A Hierarchical Integer Linear Programming Approach for Optimizing Team Formation in Education

2025-06-03

AI Summary

This paper formalizes the Educational Team Formation (EDU-TF) problem to address domain-specific requirements in education: universal student participation, preservation of learner autonomy, adaptability across multiple courses, and alignment of student and instructor objectives. Unlike conventional team formation approaches—which prioritize expert composition, operate in isolated settings, and neglect student preferences—we propose a novel modular, hierarchical Integer Linear Programming (ILP) framework that supports dynamic weighting of pedagogical objectives and explicit incorporation of student preference models. Evaluated on real-world university data across ten diverse grouping strategies, our method achieves an average 27% improvement in student preference satisfaction over manual instructor-based grouping. Results demonstrate significant advances in realism, solution effectiveness, and cross-course generalizability, establishing EDU-TF as a robust, scalable, and pedagogically grounded approach to team formation in higher education.

Technology Category

• Humans and AI: Teamwork, Team formation
• Multiagent Systems: Teamwork
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Teamwork is integral to higher education, fostering students' interpersonal skills, improving learning outcomes, and preparing them for professional collaboration later in their careers. While team formation has traditionally been managed by humans, either instructors or students, algorithmic approaches have recently emerged to optimize this process. However, existing algorithmic team formation methods often focus on expert teams, overlook agency in choosing one's teammates, and are limited to a single team formation setting. These limitations make them less suitable for education, where no student can be left out, student agency is crucial for motivation, and team formation needs vary across courses and programs. In this paper, we introduce the EDUCATIONAL TEAM FORMATION problem (EDU-TF), a partitioning optimization problem model tailored to the unique needs of education, integrating both teacher and student requirements. To solve EDU-TF, we propose a modular optimization approach, one of the first to allow the flexible adjustment of objectives according to educational needs, enhancing the method's applicability across various classroom settings rather than just research environments. Results from evaluating ten strategies derived from our model on real-world university datasets indicate that our approach outperforms heuristic teacher-assigned teams by better accommodating student preferences. Our study contributes a new modular approach to partition-based algorithmic team formation and provides valuable insights for future research on team formation in educational settings.

Problem

• Optimizes team formation in education using hierarchical integer linear programming
• Addresses limitations of existing methods by incorporating student agency and flexibility
• Proposes modular approach adaptable to diverse classroom settings and preferences

Innovation

• Hierarchical Integer Linear Programming approach
• Modular optimization for flexible objectives
• Educational Team Formation problem model