Fair Knowledge Tracing in Second Language Acquisition
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866910761301835776 |
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| author | Tang, Weitao Chen, Guanliang Zu, Shuaishuai Luo, Jiangyi |
| author_facet | Tang, Weitao Chen, Guanliang Zu, Shuaishuai Luo, Jiangyi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18048 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fair Knowledge Tracing in Second Language Acquisition Tang, Weitao Chen, Guanliang Zu, Shuaishuai Luo, Jiangyi Human-Computer Interaction Artificial Intelligence Computers and Society Machine Learning 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. |
| title | Fair Knowledge Tracing in Second Language Acquisition |
| topic | Human-Computer Interaction Artificial Intelligence Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2412.18048 |