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The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
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Finding optimal mentor-mentee matches: A case study in applied two-sided matching.

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This study applies Two-Sided Matching to pair mentors and mentees in higher education. Evolutionary heuristics and local search effectively identify high-quality matches based on participant preferences.

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Area of Science:

  • Operations Research
  • Higher Education Administration
  • Computer Science

Background:

  • Two-Sided Matching is a key algorithm for resource allocation based on preferences.
  • Existing applications include college admissions and school choice systems.
  • This research extends Two-Sided Matching to the higher education mentor-mentee context.

Purpose of the Study:

  • To adapt and evaluate Two-Sided Matching algorithms for mentor-mentee pairings.
  • To address the complexity of matching with both positive and negative preferences (avoiding certain matches).
  • To identify optimal algorithms for generating high-quality mentor-mentee matches.

Main Methods:

  • Comparison of various approximation algorithms and heuristics for Two-Sided Matching.
  • Evaluation of algorithms' effectiveness in finding desirable matchings.
  • Utilized a combination of evolutionary heuristics and local search techniques.

Main Results:

  • The general formulation of Two-Sided Matching with avoidance preferences is NP-hard.
  • A hybrid approach combining evolutionary heuristics and local search demonstrated superior performance.
  • This method successfully generated high-quality mentor-mentee matches close to ideal preferences.

Conclusions:

  • The developed matching approach is effective for higher education mentorship programs.
  • Combining evolutionary heuristics and local search offers a robust solution for complex matching problems.
  • This facilitates improved mentor-mentee relationships through preference-based allocation.