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Journal of Computing in Higher Education

, Volume 27, Issue 1, pp 28–46 | Cite as

Motivation, instructional design, flow, and academic achievement at a Korean online university: a structural equation modeling study

  • Young Ju Joo
  • Eunjung Oh
  • Su Mi Kim
Article
First Online:

Abstract

The purpose of this study is to examine the structural relationships among self-efficacy, intrinsic value, test anxiety, instructional design, flow, and achievement among students at a Korean online university. To address research questions, the researchers administered online surveys to 963 college students at an online university in Korea enrolled in a Computer Application course. Structural equation modeling was conducted to investigate the structural relationships among the variables. Findings indicated that (1) self-efficacy and instructional design had statistically significant direct effects on flow, (2) self-efficacy, intrinsic value, and flow had statistically significant direct effects on achievement, and (3) flow mediates self-efficacy and achievement, and instructional design and achievement.

Keywords

Online learning Motivation Instructional design Flow Achievement 
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Notes

Acknowledgments

This work was supported by a National Research Foundation of Korea Grant funded by the Korean Government (2012-045331).

References

  1. Ames, C., & Archer, J. (1988). Achievement goals in the classroom: Students’ learning strategies and the motivation process. Journal of Educational Psychology, 80, 260–267.CrossRefGoogle Scholar
  2. Bandalos, D. L. (2002). The effects of item parceling on goodness-of-fit and parameter estimate bias in structural equation modeling. Structural Equation Modeling, 9(1), 78–102.CrossRefGoogle Scholar
  3. Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215.CrossRefGoogle Scholar
  4. Bandura, A. (1986). Social foundation of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  5. Bernard, R. M., Abrami, P. C., Gorokhovski, E., Wade, A., Tamim, R., Surkes, M., & Bethel, E. C. (2009). A meta-analysis of three interaction treatments in distance education. Review of Educational Research, 79(3), 1243–1289.CrossRefGoogle Scholar
  6. Bloom, B. S. (1976). Human characteristics and school learning. NY: McGraw-Hill.Google Scholar
  7. Bonaccio, S., Reeve, C. L., & Winford, E. C. (2012). Text anxiety on cognitive ability test can result in differential predictive validity of academic performance. Personality and Individual Differences, 52(4), 497–502.CrossRefGoogle Scholar
  8. Branch, R. M., & Merrill, M. D. (2012). Characteristics of Instructional Design Models. In R. A. Reiser & J. V. Dempsey (Eds.), Trends and issues in instructional design and technology (pp. 8–16). Boston: Pearson.Google Scholar
  9. California State University (n.d.). Rubric for Online Instruction. Retrieved 15 May 2013. http://www.csuchico.edu/roi/the_rubric.shtml
  10. Cassady, J. C., & Johnson, R. E. (2002). Cognitive test anxiety and academic performance. Contemporary Educational Psychology, 27(2), 270–295.CrossRefGoogle Scholar
  11. Cha, S., & Kim, I. (2008). Study on the relation between test anxiety, coping strategy and achievement in mathematics. Journal of Science Education, 32(1), 55–71.Google Scholar
  12. Cho, M.-H., & Jonassen, D. (2009). Development of the human interaction dimension of the Self-Regulated Learning Questionnaire in asynchronous online learning environments. Educational Psychology, 29, 117–138.CrossRefGoogle Scholar
  13. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York: Harper Perennial.Google Scholar
  14. Daniels, L. M., Haynes, T. L., Stupnisky, R. H., Perry, R. P., Newall, N. E., & Pekrun, R. (2008). Individual differences in achievement goals: A longitudinal study of cognitive, emotional, and achievement outcomes. Contemporary Educational Psychology, 33(4), 584–608.CrossRefGoogle Scholar
  15. Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132.CrossRefGoogle Scholar
  16. E-learning white paper. (2013). 2012 E-learning industry status report. Korea, Seoul: Korea National IT Industry Promotion Agency.Google Scholar
  17. Eom, S. B., & Arbaugh, J. B (Eds) (2011). Student Satisfaction and Learning Outcomes in E-Learning: An introduction to empirical research. Information Science Reference.Google Scholar
  18. Fornell, C. R., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.CrossRefGoogle Scholar
  19. Gall, M. (2003). Educational research: An introduction (7th ed.). Boston: Allyn and Bacon.Google Scholar
