As I complete EDU 772: Introduction to Robotics and Microcontrollers, I find myself reflecting not just on what I learned, but on how much my view of teaching technology has expanded. This course moved robotics from something that felt “technical” into something that feels deeply pedagogical. It helped me see robotics not simply as coding or hardware, but as a framework for thinking, designing, problem solving, and learning.
The Most Important Things I Learned
Robotics Is About Thinking, Not Just Technology
One of the most important lessons I learned is that robotics education is not primarily about the robot. It is about computational thinking and systems thinking. Whether working with the Raspberry Pi, writing Python code to control LEDs, or programming the Edison robot to respond to sensors, the deeper learning came from understanding how software interacts with hardware and how inputs lead to outputs.
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Students are not just “making things move.” They are learning cause and effect, logic, sequencing, debugging, iteration, and persistence. Robotics forces students to think algorithmically. When something does not work, they must test assumptions, revise code, and try again. That mindset mirrors mathematical problem solving and scientific reasoning.
As a high school math teacher, this realization is significant. I now see robotics as a bridge between abstract mathematical thinking and tangible real-world application. For example, when students program a robot to turn a certain number of degrees, they are applying proportional reasoning and understanding measurement in a physical context. In the future, I plan to intentionally connect robotics tasks to algebraic reasoning, coordinate geometry, and logical structures that my students already encounter in class.
The Power of Sensors and Real-World Interaction
Another key learning experience was understanding how sensors make robotics meaningful. Programming movement alone is procedural. Programming response to sensor input makes the robot interactive and intelligent.
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| Image Source: https://woz-u.com/blog/the-evolution-of-coding-what-programming-languages-are-prominent-today/ |
Working with light sensors, obstacle detection using infrared sensors, line tracking, and clap sensors showed me how robots “perceive” their environment. The robot is not thinking in a human way. It is continuously reading data, comparing it to conditions, and responding accordingly. That simple model of input → processing → output is powerful.
In my future work, I want students to understand that this same model exists in nearly all modern technology, from automated cars to smart home systems. Robotics gives students a concrete way to understand automation, artificial intelligence, and microcontroller systems that shape the world around them. This is especially important as students increasingly interact with AI systems without understanding how they function.
Ideas I Can Implement in a Robotics Course or Club
Project-Based, Scenario-Driven Challenges
One of the most impactful ideas from this course is designing robotics lessons around real-world scenarios rather than isolated skills. For example, instead of simply teaching obstacle detection, students can complete a challenge such as designing an autonomous delivery robot that must navigate around barriers and reach a destination safely.
This approach increases engagement and purpose. Students are not just writing code to satisfy a checklist. They are solving a problem. They must test, revise, and iterate.
I have already begun using this mindset in my classroom. Even in algebra, I design tasks that feel like challenges rather than worksheets. In a robotics club or future robotics course, I would structure the curriculum around increasingly complex missions. Early challenges might involve basic movement and sensor use. Later challenges could integrate multiple sensors, decision trees, and optimization strategies.
This aligns with my broader teaching philosophy that students learn best when they create, test, fail safely, and revise.
Facilitator Over Lecturer
Another idea that deeply influenced me is the facilitator model of teaching robotics. Robotics classrooms can look messy. Students are testing, troubleshooting, collaborating, and sometimes failing publicly. But that is where learning happens.
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This course reinforced the idea that the teacher does not need to be the constant knowledge provider. Instead, the teacher can design structured challenges, provide scaffolds, and guide reflection. Students learn by doing.
I have already implemented this in small ways through coding activities and technology integration in my math classes. Instead of walking students step by step through every process, I increasingly give them structured exploration tasks. In a robotics club, I would intentionally create a culture where debugging is normalized, collaboration is expected, and iteration is celebrated.
Over time, I would love to see robotics become a space at Benet where students from different disciplines collaborate, math students, coding students, engineering-minded students, all working together to solve authentic problems.
Final Reflection
EDU 772 has strengthened both my technical understanding and my instructional philosophy. I learned how microcontrollers function, how sensors allow environmental interaction, and how software controls hardware. More importantly, I learned that robotics is a vehicle for developing resilience, logical thinking, and creativity.
As I look toward the future, whether that involves building a robotics elective, expanding a robotics club, or integrating microcontrollers into math instruction, I see robotics not as an “extra,” but as a powerful tool for preparing students for a world shaped by automation, AI, and computational systems.
This course did not just teach me how to program a robot. It reshaped how I think about teaching problem solving itself.