Abstract for: A Multidimensional Comprehension Index of Systems Thinking

Systems Thinking (ST) continues to remain a beneficial approach to address complex problems. How to measure one's comprehension and understanding of ST has become more of a prominent issue. Despite all the attention and effort to measure ST skills, a quantitative approach that captures how ST's multidimensionality affects ST assessment is still missing. This paper proposed a systematic and rigorous approach to create a Multidimensional Comprehension Index of ST (MCIST). First, we used the Lake Urmia Vignette (LUV) measurement tool (Davis et al., 2020) to collect and measure four types of ST skills on 143 undergraduate students. Second, we form a conceptual framework based on these ST skills and transform this framework into the quantifiable tool, MCIST, using Data Envelopment Analysis (DEA). Third, we use the MCIST to benchmark the performance of comprehension ST thinking on each participant. Lastly, we perform meta-analyses to validate how the MCIST score relates to the participants' responses. The result shows that: (i) DEA serves as a robust approach to capture ST's multidimensional characteristics. (ii) the MCIST model can capture multiple ST skills, including loops, connectivity, and middle nodes, without influence by the length of the responses.