Project

Food Security Module

Report by:

Aninakwah Vera Yeboah

August 1, 2025

Citation

Quansah, S., Asante, V., Yeboah, V. A., & Nsiah, N. A. A. (2023)

Physical Computing for Smart Food Security Solutions

Abstract

Background

A food security learning module was implemented in Cape Coast, Ghana, engaging 51 children aged 7–15 from 34 communities. The initiative focused on building learners’ skills in coding, physical computing, and creative problem-solving, with an emphasis on addressing agricultural challenges and promoting food security.

Objective

The program aimed to empower learners to apply interdisciplinary STEAM practices—particularly coding and physical computing—to build smart systems that enhance farming processes. These learner-driven innovations were designed to address real-world agricultural challenges and contribute to SDG 2: Zero Hunger.

Methods

Using a constructionist and culturally responsive approach, 19 sessions were delivered through a hybrid format, combining virtual tutorials with in-person workshops. Learners engaged in hands-on projects involving coding, micro:bit programming, electronics, and sustainable agriculture practices.

Results

71.1% of participants reported increased confidence in developing their own innovations. Learners successfully applied STEAM concepts to solve real-world challenges in agriculture, including irrigation, pest detection, and seedling transplantation.

Conclusion

The hybrid, learner-centered model fostered collaboration, agency, and problem-solving. The program demonstrates the transformative potential of equitable STEAM education in enabling young learners to become socially conscious innovators addressing food security in underserved contexts.

1.0 Introduction

The Food Security Module empowered learners aged 7–15 to tackle local agricultural challenges through STEAM, equipping them for participation in the Fourth Industrial Revolution. Grounded in 21st-century skills (Trilling & Fadel, 2009), the initiative emphasized problem-solving, creativity, collaboration, and technological fluency.

The module encouraged learners to develop projects addressing SDGs, particularly SDG 2 (Zero Hunger), through constructionist methods (Papert, 1980; Kafai, 1995). By integrating coding, physical computing, and design thinking, learners built real-world agricultural solutions to issues like water use, plant health, and pest management.

A spiral curriculum (Bruner, 1960) structured learning progressively, introducing core STEAM concepts and allowing learners to revisit and deepen their understanding across project cycles.

2.0 Method

2.1 Participants

51 learners (47.1% male, 52.9% female) from 34 communities in Cape Coast participated in the program.

2.2 Mode of Delivery

To ensure accessibility and equity, the program adopted a hybrid learning model:

Video Tutorials:
Enabled flipped classroom learning for asynchronous engagement.

Virtual Sessions:
Facilitated collaborative, inquiry-driven learning each Friday.

In-Person Workshops:
Provided hands-on access to tools like micro:bits, sensors, and recycled materials on Saturdays.

Universal Design for Learning (CAST, 2018) informed the learning model, ensuring inclusivity for learners with varying backgrounds and abilities.

3.0 Results

3.1 Learning Outcomes

Aligned with Bloom’s Taxonomy (Anderson & Krathwohl, 2001), learners:

Understood food security’s impact on community well-being and sustainability.
Gained awareness of technologies like physical computing and AI for agriculture.
Developed computational thinking skills (Wing, 2006), including abstraction, decomposition, and algorithmic design for creating farming solutions.
Built confidence in designing SDG-aligned prototypes, such as AI pest detection and soil moisture detectors.

3.2 Notable Projects

AI and Machine Learning for Vegetable Classification (SDG 3): Learners developed machine learning models using micro:bits to classify vegetables into health categories, applying technology (AI coding), science (nutritional science), mathematics (data analysis), and engineering (system integration). This improved crop management efficiency, promoting safer and healthier food production for community well-being.

AI and Machine Learning for Pest Detection (SDG 15): Learners built AI models to identify pests affecting tomatoes, lettuce, and cabbages, using technology (machine learning, physical computing), science (plant biology), mathematics (model accuracy), and engineering (system design). This mitigated crop damage, supporting sustainable agriculture and biodiversity preservation.

Seedling Transplantation (SDG 2): Learners gained hands-on expertise in transplanting seedlings into grow bags, applying science (plant growth),and mathematics (spacing calculations). This optimized plant growth and yield, directly enhancing food security in local communities.

Smart Irrigation System (SDGs 6 & 12): Learners engineered automated watering systems with micro:bits, using technology (coding), engineering (system assembly), science (plant water needs), and mathematics (flow calculations). This promoted efficient water use and crop health, supporting sustainable agriculture and water conservation.

Micro:bit Plant Growth Monitor System (SDG 12): Learners built a monitor to measure soil moisture, temperature, and sunlight, using technology (micro:bit ), science (environmental factors), engineering (using sensors and actuators), and mathematics (data analysis). This ensured optimal growth conditions, advancing responsible resource use in agriculture.

