Vol. 32 No. 1 (2017)
Research Articles

Predicting Success in an Online Undergraduate-Level Introductory Statistics Course using Self-Efficacy, Values, and Typical Mode of Instruction

Whitney Alicia Zimmerman
The Pennsylvania State University Department of Statistics

Published 2017-09-25


  • Online education,
  • statistics education,
  • online learning self-efficacy,
  • self-efficacy for learning statistics

How to Cite

Zimmerman, W. A. (2017). Predicting Success in an Online Undergraduate-Level Introductory Statistics Course using Self-Efficacy, Values, and Typical Mode of Instruction. International Journal of E-Learning & Distance Education Revue Internationale Du E-Learning Et La Formation à Distance, 32(1). Retrieved from https://ijede.ca/index.php/jde/article/view/1011


Expectancies of success and values were used to predict success in an online undergraduate-level introductory statistics course. Students who identified as primarily face-to-face learners were compared to students who identified as primarily online learners. Expectancy-value theory served as a model. Expectancies of success were operationalized as self-efficacy for learning online and self-efficacy for learning statistics.  Values were separated into the worth of learning statistics and the value of grades in the course. The purpose of this study was to determine if there are differences in the variables that may be used to predict final exam scores and successful course completion in typically face-to-face and typically online students, because there are differences in the populations of students who tend to take courses in these two different formats (i.e., traditional and adult learners). In predicting final exam grades there were no interactions with typical mode of instruction, though worth of statistics was a significant covariate and there was a main effect for typical mode of instruction. In predicting successful course completion, there were interactions between typical mode of instruction and one of the online learning self-efficacy subscales as well as the worth of statistics scale. These results are discussed in relation to the application of mainstream motivational models in the populations of traditional and adult learners.


Les attentes de succès et valeurs ont été utilisées pour prédire la réussite dans un cours en ligne d’introduction aux statistiques de niveau licence. Des étudiants identifiés comme apprenant principalement en face-à-face ont été comparés à d’autres apprenant principalement en ligne. La théorie de l’attente-valeur a servi de modèle. Les attentes de réussite ont été opérationnalisées en prenant en considération l’auto-efficacité dans l’apprentissage en ligne et l’auto-efficacité dans l’apprentissage des statistiques. Les valeurs ont été prises en considération en distinguant la pertinence d’apprendre les statistiques et la valeur des niveaux dans le cours. L’objectif de cette étude était de déterminer s’il y avait des différences dans les variables, qui pourraient être utilisées pour prédire les scores finaux d’examen et l’achèvement réussi des cours des étudiants typiquement en face-à-face et typiquement en ligne, sachant qu’il y a des différences dans les populations des étudiants qui tendent à suivre des cours dans ces deux différents formats (c’est-à-dire, les étudiants traditionnels vs les apprenants adultes). Il a été constaté que la prédiction des résultats finaux des examens n’avait pas de lien avec le mode d’instruction typique, même si la valeur des statistiques est une covariante significative et que l’effet du mode d’instruction typique est notable. La prédiction de l’achèvement des cours implique, quant à elle, une mise en lien du mode typique d’instruction et d’une des sous-échelles d’auto-efficacité de l’apprentissage en ligne de même que de la valeur de l’échelle statistique. Ces résultats sont discutés à la lumière de l’application des principaux courants motivationnels des populations d’apprenants traditionnels et d’apprenants adultes.


