Reaction 11: Data-Driven Instructional Systems

Anarchist Org Chart:

Reading this week:

Halverson, R.; Grigg, J.; Prichett, R.; Thomas, C. (2007). The New Instructional Leadership: Creating Data-Driven Instructional Systems in School. Journal of School Leadership. 17: 159-193.

Thorn, C.A. (2001, November 19). Knowledge Management for Educational Information Systems: What Is the State of the Field?. Education Policy Analysis Archives. 9(47). Retrieved September 5, 2007 from

Unit of analysis. That seemed to be the thing that kept popping out at me this week. This was stated directly by Thorn (2001) that if the student is the unit of interest, then the data gathered should be attributes about the student. Student Information Systems, however, tend to be designed to produce reports for district-level analysis, not for the classroom. Halverson et al. (2007) found a mismatch or inoperability of data in the district’s high-tech data storage as opposed to the local collection and storage of low-tech data. The logical goal of the proposed data-driven instructional system is thus to link the results of summative data with formative information systems that teachers can use to improve instruction. The goal of practical measurement, as proposed by Yeager et al. (2013) is that “educators need data closely linked to specific work processes and change ideas being introduced in a particular context” (p.12).

In the past, I have associated data-driven decision making as context-blind work whose sole purpose was to improve standardized test scores. The readings this week as well as the networked improvement communities (Bryk et al., 2010) from a few weeks ago has given me a different perspective on what it means to use data to inform instruction, design, and communities. The example of how professional communities can come together to engage “in tasks that systematically acquire data, reflect on data, use data to inform program design, and learn from program design efforts” (Halverson et al. 2007, p.162). Ironically, “education, like many other enterprises, actually has more knowledge, tools, and resources than its institutions routinely use well” (Yeager et al. 2013, p.7). Successful data-driven instructional systems that provide a social and technical structure to guide decisions are changing what leadership practice looks like and is also changing what quality teaching looks like.

I continue to struggle with the narrowing scope of what counts as learning in a data-driven system. In the same way that Western medicine lacks a holistic approach makes me wary to follow in the path of how healthcare has standardized practice through outcomes data. (Morris & Hiebert, 2011) If every action has a practical measurement, where is the latitude for creativity or spontaneity? In a system where teachers or leaders have to justify every action, will we ask kids to justify or measure their every action? If everything is measured, when do we have time to fail without consequences (i.e. learn)? Furthermore, is it sort of like telling schools, “you can go out and play once you’ve finished your homework?” Schools in suburban and/or affluent districts, who will not have to focus as much on test scores, will continue to provide project-based learning or art classes, while low-performing schools will narrow to spending all their time on skills. I am firmly in the camp that kids need the creative, idea-generating, safe-for-failure, engaging experiences first and skill development second. Kids should play and explore and then their learning will have meaning. How do data-driven instructional systems work with inquiry-based learning? How do we design data systems to treat data as an indicator of a moment in time and not a permanent property of a learner? How do we see data as descriptive and not constituitive? My guess is this has a lot to do with remembering your unit of analysis…

Additional References:

Yeager, D., Bryk, A. S., Muhich, J., Hausman, H., & Morales, L. (2013). Practical Measurement. Working Paper published at the Carnegie Foundation.

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