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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Ames, C., & Archer, J. (1988). Achievement goals in the classroom: Students’ learning strategies and the motivation process. Journal of Educational Psychology, 80, 260–267.
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.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215.
Bandura, A. (1986). Social foundation of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.
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.
Bloom, B. S. (1976). Human characteristics and school learning. NY: McGraw-Hill.
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.
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.
California State University (n.d.). Rubric for Online Instruction. Retrieved 15 May 2013. http://www.csuchico.edu/roi/the_rubric.shtml
Cassady, J. C., & Johnson, R. E. (2002). Cognitive test anxiety and academic performance. Contemporary Educational Psychology, 27(2), 270–295.
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.
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.
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York: Harper Perennial.
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.
Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132.
E-learning white paper. (2013). 2012 E-learning industry status report. Korea, Seoul: Korea National IT Industry Promotion Agency.
Eom, S. B., & Arbaugh, J. B (Eds) (2011). Student Satisfaction and Learning Outcomes in E-Learning: An introduction to empirical research. Information Science Reference.
Fornell, C. R., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.
Gall, M. (2003). Educational research: An introduction (7th ed.). Boston: Allyn and Bacon.
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.
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.
Hair, J. T., Anderson, R. E., Tatham, R. L., & Black, W. C. (1992). Multivariate data analysis with readings. New York: Macmillan.
Harroff, P., & Valentine, T. (2006). Dimensions of program quality in web-based adult education. The American Journal of Distance Education, 20(1), 7–22.
Herrington, J., Oliver, R., & Reeves, T. C. (2006). Authentic tasks online: A synergy among learner, task and technology. Distance Education, 27(2), 233–248.
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.
Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of Marketing, 60, 50–68.
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.
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.
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.
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.
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.
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.
Keller, J. M. (2008). First principles of motivation to learn and e3-learning. Distance Education, 29(2), 175–185.
Kiili, K. (2005). Participatory multimedia learning: Engaging learner. Australasian Journal of Educational Technology, 21(3), 303–322.
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.
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.
Kline, R. B. (2005). Principles and practice of structural equation modeling. New York: The Guilford Press.
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.
Lee, M., & Yang, Y. (2002). Development of a students’ evaluation instrument for web-based course. Journal of Educational Technology, 18(1), 175–192.
Lim, C. (2001). The development and effects of a web-based self-regulated learning support system. Journal of Educational Technology, 17(3), 53–83.
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.
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.
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.
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.
Meece, J., Blumenfeld, P. C., & Hoyle, R. (1988). Students’ goal orientations and cognitive engagement in classroom activities. Journal of Educational Psychology, 80, 514–523.
Moon, S. B. (2009). Understanding and application of structural equation modeling. Seoul, South Korea: Hakjisa.
Moore, M. G., & Kearsley, G. (1996). Distance education: A systems view. Belmont, CA: Wadsworth.
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.
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.
Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66(4), 543–578.
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.
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.
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.
Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25, 54–67.
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.
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.
Shin, N. (2006). Online learner’s ‘flow’ experience: An empirical study. British Journal of Educational Technology, 37(5), 705–720.
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.
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.
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.
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.
Wigfield, A., & Cambria, J. (2010). Students’ achievement values, goal orientations, and interest: Definitions, development, and relations to achievement outcomes. Developmental Review, 30, 1–35.
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.
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.
Zimmerman, B. J., & Schunk, D. H. (1989). Self-regulated learning and academic achievement: Theory, research, and practice. New York: Springer.
This work was supported by a National Research Foundation of Korea Grant funded by the Korean Government (2012-045331).
About this article
Cite this article
Joo, Y.J., Oh, E. & Kim, S.M. Motivation, instructional design, flow, and academic achievement at a Korean online university: a structural equation modeling study. J Comput High Educ 27, 28–46 (2015). https://doi.org/10.1007/s12528-015-9090-9
- Online learning
- Instructional design