At the intersection of data and evaluation, here is a hypothetical scenario:
A young teacher meets an evaluator for a mid-year meeting.
“85 % of the students are meeting the goal of 50% or better, in fact they just scored an average of 62.5%,” the young teacher says.
“That is impressive,” the evaluator responds noting that the teacher had obviously met his goal. “Perhaps,you could also explain how the data illustrates individual student performance and not just the class average?”
“Well,” says the teacher offering a printout, “according to the (Blank) test, this student went up 741 points, and this student went up….” he continues to read from the spreadsheet, “81points…and this student went up, um, 431 points, and…”
“So,” replies the evaluator, “these points mean what? Grade levels? Stanine? Standard score?”
“I’m not sure,” says the young teacher, looking a bit embarrassed, “I mean, I know my students have improved, they are moving up, and they are now at a 62.5% average, but…” he pauses.
“You don’t know what these points mean,” answers the evaluator, “why not?”
This teacher who tracked an upward trajectory of points was able to illustrate a trend that his students are improving, but the numbers or points his students receive are meaningless without data analysis. What doesn’t he know?
“We just were told to do the test. No one has explained anything…yet,” he admits.
There will need to be time for a great deal of explaining as the new standardized tests, Smarter Balanced Assessments (SBAC) and the Partnership for Assessment of Readiness for College and Careers (PARCC), that measure the Common Core State Standards (CCSS) are implemented over the next few years. These digital tests are part of an educational reform mandate that will require teachers at every grade level to become adept at interpreting data for use in instruction. This interpretation will require dedicated professional development at every grade level.
Understanding how to interpret data from these new standardized tests and others must be part of every teacher’s professional development plan. Understanding a test’s metrics is critical because there exists the possibility of misinterpreting results. For example, the data in the above scenario would appear that one student (+741 points) is making enormous leaps forward while another student (+81) is lagging behind. But suppose how different the data analysis would be if the scale of measuring student performance on this particular test was organized in levels of 500 point increments. In that circumstance, one student’s improvement of +741 may not seem so impressive and a student achieving +431 may be falling short of moving up a level. Or perhaps, the data might reveal that a student’s improvement of 81 points is not minimal, because that student had already maxed out towards the top of the scale. In the drive to improve student performance, all teachers must have a clear understanding of how the results are measured, what skills are tested, and how can this information can be used to drive instruction.
Therefore, professional development must include information on the metrics for how student performance will be measured for each different test. But professional development for data analysis cannot stop at the powerpoint! Data analysis training cannot come “canned,” especially, if the professional development is marketed by a testing company. Too often teachers are given information about testing metrics by those outside the classroom with little opportunity to see how the data can help their practice in their individual classrooms. Professional development must include the conversations and collaborations that allow teachers to share how they could use or do use data in the classroom. Such conversations and collaborations with other teachers will provide opportunities for teachers to review these test results to support or contradict data from other assessments.
Such conversations and collaborations will also allow teachers to revise lessons or units and update curriculum to address weakness exposed by data from a variety of assessments. Interpreting data must be an ongoing collective practice for teachers at every grade level; teacher competency with data will come with familiarity.
In addition, the collection of data should be on a software platform that is accessible and integrated with other school assessment programs. The collection of data must be both transparent in the collection of results and secure in protecting the privacy of each student. The benefit of technology is that digital testing platforms should be able to calculate results in a timely manner in order to free up the time teachers can have to implement changes suggested because of data analysis. Most importantly, teachers should be trained how to use this software platform.
Student data is a critical in evaluating both teacher performance and curriculum effectiveness, and teachers must be trained how to interpret rich pool of data that is coming from new standardized tests. Without the professional development steps detailed above, however, evaluation conversations in the future might sound like the response in the opening scenario:
“We just were told to do the test. No one has explained anything…yet.”
Used Books in Class 