Content Validity and Inter-Rater Reliability of Computational Thinking and Green Computing Awareness Instruments in Software-Based Statistics Learning
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Abstract
Purpose – Computational Thinking (CT) and Green Computing Awareness (GCA) have thus far been studied separately and have rarely been developed within the same learning context; therefore, instruments that can assess both constructs within a shared technology-based Applied Statistics learning environment are needed. This study was an instrument development research project using a quantitative psychometric approach aimed at developing instruments for CT and GCA and evaluating their content validity and inter-rater agreement among experts.
Methods – The CT instrument comprises 12 case-study-based essay items covering abstraction, decomposition, pattern recognition, and algorithmic thinking. The GCA instrument comprises 10 questionnaire items covering environmental impact awareness, perceptions of efficiency and sustainability, and responsible digital behavior. Content validity was assessed through expert judgment involving two experts for each instrument using Aiken’s V, while expert-rating consistency was assessed using the Intraclass Correlation Coefficient (ICC).
Findings – The CT instrument obtained an average Aiken’s V of 0.958 and an ICC of 1.000, indicating excellent content validity and consistency among expert ratings. The GCA instrument obtained an average Aiken’s V of 0.925 and an ICC of 0.561, indicating excellent content validity and moderate expert-rating consistency.
Research Implications – The findings provide initial evidence supporting further development of the instruments for technology-based Applied Statistics learning. Testing with student respondents is required to establish empirical validity and reliability before broader implementation.
Keywords
- computational thinking
- content validity
- green computing awareness
- inter-rater reliability
- software-based statistics learning
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