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Authors: R. Campagni ; D. Merlini and M. C. Verri

Affiliation: Università di Firenze, Italy

ISBN: 978-989-758-240-0

ISSN: 2184-5026

Keyword(s): Educational Data Mining, Clustering, Student Progressions, Self Assessment Test.

Related Ontology Subjects/Areas/Topics: Computer-Supported Education ; Learning/Teaching Methodologies and Assessment ; Metrics and Performance Measurement

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.

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Paper citation in several formats:
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, ISSN 2184-5026, pages 46-56. DOI: 10.5220/0006323400460056

@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},
}

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

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