Evaluation of students programming skills on a computer programming course with a hierarchical clustering algorithm

This Research Full Paper presents a computational hierarchical clustering approach to identify groups of college students from a Computer Science 1 (CS1) course that need extra help with the programming content. We first defined a set of features that characterize the student's programming skills in a CS1 course. Next, we applied a hierarchical clustering algorithm to bring together the students with similar skills by analyzing the source code they developed for different tasks. Finally, we evaluated the quality of the model and analyzed the different clusters. We processed a total of 630 source code tasks, for which our results indicate the formation of three clusters. We have found that one of the clusters has a large number of students with possible difficulties in programming. The other two clusters, although having different coding behaviors, reached high levels of knowledge about the contents taught. We conclude that features extracted from the students source codes can be used to group students into clusters that indicate their performance trends throughout the course.