Title:
Supporting Feedback Loop Reasoning in Simulated Systems with Computer-Based Scaffolding
Supporting Feedback Loop Reasoning in Simulated Systems with Computer-Based Scaffolding
Author(s)
Dunbar, Terri
Advisor(s)
Gorman, Jamie C.
Catrambone, Richard
Catrambone, Richard
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Abstract
Feedback loops are a critical part of systems and a frequent source of misconceptions. These misconceptions are thought to occur because people inappropriately apply their everyday experiences of causality to the types of causal feedback loops present in systems. Feedback loop reasoning can improve with training; however, misconceptions such as failing to close the loop are particularly resistant to change. Two experiments investigated whether factors known to improve positive transfer with other cognitive skills could overcome learners’ misconceptions about feedback loops during simulation training, including learning from multiple examples, similarity to the training context, scaffolding, and desirable difficulties. Results revealed that similarity and potentially cognitive load had the largest impacts on transfer, and the type of scaffolding used or how it was sequenced over training had little effect. Near transfer only occurred for participants who learned from balance systems where the goal is to maintain system equilibrium by counterbalancing relationships, and not with pattern systems where the goal is to determine how spatial patterns emerge from local interactions. There was no evidence of far transfer. Across both experiments, participants also closed the loop more frequently when learning from balance systems. Overall, the current studies suggest that researchers need to carefully consider the type of system used during simulation training because subtle manipulations can lead to different learning experiences. Existing theories of system misconceptions are unable to satisfactorily explain why these performance differences occurred. Instead, the results and their implications are discussed using cognitive load theory.
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Date Issued
2023-04-12
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Text
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Dissertation