Module 4 Field Activity: Electronics, Programming, IoT and Multidisciplinary Learning¶
Assignments¶
- Choose a collaborator and content
- Set learning objectives.
- Develop and test the electronics / programming part of the lesson
- Develop the Assessment protocol for the lesson plan
- Develop the lesson plan using the SCOPES template
- Test the lesson plan to receive feedback.
- Reflection Questions.
- Post your lesson to the SCOPES website.
1. Choose a collaborator and content¶
For this lesson plan, I have chosen to collaborate with Kris, a colleague from the lab. Although we both work in the same lab and share a common professional environment, we bring different perspectives and experiences to the project. My approach is more focused on the educational design and implementation of the activity, while Kris contributes extensive experience in fab labs and in a wide variety of projects involving both digital fabrication and traditional making skills. His contribution to this lesson plan will be to support the design and construction of the physical kit needed for the activity, suggesting practical ways to build its different parts.
Lesson idea¶
For this Field Activity, I will develop an interdisciplinary lesson based on the subjects Technology and Civic Values and Ethics from the Spanish curriculum established in Royal Decree 217/2022, of 29 March, establishing the organisation and minimum curriculum of Compulsory Secondary Education.
In this lesson, students will design and build a smart recycling system. They will create a prototype of a smart bin that uses a servo motor to open automatically when the correct type of waste item is presented. To do this, students will train a machine learning model to recognise different categories of waste, such as plastic, cardboard, or glass. They will then connect this model to a physical computing system controlled by a Micro:bit using tools such as Teachable Machine and Make:AI Robots.
This lesson integrates technological design, programming, electronics, and artificial intelligence with ethical reflection on waste prevention, sustainability, and responsible resource management.
Content from both disciplines¶
- Technology¶
Products and materials¶
- Strategies for selecting materials based on their properties and design requirements.
Computational thinking, automation and robotics¶
- Components of programmed control systems: controllers, sensors, and actuators.
- The computer and mobile devices as programming and control tools.
- Introduction to artificial intelligence and big data: applications.
- Civic Values and Ethics¶
- Sustainable lifestyles: waste prevention and the sustainable management of resources.
- The Sustainable Development Goals (SDG 2030), with special emphasis on those related to climate sustainability.
2. Set learning objectives.¶
- Students will build, and program an automatic prototype that responds autonomously to a design brief.
- Students will train and apply a basic AI model and integrate it into a physical computing system.
- Students will explain how waste prevention and correct recycling contribute to sustainable resource management.
- Students will identify and explain how their project contributes to at least one Sustainable Development Goal, especially those related to responsible consumption and climate sustainability.
3. Develop and test the electronics / programming part of the lesson¶
It is time to test the idea for the project: to design and build a recycling system that uses machine-learning models to ensure that only the correct items are placed in the corresponding recycling bin.
To build and test the prototype, I am using the following resources:
- Micro:bit
- Nezha expansion board
- Servo motor
- Bin or container with a hinged lid
- Items representing different types of recyclable materials to test the smart system, such as glass, cardboard, plastics, organic waste, and electronics
- Laptop
For this first prototype, I created a bin using Lego blocks. I also made three different objects: a bottle to represent glass, a brown block to represent cardboard, and a yellow block to represent plastic containers.
Software used¶
MakeCode (https://makecode.microbit.org/)¶
This is used to program the micro:bit. I used the Nezha extension in order to control the servo motor.
Teachable Machine (https://teachablemachine.withgoogle.com/)¶
This is a Google tool for creating simple machine-learning models in the browser. You collect examples for different classes, train the model, test it, and then export it. It supports image, audio, and pose classification.
MAKE:AI ROBOTS (https://makeairobots.com/)¶
This site allows the micro:bit to use the computer’s webcam to recognise images. It can recognise facial expressions, poses, and objects. First, the starting code is created for the micro:bit, and then the image-recognition model is built using Teachable Machine. MAKE: AI ROBOTS makes it possible to link the Teachable Machine model with the micro:bit.
MakeCode Program¶
The MakeCode program works by receiving the classification result sent from the laptop to the micro:bit through the USB serial connection.
At the start, the code uses “serial redirect to USB” so that the micro:bit can listen to the label coming from MAKE: AI ROBOTS.
The main part of the code is the block “on data received new line”. Each time the AI model recognises an object, the text label is sent to the micro:bit and stored in the variable SerialData. The program then checks that label using a series of if / else if conditions:
- If the label is “Glass”, the micro:bit displays Glass.
- If the label is “Cardboard”, it displays card/paper.
- If the label is “Plastics”, this is treated as the correct item for the bin, so the micro:bit:
- displays Plastics
- plays a melody
- shows an icon
- moves the servo on S1 to 90° to open the lid
- waits 5 seconds
- moves the servo back to 0° to close the lid
- If the label is “no_item”, it only shows an icon, meaning that no object is being detected.
The A and B buttons on the micro:bit are used to open and close the lid manually, independently of the learning model.
Training the Learning Model¶
Teachable Machine is designed to let you build simple machine-learning (ML) models in the browser without coding.
I created four classes. A class is each category I want the model to recognise, such as Plastics, Cardboard, Glass, and no_item.