  20. Ghani, J. A., & Deshpande, S. P. (1994). Task characteristics and the experience of optimal flow in human–computer interaction. The Journal of Psychology, 128(4), 381–391.CrossRefGoogle Scholar
  21. Gore, P. A, Jr. (2006). Academic self-efficacy as a predictor of college outcomes: Two incremental validity studies. Journal of Career Assessment, 14(1), 92–115.CrossRefGoogle Scholar
  22. Hair, J. T., Anderson, R. E., Tatham, R. L., & Black, W. C. (1992). Multivariate data analysis with readings. New York: Macmillan.Google Scholar
  23. Harroff, P., & Valentine, T. (2006). Dimensions of program quality in web-based adult education. The American Journal of Distance Education, 20(1), 7–22.CrossRefGoogle Scholar
  24. Herrington, J., Oliver, R., & Reeves, T. C. (2006). Authentic tasks online: A synergy among learner, task and technology. Distance Education, 27(2), 233–248.CrossRefGoogle Scholar
  25. Hodges, C. B. (2008). Self-efficacy in the context of online learning environments: A review of the literature and directions for research. Performance Improvement Quarterly, 20(3–4), 7–25.CrossRefGoogle Scholar
  26. Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 60, 50–68.CrossRefGoogle Scholar
  27. Jackson, S. A., & Marsh, H. (1996). Development and validation of a scale to measure optimal experience: The flow state scale. Journal of Sport and Exercise Psychology, 18, 17–35.Google Scholar
  28. Johnson, R. D., Hornik, S., & Salas, E. (2008). An empirical examination of factors contributing to the creation of successful e-learning environments. International Journal of Human-Computer Studies, 66(5), 356–369.CrossRefGoogle Scholar
  29. Jonassen, D., & Kim, B. (2009). Arguing to learn and learning to argue: Design justifications and guidelines. Educational Technology Research and Development, 58(4), 439–457.CrossRefGoogle Scholar
  30. Joo, Y. J., & Kim, E. (2004). The development of student ratings to evaluate course in cyber university. Ewha Journal of Educational Research, 35(2), 1–21.Google Scholar
  31. Joo, Y. J., Kim, N., & Jo, H. (2008). Test development and verifying the validity and reliability for measuring a effectiveness of e-learning course in cyber university. Journal of the Korean Association of Information Education, 12(1), 109–120.Google Scholar
  32. Joo, Y. J., Lim, K. Y., & Kim, S. M. (2012). A model for predicting flow and achievement in corporate e-learning. Educational Technology & Society, 15(1), 313–325.Google Scholar
  33. Keller, J. M. (2008). First principles of motivation to learn and e3-learning. Distance Education, 29(2), 175–185.CrossRefGoogle Scholar
  34. Kiili, K. (2005). Participatory multimedia learning: Engaging learner. Australasian Journal of Educational Technology, 21(3), 303–322.Google Scholar
  35. Kim, C., Park, S. W., & Cozart, J. (2014). Affective and motivational factors of online math learning. British Journal of Educational Technology, 45(1), 171–185.CrossRefGoogle Scholar
  36. Kishton, J. M., & Widaman, K. F. (1994). Unidimensional versus domain representative parceling of questionnaire items: An empirical example. Educational and Psychological Measurement, 54(3), 757–765.CrossRefGoogle Scholar
  37. Kline, R. B. (2005). Principles and practice of structural equation modeling. New York: The Guilford Press.Google Scholar
  38. Lebow, D., & Wager, W. W. (1994). Authentic activity as a model for appropriate learning activity: Implications for emerging instructional technologies. Canadian Journal of Educational Communication, 23(3), 144–231.Google Scholar
  39. Lee, M., & Yang, Y. (2002). Development of a students’ evaluation instrument for web-based course. Journal of Educational Technology, 18(1), 175–192.Google Scholar
  40. Lim, C. (2001). The development and effects of a web-based self-regulated learning support system. Journal of Educational Technology, 17(3), 53–83.Google Scholar
  41. Mandler, L. G., & Sarason, S. B. (1952). The effect of differential instructions on anxiety and learning. Journal of Abnormal Social Psychology, 47(2), 166–173.CrossRefGoogle Scholar
  42. Marks, H. M. (2000). Student engagement in instructional activity: Patterns in the elementary, middle, and high school years. American Educational Research Journal, 37, 153–184.CrossRefGoogle Scholar
  43. Martin, A. (2009). Motivation and engagement across the academic lifespan: A developmental construct validity study of elementary school, high school, and university/college students. Educational and Psychological Measurement, 69, 794–824.CrossRefGoogle Scholar
