Using Learning Analytics to Study Novice Learners' Work on Quantum Circuit Simulator Tasks
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.
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BibTeX
@misc{jaakola2026using,
title = {{Using Learning Analytics to Study Novice Learners' Work on Quantum Circuit Simulator Tasks}},
author = {Juuso Jaakola and Vlad Stirbu and Teiko Heinosaari},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.09499},
eprint = {2610.09499},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.09499},
}
RIS
TY - UNPB
TI - Using Learning Analytics to Study Novice Learners' Work on Quantum Circuit Simulator Tasks
AU - Juuso Jaakola
AU - Vlad Stirbu
AU - Teiko Heinosaari
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2610.09499
UR - https://arxiv.org/abs/2610.09499
AB - 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.
ER -