I then added examples for each class using the webcam. The model learns from the examples that are provided.
After collecting the samples, I clicked Train Model. Teachable Machine splits the samples into training and test sets, and it also allows the user to adjust advanced settings such as epochs, batch size, and learning rate if needed. For most beginner projects, the default settings are enough to get started.
Once the training was completed, I tested the model and it identified each item accurately in my test setup.
If predictions are weak or inconsistent, it is necessary to improve the model by collecting better examples. It is important to use similar lighting to the final setup, include different angles and distances, and make sure the background is not influencing the model too much.
Teachable Machine makes it clear that the model is learning from examples rather than understanding the object in a human way, so the quality of the samples is very important.
Linking the Learning Model to the micro:bit¶
Within Teachable Machine, I exported the model to the cloud. A shareable link was then generated, which I copied.
Next, I opened MAKE: AI ROBOTS and selected the option: “3. Connect your micro:bit to your AI!”
I then selected the correct serial port.
In the next window, I was asked to paste the Google Teachable Machine model link and select the camera and audio sources.
The following window then displayed the image-recognition model together with the live camera view.
Now the learning model and the micro:bit are linked so, when we show the correct item (in this case the yellow block , plastics) the lid of the bin show open.
The following video shows the prototype and learning model working.
So far, this prototype works well, but for the lesson I would like to further develop the hardware and explore whether it can work with mini servos, which are very popular and more widely available than the one included in the Nezha kit.
We considered giving students the opportunity to build their own bins, allowing them to choose from a range of materials such as Lego bricks, cardboard, reused containers, plywood, or similar materials.
After building a variety of containers with different materials to replicate recycling bins, we concluded that this would be a suitable option for a longer project developed over several sessions, in which learners could sketch design ideas, select and justify materials, and build their own prototypes.
However, as we do not usually have the opportunity to work with groups of learners over extended periods, we decided to develop a kit incorporating a mini servo that can be assembled in just a few minutes and reused for other lessons or STEAM workshops.
To build the kit, my colleague designed a press fit box that includes a support for a mini servo.
Based on this design, I created an exploded view that students could use to assemble the kit and to set the initial position of the servo arm (lid closed) and its final position (lid open).
4. Develop the Assessment protocol for the lesson plan¶
When assessment happens¶
| Stage | What is assessed | Who assesses |
|---|---|---|
| Assembly and setup | Building the bin and connecting components correctly | Teacher |
| Programming and testing | Manual button control, AI integration, and system response | Teacher |
| Final demonstration | Prototype performance and explanation | Teacher + Peers |
| Final reflection | Recycling, sustainability, and SDG understanding | Self + Teacher |
| Criterion | Learning Objective | 4 - Excellent | 3 - Good | 2 - Developing | 1 - Beginning |
|---|---|---|---|---|---|
| Assembly and construction of the smart bin | LO1 | Assembles the bin correctly and independently. Connects the electronics accurately and safely. The prototype is stable and ready to use. | Assembles the bin and connects most components correctly, with minor mistakes or some teacher support. | Completes only part of the assembly or needs significant help to connect components correctly. | Is unable to assemble the bin or connect the electronics so the system can work. |
| Programming and autonomous prototype behavior | LO1 | Programs the prototype so it works correctly in manual and automatic mode. The lid responds reliably to the intended actions. | Programs the prototype so it mostly works, although there may be minor errors or some support needed. | Produces partial code, but the system does not respond consistently or only one part works. | Cannot program the prototype to perform the required actions. |
| Training and integration of the AI model | LO2 | Trains an effective model, tests it, and integrates it successfully with the physical system. The bin opens only when the correct item is recognized. | Trains and integrates the model successfully, though performance is not always consistent. | Trains a model with limited success or incomplete integration with the prototype. | Is unable to train the model effectively or connect it to the prototype. |
| Understanding of recycling, sustainability, and SDGs | LO3, LO4 | Clearly explains how correct recycling and waste prevention support sustainable resource management, and accurately connects the project to one or more SDGs. | Explains recycling and sustainability clearly and identifies a relevant SDG with a reasonable explanation. | Shows partial understanding of recycling or SDGs, but the explanation is basic or unclear. | Shows very limited understanding of recycling, sustainability, or the SDG connection. |
5. Develop the lesson plan using the SCOPES template¶
6. Test the lesson plan with learners.¶
7. Reflection Questions¶
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1. Collaboration: Reflect on how you worked with colleagues or FLA participants during the Field Activity. At what stages of development and testing did the collaborator contribute? Please be detailed in your description. How did your collaborator’s perspective change the way you developed the lesson?
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2. Instructional Challenges: What challenges did you encounter while teaching this lesson? How did you address or plan to address them? How are diverse learners’ needs being met in the lesson plan facilitation?
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3. Integrating Disciplines: Where does your lesson plan fall on the continuum and why? How might you move the lesson plan along the continuum to the next level?
- Multidisciplinary
- Interdisciplinary
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Transdisciplinary
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4. AI Usage: If you used AI, describe how it was used and in which steps of the Field Activity.
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5. Reflect on the course in general:
- How has your teaching changed as a result of this course?
- What are some concepts that you would like to learn more about?
- How can you support other teachers in your practice to use digital fabrication with their students?