A Clustering-based Computational Model to Group Students with Similar Programming Skills from Automatic Source Code Analysis Using Novel Features

Throughout a programming course, students develop various source code tasks. Using these tasks to track students' progress can provide clues to the strengths and weaknesses found in each learning topic. This practice allows the teacher to intervene in learning in the first few weeks of class and maximize student gains. However, the biggest challenge is to overcome the amount of work required of the teacher in the manual analysis of all tasks. In this context, our main research objective is to automatically group students with similar programming skills based on the analysis of their submitted source codes. Our research is applied and uses an experimental procedure. First, we prepared the database, with more than 700 real-world source code tasks written in C Language, and distributed it in five different learning topics. Afterward, we define a set of features to be extracted from each learning topic. We defined and extracted 23 features from the source code for five learning topics. Then, we preprocess our database and extract the proposed features. Finally, we grouped the students. After performing the grouping, we obtained four groups of students, which were analyzed using a cluster midpoint calculation. Our results support the monitoring of students throughout the term, offering the teacher the freedom to create new exercises and waiving the obligation of any specific programming environment. We believe that these results can support the teacher in pedagogical decisions closer to the needs of each group of students.