La Calidad de la Docencia Online en la Educación Superior: Un Nuevo Enfoque para su Medición

  1. José M. Ramírez-Hurtado 1
  2. Esteban Vázquez-Cano 2
  3. Víctor E. Pérez León 3
  4. Alfredo G. Hernández-Díaz 1
  1. 1 Universidad Pablo de Olavide
    info

    Universidad Pablo de Olavide

    Sevilla, España

    ROR https://ror.org/02z749649

  2. 2 Universidad Nacional de Educación a Distancia
    info

    Universidad Nacional de Educación a Distancia

    Madrid, España

    ROR https://ror.org/02msb5n36

  3. 3 Universidad de Sevilla
    info

    Universidad de Sevilla

    Sevilla, España

    ROR https://ror.org/03yxnpp24

Revista:
REICE: Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación

ISSN: 1696-4713

Año de publicación: 2022

Volumen: 20

Número: 3

Páginas: 81-100

Tipo: Artículo

Otras publicaciones en: REICE: Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación

Resumen

The use of the Internet and the development of new technologies have introduced remarkable changes in the teaching-knowledge process in higher education. Due to the Covid-19 pandemic, most of universities have had to change from the traditional face-to-face teaching to online teaching methods (eLearning). For this reason, the present research aims to measure the quality of service perceived by the students about the eLearning process during the period of the Covid-19. To attain this goal, a variant of the Importance-Performance-Analysis (Gap-IPA) method is used. The importance is evaluated by means of the Structural Equation Method. The data was gathered from a sample of 467 students from a Spanish southern University, who receive face-to-face teaching under normal or customary circumstances. The results show that for improving the quality of online teaching priority action must be taken on the following aspects: interaction between students, concentration during online lessons, revision of online tests, system utility and diversity of assessment methods. Thefindings of this study allow to guide educational managers in the correct definition of their strategies.CÓMO CITAR:Ramírez-Hurtado, J. M., Vázquez-Cano, E., Pérez León, V. E. y Hernández-Díaz, A. G. (2022). La calidad de la docencia online en la educación superior: Un nuevo enfoque para su medición. REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación, 20(3), 81-100.https://doi.org/10.15366/reice2022.20.3.005*Contacto: jmramhur@upo.esISSN: 1696-4713revistas.uam.es/reiceRecibido: 25 de enero 20211ª Evaluación: 23 de abril 20212ª Evaluación: 19 de julio 2021Aceptado: 9de octubre2021

