Chrysikos, Alexandros, Ravi, Indrajitrakuraj, Stasinopoulos, Dimitrios, Rigby, Robert A. and Catterall, Stephen (2023) Retention of computing students in a London-based university during the Covid-19 pandemic using learned optimism as a lens: a statistical analysis in R. In: Computing Conference (formerly called Science and Information (SAI) Conference), 22-23 June 2023, London, UK.
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Abstract / Description
The aim of this research project is to investigate the low retention rate among the foundation and first year undergraduate students from the School of Computing and Digital Media in a London based university. Specifically, the research is conducted during the Covid-19 pandemic using learned optimism as a lens. The research will aid the university to improve retention rate as the overall dropout has been increasing in the last few years. The current study employed an exploratory investigation approach by using statistical modelling analysis in R to predict behavioural patterns. The quantitative data analysis conducted aims to support the efforts of the School of Computing and Digital Media of a London based university to re-evaluate its retention strategies in foundation and first year computing students. The main outcomes of the analysis is that students with a foreign qualification are optimistic, while students with other or not known qualification are mildly pessimistic. In addition, students with a BTECH, Higher Education diploma or A level qualification are generally more pessimistic especially if they are also black ethnicity, or are also not black ethnicity, aged under 34 and British.
Item Type: | Conference or Workshop Item (Paper) |
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Uncontrolled Keywords: | Learned Optimism, Student Retention, Computing, R Programming, Quantitative Research, Data Analysis |
Subjects: | 000 Computer science, information & general works 300 Social sciences 300 Social sciences > 370 Education |
Department: | School of Computing and Digital Media |
Depositing User: | Alexandros Chrysikos |
Date Deposited: | 23 Feb 2023 09:19 |
Last Modified: | 23 Feb 2023 09:19 |
URI: | https://repository.londonmet.ac.uk/id/eprint/8328 |
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