Advertisement

Utilizing Learning Analytics in Small Institutions: A Study of Performance of Adult Learners in Online Classes

  • Ellina ChernobilskyEmail author
  • Susan Hayes
Chapter

Abstract

Understanding what learning analytics can and cannot do is extremely important for small, tuition-driven colleges and universities with limited resources and smaller datasets. Online learning has become an appealing course delivery format for students, especially adult students who balance studies with work. For the university, offering online courses provides an opportunity for the recruitment and retention of adult students. To understand how adult students perform in online courses, the researchers studied archival data from 547 online undergraduate course registrants from a 2-year period. The dataset was mined to determine trends and patterns of student success, as determined by the final grade earned in the online courses. The analysis revealed that adult students have higher failure rates and lower average grades as compared to traditional students, particularly in accelerated 7-week format courses. In addition, students had lower success rates in foundational core curriculum courses, which are the basis of learning key content and competencies. The results of the study suggest that student success issues can be identified with analyzing even small datasets. The results may inform college leaders about potential issues as well as new ways to help students achieve.

Keywords

Learning analytics Adult students Online learning Retention Tuition-driven liberal arts university 

References

  1. Akyol, Z., & Garrison, D. R. (2008). The development of a community of inquiry over time in an online course: Understanding the progression and integration of social, cognitive and teaching presence. Journal of Asynchronous Learning Networks, 12(3–4), 3–22.Google Scholar
  2. Alhassan, A. M. (2012). Factors affecting adult learning and their persistence: A theoretical approach. European Journal of Business and Social Sciences, 1(6), 150–168. http://www.ejbss.com/recent.aspx Google Scholar
  3. Anastasi, J. S. (2007). Full-semester and abbreviated summer courses: An evaluation of student performance. Teaching of Psychology, 34(1), 19–22.CrossRefGoogle Scholar
  4. Bach, C. (2010). Learning analytics: Targeting instruction, curricula and student support. Proceedings from the 8th International Conference on Education and Information Systems, Technologies and Applications: EISTA 2010, Orlando, FL. http://www.iiis.org/cds2010/cd2010sci/eista_2010/index.aspGoogle Scholar
  5. Bawa, P. (2016). Retention in online courses: Exploring issues and solutions - a literature review. SAGE Open, 6(1), 215824401562177.  https://doi.org/10.1177/2158244015621777 CrossRefGoogle Scholar
  6. Bean, J., & Metzner, B. (1985). A conceptual model of non-traditional undergraduate student attrition. Review of Educational Research, 55(4), 485–540.CrossRefGoogle Scholar
  7. Bowen, W., Chingos, M., & McPherson, M. (2009). Crossing the finish line: Completing college at America’s public universities. Princeton, NJ: Princeton University Press.Google Scholar
  8. Boyer, A., & Bonnin, G. (2017). Higher education and the revolution of learning analytics. Oslo, Norway: International Council for Open and Distance Education.Google Scholar
  9. Carr, S. (2000, February 11). As distance education comes of age, the challenge is keeping the students (p. A39). Washington, DC: Chronicle of Higher Education, Information Technology Section. Retrieved from http://chronicle.com
  10. Casey, D. M. (2008). A journey to legitimacy: The historical development of distance education through technology. TechTrends: Linking Research and Practice to Improve Learning, 52(2), 45–51.CrossRefGoogle Scholar
  11. Chametzky, B. (2018). Communication in online learning: Being meaningful and reducing isolation. In A. Scheg & M. Shaw (Eds.), Fostering effective student communication in online graduate courses (pp. 20–41). Hershey, PA: IGI Global.CrossRefGoogle Scholar
  12. Chen, B. (2015). From theory use to theory building in learning analytics: A commentary on “Learning Analytics to Support Teachers during Synchronous CSCL”. Journal of Learning Analytics, 2(2), 163–168.  https://doi.org/10.18608/jla.2015.22.12 CrossRefGoogle Scholar
