MC-Dropout uncertainty in DKT, SAKT and AKT models allows targeted abstention that raises accuracy 2.3-3.0 points and captures 77-90% architecture-specific epistemic signal unexplained by IRT or psychometric factors.
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cs.LG 2years
2025 2representative citing papers
CTGAN and LLMs generate synthetic student data that passes statistical and predictive utility checks for learning analytics.
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Knowing When to Defer: Selective Prediction for Responsible Knowledge Tracing
MC-Dropout uncertainty in DKT, SAKT and AKT models allows targeted abstention that raises accuracy 2.3-3.0 points and captures 77-90% architecture-specific epistemic signal unexplained by IRT or psychometric factors.
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Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation
CTGAN and LLMs generate synthetic student data that passes statistical and predictive utility checks for learning analytics.