## 5 Discussion  
In this study, we proposed a staged approach to computer vision education, exploring ways to apply elements of data science and computational thinking by integrating neural network-based AI and symbolic AI. In this discussion, we evaluate the significance and potential impact of the proposed staged framework based on the research findings and suggest implications for educational application and future research directions. The staged approach to computer vision education was designed to enable learners to effectively grasp core concepts by integrating both neural network-based AI and symbolic AI in each learning stage. By incorporating symbolic AI, students engaged in defining explicit rules and logical criteria, which enhanced their understanding of knowledge representation and reasoning. This integrated approach allowed learners to compare symbolic reasoning with neural learning, providing a more comprehensive understanding of AI methodologies. In the first stage--image recognition--no-code platforms were utilized to allow learners to grasp basic computer vision concepts without prior programming experience. This significantly lowered the initial barriers to learning, especially for novice learners. Learners \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Question** & **Category** & \(M\) & \(SD\) & \(t\) & \(p\) \\ \hline Perception of Computer Vision Education 1: Do you think computer vision education will be interesting? & Pre & 3.5 & 0.877 & 6.345 & 0.000 \\ \cline{2-6} Perception of Computer Vision Education 2: Do you think computer vision education will be beneficial? & Post & 4.625 & 0.628 & 6.176 & 0.000 \\ \hline Subject Knowledge 1: Are you aware of how the color of acid-base indicators changes with pH? & Pre & 2.3 & 1.091 & 9.531 & 0.000 \\ \hline Use of AI 1: Can you predict pH values of substances using a computer vision machine learning model? & Pre & 2.3 & 0.939 & 8.834 & 0.000 \\ \cline{2-6} Use of AI 2: Do you understand how computer vision and AI are applied in inquiry-based activities? & Pre & 1.9 & 1.109 & 5.561 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Results of the pilot implementation. experienced problem-solving processes through hands-on activities and increased engagement by practically applying data collection and preprocessing--the initial stages of data science. This approach provided a crucial foundation for understanding computer vision technologies in an experimental educational environment. According to [39], no-code AI platforms can democratize AI education by making it accessible to a broader audience. The second stage, image retrieval and captioning, was designed to let learners experience more advanced computer vision technologies. Teachers had the flexibility to choose between basic block coding (e.g., AI blocks in Scratch 3 or mBlock V5) and basic Python coding (utilizing OpenCV 4) based on the learners' proficiency levels. By using block-based programming languages, learners with limited programming experience could implement image retrieval and captioning without complex programming. Alternatively, for learners ready to advance, Python coding provided exposure to text-based programming and OpenCV 4 libraries. Through data visualization and analysis processes, they developed the ability to make better decisions using visual data. However, some experts pointed out that limitations of block-based coding might restrict the implementation of more complex functions [40]. This suggests the need for concurrent learning with text-based programming languages. Future research should develop educational strategies that organically connect block-based coding with text-based programming to facilitate a smooth transition [41]. In the third stage, object detection and image segmentation, teachers could select between advanced block coding (e.g., supervised learning blocks in Scratch 3 or mBlock V5) and advanced Python coding (utilizing OpenCV 4 or YOLOV8), depending on learners' skill levels.