Monitoring Environmental Temperature

Project Report

Monitoring Environmental Temperature

Report by:

Aninakwah Vera Yeboah

December 1, 2025

Citation

Quansah, S., Asante, V., Aninakwah, V. Y., Debrah, A. B., & Nsiah, N. A. A. (2023)

Abstract

This project engaged 68 learners (ages 7–15) in exploring environmental sensing by using the BBC micro:bit’s built-in temperature sensor, a practical example of IoT technologies in STEM education (Mersinllari & Papajorgji, 2022). Learners programmed their micro:bits to capture and display temperature readings, then collected and compared data across different locations, a hands-on approach shown to deepen scientific understanding through field-based inquiry (Knowles et al., 2018; BBC Teach, n.d.).

This experience supported digital literacy through coding and sensor use, scientific inquiry through real-world data collection and comparison, and environmental awareness by connecting learners’ observations to broader patterns. It aligns with SDG 4: Quality Education and SDG 13: Climate Action by combining access to applied learning with a focus on environmental monitoring.

1.0 Introduction

Sensors are essential to modern technology, allowing devices to collect data from their surroundings to support decision-making and automation (Evans, 2011). Among them, temperature sensors have wide-ranging applications, from weather forecasting and precision agriculture to health monitoring and smart home systems.

In this project, learners explored the principles of sensors and temperature measurement by programming the BBC micro:bit to detect and display environmental temperatures. They conducted field-based measurements across different locations, gaining technical skills in coding and hardware use alongside scientific skills in observation, data collection, and analysis.

Evidence from research on inquiry-based STEM education (Hmelo-Silver et al., 2007) shows that such hands-on, real-world activities deepen conceptual understanding, strengthen critical thinking, and make learning more relevant and engaging for young learners.

2.0 Method

2.1 Learners

Total learners: 68
Gender representation: 34 males (50%), 34 females (50%)
Age range: 7–15 years
Learners aged 7–9: Used block-based coding
Learners aged 10–15: Used Python programming

2.2 Mode of Delivery

Virtual Live Sessions: Introduction to sensors, temperature, and how temperature sensing is used in technology.
In-Person Sessions: Programming the BBC micro:bit to detect and display temperature, moving to different locations to record readings, and discussing real-world applications.

3.0 Results

3.1 Learning Outcomes

Understood the concept of sensors and their applications.
Learned about temperature as a measurable physical property.
Programmed the BBC micro:bit to read and display temperature data.
Collected and compared environmental data from multiple locations.
Reflected on the use of temperature sensing in real-world devices.

3.2 Project Activities

Introduction to sensors and their functions.
Learning how temperature sensors work.
Writing code to read temperature values from the micro:bit.
Moving around different areas to measure temperature variations.

3.3 Materials Used

BBC micro:bit
Computers with MakeCode and Python editors
Data recording sheets

3.4 Project Code Repository

Learners utilized the BBC micro:bit and Microsoft MakeCode to develop programs that enabled the micro:bit to function as an environmental temperature sensor, incorporating the event handling concept.

Below are sample codes created and tested by the learners:

4.0 Discussion

Real-World Relevance (Situated Learning Theory – Lave & Wenger, 1991):
Learners connected the micro:bit temperature sensor to familiar devices such as mobile phones, refrigerators, laptops, and wearable health monitors, anchoring abstract concepts in authentic, everyday contexts.
Active Meaning-Making (Constructivism – Piaget, 1972; Vygotsky, 1978):
Programming the micro:bit, collecting temperature data, and interpreting results enabled learners to integrate new knowledge with prior experiences through hands-on engagement.
Inquiry and Scientific Thinking (Hmelo-Silver et al., 2007):
Learners formulated questions, compared temperature readings across environments, and engaged in evidence-based reasoning, reinforcing data literacy and critical thinking.
Problem-Solving Skills (Polya, 1945; Jonassen, 2000):
Learners applied computational thinking and iterative testing to troubleshoot coding errors, refine data collection methods, and design solutions that could address temperature-related challenges in their local communities.
Interdisciplinary Skill Development (STEM–STEAM Integration – Yakman, 2008; Beers, 2011): The project fostered programming skills, scientific measurement, and data analysis while encouraging learners to make cross-disciplinary connections between technology, science, and environmental studies.

5.0 Feedback and Reflections

Learners expressed excitement at creating a temperature-sensing device comparable to those embedded in laptops, mobile phones, and other smart technologies, fostering a sense of technological agency and relevance.

From facilitators observation, taking learners outside the classroom to measure temperature in different environments significantly increased engagement, demonstrating the value of hands-on, experiential learning in sustaining curiosity and participation.

6.0 Challenges Faced

Unstable internet connectivity adversely affected the flow of the sessions.

Limited access to computers required some learners to wait for their peers before coding on the micro:bit, resulting in session delays and a disrupted flow.

7.0 Conclusion

The Monitoring Environmental Temperature project provided a structured, inquiry-driven context for learners to engage with core concepts in environmental sensing and computational thinking. Drawing on principles of constructivist learning (Piaget, 1972) and experiential learning (Kolb, 1984), the activity positioned learners as active investigators, enabling them to generate, interpret, and apply data within authentic contexts.

Creating programming tasks within a meaningful real-world problem, monitoring and interpreting temperature variation, the project fostered situated cognition (Brown, Collins, & Duguid, 1989), where knowledge acquisition was tightly coupled with its application. Learners developed transferable skills in measurement, data analysis, and problem-solving while cultivating environmental awareness aligned with SDG 4 (Quality Education) and SDG 13 (Climate Action).

8.0 Contributors

Sam Quansah – Principal Investigator & Curriculum Designer & Curriculum Designer
Vera Yeboah Aninakwah – Lead Facilitator & Code Developer
Nana Adwoa Nsiah – Instructional Facilitator
Victor Ofori Asante – Instructional Facilitator

9.0 References

Evans, D. (2011). The Internet of Things: How the Next Evolution of the Internet Is Changing Everything. Cisco.
Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark. Educational Psychologist, 42(2), 99–107.
BBC micro:bit. (n.d.). Temperature sensor. Retrieved from https://microbit.org
United Nations (2015). Sustainable Development Goals – Goal 4: Quality Education; Goal 13: Climate Action.

Project Report