EFFECTS OF INSTRUCTIONAL CODING STRATEGIES ON ELEMENTARY AND MIDDLE-SCHOOL STUDENTS’ COMPUTATIONAL THINKING

Abstract

Coding education develops core competencies—such as problem solving and algorithmic thinking—that are essential for success in today’s digital society. Various instructional approaches are widely used to cultivate computational thinking (CT). This study examined the effects of three commonly used coding instructional strategies (block-based coding, unplugged coding, and educational robotics) on students’ CT skills. A 2×3 factorial model with grade level and instructional approach as factors was tested using a ten-week quasi-experimental non-equivalent groups pretest–posttest design implemented as an after-school program. A total of 123 students participated. A study-specific multiple-choice CT assessment was developed and validated (KR-20 = 0.969). Results showed that all three instructional approaches produced statistically significant pre-to-post gains in CT across both grade levels. Although elementary students began with lower pretest scores than middle-school students, the interventions reduced this gap: after instruction, gains were similar across grade levels. After controlling for pretest scores, there was no significant interaction between grade level and instructional approach on posttest CT scores. These findings suggest that unplugged activities, block-based programming, and educational robotics can each effectively support CT development when implemented with a common curriculum and comparable instructional design.