Educational Technology International

The Korean Society for Education Technology (한국교육공학회)
권호 표지
DOI QR코드

DOI QR Code

Exploring Online Learning Profiles of In-service Teachers in a Professional Development Course

  • Received : 2017.09.06
  • Accepted : 2017.09.25
  • Published : 2017.10.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study aimed to explore online learning profiles of in-service teachers in South Korea, focusing on video lecture and discussion activities. A total of 269 teachers took an online professional development course for 14 days, using an online learning platform from which web log data were collected. The data showed the frequency of participation and the initial participation time, which was closely related to procrastinating behaviors. A cluster analysis revealed three online learning profiles of in-service teachers: procrastinating (n=42), passive interaction (n=136), and active learning (n=91) clusters. The active learning cluster showed high-level participation in both video lecture and discussion activities from the beginning of the online course, whereas the procrastinating cluster was seldom engaged in learning activities for the first half of the learning period. The passive interaction cluster was actively engaged in watching video lectures from the beginning of the online course but passively participated in discussion activities. As a result, the active learning cluster outperformed the passive interaction cluster in learning achievements. The findings were discussed in regard to how to improve online learning environments through considering online learning profiles of in-service teachers.

Keywords

References

  1. Alario-Hoyos, C., Munoz-Merino, P. J., Perez-Sanagustin, M., Delgado Kloos, C., & Parada, G. (2016). Who are the top contributors in a MOOC? Relating participants' performance and contributions. Journal of Computer Assisted Learning, 32(3), 232-243.
  2. Alstete, J. W., & Beutell, N. J. (2004). Performance indicators in online distance learning courses: a study of management education. Quality Assurance in Education, 12(1), 6-14.
  3. Ariely, D., & Wertenbroch, K. (2002). Procrastination, deadlines, and performance: self-control by precommitment. Psychological Science, 13(3), 219-224.
  4. Arnold, K. E., & Pistilli, M. D. (2012). Course signals at Purdue: Using learning analytics to increase student success. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 267-270). ACM.
  5. Auvinen, T., Hakulinen, L., & Malmi, L. (2015). Increasing students' awareness of their behavior in online learning environments with visualizations and achievement badges. IEEE Transactions on Learning Technologies, 8(3), 261-273.
  6. Breslow, L., Pritchard, D. E., DeBoer, J., Stump, G. S., Ho, A. D., & Seaton, D. T. (2013). Studying learning in the worldwide classroom: Research into edX's first MOOC. Research & Practice in Assessment, 8.
  7. Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219-243.
  8. Cho, Y. H., Park, H. J., Kim, J., Suk, Y., & Lee, S. (2015). Exploring roles of feedback to facilitate online discussion. Asian Journal of Education, 16(2), 289-313.
  9. Coetzee, D., Fox, A., Hearst, M. A., & Hartmann, B. (2014). Should your MOOC forum use a reputation system?. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (pp. 1176-1187). ACM.
  10. Geri, N., & Winer, A. (2015). Patterns of online video lectures use and impact on student achievement. In Proceedings of the 10th Chais Conference for Innovation and Learning Technologies: Learning in the technological era (pp. 9-15). ACM.
  11. Hew, K. F. (2016). Promoting engagement in online courses: What strategies can we learn from three highly rated MOOCS. British Journal of Educational Technology, 47(2), 320-341.
  12. Howell, A. J., & Buro, K. (2009). Implicit beliefs, achievement goals, and procrastination: A mediational analysis. Learning and Individual Differences, 19(1), 151-154.
  13. Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM Computing Surveys (CSUR), 31(3), 264-323.
  14. Jeong, Y. (2004). A study on the improvement and analysis of the teachers' distance learning management system. Journal of the Korean Association of Information Education, 8(1), 15-25.
