With any airplane crash, there are many unanswered questions as to what brought the plane down. Investigators turn to the airplane's flight data recorder (FDR) and cockpit voice recorder (CVR), also known as the "black box" for answers.
After finding the black box, investigators take the recorders to a lab where they can download the data from the recorders and attempt to recreate the events of the accident. A team of experts is usually brought in to interpret the recordings stored on a CVR. This can be a painstaking process and may take weeks to complete.
However, we invest and even demand this kind of analysis, care and precision because with any airplane crash we are dealing with a matter of life and death, and every life, regardless of where it originated or where it was heading on that particular journey, had intrinsic value.
Teachers, like airplanes, take willing passengers on a journey, one that explores the counters of their mind and world and when done well can reach an unlimited array of destinations. However, when unsuccessful, like a plane crash, it can kill a livelihood.
Today, we understand so little about the process of making great teachers as opposed to poor ones. Lauded colleges of education prepare the next generation of teachers and yet their experience in the workplace of classrooms is widely mixed. We have yet to create a consistent, great-teacher making institution. Most of our indicators of teaching success are lagging indicators of such success. We know great teachers when we see them just as easily as we identify poor teachers when we see them. But how do we take teachers from poor to great? What are the leading indictors? How do we find and crack open the black box of teaching?
In a blended classroom where every student is accessing both their teacher and an amazing array of digital content through laptops, tablets or other devices, data is accumulating daily on how students are learning on several dimensions – speed, efficacy, proficiency, sequence and value-add. As more and more blended models of school emerge, powerfully combining technology with instruction to produce reams of data on student learning, an amazing amount of R&D and investigative analysis can be performed answering questions not previously posed.
What content or sequence of content works particularly well with 4th grade girls struggling with the learning standard of “main idea” in English language arts?
How do boys perform in math before recess versus after recess?
What is the optimal timing and length of assessments on particular students learning a given topic?
Too much academic research is in the form of University produced white papers published in journals that often take one or more years to compile, vet and disseminate. We need microforms of rapid R&D – the kind that answer a host of micro questions around learning habits that can quickly be disseminated into the field for rapid prototyping.
Blended learning models offer the promise and opportunity to get inside the “black box” of teaching, uncovering reams of student achievement data and the underlying drivers and causes of student success and failure. If we understand these causes, we can better minimize future crashes from occurring.
