Learning analytics are a set of practices and methods that center on the collection, measurement, and interpretation of data in educational systems. Learning analytics, when successfully applied, can greatly increase the efficacy and impact of educational research, improving both our academic understandings of core issues within learning sciences, as well as providing cutting edge tools and approaches for practitioners.
However, while learning analytics has a great deal to offers projects relating to STEM and Computer Science education, there are three major barriers to wider adoption:
- A lack of awareness,
- A lack of understanding on how to prepare field data for analysis,
- And a lack of understanding of applying learning analytics methods to these data.
While learning analytics has great promise for improving how we think and teach, this power has not been connected across institutions and organizations.
This motivated us to create the Data Consortium Fellows, or DCF. The DCF brought together leaders in learning analytics to train both novice and experienced researchers in the field – aiding them in implementing learning analytics in their own work. Through a series of convenings, workshops, and resources, we have brought together over 30 researchers and practitioners, with the goal of expanding and enriching the great potential of learning analytics.
DCF Funding and Team:
The Data Consortium Fellowship is an NSF Funded Project (#1549112).
Initiated in August 2015 with Matthew Berland as the Principal Investigator, DCF has connected numerous researchers, and supported a wide spectrum of collaborative learning and scholarly opportunities.
The team behind running DCF over the years has included:
And numerous other supporting faculty, graduate students, and scholars.