Learning Analytics – Beginner’s notes

I was reviewing the video streaming of the “Why Big Data & Analytics could transform the Learning Sciences and Education” session by Dragan Gasevic, Athabasca University in the Learning Analytics Summer Institutes (LASI) 2013 e-room, and a specific slide on “What values to promote?” made me pause for a bit. The context for the slide (and the entire presentation) was on Learning Analytics and this particular slide listed the following values:

Individualization, contextualization, and socialization

As I listened to the speaker explain these values in the context of how learning analytics can be used to promote them, I tried to make connections to how I’ve made the same values the basis of the instructional design philosophy for designing MOOCs – Massive Open Online Courses. The following diagram from my recently published white paper on “Instructional Design for MOOCs” summarizes the characteristics of a MOOC learning environment that form the basis of design thinking for MOOCs.


My interest in learning analytics (and therefore Big Data) is in its nascent stage and I am trying to make sense of this new fad in the higher ed world and how it impacts learning design. I’ve just started to read about this topic and so am sharing some beginners notes – a few terms and their definitions.

Big Data – “… datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze” Manyika et al. (2011)

Analytics: An overarching concept that is defined as data-driven decision making. van Barneveld, Arnold, & Campbell, 2012 adapted from Ravishanker

Business/Academic Analytics: A process for providing higher education institutions with the data necessary to support operational and financial decision making. van Barneveld, Arnold, & Campbell, 2012 adapted from Goldstein and Katz

Educational Data Mining (EDM): A process for analyzing data collected during teaching and learning to test learning theories and inform educational practice. Bienkowski, Feng, Means, 2012.

Learning Analytics: The use of analytical techniques to help target instructional, curricular, and support resources to support the achievement of specific learning goals through applications that directly influence educational practice. van Barneveld, Arnold, & Campbell, 2012 adapted from Bach.

Predictive Analytics: Uncover relationships and patterns, and can be used to predict behavior and events.

Visual Data Analytics: Discovering and understanding patterns in large datasets using visual interpretation.


Designing MOOCs

A White Paper on Instructional Design for MOOCs

The year 2012 saw a “massive” boom in the higher education world, making MOOCs—Massive Open Online Courses—the new buzzword. From talks by educators in ed-tech conferences and forums to media reporting and micro blogging by thought leaders, just about everyone connected to the world of higher education has been talking about MOOCs since then.

And rightly so. Hyped or not, MOOCs are bringing about a revolution in education, a thought affirmed by forerunners like Andrew Ng, Director, Stanford Artificial Intelligence Lab and Co-Founder of Coursera, who says …

“When one professor can teach 50,000 people, it alters the economics of education.”

 –  Andrew Ng
Director, Stanford Artificial Intelligence Lab and Co-Founder of Coursera

Indeed. So, if MOOCs are going to disrupt traditional education, then shouldn’t there be some deeper thinking around their design and development? Although the content is drawn up by subject matter experts (faculty), just how much thought is given to pedagogical and instructional design issues? Are MOOCs even reviewed for quality before they go public?

Not all MOOCs—especially the ones that are informal—probably need to go through a thorough and formal cycle of review for conformance to quality. However, if a university is considering offering MOOCs as part of their formal curriculum, would it then not be worthwhile to develop a pedagogy that is unique to the institute and that delivers a quality product to their learners, even if it does so free of cost?

This white paper draws attention to some of the design and quality aspects of MOOCs and goes on to propose an instructional design philosophy that integrates sophisticated e-learning technologies (interactive content, games, simulations, story-based approach et al) to enhance the design of MOOCs and take them a notch higher in terms of learner engagement.

Given the buzz around them, this white paper assumes that the readers from higher education are familiar with the basic definition of a MOOC. The white paper, therefore, starts off with only a brief introduction to the different types of MOOCs, more so to differentiate the more popular xMOOCs from the original cMOOCs. Thereafter, the paper remains focused on design and quality aspects of MOOCs.

In the end, a critical question—whether every university should offer its own MOOC—is raised. A question that can be answered best by the specific institute; the paper, however, presents some thoughts from articles and posts on this specific question, and then goes on to explore possible business models and partnerships a university can get into for developing a unique MOOC that could, in fact, become a signature course for the university.

Read the full white paper at http://bit.ly/14rvLkz

View a preview video at http://bit.ly/10V49Hb