Abstract for: An instrument to quantify how well students can recognize feedback loops in narratives and its use for evaluating curricula
Our newly developed instrument uses a signal detection approach to quantify how well undergraduates can distinguish between feedback loops and non-loops in 40 short narratives, and to what extent they may be biased to over- or under-identify loops. In our first use of the instrument, we found that students were biased (signal detection c) to identify a narrative as a "positive feedback loop" if the outcome of the narrative is desirable. The desirable/undesirable nature of the narrative outcome did not impact students' ability to distinguish loops from non-loops (signal detection d'). Teaching and testing students using the alternative term "reinforcing feedback loop" did not improve performance. Our next step is to develop a suite of mini-lessons for use in any course where at least one feedback loop is currently being taught, using a mutual alignment analogy approach to expand students' understanding from a single loop to a generalizable concept. Learning will be assessed using our loop detection instrument and student products created during instruction.