Abstract:
The growing education sector of the current era offers a lot of choices and different paths for the students as well as for the educators. They now have a lot more opportunities to work with someone who has specific skills or particular fields of interests. At the same time, more options poses more challenges for the students and educators while narrowing down the list of potential fellows. Finding a desired match from this large pool of students and supervisors/grad-schools, where both student and supervisor have different preferred criteria, is a mammoth task. Our study proposes a way to cut down this list to a smaller size and help both the parties in finding a suitable match. The solution we suggest is, ranking the students and supervisors using AHP and TOPSIS methods according to each of their requirements, preferences and qualities. This ultimately produces a customized list of potential matches for each of the students and supervisors and the problem becomes a linear single-objective optimization problem. From this ranked list we then pair the students with supervisors while taking into account the satisfaction of both parties about the matching. The major contribution of this work is it suggests a way to match pairs based on their criteria rather than over the participants themselves. Finally, we provide the results of our solution that proves the effectiveness of our model above other solutions which only ensures the satisfaction of one of the parties involved in such a match and also discuss the results with comparison to algorithms such as Genetic Algorithm which takes into account both parties satisfaction.
