Abstract

This research aimed to: 1) identify the components and indicators of computational science learning management for primary school teachers, and 2) assess the appropriateness and feasibility of these components and indicators for primary school teachers under Thailand’s Office of the Basic Education Commission. The study was conducted in two phases: 1) systematic review and synthesis of relevant literature from both domestic and international sources to identify key components and indicators, and 2) expert validation of the framework’s appropriateness and feasibility. Nine computational science education experts, selected through purposive sampling, evaluated the framework using assessment forms incorporating both quantitative Likert-scale ratings and qualitative open-ended feedback. Data were analyzed using content analysis for qualitative responses and descriptive statistics (mean and standard deviation) for quantitative ratings. The synthesis of relevant documents and research identified five components with 19 indicators: student analysis (3 indicators), computational science curriculum analysis (3 indicators), objective setting (2 indicators), computational science learning activity management (7 indicators), and measurement and evaluation (4 indicators). Expert validation demonstrated highest levels of appropriateness (X = 4.84) and feasibility (X = 4.93) across all components. However, qualitative feedback revealed significant implementation challenges including extensive teacher training requirements, resource disparities between schools, and the need for technological infrastructure support. While this study establishes strong theoretical validation for the framework, it represents initial expert validation rather than empirical testing. The findings provide a foundation for computational thinking instruction in Thai primary education, but future research must examine practical implementation in diverse classroom settings, assess real-world effectiveness, and address the implementation challenges identified by experts before the framework can be confidently recommended for system-wide adoption.

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