University of Twente Student Theses


Teaching (Tiny)ML using a tangible educational kit

Siderius, J.C. (2023) Teaching (Tiny)ML using a tangible educational kit.

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Abstract:Tiny Machine Learning is the process of integrating machine learning algorithms into resource-constrained edge devices. The technology is already embedded into applications from smart home speakers to industrial pipeline leak detection and will continue to make a growing impact on businesses, hobbyists and industries alike. Currently, there is little engaging educational material available on Tiny Machine Learning for students. With the help of a tangible educational kit and by project-based learning materials this research aims to fill in this void. In order to achieve engaging and interesting learning experiences, research was conducted to establish effective learning methods as well as gain background knowledge about Tiny Machine Learning. The resulting findings helped shape the design requirements for the realisation of the end product, an educational kit called TinySpark, that teaches Tiny Machine Learning with the help of a custom development board and interactive online platform. The educational kit was evaluated through a user experience test, which was followed by a semi-structured interview. Test participants were enthusiastic about TinySpark and noted that their engagement and interest in the topic had grown. According to some participants, the kit could be easily expanded by adding more modules and project material in the future. Overall, the user experience testing was a success, as participants gained knowledge on complex concepts and could autonomously deploy Tiny Machine Learning models to the development board. In conclusion, educational kits proved very engaging and useful in teaching Tiny Machine Learning to users. The development board and interactive online platform enhanced their comprehension and knowledge. By applying teaching methods like these, it is possible to effectively prepare students for a future filled with Tiny Machine Learning applications.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:53 electrotechnology, 54 computer science, 81 education, teaching
Programme:Creative Technology BSc (50447)
Awards:Creative Technology Graduation Award 2023
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