Retention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose of this paper is to analyse the scientific production in this area in higher education in journals indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. We use a bibliometric and systematic study to obtain deep knowledge of the referred scientific production. The information gathered allows us to perceive where, how, and in what ways learning analytics has been used in the latest years. By analysing studies performed all over the world, we identify what kinds of data and techniques are used to approach the subject. We propose a feature classification into several categories and subcategories, regarding student and external features. Student features can be seen as personal or academic data, while external factors include information about the university, environment, and support offered to the students. To approach the problems, authors successfully use data mining applied to the identified educational data. We also identify some other concerns, such as privacy issues, that need to be considered in the studies.

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.

Self-assessment is one of the strategies used in active teaching to engage students in the entire learning process, in the form of self-regulated academic learning. This study aims to assess the possibility of including self-evaluation in the student’s final grade, not just as a self-assessment that allows students to predict the grade obtained but also as something to weigh on the final grade. Two different curricular units are used, both from the first year of graduation, one from the international relations course (N = 29) and the other from the computer science and computer engineering courses (N = 50). Students were asked to self-assess at each of the two evaluation moments of each unit, after submitting their work/test and after knowing the correct answers. This study uses statistical analysis as well as a clustering algorithm (K-means) on the data to try to gain deeper knowledge and visual insights into the data and the patterns among them. It was verified that there are no differences between the obtained grade and the thought grade by gender and age variables, but a direct correlation was found between the thought grade averages and the grade level. The difference is less accentuated at the second moment of evaluation—which suggests that an improvement in the self-assessment skill occurs from the first to the second evaluation moment.