Feature Selection for UK Disabled Students’ Engagement Post Higher Education: A Machine Learning Approach for a Predictive Employment Model
- Submitting institution
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The University of West London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 11030
- Type
- D - Journal article
- DOI
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10.1109/ACCESS.2020.3018663
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 159530
- Volume
- 8
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2020
- URL
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- Supplementary information
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- Request cross-referral to
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- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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- Research group(s)
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- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is published in collaboration with colleagues at Solent University, Southampton. The paper presents the outcomes of the MALSEND project (£10,000), funded by Research, Innovation and Enterprise Centre, Solent University. The dataset used for the prediction model was provided by HESA. The prediction model was created using state-of-the-art machine learning algorithms, which received 96% accuracy. The employment prediction model is tailored for a number of disabilities and is our significant contribution to the area of special educational needs. The research is currently being extended to add new datasets and explore deep learning to enhance the prediction model further.
- Author contribution statement
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- Non-English
- No
- English abstract
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