Data without Context = Hearsay
We like proof. But we’ve all heard the saying Mark Twain made popular, “There are three kinds of lies: lies, damned lies, and statistics.”
It’s natural for self-interest to result in either innocent or active bias — selective sharing (“cherry picking”) means we get some data in ways that ignore context. Because of the deluge of “data,” we often rely on trusting the messenger to support our beliefs. Of course, we all know seemingly trustworthy messengers can be wrong or misinformed, yet they can be repeated frequently (ahem, remember Colin Powell to the UN on WMDs? Or Lance Armstrong any time before 2013?).
Educators making decisions on behalf of others — like which learning technologies are best for their students or schools — need intellectually-honest, trustworthy insights. And, not only do they have to trust the messenger (this is why peers are educators’ most trusted source), they also need those data-driven insights WITH context. But how do we get this at scale?
When it comes to learning technologies, teachers and administrators can’t answer the real question educators ask without contextualized data at scale. EdTech directories with 5-star rating systems provide no context or true statistical differentiation, and deep ethnographic studies and multi-variant data collection are too expensive and time-consuming to reach scale.
While “product efficacy” has received more attention lately, learning which combination of learning technologies, pedagogical practices and operational modalities actually accelerate student achievement means combining local context with a scalable platform to harness our collective experience with learning technologies.
The schools, districts, states and universities using Lea(R)n’s edtech management system, LearnTrials, are finding they have a time- and money-saving data with a powerful “context engine.” By combining public data from every school in America with teacher experience, product usage data, educator profiles, student achievement, pricing and other localized data, the smart report cards, dashboards and analyses provide administrators the context they need for instructional and budget decisions.
“I’m a believer! Not only do we have better insight, but our teachers are improving their practice,” says Dr. Adam Fried, a superintendent in New Jersey.
The Lea®n research team, led by Dr. Daniel Stanhope, focused on context from classroom teaching on up. By working with thousands of educators to design a rapid protocol, along with a research-backed edtech rubric, student-group level context informs every product insight. With four simple questions and a configurable feedback tool for the 8 most important criteria, teachers and administrators gain quantifiable insights into edtech with context.
Watch How to Grade a Learning Tool:
Context-rich data for districts is not just convenient, modern edtech management maximizes cost efficiency and return on investment for districts. Budgets for education technology will continue to expand, but administrators can’t afford to waste resources on low-impact or unused product licenses.
Sign up for a quick demo to see how LearnTrials can work in your local context to help you make cost-saving decisions that improve practices and outcomes.
Our platform helps district leaders gather feedback from teachers, while conducting product analyses, so they can effectively budget for edtech. Subscribe to our newsletter! And follow LearnTrials on Twitter!!
