## 3 Methods
### Pilot Implementation
The 'Suitability of Learning Activities Examples' received a mean score of 4.64 (\(SD\) = 0.48), confirming that the examples and activities are appropriate \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic** & **Question** & **\(M\)** & **\(SD\)** & **\(CVR\)** \\ \hline Appropriateness of Stage Division & Is the division into four stages (Image Recognition, Application of Image Recognition, Image Segmentation and Object Detection, Video Analysis) appropriate? & 4.56 & 0.50 & 1.00 \\ \hline Validity of Computer Vision Elements & Are the computer vision elements presented at each stage (e.g., image classification, object recognition) suitable for that stage? & 4.60 & 0.49 & 1.00 \\ \hline Connection with Data Science Elements & Are the data science elements appropriately connected with each stage of computer vision education? & 4.68 & 0.47 & 1.00 \\ \hline Suitability of Coding Education Levels & Are the coding education levels presented at each stage (e.g., no-code platforms, block coding, Python coding) appropriate for learners’ levels and educational goals? & 4.64 & 0.48 & 1.00 \\ \hline Relevance of Computational Thinking Elements & Do the computational thinking elements presented at each stage relate to the learning content of that stage? & 4.64 & 0.56 & 0.91 \\ \hline Suitability of Learning Activities Examples & Are the learning activities examples presented at each stage appropriate for achieving the learning objectives of that stage? & 4.64 & 0.48 & 1.00 \\ \hline Connection Between Stages & Is each stage designed to progressively deepen based on the learning content of the previous stage? & 4.72 & 0.45 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of the first expert validity assessment. \begin{table}
\begin{tabular}{c c} \hline \hline
**Domain** & **Question** & **Type** \\ \hline Perception of Computer Vision Education & 1. Do you think computer vision education will be interesting? & \\ \hline Subject Knowledge & 2. Do you think computer vision education will be beneficial? & \\ \hline Subject Knowledge & 3. Are you aware of how the color of acid-base indicators & \\ \hline Use of AI & 4. Can you predict pH values of substances using a computer vision machine learning model? & Likert 5-point scale \\ \cline{2-3}  & 5. Do you understand how computer vision and AI are applied & \\ \cline{2-3}  & in inquiry-based activities? & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Domains and questions for pilot implementation. for achieving the learning objectives. Lastly, the 'Connection Between Stages' had the highest mean score of 4.72 (\(SD=0.45\)), indicating that each stage is effectively designed to build upon the previous one, facilitating the progressive deepening of knowledge and skills. Overall, the results of the first validity assessment suggest that the stages of computer vision education are appropriately designed to match learners' levels, with meaningful connections between educational objectives and learning activities. However, some experts suggested the need for adjustments in certain stages to accommodate learners' varying levels and provide additional support where necessary.