## 1 Introduction  
Nowadays, Virtual Learning Environments (VLEs) represent mature online education platforms allowing teachers to provide courses, which contain thoroughly managed resources and myriads of different engaging activities. VLEs allow creating various educational games, quizzes, e-learning tests, or prepare multimedia materials to attract students. Considering technological progress and increasing demand for online education, VLEs have perceived fast development over the past few years [1]. VLE developers and providers have fully taken advantage of the internet and current web technologies and operate modern education systems to improve students' knowledge and skills. Educational approaches and methodologies tailored to the functionalities of the virtual platforms get rid of the limitations of traditional courses taken in classrooms and come up with higher flexibility in terms of where and when to take the courses online. Modern computational tools for data analysis, standardization of formats for storing data, and increasing computing power allow research questions to be explored widely using diverse data-driven approaches. Since virtual learning environments are designed to capture and store as much data on user interaction with the system as possible, it is not surprising that this data is analyzed using different knowledge discovery techniques to identify the learning-related problems early and suggest the most suitable form of intervention. As a result, different educational data mining and learning analytics techniques have already helped analyze and partially understand students' behavior, detect frequent learning mistakes, recommend learning paths, improve tests, and measure students' performance. Despite the rapid development and successes of data-driven research, educational institutions still face many principal difficulties such as effective VLE stakeholders' interaction and immediate feedback, which are not appropriate, insufficient, or delayed. This fact subsequently influences student's educational performance and their final outcome [2]. Student's dropout poses one such significant problem within contemporary e-learning approaches [3]. The dropout has a negative impact on educational organizations as well as stakeholders. Even though it has been the focus of many research, e-learning dropout rates generally tend to be higher than face-to-face education [4]. Therefore, the researchers thoroughly apply different knowledge discovery methods and examine the student's behavior, their characteristic features, as well as the impact of the external environment on the learning process and outcomes to minimize the dropout, create effective intervention strategy and provide immediate feedback [5]. Educational data used in dropout prediction models comes from different resources, has different size and quality. Educational data is often stored in database systems. It can consist of information about students' activities and provide end-users with course-wide statistics, giving an aggregated overview of all stakeholders' activity. Moreover, summary information about partial and final grades are also arranged about each student's performance. Finally, event logs are often used, which indirectly track every stakeholder's interaction with the platform [6]. The availability of the educational data resources for data-driven research differs depending on the researcher's privileges and the scope of the research problem. Whereas the researcher can have a considerable amount of data available in the form of logs, the amount of the data describing the students' achievements at the course level is limited. Combining these resources does not automatically correlate with the higher accuracy of the final predictions of student's dropout because the data must be often aggregated.