## 2 Related Works  
The vast amount of data available in digital repositories has provoked the emergence of data mining, as analytical tools can be used to extract meaningful knowledge from such data. Data mining has already been successfully applied to many domains, including medicine, business, robotics and computer vision, to name just a few [14; 15]. Likewise, the constant upsurge of data in educational institutions has given rise to the emergence of educational data mining and LA, with a focus on developing, researching and applying computer-based methods to discover patterns in large educational data collections that would otherwise be difficult or impossible to analyze [13; 16]. The increasing interest in the two fields is demonstrated by the increase in research of applying data mining methods to data from a variety of educational repositories. In many applications, computing data similarity is required and normally used by Machine Learning (ML) algorithms, particularly those that deal with clustering, recommendation and dimensionality reduction [14]. In education data mining, data similarity methods have many applications, particularly in adaptive learning systems and recommendation systems. For example, in automatic recommendation systems, similarity measures are extensively used for clustering of educational items or users [17]. In addition, data similarity measures are found to be useful for a better understanding of educational processes and providing decision-makers with actionable insights [18]. The applications of data similarity to educational data have been reported in various contexts. In the programming domain, data similarity approaches are explored to select a set of the most relevant remedial programming items and worked-out examples to support students who have trouble solving a code comprehension problem in the Java language [19]. In the same domain, a content-based similarity approach is applied to provides personalized access to a repository of programming examples through adaptive visualization [20]. An additional example of data similarity application in the programming domain is reported in [21], which focuses on similarity among non-graded items such as an explanatory text and videos. To measure the similarity between educational items involving both text and images, a similarity measure is proposed in [22] to measure the similarity between items based on a representation computed by a neural network. The proposed measure is suitable for the mathematical domain, where items containing both text and images are commonly used. In the mathematical domain, the similarity of word problems is specifically studied in [23]. Moreover, similarity and clustering of users in mathematics learning system is studied in [24], where the whole processing pipeline for computing similarity is described in detail. Similarly, data similarity measures are also used to analyze educational item (questions, problems) similarities for many purposes: to be used as input with clustering or visualization techniques [17]; to detect plagiarism in online exams, particularly cheating in essay questions, multiple-choice questions, and fill-in-the-blank questions [25]; to measure the degree of similarity for Indonesian essay assessment [26]; to group documents or contributions to identify the sub-topics and topic evolutions in the graduate discussion forums [27]; to compare students navigation behavior in different dimensions [28]. The review of the above-related works reveals the wide variety of contexts where data similarity methods can be employed, as well as the variety of data similarity measures that can be applied. Another variety of choices in applying data similarity methods is whether to compute data similarity via features or directly from the data. In the case of computing data similarity from features, a transformation of the data is performed.