Work Recognition Model for a Higher Education Unit

Pedro Martinez-Vazquez*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Higher Education frameworks have a strong focus on increasing students’ satisfaction. This vision increases expectations on academic performance that often derive from stringent output measures for staff. The combination of internal and external demands for workers, such as those derived from their interaction with society, could create inadequate working conditions, which highlights the need to develop objective, rational, and robust mechanisms to distribute roles and responsibilities more fairly. This paper introduces a work recognition model for managing higher education units. It accounts for individual effort per discretised academic activity over a range of teaching, research, and administration duties. The model integrates standard academic activities into a mathematical framework characterised by continuous derivable functions that can be used to visualise fluctuations of workload across a complete academic year. The established framework enables optimisation of workloads through redistribution, moving away from diagnosing issues experienced by individuals subject to imbalanced of unfair workloads, to propose a practical solution. It is also possible to enhance the architecture of the proposed model to incorporate further activity or allowances and potentially increase robustness. In addition, the numerical model is relatively easy to implement in a variety of platforms. The proposed model was tested on a case study including 26 academics, mixing three legged and teaching-focussed contracts, showing how it decreases the relative differences of workload in a proportion of 4:1, after redistribution.
Original languageEnglish
Article number2248912
Number of pages18
JournalCogent Education
Volume10
Issue number2
Early online date1 Sept 2023
DOIs
Publication statusPublished - 11 Dec 2023

Keywords

  • project allocation model
  • staff workload
  • numerical algorithm

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