Referencias bibliográficas

  • Ábalo, J., Varela, J. y Rial A. (2006). El análisis de importancia-valoración aplicado a la gestión de servicios. Psicothema, 18, 730-737.
  • Abdalla, I. (2007). Evaluating effectiveness of e-blackboard system using TAM framework: A structural analysis approach. AACE Journal, 3(15), 279-287.
  • Adkins, J., Kenkel, C. y Lim, C. L. (2005). Deterrents to online academic dishonesty. The Journal of Learning in Higher Education, 1(1), 17-22.
  • Allen, J., Bellizzi, M.G., Eboli, L., Forciniti, C. y Mazzulla, G. (2020). Identifying strategies for improving airport services: Introduction of the Gap-IPA to an Italian airport case study. Transportation Letters, 13, 243-253.https://doi.org/10.1080/19427867.2020.1861506
  • Bollen, K. A. (1989). Structural equations with latent variables. John Wiley & Sons.https://doi.org/10.1002/9781118619179
  • Chau, P. (1997). Reexamining a model of evaluation information center success using a structural equation modeling approach. Decision Sciences, 28, 309-334.https://doi.org/10.1111/j.1540-5915.1997.tb01313.x
  • Chen, S. H. (2009). Establishment of a performance-evaluation model for service quality in the banking industry. The Services Industries Journal, 29(2), 235-247.https://doi.org/10.1080/02642060802295034
  • Chiecher, A. C. y Donolo, D. S. (2011). Interacciones entre alumnos en aulas virtuales. Incidencia dedistintos diseños instructivos. Pixel-Bit. Revista de Medios y Educación, 39, 127-140.
  • Chiu, C. M. y Wang, E. T. G. (2008). Understanding web-based learning continuance intention: The role of subjective task value. Information& Management, 45, 194-201.https://doi.org/10.1016/j.im.2008.02.003
  • Chong, Y. S. y Ahmed, P. K. (2012). An empiricalinvestigation of students’ motivational impact upon university service quality perception: A self-determination perspective. Quality in Higher Education, 18(1), 37-41.https://doi.org/10.1080/13538322.2012.667261
  • Cole, M. T., Shelley, D. J. y Swartz, L. B. (2014). Online instruction, e-learning, and student satisfaction: A three years’ study. International Review of Research in Open and Distance Learning, 15(6), 111-131. https://doi.org/10.19173/irrodl.v15i6.1748
  • De Oliveira, O. J. y Ferreira, E. C. (2009, 8 de mayo). Adaptation and application of the SERVQUAL scale in higher education [Comunicación]. POMS 20th Annual Conference, Orlando (FL), Estados Unidos.
  • DeLone, W. H. y McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten years’ update. Journal of Management Information Systems, 19(4), 9-30. https://doi.org/10.1080/07421222.2003.11045748
  • Dhawan S. (2020). Online learning: A panacea in the time of covid-19 crisis. Journal of Educational Technology Systems. 49(1), 5-22. https://doi.org/10.1177/0047239520934018
  • Dick, G. P. y Tarí, J. J. (2013). A review of quality management research in higher education institutions. Kent Business School Working Paper Series, 274,13-43.
  • Eom, B. S., Wen, H. J. y Ashill, N. (2006). The determinants of students’ perceived learning outcomes and satisfaction in university online education: An empirical investigation. Decision Sciences Journal of Innovative Education, 4(2), 215-235.https://doi.org/10.1111/j.1540-4609.2006.00114.x
  • Fernández Enguita, M. (2020, 31 de marzo). Una pandemia imprevisible ha traído la brecha previsible.Cuaderno de Campo. [Blog]. https://bit.ly/2VT3kzU
  • Ficapal-Cusí, P., Torrent-Sellens, J., Boada-Grau, J. y Sánchez-García, J. C. (2013). Evaluación del e-learning en la formación para el empleo: Estructura factorial y fiabilidad. Revista de Educación, 361, 9-7. https://doi.org/10.4438/1988-592X-RE-2013-361-232
  • Fornell, C. y Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 30-50.https://doi.org/10.2307/3151312
  • Franklin, K. K. y Shemwell, D.W. (1995, 30 de octubre). Disconfirmation theory: An approach to student satisfaction assessment in higher education[Ponencia]. Annual Meeting of the Mid-South Educational Research Association Conference. New Orleans, Estados Unidos.
  • García-Peñalvo, F. J., Corell, A., Abella-García, V. y Grande, M. (2020). La evaluación online en la educación superior en tiempos de la Covid-19. Education in the Knowledge Society, 21, art. 12.https://doi.org/10.14201/eks.23086
  • Goñi, J. M. (2011). Las finalidades del currículo de matemáticas en secundaria y bachillerato. En J. M. Goñi (Ed.), Didáctica de las matemáticas (pp. 9-25). Editorial Graó.
