## 4 Results
### Changes in Awareness before and after SW - AI Training
#### 4.2.7. Conception of SW - AI  
Students' responses to their conception of SW - AI were analyzed by period to compare how it changed pre-camp and post-camp. TF-IDF analysis was performed first, just like the analysis on feelings towards SW - AI. Moreover, data pre-processing on stop words and synonyms was performed to enhance accuracy of analysis as a precedent. Responses were in the order of \"AI\", \"robot\", \"computer\", \"coding\", and \"future\" based on pre-camp  
Figure 3: Schematization of feelings on SW - AI post-camp (LDA topic modeling).  
Figure 2: Schematization of feelings on SW - AI pre-camp (LDA topic modeling).  
analysis, most of which are concepts related to computers. \"Fun\" being mentioned by some suggests that students have a positive perception, although they do not have an accurate grasp of the concept. Analysis post-camp shows responses in the order of \"AI\", \"robot\", \"computer\", \"machine\", and \"future\", which suggests that high-frequency words were similar pre-camp and post-camp. Responses on what students have learned in the education camp show that the students paid deep attention to education contents. The pre-camp and post-camp results of TF-IDF analysis on changes in students' conception of \(\text{SW}\cdot\text{AI}\) are shown in Table 12.  
Changes in conception of \(\text{SW}\cdot\text{AI}\) pre-camp and post-camp were analyzed via LDA topic modeling, and it was divided into five topics. The results of response analysis of \(\text{SW}\cdot\text{AI}\) conception pre-camp showed that \"AI\" and \"robot\" were included in all groups. \"Computer\" and \"future\" were also included in the majority of groups. \"AI\" was included in all groups and \"robot\" in all groups except Group 3 based on the analysis results of the post-camp response. \"Future\" was also in Groups 1, 2, and 4. The pre-camp and post-camp analysis results of changes in \(\text{SW}\cdot\text{AI}\) conception based on LDA topic modeling are shown in Table 13.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Pre** & \multicolumn{2}{c}{**Post**} \\ \hline
**Group** & **Word** & **Group** & **Word** \\ \hline
1 & robot, AI, computer, data, future AI, robot, computer, machine, science & 1 & robot, AI, computer, science, future AI, robot, computer, AI  
The results of LDA topic modeling can be clearly classified through schematization. As shown in Figure 4, Group 2's topic was relatively big pre-camp. This indicates that words in Group 2 functioned as key words in the responses. Big overlap in Group 1 and Group 3 means there were similar words without a clear distinction of themes. On the other hand, Figure 5 is a schematization of post-camp responses. Post-camp, the size of Group 1 is considerably bigger than in other groups when comparing with Figure 4 In post-camp responses, words in Group 1 constituted a significant portion of the responses. Topics did not overlap, but Group 3 and Group 5 are very close, which means low discriminant validity.  
Prior to the commencement of the camp, when questioned about SW and AI, a significant number of students often responded either by merely mentioning SW and AI themselves or by providing emotionally driven answers unrelated to the actual concepts of SW and AI. However, post-camp, a discernible shift in their responses was observed, tending more towards detailed conceptual understandings. One student, reflecting on their experience, remarked, \"Having only heard about AI before, the opportunity offered by the  
Figure 4: SW \(\cdot\) AI concept schematization pre-camp (LDA topic modeling).  
Figure 5: SW \(\cdot\) AI concept schematization post-camp (LDA topic modeling).  
camp to practically create and engage with it was immensely rewarding. This experience deepened my understanding of both SW and AI.\"