  13. Chernobilsky, E., Jasmine, J., & Ries, E. D. (2016). Action research and data mining. In S. ElAtia, D. Ipperciel, & O. Zaïane (Eds.), Data mining and learning analytics: Applications in educational research (pp. 67–78). Hoboken, NJ: Wiley.CrossRefGoogle Scholar
  14. Cochran, J. D., Campbell, S. M., Baker, H. M., & Leeds, E. M. (2014). The role of student characteristics in predicting retention in online courses. Research in Higher Education, 55, 27–48.CrossRefGoogle Scholar
  15. Collins, R. A., Kang, H., Biniecki, S. Y., & Favor, J. (2015). Building an Accelerated Online Graduate Program for Military Officers. Online Learning, 19(1), 102–111.Google Scholar
  16. Dawson, S., Mirriahi, N., & Gasevich, D. (2015). Importance of theory in learning analytics in formal and workplace settings. Journal of Learning Analytics, 2(2), 1–4.  https://doi.org/10.18608/jla.2015.22.1 CrossRefGoogle Scholar
  17. De Vito, K. (2009). Implementing adult learning principles to overcome barriers of learning in continuing higher education programs. Online journal of Workforce Education and Development, 3(4), 1–10.Google Scholar
  18. Donaldson, J. F. (2001, April). Accelerated degree programs: Policy implications and critique: What we know about adult learners and its implication for policy. Paper presented at the Annual Meeting of the American Educational Research Association, Seattle, WA.Google Scholar
  19. EDUCAUSE Learning Initiative (2011, December). Seven things you should know about first generation learning analytics. Retrieved from https://library.educause.edu/~/media/files/library/2011/12/eli7079-pdf.pdf. Accessed 23 Jan 2018.
  20. Geltner, P., & Logan, R. (2001). The influence of term length on student success (report no. 2001.4.1.0). Santa Monica, CA: Santa Monica College.Google Scholar
  21. Henschke, J. A. (2011). Considerations regarding the future of andragogy. Adult Learning, 22(1), 34–37.CrossRefGoogle Scholar
  22. Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., & Hall, C. (2016). NMC horizon report: 2016 higher education edition. Austin, TX: The New Media Consortium.Google Scholar
  23. Kauffman, H. (2015). A review of predictive factors of student success in and satisfaction with online learning. Research in Learning Technology, 23, 1–13.  https://doi.org/10.3402/rlt.v23.26507 CrossRefGoogle Scholar
  24. Kemp, N., & Grieve, R. (2014). Face-to-face or face-to-screen? Undergraduates’ opinions and test performance in classroom vs. online learning. Frontiers in Psychology, 5, 1–11.  https://doi.org/10.3389/fpsyg.2014.01278 CrossRefGoogle Scholar
  25. Knowles, M. (1980). The modern practice of adult education: From pedagogy to andragogy (2nd ed.). New York, NY: Cambridge Books.Google Scholar
  26. Lambert, C., Erickson, L., Alhramelah, A., Rhoton, D., Lindbeck, R., & Sammons, D. (2014). Technology and adult students in higher education: A review of the literature. Issues and Trends in Educational Technology, 2(1), article 3.CrossRefGoogle Scholar
  27. Lee, N., & Horsfall, B. (2010). Accelerated learning: A study of faculty and student experiences. Innovative Higher Education, 35(3), 91–202.CrossRefGoogle Scholar
  28. Leitner, P., Ebner, M., & Khalil, M. (2017). Learning analytics in higher education - a literature review. In A. Pena-Ayala (Ed.), Learning analytics: Fundamentals, applications, and trends: A view of the current state of the art to enhance e-learning (pp. 1–23). Cham, Switzerland: Springer.Google Scholar
  29. Levy, Y. (2007). Comparing dropouts and persistence in e-learning courses. Computers and Education, 48, 185–204.  https://doi.org/10.1016/j.compedu.2004.12.004 CrossRefGoogle Scholar
  30. Long, P., & Siemens, G. (2011). Penetrating fog: Analytics in learning and education. EDUCAUSE Review, 46(5), 31–40.Google Scholar
  31. Mattingly, K. D., Rice, M. C., & Berge, Z. L. (2012). Learning analytics as a tool for closing the assessment loop in higher education. Knowledge management and e-learning. An International Journal, 4(3), 236–247.Google Scholar
  32. Mealman, C. A., & Lawrence, R. L. (2000). You’ve come a long way baby: The roots and resistance of cohort-based accelerated learning. Paper presented at the Adult Higher Education Association Conference, Chicago, IL.Google Scholar
