La mejora de satisfacción de los estudiantes de grado de Relaciones Laborales y Recursos Humanos con el uso de las Ticsefectos de las estrategias de aprendizaje y de las experiencias de flujo

Referencias bibliográficas

• Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), (pp. 295-336). Mahwah, NJ: Lawrence Erlbaum Associate.
• Csikszentmihalyi, M. (2013). Flow: The psychology of happiness. Random House.
• Dansereau, D. (1985): A Learning strategy research@, en J. Segal, S. Chipman y R. Glaser (eds.) Thinking and learning skills, 1, 209-239, Hillsdale, N.J.: Lawrence Erlbaum Associates, Publishers.
• Delone, W. H., y Mclean, E. R. (2003). The Delone and Mclean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9–30.
• Esteban-Millat, I., Martínez-López, F. J., Huertas-García, R., Meseguer, A., y RodríguezArdura, I. (2014). Modelling students’ flow experiences in an online learning environment. Computers & Education, 71, 111-123.
• Fornell, C., y Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, XXVII(February), 39-50.
• Graham, S., y Golan, S. (1991). Motivational influences on cognition: Task involvement, ego involvement, and depth of information processing. Journal of Educational Psychology, 83(2), 187.
• Hair, J. F., Anderson, R. L., y Tatham, W. C. (2006). Multivariate data analysis. Upper Saddle River, NJ: Pearson.
• Kiili, K. (2005). Content creation challenges and flow experience in educational games: The IT-Emperor case. The Internet and Higher Education, 8(3), 183-198.
• Kim, K. J., y Bonk, C. J. (2006). The future of online teaching and learning in higher education: the survey says. Educause Quarterly, 29, 22–30.
• Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 3(2), 205–223.
• Kuo, Y.-C., Walker, A. E., Schroder, K. E. E., y Belland, B. R. (2014). Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20(0), 35-50. doi: http://dx.doi.org/10.1016/j.iheduc.2013.10.001
• Lee, E. (2001). The relations of motivation and cognitive strategies to flow experience. Journal of Educational Psychology, 15(3), 199–216.
• Lee, Y. K., Tseng, S. P., Liu, F. J., y Liu, S. C. (2007). Antecedents of learner satisfaction toward e-learning. The Journal of American Academy of Business, Cambridge, 11, 161–168.
• Liaw, S.S., y Huang, H.M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60, 14-24.
• Lindgaard, G., y Dudek, C. (2003). What is this evasive beast we call user satisfaction? Interacting with Computers, 15(3), 429–452.
• Lowerison, G., Sclater, J., Schmid, R. F., y Abrami, P. C. (2006). Student perceived effectiveness of computer technology use in post-secondary classrooms. Computers & Education, 47(4), 465-489.
• Massimini, F., y Delle Fave, A. (2000). Individual development in a bio-cultural perspective. American Psychologist, 55(1), 24.
• Massimini, F., Csikszentmihalyi, M., y delle Fave, A. (1988). Flow and biocultural evolution. In M. Csikszentmikalyi, y I. S. Csikszentmihalyi (Eds.), Optimal experience: Psychological studies of low in consciousness (pp. 60–81). New York: Cambridge University Press.
• Miles, J., y Shevlin, M. (2001). Applying regression and correlation. London: Sage Publications.
• Motiwalla, L. F. (2007). Mobile learning: A framework and evaluation. Computers & Education, 49(3), 581-596.
• Nunnally, J. C. (1978). Psychometric theory. New York: McGraw-Hill.
• Pearce, J. M., Ainley, M., y Howard, S. (2005). The ebb and flow of online learning. Computers in human behavior, 21(5), 745-771.
• Pekrun, R., Goetz, T., Titz, W., y Perry, R. P. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational psychologist, 37(2), 91-105.
• Pintrich, P. R. (1988). A process‐oriented view of student motivation and cognition. New directions for institutional research, 1988(57), 65-79.
• Pintrich, P. R., y Garcia, T. (1994). Self-regulated learning in college students: Knowledge, strategies, and motivation. Student motivation, cognition, and learning, 113-133.
• Podsakoff, P. M., y Organ, D. W. (1986). Self-reports in organizational research: Problems and prospects. Journal of Management, 12, 69-82.
• Ringle, C. M., Wende, S., y Will, A. (2005). SmartPLS 2.0 (M3) beta. Sevillano, M.L. (2005): Estrategias innovadoras para una enseñanza de calidad, Madrid: Pearson Prentice Hall.
• Shee, D., y Wang, Y. H. (2008). Multi-criteria evaluation of the web-based e-learning system: a methodology based on learner satisfaction and its applications. Computers & Education, 50, 894–905.
• Somuncuoglu, Y., y Yildirim, A. (1999). Relationship between achievement goal orientations and use of learning strategies. The Journal of Educational Research, 92(5), 267-277.
• Sternberg, R. J., y Davidson, J. E. (1995). The nature of insight. The MIT Press.
• Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., y Yeh, D. (2008). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50, 1183–1202.
• Virvou, M., y Katsionis, G. (2008). On the usability and likeability of virtual reality games for education: the case of VR-ENGAGE. Computers & Education, 50, 154–178.