Abstract:

The teaching-learning process in programming in university with freshmen is often associated with high failure and dropout rates. These outcomes frustrate both students and teachers and there is a need to verify the causes of these failures. By predicting the causes of these problems, we can try to control them, or at least try to plan the courses to try to avoid failure in the identified cases.
The purpose of this paper is to analyze the scientific production concerning the prediction of students’ performance in introductory programming courses. This analysis regards articles indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. The sample includes a total of 30 articles. The results obtained by bibliometric analysis show when and where those documents were published, who are the authors and what is the focus of said articles. We also analyzed the most cited documents. We made a summary of the articles. We were able to obtain a global overview of the theme, obtaining a strong analysis that is useful for teachers in the process of helping students achieve success in introductory programming courses at universities.

Keywords:

Educational data mining, CS1, programming courses, bibliometrics.