Computational Thinking (CT) is crucial in today’s world, but the lack of a shared paradigm poses challenges for researchers addressing key issues like effective teaching. Di- verse stakeholder motivations further complicate consensus on definitions, methods, and metrics. This research proposes a CT meta-framework, integrating cognitive science and stakeholder hypotheses, to disambiguate definitions and clarify experimental methods for a CT transfer experiment. The framework’s success is measured by its ability to disambiguate transfer and algorithm definitions, determine an algorithmic thinking metric, and design a transfer case study method. The research involves three steps: 1) literature review and coding to identify CT per- spectives and provide nomenclature for them; 2) integrating cognitive science concepts to develop the CT meta-framework; and 3) applying the meta-framework in a transfer case study to disambiguate definitions and identify limitations. This research aims to bridge the gap between CT perspectives and facilitate a more cohesive approach to CT research and education.