This post Lies at the intersection of 3 recent events:
- My intuition (and experience) on the need to make tech easy for teachers to adapt into practice
- A recent post by Jose Ferreira on Big Data and the Mathematics of Effectiveness and
- Comments of charter school educators I heard at the recent NYEdTech Meet up on 4-15
First, I believe that design should be an important factor in the coming ed tech revolution in educational practice. Tech must be designed in 1 of 2 ways. Either design it in a way that it can easily be adapted to existing practice (one comment at nyedtech was; “I don’t have 2 professional development days to learn a new computer program”.) or we should see a redesign of practice that is both relatively easy to implement and worth the effort. I believe that real progress will require some type of redesign, but it has to fit the larger picture of what is needed in education as it evolves into a data intensive practice and it must make teacher’s work more productive. Anything that increases the workload will not cut it. My own take is in some version of the flipped classroom that involves adapted learning. Lower level knowledge tasks are handled by technology and are linked to higher level skills that are more teacher intensive.
Data intensive technology is certainly the future of education, but as InBloom has highlighted, people are very sensitive about students data. InBlooms CEO Iwan Streichenberger and Jose Ferreira both characterize this sensitivity as a misunderstanding, however this mischaracterizes and trivializes valid concerns. For data to have meaning, it must be embedded in practice. What critics of InBloom were mostly worried about were potential problem in practice. The Reuters Article K-12 student database jazzes tech startups, spooks parents, Quotes Frank Catalano:
“The hype in the tech press is that education is an engineering problem that can be fixed by technology,” said Frank Catalano of Intrinsic Strategy, a consulting firm focused on education and technology. “To my mind, that’s a very naive and destructive view.”
We need to pull back and think small, not big. . . . By precisely packaging and identifying what data is gathered, how it will be analyzed (or “mined”), and what result is anticipated, you remove the vague what-ifs. Everyone is then judging discrete products that can be understood, poked, prodded and dissected. . . . Transparent. Tangible. Aiming for trust. It’s not a perfect plan. But it sure as hell has got to be better than what’s happening now.
Finally there was a comment by Dr. Eric Tucker of the Brooklyn Lab School on the schools role in identity formulation. This wasn’t highlighted in the wrap-up, but I think it deserves recognition that the impact of data should be conceived as a educational outcome, not the solution of an engineering problem. Students are not widgets. Nore are they data points. We must not loose sight that we are building educated people and the core of that process is found in identity formulation.