## 4 Evaluation
### Results Analysis
#### 4.3.1 Results Analysis for Section 1
Through the system's provision of targeted practice sets and real-time feedback, the students were able to progressively overcome challenging topics and ultimately achieve a high level of final accuracy. Figure 8: Solution results for Group 1 in Section 1. The test results of Group 2 are shown in Figure 10, where the x-axis represents question numbers and the y-axis indicates both the average number of attempts per question and the average accuracy rates. The overall response patterns are generally consistent with those observed in Group 1. However, the data distribution in Group 2 is more polarized. We observed that first-attempt accuracy for certain units was lower than that of Group 1. More students had no relevant background knowledge. The main difficulties were concentrated in the following section. Questions 12 and 14 pertain to combinational logic, a concept not typically encountered in general programming courses. For beginners, unfamiliar subject matters often result in lower accuracy and higher average submission counts. Nevertheless, through the system's effective instructions and opportunities for repeated practice, students were ultimately able to develop a sound understanding of these topics. Questions 16, 17, and 18 showed relatively weak performances in both groups, indicating that the content relating to synchronous and asynchronous circuits poses a particular challenge, as it is specific to _Verilog_ and more conceptually demanding. After working through the consecutive practice set designed by the system, the first-attempt accuracy improved significantly, further demonstrating the positive impact of targeted question sequencing on comprehension. Question 20 obtained the lowest accuracy due to students' limited exposure to gate-level circuit representations compared to more familiar design levels, such as dataflow or behavioral. However, as students progressed through the subsequent related questions, their accuracy steadily improved, reflecting the effectiveness of the system's instructional explanations and sequencing in helping students correct misconceptions and solidify their understanding of the underlying concepts. Overall, most of the students were able to achieve close to full marks in the end, while the groups with a higher average number of submissions usually had lower first-submission scores and needed repeated revisions to approach 100%. The multiple attempts mechanism can effectively help students gradually improve their accuracy in answering questions. Different students have different initial levels of understanding, but the overall final effects are quite good, which means that this system can achieve similar results for students of different levels. Figure 9: Individual student results for Group 1 in section 1. Individual Student Performance in Group 2  
As shown in Figure 11, approximately 62% of students achieved a first-attempt accuracy of 85% or higher. The result was close to the one observed in Group 1. This indicates that the majority of students were able to comprehend the question requirements on their initial attempt and reach fully correct answers within two to three submissions. However, in contrast to Group 1, students with lower first-attempt accuracy in Group 2 exhibited a marked increase in their average number of submissions. This suggests that, compared to students with a background in computer science, these learners were less familiar with the contents and required more iterative attempts to fully understand and correctly solve the problems. Notably, despite facing unfamiliar materials, these students did not give up. Instead, they persisted, with the support of the system, ultimately achieving correct solutions. This outcome underscores the system's clear and positive impact as an effective learning aid for students seeking to acquire _Verilog_ proficiency.