  44. Martin, A. J., & Jackson, S. A. (2008). Brief approaches to assessing task absorption and enhanced subjective experience: Examining ‘short’ and ‘core’ flow in diverse performance domains. Motivation and Emotion, 32(3), 141–157.CrossRefGoogle Scholar
  45. Meece, J., Blumenfeld, P. C., & Hoyle, R. (1988). Students’ goal orientations and cognitive engagement in classroom activities. Journal of Educational Psychology, 80, 514–523.CrossRefGoogle Scholar
  46. Moon, S. B. (2009). Understanding and application of structural equation modeling. Seoul, South Korea: Hakjisa.Google Scholar
  47. Moore, M. G., & Kearsley, G. (1996). Distance education: A systems view. Belmont, CA: Wadsworth.Google Scholar
  48. Morris, L. V., Wu, S. S., & Finnegan, C. L. (2005). Predicting retention in online general education courses. American Journal of Distance Education, 19(1), 23–36.CrossRefGoogle Scholar
  49. Multon, K. D., Brown, S. D., & Lent, R. W. (1991). Relation of self-efficacy beliefs to academic outcomes: A meta-analytic investigation. Journal of Counseling Psychology, 38, 30–38.CrossRefGoogle Scholar
  50. Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66(4), 543–578.CrossRefGoogle Scholar
  51. Parks-Stamm, E. J., Gollwitzer, P. M., & Oettingen, G. (2010). Implementation intentions and test anxiety: Shielding academic performance from distraction. Learning and Individual Differences, 20(1), 30–33.CrossRefGoogle Scholar
  52. Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33–40.CrossRefGoogle Scholar
  53. Puzziferro, M. (2008). Online technologies self-efficacy and self-regulated learning as predictors of final grade and satisfaction in college-level online courses. American Journal of Distance Education, 22(2), 72–89.CrossRefGoogle Scholar
  54. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25, 54–67.CrossRefGoogle Scholar
  55. Sass, D. A., & Smith, P. L. (2006). The effects of parceling unidimensional scales on structural parameter estimates in structural equation modeling. Structural Equation Modeling, 13(4), 566–586.CrossRefGoogle Scholar
  56. Shen, D., Cho, M.-H., Tsai, C.-L., & Marra, R. (2013). Unpacking online learning experiences: Online learning self-efficacy and learning satisfaction. Internet and Higher Education., 19, 10–17.CrossRefGoogle Scholar
  57. Shin, N. (2006). Online learner’s ‘flow’ experience: An empirical study. British Journal of Educational Technology, 37(5), 705–720.CrossRefGoogle Scholar
  58. Skinner, E. A., Wellborn, J. G., & Connell, J. P. (1990). What it takes to do well in school and whether I’ve got it: The role of perceived control in children’s engagement and school achievement. Journal of Educational Psychology, 82, 22–32.CrossRefGoogle Scholar
  59. Sobel, M. E. (1982). Asymptotic intervals for indirect effects in structural equation models. In S. Leinhart (Ed.), Sociological methodology (pp. 290–312). San Francisco: Jossey-Bass.Google Scholar
  60. Song, I., & Park, S. (2000). A study on the relationships of goal orientation, self-regulated learning, and academic achievement. Journal of Education Psychology, 14(2), 29–64.Google Scholar
  61. Spinath, B., Spinath, F. M., Harlaar, N., & Plomin, R. (2006). Predicting school achievement from general cognitive ability, self-perceived ability, and intrinsic value. Intelligence, 34(4), 363–374.CrossRefGoogle Scholar
  62. Wigfield, A., & Cambria, J. (2010). Students’ achievement values, goal orientations, and interest: Definitions, development, and relations to achievement outcomes. Developmental Review, 30, 1–35.CrossRefGoogle Scholar
  63. Wolters, C. (2004). Advancing achievement goal theory: Using goal structures and goal orientations to predict students’ motivation, cognition, and achievement. Journal of Educational Psychology, 96(2), 236–250.CrossRefGoogle Scholar
  64. Wu, C. H., Tzeng, Y. L., & Huang, Y. M. (2014). Understanding the relationship between physiological signals and digital game-based learning outcome. Journal of Computers in Education, 1(1), 81–97.CrossRefGoogle Scholar
  65. Zimmerman, B. J., & Schunk, D. H. (1989). Self-regulated learning and academic achievement: Theory, research, and practice. New York: Springer.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Educational TechnologyEwha Womans UniversitySeoulKorea
  2. 2.Department of Education Policy, Organization and LeadershipUniversity of Illinois at Urbana-ChampaignChampaignUSA
  3. 3.POSCO Corporate Future Creating AcademyEwha Womans UniversitySeoulKorea

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