e-Pedagogium 2019, 19(2):59-67 | DOI: 10.5507/epd.2019.015

Measurement of selected aspects of student behavior in online courses

Michal Černýa
Kabinet informačních studií a knihovnictví, Filozofická fakulta Masarykovy univerzity, Brno, Czech Republic

The paper analyzes the possibilities of using learning analytics outside of LSM, for example in educational environments that are not associated with common large systems such as Moodle or Blackboard, but which can only be analyzed through web analytics tools. This topic has been marginalized in the discourse by the educational public for a long time. My research and methodological attention show that the data that can be obtained from these environments about the learning and information behaviour of students can be of interest to the design of the whole educational environment and can help with significant changes in the design of courses, measuring the attractiveness or clarity of individual pages etc. In this paper, we focus primarily on the combination of Google Analytics and Smartlook or Hotjar - tools for creating heatmaps.

Keywords: learning analytics, Google Analytics, Hotjar, heatmaps, web analytics, data driven education, online courses, LMS, learning environment.

Accepted: August 5, 2019; Published: September 1, 2019  Show citation

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Černý, M. (2019). Measurement of selected aspects of student behavior in online courses. e-Pedagogium, 19(2), 59-67. doi: 10.5507/epd.2019.015
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    References

    1. de Almeida Ribeiro, J. P. (2016). The use of web analytics on a small data set in an online media company: shifter's case study. Lisboa: NOVA IMS.
    2. Bergs, H. (2017). Gaining insights into website traffic and online consumer behavior with Google Analytics. (Master thesis). Reykjavík: Reykjavík University.
    3. Clark, R. C., Nguyen, F., & Sweller, J. (2011). Efficiency in Learning: Evidence-Based Guidelines to Manage Cognitive Load. New York, NY: John Wiley & Sons.
    4. Clifton, B. (2012). Advanced web metrics with Google Analytics. Indianapolis: John Wiley & Sons.
    5. Cutroni, J. (2010). Google Analytics: Understanding Visitor Behavior. Sebastopol, CA: O'Reilly Media, Inc.
    6. Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5-6), 304-317. Go to original source...
    7. Ferguson, R., & Shum, S. B. (2012, April). Social learning analytics: five approaches. In Dawson, S., & Association for Computing Machinery. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. New York, NY: ACM. Go to original source...
    8. Luo, H., Rocco, S., Schaad, C., & 2015 International Conference of Educational Innovation through Technology (EITT). (October 01, 2015). Using Google Analytics to Understand Online Learning: A Case Study of a Graduate-Level Online Course. In Hao Yang, H. et al. Proceedings of EITT 2015 The Fourth International Conference of Educational Innovation through Technology. Los Alamitos: IEEE Computer Society. DOI: 10.1109/EITT.2015.62. Go to original source...
    9. McGuckin, C., & Crowley, N. (November 01, 2012). Using Google Analytics to evaluate the impact of the CyberTraining project. Cyberpsychology, Behavior, and Social Networking, 15(11), 625-629. Go to original source...
    10. Means, B., Toyama, Y., Murphy, R., Bakia, M., Jones, K., SRI International., & United States. (2010). Evaluation of Evidence-Based practices in Online Learning: A Meta-Analysis and Review of Online Learning Studies. Washington D.C.: U.S Department of Education. Retrieved from https://www2.ed.gov/rschstat/eval/tech/evidence-based-practices/finalreport.pdf.
    11. Morris, L. V., Finnegan, C., & Wu, S. S. (2005). Tracking student behavior, persistence, and achievement in online courses. The Internet and Higher Education, 8(3), 221-231. Go to original source...
    12. Romanowski, B., & Konak, A. (2016). Using Google Analytics to Improve the Course Website of a Database Course. In ASEE Mid-Atlantic Regional Conference Papers. October 21-22, 2016. Hempstead, NY: Hofstra University. Retrieved from https://www.hofstra.edu/pdf/academics/colleges/seas/asee-fall-2016/asee-midatlantic-f2016-konak.pdf.
    13. Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400. Go to original source...
    14. Siemens, G., & Baker, R. S. (2012, April). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge. New York, NY: ACM. Go to original source...
    15. Siemens, G., & Long, Ph. (2011). Penetrating the fog: analytics in learning and education. EDUCAUSE review 46(5). 31-40.
    16. Slavin, R. E. (2008). Cooperative learning, success for all, and evidence-based reform in education. Éducation et Didactique, 2(2), 149-157. Go to original source...
    17. Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the general data protection regulation. International Data Privacy Law, 7(2), 76-99. Go to original source...

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