Modular learning for preparing preschool teachers to develop algorithmic skills in early childhood

Abstract

Modular learning (ML) provides flexibility in the educational process, supports individualized learning, and emphasizes the practical competencies of future educators. This study assessed the impact of ML on the effectiveness of training future educators to develop algorithmic skills (AS) in preschool children. The study employed a quantitative approach using an experimental design. A total of 320 students were selected from Abai Kazakh National Pedagogical University. The assignment procedure was randomized within each program to ensure a balanced distribution of participants across groups. Results indicated that the experimental group (EG) demonstrated significant improvements in professional competencies, confidence in applying AS, and practical skills. Differences between the experimental and control groups (CG) were statistically significant across all measures (p<0.001). The findings confirm that a ML approach, combining theory, practice, and reflection, effectively enhances the readiness of future preschool teachers to foster algorithmic thinking in children. These results highlight the efficacy of ML for improving teacher training programs and suggest its applicability in diverse educational contexts.