Learner Attrition in an Advanced Vocational Online Training: The Role of Computer Attitude, Computer Anxiety, and Online Learning Experience

Klaus D. Stiller 1  and Annamaria Köster 2
  • 1 Department of Pedagogy, University of Regensburg, Germany
  • 2 University of Duisburg-Essen, Germany

Abstract

Online learning has gained importance in education over the last 20 years, but the well-known problem of high dropout rates still persists. According to the multi-dimensional learning tasks model, the cognitive (over)load of learners is essential to attrition when dealing with five challenges (e.g. technology, user interface) of an online training (Tyler-Smith, 2006). The experienced load might depend on learner characteristics. The study explored the extent that learners dropping out from a vocational video-based online training about media design for employees of micro, small and medium-sized enterprises differ from working learners’ online learning experience, computer attitude, and computer anxiety. The data were collected from 72 of 128 registered employees who completed a questionnaire before starting the course to analyze differences between the dropout group (submitted no solutions to online training tasks; n = 19) and the active learner group (submitted at least one of 13 task solutions; n = 53). No differences were found in online learning experience, but the dropout group reported more negative attitudes towards computers and a higher level of computer anxiety than the active learner group.

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

  • 1. Allen, I. E., & Seaman, J. (2010). Learning on Demand. Online Education in the United States, 2009. Babson Park, MA: Babson Research Group.

  • 2. Allen, I. E., & Seaman, J. (2015). Grade level: Tracking online education in the United States. Babson Park, MA: Babson Research Group.

  • 3. Ajzen, I. (1991). The theory of planned behaviour. Organizational Behavior and Human Decision Processes, 50, 179-211.

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

  • 5. Bernard, R. M., Brauer, A., Abrami, P. C., & Surkes, M. (2004b). The development of a questionnaire for predicting online learning achievement. Distance Education, 25(1), 31-47.

  • 6. Dodd, C., Kirby, D., Seifert, T., & Sharpe, D. (2009). The impact of high school distance e-learning experience on rural students’ university achievement and persistence. Online Journal of Distance Learning Administration, 12(1). Retrieved from http://www.westga.edu/~distance/ojdla/spring121/dodd121.html

  • 7. Elis, P. (2010). The essential guide to effect sizes: Statistical power, meta-analysis, and the interpretation of research results. Cambridge: Cambridge University Press.

  • 8. Fritsch, H. & Ströhlein, G. (1988). Mentor support and academic achievement. Open Learning, 3(2), 27-32.

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

  • 10. 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(7), 1125-1129.

  • 11. 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.

  • 12. 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.

  • 13. Hachey, A. C., Wladis, C. W., & Conway, K. M. (2014). Do prior online course outcomes provide more information than G.P.A. alone in predicting subsequent online course grades and retention? An observational study at an urban community college. Computers & Education, 72, 59-67.

  • 14. Herbert, M. (2006). Staying the course: A study in online student satisfaction and retention. Online Journal of Distance Learning Administration, 9(4). Retrieved March 3, 2016, from http://www.westga.edu/~distance/ojdla/winter94/herbert94.htm

  • 15. Kember, D. (1989). A longitudinal-process model of drop-out from distance education. Journal of Higher Education, 60(3), 278-301.

  • 16. Kranzow, J. (2013). Faculty leadership in online education: Structuring courses to impact student satisfaction and persistence. Journal of Online Learning and Teaching, 9(1), 131-139.

  • 17. Lakhal, S., & Bazinet, N. (2015). Technological factors explaining student dropout from online courses in higher education: A review. In S. Carliner, C. Fulford & N. Ostashewski (Eds.), Proceedings of EdMedia: World Conference on Educational Media and Technology 2015 (pp. 1806-1811). Chesapeake, VA: AACE. Retrieved from http://www.editlib.org/p/151456/

  • 18. Lee, Y., & Choi, J. (2011). A review of online course dropout research: Implications for practice and future research. Educational Technology Research and Development, 59(5), 593-618.

  • 19. Levy, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers & Education, 48(2), 185-204.

  • 20. 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(3), 347-359.

  • 21. Myers, M. P., & Schiltz, P. M. (2012). Use of Elluminate in online teaching of statistics in the health sciences. Journal of Research in Innovative Teaching, 5(1), 53-62.

  • 22. Park, J. (2007). Factors related to learner dropout in online learning. In F.M. Nafukho, T.H. Chermack & C.M. Graham (Eds.), Proceedings of the 2007 Academy of Human Resource Development Annual Conference (pp. 25-1-25-8). Indianapolis, IN: AHRD.

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

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

  • 25. 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.

  • 26. Richter, T., Naumann, J., & Horz, H. (2010). Eine revidierte Fassung des Inventars zur Computerbildung (INCOBI-R). Zeitschrift für Pädagogische Psychologie, 24(1), 23-37.

  • 27. Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. The Internet & Higher Education, 6(1), 1-16.

  • 28. Rowntree, D. (1998). Teaching through self-instruction: How to develop open learning materials. London: Routledge.

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

  • 30. 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, 205-219.

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

  • 32. Stiller, K. D. (2015). 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. Retrieved from http://www.editlib.org/p/151412/

  • 33. Stiller, K. D., & Bachmaier, R. (2014). NiceDesign4SME: A video-based online training course. In J. Viteli & M. Leikomaa (Eds.), Proceedings of EdMedia: World Conference on Educational Media and Technology 2014 (pp. 373-383). Chesapeake, VA: AACE. Retrieved from http://www.editlib.org/p/147526

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

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

  • 36. 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(2), 73-85.

  • 37. U.S. Department of Education (2010). Evaluation of evidence-based practices in online learning: A metaanalysis and review of online learning studies. Retrieved January 19, 2016, from http://www2.ed.gov/rschstat/eval/tech/evidence-based-practices/finalreport.pdf

  • 38. 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(4), 361-377.

OPEN ACCESS

Journal + Issues

Search