Dropout in an Online Training for Trainee Teachers

Klaus D. Stiller 1  and Regine Bachmaier 2
  • 1 Department of Educational Science, Computer Centre, University of Regensburg, , Regensburg, Germany
  • 2 Computer Centre, University of Regensburg, , Regensburg, Germany

Abstract

High dropout rates are still a problem with online training. It is strongly suggested that learner characteristics influence the decision to persist in an online course or to drop out. The study explored the differences in domain-specific prior knowledge, motivation, computer attitude, computer anxiety, and learning skills between dropouts and active learners who enrolled in a vocational online training about media pedagogy for teachers. The data were collected from 575 trainee teachers from which three groups were formed: (a) students who only registered (n = 72) and (b) students who started learning but failed to complete any of the course modules (n = 124) and (c) active students who completed at least one module (n = 379). A dropout rate of 34.1% was observed. In general, only small effects were found. Students dropping out were older, had less prior knowledge, and lower skills in arranging an adequate learning environment.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 1. Allen, I. E., & Seaman, J. (2016). Online report card: Tracking online education in the United States. Babson Park, MA: Babson Research Group.

  • 2. Amadieu, F., Tricot, A., & Mariné, C. (2009). Exploratory study of relations between prior knowledge, comprehension, disorientation and on-line processes in hypertext. The Ergonomics Open Journal, 2, 49-57.

  • 3. Bernard, R. M., Abrami, P.C., Lou, Y., Borokhovski, E., Wade, A., Wozney, L., Wallet, P.A., Fiset, M., & Euan, B. (2004). How does distance education compare with classroom instruction? A meta-analysis of the empirical literature. Review of Educational Research, 74, 379-439.

  • 4. Castles, J. (2004). Persistence and the adult learner: Factors affecting persistence in Open University students. Active Learning in Higher Education, 5, 166-179.

  • 5. Chyung, S. Y. (2001). Systematic and systemic approaches to reducing attrition rates in online higher education. American Journal of Distance Education, 15(3), 36-49.

  • 6. Federal Ministry of Education and Research (2016). Education and Research in Figures 2016. Retrieved from https://www.bmbf.de/pub/Education_and_Research_in_Figures_2016.pdf

  • 7. Friedrich, H. F., & Mandl, H. (1992). Lern- und Denkstrategien - ein Problemaufriß. In H. Mandl & H. F. Friedrich (Eds.), Lern- und Denkstrategien. Analyse und Intervention (pp. 3-54). Göttingen: Hogrefe.

  • 8. Gagné, R. M., Briggs, L. J., & Wager, W. W. (1992). Principles of instructional design. Belmont, CA: Wadsworth/Thomson Learning.

  • 9. Gazza, E. A., & Hunker, D. F. (2014). Facilitating student retention in online graduate nursing education programs: A review of the literature. Nurse Education Today, 34, 1125-1129.

  • 10. Grau-Valldosera, J., & Minguillón, J. (2014). Rethinking dropout in online higher education: The case of the Universitat Oberta de Catalunya. The International Review of Research in Open and Distributed Learning, 15(1), 290-308.

  • 11. Griese, B., Lehmann, M., & Roesken-Winter, B. (2015). Refining questionnaire-based assessment of STEM students’ learning strategies. International Journal of STEM Education, 2(12), 1-12.

  • 12. Hart, C. (2012). Factors associated with student persistence in an online program of study: A review of the literature. Journal of Interactive Online Learning, 11, 19-42.

  • 13. Hartnett, M., St. George, A., & Dron, J. (2011). Examining motivation in online distance learning environments: Complex, multifaceted and situation-dependent. The International Review of Research in Open and Distance Learning, 12(6), 20-38.

  • 14. Hauser, R., Paul, R., & Bradley, J. (2012). Computer self-efficacy, anxiety, and learning in online versus face to face medium. Journal of Information Technology Education: Research, 11, 141-154.

  • 15. Holder, B. (2007). An investigation of hope, academics, environment, and motivation as predictors of persistence in higher education online programs. The Internet and Higher Education, 10, 245-260.

  • 16. Ivankova, N. V., & Stick, S. L. (2007). Students’ persistence in a distributed doctoral program in educational leadership in higher education: A mixed methods study. Research in Higher Education, 48, 93-135.

  • 17. Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38, 23-31.

  • 18. Kalyuga, S. (2014). The expertise reversal principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 576-597). New York, NY: Cambridge University Press.

  • 19. Keller, J. M., & Kopp, T. W. (1987). An application of the ARCS model of motivational design. In C. M. Reigeluth (Eds.), Instructional theories in action: Lessons illustrating selected theories and models (pp. 289-320). Hillsdale: Erlbaum.

  • 20. Kember, D. (1989). A longitudinal-process model of drop-out from distance education. Journal of Higher Education, 60, 278-301.

  • 21. Kranzow, J. (2013). Faculty leadership in online education: Structuring courses to impact student satisfaction and persistence. Journal of Online Learning and Teaching, 9, 131-139.

  • 22. Lee, Y., & Choi, J. (2011). A review of online course dropout research: Implications for practice and future research. Educational Technology Research and Development, 59, 593-618.

  • 23. Lee, Y., Choi, J., & Kim, T. (2013). Discriminating factors between completers of and dropouts from online learning courses. British Journal of Educational Technology, 44, 328-337.

