## 3 Methodology
### Data Understanding
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **Count** & **Mean** & **Std** & **Min** & **25\%** & **50\%** & **75\%** & **Max** \\ \hline intro & 119.0 & 94.509804 & 30.101066 & 0.0 & 97.25 & 110.0 & 110.00 & 110.0 \\ \hline intro-VPL & 119.0 & 46.196078 & 23.239621 & 0.0 & 40.00 & 60.0 & 60.00 & 60.0 \\ \hline methods & 119.0 & 123.421569 & 59.649544 & 0.0 & 102.50 & 159.0 & 160.00 & 160.0 \\ \hline methods-VPL & 119.0 & 348.843137 & 224.051875 & 0.0 & 107.50 & 346.0 & 584.50 & 630.0 \\ \hline encap & 119.0 & 108.745098 & 58.633901 & 0.0 & 71.75 & 146.5 & 150.00 & 150.0 \\ \hline encap-VPL & 119.0 & 71.598039 & 38.642115 & 0.0 & 40.00 & 100.0 & 100.00 & 100.0 \\ \hline constructors & 119.0 & 79.117647 & 44.749533 & 0.0 & 51.00 & 105.5 & 110.00 & 110.0 \\ \hline constructors-VPL & 119.0 & 112.637255 & 63.217179 & 0.0 & 60.00 & 139.0 & 165.00 & 165.0 \\ \hline classes1 & 119.0 & 52.401961 & 34.236007 & 0.0 & 0.00 & 73.0 & 80.00 & 80.0 \\ \hline classes2 & 119.0 & 109.450980 & 71.353482 & 0.0 & 32.50 & 150.0 & 169.75 & 170.0 \\ \hline classes2-VPL & 119.0 & 54.166667 & 41.345462 & 0.0 & 0.00 & 65.0 & 95.00 & 95.0 \\ \hline static & 119.0 & 57.362745 & 33.316303 & 0.0 & 40.00 & 77.0 & 80.00 & 80.0 \\ \hline static-VPL & 119.0 & 147.950980 & 109.225027 & 0.0 & 38.00 & 170.0 & 270.00 & 270.0 \\ \hline inher & 119.0 & 116.568627 & 75.730569 & 0.0 & 40.00 & 161.5 & 186.50 & 190.0 \\ \hline inher-VPL & 119.0 & 139.941176 & 110.768944 & 0.0 & 27.00 & 158.5 & 270.00 & 270.0 \\ \hline polymorphism & 119.0 & 45.107843 & 31.401238 & 0.0 & 0.00 & 69.0 & 70.00 & 70.0 \\ \hline polymorphism-VPL & 119.0 & 26.647059 & 21.434518 & 0.0 & 0.00 & 45.0 & 45.00 & 45.0 \\ \hline \end{tabular}
\end{table} 
Table 2: Basic exploratory data analysis of the input dataset. attributes were not removed from the dataset for several reasons. Firstly, this correlation was expected because students were motivated to complete all the quizzes as part of their learning process. Secondly, planned clustering methods are not sensitive to the correlation between input features. Finally, removing individual features can significantly impact the final distribution and number of clusters. However, these correlations should be considered if principal component analysis (PCA) is applied. Figure 4: Correlation matrix between the attributes, which evaluate the level of comprehension of the micro-learning unit in individual chapters of the course. Figure 3: Boxplot of the features related to each student’s performance in modules with micro-learning units and quizzes. Figure 5 shows the box plots representing grades received from programming assignments. The students could receive different grades depending on the number of unique tasks, number of attempts, and used hints. The lower position of the means in boxplots is worth mentioning, as it differs in comparison to the previous groups of attributes. The higher difficulty of the programming tasks probably caused this difference. Figure 5: Boxplot of the features related to each student’s performance in automatically evaluated programming assignments. Figure 6: Correlation matrix between the attributes, which belong to programming assignments, automatically evaluated by the platform. The impact of a wider distribution of the students' achievements in programming assignments is also evident in the correlation matrix (Figure 6). [PERSON]'s rank correlation coefficients were lower. Therefore, it was not necessary to remove any attributes for the same reasons as those previously considered.