University Student Progressions and First Year Behaviour

R. Campagni, D. Merlini, M. C. Verri

2017

Abstract

Advanced mining techniques are used on educational data concerning university students. In particular, cluster analysis is used to predict the university careers of students starting from their first year performance and the results of the self assessment test. The analysis of the entire careers highlights three groups of students strongly affected by the results of the first year: high achieving students who start medium-high and increase their performance over the time, medium achieving students who maintain their performance throughout the entire course of study, low achieving students unable to improve their performance who often abandon their studies. This kind of knowledge can have practical implications on the involved laurea degree.

Paper Citation

in Harvard Style

Campagni R., Merlini D. and Verri M. (2017). University Student Progressions and First Year Behaviour . In Proceedings of the 9th International Conference on Computer Supported Education - Volume 2: CSEDU, ISBN 978-989-758-240-0, pages 46-56. DOI: 10.5220/0006323400460056

in Bibtex Style

@conference{csedu17,
author={R. Campagni and D. Merlini and M. C. Verri},
title={University Student Progressions and First Year Behaviour},
booktitle={Proceedings of the 9th International Conference on Computer Supported Education - Volume 2: CSEDU,},
year={2017},
pages={46-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006323400460056},
isbn={978-989-758-240-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Supported Education - Volume 2: CSEDU,
TI - University Student Progressions and First Year Behaviour
SN - 978-989-758-240-0
AU - Campagni R.
AU - Merlini D.
AU - Verri M.
PY - 2017
SP - 46
EP - 56
DO - 10.5220/0006323400460056