SHAREE Conference 2027

Simple and Cheap Arduino-Based Teaching Environment in STEAM

Submitted Abstract

Abstract length (max 200 words, approximately 1700 characters including spaces)

Purpose The study presents an affordable, internally developed open source Arduino-based ICT teaching platform designed to improve hands-on learning in programming and STEAM education. By integrating sensors and peripherals into a fixed, portable environment, the platform simplifies classroom use, reduces teacher intervention, and supports interdisciplinary learning.

Design/methodology The platform was developed iteratively based on feedback from pupils and teachers. It incorporates sensors, LEDs, displays, and environmental monitoring to support practical activities across programming, mathematics, physics, and home economics. AI-assisted programming tools were included to support learning.

Findings The platform was evaluated over two academic years during ICT lessons at the Finnish school of Tallinn, Estonia. Pupils successfully created functional projects, often with AI support, despite limited understanding of programming fundamentals. The platform increased engagement, encouraged creativity, and simplified classroom organization.

Research limitations The study involved a small sample from a single school with strong teacher support, limiting generalizability. Quantitative analysis was constrained, and AI-assisted programming made it difficult to distinguish programming knowledge from AI-supported task completion.

Implications The platform promotes project-based and interdisciplinary learning, supports scientific data collection, and enhances student motivation through interactive feedback, making it a versatile tool for STEAM education.

Keywords STEAM, MICROPROCESSOR, ARDUINO, TINKERING, MAKERSPACE, MEASURING TEMPERATURE

Original

Purpose The purpose of implementing and testing affordable and versatile Arduino-based teaching environments is to enhance educational experiences. This approach addresses specific challenges faced in teaching micro controllers, such as the lack of fixed positions for sensors on the table. The movement of the sensors can disrupt the functionality of the system. The system is designed to minimize the teacher intervention, as essential components are already integrated, making it easier for students to engage with the technology. This approach facilitates straightforward storage and encourages creativity, as the readily available peripherals intrigue students and spark their imagination. The multi oriented nature of this system allows educators to incorporate it into a wide array of subjects, including mathematics, programming, physics, or arts. By providing a hands-on learning experience, the environment fosters critical thinking and problem-solving skills.

Design/methodology The design of the platform is informed by ideas and suggestions provided by pupils, including questions such as how a machine can determine whether it is moving, how to use visual indicators (such as LED strips, or OLED screens), sound, or text-based feedback, and whether it can sense environmental conditions such as temperature or air quality. These features can be readily incorporated into subjects such as Home Economics, Physics, or Mathematics, providing practical and interdisciplinary learning opportunities. The study design was developed based on feedback from both students and teachers, and is still under continuous development. Although certain challenges remain, pupils have demonstrated the ability to produce exceptionally high-quality outcomes with the support of AI-assisted tools. Nonetheless, the resources for teachers are being updated and will include both text-based and video content.

Findings The system was evaluated over a two-year period during ICT lessons in a school setting at the Finnish school of Tallinn, Estonia. The learning activities during the classes were designed to introduce fundamental programming concepts, including loops, conditionals, and variables. The findings indicate that, with the support of AI-assisted tools, pupils are often able to create the desired program behavior even without first mastering these underlying programming principles. While this may reduce the emphasis on traditional coding skills, it can support learners who might otherwise struggle with programming and can accelerate the development of new ideas and creative solutions. In addition, the platform contributes to more efficient classroom management by simplifying the organization, monitoring, and implementation of learning activities.

Research limitations Some limitations should be considered when interpreting the findings. The sample size is relatively small and limited to a single school, reducing the generalizability of the results. In addition, participants were predominantly interested in engineering and computing, which may not reflect the wider student population. The study was conducted in a small-school environment where teacher provides substantial individual support. Such conditions may differ from those in larger educational settings. Furthermore, the platform currently supports a limited range of peripherals and sensors, selected primarily based on student interest. Quantitative analysis was restricted by the small sample size and the exploratory nature of the research. The AI-assisted programming tools also makes it difficult to separate programming knowledge from AI-supported task completion. Although the study spanned two academic years, the research and development remains ongoing.

