Fair Knowledge Tracing in Second Language Acquisition

• URL: http://arxiv.org/abs/2412.18048v1
• Date: Mon, 23 Dec 2024 23:47:40 GMT
• Title: Fair Knowledge Tracing in Second Language Acquisition
• Authors: Weitao Tang, Guanliang Chen, Shuaishuai Zu, Jiangyi Luo,
• Abstract summary: This study evaluates the fairness of two predictive models using the Duolingo dataset's en_es (English learners speaking Spanish), es_en (Spanish learners speaking English), and fr_en (French learners speaking English) tracks. Deep learning outperforms machine learning in second-language knowledge tracing due to improved accuracy and fairness.
• Score: 3.7498611358320733
• License:
• Abstract: In second-language acquisition, predictive modeling aids educators in implementing diverse teaching strategies, attracting significant research attention. However, while model accuracy is widely explored, model fairness remains under-examined. Model fairness ensures equitable treatment of groups, preventing unintentional biases based on attributes such as gender, ethnicity, or economic background. A fair model should produce impartial outcomes that do not systematically disadvantage any group. This study evaluates the fairness of two predictive models using the Duolingo dataset's en\_es (English learners speaking Spanish), es\_en (Spanish learners speaking English), and fr\_en (French learners speaking English) tracks. We analyze: 1. Algorithmic fairness across platforms (iOS, Android, Web). 2. Algorithmic fairness between developed and developing countries. Key findings include: 1. Deep learning outperforms machine learning in second-language knowledge tracing due to improved accuracy and fairness. 2. Both models favor mobile users over non-mobile users. 3. Machine learning exhibits stronger bias against developing countries compared to deep learning. 4. Deep learning strikes a better balance of fairness and accuracy in the en\_es and es\_en tracks, while machine learning is more suitable for fr\_en. This study highlights the importance of addressing fairness in predictive models to ensure equitable educational strategies across platforms and regions.

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