Assessment Education Perspectives

Making Sense of Assessment Data Results – Three Simple Concepts

Making Sense of Assessment Data Results – Three Simple Concepts

When it comes to assessment data, there is a need to distill the information to observe trends and patterns in learning. In fact, this is right where data literacy and assessment literacy come together. Often, assessment data bogs teachers down when they are trying to connect it back to instructional strategies. Since time is of the essence for most teachers, a deep dive into statistical analysis and psychometrics just isn’t in the cards.

There are several data measurement concepts, however, that teachers can use to get more comfortable interpreting and communicating the data and evidence that larger scale assessments produce. Here are three that can be very helpful:

1. Measurement scale. Generally speaking, content from curriculum standards is assigned a level of difficulty on a measurement scale. Students are then given a scale score that is based on the average difficulty level of the questions that they answer. Understanding where students are on the measurement scale can help teachers understand how well a student understands a body of content covered on a test. They can then use this information (for example) to group students based on instructional needs.

2. Mean and median. The mean and median are both part of Measures of Central Tendency, which help describe the center of a set of data. The mean is a method of communicating the average of a set of numbers, whereas the median is the middle number in a given set. Teachers can use these pieces of data to inform grading and generate summaries of student performance toward learning targets.

3. Standard error of measurement. It’s a known fact that if you administer an assessment to a student on one day, then again on another day, the results will be different – for better or worse. It could be due to the weather, how the student is feeling, or the environment in which you’re giving the test. The variation between these two scores would be called the standard error of measurement. By associating a standard error of measurement to a test result, the teacher can better understand if the student wasn’t giving their best effort or if they truly need more assistance in a given subject.

While there are certainly other data measurement concepts, these three can help teachers make assessment results matter – a big component in our efforts to improve assessment literacy. For more measurement concepts, check out this page on our site, where you can download the terms for reference.

If you have some quick tips on using data measurement, or remembering statistical terminology, share them with us on Facebook or Twitter (@Assess2Learn).