# Effectiveness and Design of PBL-Based Project Approach for Non-Major University Computing Courses
## 4 Results  
A comparative analysis of course evaluation results from the second semester of 2023 and the first semester of 2024 within the scope of this case study reveals consistent improvements across all evaluation metrics. These findings highlight the positive impact of implementing Project-Based Learning (PJBL) in non-major computing education. As shown in Table 3, the mean scores for all evaluation items improved significantly between the control group (C) and the experimental group (E), with t-values confirming the statistical significance of these changes (\(p<0.05\)). Table 3 presents a comparison of evaluation scores between the control group (2023) and the experimental group (2024). The groups followed identical instructional content but differed in their assessment methods. Significant p-values are highlighted in bold. The category \"Overall Satisfaction\" increased from 4.57 in 2023 to 4.74 in 2024, with a mean difference of 0.17 (t = \(-\)2.147, \(p\) = 0.039). Notably, when compared to the university-wide averages of 4.53 in 2023 and 4.54 in 2024, this course consistently outperformed institutional benchmarks. \"Teaching Method Effectiveness\" demonstrated one of the most notable improvements, with the mean score rising from 4.43 in 2023 to 4.77 in 2024, reflecting a mean difference of 0.34 (t = \(-\)4.187, \(p\) = 0.0001). This result underscores the capacity of PJBL to enhance students' perceptions of instructional quality. Similarly, the category \"Systematic Organization and Alignment\" exhibited the most significant enhancement, increasing from 4.37 to 4.74 (mean difference = 0.37, t = \(-\)4.581, \(p\) = 0.00003), highlighting PJBL's role in structuring a coherent learning experience. Other areas such as \"Feedback Appropriateness\" and \"Participation and Feedback\" also saw substantial gains, with mean differences of 0.30 (t = \(-\)3.724, \begin{table}
\begin{tabular}{l} \hline \hline
**Evaluation Questions** \\ \hline The lecture was generally satisfactory. \\ The course content and preparation were adequate. \\ The teaching methods were effective. \\ The lecture plan was helpful for course selection and learning. \\ The feedback on assignments and tests was satisfactory. \\ The practice activities were well organized and facilitated learning. \\ Students were encouraged to actively participate, and feedback was appropriate. \\ \hline \hline \end{tabular} This table summarizes the survey questions used for evaluating the course.  
\end{table} 
Table 2: Course evaluation survey items. \(p=0.001\)) and \(0.27\) (t \(=-3.167\), \(p=0.004\)), respectively. These metrics reflect the emphasis on clear, constructive feedback and active engagement in PJBL environments. Figure 2 visually represents the evaluation score improvements between the control group (C) and the experimental group (E). The control group (C) followed traditional instructional methods, while the experimental group (E) engaged in a PBL-based project approach. Notably, the standard deviations ranged from 0.7 to 0.8 for most items, indicating a high degree of response consistency. However, slight increases in standard deviation for certain metrics in 2024 suggest that PJBL may evoke diverse reactions due to its innovative and collaborative approach. In summary, the course not only exceeded the university-wide averages but also showed statistically significant improvements across all evaluation items. These results confirm the positive impact of PJBL on enhancing student engagement, satisfaction, and learning outcomes in non-major computing education. The results of this study provide insights into how project-based learning influences students' problem-solving abilities, collaboration experiences, and computational thinking. These findings contribute to developing effective computing education models for non-major students, emphasizing the significance of collaborative problem-solving in real-world contexts.