## 2 Related Works
The process helps to identify relevant attributes in the dataset that contribute to the accuracy of the prediction model, for instance, by the last activity on the student's corresponding course to predict a drop out [4]. Strategies effective for one kind of datasets may not be sufficient for another one. In this case, it is often necessary to manually develop new strategies for extracting features [6]. On the other hand, decision trees, rule-based classifier belong to the white-box models that are more understandable and easily interpretable because they expose the reasoning process underlying the predictions. Another possibility is to use clustering algorithms or association rule mining. Correlation analysis of grades achieved from courses and attributes defining credits obtained by students and their average grades can also be valuable [19]. [PERSON] et al. [16] realized a review of contemporary LA research direction. They distinguish different sets of predictors of students' dropout. The first set of predictors is based on online behavior data, mainly log files and trace data. In this context, one of the difficult challenges is to determine how to engage students to participate in online learning [20]. The second set of predictors is based on students' background information, such as demographics, socioeconomic status, prior academic experience, and performance [7]. Many studies usually also examine student-related records, e.g., the age, gender, status, because they are readily available in the information systems of each university. It is proven that attributes like students' average grade or average grade obtained in a course positively affect prediction [21]. Analysis of students' logs together with some other predictors, such as the number of discussion posts, number of forum views, number of quiz views, number of module views, number of active days, and social network activities, were aimed to predict the dropout through the implementation of a decision tree [22]. Similarly to the research presented in this paper, [PERSON] et al. [23] considered the popular types of variables, such as task submissions, assignment switches (i.e., the amount of times a learner goes from one assignment to another), active days, and total points. They concluded that these features could predict the students' success. However, they did not discuss the role of the smaller dataset and performance metrics in detail. [PERSON] et al. noted that other special activities, like programming assignments, can also be considered to predict the final score in the course effectively [12]. The assignments have also been analyzed in [24; 25]. Both studies confirmed that assignments could be successfully used for prediction students' learning outcomes. Considering the role of forum posts as predictors is also a significant subject of research. They can be an appropriate source of predictive elements (e.g., the number of posts, number of comments, number of votes up, and so on) that are not normally available in face-to-face courses [1]. [PERSON] et al. [26] demonstrated that the courses in which teachers used predictive methods delivered at least 15% higher output than the classes without their use [1]. Nevertheless, it is challenging to reach this state because the fewer data is available, the less accurate prediction can be obtained. Moreover, researchers are trying to find extra features that can contribute to a deeper understanding of students. Overall, all these approaches produced acceptable results, allowing the prediction of students' performance to achieve overall accuracy varying from 65% to 90% [4]. It is a considerable challenge to optimize the results obtained by predictions because different algorithms typically present a large variance in performance rates depending on the combination of several features (e.g., balance among classes, amount of data, input variables, and others) [1].