Data mining techniques for predicting teacher evaluation in higher education: A systematic literature review

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.