Sequentially Analyzing and Modeling Causal Mapping Processes that Support Causal Understanding and Systems Thinking

Abstract

A growing body of research shows that deeper understanding of complex systems can be achieved when students construct causal maps to articulate, explore, and refine their understanding. However, to what extent is students’ maps an actual measure of their causal understanding versus the efficacy of different processes used by students to construct their map? To address this question, a set of methods was developed and implemented in this study to mine data from the causal mapping tool, jMAP; sequentially analyze the mined data; and construct transitional state diagrams to visualize, model, and compare students’ causal mapping behaviors to identify differences in action sequences performed by students that created maps of high versus low accuracy. The causal maps of 17 graduate students enrolled in an online course on collaborative learning at Florida State University (USA) were sequentially analyzed and used to illustrate how the proposed methods can be implemented to capture, analyze, model, and identify causal mapping and reasoning processes that produce deeper causal understanding. Identifying and validating the most effective processes will enable software developers to create causal mapping software that can standardize the map construction process, control for individual differences in mapping skills, enable students to produce causal maps that accurately reflect their causal understanding (and/or help students to produce more accurate causal maps), and thus enable instructors to use such mapping tools to conduct automated large-scale assessments of students’ causal understanding and systems thinking skills.