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Exploring student predictive model that relies on institutional databases and open data instead of traditional questionnaires

Sarker, Farhana, Tiropanis, Thanassis and Davis, Hugh C. (2013) Exploring student predictive model that relies on institutional databases and open data instead of traditional questionnaires At Linked Learning 2013: 3rd International Workshop on Learning and Education with the Web of Data (LILE2013), Brazil. 13 - 17 May 2013. 6 pp, pp. 413-418.

Record type: Conference or Workshop Item (Paper)

Abstract

Research in student retention and progression to completion is traditionally survey-based, where researchers collect data through questionnaires and interviewing students. The major issues with survey-based study are the potentially low response rates and cost. Nevertheless, a large number of datasets that could inform the questions that students are explicitly asked in surveys is commonly available in the external open datasets. This paper describes a new student predictive model for student progression that relies on the data available in institutional internal databases and external open data, without the need for surveys. The results of empirical study for undergraduate students in their first year of study shows that this model can perform as well as or even out-perform traditional survey-based ones.

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More information

e-pub ahead of print date: 14 May 2013
Venue - Dates: Linked Learning 2013: 3rd International Workshop on Learning and Education with the Web of Data (LILE2013), Brazil, 2013-05-13 - 2013-05-17
Organisations: Faculty of Physical Sciences and Engineering

Identifiers

Local EPrints ID: 353484
URI: http://eprints.soton.ac.uk/id/eprint/353484
ISBN: 978-1-4503-2038-2
PURE UUID: 05d13dc1-da4d-49fb-b97b-820c725489fd
ORCID for Thanassis Tiropanis: ORCID iD orcid.org/0000-0002-6195-2852
ORCID for Hugh C. Davis: ORCID iD orcid.org/0000-0002-1182-1459

Catalogue record

Date deposited: 07 Jun 2013 10:31
Last modified: 18 Jul 2017 04:03

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Contributors

Author: Farhana Sarker
Author: Hugh C. Davis ORCID iD

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