## 3 Data Preparation
### Data Cleaning and Feature Selection
As was mentioned earlier, the course used as the primary source of data contained 13 sections with more than 30 interactive activities, which required continual students' activity. These activities could be divided into assignments, tests, project, and exam. The partial results obtained from these activities and the number of accesses to the course could be considered an ideal input set of features for machine learning algorithms at first glance. Therefore, they were intended to process using standard feature selection or dimensionality reduction technique. Consequently, different levels of features granularity were considered, from the individual activity levels, over different kinds of their categorization or aggregation with the aim to apply some feature selection technique. Although the importance of this technique increases with the number of the features in the input dataset, the restricted number of features and records influenced the final decision not to use the automated feature selection technique and replace it with the aggregation based on the category of activity. In other words, even though the partial points obtained from the individual activities could be considered in the input dataset as individual features, they were replaced by the total sum for the category of activity in the observed period of the term. As a result, the application of the automated feature selection method was not needed for this study due to a limited number of independent variables, which were created from the original features based on their aggregation. In addition to the number of features, another reason for this decision was that the points had the same impact on the final grade. The assignments and tests were evenly distributed in the first eight sections of the course, and their sequence did not play a significant role in this research. The final reason is that, as was mentioned earlier, the course was used over four academic years. During this period, not all assignments and tests were always used. As a result, several features should be removed or require applying suitable imputation methods to be correctly included in the dataset. These approaches would result in a further reduction of an already limited dataset. Therefore, the partial results of different activitieswere aggregated, reflecting that the partial assignments had the same share of the final grade in the courses in all academic years. Simultaneously, alternative techniques of input feature aggregating based on course topics and weeks during the semester were examined. However, they were finally considered less perspective than the aggregation based on the categories. As a result, the following five attributes reflecting the categories of the course activities were considered in the presented research (Table 1):  
* _Accesses_ represent the total number of course views by a student in the observed period. * _Assignments_ represent a total score from different types of evaluated activities within the observed period. * _Tests_ represent a total score from the midterm and final tests during the semester. * _Exam_ and _Project_ features were also considered. While offering high analytic value, these were not included since they were realized in the last third of the term, and their values are directly tied to completing the course. Those values were not available at the time of expected prediction (during a semester when features _Project_ and _Exam_ are unknown yet). A standard Pearson correlation was performed to find the correlations between the individual independent variables and final results, represented by the variable _result_points_. This variable represents the total sum of partial points, which the student could get during the course.