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

Miguel Angel Prada*, Manuel Dominguez, Jose Lopez Vicario, Paulo Alexandre Vara Alves, Marian Barbu, Michal Podpora, Umberto Spagnolini, Maria J.Varanda Pereira, Ramon Vilanova

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number9272294
Pages (from-to)212818-212836
Number of pages19
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Keywords

  • Drop-out prediction
  • educational data mining
  • performance prediction
  • visual analytics

Fingerprint

Dive into the research topics of 'Educational Data Mining for Tutoring Support in Higher Education: A Web-Based Tool Case Study in Engineering Degrees'. Together they form a unique fingerprint.

Cite this