A Management Support System for Directing and Monitoring the Activities of University Academic Staff

Finlay, Paul N.; Gregory, Geoffrey

doi:10.1038/sj/jors/0450604

Cited by 2 publications

(5 citation statements)

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“…The development and promotion of academic careers depends highly on the achievement and accomplishment in teaching, research and community services. What is notable from this practice has been the tendency to segment research and teaching, as a result of greater rewards given to research than to teaching (Finlay and Gregory, ; Nixon, ).…”

Section: Contextmentioning

confidence: 99%

Performance Indicators in Indonesian Universities: The Perception of Academics

Gaus

Hall

2016

Higher Education Quarterly

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This study aimed to explore the perceptions of Indonesian academics towards the implementation of Performance Indicators (PIs) on teaching and research. The study was a case study using semi‐structured interviews, conducted with 30 academics in three state universities in Indonesia. The results of the study revealed academics believed that outcome indicators used in PIs for control mechanisms have neglected the humane side of the teaching and learning process, and scientific research. Academics also perceived that PIs are valuable for improving daunting workplace conditions and poor institutional culture if they are used for the improvement of internal management. The findings of this study, if seriously considered, may provide information for policy makers to revisit and refine the existing indicators used to measure academics’ teaching and research performance. This can be implemented by taking into account the collegial nature, organised anarchy, of a bottom‐up approach that emphasises the emancipation of academics to determine what elements of their teaching and research should be covered and be measured. This research argues that indicators used for PIs can be a blend of quantitative and qualitative measures, which compensate for each other.

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Section: Contextmentioning

confidence: 99%

Performance Indicators in Indonesian Universities: The Perception of Academics

Gaus

Hall

2016

Higher Education Quarterly

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“…Multiple, un-related research work [26][27] [28] carried out across universities re-affirmed the need for a well-structured approach towards a quantitative workload model. A notable step in this direction was the Supervision, Teaching, Administration and Research Model or 'STAR' as introduced by Finlay and Gregory [21]. In this model;  Supervision referred to the guiding of undergraduate students through their projects and industrial training assignments, as well as the research work of post-graduate students.…”

Section: The Star Modelmentioning

confidence: 99%

“…Teaching, research, administration/service, and student supervision -these are the four corecomponents of an average faculty's workload, as have been described in [21]. However, the quantization and comparison of different types of workload is a challenging task, and makes the resulting workload model extremely complex and difficult to comprehend from an external (faculty) point of view.…”

Section: Components Of Faculty Workloadmentioning

confidence: 99%

“…The teaching and supervision components of the workload are the ones that can be adjusted the most by the administration. For further simplification, we combine the teaching and supervision aspects of the popular STAR workload [21] into teaching itself -with a distinction of 'formal teaching' for lectures/tutorials/labs and 'informal teaching for student supervisions.…”

Section: Teaching Contributions and Relaxationsmentioning

confidence: 99%

“…Research contributions have been measured in terms of publications, patents and research grants secured in existing literature [21] The value of parameters can vary. An example set of values is given below.…”

Section: Research Relaxationmentioning

confidence: 99%

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Algorithms and data structures for automated teaching workload allocation

Sood¹

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Fair allocation of teaching workload to academic staff is one of the most important administrative duties of an educational institution. However, it is often managed manually with limited data and, often with an arbitrary set of constraints due its complexity, thereby resulting in sub optimal allocations. The objective of this research is to develop efficient methods to automatic the realization of a fair and transparent workload allocation system. A detailed exploration of past attempts, aimed at improving the workload allocation at tertiary institutions, was undertaken to gain a deeper understanding of the various approaches and challenges associated with the process. In particular, it was observed that various approaches were adopted to quantify a variety of components of the workload during the allocation process covering teaching, research and administrative duties. This motivated the development of an efficient time-based workload model called the Workload Unit (WLU) Model as the quantitative framework for measuring and comparing different faculty workloads. This involved the utilization of a combination of weightages corresponding to nature of teaching duties, the number and appointment of faculty as well as their availability for undertaking teaching duties, and attributes (such as class size, level of course, preparation requirements, etc.) of the courses to be offered. In addition, the proposed workload model takes into account the research activeness and administrative duties carried out by each faculty. The resulting workload is separated into two components, namely formal and informal teaching for the purpose of systematic workload allocation. Next, an efficient workload allocation method for formal teaching comprising of lectures, tutorials and labs was proposed by taking into consideration of faculty preference and performance, School policies and priorities and availability of teaching expertise. A novel combined cost function was proposed to determine the feasibility of individual allocations called the Feasibility Index (FI). A greedy approach, with activity-type and course-priority heuristics, is implemented to optimize the quality of the allocations. The proposed approach was validated against the workload allocations made at one of the schools of a University consisting of 100 teaching faculty, 120 courses and 1500 students. The resulting allocations improved workload distribution and quality of assignments over existing manual processes,

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A Management Support System for Directing and Monitoring the Activities of University Academic Staff

Cited by 2 publications

References 0 publications

Performance Indicators in Indonesian Universities: The Perception of Academics

Performance Indicators in Indonesian Universities: The Perception of Academics

Algorithms and data structures for automated teaching workload allocation

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