Implications The findings suggest several implications for educational practice. First, hands-on and project-based activities appear to increase pupil engagement and interest in STEAM-related subjects, including electronics, programming, and engineering. The platform enables learners to explore real-world phenomena, such as gradual changes in temperature and other environmental variables, supporting the development of scientific reasoning and data literacy. In addition, the use of LED lighting and other interactive components provides immediate visual feedback, which can enhance motivation and understanding of computational concepts. The integration of sensors and measurement tools also allows the platform to function as a scientific data-collection system, creating opportunities for interdisciplinary learning across subjects such as physics, technology, and home economics. These features may help connect abstract concepts to practical applications and encourage creative experimentation.

Keywords STEAM, MICROPROCESSOR, ARDUINO, TINKERING, MAKERSPACE, MEASURING TEMPERATURE

The paper

Pedagogical Foundations and the Historical Development of STEAM, Makerspaces, and Open Hardware in Education

The integration of science, technology, engineering, arts, and mathematics (STEAM) into education reflects a broader shift toward interdisciplinary, inquiry-based, and project-oriented learning. Rooted in constructivist and constructionist educational theories, STEAM emphasizes active knowledge creation through experimentation, collaboration, and creative problem-solving rather than passive acquisition of information. The inclusion of makerspaces, open-source electronics platforms such as Arduino, programmable robotics (including LEGO systems), and community-driven "hacker" initiatives has significantly expanded opportunities for authentic learning in school subjects ranging from physics and chemistry to home economics and culinary education.

The educational philosophy underlying modern STEAM can be traced to the work of John Dewey, who argued that meaningful learning emerges through experience and reflection. Later, Seymour Papert extended these ideas through constructionism, proposing that learners develop deeper understanding by creating tangible artifacts that are personally meaningful. These theoretical perspectives have become central to contemporary makerspaces, where students design, prototype, test, and refine physical and digital projects while integrating scientific reasoning with engineering design (Papert, 1980).

The term STEM was popularized during the early 2000s to strengthen education in science and engineering, particularly in response to workforce demands and technological innovation. Subsequently, the addition of the Arts transformed STEM into STEAM, recognizing that creativity, design thinking, communication, and aesthetics are essential components of innovation (Yakman, 2008). Today, STEAM education seeks not only to develop disciplinary knowledge but also to foster critical thinking, collaboration, creativity, and digital literacy—competencies frequently described as essential twenty-first-century skills (Henriksen, 2017).

A significant practical manifestation of STEAM pedagogy is the makerspace. Makerspaces are collaborative learning environments equipped with digital fabrication tools, electronics, programmable devices, and traditional craft materials that encourage experimentation and iterative design. Rather than following predetermined laboratory procedures, learners formulate problems, construct prototypes, evaluate outcomes, and improve their solutions. Research demonstrates that makerspaces promote learner autonomy, motivation, and interdisciplinary understanding while supporting inclusive participation across diverse learner groups (Sheridan et al., 2014; Martin, 2015).

Among the most influential educational technologies within makerspaces is the Arduino platform. Introduced in 2005 as an open-source microcontroller ecosystem, Arduino was developed to make embedded electronics and physical computing accessible to artists, designers, educators, and novice programmers. Its combination of low cost, open-source hardware and software, extensive documentation, and a large international user community has led to widespread adoption across engineering, STEM, and interdisciplinary educational settings (El-Abd, 2017, Tselegkaridis, 2024). Its affordability, extensive documentation, and large global community have made it one of the most widely adopted educational platforms for teaching programming, electronics, data acquisition, and automation. Students can construct weather stations, environmental monitoring systems, smart home prototypes, or laboratory instruments using inexpensive sensors that measure temperature, humidity, light intensity, pressure, pH, electrical conductivity, or motion.

Similarly, LEGO educational systems, particularly LEGO Education and LEGO Mindstorms (and their successors), have played a major role in introducing engineering concepts to learners through programmable robotics. These systems enable students to investigate mechanical design, control systems, mathematics, and computational thinking while maintaining a playful and highly motivating learning environment. Numerous studies have shown that robotics activities support problem-solving abilities, teamwork, and conceptual understanding in STEM subjects (Eguchi, 2014).

