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Educational Technology Research and Development

, Volume 65, Issue 5, pp 1285–1304 | Cite as

Analysis of student activity in web-supported courses as a tool for predicting dropout

  • Anat Cohen
Development Article

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.

Keywords

Learning Management Systems Predicting course dropout Web-supported Learning Learning analytics 

Notes

Funding

This study was not funded.

Compliance with ethical standards

Conflict of interest

The author declares that she has no conflict of interest.

References

  1. 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.
  2. 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
  3. 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
  4. Astin, A. (1999). Student involvement: A developmental theory for higher education. Journal of College Student Development, 40(5), 518–529.Google Scholar
  5. 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.CrossRefGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. 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.CrossRefGoogle Scholar
  8. 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.CrossRefGoogle Scholar
  9. 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.Google Scholar
  10. Bonk, C. J., & Graham, C. R. (Eds.). (2006). The handbook of blended learning: Global perspectives, local designs. San Francisco, CA: Pfeiffer Publishing.Google Scholar
  11. Brandl, K. (2005). Are you ready to “MOODLE”? Language Learning & Technology, 9(2), 16–23.Google Scholar
  12. 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.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. Callahan, J. T. (2007). Assessing online homework in first-semester calculus. PRIMUS, 26(6), 545–556.CrossRefGoogle Scholar
  15. 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.
  16. 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
  17. 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.CrossRefGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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.
  20. 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.CrossRefGoogle Scholar
  21. 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
  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.Google Scholar
  23. Cohen, A., & Soffer, T. (2015). Academic instruction in a digital world: The virtual TAU case. Procedia - Social and Behavioral Sciences, 177, 9–16.CrossRefGoogle Scholar
  24. 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
  25. 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
  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.CrossRefGoogle Scholar
  27. 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.CrossRefGoogle Scholar
  28. Frankola, K. (2001). Why online learners dropout. Workforce, 80(10), 53–63.Google Scholar
  29. Gentry, R. (2014). Sustaining college students’ persistence and achievement through exemplary instructional strategies. Research in Higher Education, 24, 1–14.Google Scholar
  30. 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.
  31. 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
  32. 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.CrossRefGoogle Scholar
  33. 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.CrossRefGoogle Scholar
  34. 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.CrossRefGoogle Scholar
  35. 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
  36. 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.CrossRefGoogle Scholar
  37. 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.CrossRefGoogle Scholar
  38. 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
  39. Levin, J., Barak, A., & Yaar, E. (1979). College dropout and some of its correlates. Megamot, 24(4), 564–573 [Hebrew].Google Scholar
  40. Levy, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers & Education, 48, 185–204. doi: 10.1016/j.compedu.2004.12.004.CrossRefGoogle Scholar
  41. 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.
  42. 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.CrossRefGoogle Scholar
  43. 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.CrossRefGoogle Scholar
  44. 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.CrossRefGoogle Scholar
  45. 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.CrossRefGoogle Scholar
  46. 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.Google Scholar
  47. 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.CrossRefGoogle Scholar
  48. 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.
  49. 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.
  50. 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.CrossRefGoogle Scholar
  51. 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.CrossRefGoogle Scholar
  52. 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
  53. Parker, A. (2003). Identifying predictors of academic persistence in distance education. USDLA Journal, 17(1), 55–62.Google Scholar
  54. Rice, W. H. (2006). Moodle e-learning course development (3rd ed.). Birmingham, UK: Packt Publishing.Google Scholar
  55. 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..CrossRefGoogle Scholar
  56. Romero, C., & Ventura, S. (2006). Data mining in E-learning. Southampton, UK: WIT Press.CrossRefGoogle Scholar
  57. 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.CrossRefGoogle Scholar
  58. 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.CrossRefGoogle Scholar
  59. 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
  60. Scheffel, M., Drachsler, H., Stoyanov, S., & Specht, M. (2014). Quality Indicators for Learning Analytics. Educational Technology & Society, 17(4), 117–132.Google Scholar
  61. 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.CrossRefGoogle Scholar
  62. 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.CrossRefGoogle Scholar
  63. Tait, H., & Entwistle, N. (1996). Identifying students at risk through ineffective study strategies. Higher Education, 31(1), 97–116. doi: 10.1007/BF00129109.CrossRefGoogle Scholar
  64. 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.CrossRefGoogle Scholar
  65. Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition (2nd ed.). Chicago: University of Chicago Press.Google Scholar
  66. 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.CrossRefGoogle Scholar
  67. 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.CrossRefGoogle Scholar
  68. 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.CrossRefGoogle Scholar
  69. Yingling, M. (2006). Mobile moodle. Journal of Computing Sciences in Colleges, 21(6), 280–281.Google Scholar
  70. 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.CrossRefGoogle Scholar

Copyright information

© Association for Educational Communications and Technology 2017

Authors and Affiliations

  1. 1.The School of EducationTel Aviv UniversityTel AvivIsrael

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