Learning analytics focus on the collection and use of data about students, courses, and academic programs for the purpose of improving teaching and learning. Educational Data Mining, EDM, is concerned with using data collected from educational settings to better understand students and settings for learning. The two fields are closely allied.
The following link provides a well-referenced introduction to learning analytics, a relatively new field of data collection for educational purposes. The site offers definitions of terms, history, methods, uses, software, ethical issues, and resources.
The link below provides information on educational data mining, including such topics as users and stakeholders, costs and challenges, and criticisms.
“Learning Analytics: Readiness and Rewards,” Norm Friesen, Boise State University. (Canadian Journal of Learning and Technology, Volume 39 (4), Fall 2013.
Introduces the field of learning analytics; advises “an incremental approach to institutional preparedness.”
Learning and Knowledge Analytics.
A site devoted to Learning and Knowledge Analytics. Supports a conference, a journal, and an open free online course “Introduction to Learning and Knowledge Analytics,” syllabus provided. The site offers interviews, recordings, and blogs.
Examples of the Use of Learning Analytics
The following two sites offer examples of learning analytics at the University of Michigan’s SLAM Program.
Student Learning and Analytics at Michigan (SLAM).
This site offers slides, videos, and other materials from a speaker series at the University of Michigan on student learning analytics from 2011 to the present. Topics range from analytics of UM’s MOOC courses to UM projects by learning analytics fellows. Speaker series includes presentations from researchers at several universities.
U-M Learning Analytics Fellows’ Projects 2013 and 2014.
Links to posters, handouts, and videos for an overview of these projects from a wide variety of disciplines at U-M.
Other examples of Learning Analytics for Educational Purposes
Learning Analytics, EDUCAUSE.edu
This EDUCAUSE site links to a wide variety of articles and reports on learning analytics in higher education, including examples of their use.
Learning Analytics at Penn State University.
An introduction to analytics, posts on necessary infrastructure, annotated list of learning analytics research, and links to information on learning analytics in use at other universities.
Societies, Associations, Journals
Society for Learning Analytics Research (SoLAR).
The Society is an “inter-disciplinary network of leading international researchers who are exploring the role and impact of analytics on teaching, learning, training and development.” Organizes conferences, holds a summer institute, and supports initiatives.
Publishes the Journal of Learning Analytics, a peer-reviewed, open-access journal. Current issue and archives are available at http://solaresearch.org/stay-informed/journal/.
Journal of Educational Technology & Society, Vol.15, No. 3, 2012.
Special issue on learning analytics, guest editors George Siemens and Dragan Gasevic. Entire issue is open-access online at:
International Educational Data Mining Society.
The society’s aim is “to support collaboration and scientific development” in the emerging discipline of data mining from educational settings.
Sponsors the Journal of Educational Data Mining, JEDM, a free, open-access journal at http://www.educationaldatamining.org/JEDM/index.php/JEDM.
The entire issue of EDUCAUSE July/August 2014 is dedicated to learning analytics. Full articles free online can be accessed at http://er.educause.edu/ero/articles.
Examples of Learning Analytics Software and Uses
SNAPP, Social Networks Adapting Pedagogical Practice.
A diagnostic tool that “performs real-time social network analysis” of posts and interactions.
Provides teachers with feedback on the learning processes going on in a web-based learning environment.
SAM, Student Activity Monitor.
A teaching and learning tool designed to increase self-reflection and awareness among students and also provide teachers with analysis of student activities.
This site shows how Beestar’s Location Intelligence Platform solves five pressing educational problems. Example: early dropout notification & prevention.