When asked publicly or privately about high stakes assessments for teachers and schools, we always say the same thing: don’t go there. Using value-added models based of student test scores to reward or punish teachers misdiagnoses educator motivation, guides educators away from good assessment practices, and unnecessarily exposes them to technical and human testing uncertainties. Now, to be clear, we do use and value standardized tests in our work. But here’s a 10,000-foot view of why we advise against the high stakes use of value-added models in educator assessments:
- Using value-added scores to determine teacher pay misdiagnoses teacher motivation.
When Wayne Craig, then Regional Director of the Department of Education and Early Childhood Development for Northern Melbourne, Australia, sought to drive school improvement in his historically underperforming district, he focused on building teachers’ intrinsic motivation rather than the use of external carrots and sticks. His framework for Curiosity and Powerful Learning presented a matrix of theories of action that connect teacher actions to learning outcomes. Data informs the research that frames core practices, which then drive teacher inquiry and adoption. The entire enterprise is built on unlocking teacher motivation and teachers’ desire to meet the needs of their students. (more…)
Continue readingAs educators, we talk about data, collect data, wade through data, analyze data, and draw conclusions from data that hopefully demonstrate how and why our interventions led to the achievement of our goals. But sometimes there seems to be so much data, so many things we could measure, that it’s difficult to know where to start.
Burying one’s head in the sand – i.e., not planning for the appropriate collection and use of data to drive decision-making – is clearly not the answer. But where to begin? In a guest blog post for ASCD, 30-year educator, administrator and author Craig Mertler shared his top five ways to achieve strategic data use in planning and decision-making. We’ve adapted them here:
1. Find your focus. Planning starts with identification. Mertler suggests zeroing in on a specific “problem of practice” that you want to improve or otherwise address and using that to brainstorm about the types of data you may wish to collect. (more…)
Continue readingOur past two posts covered both the “why” of measuring implementation and some of the common challenges to doing so. In this third and final post, we’ll look at what is most useful to measure.
Implementation measures are particular to each program and should take into account the specific actions expected of program participants: who is doing what, when, where, how often, etc. Participants may be teachers, students, administrators, parents, advocates, tutors, recruiters, or institutions (e.g., regional centers, schools, community organizations). Specific measures should help stakeholders understand whether, how, and with what intensity a program is being put into place. Moreover, for programs with multiple sites or regions, understanding differences among them is critical.
In our last post, we shared four reasons why educators should be measuring implementation: here we’ll look at four common challenges to strong implementation measurement.
1. Differential definitions. What happens when different units of your program operate with different working definitions of a measure?
Take tutoring, for example, in a multi-site program, where each site is asked to report the number of hours per week a participant is tutored. Site A takes attendance and acknowledges that, although the after school program runs for 1.5 hours, only .5 hours are spent tutoring. So Site A reports the number of days a student attends, multiplied by .5: e.g., if Jose attends for 3 days, Site A reports 1.5 hours of tutoring. Site B calculates 1.5 hours of tutoring per day times 5 days per week, per participant: So if Jose is a participant that week, regardless of how often he attends, Site B reports 7.5 hours of tutoring. (more…)
Continue readingUnderstanding implementation is critical to both program improvement and program evaluation. But measuring implementation is typically undervalued and often overlooked. This post is one of three in a series that focuses on measuring implementation when evaluating educational programs.
“Fidelity of implementation” ranks next to “scientifically based research” on our list of terms thrown about casually, imprecisely, and often for no other reason than to establish that one is serious about measurement overall. Sometimes there isn’t even a specified program model when the phrase pops up, rendering fidelity impossible. Other times we think all stakeholders are on the same page and so don’t bother to measure implementation at all.
That should change. Here’s why. (more…)
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