Quantum Sparse Autoencoders for Q-Matrix Estimation in Cognitive Diagnosis
Q-matrices play a central role in cognitive diagnosis within educational data mining (EDM), specifying which latent skills each assessment item requires. Data-driven Q-matrix estimation remains challenging when assessments involve many correlated skills and when real response patterns depart from idealized generative assumptions. We introduce a novel quantum sparse autoencoder (QSAE) for Q-matrix estimation, which, to the best of our knowledge, is the first application of quantum machine learning (QML) to cognitive diagnosis. Overall, the QSAE embeds each student's binary response vector into
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- PossiblePossibly related (embedding) · 52%FAU Researchers Develop Quantum Machine Learning Framework for Heart Disease Prediction - Newswise →
- PossiblePossibly related (embedding) · 50%FAU Researchers Use Quantum Machine Learning to Predict Heart Disease - The Quantum Insider →
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%mudler/LocalAI →
“Shared author/contributor keys: guo”
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“Shared author/contributor keys: wang”
- LinkedLinked via arxiv author · 85%Bowen Guo →
“Quantum Sparse Autoencoders for Q-Matrix Estimation in Cognitive Diagnosis”
- LinkedLinked via arxiv author · 85%Xiang Li →
“Quantum Sparse Autoencoders for Q-Matrix Estimation in Cognitive Diagnosis”