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.