Learning together better : the structured design of learning teams

There is a great need among educators for a way to quickly assign teams in large or distance learning classrooms in a manner superior to random assignment or student self-selection. Forming teams based on knowledge of students' characteristics is too time-consuming for large classrooms, yet research has shown that the characteristics of individuals greatly affect the quality of the teamwork experience. This thesis provides an automated method to quickly assign students to teams based on individual characteristics. We begin with a thorough review of the literature on how individuals' characteristics affect team behavior, focusing on the level of diversity of four main classes of traits - knowledge/skills/abilities, demographics, personality, and motivation. By forming teams that have diversity on some of these traits and homogeneity on others, we will be able to improve performance over randomly assigned teams. We frame this problem from a group dynamics perspective, measuring the compatibility of every dyad of students within a team. We propose, for several group environments, which traits should be homogeneous and which heterogeneous, and how important each trait is, and use these values to create an equation for a student compatibility score, a number representing how well a pair of students will work together. We then simulate team assignment to determine which of several heuristics is most efficient. A combination of random generation and pairwise exchange is found to be the best, forming teams with average compatibilities 307% higher than the average randomly generated team. The code for this program is included in the appendices.

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2004

Author dc:creator

• Timmers, Kendell M. (Kendell MacQueen), 1978-

Advisor dc:contributor.advisor

• Richard C. Larson.

• Operations Research Center.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/17729

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/17729