  1. Allen, I. E., Seaman. (2016). Online report card: Tracking online education in the United States. Retrieved from http://onlinelearningsurvey.com/reports/onlinereportcard.pdf
  2. Alqurashi, E. (2016). Self-efficacy in online learning environments: A literature review. Contemporary Issues in Education Research, 9(1), 45-51.
  3. Bandura, A. (1977). Social learning theory, Englewoods Clidds, NJ: Prentice-Hall Inc.
  4. Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122-147.
  5. Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: W. H. Freeman and Company.
  6. Cruise, R. J., Cash, R. W., & Bolton, D. L. (1985). Development and validation of an instrument to measure statistical anxiety. Proceedings of the American Statistical Association, Section of Statistical Education, Las Vegas, NV.
  7. Eccles, J., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109-132.
  8. Finney, S. J., & Schraw, G. (2003). Self-efficacy beliefs in college statistics courses. Contemporary Educational Psychology, 28(2), 161-186. doi: 10.1016/S0361-476X(02)00015-2
  9. Glenberg, A. M., Sanocki, T., Epstein, W., & Morris, C. (1987). Enhancing calibration of comprehension. Journal of Experimental Psychology: General, 116(2), 119–136.
  10. Gordon, S. (2004). Understanding students’ experiences of statistics in a service course. Statistics Education Research Journal, 3(1), 40-59. Retrieved from http://iase-web.org/documents/SERJ/SERJ3(1)_gordon.pdf
  11. Gorges, J. (2015). Out of school, out of mind? An expectancy-value analysis of adult learners’ motivation. Journal of Cognitive Education and Psychology, 14(2), 263-264.
  12. Hanna, D., Shevlin, M., & Dempster, M. (2008). The structure of the statistics anxiety rating scale: A confirmatory factor analysis using UK psychology students. Personality and Individual Differences, 45(1), 68-74.
  13. Hood, M., Creed, P. A., & Neumann, D. L. (2012). Using the expectancy value model of motivation to understand the relationship between student attitudes and achievement in statistics. Statistics Education Research Journal, 11(2), 72-85. Retrieved from https://www.stat.auckland.ac.nz/~iase/serj/SERJ11(2)_Hood.pdf
  14. Jameson, M. M., & Fusco, B. R. (2014). Math anxiety, math self-concept, and math self-efficacy in adult learners compared to traditional undergraduate students. Adult Education Quarterly, 64(4), 306-322.
  15. Knowles, M. (1984). The adult learner: A neglected species, (3rd ed.). Houston, TX: Gulf Publishing Co.
  16. Knowles, M. S., Swanson, R. A., & Holton, E. F. (2005). The adult learner: The definitive classic in adult education and human resource development, (6th ed.). San Burlington, MA: Elsevier.
  17. Ko, S., and S. Rossen. 2010. Teaching online: A practical guide, (3rd ed.). New York, NY: Routledge.
  18. Lent, R. W., Brown, S. D., & Larkin, K. C. (1984). Relation of self-efficacy expectations to academic achievement and persistence. Journal of Counseling Psychology, 31(3), 356–362.
  19. Malhotra, N. K. (2015). The effects of anxiety and self-efficacy on adult undergraduate finance students. International Journal of Arts & Sciences, 8(2), 539-547.
  20. Moore, M. (1986). Self-directed learning and distance education. Journal of Distance Education/ Revue de l’Enseignement a Distance, 1(1), 7-24. Retrieved from http://www.ijede.ca/index.php/jde/article/view/307/762
  21. Onwuegbuzie, A. J., & Wilson, V. A. (2003). Statistics anxiety: Nature, etiology, antecedents, effects, and treatments- a comprehensive review of the literature. Teaching in Higher Education, 8(2), 195-209.
  22. Pajares, F. (1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66(4), 543–578.
  23. Papousek, I., Ruggeri, K., Macher, D., Paechter, M., Heen, M., Weiss, E. M., Schulter, G., & Freudenthaler, H. H. (2012). Psychometric evaluation and experimental validation of the Statistics Anxiety Rating Scale. Journal of Personality Assessment, 94(1), 82-91.
  24. Schau, C., Stevens, J., Dauphinee, T. L., & Del Vecchio, A. (1995). The development and validation of the survey of attitudes toward statistics. Educational and Psychology Measurement, 55(5), 868-875.
  25. Schunk, D. H. (1981). Modeling and attributional effects on children’s achievement: A self-efficacy analysis. Journal of Educational Psychology, 73(1), 93–105.
  26. Schunk, D. H. (2012). Social cognitive theory. In K. R. Harris, S. Graham, T. Urdan, C. B. McCormick, G. M. Sinatra, J. Sweller, & J. Brophy (Eds.), APA Educational Psychology Handbook (pp. 101–124). Washington, DC: American Psychological Association.
  27. Shen, D., Cho, M., Tsai, C., & Marra, R. (2013). Unpacking online learning self-efficacy and learning satisfaction. Internet and Higher Education, 19, 10-17.
  28. Wigfield, A. & Eccles, J. S. (2000). Expectancy-value theory of achievement motivation. Contemporary Educational Psychology, 25, 68-81.
  29. Zimmerman, W. A., & Goins, D. D. (2015). Calibration of self-efficacy for conducting a chi-square test of independence. Statistics Education Research Journal, 14(2). http://iase-web.org/documents/SERJ/SERJ14%282%29_Zimmerman.pdf
  30. Zimmerman, W. A., Johnson, G., & Shumway, D. (2016, October 26). Using attitudes and anxieties to predict end-of-course outcomes in online and face-to-face introductory statistics course. Paper presented at the Annual Meeting of the Northeastern Educational Research Association, Trumbull, CT.
  31. Zimmerman, W. A., & Kulikowich, J. M. (2016). Online learning self-efficacy in students with and without online learning experience​. American Journal of Distance Education, 30(3).