Soil Moisture Detector with Micro:bit (SDG 13): Learners designed a soil moisture detector using micro:bits, integrating technology (coding), science (soil chemistry), engineering (sensor construction), and mathematics (data calibration). This optimized irrigation, reducing plant stress and supporting climate-resilient farming.

Sowing Seeds Using Cocopeat and Soil (SDG 15): Learners sowed cabbage, lettuce, and tomato seeds in cocopeat and soil, applying science (plant propagation), engineering (substrate preparation), and arts (community education designs). This enhanced seedling health, supporting biodiversity and ecosystem health.

Circular Economy Practices (SDG 12): Learners used cocopeat and recycled plastic bottles for planting, applying science (growth mediums), engineering (structural design), arts (community engagement), and mathematics (resource calculations). This reduced waste and promoted sustainable agriculture, aligning with circular economy principles.

3.3 Projects Code Repository

This repository features code used by K–12 learners to prototype STEAM-based solutions for local food security challenges using the BBC micro:bit. Learners used Scratch, Microsoft MakeCode for Micro:bit and MicroPython, Google Teachable Machine, to create smart farming systems, and optimize farming practices in low-resource settings.

4.0 Discussion

4.1 Problem-Solving and Innovation

Through design thinking (Brown, 2009), learners developed iterative problem-solving skills, emphasizing empathy, ideation, prototyping, and testing. The creative learning spiral (Resnick, 2017), imagining, creating, playing, sharing, reflecting, enabled innovative solutions to food security challenges, fostering resilience and critical thinking.

4.2 Interdisciplinary Collaboration

Team-based projects integrated electronics, science, arts, and engineering, aligning with connectivist learning theory (Siemens, 2005). Learners collaborated on solutions like AI pest detection, mirroring real-world STEAM applications (Lave & Wenger, 1991). For example, the smart irrigation system combined coding, system design, and plant science, addressing food security holistically.

4.3 Creativity and Critical Thinking

Informed by Vygotsky’s Zone of Proximal Development (1978), learners progressed from scaffolded tasks to self-directed projects. Tinkering with micro:bits and recycled materials fostered intrinsic motivation and agency, key to constructionist pedagogy (Papert, 1980). Creative solutions, like cocopeat-based planting, addressed food security while promoting sustainability.

5.0 Learning Environment and Agency

The program created learning spaces that maximized participation and impact:

Project-Based Learning (PBL): Real-world projects like AI pest detection fostered ownership and systems thinking, aligning with SDG 2.
Makerspaces: Hands-on experience with micro:bits and recycled materials encouraged experimentation and resilience (Martinez & Stager, 2013).
Culturally Responsive Pedagogy: Projects tied to local farming needs (e.g., cocopeat use) made learning relevant and identity-affirming (Gay, 2010).
Hybrid Delivery: Virtual and in-person sessions ensured accessibility, per UDL principles (CAST, 2018).
Mentorship: Facilitators and expert-led sessions (e.g., cocopeat workshops) bridged theory and practice, supporting critical thinking (Vygotsky, 1978).

Fail-Forward Culture: Iterative design cycles built adaptability and confidence despite resource constraints.

6.0 Feedback and Reflections

Post-program feedback revealed that 71.1% of learners felt more confident in independently developing and pitching their own projects. Facilitators observed increased creativity, problem ownership, and collaboration among participants. Many learners began initiating their own ideas beyond the structured sessions.

7.0 Challenges Faced

Technology Access: Limited micro:bits and sensor kits constrained some teams’ ability to complete projects.
Financial Barriers: A few learners discontinued participation due to cost-related challenges, highlighting the need for sustainable support models.

8.0 Conclusion

The Food Security Module demonstrated the transformative impact of constructionist, culturally grounded STEAM education in underserved communities. By combining coding, electronics, and hands-on learning with real-world agricultural issues, learners developed socially relevant innovations aligned with SDGs.

9.0 Learning Module Contributors

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

10.0 References

Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. Longman.
Bruner, J. S. (1960). The process of education. Harvard University Press.
Brown, T. (2009). Change by design: How design thinking creates new alternatives for business and society. Harvard Business Press.
CAST. (2018). Universal Design for Learning Guidelines version 2.2. http://udlguidelines.cast.org
Gay, G. (2010). Culturally responsive teaching: Theory, research, and practice (2nd ed.). Teachers College Press.
Kafai, Y. B. (1995). Minds in play: Computer game design as a context for children’s learning. Lawrence Erlbaum Associates.
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.
Martinez, S. L., & Stager, G. S. (2013). Invent to learn: Making, tinkering, and engineering in the classroom. Constructing Modern Knowledge Press.
Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books.
Resnick, M. (2017). Lifelong Kindergarten: Cultivating creativity through projects, passion, peers, and play. MIT Press.
Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1). http://www.itdl.org/Journal/Jan_05/article01.htm
Trilling, B., & Fadel, C. (2009). 21st century skills: Learning for life in our times. Jossey-Bass.
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215