• The Spark

A Model for Evidence-Based Innovation in Education

Author:  Dr. David Dockterman, Lecturer, Harvard Graduate School of Education  | 09/21/2016

TeacherAt a time when the US education system leaves nearly two-thirds of all eighth grade students below proficient in math and reading, approaches that are new and different have great appeal. Districts across the USA have created innovation zones to encourage new ideas and approaches. Charter schools have long promised positive change by releasing school leaders and teachers from the bounds of union rules and legislative handcuffs. But we can’t keep offering the same educational experience to our children and expect different results: we need something new. Innovative, though, isn’t necessarily effective.

An evidence-based approach towards developing new educational practices and programs increases the chances of the innovation being effective. Many elements of this approach are already in use. My hope here is to provide an accessible framework for classroom teachers, administrators, publishers and educational technology entrepreneurs.

Where Innovation Originates

We are all innovators. Sometimes we innovate to solve a problem. Sometimes it’s to tease our desire for novelty. Sometimes it’s about staying ahead of the competition, in business and socially. These innovations can be small and mundane – tinkering with a recipe or creating a technology workaround – or comprehensive and unexpected, such as the sharing economy or driverless cars.

Our drive for innovation pervades education as well. As a teacher I was constantly inventing and tweaking lessons to enliven my classroom or provide a more effective path to learning. Today’s entrepreneurial environment for educational technology, with pitch competitions and incubators, promises financial rewards for all kinds of innovative methods, policies, and products.

Where do those innovative ideas in education come from? While rhetoric for research- and evidence-based approaches remains high, I argue that theory is the real driver of new practices. In the model I present here, research encompasses evidence of all kinds. Research captures results but doesn’t necessarily explain why those results occurred.

That’s the job of theory. We all carry theories of learning in our heads, even if often we don’t explicitly define them. Imagine, for instance, I encounter a young girl struggling to tie her shoelaces. How would I respond? Would I tie them for her? Would I model each step and have her repeat what I did? Would I hand her a URL of a shoelace-tying video to watch overnight and work with her on the steps the next day? My decision likely rests on a theory of action most appropriate for the timing and circumstance. That theory is fed by my own experiences, those shared by others, and maybe – just maybe – some evidence from rigorous scientific research. That collection of evidence broadly constitutes the research directing my theory. My experience with the young girl adds to that evidence base, potentially reinforcing or prompting a revision of my theory of action.

Teaching on a smart boardIn this dynamic relationship, innovation is fueled by both an openness to a wide range of evidence and by a willingness to test and revise theories. The use of higher-quality research from the learning sciences (and beyond) coupled with a commitment to rigorous testing and revision can help us generate more effective innovations.

From Story to Problem Definition

A critical first step in this innovation process involves making sure we’re solving the right problem. For guidance on this step I recommend invoking what Richard Neustadt and Ernest May call the “Goldberg Rule”. In their book Thinking in Time: The Uses of History for Decision-Makers (1986), Neustadt and May describe how Avram Goldberg, then the CEO of the New England supermarket chain Stop & Shop, responded when one of his managers came to him with an issue. Rather than ask the manager what the problem was, Goldberg would encourage the manager to tell the story of what was happening, so Goldberg could figure out what the real problem was.

Different people can look at the same situation and see different problems. The story contains the background information, the nuance, and the context. Knowing the story, Neustadt and May argued, helps reveal motive and normalcy, what’s driving the actions we want to control. Then we can discern the true problem. Doctors similarly collect a patient’s story – medical, personal, and historical – to uncover what might be behind a set of symptoms. The back-story matters.

We can see this same pattern in education. Data, for instance, may show that a school has a high dropout rate, but they don’t indicate why. Unraveling the story, though, might reveal that many older, low-performing students stop coming to school because they’re embarrassed that they can’t read. Keep digging, and other possible causes could emerge. Sometimes the problem is clear, but often it’s muddy. Tackling a high dropout rate by adding more vocational and theoretically high-interest courses, for example, might help encourage some students, but it misses the underlying problem of student reluctance to put their self-esteem at risk because of low reading ability. Taking in the full context, embracing the data and the surrounding narrative can guide problem definition.

So we need to start by telling the story of the situation we want to change. It should be a rich story, encompassing the students, the teachers, and the institutional and community cultures. In the world of human-centered design, innovators would go and live with the community they want to impact. They would observe the routines of its members and work to ascertain the motives and pressures driving their decisions and actions.

Then we need to share our story with others. Outsiders offer additional perspectives, bringing their own experience-based theories. As particular elements seem to gain importance – like adolescent motivation, the use of norms, best practices for teaching reading to older students, the need to feel valued, and so on – we can delve into them with research from the learning and behavioral sciences. The story helps us define the problem, and we need to be ready to make revisions, because problem definition, like innovation, is a dynamic process itself.

For more tips on how to bring evidence-based innovation into your classroom, check out Dr. Dockterman’s original article on npj Science of Learning community.

References:

Bryk AS, Gomez LM, Grunow A LeMahieu P. Learning to improve: How America's schools can get better at getting better. Harvard Education Press: Cambridge, MA, USA, 2015.

Ash K. School Districts Embrace Second Generation of ‘Innovation Zones’. Education Week 33, 21 (2014).

U.S. Department of Education, Institute of Education Sciences, National Center for Education Statistics, National Assessment of Educational Progress (NAEP), various years, 1990–2015 Mathematics and Reading Assessments.

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