RESUMO
Teacher evaluation is presented as an object of study of great interest, where multiple efforts converge to establish models from the association of heterogeneous data from academic actors, one of these is the students' community, who stands out for their contribution with rich data information for the establishment of teacher evaluation in higher education. This study aims to present the search results for references on the prediction of teacher evaluation based on the associated data provided by the performance of university students. For this purpose, a systematic literature review was carried out, established by the phases of planning (search objective, research questions, inclusion and exclusion criteria), search and selection (literature control group and keywords, the definition of the search string, results filtering), and extraction (synthesis of the contributions). As a result, a set of references on the application of predictions is obtained, focused on educational data mining techniques, such as Fuzzy logic, Fuzzy clustering, Fuzzy Neural Network (FNN), Neural networks, multilayer perceptron (MLP), Decision Trees, Logistic Regression, Random Forest Classifier, Naïve Bayes Classifier, Support Vector Machine (SVM), K-Nearest-Neighbor (KNN), and Associative classification model. In conclusion, prediction and mining techniques have been widely explored; however, teacher evaluation is in the process of growth with particular emphasis on fuzzy principles, considering that human decision-making is developed with uncertainty, which is strongly related to human behavior.
RESUMO
School dropout is a structural problem which permanently penalizes students and society in areas such as low qualification jobs, higher poverty levels and lower life expectancy, lower pensions, and higher economic burden for governments. Given these high consequences and the surge of the problem due to COVID-19 pandemic, in this paper we propose a methodology to design, develop, and evaluate a machine learning model for predicting dropout in school systems. In this methodology, we introduce necessary steps to develop a robust model to estimate the individual risk of each student to drop out of school. As advancement from previous research, this proposal focuses on analyzing individual trajectories of students, incorporating the student situation at school, family, among other levels, changes, and accumulation of events to predict dropout. Following the methodology, we create a model for the Chilean case based on data available mostly through administrative data from the educational system, and according to known factors associated with school dropout. Our results are better than those from previous research with a relevant sample size, with a predictive capability 20% higher for the actual dropout cases. Also, in contrast to previous work, the including non-individual dimensions results in a substantive contribution to the prediction of leaving school. We also illustrate applications of the model for Chilean case to support public policy decision making such as profiling schools for qualitative studies of pedagogic practices, profiling students' dropout trajectories and simulating scenarios.