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Expanding Students’ Social Networks via Optimized Team Assignments

Published: 2022/07/14
  • Alessandro Hill
  • Steffen Peuker
  • Categories Network Optimization, Other Topics Tags classroom social networks, dynamic social network analysis, Education, mathematical optimization, student collaboration Short URL: https://optimization-online.org/?p=19100

    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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    Unpublished, submitted.

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    alternating direction method of multipliers approximation algorithms augmented lagrangian method bilevel optimization Branch-and-Bound branch-and-cut chance constraints column generation combinatorial optimization complexity compressed sensing conic optimization convex optimization cutting planes decomposition derivative-free optimization distributionally robust optimization duality dynamic programming first-order methods global convergence global optimization heuristics integer programming interior point methods large-scale optimization linear programming machine learning mixed-integer linear programming mixed-integer nonlinear programming mixed-integer programming nonconvex optimization nonlinear optimization nonlinear programming nonsmooth optimization optimal control optimization proximal point algorithm quadratic programming robust optimization semidefinite programming stochastic optimization stochastic programming trust-region methods unconstrained optimization

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