Maximize Your Mining, Part Two: Individualized Analysis and a "Can Do" Culture Are Key to Schools Committed to Sustained Student Achievement
McIntire, Todd, Technology & Learning
In the April issue of Technology & Learning, part one of this article described the first two stages that schools move through as they learn to link data to higher student achievement (www.techlearning.com/story/showArticle.jhtml?articleID=160400818). In stage one, schools make initial efforts to contextualize the many data sources available, but their data analysis is mostly for its own sake. Few of the findings make their way into classroom practice, and the connection of data analysis to student achievement remains tenuous.
As individuals and stakeholder groups develop experience and sophistication in understanding the limitations, context, and implications of various data sources, they move into stage two--using data to improve educational efficiency. In this phase, the school does everything it can to maximize the performance of students who are on the verge of moving to the next level and thereby get as many as possible over the bar. Schools in stage two that are persistent in gathering formative assessment data and adjusting teaching to fill the identified gaps in student performance are able to achieve significant gains in school performance for a few years as they remove the slack in the system and push up the "bubble" students to the next level.
In the long run, however, schools that stay in stage two--dedicated to improving efficiency by focusing on the students at the margin--will run headlong into the ever rising NCLB standards. Data analysis that focuses on improving efficiency works mostly at the edges of the problem, and eventually the school will pull all of the slack out of the system.
Imagine an electronics company that has a strong history in the VCR business. A few years ago, this company noticed that their profits were beginning to slip. To address the problem, they reduced costs by improving the efficiency of VCR production. They modernized their plants, streamlined their efforts, and consequently were able to generate better margins for each VCR sold. For a few quarters, the company's profits rose. Soon, however, the pattern reversed itself and no matter how much effort was made to improve the efficiency of the production process, the company could not meet its profit targets.
The company had responded aggressively to dwindling profits, but its efforts to improve efficiency could only take it so far. The market had changed and the product they produced was no longer in demand, so the company's only hope would be to fundamentally change their work. They would need to develop new products--such as DVD players or DVRs--to meet the needs of the changed market.
Like our fictional electronics company, American schools are operating in a radically changing marketplace. Schools are being required to produce greater numbers of higher performing students. Improving educational efficiency is an absolutely necessary response to these changes, but schools that focus solely on improving efficiency will ultimately stall in their efforts to sustain growth. For schools to reach the ever increasing standards of NCLB, they must do more than push marginal students over the next NCLB threshold. Moreover, to reach a plane of sustained growth and improvement, they must develop transformational ways of working and produce fundamentally different kinds of results.
The move to stage three--analysis for sustained achievement--requires that a school adopt a core belief that all its students can perform to higher standards. While most educators will agree with this concept in principle, the challenge is establishing structures and systems that fundamentally change the practices of the school to support this philosophy. In stage three, schools combine data analysis and organizational reform to create an environment driven by an abiding belief that all students can and will meet the standards. This is a major shift in the way schools operate, and it requires careful collection and analysis of data to sustain its implementation and maintain its progress. …