The growing availability of low-cost sensors and open-source hardware has also encouraged educational practices inspired by the hacker and maker movements. In this context, the term "hacker" refers not to illegal computer activities but to a culture of curiosity, creativity, open knowledge sharing, and technological exploration. Hackerspaces and Fab Labs emphasize learning through experimentation, reverse engineering, collaborative problem-solving, and open-source development. Educational adaptations of these principles encourage students to understand how technologies function internally rather than merely consuming finished products (Blikstein, 2013).

These approaches have proven particularly valuable in school physics and chemistry laboratories. Arduino-based data logging allows students to perform real-time measurements of physical phenomena such as acceleration, force, magnetic fields, electrical circuits, heat transfer, and oscillatory motion. In chemistry education, digital sensors can continuously monitor variables including temperature, pH, dissolved oxygen, conductivity, gas concentration, or reaction kinetics, enabling more precise quantitative investigations than traditional manual methods. Automated data collection also allows greater emphasis on experimental design, data interpretation, uncertainty analysis, and scientific reasoning.

Interestingly, STEAM principles have increasingly been incorporated into home economics education, where cooking provides an authentic interdisciplinary context for scientific inquiry. Food preparation naturally involves concepts from chemistry, physics, biology, mathematics, engineering, and nutrition. Arduino-compatible digital thermometers, load cells for precision weighing, humidity sensors, infrared temperature sensors, and timers enable students to investigate processes such as protein denaturation, starch gelatinization, caramelization, fermentation, heat transfer, energy efficiency, and moisture loss during cooking. Students may compare different heating methods, optimize baking conditions, or collect quantitative data to evaluate recipes scientifically. Such investigations transform cooking from a procedural activity into an evidence-based design challenge that integrates measurement, experimentation, and reflection while simultaneously developing practical life skills.

Overall, the convergence of STEAM pedagogy, makerspaces, open-source electronics, programmable robotics, and hacker-inspired learning communities represents an important evolution in contemporary education. These approaches encourage learners to become active investigators, designers, and creators who connect theoretical knowledge with practical applications across multiple disciplines. By integrating precise digital measurements into physics, chemistry, and home economics, educators can provide authentic learning experiences that strengthen scientific literacy, technological competence, creativity, and lifelong problem-solving skills.

References (with DOI or stable links)

El-Abd, M. (2017). A Review of Embedded Systems Education in the Arduino Age: Lessons Learned and Future Directions. International Journal of Engineering Pedagogy, 7(2), 79–93. https://doi.org/10.3991/ijep.v7i2.6845

Tselegkaridis, S., Sapounidis, T., & Papakostas, D. (2024). Learning Circuits and Coding with Arduino Board in Higher Education Using Tangible and Graphical User Interfaces. Information, 15(5), 245. https://doi.org/10.3390/info15050245

Blikstein, P. (2013). Digital fabrication and "making" in education: The democratization of invention. In J. Walter-Herrmann & C. Büching (Eds.), *FabLabs: Of Machines, Makers and Inventors* (pp. 1–21). Transcript Verlag. https://inventingtolearn.org/wp-content/uploads/2023/10/Blikstein-2013-Digital-fabrication-and-%E2%80%98making-in-education-The-democratization-of-invention5.pdf https://www.researchgate.net/publication/281495128_Digital_Fabrication_and_'Making'_in_Education_The_The_Democratization_of_Invention

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Eguchi, A. (2014). Robotics as a learning tool for educational transformation. In *Proceedings of the 4th International Workshop Teaching Robotics, Teaching with Robotics* (pp. 27–34). https://www.terecop.eu/TRTWR-RIE2014/files/00_WFr1/00_WFr1_04.pdf https://doi.org/10.1007/978-3-319-16369-7_3

Henriksen, D. (2017). Creating STEAM with design thinking: Beyond STEM and arts integration. *The STEAM Journal, 3*(1), Article 11. Not openly available. https://doi.org/10.5642/steam.20170301.11

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Papert, S. (1980). *Mindstorms: Children, Computers, and Powerful Ideas*. Basic Books. https://mitpress.mit.edu/9780465046744/mindstorms/

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