# Assigning Candidate Tutors to Modules: A Preference Adjustment Matching Algorithm (PAMA)
## 4 The Preference Adjustment Matching Algorithm (PAMA)
### The PAMA Logic
While its successful application at the Hellenic Open University, PAMA does have limitations, the algorithm prioritizes the preferences of tutors, and in specific cases, when the available slots exceed the total number of tutors, it is possible for a module to be left without assigned tutors. In such cases, a future improvement could involve implementing a restriction that ensures modules are not left completely empty but instead have partial assignments, thus covering some slots in a proportional manner. Another limitation of the algorithm is its sensitivity to the way tutor preferences are formulated. The algorithm emphasizes satisfying these preferences, which, however, may be random or defined by the tutors without much consideration. Finally, the algorithm has a complexity of O(M \(*\) T \(*\) P), which is generally acceptable, but for very large-scale applications, it may become computationally expensive. Future research efforts involve the utilization of multi-criteria optimization techniques to provide more balance in requirements and preferences, considering parameters such as prior teaching experience, minimizing changes in successful assignments from past years, student evaluation, and learning outcomes. Moreover, effort will also be made to support quota-based candidate allocations (for example, ensuring that 10% are early-career scientists). Dynamic methods are another promising approach where adaptive mechanisms change algorithm parameters dynamically according to prior performance. Considering measures such as drop out and module completion rate, the algorithm would learn automatically to adjust the balance between tutor preferences and module rankings. This approach would create a feedback loop in which well-performing assignment patterns are reinforced, and poor-performing ones are updated, leading to continuous improvement within the matching process and improved academic performance from one academic term to the next. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Academic** & **Modules** & **Available** & **Tutors** & **Tutors’** & **PARTA Time** & **GS Time** & **Differences** \\
**Period** & **Modules** & **Positions** & **(Candidates)** & **Preferences** & **(sec)** & **(sec)** & **(Assignments)** \\ \hline
2024-2025 & 620 & 1906 & 3937 & 9768 & 0.464314 & 0.015043 & 9 \\ \hline
2023–2024 & 565 & 2155 & 4399 & 9975 & 0.543067 & 0.017999 & 2 \\ \hline
2022–2023 & 476 & 2047 & 4463 & 9939 & 0.494699 & 0.019013 & 0 \\ \hline
2021–2022 & 421 & 2304 & 4419 & 9605 & 0.547184 & 0.023001 & 2 \\ \hline
2018–2019 & 39 & 173 & 308 & 575 & 0.003999 & 0.000998 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of PAMA Execution in Comparison to the Gale–Shapley Algorithm in the Context of Assignments Over Five Academic Periods. Machine learning algorithms could address this automatically, predicting tutors' preferences based on historical teaching assignments, academic backgrounds, and research interests, eliminating the need for explicit preference collection. Apart from that, standards of fairness would assist in dealing with equity problems by distributing the same percentage of introductory and advanced courses to every faculty member in a way that prevents the conditions where new tutors receive only large introductory classes and veteran faculty receive only small, advanced classes. These metrics would also monitor gender balance in high-profile course assignments to construct a more equitable learning climate supportive of faculty growth at all career stages. Conceptualization, N.K. ; methodology, N.K., G.V., D.P. and V.S.V. ; software, N.K. ; validation, N.K. and D.P. ; formal analysis, D.P. ; resources, N.K. ; data curation, N.K. ; writing--original draft preparation, N.K., G.V., D.P. and V.S.V. ; writing--review and editing, N.K., G.V., D.P. and V.S.V. ; visualization, N.K. and D.P. ; supervision, V.S.V. All authors have read and agreed to the published version of the manuscript. This research received no external funding.