  33. Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2010). Evaluation of evidence-based practices in online learning: A meta-analysis and review of online learning studies. Washington, DC: U.S. Department of Education.Google Scholar
  34. Messina, R. C. (1996). Power package: An alternative to traditional course scheduling (ERIC Document Reproduction Service NO. ED 396787).Google Scholar
  35. Millett, C. M., Stickler, L. M., & Wang, H. (2015). Accelerated nursing degree programs: Insights into teaching and learning experiences (research report no. RR-15-29). Princeton, NJ: Educational Testing Service.  https://doi.org/10.1002/ets2.12078 CrossRefGoogle Scholar
  36. National Center for Education Statistics. (2015). Distance education in postsecondary institutions. Retrieved from https://nces.ed.gov/programs/coe/indicator_sta.asp
  37. Nguyen, T. (2015). The effectiveness of online learning: Beyond no significant difference and future horizons. MERLOT Journal of Online Teaching and Learning, 11(2), 309–319.Google Scholar
  38. Park, J.-H., & Choi, H. J. (2009). Factors influencing adult learners’ decision to drop out or persist on online learning. Educational Technology and Society, 12(4), 207–217.Google Scholar
  39. Picciano, A. G. (2012). The evolution of big data and learning analytics in American higher education. Journal of Asynchronous Learning Networks, 16(3), 9–20.Google Scholar
  40. Piety, P. J. (2013). Assessing the educational data movement. New York, NY: Teachers College Press.Google Scholar
  41. Pino, D. (2008). Web-based English as a second language instruction and learning: Strengths and limitations. Distance Learning, 5(2), 65–71. Retrieved from http://www.infoagepub.com/index.php?id=89&i=59 Google Scholar
  42. Romero, C., & Ventura, S. (2013). Data mining in education. WIREs Data Mining Knowledge Discovery, 3, 12–27.  https://doi.org/10.1002/widm.1075 CrossRefGoogle Scholar
  43. Ross-Gordon, J. (2011). Research on adult learners: Supporting the needs of a student population that is no longer nontraditional. Peer Review, 13, 26–29.Google Scholar
  44. Samaroo, S., Cooper, E., & Green, T. (2013). Pedandragogy: A way forward to self-engaged learning. New Horizons in Adult Education & Human Resource Development, 25(3), 76–90.CrossRefGoogle Scholar
  45. Scheffel, M., Drachsler, H., Stoyanov, S., & Specht, M. (2014). Quality indicators for learning analytics. Educational Technology & Society, 17(4), 117–132.Google Scholar
  46. Seamon, M. (2004). Short and long-term differences in instructional effectiveness between intensive and semester-length courses. Teachers College Record, 106(4), 852–874.  https://doi.org/10.1111/j.1467-9620.2004.00360.x CrossRefGoogle Scholar
  47. Shacklock, X. (2016). From bricks to clicks: The potential of data and analytics in higher education. The Higher Education Commission (HEC) report.Google Scholar
  48. Shaw, M., Chametzky, B., Burrus, S. W., & Walters, K. J. (2013). An evaluation of student outcomes by course duration in online higher education. Online Journal of Distance Learning Administration, 16(4), 1–33.Google Scholar
  49. Sheldon, C., & Durdella, N. (2010). Success rates for students taking compressed and regular length developmental courses in the community college. Community College Journal of Research and Practice, 35, 39–54.  https://doi.org/10.1007/s10755-010-9141-0 CrossRefGoogle Scholar
  50. Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529.  https://doi.org/10.1177/0002764213479366 CrossRefGoogle Scholar
  51. Stack, S. (2015). Learning outcomes in an online vs traditional course. International Journal for the Scholarship of Teaching and Learning, 9(1), article 5.  https://doi.org/10.20429/ijsotl.2015.090105 CrossRefGoogle Scholar
  52. Tatum, B. C. (2010). Accelerated education: Learning on the fast track. Journal of Research in Innovative Teaching, 3(1), 34–50.Google Scholar
  53. van Barneveld, A., Arnold, K. E., & Campbell, J. P. (2012). Analytics in higher education: Establishing a common language. EDUCAUSE Learning Initiative, 1, 1–11.Google Scholar
  54. Wagner, E., & Ice, P. (2012). Data changes everything: Delivering on the promise of learning analytics in higher education. EDUCAUSE Review, 47(4), 32.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Caldwell UniversityCaldwellUSA

Personalised recommendations