## 4 Evaluation
### Discussion  
The findings of the evaluation show that the adaptive user interface system, incorporating dynamically adjusted fuzzy weights and reinforcement learning, significantly improved learning efficiency, accuracy, engagement, and user satisfaction. The results confirm that a real-time, data-driven UI adaptation positively affects the learning experience, especially for students who initially faced challenges with programming exercises. Following, we discuss these findings in detail, analyzing their implications and comparing them to existing research. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Phase 1 Avg.** & **Phase 2 Avg.** & **Change (\%)** \\ \hline Hint Requests per & & & \\ Exercise & & & \\ UI Interaction time (s) & 45.88 \(\pm\) 0.26 & 38.88 \(\pm\) 0.24 & \(-\)15.3 \\ Code scrolls per Exercise & 7.08 \(\pm\) 0.08 & 5.98 \(\pm\) 0.08 & \(-\)15.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Engagement and interaction tracking. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Usability Factor** & **Phase 1 Avg. Rating** & **Phase 2 Avg. Rating** & **Change (\%)** \\ \hline UI Adaptability to Learning Speed & \(3.2\pm 1.0\) & \(4.1\pm 0.9\) & 28.1 \\ Effectiveness of Adaptive Hints & \(3.1\pm 1.0\) & \(4.0\pm 1.0\) & 29.0 \\ Perceived Cognitive Load Reduction & \(2.9\pm 1.2\) & \(3.8\pm 1.1\) & 31.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Students’ experiences with AUI. Figure 6: Engagement and interaction tracking. A key objective of the system was to optimize the time students spent on exercises, ensuring that they neither rushed through problems without sufficient challenge nor spent excessive time struggling without appropriate support. The results confirmed that slow learners exhibited the greatest reduction in time spent per exercise (15.5%), followed by moderate learners (11.0%) and fast learners (4.6%). These findings suggest that the reinforcement learning-optimized fuzzy weights played a critical role in fine-tuning UI interventions, ensuring that slow learners received more effective guidance without becoming overly dependent on hints. The significant reduction in error rates, particularly among slow learners (30.9%), further validates the system's effectiveness. The decrease in errors among moderate learners (22.9%) and fast learners (12.1%) also indicates that adaptive UI modifications improved students' accuracy by providing appropriately timed hints and structured assistance. The large improvement for slow learners suggests that reinforcement learning helped optimize the timing and necessity of UI interventions, preventing premature hints that could reduce engagement or delayed hints that could lead to frustration. The success rate improvement, which measures the percentage of exercises completed without external assistance, provides additional confirmation of the system's impact. The most notable increase occurred among slow learners (+28.5%), showing that adaptive hints, step-by-step guidance, and difficulty adjustments helped them gain greater independence. Moderate learners also demonstrated a meaningful improvement (+7.9%), suggesting that the reinforcement learning mechanism effectively refined the balance between challenge and support. The relatively small improvement for fast learners (+2.4%) indicates that these students were already capable of completing exercises successfully and therefore required fewer UI modifications. Beyond direct performance metrics, this study also examined how students interacted with the adaptive UI by tracking hint requests, UI interaction time, and scrolling behavior. The 26.6% reduction in hint requests suggests that students gradually became more confident and self-sufficient, requiring less system-generated assistance over time. The 15.3% decrease in UI interaction time indicates that students became more focused and efficient in navigating the system, and the 15.5% decrease in code scrolling suggests improved understanding of exercises, reducing the need to revisit instructions repeatedly.