Stanford calls for education data science field; presents national road map to analytics success
In a truly comprehensive report, with advice and suggestions from over 800 teachers and administrators, a Stanford-led national Workgroup calls for a new movement in learning analytics—redefining the field and offering a seemingly scalable road map to success.
The Workgroup, report, and road map were developed, because as Roy Pea, the David Jacks Professor of Education and Learning Sciences at the Stanford Graduate School of Education and lead investigator of the report, explains: the technology behind analytics has progressed faster than education’s ability to use it.
“Data science, as a distinct professional specialization, is in its infancy,” notes Pea. “What we are calling for is an even newer specialization, ‘education data science.’ Technology has run ahead of the readiness and human capital in this emerging field; demand is ahead of supply and will continue to be without a systematic effort at capacity building in the form of training programs and field building.
One of the first steps the report recognizes in better utilizing massive amounts of student learning data toward analytics is in redefining the specialization of the field, allowing for proper training.
It suggests “bringing current education faculty—especially those who study psychometrics and education measurement—into learning analytics, as well as reaching out to faculty in computer science, statistics, bioinformatics, business intelligence, particle physics, and other fields that do advanced work with large data sets.
Outside of pure data analysis, education data science would also need to leverage the expertise of cyberlearning (learning that is mediated by networked computing and communications technologies) experts, and cyberinfrastructure (the distributed computer, information, and communication technologies combined with the personnel and integrating components that provide a long-term platform for open learning) experts.
“To build the field of learning analytics that can meet the challenge of personalized learning through cyberlearning infrastructures will require leveraging the talents, skills, and other resources from the academy, nonprofits, industry, private foundations, and governmental agencies,” explained Pea.
(Next page: Road-mapping the future of learning analytics)