## 3 Data Preparation
### Data Cleaning and Feature Selection  
Data preparation involves cleaning of data, data joining, attributes transforming, and feature selection or reduction. Various methods can be applied to analyze the data based on the data understanding in order to uncover hidden patterns [27]. The output from this phase is a construction of the final dataset deemed to be imported from the initial raw data into the modelling tool. The educational data is often either sparse, noisy, inconsistent, or include several attributes that are irrelevant. Therefore, this step is usually the one where the aim is to remove all unwanted data characteristics. Noisy data can be smoothed by binning, regression, or clustering. There are also several techniques on how to overcome the problem of missing values. The incomplete records can be removed or imputed with a global constant or the most probable value [9]. However, the semantics of the missing values should be taken into account in all cases individually. As was already mentioned, the educational data used in this paper represents 261 unique students enrolled in the e-learning course used in blended study form over four academic years. The results of interactive learning activities were exported using a conventional tool of LMS Moodle. Therefore, it was not required to identify individual students. The personal data have been removed. Simultaneously, the detailed records about their activity in the form of application logs were also exported. However, this data had to be cleaned in contrast to the grades received by the students. The reason is that these records contained information about other stakeholders like teachers and administrators, as well as information about activities,  
Figure 1: Distribution of students’ dropout over several academic years (orange—number of failed students, blue—number of students who successfully finished the course). which did not relate directly to the learning process. After cleaning, the records were aggregated for each student and joined with his partial outcomes. The number of records represents a persistent issue in the educational technology, learning analytics and educational data mining research field [18]. This problem can be overcome if the research methods use only the logs, representing the digital traces of the VLE stakeholders' behavior. On the other hand, if the input dataset should also contain the students' achievements or grades, the number of unique records is radically reduced, and the records should be aggregated [29]. The reason is that data about the students' partial achievements should be often aggregated to minimize the problem with data incompleteness, presence of missing values or voluntary character of the assignments, which the student could submit. The missing values cannot be easily removed because of their semantics. It may not have been known if the students submitted the assignment and received zero points, did not participate in the activity, or selected other voluntary assignment. Simultaneously, this record cannot be removed from the already limited size of the dataset. Considering research reviews in the learning analytics and educational data mining domain, the dataset used in this case study can be considered sufficient [18]. Simultaneously, it is necessary to take into account the reduction of results interpretation and more limited options to generalize the obtained results when machine learning techniques are applied for such scarce datasets [30]. Precise and robust prediction models depend on thoroughly selected features extracted from the studied dataset [8]. Therefore, the feature extraction process is critical as it directly impacts the performance of any prediction model. According to previous works, different studies often use different features [7]. However, they all generally refer to the students' interactions with the course content and the traces of their activities on the VLE platform. They represent the natural behavioral features that the LMS Moodle platform keeps track of [31; 32].