Data Driven Assumption Making

We’ve been taught to use the available data to make decisions for students, programming, curriculum, and policy. So we collect our numbers, issue our surveys, conduct our focus committee meetings, and establish a belief and draft a plan of action. But when we sit in our offices staring at numbers, looking at charts, and pondering the issues, we first make assumptions about what the data is telling us.

The high school administration will be proposing an alteration to our current grade scale to reflect the normal grade scales in our state and county. Believe it or not, we issue three forms of an “F” (F+, F, F-). Don’t ask why – it isn’t worth explaining. What is important is the bottom tier our scale:

  • D (72-70)
  • F+ (69-65)
  • F (64-50)
  • F- (49-0)

We simply want to make our scale normal. We propose that the D becomes a C-, the F+ a D, and any average below 65 and F. Simple. Accepted. Widely practiced. Some teachers wouldn’t think so, though. Some claim that our new proposed grade scale would decrease the rigor of our grading.

In the spirit of “You Gotta Be Kidding Me!”, I collected the teachers’ grades data using PowerSchool.

I have a task for you. Look at the graph. The graph represents the total grade distribution for our high school for the first marking period. What assumptions about rigor, assessments, or standards can you make from the data? I’ll tell you mine if you tell me yours.

Author: Michael Parent, Ed.D

Father, husband, school administrator in NJ. "Education cures poverty".

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