Advertisement

College Students’ Computer Self-efficacy, Intrinsic Motivation, Attitude, and Satisfaction in Blended Learning Environments

  • Yanhong Li
  • Harrison Hao YangEmail author
  • Jin Cai
  • Jason MacLeod
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10309)

Abstract

The purpose of this study was to examine the relationships between students’ computer self-efficacy, intrinsic motivation, attitude, and satisfaction in blended learning environments. The participants were 239 college students enrolled at Hubei University in China. A survey including four existing instruments was used for data collection. The results of structural equation modeling analysis suggested students’ attitudes toward online and face-to-face classes were the most influential toward to satisfaction in blended learning environments. Higher levels of intrinsic motivation were seen to be influential toward satisfaction in blended learning environments. Computer self-efficacy was seen to influence intrinsic motivation and attitudes, but not found to influence satisfaction in blended learning environments.

Keywords

Blended learning Computer self-efficacy Intrinsic motivation Attitude Satisfaction 

Notes

Acknowledgments

The work was supported by the Key Project of Philosophy and Social Science Research, Ministry of Education of China (14JZD044).

References

  1. 1.
    Garrison, D.R., Kanuka, H.: Blended learning: Uncovering its transformative potential in higher education. Internet High. Educ. 2, 95–105 (2004)CrossRefGoogle Scholar
  2. 2.
    Singh, H.: Building effective blended learning programs. Educ. Technol. 43(6), 51–54 (2003)Google Scholar
  3. 3.
    Osguthorpe, R.T., Graham, C.R.: Blending learning environments: definitions and directions. Q. Rev. Distance Educ. 3, 227–233 (2003)Google Scholar
  4. 4.
    Yang, H., Wang, S.: Cases on Online Learning Communities and Beyond: Investigations and Applications. Information Science Reference/IGI Global, Hershey (2012)Google Scholar
  5. 5.
    Yang, H., Wang, S.: Cases on e-Learning Management: Development and Implementation. Information Science Reference/IGI Global, Hershey (2012)Google Scholar
  6. 6.
    So, H.J., Brush, T.A.: Student perceptions of collaborative learning, social presence and satisfaction in a blended learning environment: relationships and critical factors. Comput. Educ. 51, 318–336 (2008)CrossRefGoogle Scholar
  7. 7.
    Abrantes, J.L., Seabra, C., Lages, L.F.: Pedagogical affect, student interest, and learning performance. J. Bus. Res. 60, 960–964 (2007)CrossRefGoogle Scholar
  8. 8.
    Wu, J.H., Tennyson, R.D., Hsia, T.L.: A study of student satisfaction in a blended e-learning system environment. Comput. Educ. 1, 155–164 (2010)CrossRefGoogle Scholar
  9. 9.
    Deci, E.L., Ryan, R.M.: Intrinsic Motivation and Self-determination in Human Behavior. Plenum, New York (1985)CrossRefGoogle Scholar
  10. 10.
    Ryan, R.M., Deci, E.L.: Self-determination theory and facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 55, 68–78 (2000)CrossRefGoogle Scholar
  11. 11.
    Ryan, R.M., Deci, E.L.: Intrinsic and extrinsic motivations: classic definitions and new directions. Contemp. Educ. Psych. 25, 54–67 (2000)CrossRefGoogle Scholar
  12. 12.
    Chao, C.Y., Chen, Y.T., Chuang, K.Y.: Exploring students’ learning attitude and achievement in flipped learning supported computer aided design curriculum: a study in high school engineering education. Comput. Appl. Eng. Educ. 4, 514–526 (2015)CrossRefGoogle Scholar
  13. 13.
    Compeau, D.R., Higgins, C.A.: Computer self-efficacy: development of a measure and initial test. MIS Q. 19, 189–211 (1995)CrossRefGoogle Scholar
  14. 14.
    Lim, C.K.: Computer self-efficacy, academic self-concept, and other predictors of satisfaction and future participation of adult distance learners. Am. J. Distance Educ. 2, 41–51 (2001)CrossRefGoogle Scholar
  15. 15.
    Holley, D., Oliver, M.: Student engagement and blended learning: portraits of risk. Comput. Educ. 3, 693–700 (2010)CrossRefGoogle Scholar
  16. 16.
    Ajzen, I., Fishbein, M.: Understanding Attitudes and Predicting Social Behavior. Prentice Hall, New Jersey (1980)Google Scholar
  17. 17.
    Lu, Y., Zhou, T., Wang, B.: Exploring Chinese users’ acceptance of instant messaging using the theory of planned behavior, the technology acceptance model, and the flow theory. Comput. Hum. Behav. 1, 29–39 (2009)CrossRefGoogle Scholar
  18. 18.
    Teo, T.: Assessing the computer attitudes of students: an Asian perspective. Comput. Hum. Behav. 4, 1634–1642 (2008)CrossRefGoogle Scholar
  19. 19.
    Mitchell, A., Honore, S.: Criteria for successful blended learning. Indust. Commer. Train. 3, 143–149 (2007)CrossRefGoogle Scholar
  20. 20.
    Dahalan, N., Hassan, H., Atan, H.: Student engagement in online learning: learners attitude toward e-mentoring. Procedia-Soc. Behav. Sci. 67, 464–475 (2012)CrossRefGoogle Scholar
  21. 21.
    Pintrich, P.R., De Groot, E.V.: Motivational and self-regulated learning components of classroom academic performance. J. Educ. Psych. 1, 33–40 (1990)CrossRefGoogle Scholar
  22. 22.
    Akkoyunlu, B., Yılmaz-Soylu, M.: Development of a scale on learners’ views on blended learning and its implementation process. Internet High. Educ. 1, 26–32 (2008)CrossRefGoogle Scholar
  23. 23.
    Bentler, P.M., Chou, C.P.: Practical issues in structural modeling. Sociol. Methods Res. 16(1), 78–117 (1987)CrossRefGoogle Scholar
  24. 24.
    Gefen, D., Straub, D.W.: A practical guide to factorial validity using PLS-graph: tutorial and annotated example. Commun. Infor. Syst. 5, 91–109 (2005)Google Scholar
  25. 25.
    Chin, W.W.: The partial least squares approach to structural equation modeling. In: Marcoulides, G.A. (ed.) Modern Methods for Business Research, pp. 298–336. Erlbaum, New Jersey (1998)Google Scholar
  26. 26.
    Shevlin, M., Miles, J.N.: Effects of sample size, model specification and factor loadings on the GFI in confirmatory factor analysis. Pers. Indiv. Differ. 1, 85–90 (1998)CrossRefGoogle Scholar
  27. 27.
    Fornell, C.D., Larcker, F.: Evaluating structural equation models with unobservable variables and measurement errors. J. Marketing Res. 2, 39–50 (1981)CrossRefGoogle Scholar
  28. 28.
    Chatterjee, S., Hadi, A.S.: Regression Analysis by Example. Wiley, New York (2000)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yanhong Li
    • 1
  • Harrison Hao Yang
    • 2
    • 3
    Email author
  • Jin Cai
    • 1
  • Jason MacLeod
    • 2
  1. 1.Collaborative & Innovative Center for Educational TechnologyCentral China Normal UniversityWuhanChina
  2. 2.School of Educational Information TechnologyCentral China Normal UniversityWuhanChina
  3. 3.School of EducationState University of New York at OswegoOswegoUSA

Personalised recommendations