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Social Network Methods for Assigning Students to Teams.

William B Hansen1, Kelly L Rulison2

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Summary
This summary is machine-generated.

Network-informed student team assignments can improve classroom interventions. Using social network data and student interests, with a pruning algorithm, created the most effective teams for positive peer influence.

Keywords:
AdolescentsInterventionSchoolsSocial networkTeam formation

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Area of Science:

  • Educational Psychology
  • Social Network Analysis
  • Intervention Science

Background:

  • Classroom organization often involves student grouping.
  • Network-informed interventions leverage peer influence for positive outcomes.
  • Optimal methods for social network-based student team assignment remain under-explored.

Purpose of the Study:

  • To identify and compare seven distinct methods for creating student teams using various data sources and assignment algorithms.
  • To evaluate the effectiveness of different team assignment strategies based on network data and student characteristics.

Main Methods:

  • Survey data from 247 5th-8th graders in rural schools were collected, assessing social networks, sociability, values, interests, and school bonding.
  • Seven team assignment methods were tested, forming 4-person teams around popular students with normal school bonding scores.
  • At-risk students were integrated in later assignment rounds, and team assignments were evaluated on team bonding, affiliation patterns, and shared values/interests.

Main Results:

  • Two methods demonstrated the most promising outcomes: one using solely social network data, and another combining social network data with student values and interests.
  • A pruning algorithm, similar to Girvan and Newman's (2002) approach, which prioritized linking weakly connected students, yielded the most positive results.
  • This method maximized the potential for suitable peer matches, enhancing team cohesion and intervention effectiveness.

Conclusions:

  • Network-informed team assignment strategies, particularly those incorporating social network data and student interests, show significant promise for educational interventions.
  • The use of specific network analysis algorithms can optimize team formation for positive peer influence and intervention diffusion.
  • These findings offer practical guidance for educators and intervention scientists in designing effective, network-based classroom strategies.