Algorithms and data structures for automated teaching workload allocation

Abstract

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,

Holistic teaching workload allocation for research-intensive universities

“…They also proposed using research and service workloads by directly relaxing the teaching workload expectations from a faculty. [12].…”

“…Sood [12] identified another management priority -the need to ensure that the best faculty are allocated to earlier-year courses, which are critical in setting the foundation for the academic journey of students. This was identified as a key priority, as it ensures that the students get the right amount of guidance in the early years, while they gain more autonomy and independence in the later years, and build the skills to self-learn.…”

“…In 2017, Sood [12] explored a greedy solution because it "offers multiple advantages, especially over other alternatives like Genetic Algorithms and Linear programming. It is simple to implement, and as a result simple to explain in an organizational setting like a university".…”

“…Sood also explored the application of the Kuhn-Munkres algorithm, commonly known as the Hungarian algorithm, to allocating Student Final-Year Projects. However, this wasn't explored for the course allocation problem [12]. The Hungarian Algorithm [22] is the go-to algorithm for one-to-one matrix-optimization problems, but it isn't built to directly apply to many-to-many solutions.…”

“…Research output was also found to be a lagging indicator of the research workload. Sood's approach of using the number of research staff supervised by a faculty to measure research workload proved effective in this regard [12]. However, more research is required to account for the economies of scale achieved by the hierarchical nature of research supervision.…”