academic engagement, higher education, student interactivity, virtual campusesAbstract
Student interactivity in virtual campuses has gained particular relevance following the COVID-19 pandemic, as its direct influence on academic engagement in higher education has become evident. This study aimed to evaluate how four dimensions of student interactivity—interaction modalities, technological medium, interactive content, and the facilitator–participant relationship—explain academic engagement among students from five higher education institutions with physical campuses in the department of Norte de Santander, Colombia. The research was conducted under a positivist paradigm, using a quantitative approach and a non-experimental, cross-sectional, explanatory–confirmatory design. A Likert-type questionnaire was administered to a sample of 266 students, and the data were analyzed using exploratory factor analysis, confirmatory factor analysis, composite reliability (CR), convergent validity (AVE), discriminant validity (HTMT), common method bias assessment, and covariance-based structural equation modeling (CB-SEM). The results showed that the exploratory factor analysis explained 67% of the variance in the student interactivity components scale and 59% of the variance in the academic engagement scale. Furthermore, the structural model revealed positive and statistically significant effects of student interactivity dimensions on academic engagement, with the facilitator–participant relationship emerging as the predictor with the highest standardized weight. The study provides contextualized empirical evidence and offers theoretical, methodological, and practical implications for strengthening virtual higher education.
Downloads
References
A. Bozkurt et al., “A global outlook to the interruption of education due to COVID-19 pandemic: Navigating in a time of uncertainty and crisis,” Asian Journal of Distance Education, vol. 15, no. 1, pp. 1–126, 2020.
M. Area-Moreira, A. San Nicolás-Santos y F. J. García-Peñalvo, “Modelos de blended learning en educación superior,” Pixel-Bit. Revista de Medios y Educación, no. 64, pp. 7–46, 2022.
R. M. Bernard, P. C. Abrami, E. Borokhovski, C. A. Wade, R. M. Tamim, M. A. Surkes y E. C. Bethel, “A meta-analysis of three types of interaction treatments in distance education,” Review of Educational Research, vol. 79, no. 3, pp. 1243–1289, 2009.
E. R. Kahu y K. Nelson, “Student engagement in the educational interface: Understanding the mechanisms of student success,” Higher Education Research & Development, vol. 37, no. 1, pp. 58–71, 2018.
J. Cabero-Almenara, J. Barroso-Osuna y A. Palacios-Rodríguez, Literacy and Digital Inclusion in Higher Education, Cham, Switzerland: Springer, 2024.
F. J. García-Peñalvo, “Transformación digital en las universidades: Implicaciones educativas,” Education in the Knowledge Society, vol. 22, pp. 1–10, 2021.
D. R. Garrison, T. Anderson y W. Archer, “The first decade of the Community of Inquiry framework: A retrospective,” The Internet and Higher Education, vol. 13, no. 1–2, pp. 5–9, 2010.
G. J. Hwang, C. L. Lai y S. Y. Wang, “Seamless flipped learning: A mobile technology-enhanced flipped classroom with effective learning strategies,” Journal of Computers in Education, vol. 2, no. 4, pp. 449–473, 2015.
P. M. Moreno-Marcos, C. Alario-Hoyos, I. Estévez-Ayres y C. Delgado-Kloos, “Comparing different approaches for learning analytics interventions in MOOCs,” IEEE Transactions on Learning Technologies, vol. 12, no. 3, pp. 384–401, 2019.
T. Anderson, The Theory and Practice of Online Learning, 3rd ed. Edmonton, Canada: AU Press, 2019.
RStudio Team, RStudio: Integrated Development Environment for R, Boston, MA, USA, 2020.
R Core Team, R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing, 2023.
W. G. Cochran, Sampling Techniques, 3rd ed. New York, NY, USA: Wiley, 1977.
J. C. Anderson y D. W. Gerbing, “Structural equation modeling in practice: A review and recommended two-step approach,” Psychological Bulletin, vol. 103, no. 3, pp. 411–423, 1988.
R. B. Kline, Principles and Practice of Structural Equation Modeling, 5th ed. New York, NY, USA: Guilford Press, 2023.
American Educational Research Association (AERA), American Psychological Association (APA) y National Council on Measurement in Education (NCME), Standards for Educational and Psychological Testing, Washington, DC, USA, 2018.
P. J. Ferrando y U. Lorenzo-Seva, “Assessing the quality and appropriateness of factor solutions and factor score estimates in exploratory item factor analysis,” Psicothema, vol. 34, no. 1, pp. 1–10, 2022.
U. Lorenzo-Seva, “Promin: A method for oblique factor rotation,” Behavior Research Methods, vol. 54, no. 6, pp. 2999–3011, 2022.
S. Lloret-Segura, A. Ferreres-Traver, A. Hernández-Baeza y I. Tomás-Marco, “El análisis factorial exploratorio de los ítems: Una guía práctica,” Anales de Psicología, vol. 33, no. 2, pp. 417–432, 2017.
I. Izquierdo, E. Olea y A. Abad, “Exploratory factor analysis in validation studies: Uses and recommendations,” Psicothema, vol. 26, no. 3, pp. 395–400, 2014.
R. E. Schumacker y R. G. Lomax, A Beginner’s Guide to Structural Equation Modeling, 4th ed. New York, NY, USA: Routledge, 2016.
P. Lei y Q. Wu, “Introduction to structural equation modeling: Issues and practical considerations,” en Handbook of Structural Equation Modeling, R. H. Hoyle, Ed. New York, NY, USA: Guilford Press, 2012, pp. 92–109.
T. A. Brown, Confirmatory Factor Analysis for Applied Research, 2nd ed. New York, NY, USA: Guilford Press, 2015.
P. M. Bentler, “Comparative fit indexes in structural models,” Psychological Bulletin, vol. 107, no. 2, pp. 238–246, 1990.
L. Hu y P. M. Bentler, “Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives,” Structural Equation Modeling, vol. 6, no. 1, pp. 1–55, 1999.
R. C. MacCallum, M. W. Browne y H. M. Sugawara, “Power analysis and determination of sample size for covariance structure modeling,” Psychological Methods, vol. 1, no. 2, pp. 130–149, 1996.
R. McDonald, Test Theory: A Unified Treatment, Mahwah, NJ, USA: Erlbaum, 1999.
J. F. Hair, G. T. M. Hult, C. M. Ringle y M. Sarstedt, “A primer on partial least squares structural equation modeling (PLS-SEM),” Journal of Advertising, vol. 46, no. 1, pp. 184–185, 2017.
Y. Rosseel, “lavaan: An R package for structural equation modeling,” Journal of Statistical Software, vol. 48, no. 2, pp. 1–36, 2012.
A. A. Beaujean, Latent Variable Modeling Using R: A Step-by-Step Guide, New York, NY, USA: Routledge, 2014.
M. Revelle, psych: Procedures for Psychological, Psychometric, and Personality Research, Evanston, IL, USA: Northwestern University, 2023.
U. Lorenzo-Seva y P. J. Ferrando, FACTOR: A Computer Program to Fit the Exploratory Factor Analysis Model, Tarragona, Spain, 2022.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Ciro Antonio Carvajal Labastida, Milvia Lissette Peñaloza de Arias
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
