# Effect of Block-Based Python Programming Environment on Programming Learning
## 5 Results  
The results of analyzing the effect of the \"programming learning environment\" factor on \"positive perceptions of programming\" are as follows: The \"programming learning environment\" factor includes \"understanding of programming instructions, usage confidence, and usefulness\". The analysis of variance results show that the usability factor had a statistically significant effect on \"positive perceptions of programming\". Table 7 shows the results of the detailed analysis of the factors that affected the \"positive perceptions of programming\".  
Table 8 represents the analysis results. \"understanding of programming instructions\" had a statistically significant effect on \"positive perceptions of programming\" with a Beta value of 0.462 and a significance level of 0.5. The \"usage confidence\" factor was also statistically significant with a Beta value of 0.248 (\(p<0.05\)). Therefore, it can be considered that using the programming environment and increasing the confidence made the programming perceptions more positive.  
The B-value was used to predict the programming perceptions based on the programming learning environment. The following regression equation was derived using the B (unstandardized coefficient) value of the three factors, and the perceptions of the programming value can be predicted based on \"understanding of programming instructions\" and \"usage confidence\".  
1. All Students \(\mathrm{R_{eg}}\) = 2.393 + 0.342 \(\times\) (Understanding of Programming Instructions) + 0.178 \(\times\) (Usage Confidence);
2. Middle School Students \(\mathrm{R_{M}}\) = 0.426 \(\times\) (Usefulness) + 2.804;
3. High School Students \(\mathrm{R_{H}}\) = 0.280 \(\times\) (Usefulness) + 0.251 \(\times\) (Understanding of Programming Instructions) + 2.395;
4. University Students \(\mathrm{R_{U}}\) = 0.375 \(\times\) (Understanding of Programming Instructions) + 0.277 \(\times\) (Usage Confidence) + 1.724.  
For example, out of all students (a), the positive perceptions toward programming of those who answered 5 for \"understanding of programming instructions\" and 3 for \"usage  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**ANOVA**} \\ \hline
**Model** & **Sum of Squares (SS)** & **df** & **Mean Square (MS)** & **F** & \\ \hline Regression & 16.144 & 2 & 8.072 & 53.133 & 0.000 \\ \hline Residual & 18.990 & 125 & 0.152 & & \\ \hline Total & 35.134 & 127 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: The analysis of variance summary of the correlation.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **B** & **Std. Error** & **Beta** & **t** & **Sig.** \\ \hline (Constant) & 2.393 & 0.225 & & 10.612 & 0.000 \\ \hline Understanding of Programming Instructions & 0.342 & 0.082 & 0.462 & 4.170 & 0.000 \\ \hline Usage Confidence & 0.178 & 0.080 & 0.248 & 2.233 & 0.027 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The regression coefficient of the correlation.  
confidence\" was calculated as \(\text{R}_{\text{eg}}=2.393+(0.342\times 5)+(0.178)\times 3\), and their perception toward programming was 4.637.  
The explanatory power of the \"programming learning environment\" factor with respect to \"positive perceptions of programming\" was 45.9%. Of this, \"understanding of programming instructions\" was 43.8% and \"usage confidence\" was 2.1%. When we examined results by school level, the explanatory power of \"usefulness\" on \"positive perceptions of programming\" was found to be 45.7% for middle school students. For high school students, \"usefulness\" was found to be 41.1%, whereas \"understanding of programming instructions\" was found to be 9.8%. For university students, \"understanding of programming instructions\" and \"usage confidence\" were found to be 52.0% and 2.7%, respectively.