Big ah ha's coming from listening to learners

The best part of my job is listening.  I love listening to the thoughts of our scholars when we try new things in the classroom.  As we embarked on the Competency X journey to improve science and engineering practices for our scholars, we started hearing lots of questions.  

• What is a good artifact?
• What do I write?
• Why am I doing this?

These questions caused our science team to tap the brakes and look back at the descriptions of artifacts of learning we wanted from scholars to earn digital badges.  Given the large industry based design team for the badges, we weren’t surprised by the inconsistency in what we wanted from scholars.  Some badges asked for two products and one reflection.  Others wrapped the reflection into the products we wanted to see from scholars.  

The problem with this diversity of artifacts in our system is that kids are constantly guessing what is expected of them from badge to badge.  This is also an equity concern because we need to provide scaffolds so that all scholars can independently access these industry desired badges.  This requires us to build sufficient supports (e.g., visual cues in the classroom and sentence stems) for diverse learners to achieve success.  

To provide some consistency in what we expect as evidence, we took a look at all the artifacts across the badges and inductively coded them for what we wanted scholars to show.

We saw some artifacts that were work samples or performances like the following:

• Create an artistic representation of your experiment, results, and analysis for a lay audience.

          OR

• In 1 minute, you can orally deliver your pitch to a person that understands your work without any notes or visuals.  Your performance is video recorded or observed by an assessor (i.e., teacher or internship mentor).

Others were manuals and how to’s:

• Create a user manual for a laboratory technician on how to use your tool.
  • Describe the technique and how it is commonly used in the laboratory.

Some were reflections on the nature of how the scholar acted as a scientist:

• Reflect on what your data does (or doesn’t) say about you as a scientist or technician.  Write about the results and the reasons why particular data points are not useable and how to amend the procedure in order to improve the results.

          OR

• Reflect on how different representations of data can tell a certain story and why you might represent your data in different ways for different audiences.

The codes that began to emerge from these artifacts included manuals, work samples, tutorials, metacognitive reflections, art, troubleshooting documents, and live performances.  We looked for what was common across these codes and grouped them into 3 categories.  Click on each of the following to view:

• Process work samples
• Product work samples
• Metacognitive reflection  

Each badge now has only 3 artifacts, one from each of these categories.  This allows scholars grow as curators of learning because we have consistent expectations for evidence from badge to badge in our system.  We can also provide scaffolds to scholars that require modifications to assessments, such as the following for a Metacognitive Reflection:

• From this project, I learned ______.
• One skill I could not perform very well but now I can is ______.
• I think I can use this new skill next time I ______.
• Now, I think I want to learn more about _____.

The system is growing and improving as we monitor how it functions in the classroom.  Instructional rounds are helping us identify practices that support scholars with curating artifacts.  Release time for teachers is helping us share stories and insights like the big “ah ha” described in this post.  The most important monitoring function we do is listen to our scholars.  We run focus groups and survey them on what they feel is working and could be improved in Competency X.


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