SPEET: visual data analysis of engineering students performance from academic data
Year: 2018
Type: conference-paper
Abstract: This paper presents the steps conducted to design and develop an IT Tool for Visual Data Analysis within the SPEET (Student Profile for Enhancing Engineering Tutoring) ERASMUS+ project. The global goals of the project are to provide insight into student behaviours, to identify patterns and relevant factors of academic success, to facilitate the discovery and understanding of profiles of engineering students, and to analyse the differences across European institutions. Those goals are partly covered by the visualisations that the proposed tool comprises. Specifically, the aim is to provide support to the staff involved in tutoring, facilitating the exploratory analysis that might lead them to discover and understand student profiles. For that purpose, visual interaction and two main approaches are used, one based on the joint display of interconnected visualisations and the other focused on dimensionality reduction. The tool is validated on a data set that includes variables present in a typical student record.
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
Funders: European Commission, Erasmus+
OpenAlex ID: W2918292131