New movement in learning analytics could change education

Stanford calls for education data science field; presents national road map to analytics success

data-learning-analyticsIn 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)

According to the Learning Analytics Workgroup—which consists of 37 leaders in the field from companies, universities, government, foundations, and nonprofits, and funded by the Bill & Melinda Gates Foundation and the MacArthur Foundation—the potential for learning analytics is massive.

Not only could learning analytics make it possible to personalize all students’ learning on a massive scale, fundamentally transforming how students are taught, but could also change how teachers are prepared, how research is conducted, how education-related information is used and managed, and how funds are allocated.

If everything goes according to the Workgroup’s vision, and the field of education data science evolves and thrives, incorporating emerging technologies to further enhance analytics would be a must.

For example, sensing systems for learning, or sensors for interaction (e.g. wearable technology), could use data beyond measuring cognitive abilities to include indicators of student interactions during learning activities, visual preferences, student moods and mindsets, and much more.

It’s what Pea calls an evolving “predictive learner model,” which could “get the greatest percentage of learners to competency in the shortest time at the lowest cost.”

But…first education data science for learning analytics has to get there.

The report, which draws on a series of meetings held over the past three years by the Workgroup, gives one of the most comprehensive breakdowns of what’s needed to further the success of learning analytics in education, including:

  • A conceptual framework for building the field
  • Critical questions for understanding how to build the field
  • Articulating and prioritizing new tools, approaches, policies, markets, and programs
  • Determining resources needed to address priorities
  • Specific funding recommendations
  • Outlining value propositions for different stakeholders
  • A road map to implement field-building strategy and how to evaluate progress

The road map is designed for policy makers, foundation leaders and researchers to be able to support and advance learning analytics, and cites many recent advances—the Common Core, growth in data mining, more detailed measures of effective teaching, blended learning models, and digital tools—toward realizing the promise of the field.

To read the executive summary of the report, click here.

To read the full report, click here.

To read more about Stanford’s ‘Lytics Lab’ and the University’s progress in using learning analytics, click here.

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