Abstract
Version control systems’ usage is a highly demanded skill in information and communication technology professionals. Thus, their usage should be encouraged by educational institutions. This work demonstrates that it is possible to assess if a student can pass a computer science-related subject by monitoring its interaction with a version control system. This paper proposes a methodology that compares the performance of several machine learning models so as to select the appropriate predicting model for the assessment of the students’ achievements. To fit predicting models, three subjects of the Degree in Computer Science at the University of León are considered to obtain the dataset: computer organization, computer programming, and operating systems extension. The common aspect of these subjects is their assignments, which are based on developing one or several programs with programming languages such as C or Java. To monitor the practical assignments and individual performance, a Git repository is employed allowing students to store source code, documentation, and supporting control versions. According to the presented experience, there is a huge correlation between the level of interaction for each student and the achieved grades.
Highlights
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
In [2], the students’ probability to pass or fail a course was determined. 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
The impact on education was truly positive. 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
Summary
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. 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. The impact on education was truly positive. 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
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