Defining Analytics

This is the week 2 topic from the Learning Analytics and Knowledge: LAK12 MOOC. It provides a good outline of the emerging area, and acknowledges the different areas and approaches that have influenced it. Click here to view the original link.

Week 2: What are learning analytics?

Date: January 30 - February 5

In week one of LAK12, we explored the context and change pressures driving interest (and adoption) of analytics in many sectors. The volume, velocity, and variety of data are among the key factors explored. The term "big data" was also introduced last week. Big data is gaining attention as a "new" buzzword, but Diebold coined the term (.pdf) in 2000 to describe "the explosion in the quantity (and sometimes, quality) of available and potentially relevant data". The data trails we now leave in our daily digital interactions can reveal our interests, sentiments, even beliefs and opinions. This digitization (or externalization) has strong positive and negative benefits. As Latour states:

"Imagination no longer comes as cheaply as it did in the past. The slightest move in the virtual landscape has to be paid for in lines of code.
If it is rather useless to try to decide whether we are ready to upload our former selves into these virtual worlds or not, it is more rewarding to notice another much more interesting difference between the two industries and technologies of imagination. Apart from the number of copies sold and the number and length of reviews published, a book in the past left few traces. Once in the hands of their owners, what happened to the characters remained a private affair. If readers swapped impressions and stories about them, no one else knew about it.
The situation is entirely different with the digitalisation of the entertainment industry: characters leave behind a range of data."

In education, analytics are described by various terms: educational data mining, academic analytics, and learning analytics. Significant overlap exists in each of these areas. For the purposes of this course, we will use the term “learning analytics”. This week will give us an opportunity to explore the term in more detail and also to define how it differs from other terms (such as EDM and academic analytics).


  • Learning analytics:
    • The measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. (SoLAR:
  • Academic analytics:
  • Educational Data Mining:
    • Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in.
      Whether educational data is taken from students' use of interactive learning environments, computer-supported collaborative learning, or administrative data from schools and universities, it often has multiple levels of meaningful hierarchy, which often need to be determined by properties in the data itself, rather than in advance. Issues of time, sequence, and context also play important roles in the study of educational data. (IEDMS:


Goldstein, P. J. (2005) Academic Analytics: Uses of Management Information and Technology in Higher Education

Siemens, Long:

What is a career in big data?

Baker, R. S.J.d. Data Mining for Education:

Analytics: The widening gap (a bit of a flash back to last week, but compartmentalized curriculum isn't good :)): (you will need to register (free))


Ryan S.J.d. Baker from the International Educational Data Mining Societywill be joining us on Tuesday, January 31 at 1 pm mountain time (time zone conversions here. The session will be held here in Blackboard Collaborate.

George Siemens will be presenting an online session on February 1 at 11 am mountain time Time zone conversions here for EDUCAUSE “Leaping the Chasm: Moving from buzzwords to implementation of analytics”

Last modified: Tuesday, 1 December 2015, 2:55 PM