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Figure 1: Distribution of student preferences for academic staff members as potential supervisors TD. EOS SE IEE IEEE ISIEE! SOI IEEE IESE! EIA SO SISN IEA IEE he proposed algorithm for student-supervisor allocation was implemented in R using the Ips ickage, and the results were obtained on a real dataset of 34 students and 9 academic staff memb igure 1 presents the frequencies of students’ selections for each of the 9 academic staff members as t ‘ and 2™ preferences. It appears that P8 was the most popular among students as potential supervi espite the variation in student preference across the academic staff members, the algorithm was abl ‘ovide an optimal solution that minimised the workload imbalances among the staff members. | location process was deemed successful, with an objective function value of 60 indicating that ymnstraints were met appropriately. Further, the average assigned preference score of 1.76 reflects ost of the 34 students were able to obtain one of their first or second choice preferred supervisors own in Figure 2. This suggests that the allocation process aligned well with the student preferences, sulted in a satisfactory solution for both the staff members and the student population.

Figure 1 Distribution of student preferences for academic staff members as potential supervisors TD. EOS SE IEE IEEE ISIEE! SOI IEEE IESE! EIA SO SISN IEA IEE he proposed algorithm for student-supervisor allocation was implemented in R using the Ips ickage, and the results were obtained on a real dataset of 34 students and 9 academic staff memb igure 1 presents the frequencies of students’ selections for each of the 9 academic staff members as t ‘ and 2™ preferences. It appears that P8 was the most popular among students as potential supervi espite the variation in student preference across the academic staff members, the algorithm was abl ‘ovide an optimal solution that minimised the workload imbalances among the staff members. | location process was deemed successful, with an objective function value of 60 indicating that ymnstraints were met appropriately. Further, the average assigned preference score of 1.76 reflects ost of the 34 students were able to obtain one of their first or second choice preferred supervisors own in Figure 2. This suggests that the allocation process aligned well with the student preferences, sulted in a satisfactory solution for both the staff members and the student population.

Related Figures (2)

The quality of the solution was evaluated based on the number of students assigned to their 1‘ and ‘ preferences, using Receiver Operating Characteristic (ROC) analysis. The True Positive Rate (TPR) calculated as the proportion of students who were assigned to one of their top two preferred superviso and the False Positive Rate (FPR) as the proportion of students assigned to supervisors that were not o of their top two preferences. Figure 3 depicts a ROC curve that represents the trade-off between the T] and FPR of the allocation output. The resulting ROC curve is located above, and deviating from t reference line, where the TPR is high and the FPR is low, indicating that the algorithm’s over performance is commendable; it successfully matches a sizable proportion of students to their preferr supervisors, while efficiently minimising the number of students assigned to their less preferr supervisors.
The quality of the solution was evaluated based on the number of students assigned to their 1‘ and ‘ preferences, using Receiver Operating Characteristic (ROC) analysis. The True Positive Rate (TPR) calculated as the proportion of students who were assigned to one of their top two preferred superviso and the False Positive Rate (FPR) as the proportion of students assigned to supervisors that were not o of their top two preferences. Figure 3 depicts a ROC curve that represents the trade-off between the T] and FPR of the allocation output. The resulting ROC curve is located above, and deviating from t reference line, where the TPR is high and the FPR is low, indicating that the algorithm’s over performance is commendable; it successfully matches a sizable proportion of students to their preferr supervisors, while efficiently minimising the number of students assigned to their less preferr supervisors.
Figure 3: ROC curve showing the trade-off between correctly allocated students and those not correctly allocated based on their desired preferences.
Figure 3: ROC curve showing the trade-off between correctly allocated students and those not correctly allocated based on their desired preferences.
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