In this study we analyzed the curricula of 65 university students to investigate the impact of activities progression on student performances. Clustering curricula based on activity order and type we discovered a significant incidence on performance, validating the predictive power of curricula. Nevertheless, we discovered that the characterization of clusters is mainly due to non mandatory activities, selected by a student to personalize his curriculum, while activity order is very less relevant. This observation rejects the idea that activities progression has impact on performance, resulting rather as a consequence of student choices.

On the predictive power of university curricula / A. Azzini, P. Ceravolo, N. Scarabottolo, E. Damiani - In: Global Engineering Education Conference (EDUCON)[s.l] : IEEE, 2016. - pp. 1-4 (( convegno Global Engineering Education Conference tenutosi a Abu Dhabi nel 2016.

On the predictive power of university curricula

A. Azzini
Primo
;
P. Ceravolo
Secondo
;
N. Scarabottolo
Penultimo
;
E. Damiani
2016

Abstract

In this study we analyzed the curricula of 65 university students to investigate the impact of activities progression on student performances. Clustering curricula based on activity order and type we discovered a significant incidence on performance, validating the predictive power of curricula. Nevertheless, we discovered that the characterization of clusters is mainly due to non mandatory activities, selected by a student to personalize his curriculum, while activity order is very less relevant. This observation rejects the idea that activities progression has impact on performance, resulting rather as a consequence of student choices.
educational data mining; experimental study; university curricula; students performances
Settore INF/01 - Informatica
2016
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/378588
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