2 Universidad de Leon (SPAIN)
3 University Dunarea de Jos, Galati (ROMANIA)
4 Instituto Politecnico de Bragan├ža (PORTUGAL)
5 Opole University of Technology (POLAND)
6 Politecnico de Milano (ITALY)
About this paper:
Appears in: ICERI2017 Proceedings
Publication year: 2017
Pages: 1648-1656
ISBN: 978-84-697-6957-7
ISSN: 2340-1095
doi: 10.21125/iceri.2017.0514
Conference name: 10th annual International Conference of Education, Research and Innovation
Dates: 16-18 November, 2017
Location: Seville, Spain
Using data for making decisions is not new; companies use complex computations on customer data for business intelligence or analytics. Business intelligence techniques can discern historical patterns and trends from data and can create models that predict future trends and patterns. Analytics, broadly defined, comprises applied techniques from computer science, mathematics, and statistics for extracting usable information from very large datasets.

Data has always been generated and used to inform decision-making. However, most of this was structured and organized, through regular data collections, surveys, etc. What is new, with the invention and dominance of the Internet and the expansion of digital systems across all sectors, is the amount of unstructured data we are generating. This is what we call the digital footprint as the traces that individuals leave behind when they interact with their increasingly digital world. Data analytics is the process where data is collected and analyzed in order to identify patterns, make predictions, and inform business decisions. Our capacity to perform increasingly sophisticated analytics is changing the way we make predictions and decisions, with huge potential to improve competitive intelligence.

This paper presents the collaborative experience that is under development as the European ERASMUS+ project SPEET (Student Profile for Enhancing Engineering Tutoring). This project goal emerges from the potential synergy among:
a) the huge amount of academic data actually existing at the academic departments of faculties and schools, and
b) the maturity of data science in order to provide algorithms and tools to analyze and extract information from what is more commonly referred to Big Data.

A rich picture can be extracted from this data if conveniently processed. The main purpose of this project is to apply statistical learning methods algorithms to process this data in order to extract information about and to identify student profiles. An idea of the student profile we are referring to within the project scope is, for example: Students that finish degree on time, Students that are blocked on a certain set of subjects, Students that leave degree earlier, etc.

The final aim of the project is the development of an IT tool to be produced in order to help disseminate the study and allow other faculties and schools to conduct similar study for the benefit of the students and their performances. In this paper, the first steps and conclusions are presented. Specifically, the definition of the academic data format, arquitecture of the IT tool, proposed algorithms for data exploitation as well as first results concerning partner data analysis.

The main question that will be asked once these student profiles are determined is regarding once a new student gets enrolled, could we know as in advance as possible which profile this student obeys to? This would definitively help tutoring these students and elaborate specific recommendations in order to avoid early leaving, increase motivation and better pass blocking subjects, etc.
Educational Data Mining, ERASMUS+, Student tutoring.