Educational Data Mining for Tutoring Support in Higher Education: A Web-Based Tool Case Study in Engineering Degrees

• Published in: IEEE access Vol. 8; pp. 212818 - 212836
• Main Authors: Prada, Miguel Angel, Dominguez, Manuel, Vicario, Jose Lopez, Alves, Paulo Alexandre Vara, Barbu, Marian, Podpora, Michal, Spagnolini, Umberto, Pereira, Maria J. Varanda, Vilanova, Ramon
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Classification; Clustering; Colleges & universities; Data analysis; Data mining; Data visualization; Drop-out prediction; Education; educational data mining; Engineering; Engineering education; Europe; Higher education; Higher education institutions; Histograms; Impact analysis; performance prediction; Software; Software development tools; Students; Tutoring; Visual analytics; Visualization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3040858

This paper presents a web-based software tool for tutoring support of engineering students without any need of data scientist background for usage. This tool is focused on the analysis of students' performance, in terms of the observable scores and of the completion of their studies. For that purpose, it uses a data set that only contains features typically gathered by university administrations about the students, degrees and subjects. The web-based tool provides access to results from different analyses. Clustering and visualization in a low-dimensional representation of students' data help an analyst to discover patterns. The coordinated visualization of aggregated students' performance into histograms, which are automatically updated subject to custom filters set interactively by an analyst, can be used to facilitate the validation of hypotheses about a set of students. Classification of students already graduated over three performance levels using exploratory variables and early performance information is used to understand the degree of course-dependency of students' behavior at different degrees. The analysis of the impact of the student's explanatory variables and early performance in the graduation probability can lead to a better understanding of the causes of dropout. Preliminary experiments on data of the engineering students from the 6 institutions associated to this project were used to define the final implementation of the web-based tool. Preliminary results for classification and drop-out were acceptable since accuracies were higher than 90% in some cases. The usefulness of the tool is discussed with respect to the stated goals, showing its potential for the support of early profiling of students. Real data from engineering degrees of EU Higher Education institutions show the potential of the tool for managing high education and validate its applicability on real scenarios.