Using learning analytics to predict (and improve) student success: A faculty perspective

Journal of Interactive Online Learning

Volumn 12, Issue 1, 2013, Pages 17-26

  Dietz Uhler, Beth ^a   Hurn, Janet E ^a  

^a MIAMI UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84880710773     PISSN: 15414914     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (27)

1. Abdous, M., He, W., & Yen, C. (2012). Using data mining for predicting relationships between online question theme and final grade. Educational Technology & Society, 15(3), 77-88.
2. Brandon, K. (2009). Investing in Education: The American Graduation Initiative. Office of SociaI Innovation and Civic Participation. Retrieved November 22, 2012, from http://www.whitehouse.gov/blog/Investing-in-Education-The-American-Graduation-Initiative
3. Campbell, J. P., DeBlois, P. B., & Oblinger, D. (2007). Academic analytics: A new tool for a new era. EDUCAUSE Review, 42(4), 40-57.
4. Dietz-Uhler, B., Hurn, J.E., & Hurn, B. (2012). Making use of data in an LMS to predict student performance: A learning analytics investigation. Unpublished manuscript.
5. Dringus, L. P. (2012). Learning analytics considered harmful. Journal of Asynchronous Learning Networks, 16(3), 87-100.
6. Dyckhoff, A. L., Zielke, D., Bultmann, M., Chatti, M. A., & Schroeder, U. (2012). Design and implementation of a learning analytics toolkit for teachers. Educational Technology & Society, 15(3), 58-76.
7. Dziuban, C., Moskal, P., Cavanaugh, T., & Watts, A. (2012). Analytics that inform the university: Using data you already have. Journal of Asynchronous Learning Networks, 16(3), 21-38.
8. Falakmasir, M. & Jafar, H. (2010). Using educational data mining methods to study the impact of virtual classrooms in e-learning. Paper presented at the Proceedings of the 3rd International Conference on Educational Data Mining, Pittsburgh, PA. Retrieved from http://alum.sharif.edu/~falakmasir/images/EDMPaper.pdf
9. Greller, W. & Drachslrer, H. (2012). Translating learning into numbers: A generic framework for learning analytics. Educational Technology & Society, 15(3), 42-57.
10. Goldstein, P. & Katz, D. (2005). Academic analytics: The uses of management information and technology in higher education. EDUCAUSE Center for Applied Research. Retrieved from http://net.educause.edu/ir/library/pdf/ekf/ekf0508.pdf
11. Hrabowski, F. A., Suess, J. J., & Fritz, J. (2011). Assessment and analytics in institutional transformation. EDUCAUSE Review Online. Retrieved from http://www.educause.edu/ero/article/assessment-and-analytics-institutionaltransformation
12. Ice, P., Diaz, S., Swan, K., Burgess, M., Sharkey, M., Sherrill, J., Huston, D., & Okimoto, H. (2012). The PAR framework proof of concept: Initial findings from a multi-institutional analysis of federated postsecondary data. Journal of Asynchronous Learning Networks, 16(3),63-86.
13. IBM. (2001). Analytics for achievement: Understand success and boost performance in primary and secondary education. Retrieved from http://public.dhe.ibm.com/common/ssi/ecm/en/ytw03149caen/YTW03149CAEN.PDF
14. Johnson, L., Adams, S., and Cummins, M. (2012). The NMC Horizon Report: 2012 Higher Education Edition. Austin, Texas: The New Media Consortium.
15. Jones, S. J. (2012). Technology review: The possibilities of learning analytics to improve learner centered decision making. The Community College Enterprise, Spring, 2012, 89-92.
16. Long, P. D. & Siemens, G. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review Online. Retrieved from http://www.educause.edu/ero/article/penetrating-fog-analytics-learning-and-education
17. Macfadyen, L. P. & Dawson, S. (2012). Numbers are not enough: Why e-learning analytics failed to inform an institutional strategic plan. Educational Technology & Society, 15(3), 149-163.
18. Manaei-Bidgoli, B., Kashy, D. A., Kortmeyer, G., & Punch, W. (2003). Predicting student performance: An application of data mining methods with an educational web-based system (LON-CAPA). Paper presented at the 33rd ASEE/IEEE Frontiers in Education Conference, Boulder, CO. Retrieved from http://lon-capa.org/papers/v5-FIE-paper.pdf
19. 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 & E-Learning: An International Journal, 4(3), 236-247.
20. May, T. A. (2011). Analytics, University 3.0, and the future of information technology. EDUCAUSE Review Online. Retrieved from http://net.educause.edu/ir/library/pdf/ERM1159.pdf
21. Olmos, M. & Corrin, L. (2012). Learning analytics: A case study of the process of design of visualizations. Journal of Asynchronous Learning Networks, 16(3), 39-49.
22. 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.
23. Shum, S. B. & Ferguson, R. (2012). Social learning analytics. Educational Technology & Society,15(3), 3-26.
24. Siemens, G. (2010). What are learning analytics. ELEARNSPACE: Learning, networks, knowledge, technology, community. Retrieved from http://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/
25. Smith, V. C., Lange, A., & Huston, D. R. (2012). Predictive modeling to forecast student outcomes and drive effective interventions in online community college courses. Journal of Asynchronous Learning Networks, 16(3), 51-61.
26. Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-driven research to support learning and knowledge analytics. Educational Technology & Society, 15(3), 133-148.
27. Watters, A. (2012). Learning analytics: Lot of education data...Now what? Hack Education. Retrieved from http://hackeducation.com/2012/05/04/learning-analytics-lak12/