A machine learning conceptual approach to detect patterns in subject areas and performance of university students with special educational needs and disabilities (MALSEND)

Sobnath, Drishty, Isiaq, Olufemi, Rehman, Ikram and Nasralla, Moustafa (2019) A machine learning conceptual approach to detect patterns in subject areas and performance of university students with special educational needs and disabilities (MALSEND). In: Springer ICT Congress (ICICT), 25-26 Feb 2019, London, UK. (In Press)

[img]
Preview
PDF
A Machine Learning Conceptual Approach to Detect Patterns in Subject Areas and Performance of University students with Special Educational Needs and Disabilities (MALSEND).pdf - Accepted Version

Download (137kB) | Preview

Abstract

Universities and colleges in the UK welcome almost 30,000 disabled students each year. Re-search shows that the dropout from education in the EU for the disabled is at 31.5%, much higher compared to only 12.3% for non-disabled students. Supporting young students who require special educational needs in pursuing higher education is an ambitious and necessary step that needs to be adopted by tertiary education providers worldwide. We propose, MALSEND, a project aiming to develop a platform based on machine and human intelligence to understand learning disability patterns in Higher Education. The platform will analyse da-tasets from universities in the previous years and will help to discover any trends in subject areas and performance among autistic students, dyslexic students or students having attention deficit hyperactive disorder (ADHD), among others. Analysing variables such as students’ courses, modules, performances and other engagement-indices will give new insights on re-search questions, career advice and institutional policy making. This paper describes the activ-ities of the development phases of this concept.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: MALSEND, machine learning, special educational needs, performance, unsupervised learning
Subjects: Computing
Depositing User: Ikram Rehman
Date Deposited: 10 Jul 2019 08:24
Last Modified: 10 Jul 2019 08:24
URI: http://repository.uwl.ac.uk/id/eprint/6226

Downloads

Downloads per month over past year

Actions (login required)

View Item View Item

Menu