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The Gap Between Data-Driven Decision Making and the Achievement Datum Base
|Posted By: Dick Schutz on May 9, 2006|
The conclusion I draw from the article is that the people in these schools are more rational and reflective than the people at the top of the EdChain who recklessly hurl down empty shibboleths like “accountability” and “data based decision making” from on high—free of any “accountability” and “data-based decision making” themselves. But finger-pointing and game-blaming will get us nowhere.
Tear Down Those Barriers. Mr./Ms._________!
(An “eighth barrier” is that no single individual or entity fills the blank, but that’s not an obstacle from a CI/OL perspective. Let’s look at the “Barriers:”
Barrier 1: Many teachers have developed their own personal metric for judging the effectiveness of their teaching and often this metric differs from the metrics of external parties (e.g., state accountability systems and school boards).
That’s a straw barrier. It applies to individuals in any endeavor—scientists, bakers, you-name-it.
Barrier 2: Many teachers and administrators base their decisions on experience, intuition and anecdotal information (professional judgment) rather than on information that is collected systematically.
Another straw barrier. All professions do this. The barrier lies in the dearth of “information that is collected systematically,” rather than in the personnel.
Barrier 3: There is little agreement among stakeholders about which student outcomes are most important and what kinds of data are meaningful.
My interpretation of the data on this matter there is more agreement on this matter than is customarily believed—after you scrape off all the rhetoric. But it’s an empirical question. The only “outcome” now in play is scaled scores on standardized achievement tests. That datum doesn’t give much data to “drive.”
Barrier 4: Some teachers disassociate their own performance and that of students, which leads them to overlook useful data.
Well, although the two matters are interdependent, they are different. The barrier resides in trying to drag both matters through the needle’s eye of the standardized test scale scores.
Barrier 5: Data that teachers want─ about ‘‘really important outcomes’’─ are rarely available and are usually hard to measure.
“Hard to measure?” Psychometrists today suffer from low expectations and resistance to change. When no effort is being made to acknowledge, observe, record, and analyze “what the teachers want” of course the phenomena are going to be “hard to measure.” Very rudimentary psychometric methodology is up to the task, but it has to be applied.
Barrier 6: Schools rarely provide the time needed to collect and analyze data.
A true barrier. But it lies in the fact that “data” are viewed as extrinsic to school operations and something that is imposed on everyday school life. This view all-but assures that the data will be artificial and/or obsolete buy the time they become available.
An alternative view is to focus on the real-time accomplishments and services schools are delivering—using the same InfoTech orientation and methodology that is being used in the Corporate world. That’s a longer story. But the point here, is that tearing down the barrier is tractable and feasible.
Barriers 7: Data have often been used politically, leading to mistrust of data and data avoidance.
“Often” is stating the barrier too mildly. Ideology rules—within the profession and in the government mandates currently being levied on schools. Again the barrier is at the top of the EdChain, not the bottom.
The data-driven conclusion I draw from the article, is the personnel in these schools, which very likely represent “best practice” schools are making the best “data-based” decisions possible, but they are doing so under superimposed conditions that irrationally inhibit rather than foster the desired/aspired practice.
CL/OL is in one sense a carrot-on-a-stick ideal that’s never attained. In another sense, however, self-corrections are very feasible. The data that Ingram, Seashore, and Schroeder present provide clear guidance on some self-corrections that are in order. All we need is data-driven decision makers at the top to implement the corrections.