# The Establishment and Evaluation Model of the Thematic Deep-Learning Teaching Module
## 4 Results
### Teaching Strategies and Evaluation  
The diagram of the experimental teaching and evaluation process is shown in Figure 2. The evaluation process was deployed during the semester. This designed module aimed to be practically applied in embedded courses for teaching experiments. The psychomotor evaluation scale assessed learners' skills performance, while the affective evaluation scale gauged students' emotional feedback regarding the teaching of the embedded IoT smart home robot. After the experimental teaching, four dimensions--teaching materials and equipment, cognitive development, skills performance, and self-exploration--were used to evaluate the emotional attitudes of the students in the experimental group using a five-point Likert scale. This study's learning benefit assessment scale included a thematic psychomotor assessment scale and a thematic affective scale (Appendix A). The thematic psychomotor assessment scale was adopted from the research of [PERSON] et al. [10]. This study considered some indicators in the developed evaluation scale, including software design, controller use, hardware construction, software and hardware system integration, IoT technology practice, system operation, etc. This integration improves skills. To assess the accuracy of the scale, the two evaluation scales were used in automatic measurement technology courses, evaluating the effectiveness of thematic teaching in robot design and practice courses. The psychomotor assessment scale in this study was adopted from [PERSON] et al. [10]. The original Kendall's W of the scale was used to test the consistency and reliability of the ratings of the three teachers. The results showed that [PERSON]'s W was 0.885; the chi-square value was 99.232; the \(p\)-value was less than 0.05, showing significance; and [PERSON]'s \(\alpha\) reliability was 0.928. This study added technical ability indicators of Python and AI model (ANN and CNN) building capabilities, and the revised skills evaluation scale is shown in Appendix A. The skill scores of the pre- and post-tests were conducted on the basis of the skills assessment scale of the Environment Sensing ML Themed Robot Teaching Course. Each aspect was evaluated using a 5-point Likert scale. The total score of the evaluation form is 50 points. Each question was scored as follows: very good = 5, good = 4, fair = 3, poor = 2, and very poor = 1, for a total score of 10 to 50. Pre-tests and post-tests were conducted using paired-sample \(t\)-tests to assess the effectiveness of the developed teaching materials and devices in improving students' skills performance. In the pre-test, [PERSON]'s W was used to test the consistency and reliability of the three teachers' ratings. The results showed a [PERSON]'s W of 0.879, a chi-square value of 99.315, and a significance level of \(p<0.05\), confirming the consistency of the grading. Table 5 presents the statistical results of the paired-sample \(t\)-test for skills performance. Table 6 details the differences between pre-test and post-test scores and the \(t\)-test results, showing an average difference of \(\mathrm{M}=-25.02\). The \(t\)-test result was \(t=-38.177\), with df = 37 and a significance level of 0.001. These results indicate that students significantly improved their skills performance after taking the course. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Paired Variables** & **N** & **M** & **SD** & **SE** & \(t\) \\ \hline Pre-test & 38 & 15.82 & 1.92 & 0.29 & \\ \hline Post-test & 38 & 40.53 & 5.08 & 0.79 & \(-38.177\) *** \\ \hline \end{tabular}
\end{table} 
Table 5: The paired-sample \(t\)-test results. After finding that the 38 students' skills performance improved after receiving experimental teaching, the reliability of the skills assessment in the pre-test and post-test was tested using [PERSON]'s W concordance coefficient.