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Algorithmic bias research often evaluates models in terms of traditional demographic categories (e.g., U.S. Census), but these categories may not capture nuanced, context-dependent identities relevant to learning. This study evaluates four affect detectors (boredom, confusion, engaged concentration, and frustration) developed for an adaptive math learning system. Metrics for algorithmic fairness (AUC, weighted F1, MADD) show subgroup differences across several categories that emerged from a free-response social identity survey (Twenty Statements Test; TST), including both those that mirror demographic categories (i.e., race and gender) as well as novel categories (i.e., Learner Identity, Interpersonal Style, and Sense of Competence). For demographic categories, the confusion detector performs better for boys than for girls and underperforms for West African students. Among novel categories, biases are found related to learner identity (boredom, engaged concentration, and confusion) and interpersonal style (confusion), but not for sense of competence. Results highlight the importance of using contextually grounded social identities to evaluate bias. 

Students' reading ability affects their outcomes in learning software even outside of reading education, such as in math education, which can result in unexpected and inequitable outcomes. We analyze an adaptive learning software using Bayesian Knowledge Tracing (BKT) to understand how the fairness of the software is impacted when reading ability is not modeled. We tested BKT model fairness by comparing two years of data from 8,549 students who were classified as either "emerging" or "non-emerging" readers (i.e., a measure of reading ability). We found that while BKT was unbiased on average in terms of equal predictive accuracy across groups, specific skills within the adaptive learning software exhibited bias related to reading level. Additionally, there were differences between the first-answer mastery rates of the emerging and non-emerging readers (M=.687 and M=.776, difference CI=[0.075, 0.095]), indicating that emerging reader status is predictive of mastery. Our findings demonstrate significant group differences in BKT models regarding reading ability, exhibiting that it is important to consider—and perhaps even model—reading as a separate skill that differentially influences students' outcomes."]} 

Adaptive learning systems are increasingly common in U.S. classrooms, but it is not yet clear whether their positive impacts are realized equally across all students. This study explores whether nuanced identity categories from open-ended self-reported data are associated with outcomes in an adaptive learning system for secondary mathematics. As a measure of impact of these social identity data, we correlate student responses for 3 categories: race and ethnicity, gender, and learning identity—a category combining student status and orientation toward learning—and total lessons completed in an adaptive learning system over one academic year. Results show the value of emergent and novel identity categories when measuring student outcomes, as learning identity was positively correlated with mathematics outcomes across two statistical tests. 

Abstract Automated, data‐driven decision making is increasingly common in a variety of application domains. In educational software, for example, machine learning has been applied to tasks like selecting the next exercise for students to complete. Machine learning methods, however, are not always equally effective for all groups of students. Current approaches to designing fair algorithms tend to focus on statistical measures concerning a small subset of legally protected categories like race or gender. Focusing solely on legally protected categories, however, can limit our understanding of bias and unfairness by ignoring the complexities of identity. We propose an alternative approach to categorization, grounded in sociological techniques of measuring identity. By soliciting survey data and interviews from the population being studied, we can build context‐specific categories from the bottom up. The emergent categories can then be combined with extant algorithmic fairness strategies to discover which identity groups are not well‐served, and thus where algorithms should be improved or avoided altogether. We focus on educational applications but present arguments that this approach should be adopted more broadly for issues of algorithmic fairness across a variety of applications.