Determinantes de la calidad, la satisfacción y el aprendizaje percibido de la e-formación del profesorado universitario

  1. Víctor Abella
  2. Vanesa Ausín
  3. Vanesa Delgado
  4. David Hortigüela
  5. Hugo J. Solano
Journal:
Revista mexicana de investigación educativa

ISSN: 1405-6666

Year of publication: 2018

Volume: 23

Issue: 78

Pages: 733-760

Type: Article

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Abstract

Resumen: El objetivo de esta investigación fue determinar la influencia de los factores pedagógicos y de diseño instruccional, controlados por los instructores, sobre el aprendizaje, la satisfacción y la calidad percibidos por los participantes en cursos de formación de profesorado universitario impartidos en formato e-learnig. Participaron 109 profesores universitarios. Como procedimiento de análisis estadístico se utilizó la regresión por mínimos cuadrados parciales (Partial Least Squares, PLS). Los resultados mostraron que los factores rigor y contenido del curso fueron los predictores de satisfacción (R2 = .66) y calidad (R2 = .55); en tanto los factores contenido del curso, estructura del curso, rigor, interacción profesor-instructor e interacción entre profesores mostraron un impacto positivo y significativo sobre el aprendizaje percibido (R2 = .70).

Bibliographic References

  • Alavi, Marya. (1994). ComputerMediated collaborative learning: An empirical evaluation. MIS Quarterly. 18. 159
  • Abella, Víctor,López, Carlos,Ortega, Natividad,Sánchez, Pedro,Lezcano, Fernand. (2011). Implantación de UBUVirtual en la Universidad de Burgos: evaluación y expectativas de uso. EDUCTEC: Revista Electrónica de Tecnología Educativa. 38.
  • Ausín, Vanesa,Abella, Víctor,Delgado, Vanesa,Hortigüela, Davi. (2016). Aprendizaje Basado en Proyectos a través de las TIC. Una experiencia de innovación docente desde las aulas universitarias. Formación Universitaria. 9. 31
  • Barclay, Donald,Higgins, Chistopher,Thompson, Ro. (1995). The partial least squares (PLS) approach to causal modeling: personal computer adoption and use as an illustration. Technology Studies. 2. 285-309
  • Beqiri, Mirjeta S.,Chase, Nancy M.,Bishka, Aten. (2010). Online course delivery: An empirical investigation of factors affecting student satisfaction. Journal of Education for Business. 85. 95-100
  • Blau, Gary,Drennan, Rob B.,Hochner, Arthur,Kapanjie, Dari. (2016). Perceived learning and timely graduation for business undergraduates taking an online or hybrid course. Journal of Education for Business. 91. 347
  • Bruckman, Am. (2002). The future of e-learning communities. Communications of the ACM. 45. 60
  • Cheng, Gary,Chau, Julian. (2016). Exploring the relationships between learning styles, online participation, learning achievement and course satisfaction: An empirical study of a blended learning course. British Journal of Educational Technology. 47. 257
  • Chin, Wynn. (1998). Modern methods for business research. Lawrence Erlbaum Associates. Mahwah.
  • Chin, Wynn. (2010). Handbook of partial least squares: Concepts, methods and applications. Springer-Verlag. Berlín.
  • De Juanas, Ángel,Diestro, Alfons. (2011). Cambios educativos y formativos para el desarrollo sostenible. Universidad de Murcia: Servicio de Publicaciones edit.um.
  • Dijkstra, Theo,Henseler, Jör. (2015). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics and Data Analysis. 81. 10-23
  • Dykman, Charlene,Davis, Charle. (2008). Online education forum - Part three: A quality online educational experience. Journal of Information Systems Education. 19. 281
  • Eom, Sean B.,Wen, H. Joseph,Ashill, Nicholla. (2006). The determinants of students’ perceived learning outcomes and satisfaction in university online education: an empirical investigation. Decision Sciences Journal of Innovative Education. 4. 215
  • Evermann, Joerg,Tate, Mar. (2016). Assessing the predictive performance of structural equation model estimators. Journal of Business Research. 69. 4565
  • Falk, Frank,Miller, Nanc. (1992). A primer for soft modeling. The University of Akron. Akron, Ohio.
  • Fornell, Claes,Larcker, David F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research. 18. 39-50
  • Hair, Joseph,Hult, Tomas,Ringle, Chiristian,Sarstedt, Mark. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). SAGE Publications. Thousand Oaks.
  • Halawi, Leila A.,McCarthy, Richard V.,Pires, Sandr. (2009). An evaluation of e-learning on the basis of Bloom’s taxonomy: An exploratory study. Journal of Education for Business. 84. 374
  • Henseler, Jorg,Dijkstra, Theo,Sarstedt, Marko,Ringle, Christian,Diamantopoulos, Adamantios,Straub, Detmar,Ketchen, David,Hair, Josehp,Hult, Thomas,Calantone, Roge. (2014). Common beliefs and reality about PLS: Comments on Ronkko and Evermann (2013). Organizational Research Methods. 17. 182-209
  • Henseler, Jorg,Hubona, Geoffrey,Ray, Paulin. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems. 116. 2-20
