## 4 Evaluation
### Results Analysis
#### 4.3.2 Section 2 Analysis
Some students performed well in their first attempt, but others needed multiple attempts before gradually improving their performance. This diverse first-time answer performance and the high levels of final results indicate that the teaching and exercise design of the proposed system has a good learning assistance effect, enabling students to quickly adjust their mistakes and significantly improve their learning outcomes. The test results are shown in Figure 14. The overall answer curve is roughly consistent with the trend of Group 1, indicating that the challenges faced by the students in the two groups in conceptual understanding are highly consistent. However, since Group 2 includes students that have no computer science background, the Average Accuracy of the First Submission is significantly lower than that of Group 1. This reflects the lack of basic programming and hardware logic knowledge, which creates a high hurdle for beginners. From the data, we can see that both Group 1 and Group 2 have a higher average number of comparisons for Question 7, but they are able to achieve a very high final accuracy rate. It was found that the question contained a trap and students were required to confirm the definition again and again before they could provide the correct answer, which should be improved in future works. To assess students' ability to integrate prior knowledge, we designed Questions 10, 11, and 12 as review questions, similarly to those in Section 1. Figure 14 shows the average accuracy upon first submission for these three questions significantly improved after repeated practice, demonstrating that alternating new and previously learned concepts can effectively reinforce understanding. Compared to Group 1, Group 2 had more difficulty  
Figure 13: Individual student performance for Group 1 in Section 2.  
with questions related to numerical definition and bit manipulation in _Verilog_, such as value declaration, truncation, and extension, indicating that such abstract conversion and symbol extension mechanisms are particularly challenging for students without any information technology background. To reduce this gap, we are considering adding more practical exercises regarding \"hardware data types\" and \"bit operations\" and using visual waveform figures to increase students' intuitive understanding. Additionally, introducing small tests to help students review and reinforce their knowledge is another direction for future improvement. In summary, students in Group 2 were still able to achieve a high accuracy rate, comparable to that of Group 1, through the repeated practice and instant feedback provided by the system, indicating that this learning platform has good adaptability and teaching effectiveness for students from different backgrounds. Individual Student Performance in Group 2  
As can be observed from Figure 15, the vast majority of students eventually achieved 90% or even close to 100%, with only a few students achieving slightly below 80%. Taken together, the results indicate that (1) some students already had a good understanding upon their initial submission and can maintain high scores with only one or two attempts, (2) although some students do not score well in their first submission, they can significantly improve their accuracy after multiple revisions, and (3) the number of submissions often reflects the depth of students' initial understanding, and more attempts mean that they need more attempts to get close to full marks. Overall, this chart highlights the value of multiple attempts and immediate feedback, proving that even if the initial performance gap is quite large, most learners can eventually improve their performance to a level similar to that of higher-scoring students through subsequent efforts. Figure 14: Solution results for Group 2 in Section 2.