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.