One avenue of research around learning analytics is the identification of interesting or useful patterns. For example, one comment pattern is that the students who use an LMS more tend to get better results (but not always).

These patterns might be useful for:

  • Informing academic staff around the design and running of online courses.
  • Informing students about potential implications of their behaviour (e.g. the Signals project at Purdue University
  • Inform administrators etc, and
  • Identify or be used as part of research.

The following is intended to be a place to list the patterns we’ve found either through our own work or through literature and online. Some or many of these patterns are tentative, most are indications of correlation and not causation, and many are limited by arising from data from within a single LMS, single institution, limited time frame, or subset of courses.

Pattern Sample Source
Use of asynchronous communication tools increases student visits to course sites 1200 students, 10 online business courses, 1 institution, 1 teaching period (Greenland, 2011)
The more students use the LMS the better their grades ?? (Dawson, McWilliam & Tan, 2008)
The more students use the LMS the better their grades –
Only for off-campus students, but not necessarily for on-campus students or students in courses where there is low staf participation
?? (Beer et al, 2009)
No pattern found 2008-2009, 2 postgrad programs with 8 courses each. Courses lasting 2 to 9 weeks. 48 students. (Pascual-Miguel, et al, 2010)
Older students use the LMS more then younger students 28,000 undergraduate distance education students Beer, 2009
LMS feature usage has approached a stable usage pattern, primarily focused on information transmission. On-campus courses only, 2005-2007, 842 course sites (Malikowski, 2010)
The later a students starts using the course site, the lower their final grade 5900 students Beer, 2012
New online students with full loads drop out more than part-time 640,000 students, 6 institutions (Fain, 2012)
Students tend to interact only with students of a similar academic ability unknown (Dawson, McWilliam, 2008)
In forums, staff tend to interact with high-performing students unknown (Dawson, McWilliam, 2008)


Beer, C., Jones, D., & Clark, K. (2009). The indicators project identifying effective learning, adoption, activity, grades and external factors. Same places, different spaces. Proceedings ascilite Auckland 2009. Auckland, New Zealand. Retrieved from

Dawson, S., McWilliam, E., & Tan, J. P. L. (2008). Teaching smarter: How mining ICT data can inform and improve learning and teaching practice. Melbourne. Retrieved from

Dawson, S., & McWilliam, E. (2008). Investigating the application of IT generated data as an indicator of learning and teaching performance. Queensland University of Technology and the University of British Columbia. Retrieved from

Greenland, S. (2011). Using log data to investigate the impact of (a) synchronous learning tools on LMS interaction. In G. Williams, P. Statham, N. Brown, & B. Cleland (Eds.), ASCILITE 2011 (pp. 469-474). Hobart, Australia. Retrieved from

Malikowski, S. (2010). A Three Year Analysis of CMS Use in Resident University Courses. Journal of Educational Technology Systems, 39(1), 65-85. Retrieved from

Pascual-Miguel, F., Chaparro-Peláez, J., Hernández-García, Á., & Iglesias-Pradas, S. (2010). A Comparative Study on the Influence between Interaction and Performance in Postgraduate In-Class and Distance Learning Courses Based on the Analysis of LMS Logs. In M. D. Lytras, P. Ordonez De Pablos, D. Avison, J. Sipior, Q. Jin, W. Leal, L. Uden, et al. (Eds.), Technology Enhanced Learning. Quality of Teaching and Educational Reform (pp. 308-315). Springer Berlin Heidelberg.


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