Arduino-based fine particulate matter STEM program: enhancing problem-solving and collaboration in a post-pandemic blended high school setting

After the pandemic, the need for research on teaching and learning methods that promote motivation, engagement, and soft skills in blended learning environments has increased. This study presents an Arduino-based STEM program centered on fine particulate matter measurement to enhance high school students' problem-solving abilities and collaborative thinking. Conducted in a blended learning setting, the program guided learners to build fine particulate matter sensors, collect real air quality data, and discuss potential solutions at personal, community, and national levels. The research utilized a mixed-methods approach, analyzing quantitative pre-and post-surveys (48 items) and qualitative interviews and reflection reports. Quantitative analysis results showed significant improvements in math/science self-efficacy (e.g., "I quickly understand scientific concepts," <i>p</i> = 0.001, Cohen's <i>d</i> = 0.80) and collaboration (e.g., "I actively exchange opinions in math classes," <i>p</i> < 0.05). Qualitative analysis revealed that students felt more confident debugging block-based codes and recognized the social relevance of scientific data. Additionally, many students expressed increased awareness of fine particulate matter and environmental issues and a willingness to address real-life problems using computing tools. These findings highlight the importance of integrating real-world STEM programs into blended learning environments post-pandemic. By harmonizing technical skills (Arduino assembly, data analysis) with soft skill development (communication, teamwork), the program inspired learners to perceive STEM as both practical and interdisciplinary.