Analysis of student activity in web-supported courses as a tool for predicting dropout
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Persistence in learning processes is perceived as a central value; therefore, dropouts from studies are a prime concern for educators. This study focuses on the quantitative analysis of data accumulated on 362 students in three academic course website log files in the disciplines of mathematics and statistics, in order to examine whether student activity on course websites may assist in providing early identification of learner dropout from specific courses or from degree track studies in general. It was found in this study that identifying the changes in student activity during the course period could help in detecting at-risk learners in real time, before they actually drop out from the course. Data examination on a monthly basis throughout the semester can enable educators and institutions to flag students that have been identified as having unusual behavior, deviating from the course average. It was found that a large percentage of students (66%) who had been marked as at-risk actually did not finish their courses and/or degree. The presented analysis allows instructors to observe website student usage data during a course, and to locate students who are not using the system as expected. Furthermore, it could enable university decision makers to see the information on a campus level for initiating intervention programs.
KeywordsLearning Management Systems Predicting course dropout Web-supported Learning Learning analytics
This study was not funded.
Compliance with ethical standards
Conflict of interest
The author declares that she has no conflict of interest.
- Aburdene, M. F., & Kozick, R. J. (1997). A project-oriented course in probability and statistics for undergraduate electrical engineering students. In Proceedings of the frontiers in education conference, 27th annual conference. Teaching and learning in an era of change (Vol. 2, pp. 598–603) doi: 10.1109/FIE.1997.635870.
- Ai, J., & Laffey, J. (2007). Web mining as a tool for understanding online learning. Merlot Journal of Online Learning and Teaching, 3(2), 160–169.Google Scholar
- Alier, M., Casany, P., & Casado, P. (2007). A mobile extension of a web based moodle virtual classroom. In Proceedings of the E-challenges comference (pp. 11–26).Google Scholar
- Astin, A. (1999). Student involvement: A developmental theory for higher education. Journal of College Student Development, 40(5), 518–529.Google Scholar
- Bonk, C. J., & Graham, C. R. (Eds.). (2006). The handbook of blended learning: Global perspectives, local designs. San Francisco, CA: Pfeiffer Publishing.Google Scholar
- Brandl, K. (2005). Are you ready to “MOODLE”? Language Learning & Technology, 9(2), 16–23.Google Scholar
- Campbell, J. (2007). Utilizing student data within the course management system to determine undergraduate student academic success: An exploratory study (Doctoral dissertation, Purdue University). Retrieved from http://gradworks.umi.com/32/87/3287222.html.
- Campbell, J. P., DeBlois, P. B., & Oblinger, D. G. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42(4), 42–57.Google Scholar
- Cheng, J., Kulkarni, C., & Klemmer, S. (2013). Tools for predicting drop-off in large online classes. In Proceedings of the computer supported cooperative work companion (pp. 121–124). doi: 10.1145/2441955.2441987.
- Cohen, A., & Nachmias, R. (2012). The implementation of a cost effectiveness analyzer for web-supported academic instruction: An example from life science. International Journal on E-Learning, 11(2), 5–22.Google Scholar
- Cohen, A., & Shimony, U. (2016). Dropout prediction in a massive open online course using learning analytics. In Proceedings of E-Learn 2016 world conference on E-learning in corporate, government, healthcare, and higher education, organized by the AACE-Association for the Advancement of Computing in Education and the International Journal on E-Learning, Washington, USA.Google Scholar
- Dawson, S., McWilliam, E., & Tan, J. P.-L. (2008). Teaching Smarter: How mining ICT data can inform and improve learning and teaching practice. In Proceedings of the Australasian Society for computers in learning in tertiary education (pp. 221–230).Google Scholar
- Dietz-Uhler, B., & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12(1), 17–26.Google Scholar
- Frankola, K. (2001). Why online learners dropout. Workforce, 80(10), 53–63.Google Scholar
- Gentry, R. (2014). Sustaining college students’ persistence and achievement through exemplary instructional strategies. Research in Higher Education, 24, 1–14.Google Scholar
- Graf, S., & List, B. (2005). An evaluation of open source e-learning platforms stressing adaptation issues. In Proceedings of the fifth IEEE international conference on advanced learning technologies, IEEE, Computer Society (pp. 1–3). doi: 10.1109/ICALT.2005.54.
- Grau-Valldosera, J., & Minguillón, J. (2014). Rethinking dropout in online higher education: The case of the Universitat Oberta de Catalunya. The International Review of Research in Open and Distance Learning, 15(1), 290–308.Google Scholar
- Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2014). NMC horizon report: 2014 Higher education edition. Austin, TX: The New Media Consortium.Google Scholar
- Levi-Gamlieli, H., Cohen, A., & Nachmias, R. (2015). Detection of overly intensive learning by using weblog of course website. Technology, Instruction, Cognition and Learning (TICL), 10(2), 151–171.Google Scholar
- Levin, J., Barak, A., & Yaar, E. (1979). College dropout and some of its correlates. Megamot, 24(4), 564–573 [Hebrew].Google Scholar
- Lohr, S. (2012). The age of big data. The New York Times. Available at: http://www.nytimes.com/2012/02/12/sunday-review/big-datas-impact-in-the-world.html?pagewanted=all&_r=0.
- Méndez, G., Ochoa, X., & Chiluiza, K. (2014). Techniques for data-driven curriculum analysis. In Proceedings of the fourth international conference on learning analytics and knowledge (pp. 148–157). doi: 10.1145/2567574.2567591.
- Ngom, B., Guillermet, H., & Niang, I. (2012). Enhancing Moodle for offline learning in a degraded connectivity environment. In Proceedings of the international conference on multimedia computing and systems (pp. 858–863). doi: 10.1109/ICMCS.2012.6320168.
- Park, J.-H., & Choi, H. J. (2009). Factors influencing adult learners’ decision to drop out or persist in online learning. Educational Technology & Society, 12(4), 207–217.Google Scholar
- Parker, A. (2003). Identifying predictors of academic persistence in distance education. USDLA Journal, 17(1), 55–62.Google Scholar
- Rice, W. H. (2006). Moodle e-learning course development (3rd ed.). Birmingham, UK: Packt Publishing.Google Scholar
- Santana, M. A., Costa, E. B., Neto, B. F. D. S., Silva, I. C. L., & Rego, J. B. (2015). A predictive model for identifying students with dropout profiles in online courses. In Proceeding of the 8th international conference on educational data mining, EDM workshops.Google Scholar
- Scheffel, M., Drachsler, H., Stoyanov, S., & Specht, M. (2014). Quality Indicators for Learning Analytics. Educational Technology & Society, 17(4), 117–132.Google Scholar
- Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). Chicago: University of Chicago Press.Google Scholar
- Yingling, M. (2006). Mobile moodle. Journal of Computing Sciences in Colleges, 21(6), 280–281.Google Scholar