The learning of computer programming subjects has always brought challenges to any class of computer students, due to the difficulties linked to its use. This scenario greatly contributes to the demotivation of the student and, thus, the increased dropout of courses. Generally, teachers who work in these disciplines have signs about which students will become good programmers, but it is difficult to detect which need help in the learning process. This article proposes the use of a Bayesian model that helps in the identification of students with difficulties in the computer programming subjects. The research uses a mixed approach, quantitative and qualitative. An experiment, with an informal character, was carried out with students who were studying programming subjects. This set of students was presented to five specialist teachers in order to identify which ones would need help with the learning of programming. The same set was presented to the Bayesian model. The results showed that the model can help in the identification of students who present difficulties, with the potential to contribute to the learning process.
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