<ul><li>This study evaluates the fairness of two predictive models in second-language acquisition using the Duolingo dataset.</li><li>Key findings include the superiority of deep learning over machine learning in terms of accuracy and fairness in second-language knowledge tracing.</li><li>Both models show a bias towards mobile users over non-mobile users.</li><li>Machine learning exhibits stronger bias against developing countries compared to deep learning.</li></ul>

Fair Knowledge Tracing in Second Language Acquisition

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