## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Paper** & **Summary** & **Dataset** & **Clustering Method** & **Number of Clusters** & **Validation** \\ \hline \multirow{4}{*}{[PERSON] [6]} & They introduced a study that applies & \multirow{4}{*}{Mooble} & \multirow{4}{*}{Mooble} & \multirow{4}{*}{Hierarchical method and K-Means method} & \multirow{4}{*}{4} & \multirow{4}{*}{N/A} \\  & unsupervised learning to identify how students’ & & & & \\  & engagement and demographic features in & & & & \\  & online learning activities on affect their & & & & \\  & learning achievement. & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [7]} & They analyzed the interaction of university & \multirow{4}{*}{[PERSON] et al. [7]} & \multirow{4}{*}{Mobile} & \multirow{4}{*}{Hierarchical method and K-Means method} & \multirow{4}{*}{4} & \multirow{4}{*}{N/A} \\  & students in V1E, comparing clustering and & & & & \\  & classification methods. They stated that both & & & & \\  & groups of methods could contribute a better & & & & \\  & comprehension of the learning process. & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [5]} & They focused on generating a matter dataset & \multirow{4}{*}{Mobile} & \multirow{4}{*}{Mobile} & \multirow{4}{*}{K-Means method} & \multirow{4}{*}{4} & \multirow{4}{*}{N/A} \\  & through a reduced number of features without & & & & \\  & computing the number of records. Their & Student’ Academic Performance & & & \\  & approach combined the strengths of & & & & \\  & classification and clustering for better & & & & \\  & prediction results. & & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [6]} & They applied clustering using three different & \multirow{4}{*}{OULAD} & \multirow{4}{*}{Gaussian Metrics, Hierarchical and K-prototype} & \multirow{4}{*}{3} & \multirow{4}{*}{Index, Silhouette score, and Davies-Buoltan} \\  & techniques to identify students with a low level & & & & \\  & of engagement in learning. As a result, they & & & & \\  & identified clusters that can be further analyzed. & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [5]} & They provided a detailed review of the & \multirow{4}{*}{Moodle} & \multirow{4}{*}{K-Means, K-Models,} & \multirow{4}{*}{Mobile} & \multirow{4}{*}{Mobile} \\  & clustering algorithms in an educational context. & & & & \\  & They proposed an evaluation approach that & & & & \\  & compared several clustering methods using & \multirow{4}{*}{Moodle} & \multirow{4}{*}{Custering, DBSCAN, OPTICS} & \multirow{4}{*}{3} & \multirow{4}{*}{Index, Silhouette score, and Davies-Buoltan} \\  & multiple internal and external performance & & & & \\  & measures for one level-world educational & & & & \\  & datasets of different sizes. & & & \\ \hline \multirow{4}{*}{[PERSON] et al. [5]} & They compared different clustering algorithms using an educational dataset and recommended & \multirow{4}{*}{University Dataset of Students’ Profiles} & \multirow{4}{*}{K-Means, Clara, PAM, PANN, Hierarchical, AGENS, DIANA} & \multirow{4}{*}{3} & \multirow{4}{*}{Silhouette coefficient, Dours’ indices} \\  & the ones with the highest performances. & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: A summary of related scientific publications that dealt with clustering analysis in the learning analytics domain. This analysis of the related works confirms that there is a general agreement that clustering methods can contribute to a better understanding of the students' behavior and achievements in VLE, but further research is needed. Moreover, despite the popularity of learning programming platforms, including micro-learning platforms, the number of research studies that apply clustering is limited.