# The Establishment and Evaluation Model of the Thematic Deep-Learning Teaching Module
## 4 Results  
### Establishment of Technical Ability Indicators and Planning of Teaching Content  
This study referred to the technical capability indicators considered in the research results of [PERSON] et al. [10], which included (1) software function design; (2) the use of controllers; (3) hardware structure construction (including I/O equipment); (4) software and hardware system integration; (5) IoT function technology practice; (6) AI module construction (ANN, CNN, and KNN); and (7) system operation. These seven technical capability indicators serve as the basis for the technical capability evaluation scale. Considering the rapid growth of DL technology, and after expert consultation and a literature review, the theme-oriented curriculum planning of the DL robot teaching module was structured as follows: Chapter 1: Robot Technology (including DL technology); Chapter 2: Software Design; Chapter 3: myRIO Technology; and Chapter 4: DL-themed Robot Design Implementation. The compiled technical capability indicators are listed in Table 1. The 14 technical capability indicators across all four chapters in Table 1 serve as the primary basis for learning evaluation, while the seven indicators in Chapter 4 are the main foundation for curriculum planning in thematic teaching. Based on the 14 technical capability indicators summarized in Table 1, 15 experts and scholars were invited to complete the FDM questionnaire and participate in interviews. After collecting the questionnaires, the double-triangle fuzzy technique was used to test the degree of consistency. The implementation results are shown in Table 2.  
Zi, Mi, and Gi are the parameters of optimistic and conservative triangular fuzzy models [25]. Table 2 presents the data for each item. According to the findings of the convergence state, the expert consensus value Gi was calculated for the evaluation items that completed convergence. From the results of the convergence state in the table, it can be seen that all indicators in this questionnaire reached a consensus among the expert scholars, with no situation where the expert opinions could not effectively converge when Zi > Mi. The design of theme-based robots and practical courses was planned corresponding to the technical capability index test value (\(\text{Mi}-\text{Zi}\)) and expert consensus value (\(\text{Gi}\)), as shown in Table 3. Table 4 shows the unit configuration planned for the theme-based DL robot teaching course. This planning mode can be used depending on different DL target themes. As shown in Table 1, Ch 4 focuses on the thematic design and implementation of DL robots, with the curriculum scheduled for four weeks of thematic teaching. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Teaching Module Course Planning** & \multicolumn{3}{c}{**Teaching Module Technical Capability Indicators**} \\ \hline Ch1 Robot Technology & 1. & Ability to understand the current situation of the robot industry (cognitive part) \\ Ch1 Robot Technology & 2. & Robot structure and design theoretical capabilities (cognitive part) \\  & 3. & AI and DL technology (cognitive part) \\ \hline Ch2 Software Design & 1. & LabVIEW design capabilities \\  & 2. & Python design ability \\ \hline Ch3 myRIO Technology & 1. & myRIO controller setting and usage capabilities \\  & 2. & System integration capabilities of myRIO controller \\ \hline Ch4 Thematic Robot Design and Implementation of DL & 4. & Robot IoT function construction technical capabilities \\  & 5. & Robot AI Modules construction capabilities (ANN, CNN, and KNN) \\  & 6. & Integration and construction capabilities of the robot control system and AI Modules \\  & 7. & Robot system operation capabilities \\ \hline \hline \end{tabular} The technical capability indicators of the first three chapters will also be included in this unit.  
\end{table} 
Table 1: Technical capability indicators for DL robotic environmental monitoring module.