Abstract
Adaptive learning gives learners control of context, pace, and scope of their learning experience. This strategy can be implemented in online learning by using the “Adaptive Release” feature in learning management systems. The purpose of this study was to use learning analytics research methods to explore the extent to which the adaptive release feature affected student behavior in the online environment and course performance. Existing data from two sections of an online pre-service teacher education courses from a Southeastern university were analyzed for this study. Both courses were taught by the same instructor in a 15 weeks time period. One section was designed with the adaptive release feature for content release and the other did not have the adaptive release feature. All other elements of the course were the same. Data from five interaction measures was analyzed (logins, total time spent, average time per session, content modules accessed, time between module open and access) to explore the effect of the adaptive release feature. The findings indicated that there was a significant difference between the use of adaptive release and average login session. Considered as the average time of module access across the entire course, adaptive release did not systematically change when students accessed course materials. The findings also indicated significant differences between the experimental and control courses, especially for the first course module. This study has implications for instructors and instructional designers who design blended and online courses.



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Anderson, J. R. (1983). The architecture of cognition. New York, NY: Psychology Press.
Armani, J. (2004). Shaping learning adaptive technologies for teachers: A proposal for an adaptive learning management system. In Proceedings of IEEE international conference on advanced learning technologies, 2004 (pp. 783–785).
Baepler, P., & Murdoch, C. J. (2010). Academic analytics and data mining in higher education. Retrieved from http://dspaceprod.georgiasouthern.edu:8080/jspui/bitstream/10518/4069/1/_BaeplerMurdoch.pdf.
Beer, C., Jones, D., & Clark, D. (2012). Analytics and complexity: Learning and leading for the future. In ASCILITE conference (Vol. 2012, No. 1). Retrieved from http://beerc.files.wordpress.com/2009/11/ascillite2012_final_submission.pdf.
Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief (pp. 1–57). Washington, DC: Office of Educational Technology, US Department of Education.
Brusilovsky, P. (2004). Knowledge tree: A distributed architecture for adaptive e-learning. In Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters. ACM (pp. 104–113).
Campbell, J. P. (2007). Utilizing student data within the course management system to determine undergraduate student academic success: An exploratory study. A. G. Rud. United States—Indiana, Educational Studies.
Dahlstrom, D. E., Brooks, C., & Bichsel, J. (2014). The current ecosystem of learning management systems in higher education: Student, faculty, and IT perspectives. Research report. Louisville, CO: ECAR, September 2014. Available from http://www.educause.edu/ecar.
Dawson, S., McWilliam, E., & Tan, J. P. L. (2008). Teaching smarter: How mining ICT data can inform and improve learning and teaching practice. In ASCILITE 2008, Melbourne (pp. 221–230).
Elkind, J. I., Card, S. K., & Hochberg, J. (Eds.). (1990). Human performance models for computer-aided engineering. San Diego, CA: Academic Press.
Ferguson, R. (2012). The state of learning analytics in 2012: A review and future challenges. Technical Report KMI-12-01, Knowledge Media Institute, The Open University, UK. Retrieved from http://kmi.open.ac.uk/publications/techreport/kmi-12-01.
Fournier, H., Kop, R., & Sitlia, H. (2011). The value of learning analytics to networked learning on a personal learning environment. In 1st international conference on learning analytics and knowledge, Banff, Canada, 27 February–1 March 2011.
Fritz, J. (2011). Classroom walls that talk: Using online course activity data of successful students to raise self-awareness of underperforming peers. The Internet and Higher Education, 14(2), 89–97.
Howard, L., Remenyi, Z., & Pap, G. (2006). Adaptive blended learning environments. In International Conference on Engineering Education (pp. 23–28).
Ifenthaler, D., & Widanapathirana, C. (2014). Development and validation of a learning analytics framework: Two case studies using support vector machines. Technology, Knowledge and Learning, 19(1–2), 221–240.
Kelly, D., & Tangney, B. (2006). Adapting to intelligence profile in an adaptive educational system. Interacting with Computers, 18(3), 385–409.
Kleinman, S. (2005). Strategies for encouraging active learning, interaction, and academic integrity in online courses. Communication Teacher, 19(1), 13–18.
Lin, C. B., Young, S. S. C., Chan, T. W., & Chen, Y. H. (2005). Teacher-oriented adaptive Web-based environment for supporting practical teaching models: a case study of “school for all”. Computers & Education, 44(2), 155–172.
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.
Magoulas, G. D., Papanikolaou, Y., & Grigoriadou, M. (2003). Adaptive web-based learning: accommodating individual differences through system’s adaptation. British Journal of Educational Technology, 34(4), 511–527.
Morris, L., Finnegan, C., & Wu, S. (2005). Tracking student behavior, persistence, and achievement in online courses. Internet and Higher Education, 8, 221–231.
Peled, A., & Rashty, D. (1999). Logging for success: Advancing the use of WWW logs to improve computer mediated distance learning. Journal of Educational Computing Research, 21(4), 413–431.
Robson, R. (1999). Course support systems: The first generation. International Journal of Telecommunications in Education, 5(4), 271–282.
Ryabov, I. (2012). The effect of time online on grades in online sociology courses. MERLOT Journal of Online Learning and Teaching, 8(1), 13–23.
Shute, V., & Towle, B. (2003). Adaptive e-learning. Educational Psychologist, 38(2), 105–114.
Sonwalkar, N. (2007). Adaptive learning: A dynamic methodology for effective online learning. Distance Learning, 4(1), 43–46.
van Harmelen, M., & Workman, D. (2012). Analytics for learning and teaching. CETIS Analytics Series, 1(3), 1–41.
Wenger, E. (1987). Artificial intelligence and tutoring systems: Computational and cognitive approaches to the communication of knowledge. Los Altos, CA: Morgan Kaufmann.
Whitmer, J., Fernandes, K., & Allen, W. (2012). Analytics in progress: Technology use, student characteristics, and student achievement. EDUCAUSE Review . Retrieved online from http://www.educause.edu/ero/article/analytics-progress-technology-use-student-characteristics-and-studentachievement.
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Martin, F., Whitmer, J.C. Applying Learning Analytics to Investigate Timed Release in Online Learning. Tech Know Learn 21, 59–74 (2016). https://doi.org/10.1007/s10758-015-9261-9
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DOI: https://doi.org/10.1007/s10758-015-9261-9