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.
Highlights
T HE availability of data is a relevant asset for institutions, because data analysis can be used to help in decision making both in the day-to-day operative as well as strategically
Most applications of visual analytics in education have been constrained to the analysis of data obtained from the interaction of students with learning management systems and other learning support platforms
PROPOSED METHODS As stated in the introduction, the ultimate aim of this work is the development of a web-based software tool for the support of predictive modeling activities of tutoring staff in a transnational context focused on engineering students, where basic data from university administration is assumed to be available
Summary
T HE availability of data is a relevant asset for institutions, because data analysis can be used to help in decision making both in the day-to-day operative as well as strategically. Author et al.: Preparation of Papers for IEEE TRANSACTIONS and JOURNALS particular, the universities collect every year data from their students including demographic details (e.g., age, address or socio-economic status) and information about their admission and academic performance (school, degree, course path, and even examination results). Sometimes this information is augmented with data obtained from questionnaires and field observations or with information about their career after graduation. In [22], interactive visualizations were used for the analysis of the correlations between activity patterns in MOOCs (massive open online courses) and dropout
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