Four studies on how instruction, technology, and policy shape who succeeds in college mathematics, and a discipline of saying plainly where each claim stops.
I study how instructional design, technology, and policy move student outcomes, especially for the students community colleges serve, using methods matched to the data and every claim scoped to what its design can support.
Not a student shortcut, a way to raise the design of teaching and assessment.
Who moves, and where in the distribution, not just the average.
Count models, panels, regularization, and honest ecological limits.
When California placed students directly into transfer-level statistics, did outcomes rise or fall, and did they improve equitably across groups?
867 enrollments, 17 semesters, one instructor held constant; logistic trend with semester-clustered SEs.
Success rose for every racial/ethnic group, but broadened access alone did not close the gap.
Direct, student-level evidence in a policy debate usually fought with aggregate numbers.
Scoped as Correlational, a single-instructor design cannot separate the reform from the instructor’s own growth over the same years.
Direct placement was associated with rising, not eroding, success, but access alone did not close racial gaps.
Single-instructor design, correlational; it cannot separate the reforms from the instructor's own growth.
Does designing exams with AI change the cognitive demand of what we ask students, and if so, in which direction?
Bloom’s revised taxonomy; matched finals coded item-by-item across two courses, same instructor.
AI raised reasoning demand in statistics and added scaffolding in algebra (mean 61.8→78.7%).
AI as amplification, acting on design intentions time had made impractical, not replacing judgment.
Replicable The paired-Bloom’s method transfers directly to other courses, instructors, and institutions.
AI as an amplification mechanism, it lets instructors act on design intentions time had made impractical, not a replacement for judgment.
The paired-Bloom's design is replicable across courses, instructors, and institutions.
When an instructor redesigns a course with AI, who moves, and where in the grade distribution does the change happen?
Official gradebooks (N = 106); quantile decomposition; Holm family-wise error control across ten comparisons.
Alignment decides value: AI-designed quizzes aligned to the exam stayed predictive; item-bank quizzes lost it.
Means hide the students open-access colleges most need to see. Companion to Paper II.
Discipline Effects reported distributionally and per-course before pooling, a reporting standard this literature still lacks.
Means hide who moved; AI-assisted design lifted the lowest performers most, and alignment governs whether formative work stays diagnostic.
Small cells; unproctored scores inflated toward the ceiling. Reported per course before pooling.
Which state characteristics track mass-shooting rates, gun policy, mental illness, or socioeconomic conditions?
50-state panel, 3,783 incidents; negative-binomial with CR2 cluster-robust SEs; LASSO + tree checks.
The post-2020 surge was broad and sustained; its geography tracks disadvantage, not policy or illness.
Shows range, count models, panel inference, regularization, triangulation.
Scoped as Ecological and observational, no causal or individual claims; sensitive to the “mass shooting” definition.
The post-2020 surge was broad and sustained, and its geography tracks socioeconomic disadvantage, not gun policy or mental illness.
Ecological and observational, no causal or individual claims, and sensitive to the “mass shooting” definition.
Together they make the case: an emerging scholar of AI and equity in postsecondary education, grounded in rigorous quantitative method, and already moving toward doctoral work on AI-powered cognitive scaffolding in statistics education, with data in hand.
“I report what the design supports, and I say plainly where it stops.”
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