Introduction

School: 2nd Primary School of Nea Erythraia, Athens, Greece
Teacher: Georgia Lascaris
Class: 6th Grade (11 to 12 years old)
Implementation period: March 2021

Goal: Solving real world problems for Sustainable Development Goal 3 Good Health and Well-being” with the help of technology: coding, Internet of Things, Artificial Intelligence – Machine Learning

Abstract

New technologies as Artificial Intelligence, the Internet of Things and smart mobile devices are already influencing and shaping every aspect of our daily lives and can be a powerful ally to help us with real global problems.
Through the European eTwinning program “Micro Circuits for Mega Solutions”, with the participation of schools from Greece, Croatia, Turkey, Spain and Latvia, the 6rth grade students from the Primary School of Nea Erythraia, Athens, Greece, are introduced to the basic concepts of:
a) coding using BBC Micro: bit (palm-sized computer )
b) The Internet of Things (IoT) 
c) Artificial Intelligence and Machine Learning
d) 17 Sustainable Development Goals

Through fun, collaborative and interdisciplinary activities, they dreamed, designed, coded, and implemented applications to simulate smart devices that could be a solution to real global problems and improve people’s daily lives. By understanding the way those new technologies work, they could realize their possibilities as well as their limits and how to use them for good (Technology for Good).

After schools’ closure because of the Covi19 pandemic, these activities were implemented through synchronous  and asynchronous distance education (Webex, eclass) using online tools such as breakout rooms, micro: bit MakeCode platform, Web2.0 simulators…. 

In March, all our activities were based on Sustainable Development Goal 3: Good Health and Wellness as the Covid19 pandemic reminded us in the harshest way of how valuable good physical and mental health are.

Implementation

1) Artificial Intelligence and Machine Learning for SDG Goal 3:Good Health and Wellness

Students created two Machine Learning models (a subset of Artificial Intelligence) using Google’s Teachable Machine tool and Pictoblox programming software (Scratch-based programming environment) to address two key issues:
a) How can a program understand whether or not someone is wearing a coronavirus mask and accordingly, displays appropriate messages.
b) How can a program check if our posture in front of the computer is correct, especially now that we spend so many hours in front of our screens.

In both cases, a Machine Learning model was:
a) created to gather and group example pictures of the categories the machine will classify (with mask / without mask, good posture / bad posture) using the Teachable Machine from Google
b) trained
c) exported to Pictoblox.
In Pictoblox, students coded a “smart” agent (using the Machine Learning extension) to recognize if people are wearing a mask or if our posture in front of our computer correct.

(due to personal data protection the above two models of machine learning were trained with the photos of the teacher and not of the students, during the synchronous distance learning courses)

2) BBC Micro: bits and sensors

Based on their knowledge of the Internet of Things and the programming of BBC Micro: bit sensors, the students created simulators of smart devices to respond to the following problems:

1) To help stop the spread of coronavirus, it is important that all retail businesses comply with their obligations to enforce physical distancing and maintain good hygiene practices. How can we use the BBC Micro:bit to help manage the number of customers entering a store?.
The Micro:bit panel simulates a digital screen outside the store and shows the number of customers allowed to enter inside. Each time a customer enters the store, by pressing the A button, this number will decrease by 1, and respectively, every time a customer leaves the store, by pressing the B key, this number will increase by 1.

2) The good health of our heart is a prerequisite for a quality life. How can the micro: bit be converted to a cardiographer?
Each time the motion sensor detects motion (heartbeat) a heart will appear in the micro: bit panel and a sharp sound will be heard, otherwise, a straight line will appear.

3) Especially now with the safety measures against the coronavirus, many elderly are forced to live alone, away from their children and relatives in order to be safe.
Most injuries in the elderly are the result of falls.
How can we create a smart device that will send an alert message to a relative if an elderly person falls?
The students used the motion sensor (accelerometer) to detect the elderly person’s movement (falling right, falling left, muzzle falling, falling on their backs) as well as the radio wave sensor for sending the alarm message wirelessly.

Conclusion

The students participated actively and with great interest in all the activities and collaborated online in plenary, through breakout rooms, used the micro:bit classroom tool and Web2.0 collaborative tools.
They were asked to design solutions that did not concern hypothetical situations, but problems of their daily lives and realized how technology can be used for good to respond to the 17 goals of sustainable development. 
They demystified Machine Learning and Artificial Intelligence, acquired a better understanding of how these new technologies work, their capabilities and their limits. Using the micro: bit sensors they realized the way their own mobile devices (smartphone, smartwatch ..)  work:  detect movements and directions, changing in heights..heart rate sensor…

Of course, distance education has many weaknesses and difficulties and the above activities will be applied during face to face learning so that students can apply their solutions in real conditions and not through simulators.