A framework using latent class analysis on student data to define personas, LLM simulations of their responses, and ridge regression improves IRT difficulty prediction for MCQs over baselines.
arXiv preprint arXiv:2407.15645 (2024)
3 Pith papers cite this work. Polarity classification is still indexing.
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A model-free method builds confidence sets for latent parameters to proxy sim-to-real discrepancies and estimates the quantile function of that proxy to produce a distribution-level fidelity profile for simulators.
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MCQ Difficulty Prediction via Modeling Learner Heterogeneity Using Data-Driven Cognitive Profiling
A framework using latent class analysis on student data to define personas, LLM simulations of their responses, and ridge regression improves IRT difficulty prediction for MCQs over baselines.
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Model-Free Assessment of Simulator Fidelity via Quantile Curves
A model-free method builds confidence sets for latent parameters to proxy sim-to-real discrepancies and estimates the quantile function of that proxy to produce a distribution-level fidelity profile for simulators.
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