Arduino-based fine particulate matter STEM program: enhancing problem-solving and collaboration in a post-pandemic...

Area of Science:

• STEM Education
• Environmental Science
• Educational Technology

Background:

• Post-pandemic, there's a growing need for effective blended learning strategies.
• Developing student motivation, engagement, and soft skills is crucial in modern education.
• Integrating real-world problem-solving enhances STEM learning outcomes.

Purpose of the Study:

• To evaluate an Arduino-based STEM program focused on fine particulate matter measurement.
• To enhance high school students' problem-solving abilities and collaborative thinking in a blended learning environment.
• To assess the program's impact on technical skills, self-efficacy, and environmental awareness.

Main Methods:

• A mixed-methods approach combining quantitative surveys and qualitative interviews.
• Students participated in building fine particulate matter sensors and collecting air quality data.
• Pre- and post-surveys (48 items) and reflection reports were analyzed.

Main Results:

• Significant improvements in math/science self-efficacy (p=0.001, d=0.80) and collaboration (p<0.05).
• Students reported increased confidence in coding and debugging.
• Enhanced awareness of air quality issues and a willingness to use computing for solutions.

Conclusions:

• Arduino-based STEM programs effectively boost problem-solving and collaboration in blended learning.
• The program successfully integrated technical skills with soft skill development.
• Real-world STEM projects foster interdisciplinary understanding and environmental consciousness.