Research fields

A connected view of how higher education works.

Each field brings a different part of the system into focus. Together they trace how institutions, policy, and student support shape who gets through college — and what happens along the way.

01

Research field

Student Access & Success

We study where opportunity narrows across the student journey — from entry and transfer to persistence, completion, and the meaning of the credentials students earn.

Background

Access to college has expanded, but access alone does not determine who benefits. Students encounter different transfer rules, advising capacity, institutional resources, and definitions of progress. Those differences accumulate, often long before they appear in a completion rate.

HERA links administrative records across institutions and states with longitudinal surveys and policy evaluation. The aim is to separate differences institutions can change from differences they merely observe, then make the evidence useful to the people designing pathways through college.

02

Research field

Higher Education Finance & Institutions

We examine how price, public funding, institutional strategy, measurement, and the academic workforce shape what universities can offer and whom they can serve.

Background

Universities make high-stakes decisions inside systems of tuition, aid, appropriations, staffing, and accountability. Yet the effects of those decisions are difficult to isolate because price changes arrive with aid changes, staffing data arrive late, and familiar measures drift over time.

Our work reconstructs the institutional conditions around a decision and follows what changes afterward. That includes causal studies of tuition policy, long-run measurement projects, and public monitors that make changes in the higher-education workforce visible while they can still inform action.

03

Research field

AI, Advising & Student Support

We test how automated systems enter the advising relationship, who they reach, which needs they miss, and when a student should be connected to a person instead.

Background

Student-support systems are adopting language models and predictive tools faster than institutions can build evidence about them. A system can increase contact while still shifting risk onto the students whose questions are unusual, urgent, or hard to classify.

HERA combines randomised trials, model audits, and close analysis of real advising conversations. We treat automation as part of a human service system: the relevant outcomes are not only accuracy and volume, but escalation, refusal, equity, and the quality of the conversations that reach an advisor.