  • Holsapple, Clyde W.,Lee-Post, Anit. (2006). Defining, assessing, and promoting e-learning success: An information systems perspective. Decision Sciences Journal of Innovative Education. 4. 67-85
  • Kauffman, Heathe. (2015). A review of predictive factors of student success in and satisfaction with online learning. Research in Learning Technology. 23.
  • Kurucay, Murat,Itnan, Fethi A. (2017). Examining the effects of learner-learner interactions on satisfaction and learning in an online undergraduate course. Computers & Education. 115. 20-37
  • Lee, Sang,Srinivasan, Sandhya,Trail, Trudian,Lewis, David,Lopez, Samanth. (2011). Examining the relationship among student perception of support, course satisfaction, and learning outcomes in online learning. Internet and Higher Education. 14. 158
  • Lee, Hye,Rha, Ilj. (2009). Influence of structure and interaction on student achievement and satisfaction in web-based distance learning. Educational Technology & Society. 12. 372
  • Marks, Ronald B.,Sibley, Stanley D.,Arbaugh, J. B. (2005). A structural equation model of predictors for effective online learning. Journal of Management Education. 29. 531
  • Moore, Michael G. (1989). Editorial: Three types of interaction. The American Journal of Distance Education. 3. 1-7
  • Morrison, Gar. (2010). Designing Effective Instruction. John Wiley & Sons. Nueva York.
  • Murphy, Elisabeth,Rodríguez-Manzanares, Marí. (2008). Revisiting transactional distance theory in a context of web-based high-school distance education. Journal of Distance Education. 22. 1-14
  • Oncu, Semiral,Ozdilek, Zehr. (2013). Learning with peers: an interdisciplinary comparative study of learner interaction and satisfaction on an instructional design course. Educational Sciences: Theory and Practice. 13. 1251
  • Oyarzun, Beth,Stefaniak, Jill,Bol, Linda,Morrison, Gar. (2017). Effects of learner-to-learner interactions on social presence, achievement and satisfaction. Journal of Computing in Higher Education. 1. 1-22
  • Paechter, Manuela,Maier, Brigitte,Macher, Danie. (2010). Students’ expectations of, and experiences in e-learning: Their relation to learning achievements and course satisfaction. Computers & Education. 54. 222
  • Palmer, S. R.,Holt, D. M. (2009). Examining student satisfaction with wholly online learning. Journal of Computer Assisted Learning. 25. 101
  • Peltier, James,Schibrowsky, John,Drago, Willia. (2007). The interdependence of the factors influencing the perceived quality of the online learning experience: A causal model. Journal of Marketing Education. 29. 140
  • Perrault, Heidi,Waldman, Lila,Alexander, Melody,Zhao, Jense. (2002). Overcoming barriers to successful delivery of distance-learning courses. Journal of Education for Business. 77. 313
  • Revilla, Melanie,Saris, Willem,Krosnick, Jo. (2014). Choosing the number of categories in agree-disagree scales. Sociological Methods & Research. 47. 73-97
  • Richardson, Jennifer,Swan, Kare. (2003). Examining social presence in online courses in relation to students’ perceived learning and satisfaction. Journal of Asynchronous Learning Networks. 7. 68-88
  • Salomon, Gill. (2013). E-tivities, the key to active online learning. Routledge. Nueva York.
  • Sarstedt, Marko,Christian, Ringle,Joseph, Hai. (2017). Handbook of Market Research. Springer. Cham.
  • Schoonenboom, Judit. (2014). Using an adapted, task-level technology acceptance model to explain why instructors in higher education intend to use some learning management system tools more than others. Computers & Education. 71. 247
  • Sebastianelli, Rose,Swift, Caroline,Tamimi, Nabi. (2015). Factors affecting perceived learning, satisfaction, and quality in the online MBA: A Structural Equation Modeling. Journal of Education for Business. 90. 296-305
  • Shea, Peter,Swan, Karen,Fredericksen, Eric,Pickett, Alexandr. (2002). Elements of quality online education: Learning effectiveness, cost effectiveness, access, faculty satisfaction, student satisfaction. Olin College-Sloan Consortium. Needham.
  • Sher, Al. (2009). Assessing the relationship of student-instructor and student- student interaction to student learning and satisfaction in web based online learning environment. Journal of Interactive Online Learning. 8. 102
  • Sun, Qin,Ganesh, Gopal. (2014). Developing and teaching an online MBA marketing research class: Implications for online learning effectiveness. Journal of Education for Business. 89. 337
  • Tessema, Mussie,Ready, Kathryn,Yu, Wei-Chou. (2012). Factors affecting college students’ satisfaction with major curriculum: Evidence from nine years of data. International Journal of Humanities and Social Science. 2. 34-44
  • Uppal, Muhammad Amaad,Ali, Samnan,Gulliver, Stephen R. (2017). Factors determining e-learning service quality. British Journal of Educational Technology. 1-15
  • Yu, Chian-So. (2011). Construction and validation of an e-lifestyle instrument. Internet Research. 21. 214
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