Abstract
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.
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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.
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).
Astin, A. (1999). Student involvement: A developmental theory for higher education. Journal of College Student Development, 40(5), 518–529.
Barefoot, B. (2004). Higher education’s revolving door: Confronting the problem of student drop out in US colleges and universities. Open Learning: The Journal of Open, Distance and e-Learning, 19(1), 9–18. doi:10.1080/0268051042000177818.
Bean, J. (1985). Interaction effects based on class level in an explanatory model of college student dropout syndrome. American Educational Research Journal, 22(1), 35–64. doi:10.3102/00028312022001035.
Ben-Tsur, D. (2007). Affairs of state and student retention: An exploratory study of the factors that impact student retention in a politically turbulent region. British Journal of Sociology of Education, 28(3), 317–332. doi:10.1080/01425690701252382.
Black, E. W., Dawson, K., & Priem, J. (2008). Data for free: Using LMS activity logs to measure community in online courses. Internet and Higher Education, 11, 65–70. doi:10.1016/j.iheduc.2008.03.002.
Blanc, R., DeBuhr, L., & Martin, D. (1983). Breaking the attrition cycle: The effects of supplemental instruction on undergraduate performance and attrition. The Journal of Higher Education, 54(1), 80–90. doi:10.2307/1981646.
Bonk, C. J., & Graham, C. R. (Eds.). (2006). The handbook of blended learning: Global perspectives, local designs. San Francisco, CA: Pfeiffer Publishing.
Brandl, K. (2005). Are you ready to “MOODLE”? Language Learning & Technology, 9(2), 16–23.
Breier, M. (2010). From ‘financial considerations’ to ‘poverty’: Towards a reconceptualization of the role of finances in higher education student drop out. Higher Education, 60(6), 657–670. doi:10.1007/s10734-010-9343-5.
Cabrera, A., Nora, A., & Casaneda, M. (1992). The role of finances in the persistence process: A structural model. Research in Higher Education, 33(5), 571–594. doi:10.1007/BF00973759.
Callahan, J. T. (2007). Assessing online homework in first-semester calculus. PRIMUS, 26(6), 545–556.
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.
Cerezo, R., Sánchez-Santillán, M., Paule-Ruiz, M. P., & Núñez, J. C. (2016). Students’ LMS interaction patterns and their relationship with achievement: A case study in higher education. Computers & Education, 96, 42–54. doi:10.1016/j.compedu.2016.02.006.
Chen, R. (2012). Institutional characteristics and college student dropout risks: A multilevel event history analysis. Research in Higher Education, 53(5), 487–505. doi:10.1007/s11162-011-9241-4.
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. (2011). What can instructors and policy makers learn about web-supported learning through web-mining. The Internet and Higher Education, 14(2), 67–76. doi:10.1016/j.iheduc.2010.07.008.
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.
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.
Cohen, A., & Soffer, T. (2015). Academic instruction in a digital world: The virtual TAU case. Procedia - Social and Behavioral Sciences, 177, 9–16.
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).
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.
Doherty, W. (2006). An analysis of multiple factors affecting retention in Web-based community college courses. The Internet and Higher Education, 9(4), 245–255. doi:10.1016/j.iheduc.2006.08.004.
Duarte, R., Ramos-Pires, A., & Gonçalves, H. (2014). Identifying at-risk students in higher education. Total Quality Management & Business Excellence, 25(7–8), 944–952. doi:10.1080/14783363.2014.906110.
Frankola, K. (2001). Why online learners dropout. Workforce, 80(10), 53–63.
Gentry, R. (2014). Sustaining college students’ persistence and achievement through exemplary instructional strategies. Research in Higher Education, 24, 1–14.
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.
Horstschräer, J., & Sprietsma, M. (2015). The effects of the introduction of Bachelor degrees on college enrollment and dropout rates. Education Economics, 23(3), 296–317. doi:10.1080/09645292.2013.823908.
Hovdhaugen, E. (2011). Do structured study programmes lead to lower rates of dropout and student transfer from university? Irish Educational Studies, 30(2), 237–251. doi:10.1080/03323315.2011.569143.
Hwang Wu-Yuin & Wang Chin-Yu. (2004). A study of learning time patterns in asynchronous learning environments. Journal of Computer Assisted Learning, 20(4), 292–304. doi:10.1111/j.1365-2729.2004.00088.x.
Johnson, L., Adams Becker, S., Estrada, V., & Freeman, A. (2014). NMC horizon report: 2014 Higher education edition. Austin, TX: The New Media Consortium.
Lazin, R., & Neumann, L. (1991). Student characteristics as predictors of drop-out from medical school: Admissions to Beer-Sheva over a decade. Medical Education, 25(5), 396–404. doi:10.1111/j.1365-2923.1991.tb00087.x.
Lee, Y., & Choi, J. (2011). A review of online course dropout research: Implications for practice and future research. Educational Technology Research and Development, 59(5), 593–618. doi:10.1007/s11423-010-9177-y.
