Concurrently, it is worth noting that the primary motivation to develop the platform was a belief that providing the learning content in the form of micro-learning units suitably combined with questions and automatically evaluated programming assignments can positively influence students' self-confidence and engagement in learning programming. This expectation was amplified by the teachers' previous negative experiences with teaching programming in two separate systems for providing educational online content and automated programming code evaluation. Therefore, this paper's main aim is to analyze students' academic performance data using unsupervised machine learning methods to identify the groups of students with similar performances and behaviors in a Java programming micro-learning course and uncover possible changes in the number of identified groups in consecutive periods of the term and based on different types of micro-learning units. The following research questions can be formulated to addresses this issue:  
1. How do the students' performances in micro-learning units with quizzes and programming assignments in introductory micro-learning courses affect the number of identified meaningful clusters? 2. How do identified groups of students with different academic performances change in composition over three periods of term? According to [PERSON] et al. [3], who published a taxonomy of machine learning approaches used in learning analytics in different learning environments, the application of ensemble and unsupervised learning clustering techniques for the identification of the heterogeneous and homogeneous groups of students is still generally lacking. In addition, available studies deal predominantly with data obtained from the LMS environments or surveys. Clustering analysis of the students' partial achievements in learning programming in a tailor-made environment is rare. Therefore, the application of clustering analysis with the evaluation of students' data obtained from a micro-learning environment and comparing changes in cluster distribution found in different term periods can be considered partially novel. This paper contributes to the discussion of whether delivering educational content in the form of micro-learning units with automated programming assignments can identify meaningful groups of students with different academic performances and whether these groups change over time. Consequently, these findings could initialize the adjustment of the educational model and initialize changes in course structure and content sequencing. Simultaneously, the presented results can evoke new ideas, which can be considered in the future development of the platform Priscilla in the context of the planned implementation of the learning analytics module and dashboard. These findings could decrease the dropout rate and achieve better academic performance. The paper is structured as follows. First, the Related Works section summarizes some interesting findings of similar studies. Consequently, all phases of the CRISP-DM methodology are described to explain the complexity of the problem of the practical adoption of machine learning methods in the case of data obtained from the micro-learning platform. The selected clustering methods and other evaluation and visualization techniques are then applied to different datasets to identify distinct groups of students with similar performances. This section also analyzes and visualizes the changes in the composition of individual groups of students to understand the differences and behavioral patterns of the found groups over time and based on types of activities. Afterwards, this paper summarizes the main findings, the limitations of this study, its implications for future changes in the micro-learning platform and educational model, and recommendations for further research directions.