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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. 

Recent research on more comprehensive models of student learning in adaptive math learning software used an indicator of student reading ability to predict students' tendencies to engage in behaviors associated with so-called "gaming the system." Using data from Carnegie Learning's MATHia adaptive learning software, we replicate the finding that students likely to experience reading difficulties are more likely to engage in behaviors associated with gaming the system. Using both observational and experimental data, we consider relationships between student reading ability, readability of specific math lessons, and behavior associated with gaming. We identify several readability characteristics of specific content that predict detected gaming behavior, as well as evidence that a prior experiment that targeted enhanced content readability decreased behavior associated with gaming, but only for students that are predicted to be less likely to experience reading difficulties. We suggest avenues for future research to better understand and model behavior of math learners, especially those who may be experiencing reading difficulties while they learn math. 

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.