Partition projects across course lab sections using the Gale-Shapley algorithm before student-to-project assignment¶

Strategy · All strategies
Evidence · no claims cited

Description¶

To support courses with labs, the article describes a supplementary algorithm run before the main assignment: it computes teams needed per lab from an instructor-defined base team size, then treats lab-to-project allocation as "a version of the college admissions problem, solvable via the Gale-Shapley algorithm," seeking a stable matching without blocking pairs. Labs rank projects by average student preference; projects rank labs by average lab skill frequency of their required skills. This ensures all students on a project share a lab, facilitating consistent collaboration, after which the main skill-weighted algorithm runs within each lab.

Design Implications¶

Context¶

Requirements¶

• Instructor-defined base team size
• Student skill and preference survey data to build both preference lists

Constraints¶

• Gale-Shapley ensures stability but does not account for skill diversity within teams, so skill-based matching proceeds separately within each lab

Target Learners¶

• capstone students enrolled in lecture-plus-lab courses

Target Learning Goals¶

• overlapping team availability during lab hours for consistent collaboration

Affordances¶

• Three Stage Llm Team Formation Methodology

Related Strategies¶

Examples¶

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Key Sources¶

• Brandon Pardi, Garret Castro, Michael Pisman, Avash Adhikari, and Santosh Chandrasekhar. (2026). Improving Capstone Team Outcomes through Dynamic Skill Matching and Preference Alignment. https://arxiv.org/abs/2606.15572