Exploring student predictive model that relies on institutional databases and open data instead of traditional questionnaires
Exploring student predictive model that relies on institutional databases and open data instead of traditional questionnaires
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.
978-1-4503-2038-2
413-418
Sarker, Farhana
e98a17ca-8004-431c-8dd6-f8254bb82257
Tiropanis, Thanassis
d06654bd-5513-407b-9acd-6f9b9c5009d8
Davis, Hugh C.
1608a3c8-0920-4a0c-82b3-ee29a52e7c1b
Sarker, Farhana
e98a17ca-8004-431c-8dd6-f8254bb82257
Tiropanis, Thanassis
d06654bd-5513-407b-9acd-6f9b9c5009d8
Davis, Hugh C.
1608a3c8-0920-4a0c-82b3-ee29a52e7c1b
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.
Linked Learning 2013: 3rd International Workshop on Learning and Education with the Web of Data (LILE2013), , Rio de Janeiro, Brazil.
13 - 17 May 2013.
.
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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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), , Rio de Janeiro, 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
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Date deposited: 07 Jun 2013 10:31
Last modified: 15 Mar 2024 03:31
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Contributors
Author:
Farhana Sarker
Author:
Thanassis Tiropanis
Author:
Hugh C. Davis
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