Abstract
The class social network is a momentous factor when it comes to educational, personal and professional student success as well as achieving course learning outcomes. Students and teachers benefit from expanded network connectivity via augmented engagement, more inclusivity, and efficient diffusion of information. We present a novel method for positively influencing the class social network. We develop an in-class grouping strategy based on optimization and sociocentric network analysis that pragmatically expands the students’ social networks. In contrast to existing routines, our technique focuses on maximizing individual student opportunities to establish new ties. Based on the knowledge of existing connections, our procedure systematically optimizes the overall number of new ties that can be established during a team project. Our data-driven approach is designed for practical use in class. We show that the underlying combinatorial problem of maximizing unrelated intra-team students can be modeled as a bin packing variant. Using an integer programming formulation, we demonstrate the efficient spreadsheet implementation. We discuss model extensions to account for high-density networks, team balancing, and teammate forcing and forbidding, allowing for hybridization using existing grouping techniques. In an empirical study, we provide evidence for the efficacy of our approach using data from 10 industrial engineering classes with 253 students and 77 project teams - in both face-to-face and virtual modes. We demonstrate the impact of our grouping method compared to random-assignment, self-selection, and maximizing existing intra-team ties. We report an impressive 62% increase of ties compared to only 17% when self-assigning.
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Notes
The template spreadsheet is available from the authors upon request.
Computations are performed using the MAPLE Graph Theory package (www.maplesoft.com).
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Acknowledgements
We are indebted to Nian Cheng and Jose Macedo for supporting the collection of class social network data and letting us generate project teams in their classes. Furthermore, we thank John Pan for his valuable comments. We are thankful for the reviewers help in improving this paper.
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Appendices
Appendix A Example for mathematical formulation
The IP formulation (F) for computing the 3 optimized teams in the example class from Sect. 4.1 is given in an LP file format below. The formulation contains 39 binary variables and 48 constraints. Note that Inequality (3) is split into two inequalities to comply with standard LP notation for solvers.
Appendix B Example for social network and grouping effect
An example class social network snapshots from our case study—before and after the team project—with optimized team assignments (Class J, IME 305, Operations Research II) is presented below. The number of ties among the 30 students increased from 57 to 108, as illustrated in Fig. 10. In Fig. 11, the 32 newly created intra-group ties (left) which presumably stem from project interaction, and the 30 new ties that were established outside of the projects (right) are depicted.
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Hill, A., Peuker, S. Expanding students’ social networks via optimized team assignments. Ann Oper Res 332, 1107–1131 (2024). https://doi.org/10.1007/s10479-023-05492-2
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DOI: https://doi.org/10.1007/s10479-023-05492-2