by David Hamilton, Director of Programs, EXPLO Elevate.
Recently, a high school robotics teacher asked me to observe her class. When I asked her what kind of feedback she was looking for, she said, “Anything you think could be better.”
On the one hand, a welcome response – she’s open to and actively seeking feedback. On the other, it’s not possible to offer meaningful input on “anything.”
In my last post, I offered how just one piece of data can provide multiple opportunities for improving our teaching, and I stressed how choosing which data to focus on starts by naming a teaching goal. This post is a case study for one piece of data in practice.
I asked the robotics teacher, “What’s one thing you’re working on in your teaching?” She admitted, “I’m worried about how I regulate my enthusiasm for student solutions. I think I get too excited about the solutions that surprise me and not enough about those from less experienced students.”
In classes like robotics, the gaps in starting knowledge can be staggering. You often have students who have never programmed an alarm clock working alongside students who have built an automaton that cooks breakfast. You can’t expect always to be the expert in the room, and it’s easy to find yourself truly amazed by some of your student’s creations.
In my previous post, I stressed the importance of gathering evidence, not opinions, and I shared three types of data to focus on when it comes to classroom observations: counting, scripting, or tracking.
Because the robotics teacher was interested in auditing her comments, we agreed on scripting. There are eight teams of students progressing through a series of challenges in this class, and I would focus on two groups: Boston Dynamics, working on the most advanced challenge, and the Robos, still working on the first challenge. I would script one thing: whatever this instructor said aloud when talking with these two groups.
Here’s what I transcribed in a 15-minute window:
With Boston Dynamics:
- “That’s amazing! How did you think of that?”
- “What a cool idea! I had no clue how you were going to fix that arm.”
- “Then what did you do?”
- “Amazing idea. Really really good.”
- “What?!?! (Said in a tone of surprise). That is such a smart approach.”
With the Robos:
- “Ok, good. What will you try next?”
- “I know it’s frustrating, but you’re doing good work. I believe in you.”
- “I know… what else could you try?”
- “This part was brilliant (gesturing to a wheel mount). I think you’re forgetting one thing about these motors.”
- “Remember, this is hard stuff. You’re programming a robot! Let’s celebrate the fact that it’s driving!”
- “Well, that’s true. It’s not turning to the left, but you have figured out how to turn it right. Let’s look at the code and see if we can find the difference.”
After the class, I handed over my transcription before offering any feedback. After reading through it, here’s what the teacher observed:
- “My comments to Boston Dynamics were all celebratory. It was clear I was amazed (genuinely) by the solutions. (Because I was) And I was very transparent about it.”
- “My comments to the Robos sound more supportive and less celebratory.”
- “However, with the Robos, I was more specific in my praise. I pointed out particular moments of success and hinted at how to solve the problem.”
- “The language I used with Boston Dynamics left little room for growth. Even though this team is still actively engaged, I might also say these same things at the end of the project.”
Her goal is to offer a more balanced approach in tone and words for all student feedback. In her next class, she plans to celebrate the Robos’ moments of discovery with the same exclamations she uses with the Boston Dynamics team. She also wants to be more specific in her reactions to Boston Dynamics. She plans to cheer on their accomplishments while challenging them to go deeper.
When we focus on one piece of data, classroom observation feels collaborative rather than evaluative. It’s also efficient. My observation lasted 15 minutes, and I only wrote down 11 quotations. The straightforward documentation method did not require additional explanation, and the instructor saw evidence supporting her initial suspicion. As a result of one piece of data, she named two changes she will test during her next class.