Reducing the students' dropout is one of the biggest challenges faced by educational institutions, especially in underdeveloped countries. Identification of the student with the highest risk of dropping out is generally used to apply corrective actions (WHO). Therefore, it is also important to determine WHEN a student will drop out, which is fundamental to planning preventive actions. In this work, we perform a study to quantitatively compare several approaches to address the early identification of dropout students in universities. We categorize our study into three main methods families, i.e., analytical methods, traditional classification methods, and probabilistic methods. The first is exploited at preprocessing step for selecting significant variables into the dropout identification task. The second uses machine learning models to classify students into dropout prone or non-dropout prone classes. The third family uses survival models to determine when the student would desert. To evaluate the predictive capacity of the classification models, the Kappa coefficient was incorporated into the usual machine learning metrics and shows that Kappa is handy for evaluating performance in unbalanced data. Similarly, in the survival models, the concordance index was applied to evaluate the predictive capacity. Our approach was applied over a real data set of Peruvian university graduate students to identify when and who will drop out.

Automatic trajectory processing has multiple applications, mainly due to the wide availability of the data. Trajectory data have a significant practical value, making possible the modeling of various problems such as surveillance and tracking devices, detect anomaly trajectories, identifying illegal and adverse activity. In this study, we show a comparative analysis of the performance of two descriptors to detect anomaly trajectories. We define Wavelet and Fourier transforms as trajectory descriptors to generate characteristics and subsequently detect anomalies. The experiments emphasize performance in the description in the coefficient feature space. For that, we used unsupervised learning, specifically clustering techniques, to generate subsets and identify which are irregular. The implications of the study demonstrate that it is possible to use descriptors in trajectories for automatic anomaly detection and the use of unsupervised learning methods that automatically segment the required information. The performance and comparative analysis of our study are demonstrated through experiments and a case study considering synthetic and real data sets that leave evidence of our contribution.