  • 24. Long, L., Dubois, C., & Faley, R. (2009). A case study analysis of factors that influence attrition rates in voluntary online training programs. International Journal on E-Learning, 8, 347-359.

  • 25. McDonald, S., & Stevenson, R. J. (1998). Effects of text structure and prior knowledge of the learner on navigation in hypertext. Human Factors, 40, 18-27.

  • 26. Osborn, V. (2001). Identifying at-risk students in videoconferencing and web-based distance education. American Journal of Distance Education, 15(1), 41-54.

  • 27. Park, J.-H., & Choi, H. J. (2009). Factors influencing adult learners' decision to drop out or persist in online learning. Educational Technology & Society, 12(4), 207-217.

  • 28. Parker, A. (2003). Identifying predictors of academic persistence in distance education. United States Distance Learning Association Journal, 17(1), 55-61.

  • 29. Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 31, 459-470.

  • 30. Reinmann, G., & Mandl, H. (2006). Unterrichten und Lernumgebungen gestalten. In A. Krapp & B. Weidenmann (Eds.), Pädagogische Psychologie. Ein Lehrbuch (pp. 613-658). Weinheim: Beltz.

  • 31. Richter, T., Naumann, J., & Groeben, N. (2000). Attitudes toward the computer: Construct validation of an instrument with scales differentiated by content. Computers in Human Behavior, 16, 473-491.

  • 32. Richter, T., Naumann, J. & Horz, H. (2010). Eine revidierte Fassung des Inventars zur Computerbildung (INCOBI-R). Zeitschrift für Pädagogische Psychologie, 24, 23-37.

  • 33. Ryan, R. M. (1982). Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory. Journal of Personality and Social Psychology, 43, 450-461.

  • 34. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25, 54-67.

  • 35. Saadé, R. G., & Kira, D. (2009). Computer anxiety in e-learning: The effect of computer selfefficacy. Journal of Information Technology Education: Research, 8, 177-191.

  • 36. Sam, H. K., Othman, A. E. A., & Nordin, Z. S. (2005). Computer self-efficacy, computer anxiety, and attitudes toward the internet: A study among undergraduates in Unimas. Educational Technology & Society, 8(4), 205-219.

  • 37. Shin, N., & Kim, J. (1999). An exploration of learner progress and drop-out in Korea National Open University. Distance Education, 20, 81-95.

  • 38. Stiller, K. (2003). Lernstrategien und Lernerfolg beim computerbasierten Wissenserwerb. Psychologie in Erziehung und Unterricht, 50, 258-269.

  • 39. Stiller, K. D. (2009). Mono- und bimodale Textpräsentationen zu Bildern in Hypermedia- Systemen. Psychologie in Erziehung und Unterricht, 56, 49-63.

  • 40. Stiller, K. D. (2015a). Linear vs. pictorial access to on-screen text and computer attitude. In S. Carliner, C. Fulford, & N. Ostashewski (Eds.), Proceedings of EdMedia: World Conference on Educational Media and Technology 2015 (pp. 158-167). Chesapeake, VA: AACE.

  • 41. Stiller, K. D. (2015b). Promoting computerized learning via pictorial access to on-screen text. In S. Carliner, C. Fulford, & N. Ostashewski (Eds.), Proceedings of EdMedia: World Conference on Educational Media and Technology 2015 (pp. 59-68). Chesapeake, VA: AACE.

  • 42. Stiller, K. D., & Köster, A. (2016). Learner attrition in an advanced vocational online training: The role of e-learning experience, computer attitude, and computer anxiety. European Journal of Open, Distance and E-Learning, 19(2), 1-14.

  • 43. Stiller, K. D., & Köster, A. (submitted). Cognitive loads and training success in a video-based online training course.

  • 44. Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22, 123-138.

  • 45. Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition. Chicago: The University of Chicago Press.

  • 46. Tomei, L. A. (2007). A theoretical model for designing online education in support of lifelong learning. In Y. Inoue (Eds.), Online education for lifelong learning (pp. 122-145). Hershey: Information Science Publishing.

  • 47. Tyler-Smith, K. (2006). Early attrition among first time eLearners: A review of factors that contribute to drop-out, withdrawal and non-completion rates of adult learners undertaking eLearning programmes. Journal of Online Learning and Teaching, 2, 73-85.

  • 48. Weinstein, C. E., & Mayer, R. E. (1986). The teaching of learning strategies. In M. C. Wittrock (Ed.), Handbook of research on teaching: Third edition (pp. 315-327). New York, NY: Macmillan.

  • 49. Wild, K.-P., & Schiefele, U. (1994). Lernstrategien im Studium: Ergebnisse zur Faktorenstruktur und Reliabilität eines neuen Fragebogens. Zeitschrift für Differentielle und Diagnostische Psychologie, 15, 185-200.

  • 50. Xenos, M., Pierrakeas, C., & Pintelas, P. (2002). A survey on student dropout rates and dropout causes concerning the students in the course of informatics of the Hellenic Open University. Computers & Education, 39, 361-377.

  • 51. Yukselturk, E., & Bulut, S. (2007). Predictors for student success in an online course. Educational Technology & Society, 10(2), 71-83.

OPEN ACCESS

Journal + Issues

Search