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.
Levin, J., Barak, A., & Yaar, E. (1979). College dropout and some of its correlates. Megamot, 24(4), 564–573 [Hebrew].
Levy, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers & Education, 48, 185–204. doi:10.1016/j.compedu.2004.12.004.
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.
Lu, J., Yu, C. S., & Liu, C. (2003). Learning style, learning patterns, and learning performance in a WebCT-based MIS course. Information & Management, 40(6), 497–507. doi:10.1016/S0378-7206(02)00064-2.
Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., & Loumos, V. (2009). Dropout prediction in e-learning courses through the combination of machine learning techniques. Computers & Education, 53, 950–965. doi:10.1016/j.compedu.2009.05.010.
Mac an Bhaird, C., Morgan, T., & O’Shea, A. (2009). The impact of the mathematics support centre on the grades of first year students at the National University of Ireland Maynooth. Teaching Mathematics and its Applications, 28, 117–122. doi:10.1093/teamat/hrp014.
Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop “early warning system” for educators: A proof of concept. Computers & Education, 54, 588–599. doi:10.1016/j.compedu.2009.09.008.
Márquez-Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Mousa Fardoun, H., & Ventura, S. (2015). Early dropout prediction using data mining: A case study with high school students. Expert Systems. doi:10.1111/exsy.12135.
McKenzie, K., & Schweitzer, R. (2001). Who Succeeds at University? Factors predicting academic performance in first year Australian university students. Higher Education Research & Development, 20(1), 21–33. doi:10.1080/07924360120043621.
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.
Nistor, N., & Neubauer, K. (2010). From participation to dropout: Quantitative participation patterns in online university courses. Computers & Education, 55, 663–672. doi:10.1016/j.compedu.2010.02.026.
Ortiz, E. A., & Dehon, C. (2013). Roads to success in the Belgian French Community’s higher education system: Predictors of dropout and degree completion at the Université Libre de Bruxelles. Research in Higher Education, 54(6), 693–723. doi:10.1007/s11162-013-9290-y.
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.
Parker, A. (2003). Identifying predictors of academic persistence in distance education. USDLA Journal, 17(1), 55–62.
Rice, W. H. (2006). Moodle e-learning course development (3rd ed.). Birmingham, UK: Packt Publishing.
Romero, C., López, M. I., Luna, J. M., & Ventura, S. (2013). Predicting students’ final performance from participation in on-line discussion forums. Computers & Education, 68, 458–472. doi:10.1016/j.compedu.2013.06.009..
Romero, C., & Ventura, S. (2006). Data mining in E-learning. Southampton, UK: WIT Press.
Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135–146. doi:10.1016/j.eswa.2006.04.005.
Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368–384. doi:10.1016/j.compedu.2007.05.016.
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.
Scheffel, M., Drachsler, H., Stoyanov, S., & Specht, M. (2014). Quality Indicators for Learning Analytics. Educational Technology & Society, 17(4), 117–132.
Spiliopoulou, M., & Pohle, C. (2001). Data mining for measuring and improving the success of web sites. Data Mining and Knowledge Discovery, 5, 85–114. doi:10.1023/A:1009800113571.
Stewart, B., Briton, D., Gismondi, M., Heller, B., Kennepohl, D., McGreal, R., et al. (2007). Choosing moodle: An evaluation of learning management systems at Athabasca University. Journal of Distance Education Technologies, 5(3), 1–7.
Tait, H., & Entwistle, N. (1996). Identifying students at risk through ineffective study strategies. Higher Education, 31(1), 97–116. doi:10.1007/BF00129109.
Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125. doi:10.3102/00346543045001089.
Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). Chicago: University of Chicago Press.
Wang, J., Doll, W. J., Deng, X., Park, K., & Yang, M. G. M. (2013). The impact of faculty perceived reconfigurability of learning management systems on effective teaching practices. Computers & Education, 61, 146–157. doi:10.1016/j.compedu.2012.09.005.
Wang, A. Y., & Newlin, M. H. (2002). Predictors of web-student performance: The role of self-efficacy and reasons for taking an on-line class. Computers in Human Behavior, 18(2), 151–163. doi:10.1016/S0747-5632(01)00042-5.
Xenos, M. (2004). Prediction and assessment of student behavior in open and distance education in computers using Bayesian networks. Computers & Education, 43(4), 345–359. doi:10.1016/j.compedu.2003.09.005.
Yingling, M. (2006). Mobile moodle. Journal of Computing Sciences in Colleges, 21(6), 280–281.
You, J. W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. The Internet and Higher Education, 29, 23–30. doi:10.1016/j.iheduc.2015.11.003.
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Cohen, A. Analysis of student activity in web-supported courses as a tool for predicting dropout. Education Tech Research Dev 65, 1285–1304 (2017). https://doi.org/10.1007/s11423-017-9524-3
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DOI: https://doi.org/10.1007/s11423-017-9524-3