  • Goos, M. y Salomons, A. (2017). Measuring teaching quality in higher education: Assessing selection bias in course evaluations. Research in Higher Education, 58, 341-364.https://doi.org/10.1007/s11162-016-9429-8
  • Grönroos, C. (1994). From marketing mix to relationship marketing: Towards a paradigm shift in marketing. Management Decision, 32(2), 4-20.https://doi.org/10.1108/00251749410054774
  • Guzmán, J. C. (2018). Las buenas prácticas de enseñanza de los profesores de educación superior. REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación, 16(2), 133-149. https://doi.org/10.15366/reice2018.16.2.008
  • Hair, J., Anderson, R., Tatham, R. L. y Black, W. C. (1998). Multivariate data analysis. Prentice-Hall.
  • Hassanzadeh, A., Kanaani, F. y Elahi,S. (2012). A model for measuring e-learning systems success in universities. Expert Systems with Applications, 39, 10959-10966.https://doi.org/10.1016/j.eswa.2012.03.028
  • Hernández, R., Murillo, F. J. y Martínez-Garrido, C. (2014). Factores de ineficacia escolar. REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación, 12(1), 103-118.
  • Hidalgo, N. y Murillo, F. J. (2017). Las concepciones sobre el proceso de evaluación del aprendizaje de los estudiantes. REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación, 15(1), 107-128. https://doi.org/10.15366/reice2017.15.1.007
  • Ho, C. L. y Dzeng, R. J. (2010). Construction safety training via e-learning: Learning effectiveness and user satisfaction. Computers & Education, 55, 858-867.https://doi.org/10.1016/j.compedu.2010.03.017
  • Hodges, C., Moore, S., Lockee, B., Trust, T. y Bond, A. (2020, 27 de marzo). The difference between emergency remote teaching andonline learning.Educause Review. https://bit.ly/3b0Nzx7
  • Kwek, L. C., Lau, T. C. y Tan, H. P. (2010). Education quality process model and its influence on students’ perceived service quality. International Journal of Business and Management, 5(8), 154-165. https://doi.org/10.5539/ijbm.v5n8p154
  • Lagrosen, S., Seyyed-Hashemi, R. y Leitner, M. (2004). Examination of the dimensions of quality in higher education. Quality Assurance in Education, 12(2), 61-69.https://doi.org/10.1108/09684880410536431
  • LeBlanc, G. y Nguyen, N. (1997). Searching for excellence in business education: An exploratory study of customer impressions of service quality. International Journal of Educational Management, 11(2), 72-79. https://doi.org/10.1108/09513549710163961
  • Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward elearning: An extension of the expectation–confirmation model. Computers & Education, 54, 506-516. https://doi.org/10.1016/j.compedu.2009.09.002
  • Lévy M. J. P., Martín F. M. T. y Román G.M.V. (2006). Optimización según estructuras de covarianzas. En J. P. Lévy M. y J. Varela (Dirs.), Modelización con estructuras de covarianzas en ciencias sociales (pp. 11-30). Netbiblo.
  • Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864-873. https://doi.org/10.1016/j.compedu.2007.09.005
  • Marks, R. B., Sibley, S. D. y Arbaugh, J. B. (2005). A structural equation model of predictor for effective online learning. Journal of Management Education, 29(4), 531-563.https://doi.org/10.1177/1052562904271199
  • Martilla, J. y James, J. (1977). Importance-performance analysis. Journal of Marketing, 41(1), 77-79. https://doi.org/10.2307/1250495
  • Martínez-Caro, E., Cegarra-Navarro, J. G. y Cepeda-Carrión, G. (2015). An application of the performance-evaluation model for e-learning quality in higher education. Total Quality Management & Business Excellence, 26(5), 632-647.https://doi.org/10.1080/14783363.2013.867607
  • Matosas-López, L., Romero-Ania, A. y Cuevas-Molano, E. (2019). ¿Leen los universitarios las encuestas de evaluación del profesorado cuandose aplican incentivos por participación? Una aproximación empírica. REICE.Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación, 17(3), 99-124. https://doi.org/10.15366/reice2019.17.3.006
  • Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior, 45, 359-374.https://doi.org/10.1016/j.chb.2014.07.044
  • Moreno Olivos, T. (2018). La evaluación docente en la universidad: Visiones de los alumnos. REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación, 3(16), 87-102. https://doi.org/10.15366/reice2018.16.3.005
