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arXiv:2610.09499

Using Learning Analytics to Study Novice Learners' Work on Quantum Circuit Simulator Tasks

Juuso Jaakola, Vlad Stirbu, Teiko Heinosaari

physics.ed-phquant-phSubmitted Oct 7, 2026• Announced Oct 8, 2026
arXivHTMLPDF

Abstract

Interactive quantum circuit simulators are increasingly used to support introductory quantum computing education for learners from varied disciplinary backgrounds. These environments can also generate detailed response logs of submitted circuits, but such data are rarely transformed into evidence that instructors can use for course redesign and development. We present a learning analytics framework for quantum circuit simulator tasks and report its task-level branch on anonymised logs from an introductory quantum computing course delivered in the TIM learning environment. The task branch characterises exercises through participation, completion, first-attempt accuracy, valid-attempt counts, sessionised active time, and observed answer-space reporting of common correct and incorrect constructions. The dataset covers the 2024-2025 cohort of 492 learners, 47,533 valid submissions, and 21 obligatory quantum circuit exercises. The framework identifies bottleneck tasks, broad solution spaces, and recurring wrong-answer patterns that are not visible from final scores alone. Its most distinctive output is a plain-text representation of the observed answer space, designed to make open-ended circuit submissions directly interpretable to instructors. We show how response logs can reveal actionable task-design issues and support concrete course redesign. A learner-level branch is retained only as an ongoing research extension and is not a central empirical contribution of the present paper.

Comments
10 pages, 8 figures. Accepted and presented as a technical paper at the Quantum Science and Engineering Education Conference (QSEEC 2026), held in conjunction with IEEE Quantum Week 2026

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Interactive quantum circuit simulators are increasingly used to support introductory quantum computing education for learners from varied disciplinary backgrounds. These environments can also generate detailed response logs of submitted circuits, but such data are rarely transformed into evidence that instructors can use for course redesign and development.
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