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Linked data, data mining and external open data for better prediction of at-risk students

Linked data, data mining and external open data for better prediction of at-risk students
Linked data, data mining and external open data for better prediction of at-risk students
Research in student retention is traditionally survey-based, where researchers use questionnaires to collect student data to analyse and to develop student predictive model. The major issues with survey-based study are the potentially low response rates, time consuming and costly. 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 that uses commonly available external open data instead of traditional questionnaires/surveys to spot ‘at-risk’ students. Considering the promising behavior of neural networks led us to develop student predictive models to predict ‘at-risk’ students. 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. The prediction performance of this study was also compared with that of logistic regression approach. The results shows that neural network slightly improved the overall model accuracy however, according to the model sensitivity, it is suggested that logistic regression performs better for identifying ‘at-risk’ students in their programme of study
652-657
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. (2014) Linked data, data mining and external open data for better prediction of at-risk students At 2nd International Conference on Control, Decision and Information Technologies (CoDIT) 2014, France. 03 - 05 Nov 2014. 6 pp, pp. 652-657. (doi:10.1109/CoDIT.2014.6996973).

Record type: Conference or Workshop Item (Other)

Abstract

Research in student retention is traditionally survey-based, where researchers use questionnaires to collect student data to analyse and to develop student predictive model. The major issues with survey-based study are the potentially low response rates, time consuming and costly. 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 that uses commonly available external open data instead of traditional questionnaires/surveys to spot ‘at-risk’ students. Considering the promising behavior of neural networks led us to develop student predictive models to predict ‘at-risk’ students. 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. The prediction performance of this study was also compared with that of logistic regression approach. The results shows that neural network slightly improved the overall model accuracy however, according to the model sensitivity, it is suggested that logistic regression performs better for identifying ‘at-risk’ students in their programme of study

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More information

e-pub ahead of print date: 16 December 2014
Venue - Dates: 2nd International Conference on Control, Decision and Information Technologies (CoDIT) 2014, France, 2014-11-03 - 2014-11-05
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 373151
URI: http://eprints.soton.ac.uk/id/eprint/373151
PURE UUID: 7b66ce0c-51b0-4214-a9a4-a26fc8de833d
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: 12 Jan 2015 13:28
Last modified: 17 Jul 2017 21:36

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Contributors

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

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