This study aims to apply a sequential analysis to explore the effect of learning motivation on online reading behavioral patterns. The study’s 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.
- learning analytics,
- sequential analysis,
- online learning,
- behavioral pattern
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- Agudo-Peregrina, Á. F., Iglesias-Pradas, S., Conde-González, M. Á., & Hernández-García, Á. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31(0), 542-550. doi: 10.1016/j.chb.2013.05.031
- Ann, L. K. (2006). Study design III: Cross-sectional studies. Evidence-Based Dentistry, 2006(7), 24-25. doi: 10.1038/sj.ebd.6400375
- Austin, K. A., Gorsuch, G. J., Lawson, W. D., & Newberry, B. P. (2011). Developing and designing online engineering ethics instruction for international graduate students. Instructional Science, 39(6), 975-997.
- Bakeman, R. (1997). Observing interaction: An introduction to sequential analysis. United Kingdom: Cambridge University Press.
- Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191-215.
- 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
- 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.
- 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. doi: 10.1016/j.chb.2010.01.011
- Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum Press.
- 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. doi: 10.1016/j.compedu.2013.08.009
- Gardner, J. S. (2008). Simultaneous media usage: Effects on attention. Virginia Polytechnic Institute and State University.
- 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
- Hou, H.-T. (2012a). Analyzing the learning process of an online role-playing discussion activity. Educational Technology & Society, 15(1), 211-222.
- Hou, H.-T. (2012b). Exploring the behavioral patterns of learners in an educational massively multiple online role-playing game (MMORPG). Computers & Education, 58(4), 1225-1233. doi: 10.1016/j.compedu.2011.11.015
- Hou, H.-T., & Wu, S.-Y. (2011). Analyzing the social knowledge construction behavioral patterns of an online synchronous collaborative discussion instructional activity using an instant messaging tool: A case study. Computers & Education, 57(2), 1459-1468. doi: 10.1016/j.compedu.2011.02.012
- Hu, Y. H., Lo, C. L., & Shih, S. P. (2014). Developing early warning systems to predict students' online learning performance. Computers in Human Behavior, 36, 469-478. doi: 10.1016/j.chb.2014.04.002
- Hung, J. L., & Zhang, K. (2008). Revealing online learning behaviors and activity patterns and making predictions with data mining techniques in online teaching. MERLOT Journal of Online Learning and Teaching, 4(4), 426-437.
- Johnson, L., Adams, S., & Cummins, M. (2012). The NMC horizon report: 2012 higher education edition. Austin, Texas: The New Media Consortium.
- Johnson, L., Adams, S., Cummins, M., Estrada, V., Freeman, A., & Ludgate, H. (2013). The NMC horizon report: 2013 higher education edition. Austin, Texas: The New Media Consortium.
- Johnson, L., Becker, S., Estrada, V., & Freeman, A. (2014). The NMC horizon report: 2014 higher education edition. Austin, Texas: The New Media Consortium.
- Kong, J. S. L., Kwok, R. C. W., & Fang, Y. (2012). The effects of peer intrinsic and extrinsic motivation on MMOG game-based collaborative learning. Information & Management, 49(1), 1-9. doi: 10.1016/j.im.2011.10.004
- Lai, C.-L., & Hwang, G.-J. (2015). A spreadsheet-based visualized mindtool for improving students' learning performance in identifying relationships between numerical variables. Interactive Learning Environments, 23(2), 230-249.
- 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
- 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.
- Long, P., & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review, September/October, 31-40.
- Ma, J., Han, X., Yang, J., & Cheng, J. (2015). Examining the necessary condition for engagement in an online learning environment based on learning analytics approach: The role of the instructor. The Internet and Higher Education, 24, 26-34. doi: 10.1016/j.iheduc.2014.09.005
- Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an "early warning system" for educators: A proof of concept. Computers & Education, 54(2), 588-599. doi: 10.1016/j.compedu.2009.09.008
- Moore, M. G. (1989). Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1-7.
- Nunnally, J. C., & Bernstein, I. H. (1994). Psychological theory (3rd ed.). New York: McGraw-Hill.
- Pellas, N. (2014). The influence of computer self-efficacy, metacognitive self-regulation and self-esteem on student engagement in online learning programs: Evidence from the virtual world of Second Life. Computers in Human Behavior, 35, 157-170. doi: 10.1016/j.chb.2014.02.048
- Raab, R. T., Ellis, W. W., & Abdon, B. R. (2001). Multisectoral partnerships in e-learning: A potential force for improved human capital development in the Asia Pacific. The Internet and Higher Education, 4(3), 217-229.
- Saadé, R. G., He, X., & Kira, D. (2007). Exploring dimensions to online learning. Computers in Human Behavior, 23(4), 1721-1739. doi: 10.1016/j.chb.2005.10.002
- Schunk, D. H., Meece, J. L., & Pintrich, P. R. (2013). Motivation in education: Theory, research, and applications (4th ed.). Upper Saddle River, NJ: Pearson.
- 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.
- Sun, J. C.-Y., Kuo, C.-Y., Hou, H.-T., & Lin, Y.-Y. (2017). Exploring learners' sequential behavioral patterns, flow experience, and learning performance in an anti-phishing educational game. Educational Technology & Society, 20(1), 45-60.
- Sun, J. C.-Y., & Rueda, R. (2012). Situational interest, computer self-efficacy and self-regulation: Their impact on student engagement in distance education. British Journal of Educational Technology, 43(2), 191-204. doi: 10.1111/j.1467-8535.2010.01157.x
- 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.2010.01.001
- Wang, S.-L., & Lin, S. S. J. (2007). The effects of group composition of self-efficacy and collective efficacy on computer-supported collaborative learning. Computers in Human Behavior, 23(5), 2256-2268. doi: 10.1016/j.chb.2006.03.005
- Yang, T.-C., Chen, S. Y., & Hwang, G.-J. (2015). The inﬂuences of a two-tier test strategy on student learning: A lag sequential analysis approach. Computers & Education, 82, 366-377.
- Yang, X., Li, J., Guo, X., & Li, X. (2015). Group interactive network and behavioral patterns in online English-to-Chinese cooperative translation activity. Internet and Higher Education, 25, 28-36.
- Yoo, S. J., Han, S.-H., & Huang, W. (2012). The roles of intrinsic motivators and extrinsic motivators in promoting e-learning in the workplace: A case from South Korea. Computers in Human Behavior, 28(3), 942-950. doi: 10.1016/j.chb.2011.12.015