## 2 Related Works  
This section summarizes the results of other related scientific papers from the educational data mining (EDM) and learning analytics domains, which apply different clustering methods to students' performance data from their interactions with a virtual learning environment. The researchers in both domains systematically analyze specific patterns of students' interactions with VLE, their performances, and the risk of early dropout or achievements [4]. [PERSON] et al. [5] also confirmed these trends, especially in higher education. They stated that specific EDM and LA methods could offer the best means of solving specific learning problems. Similarly, [PERSON] et al. [1] provided a concise review of the current state of the learning analytics research field and its typical tasks, technologies, and challenges. As a result, they stated that many learning analytics and educational data mining methods, including clustering, have been successfully applied to the student's activity and performance data stored in VLEs during the last decade. As stated by [PERSON] et al. [3], publications that dealt with unsupervised machine learning clustering techniques for the identification of the heterogeneous and homogeneous groups of students as a primary research topic are still rare. Clustering analysis was more frequently applied as a part of the data-understanding phase [6] or data-modeling phase, together with classification or prediction [7; 8]. Several papers tried to find the best clustering methods and algorithms using artificial, open-source, or real datasets [9; 10]. Regarding the selection of the best clustering method in LA research, K-Means was the most often selected [10]. The reason is that this unsupervised machine learning method is considered simple, flexible, and time-effective for the cluster analysis of educational data, which usually have linear or monotonic relationships. Other, more robust methods like DBSCAN or OPTICS, often applied in industry and engineering, were not frequently applied in the LA domain [11]. Table 1 summarizes the main characteristics of other studies focused on clustering analysis in the context of learning analytics and topics related to the study presented in this paper. As can be seen, a low number of identified clusters is typical for the education domain because of the issues related to the correct interpretation of higher values. Simultaneously, it is surprising that several studies did not apply any internal or external validation metrics, which can cause problems with the generalization of the findings. Even fewer articles dealt with clustering data about the students' performances in learning programming courses. [PERSON] et al. [12] confirmed the potential of clustering methods to understand students' performances better and identify behavioral patterns. However, they did not deal with programming courses or data from the micro-learning environment. [PERSON] et al. [13] used K-Means clustering to group students with similar performances without collecting their previous academic records or demographic information. They identified 7.7% as slow learners, 31% as average learners and the rest as advanced learners. Simultaneously, they found a correlation between these groups and academic success. [PERSON] et al. [14] classified low-income students based on the clustering analysis of their academic performance. They applied three unsupervised models, including K-Means. As a result, they identified five clusters of students and proposed actions that could increase their success. [PERSON] et al. [15] studied relationships among the self-regulation learning and affective strategies and multiple intelligence dimensions of students using clustering analysis. As a result, they identified features that positively or negatively impacted academic performance. These publications confirm the suitability of clustering analysis and selection of the K-Means method for the identification of groups with similar academic achievements.