  • Murillo, F. J., Martínez Garrido, C. y Hernández, R. (2011). Decálogo para una enseñanza eficaz. REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación, 9(1), 6-27.
  • Murillo, F. J. y Román, M. (2019). Retos en la evaluación de la calidad de la educación en América Latina. Revista Paraguaya de Educación, 8(1), 13-33.https://doi.org/10.35362/rie530559
  • Noaman, A.Y., Ragab, A. H., Madbouly, A.I., Khedra, A.M. y Fayoumi, A.G. (2017). Higher education quality assessment model: Towards achieving educational quality standard. Studies in Higher Education, 42(1), 23-46.https://doi.org/10.1080/03075079.2015.1034262
  • O’Neill, M. A. y Palmer, A. (2004). Importance-performance analysis: A useful tool for directing continuous quality improvement in higher education. Quality Assurance in Education, 12(1), 39-52. https://doi.org/10.1108/09684880410517423
  • Parasuraman, A., Zeithaml, V. A. y Berry, L. L.(1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40.
  • Picón, E., Varela, J. y Braña, T. (2011). La representación de los datos mediante el análisis de importancia-valoración. Metodología de Encuestas, 13,121-142.
  • Rindskopf, D. y Rose, T. (1988). Some theory and applications of confirmatory second-order factor analysis. Multivariate Behavioral Research, 23(1), 51-67.https://doi.org/10.1207/s15327906mbr2301_3
  • Rodrigues, H., Almeida, F., Figueiredo, V. y Lopes, S.L. (2019). Tracking e-learning through published papers: A systematic review. Computers & Education, 136, 87-98.https://doi.org/10.1016/j.compedu.2019.03.007
  • Sampson, S. E. y Showalter, M. J. (1999). The performance-importance response function: Observations and implications. Service Industries Journal, 19(3), 1-26.https://doi.org/10.1080/02642069900000027
  • Shauchenka, H. V. y Bleimann, U. (2014). Methodology and measurement system for higher education service quality estimation. Proceedings of the 2014 Conference on Education Technologies and Education, 21-28, Interlaken, Suiza.
  • Srikanthan, G. y Dalrymple, J. F. (2007). A conceptual overview of a holistic model for quality in higher education. International Journal of Educational Management, 21(3), 173-193.https://doi.org/10.1108/09513540710738647
  • Tarhini, A., Masa’deh, R., Al-Busaidi, K. A., Mohammed, A. B. y Maqableh, M. (2017). Factors influencing students’ adoption of e-learning: A structural equation modeling approach. Journal of International Education in Business, 10(2), 164-182.https://doi.org/10.1108/JIEB-09-2016-0032
  • Teas, R. K. (1993). Expectations, performance evaluation and consumer’s perception of quality. Journal of Marketing, 57(4), 18-34. https://doi.org/10.2307/1252216
  • Tejedor, S., Cervi, L., Pérez-Escoda, A., Tusa, F. y Parola, A. (2021). Higher education response in the time of coronavirus: perceptions of teachers and students, and open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7, 43.https://doi.org/10.3390/joitmc7010043
  • Telford, R. y Masson, R. (2005). The congruence of quality values in higher education. Quality Assurance in Education, 13(2), 107-119. https://doi.org/10.1108/09684880510594364
  • Thurmond, V. A., Wambach, K., Connors, H. R. y Frey, B. B. (2002). Evaluation of student satisfaction: Determining the impact of a web-based environment by controlling for student characteristics. American Journal of Distance Education, 16(3), 169-190.https://doi.org/10.1207/S15389286AJDE1603_4
  • UNESCO. (2020). Impacto del Covid-19 en la educación.UNESCO.
  • Wang, Y. S. y Liao, Y. W. (2008). Assessing e-government systems success: A validation of the Delone and Mclean model of information systems success. Government Information Quarterly, 25(4), 717-733. https://doi.org/10.1016/j.giq.2007.06.002
  • Wang, W. T. y Wang, C. C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53, 761-774.https://doi.org/10.1016/j.compedu.2009.02.021
  • Yang, C. C. (2003). Improvement actions based on the customers’ satisfaction survey. Total Quality Management and Business Excellence, 14(8), 919-930.https://doi.org/10.1080/1478336032000090842
  • Yildiz, S. M. y Kara, A. (2015). Developing alternative measures for service quality in higher education: Empirical evidence from the school of physical education and sports sciences. En Jr. L. Robinson (Eds.), Proceedings of the 2009 Academy of marketing science (AMS) annual conference. Developments in marketing science: Proceedings of the academy of marketing science. Springer. https://doi.org/10.1007/978-3-319-10864-3_102