Andres Felipe Zambrano
ABSTRACT
In today's data-driven educational technologies, algorithms have a pivotal impact on student experiences and outcomes. Therefore, it is critical to take steps to minimize biases, to avoid perpetuating or exacerbating inequalities. In this paper, we investigate the degree to which algorithmic biases are present in two learning analytics models: knowledge estimates based on Bayesian Knowledge Tracing (BKT) and carelessness detectors. Using data from a learning platform used across the United States at scale, we explore algorithmic bias following three different approaches: 1) analyzing the performance of the models on every demographic group in the sample, 2) comparing performance across intersectional groups of these demographics, and 3) investigating whether the models trained using specific groups can be transferred to demographics that were not observed during the training process. Our experimental results show that the performance of these models is close to equal across all the demographic and intersectional groups. These findings establish the feasibility of validating educational algorithms for intersectional groups and indicate that these algorithms can be fairly used for diverse students at scale.
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Index Terms
- Investigating Algorithmic Bias on Bayesian Knowledge Tracing and Carelessness Detectors
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