Assessing Students' Understanding of Complex Systems
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Introduction
Assessing student understanding of complex thinking is quite challenging. As with assessing student learning on any topic, the first step is to identify your learning goals for your students. Once you have identified learning goals, it becomes easier to choose one or more assessment tools appropriate to the task.
Here is a list of possible learning objectives related to students' thinking about complex systems (i.e., skills expected from students who exhibit complex systems thinking). While this is in no way intended to be a comprehensive list of possible learning goals, it may help you to articulate your own list.
Learning Goals
A student who demonstrates complex systems thinking can:
- Identify and explain the characteristics of a complex system
- Describe and/or model a process where there is a feedback mechanism at work
- Build a model that mimics the expected behavior of the target system
- Identify stocks/reservoirs and flows
- Correctly identify positive/negative feedbacks
- Test a model through trial and error and comparison to real-world data
- Explore the possible outcomes of a system under different parameters
- Bridge across scales: student explanations of processes show fidelity across scales (e.g., a student applies the concept of homeostasis at multiple levels)
- Create and interpret graphical information
- Predict attributes of system behavior based on specific inputs or components of the system
- Understand that a complex system is irreducible, unpredictable, historical, nonlinear, and has emergent properties, and be able to describe what these terms mean
Assessment Tools
A number of assessment tools can be used to assess students' understanding (or progress toward understanding) of complex systems. A few of these tools are listed below.
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HideConcept Maps
A concept map is a diagram with hierarchical nodes, labeled with concepts. The nodes are linked together with directional lines and are arranged from general to specific. By developing concept maps, students literally illustrate their understanding of a complex system. This method can be used for summative or formative assessment, and has the benefit of highlighting any misconceptions.
Models
Students can develop and run physical or computer models to gain an understanding of how a system works. The choices a student makes in developing a model (what components of the system to include, how they are linked, and so on), along with how the student explains his or her choices, illustrate that student's understanding of the system in question. Their ability to explain the behavior of the model (describe the outcomes given different inputs, find patterns in the output, etc.) offers further opportunity for assessment. This method can be employed for summative or formative assessment (or both).
Data Representations
An understanding of graphical representations of data is an essential component of data analysis. Students can demonstrate their understanding of complex systems by interpreting graphical data illustrating the relationships between system variables. This method can be employed for summative or formative assessment (or both). For more information about using graphs in the classroom, see the Starting Point web pages on describing and analyzing graphs.
Assessing Students' Thinking Processes
Much frustration can be avoided by engaging in formative assessment: assessing student learning during the learning process. One way to do this is to incorporate several "checkpoints" in each teaching activity or assignment where you ask students to articulate what the results are and how they got there. This serves two functions: 1) it exposes misconceptions or misapplications at an early stage, and 2) it requires students to think about what they are doing and why -- and whether their progress makes sense in the context of what they know or expect. This opens up the realm of metacognition, wherein students think specifically about their own learning and engage in self-monitoring and self-regulatory behavior. Research demonstrates that metacognition improves learning (Lovett, 2008).