Algorithms and programming are some of the most challenging topics faced by students during undergraduate programs. Dropout and failure rates in courses involving such topics are usually high, which has raised attention towards the development of strategies to attenuate this situation. Machine learning techniques can help in this direction by providing models able to detect at-risk students earlier. Therefore, lecturers, tutors or staff can pedagogically try to mitigate this problem. To early predict at-risk students in introductory programming courses, we present a comparative study aiming to find the best combination of datasets (set of variables) and classification algorithms. The data collected from Moodle was used to generate 13 distinct datasets based on different aspects of student interactions (cognitive presence, social presence and teaching presence) inside the virtual environment. Results show there are no statistically significant difference among models generated from the different datasets and that the counts of interactions together with derived attributes are sufficient for the task. The performances of the models varied for each semester, with the best of them able to detect students at-risk in the first week of the course with AUC ROC from 0.7 to 0.9. Moreover, the use of SMOTE to balance the datasets did not improve the performance of the models.

This paper describes a nationwide learning analytics initiative in Uruguay focused on the future implementation of governmental policies to mitigate student retention and dropouts in secondary education. For this, data from a total of 258,440 students were used to generate automated models to predict students at risk of failure or dropping out. Data were collected from primary and secondary education from different sources and for the period between 2015 and 2020. Such data contains demographic information about the students and their trajectories from the first grade of primary school to the second grade of secondary school (e.g., student assessments in different subjects over the years, the amount of absences, participation in social welfare programs, and the zone of the school, among other factors). Predictive models using the random forest algorithm were trained, and their performances were evaluated with F1-Macro and AUROC measures. The models were planned to be applied in different periods of the school year for the regular secondary school and for the technical secondary school ((before the beginning of the school year and after the first evaluation meeting for each grade). A total of eight predictive models were developed considering this temporal approach, and after an analysis of bias considering three protected attributes (gender, school zone, and social welfare program participation), seven of them were approved to be used for prediction. The models achieved outstanding performances according to the literature, with an AUROC higher than 0.90 and F1-Macro higher than 0.88. This paper describes in depth the characteristics of the data gathered, the specifics of data preprocessing, and the methodology followed for model generation and bias analysis, together with the architecture developed for the deployment of the predictive models. Among other findings, the results of the paper corroborate the importance given in the literature of using the previous performances of the students in order to predict their future performances.

O projeto LANSE é uma solução na nuvem que oferece serviços de predição de risco acadêmico e visualizações das interações dos estudantes. A versão atual do MVP utiliza dados do Moodle e consiste em duas ferramentas: modelos de machine learning que fornecem predições semanais sobre risco de evasão e fracasso, e um dashboard analítico que visualiza padrões de acesso e auxilia na gestão de usuários e carga horária de tutores e professores. O LANSE permite que as instituições de ensino identifiquem rapidamente estudantes em risco e intervenham proativamente, aplicando métodos corretivos em tempo hábil. As visualizações das interações dos estudantes fornecem insights valiosos, facilitando decisões informadas para melhorar a trajetória educacional. Assim, a solução LANSE não só facilita a gestão técnica para as equipes de TI, mas também melhora o acompanhamento e apoio ao desenvolvimento acadêmico, contribuindo para melhores resultados educacionais e reduzindo a evasão escolar.