## 4 Results
### Post-Tests
#### 4.2.2 H2--Using VPL Improves Student Performance in Advanced Chapters of Programming  
The same procedure was applied for testing hypothesis H2. The results of the final test realised at the end of the semester are used. The results of the Kolmogorov-Smirnov test and [PERSON]'s test are presented in Table 7.  
The data in the observed groups, as well as the variances of groups, are not significantly different from a normal distribution. Again, the ANOVA test at the 5% significance level was used to compare student test results. The null hypothesis assumes that there is not any significant difference in means. The results are summarised in Table 8.  
Since \(p\)-value \(>\alpha\), H\({}_{0}\) is accepted. The difference between the means of groups is not large enough to be considered statistically significant. The \(p\)-value equals 0.905179, [p(\(\propto\) E) = 0.0948215]. This means that, if H\({}_{0}\) were rejected, the chance of type1 error (rejecting a correct H\({}_{0}\)) would be too high: 0.9052 (90.52%). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **DF** & **Sum of Square** & **Mean Square** & **F-Statistic** & \(p\)**-Value** \\ \hline Groups (between groups) & 1 & 194,258.95 & 194,258.95 & 7.99 & 0.0055 \\ Error (within groups) & 119 & 2,893,546.19 & 24,315.51 & & \\ Total & 120 & 3,087,805.14 & 25,731.71 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Assumptions of normal distribution and homogeneity of variances. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & **Count** & **Mean** & **Median** & **Std. Dev.** & **D (Val. of K-S Test)** & \(p\)**-Value** \\ \hline
2016 & 51 & 235.97 & 227.80 & 153.29 & 0.10 & 0.60 \\
2017 & 70 & 317.12 & 366.65 & 157.82 & 0.15 & 0.09 \\ \hline \hline \multicolumn{6}{c}{**The Results of the Levene’s Test of Homogeneity of Variance**} \\ \hline
**Valid N1** & **Valid N2** & **Std.Dev.1** & **Std.Dev.2** & **F-Statistic** & \(p\)**-Value** \\ \hline
2016–2017 & 70 & 82 & 79.69 & 77.30 & 2.42 & 0.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Assumptions of normal distribution and homogeneity of variances. It follows that the hypothesis H2 cannot be accepted. In other words, the use of automated assessment object in the advanced topics of introductory programming course does not cause significant differences in student's outcomes. 2.3 H3--The Use of Microlearning Improves Student Performance in Introductory Chapters of Programming  
The compared groups are groups of students from 2018 and 2019 year. The results of the test realised in half of the semester are used. The test score distribution of the test is demonstrated in Figure 7.  
The results of the Kolmogorov-Smirnov test are presented in Table 9.  
The data in 2018 group does not significantly differ from a normal distribution, but the distribution of the test results in the 2019 group is significantly different from a normal distribution. Therefore, a non-parametric Mann-Whitney U-test should be used to investigate if the results are significantly different. The null hypothesis assumes that the means are not significantly different. The distribution of values is approximately normal. Therefore, the z-score should be used. The values of the test are presented in Table 10.  
The U-value is 3820.5, the Z-Score is 1.11105, and the \(p\)-value is 0.1335. The result is not significant at \(p<0.05\). Since \(p\)-value \(>\alpha\), the hypothesis is accepted, the difference between the averages of all groups is not large enough to be statistically significant. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **DF** & **Sum of Square** & **Mean Square** & **F-Statistic** & \(p\)**-Value** \\ \hline Groups (between groups) & 1 & 328.32 & 328.32 & 0.014 & 0.91 \\ Error (within groups) & 150 & 3,458,909.89 & 23,059.40 & & \\ Total & 151 & 3,459,238.21 & 22,908.86 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Assumptions of normal distribution and homogeneity of variances.