Visualization enhances Problem Solving in Multi-Qubit Systems: An Eye-Tracking Study
- URL: http://arxiv.org/abs/2505.21508v1
- Date: Tue, 13 May 2025 15:30:28 GMT
- Title: Visualization enhances Problem Solving in Multi-Qubit Systems: An Eye-Tracking Study
- Authors: Jonas Bley, Eva Rexigel, Alda Arias, Lars Krupp, Nikolas Longen, Paul Lukowicz, Stefan Küchemann, Jochen Kuhn, Maximilian Kiefer-Emmanouilidis, Artur Widera,
- Abstract summary: We examine the conditions under which the visualization of multi-qubit systems in addition to the mathematical symbolic Dirac Notation (DN) is associated with a benefit for solving problems on the ubiquitously used Hadamard gate operation in terms of performance, Extraneous Cognitive Load (ECL) and Intrinsic Cognitive Load (ICL)<n>We find that visualization increases performance and reduces cognitive load for participants with little experience in quantum physics.<n>In addition, representational competence is able to predict reductions in ECL with visualization, but not performance or ICL.
- Score: 1.9441907817816357
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Quantum Information Science (QIS) is a vast, diverse, and abstract field. In consequence, learners face many challenges. Science, Technology, Engineering, and Mathematics (STEM) education research has found that visualizations are valuable to aid learners in complex matters. The conditions under which visualizations pose benefits are largely unexplored in QIS education. In this eye-tracking study, we examine the conditions under which the visualization of multi-qubit systems in addition to the mathematical symbolic Dirac Notation (DN) is associated with a benefit for solving problems on the ubiquitously used Hadamard gate operation in terms of performance, Extraneous Cognitive Load (ECL) and Intrinsic Cognitive Load (ICL). We find that visualization increases performance and reduces cognitive load for participants with little experience in quantum physics. In addition, representational competence is able to predict reductions in ECL with visualization, but not performance or ICL. Analysis of the eye-tracking results indicates that problem solvers with more transitions between DN and visualization benefit less from it. We discuss the generalizability of the results and practical implications.
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