Advisor(s)
Abstract(s)
SPEET project is aimed at exploiting the potential synergy among the huge amount
of academic data actually existing at universities and the maturity of data science in
order to provide tools to extract information from students’ data. A rich picture can
be extracted from this data if conveniently processed. The purpose of this project is
to apply data mining algorithms to process this data in order to extract information
about and to identify student profiles.
In this document, the results obtained at SPEET project under the development
of the data mining tools are presented. More specifically, two mechanisms have
been developed: a clustering/classification scheme of students in terms of academic
performance and a drop-out prediction system.
The document starts by addressing the motivation of the development of data
mining tools along with the considerations taken into account for academic data
gathering. These considerations include the proposed unified dataset format and
some details about confidentiality issues. Next, the students’ clustering and classification
schemes are presented in detail. More specifically, a description of the
considered machine learning algorithms can be found. Besides, a discussion of
obtained results when considering data belonging to the different SPEET project’s
partners is addressed. Results show how groups of clusters can be automatically
identified and how new students can be classified into existing groups with a high
accuracy. Finally, the implemented drop-out prediction system is considered by
presenting several algorithms alternatives. In this case, the evaluation of the dropout
mechanism is focused on one institution, showing a prediction accuracy around
91 %.
Algorithms presented at this document are available at repositories or inline
code format, as accordingly indicated.
Description
Keywords
Academic analytics Learning analytics Big data in education Educational data mining Student profile Dropout prevention
Citation
Vicario, Jose; Vilanova, Ramon; Bazzarelli, M.; Paganoni, Anna; Spagnolini, Umberto; Torrebruno, Aldo; Prada, Miguel; Morán, Antonio; Dominguez, Manuel; Pereira, Maria João; Alves, Paulo; Podpora, Michal; Barbu, Marian (2018). Data mining tool for academic data exploitation: selection of most suitable algorithms. ERASMUS+KA2/KA203