# Academic Success Assessment through Version Control Systems
## 1 Introduction  
Learning Analytics (LA) is defined in [1] as the measurement, collection, analysis, and reporting of data about learners and their contexts in order to understand and optimize the learning process and its environments. The use of LA provides many advantages such as carrying out data mining in education and also helping lecturers to optimize their teaching as they obtain current and actual feedback of each student's learning process. Therefore, by analyzing data from both Student Institutional Systems (SISs) or Learning Management Systems (LMSs), information about the students' interaction is gathered. Such data allow lecturers to identify patterns that point out if a resource is adequate or if the learning outcomes can be achieved. According to recent literature, LA research is widely applied to estimate academic results. In particular, in [2], the students' probability to pass or fail a course was determined. By employing predictive analytics, those students whose chances to pass a subject were lower could be identified, so an intervention could be adapted in order to mitigate the academic failure. In other words, the impact on education was truly positive. However, these methods depend on the field where they are applied, the sample features, or the data source [3], as well as what has an influence on the obtained accuracy. In existing methodologies that use learning algorithms, the quality data are an essential requirement;such a drawback avoids detecting the influence of different features on academic success, as well as designing a ready-to-use tool that can be applied in general. On the contrary, the Module Evaluator (MoEv) tool presented in [4; 5] handled these problems by evaluating different parametric and non-parametric learning algorithms, automatically identifying the more accurate one for a specific problem. MoEv follows the methodology proposed in [6], but it appends a crucial step so as to generalize models: the feature extraction is done automatically by choosing the most discriminant variables. In order to design a general tool that makes it possible to analyze any data source, the first stage is to identify the most valuable information for the model. Usually, predictive models on education consider data sources such as SISs [7], the trace data recorded by LMSs and other learning environments [8], or hybrid data sources formed by a combination of them [9]. On the other hand, the use of a Version Control System (VCS) is an indispensable skill for Information and Communications Technology (ICT) professionals and demanded by companies [10]. Thus, it is widely used by lecturers during practical assignments so as to provide a meaningful learning experience. In order to facilitate software development, there are specific tools that manage the code changing named version control [11]. In this field, a version means how the product is at a certain time. Moreover, these tools usually provide version management since it is a hard task if it is done manually. Not only a VCS has to provide storage for the source code, images, or documentation, but also they can be editable, keeping a record of every performed action so as to revert changes at any time in case this is needed. This last feature is very important since it makes it possible to return an element to its previous state, which is quite common in programming. Some examples of popular VCSs are CVS, Subversion [12], or Git [13]. The authors aim to estimate students' academic achievements by supervising their daily performance with VCSs. In order to do so, there are two issues with which to deal. The first one is if academic success is related to the considered variables that are computed from the students' interaction with VCSs. The second is if it is possible to design a model that analyses those features in order to estimate the students' success automatically. Preliminary results of this approach were presented in [4; 5; 14].