  15. Jo, I. H., Kim, D., & Yoon, M. (2015). Constructing proxy variables to measure adult learners' time management strategies in LMS. Educational Technology & Society, 18(3), 214-225.
  16. Kim, J., Guo, P. J., Seaton, D. T., Mitros, P., Gajos, K. Z., & Miller, R. C. (2014). Understanding in-video dropouts and interaction peaks in online lecture videos. In Proceedings of the first ACM conference on Learning@scale conference (pp. 31-40). ACM.
  17. Kim, H. S., Kim. H., & Yu, S. (2015). A research on analyzing the performance of job training for teachers in distance education organization (RR 2015-4). Daegu: Korea Education and Research Information Service.
  18. Kizilcec, R. F., Piech, C., & Schneider, E. (2013). Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In Proceedings of the third international conference on learning analytics and knowledge (pp. 170-179). ACM.
  19. Koedinger, K. R., Kim, J., Jia, J. Z., McLaughlin, E. A., & Bier, N. L. (2015). Learning is not a spectator sport: Doing is better than watching for learning from a MOOC. In Proceedings of the Learning @ Scale conference (pp. 111-120)
  20. Kwon, T. H., Shin, S. H., & Shin, S. H. (2010). Survey on factors of interference in participation of online in service training and analysis on training - needs by teacher characteristics. Journal of Special Education, 17(2), 53-78.
  21. Levy, Y., & Ramim, M. (2012). A study of online exams procrastination using data analytics techniques. Interdisciplinary Journal of E-Learning and Learning Objects, 8(1), 97-113.
  22. Long, P., & Siemens, G. (2011). LAK '11: Proceedings of the 1st international conference on learning analytics and knowledge. New York, NY, USA: ACM. Retrieved from http://dl.acm.org/citation.cfm?id=2090116&preflayout=flat#abstract.
  23. Michinov, N., Brunot, S., Le Bohec, O., Juhel, J., & Delaval, M. (2011). Procrastination, participation, and performance in online learning environments. Computers & Education, 56(1), 243-252.
  24. Nawrot, I., & Doucet, A. (2014). Building engagement for MOOC students: Introducing support for time management on online learning platforms. In Proceedings of the 23rd International Conference on World Wide Web (pp. 1077-1082). ACM.
  25. Park, Y. J., & Jo, I. H. (2014). Design and application of visual dashboard based on learning analytics. The Journal of Educational Information and Media, 20(2), 191-216.
  26. Park, J., & Kim, M. (2011). The current state and effects of out-of-class learning activities at KNOU. Seoul: Korea National Open University.
  27. Pursel, B. K., Zhang, L., Jablokow, K. W., Choi, G. W., & Velegol, D. (2016). Understanding MOOC students: Motivations and behaviors indicative of MOOC completion. Journal of Computer Assisted Learning, 32(3), 202-217.
  28. Rha, I., Lim, C., Cho, Y. H., Choi, H., Yun, H., Yoo, M., & Jeong, E. (2017). Developing a national data metrics framework for learning analytics in Korea. Educational Technology International, 18(1), 1-25.
  29. Rakes, G. C., & Dunn, K. E. (2010). The impact of online graduate students' motivation and self-regulation on academic procrastination. Journal of Interactive Online Learning, 9(1), 78-93.
  30. Ramos, C., & Yudko, E. (2008). "Hits" (not "discussion posts") predict student success in online courses: a double cross-validation study. Computers & Education, 50(4), 1174-1182.
  31. Solomon, L. J., & Rothblum, E. D. (1984). Academic procrastination: Frequency and cognitive-behavioral correlates. Journal of Counseling Psychology, 31(4), 503-509.
  32. Song, L., & Hill, J. R. (2007). A conceptual model for understanding self-directed learning in online environments. Journal of Interactive Online Learning, 6(1), 27-42.
  33. Song, L., Singleton, E. S., Hill, J. R., & Koh, M. H. (2004). Improving online learning: Student perceptions of useful and challenging characteristics. The Internet and Higher Education, 7(1), 59-70.
  34. Wolters, C. A. (2003). Understanding procrastination from a self-regulated learning perspective. Journal of Educational Psychology, 95(1), 179.
  35. Zhang, H., Lin, L., Zhan, Y., & Ren, Y. (2016). The impact of teaching presence on online engagement behaviors. Journal of Educational Computing Research, 54(7), 887-900.