Employability of university leavers using a descriptive analytics case study

Shrestha, Subeksha, Fernando, Sandra, Patel, Preeti and Pun, Maya (2023) Employability of university leavers using a descriptive analytics case study. International Journal of Learning and Teaching, 9 (3). pp. 175-185. ISSN 2377-2905

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Abstract / Description

High and successful employment of university leavers has been a challenging key performance indicator for decades as a result of diverse life circumstances, life goals and travel need. The COVID pandemic and subsequent online delivery have added further challenges to the work-based placement and practical skill delivery, particularly in the STEM subject areas. The purpose of this study is to consider the recent past employment history and leavers data by looking into salient but yet unanswered questions about the activity of student leavers, employment type, relevance, and contribution of the degree programme and gain insight into course modification, employability support and market analysis. The latest Graduate Outcome Survey is in its infancy, and the current response rate is reportedly low. Therefore, a subset of Destination of Leavers from Higher Education (DHLE) data from an inner London university is analyzed and the results are visualized with findings. Among the participants of a computing case study, Computer Science graduates produced the highest earnings in comparison to any other courses. Additionally, undergraduate courses with the title of Computer Forensics or Business Computing produced the highest number of skilled workers in positions relevant to their qualification and produced the highest levels of employed Higher Education (HE) leavers after graduation, demonstrating that degrees that combine IT skills and other speciality skills have higher levels of proven employability.

Item Type: Article
Uncontrolled Keywords: Destination of Leavers from Higher Education (DHLE), employability, computing, survey
Subjects: 300 Social sciences > 330 Economics
300 Social sciences > 370 Education
Department: School of Computing and Digital Media
Depositing User: Sandra Fernando
Date Deposited: 07 Dec 2023 09:39
Last Modified: 09 Jan 2024 09:23
URI: https://repository.londonmet.ac.uk/id/eprint/8956


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