Using a Dynamic Systems Approach to Measure Changes in Teacher–Student Interactions

• In Event: 66.069 - Motivation and Complexity: The Merits of a Dynamic Systems Approach to Theory and Research on Motivation in Education

Abstract

Objectives

We will present an example of one dynamic systems approach to analyze teacher-student interactional change drawn from a three-year professional development program to increase student engagement (Turner et al., 2014). In particular, we will illustrate the use of an educational application of the state-space grid (SSG) technique, which enabled us to analyze different trajectories of teacher-student interaction and their quality over time.

Theoretical framework

Dynamic patterns of interpersonal activity can be represented by an educational application of the state space grid (SSG) technique (Lewis, Lamey, & Douglas, 1999). SSGs depict interactional change or stability in both real time (i.e., during one class) and in developmental time (i.e., over the year/s). The SSG methodology is sensitive to changes (or lack thereof) in teacher-student interactions and can reveal the dynamics of instructional interactions and their consequences for student engagement. In this case, the SSGs also enabled us to see both optimal and non-optimal patterns and both ‘‘matches’’ and ‘‘mismatches’’ between teacher-offered opportunities for engagement and student uptake.

Methods and Data sources

We observed instruction in six teachers’ classrooms four times a year for three years, capturing both teacher “offers” of engagement and students’ uptake of opportunities. Six categories for observed motivational support were rated “live” on a scale of 0 (low support) to 3 (high support) for each activity setting in each year for each observed teacher. These were averaged to form the composite score, Motivational Support (MS). Six categories related to student engagement, scored 0 (low uptake) to 3 (high uptake), were also averaged to form a composite score called Student Engagement (SE).

GridWare, a software program, creates SSGs from ordinal or categorical time series data (Lamey, Hollenstein, Lewis, & Granic, 2004) and represents them on a 2-D grid. The teacher and his/her students are represented as a dyad in the SSG. We plotted 4 x 4 grids ranging from 0 (none) to 3 (high quality) MS and SE. The grid represents all possible behavioral combinations of MS and SE (See Figure 1). Teacher offers of motivational support (MS) are plotted on the y-axis, and student engagement (SE) is plotted on the x-axis. Each cell on the grid represents the intersection of participant behavior (e.g., high-quality teacher motivational support, high-quality student engagement). Each node (circle) on the grid captures teachers’ level of observed motivational support and students’ observed engagement during one period of observation.

Findings

We found two patterns. Three observed teachers showed an upward trajectory, with higher quality interactions in years two and three (See Figure 3). Three teachers showed a stable pattern, with no changes for three years.

Scholarly significance

In studies of behavior change, the use of SSGs affords access to both trajectories of change (i.e., upward, erratic, etc.) and quality of interaction (e.g., low student uptake and high teacher offers). When complemented with qualitative data (e.g., video), SSGs can help explain both why and when teacher-student interaction either changed or stalled.

Authors

• Julianne C. Turner, University of Notre Dame

• Andrea Lynn Christensen, University of Notre Dame