Purpose: The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective. Design/methodology/approach: This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM. Findings: From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles. Originality/value: This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

Missing data is a significant problem found in data mining projects and data analysis. Despite being a common problem, the missing data is dealt in a simplistic way and may lead to inconsistent knowledge discovery. Through literature review, it was p

Missing data in databases are considered to be one of the biggest problems faced on data mining application. This problem can be aggravated when there is massive missing data in the presence of imbalanced databases. Several techniques as samples deletion, values imputation, values prediction through classifiers and approximation of patterns have been proposed and compared, but these comparisons do not consider adverse conditions found in real databases. In this work, it is presented a comparison of techniques used to classify records from a real imbalanced database with massive missing data, where the main objective is the database pre-processing to recover and select records completely filled for further techniques application. It was compared with other algorithms such as clustering, decision tree, artificial neural networks and Bayesian classifier, expressing their efficiency through ROC curves. Through the results, it can be verified that the problem characterization and database understanding are essential steps for a correct techniques comparison in a real problem. It was observed that artificial neural networks are an interesting alternative for this kind of problem since it was capable to obtain satisfactory results even when dealing with real-world problems.