On the predictive power of university curricula

Antonia Azzini, Paolo Ceravolo, Nello Scarabottolo, Ernesto Damiani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

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.

Original languageBritish English
Title of host publicationProceedings of 2016 IEEE Global Engineering Education Conference, EDUCON 2016
Subtitle of host publication"Smart Education in Smart Cities"
PublisherIEEE Computer Society
Pages929-932
Number of pages4
ISBN (Electronic)9781467386333
DOIs
StatePublished - 19 May 2016
Event2016 IEEE Global Engineering Education Conference, EDUCON 2016 - Abu Dhabi, United Arab Emirates
Duration: 10 Apr 201613 Apr 2016

Publication series

NameIEEE Global Engineering Education Conference, EDUCON
Volume10-13-April-2016
ISSN (Print)2165-9559
ISSN (Electronic)2165-9567

Conference

Conference2016 IEEE Global Engineering Education Conference, EDUCON 2016
Country/TerritoryUnited Arab Emirates
CityAbu Dhabi
Period10/04/1613/04/16

Keywords

  • educational data mining
  • experimental study
  • students performances
  • university curricula

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