End-of-Year Data Review and Planning for Teachers

Reviewing your data at the end of the school year is not always fun, but is definitely a MUST! This process was definitely one I had to learn to love. I didn’t love it until I saw the value. With the help of my team and our school’s “data person” (a fellow-teacher who was really good at digging in), I found some really simple ways to make a big impact on my scores. I was sold. 

I know this isn’t exciting, but grab a cup of coffee and stick with me!

 
 

I’m going to give you a somewhat vague overview of things to look for because every school is different, so keep that in mind! 

Grab a printable version of this blog post here.

What you need

Gather the following data from this year:

I put all my averages together from each class, so all 98 students’ scores were accounted for in the average (SpEd, R.Ed, and GT). 

  • Average grade for your class (what’s on report cards)

  • Average score of pre-test

  • Average score of each unit test (identified by standard)

  • Average score of each benchmark test (identified by standard)

To the right is an image of my own personal notes from my pre-test, unit tests, and benchmark data for a particular school year. It does’t have to be pretty to be effective! 

 
 

What to look at

Here are some key points to consider for your review. The key to interpreting data is to look for patterns. Find where things are consistent and where things are not consistent. 

  • Compare pre-test scores to last benchmark scores = growth

  • Compare unit test scores to bencmarh scores = consistency

  • Compare class grades to last benchmark scores = consistency

Ideally, your unit test scores and the accompanying benchmark scores should be close. If not, here are possible culprits to consider: 

  • One standard is pulling down scores in the unit test (students may have mastered it by the time they took the benchmark)

  • One standard is pulling down scores in the benchmark (students may not have reviewed this enough before the benchmark)

  • If the unit test scores are higher than the benchmark scores, then there could be a discrepancy in the types of questions on the two assessments. Do an item analysis on each and compare. 

  • If benchmark scores are higher than unit tests, double-check the rigor of the benchmark. If the rigor is on par, keep note of your review or reteaching because it was successful! 

Ideally, your class average grades and the last benchmark scores should be close. 

This could depend on your school’s grading policy, so keep that in mind.

  • If your class average grades are lower than the benchmark, then…

    • Review your assignment weights and the number of each type in the grade book 

      • Ex: Is there only one test grade in the 9-weeks, and it’s dragging students way down because it’s the only one and weighted heavily?

    • Review your scoring practice (maybe discuss with another teacher to see how their data is)

    • Review the rigor of the benchmark. Does it match the rigor of your class assignments? 

  • If your class average grades are higher than the benchmark, then you’re like me! My school’s grading policies had certain requirements that led to this, and that’s ok! The benchmarks tend to show only academic knowledge, while the class grades show both academic knowledge and work ethic. However, if you don’t think that’s the culprit, here is one thing you can consider: 

    • Review the rigor of your class assignments: Your class assignments might be “easier” than the prompts on the benchmark. There could be more data in tables, charts, or maps on the benchmark than students are used to interpreting during class assignments, etc. 

    • Review the rigor of your benchmark: is the rigor matched to the rigor of the standards? Does it extend beyond the reach of the standards? 

Hopefully, this helps you dig into your data at the end of the school year! Grab this free guide to use as you dive in! 

Printable Version

Grab this printable version of the blog post to have on hand while you dig into your data!

 
 

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