Academic Success Assessment through Version Control Systems

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.

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“…Neural network [15,18,19,24] No retraining is required because it learns events; it is applicable to real-world issues, and there are few parameters to alter, making it simple to use.…”

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Predicting the At-Risk Online Students Based on the Click Data Distribution Characteristics

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“…e loss value of each batch can be computed by ( 13) and (14). First, the softmax operation, defined as (13), is performed to scale the instance's outputs to value range [0, 1] and the sum to 1, where y i represents the probability of the instance belonging to the ith category, x i is the ith output of the input instance, K is the total number of the instance outputs (equal to the number of categories of the instances), and the category number here is two. en, the binary cross-entropy loss, defined as (14), is calculated, where y i is the ground truth category of instance i, and 􏽢 y i is the predicted probability of instance i belonging to the ground truth category.…”