Urban Political Theory and the Case of the Local Government Simulation

Abstract

This essay contributes to the growing literature on the benefits of using in-class simulations. We find that the use of a simulation in an urban politics class improved student achievement and understanding of theoretical concepts across a range of measures regardless of student GPA or major status. Special care was taken to ensure the simulation experience covered each discrete stage: preparation, interaction, and debriefing. While no absolute claim is made as to the relative importance of each stage compared to the others, clear evidence emerged regarding the pedagogical effectiveness of the simulation as a total experience.

Keywords:

• assessment
• simulation
• urban politics

Notes

Note. Response options were as follows: A Great Deal (5), A Fair Amount (4), Somewhat (3), A Little Bit (2), Not At All (1).

Note. Response options were as follows: A Great Deal, A Fair Amount, Somewhat, A Little Bit, Not At All and were recoded into the categories above.

Values in the parentheses represent the n for each category.

The lead author received Human Subjects Committee approval to employ surveys in his urban politics course.

Analytic rubrics separate the work being evaluated into specific learning outcomes in order for them to be analyzed separately. This is in contrast to a holistic rubric that provides a single rating for the entire artifact being evaluated (Conley-Sowels, Franklund, and Woodman 2010).

The instructors selected issues one through four because the Camelot text supplied these as appropriate first items to begin the simulation (see Woodworth, Gump, and Forester 2006, 95).

The effect size, r, is calculated using the following equation (Rosenthal 1991; Rosnow and Rosenthal 2005): .

The grouping of students into a composite score category was based on an effort to compare the “top half” of the class to the “bottom half” and as such groups were constructed using natural breaks in the data to resemble as closely as possible half the cases falling above and half the cases falling below the cut point.

An analysis of the grades of majors compared to nonmajors taking the course from Spring 2000 to Spring 2009, when simulations were not used (N = 944), reveals that there is a statistically significant difference (p < .05) in course grades between the two groups: majors averaged a 3.02 (B−) in the course while nonmajors averaged a 2.79 (C+) in the course. In this manner, the simulation seems to address a significant concern of the instructors regarding maximizing student learning outcomes for all students.

An analysis of the cumulative GPAs of students comparing those with a 3.01 or higher to those with a 3.0 or lower taking the course from Spring 2000 to Spring 2009, when simulations were not used (N = 944), reveals that there is a statistically significant difference (p < .05) in grades between the two groups: Those with a GPA higher than 3.0 averaged a 3.29 (B) in the course while those with a GPA of 3.0 or lower averaged a 2.48 (C) in the course. Here again, the simulation seems to address a significant concern of the instructors regarding maximizing student learning outcomes for all students since equal gains were made in scores across both GPA groupings in the simulation class.

The grouping of students into GPA categories of above and below 3.0 was based on an effort to compare students that are considered to have GPAs equivalent to a “B-” or higher to those with a “C+” or lower.