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Applying Learning Analytics to Explore the Effects of Motivation on Online Students' Reading Behavioral Patterns ARTICLE

, , , National Chiao Tung University

IRRODL Volume 19, Number 2, ISSN 1492-3831 Publisher: Athabasca University Press

Abstract

This study aims to apply a sequential analysis to explore the effect of learning motivation on online reading behavioral patterns. The study\u2019s participants consisted of 160 graduate students who were classified into three group types: low reading duration with low motivation, low reading duration with high motivation, and high reading duration based on a second-order cluster analysis. After performing a sequential analysis, this study reveals that highly motivated students exhibited a relatively serious reading pattern in a multi-tasking learning environment, and that online reading duration was a significant indicator of motivation in taking an online course. Finally, recommendations were provided to instructors and researchers based on the results of the study.

Citation

Sun, J., Lin, C.T. & Chou, C. (2018). Applying Learning Analytics to Explore the Effects of Motivation on Online Students' Reading Behavioral Patterns. The International Review of Research in Open and Distributed Learning, 19(2),. Athabasca University Press. Retrieved October 20, 2018 from .

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References

  1. Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can
  2. Ann, L.K. (2006). Study design III: Cross-sectional studies. Evidence-Based Dentistry, 2006(7), 24-25.
  3. Black, E.W., Dawson, K., & Priem, J. (2008). Data for free: Using LMS activity logs to measure community in online courses. The Internet and Higher Education, 11(2), 65-70. Doi:10.1016/J.iheduc.2008.03.002
  4. Chen, H., Chiang, R.H.L., & Storey, V.C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.
  5. Chen, K.C., & Jang, S.J. (2010). Motivation in online learning: Testing a model of self-determination theory. Computers in Human Behavior, 26(4), 741-752.
  6. Deci, E.L., & Ryan, R.M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum Press.
  7. Eryilmaz, E., Chiu, M.M., Thoms, B., Mary, J., & Kim, R. (2014). Design and evaluation of instructor-based and peer-oriented attention guidance functionalities in an open source anchored discussion system. [Article]. Computers& Education, 71, 303-321.
  8. Gardner, J.S. (2008). Simultaneous media usage: Effects on attention. Virginia Polytechnic Institute and State University.
  9. Gil-Flores, J., Torres-Gordillo, J.-J., & Perera-Rodríguez, V.-H. (2012). The role of online reader experience in explaining students’ performance in digital reading. Computers& Education, 59(2), 653-660. Doi:10.1016/J.compedu.2012.03.014
  10. Johnson, L., Adams, S., & Cummins, M. (2012). The NMC horizon report: 2012 higher education edition. Austin, Texas: The New Media Consortium.
  11. Johnson, L., Adams, S., Cummins, M., Estrada, V., Freeman, A., & Ludgate, H. (2013). The NMC horizon
  12. Liu, C.-C., Cheng, Y.-B., & Huang, C.-W. (2011). The effect of simulation games on the learning of computational problem solving. Computers& Education, 57(3), 1907-1918. Doi:10.1016/J.compedu.2011.04.002
  13. Liu, Z. (2005). Reading behavior in the digital environment: Changes in reading behavior over the past ten years. Journal of Documentation, 61(6), 700-712.
  14. Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, September/October, 31-40.
  15. Pellas, N. (2014). The influence of computer self-efficacy, metacognitive self-regulation and self-esteem
  16. Saadé, R.G., He, X., & Kira, D. (2007). Exploring dimensions to online learning. Computers in Human Behavior, 23(4), 1721-1739.
  17. Schunk, D.H., Meece, J.L., & Pintrich, P.R. (2013). Motivation in education: Theory, research, and
  18. Skinner, E., Furrer, C., Marchand, G., & Kindermann, T. (2008). Engagement and disaffection in the classroom: Part of a larger motivational dynamic? Journal of Educational Psychology, 100(4), 765-781.
  19. Sun, J.C.-Y., Kuo, C.-Y., Hou, H.-T., & Lin, Y.-Y. (2017). Exploring learners' sequential behavioral
  20. Sun, J.C.-Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their
  21. Tseng, S.-C., & Tsai, C.-C. (2010). Taiwan college students' self-efficacy and motivation of learning in online peer assessment environments. Internet and Higher Education, 13(3), 164-169. Doi:10.1016/J.iheduc.2010.01.001226

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