## 2 Related Works  
Various definitions of students' dropout were established in previous studies. The most frequent definition focuses on whether students will still be active until the last week or whether the current week is the final week in which students are active. Early identification of students at risk of dropout plays an important role in reducing the issue and allowing required conditions to be targeted. Timing considerations are, therefore, significant for the dropout problem. As a result, some analyses have shown that 75% of dropouts occur in the first weeks [7]. The prediction of dropout is often considered a time series prediction problem or a sequence labelling problem [8]. These can correspond with the final status of students [9]. On the other hand, the time dimension can be included in prediction dropout indirectly using the input features available in a particular time window, which allow selecting a suitable form of an intervention [10]. Prediction of the student's learning success or failure belongs to one of the most frequently researched topics of educational data mining (EDM) and learning analytics (LA) disciplines [3]. EDM deals with the analysis of study-related data to understand student behavior. These techniques are usually applied to provide more effective learning environments through revealing useful information for modifying the structure of courses or support the prediction of student performance and behavior [11]. On the other hand, learning analytics deals with the measurement, collection, analysis, and reporting of student data and their backgrounds in order to understand and improve learning and the environments in which it takes place [12]. The core of current prediction approaches is usually based on EDM and LA methods. One of the hottest research topics in learning analytics has been predicting the probability of students completing or failing a course, especially from the very early weeks [13], as well as EDM [14]. Once a reliable performance prediction is available, it can be used in many contexts to identify weak students and provide feedback to students or predict students' failure [15]. A successful prediction depends on the use of reliable, accurate, and significant data [9]. The size of the dataset is a typical pitfall of many research studies published in educational technology, learning analytics and educational data mining research [16]. It is challenging to reach the volume of educational data, which would fully satisfy the machine learning requirements besides using logs [8]. On the other hand, the high granularity and technical nature of the VLE logs often miss the necessary semantics, which can cause problems, how the findings should be interpreted from the educational point of view [17]. [PERSON] and [PERSON] indicated in their review of classifiers for EDM that the datasets often used in educational data mining are relatively small. They usually consist of 200 records on average on the course level [18]. Many analyzed experiments confirmed this statement. A similar data volume was used to evaluate the machine learning techniques on the individual VLE course level [18]. As [PERSON] and [PERSON] [16] stated, more educational data has not always led to better educational decisions. The same authors concluded that the reliability and validity of data and its accurate and bias-free analysis are critical for generating useful summative, real-time, or formative, and predictive or prescriptive insights for learning and teaching. [PERSON] et al. [19] stated that classification and regression methods, neural network, Bayesian networks, support vector machines, logistic regression, and linear regression could solve the student performance prediction problem. These models are often called black-box models that are difficult to understand and interpret. All of them also rely heavily on feature extraction. Feature extraction refers to the compilation of a subset of unique predictive features for the predictive problem in modelling, also known as attribute selection.