Formation of professional competencies of future biology teachers based on STEM technologies

Abstract

This study developed an integrated pedagogical model incorporating Python programming, Arduino biosensors, and virtual reality (VR) applications aimed at biology teacher training. The 20-week study involved 124 participants (62 in the experimental group (EG) and 62 in the control group (CG)), with baseline competency levels assessed at the outset. Through collaborative projects, participants created technological applications integrated with biological content. Subject knowledge scores in the EG increased by 42.2% (from 22.3 ± 4.1 to 31.7 ± 3.2), compared to a 14.9% increase in the CG (from 22.1 ± 3.9 to 25.4 ± 3.7) (p < 0.001, d = 1.89). The most substantial gains were observed in molecular biology competencies—57.7% in the EG versus 18.2% in the CG. Science, technology, engineering, and mathematics (STEM) integration indicators reached 8.7 ± 1.2 versus 3.2 ± 1.8 (p < 0.001, r = 0.84). Technological skill distribution revealed that 89% of the EG achieved intermediate or expert-level proficiency in Python, compared to just 8% in the CG. Task performance demonstrated categorical differences: success in deoxyribonucleic acid (DNA) sequence analysis reached 91.9% versus 22.6% (χ² < 0.001), and successful biosensor programming reached 85.5% versus 0%. Self-efficacy scores rose by 86.9% (from 22.1 ± 6.7 to 41.3 ± 5.1) compared to a 15.4% increase in the CG.