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Wednesday, December 7, 2011


The advent of greater and more efficient technology has changed the way in which we interact with data. Let's take a look at what Theodore Levitt said about data in his book, The Marketing Imagination.

"The difference between data and information is that while data are crudely aggregated collections of raw facts, information represents the selective organization and imaginative interpretation of those facts. Information represents the imposition of order, categories, and ideas on the collected data."

We can now easily collect, store, and retrieve incredible amounts of data. Schools are immersed in data. Personal data is acquired upon enrollment. From that point on there is a steady stream flowing from telephone numbers, height and weight, medical reference points, test scores - virtually anything and everything that can possibly be measured. While the continuous improvement in technology places data at our fingertips - it's still data. It's inert and almost useless until and unless it is converted into information that can be strategically applied to make a difference and leverage progress. In other words, schools can suffer from being DRIP: Data Rich, Information Poor.

I recently joined several teachers from Heatly at a regional workshop to learn about taking advantage of the new testing program we have acquired from the Northwest Evaluation Association (NWEA). Our school has already experienced the first of three different assessment periods. The first round of tests serve as a baseline, a platform to use to project and measure progress of each individual.

We were able to review test data and begin converting it into information that can be incorporated into instructional strategies. The figures reveal where each learner is on a spectrum of skills within the curriculum. From that point, we can determine what specific skills the learner is now prepared to engage. The information enables teachers to form temporary learning groups predicated on skills rather than the average of their overall achievement.

Too often when learning groups are formed they are ability grouped based on the grade equivalent measure of a subject. For example, two different fifth grade learners may receive the same achievement level on a test in reading. Let's say that they score a 5.6, or 5th grade sixth month. On that basis they would be perceived as similar learners and organized into a group with other learners of approximately the same score. However, when you examine the separate elements that comprise the overall reading test you might discover that one learner was high in comprehension but low in vocabulary, while the other is just the opposite, low in comprehension and high in vocabulary. On the whole, they register identical scores but in reality they have vastly different needs. Furthermore, such grouping is often static rather than dynamic. That is, once they are assigned a group they remain at that level. So, the one who needs help in vocabulary is in the same group with someone who excels in vocabulary.

Skill grouping is predicated on specific skill deficiencies. Learners are arranged and rearranged according to needs, receiving instruction in common areas of need. Once that skill is mastered they exit the group and become assigned to the next skill in the scope and sequence of the curriculum.

This constant diagnosis, prescription, intervention, and assessment represents a complicated juggling act for the teacher. Coordination of the logistics can be taxing, but opportunities for success abound.

In addition, we also obtain projections for progress that are predicated on the achievement levels and skill attainment of the learners resulting from their tests this fall. The projections point toward where the individual is expected to score when they are administered a similar test (the questions are all different) this coming spring. This metric allows us to set goals and monitor progress toward reaching the goals.

Collecting purposeful data and converting it into meaningful strategies that inform instruction will move us away from the paralysis of DRIP, DRIP, DRIP Data Rich, Information Poor.


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