Shuhan Ai | Searcharxiv

arXiv2026

Causal Fairness Analysis of ADHD Status and High School STEM Outcomes

This study applies the Causal Fairness Analysis (CFA) framework of Plecko and Bareinboim (2024) to decompose the total variation in STEM outcomes attributable to ADHD status into direct, indirect, and spurious components using Pearl's Structural Causal Model. Drawing on nationally representative data from the High School Longitudinal Study of 2009, this study examines two outcomes: cumulative STEM GPA and science identity. Total variation decomposition reveals a statistically significant ADHD penalty on STEM GPA (TV = -0.670), of which 63.3% is attributable to the direct effect (x-DE), indicating that the majority of the disparity operates through pathways not mediated by observed sociodemographic or academic confounders. In contrast, the effect on science identity is small and non-significant (TV = -0.068). Counterfactual direct effect analysis using the one-step debiased estimator further reveals that the direct effect is structured by race, with notable variation across racial and ethnic subgroups. Sensitivity analyses confirm robustness to moderate unmeasured confounding. These findings advance the understanding of ADHD-related inequities in STEM education and highlight the need for fairness-aware policies that address both direct institutional barriers and their differential impact across intersecting social identities.

stat.AP↗

arXiv2026

Discussion Network Formation and Evolution in an Online Professional Development Class: Evidence from a MOOC for K-12 Educators

Understanding how educators interact and form peer networks in online professional development contexts has become increasingly important as MOOCs for educators (MOOC-Eds) proliferate. This study examines peer discussion network formation and evolution in 'The Digital Learning Transition in K-12 Schools', a MOOC-Ed offered to U.S. and international educators in Spring 2013. Using cross-sectional and temporal exponential random graph models (ERGMs and TERGMs), the study analyzes two network subsamples: the largest connected component (N = 363) and active participants with three or more interactions (N = 227). Results reveal strong reciprocity and transitive closure effects across both networks, with participants six to nine times more likely to reciprocate interactions and over twice as likely to form ties with peers sharing common discussion partners. Assigned discussion group homophily emerged as the strongest predictor of tie formation, while regional homophily and willingness to connect also significantly influenced network structure. Temporal analysis showed discussion activity peaked mid-course before declining sharply, with network structure evolving from broadly distributed participation to concentrated interaction among a tightly connected core. These findings illuminate the mechanisms driving peer-supported learning in online professional development contexts and suggest design implications for fostering sustained educator engagement in MOOC-based learning environments.

stat.AP↗