Last edited by George Siemens about 1 year ago

Week 1: Trends and context: why learning analytics?



View this short introduction to LA (~15 min)

This week is an introduction into the trends and context driving interest in learning analytics. Parts of this week overlap with week 2 where we focus on cases and examples of analytics in education.

We live in an increasingly digital era, defined by information abundance and growing complexity. The conversations that used to evaporate are now digitized, waiting for a clever algorithm for analysis. The potential of analytics to increase employee efficiency, match the right people to the right tasks, and to improve access to help resources is tremendous. Businesses and governments have taken advantage of new data-focused tools and techniques to improve organizational efficiency and gain a competitive advantage.

This course will explore learning and knowledge analytics, including analytics methods and models, systemic use of analytics, data sources, the “soft/human/non-quantifiable” aspect of learning, as well as privacy and ethical considerations in deploying analytics. For those of you that have participated in this course in the past, you'll see three significant difference in this version: 1. hands on learning activities that introduce both the logic around an analytics project, 2. greater emphasis on tools and technologies and, 3. focus on the assumptions underlying analytics tools and projects.

The volume, velocity, and variety of data are among the key factors explored. 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 introduce the context of learning analytics and explore 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:
    • Academic analytics marries large data sets with statistical techniques and predictive modeling to improve decision making. (Campbell and Oblinger)
  • 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)

Readings and videos:

Learning Activities:

We have three learning activities for week 1:

1. Discussion: If you've taken an online course, particularly something MOOC-like in size, you are likely familiar with the obligatory "introduce yourself and tell us why you're here!" opening forum. It usually turns into a mess of hundreds of posts with limited interaction between participants. In spite of this weakness, it is still a good opportunity for participants to get a sampling of the scope of participants in the course. We have three forums for Week 1:

Pre-course discussion forum: go ahead, introduce yourself
Week 1 Discussion Forum: This will be the main forum for discussion this week. Topic for week 1: After reviewing the readings and video about the context of analytics and various definitions, what is missing? What is most important for educators?
Help/Suggestion Forumexactly like it reads

2. Join live discussion sessionLive Sessions & Guest Speakers

3. Begin planning your analytics projectAnalytics: Logic and Structure

Additional Readings:

Duval, ErikAttention please! Learning analytics for visualization and recommendation, LAK11: International Conference on Learning Analytics and Knowledge, Banff, Canada, 27 February - 1 March 2011, Proceedings of LAK11: 1st International Conference on Learning Analytics and Knowledge, pages 9-17, ACM

Computing the world: 

4th paradigm of scientific discovery:


What is a career in big data?