Authors:
Zyric N. David, Jireh Grace P. Dela Peña, Gerald P. Mayor, Cindy Dianne Sanchez, Shella D. Delgado
Addresses:
Department of Education, University of Antique-Main Campus, Sibalom, Antique, Philippines.
Abstract:
Scientific inquiry, critical thinking, and real-world problem-solving require science process skills (SPS), including seeing, measuring, classifying, inferring, communicating, and hypothesising. This descriptive quantitative study used an adopted research instrument to analyse senior high school STEM learners' SPS acquisition levels at a state institution in 2024–2025. Mean, t-tests, ANOVA, and regression analyses were used. Informed consent, confidentiality, and institutional research protocols ensured ethical conduct. Learners demonstrated high SPS, with talking, classifying, and observing being the most developed areas of skill. Women were slightly better at communicating and hypothesising than men. High-income students hypothesised well, while low-income students observed and communicated. Hypothesis was the weakest skill. Parental education and family income affected SPS acquisition. Higher education and affluence increased observational abilities and hypothesising due to better resources. The findings confirm Kolb's Experiential Learning Theory that hands-on, inquiry-based, and reflective learning improve scientific thinking and problem-solving. Reduce social disparity via SMART science. This method emphasizes hands-on, constructivist modules on observing, measuring, inferring, and hypothesising with teacher training, school-wide efforts, and enough resources. Inclusive teaching and performance-standards-aligned content are needed to close gaps and study SPS acquisition's long. This study emphasizes the necessity for fair STEM education to teach students scientific skills regardless of socioeconomic background.
Keywords: Science Education; Science Process Skills (SPS); SPS Acquisition Level; SMART Science; STEM Learners; Socioeconomic Background; Socioeconomic Inequality; Parental Education; Family Income.
Received: 07/06/2024, Revised: 26/08/2024, Accepted: 16/10/2024, Published: 03/06/2025
DOI: 10.64091/ATITL.2025.000130
AVE Trends in Intelligent Techno Learning, 2025 Vol. 2 No. 1 , Pages: 43-54