Fair Knowledge Tracing in Second Language Acquisition

In the domain of second-language acquisition, predictive modeling serves as a pivotal tool for facilitating educators in implementing diversified teaching strategies, thereby garnering extensive research attention. Despite the prevalent focus on model accuracy in most existing studies, the exploration into model fairness remains substantially underexplored. Model fairness pertains to the equitable treatment of different groups by machine learning algorithms. It ensures that the model's predictions do not exhibit unintentional biases against certain groups based on attributes such as gender, ethnicity, age, or other potentially sensitive characteristics. In essence, a fair model should produce outcomes that are impartial and do not perpetuate existing prejudices, ensuring that no group is systematically disadvantaged. In this research, we evaluate the fairness of two predictive models based on second-language learning, utilizing three tracks from the Duolingo dataset: en_es (English learners who speak Spanish), es_en(Spanish learners who speak English), and fr_en(French learners who speak English). We measure (i) algorithmic fairness among different clients such as iOS, Android and Web and (ii) algorithmic fairness between developed countries and developing countries. Our findings indicate: 1) Deep learning exhibits a marked advantage over machine learning when applied to knowledge tracing based on second language acquisition, owing to its heightened accuracy and fairness.