What Universities Discovered When They Tracked Email Response Times
Two universities embedded tracking in their email systems to measure how quickly faculty and staff responded to student messages. The findings revealed inequities most administrators had not suspected.
In 2023, two major US universities (one private, one public) began embedding response time tracking in their email systems to study how quickly faculty and staff responded to student messages. The intent was to identify pinch points and improve student support. The results, published in 2025, revealed patterns that universities had not anticipated and that explain some of the persistent inequities in student experience.
The study tracked roughly 1.4 million student-initiated emails over two academic years. Response times were measured from the moment a student sent an email to the moment a faculty or staff member responded substantively.
The Aggregate Pattern
The median response time across both universities was 19 hours during business days. The number was longer than most administrators had expected. About 23 percent of student emails received no response within five business days, and 11 percent received no response at all during the academic term in which they were sent.
The non-response rate varied substantially by category. Routine course questions to faculty received responses 89 percent of the time. Questions to academic advisors received responses 84 percent of the time. Questions to financial aid offices received responses 71 percent of the time. Questions to registrars received responses 67 percent of the time.
The Identity Pattern
The more troubling finding was that response times varied by characteristics of the sending student. After controlling for the type of question, the recipient, and the time of sending, students were significantly more likely to receive timely responses when their names suggested certain demographic backgrounds. Students with names suggesting non-Western origin received responses 22 percent slower on average than students with names suggesting Anglo origin. Female-coded names received responses 8 percent slower than male-coded names.
The gaps were larger for some recipients than others. Some departments showed no name-based variation in response times. Others showed substantial gaps. The pattern was not concentrated in any one type of office or any one type of staff.
Why This Happens
The researchers conducted follow-up interviews with 240 faculty and staff to understand the patterns. The dominant explanation was not deliberate bias but cognitive load and pattern recognition. Faculty and staff with high email volumes tended to triage messages quickly, often based on partial reading of subject lines and sender names. The triage was faster for messages from senders who pattern-matched against the recipient’s typical fluent communicators.
“I am not slower to respond to international students because I do not value them,” said one faculty member quoted in the study. “I am slower because I have to read their messages more carefully, and I do that when I have time to read carefully, which is often not the same as when their message arrives.”
The Effect on Students
The response time gaps had measurable effects on student outcomes. Students whose emails received slower responses were less likely to follow up on academic problems, less likely to seek help during difficult periods, and reported lower satisfaction with the university overall. The cumulative effect, over four years, contributed to the persistent gaps in academic and persistence outcomes that the universities had been working to close through other means.
“We were investing significantly in retention and equity programs,” said one of the university’s deans, who asked not to be named because the data is part of ongoing institutional work. “We had not realized that something as basic as how quickly we answered email was undermining those investments.”
What the Universities Did
Both universities introduced systemic changes after the data was analyzed. Email triage systems were redesigned to randomize the order in which messages were presented to faculty and staff, removing the implicit sorting by sender pattern. Required training was implemented for staff in offices that showed the largest response time gaps. Tools were provided to help faculty and staff manage email volumes more equitably, including templates and automated initial acknowledgments.
The changes produced measurable improvements within a year. The name-based response time gap narrowed by roughly 60 percent at the private university and 45 percent at the public university. Student satisfaction with email-based interactions rose modestly.
The Lessons for Other Institutions
The study has implications for institutions that have not measured their own response time patterns. Many universities have invested in student-facing technology improvements without examining the workflows that affect student experience most directly. Email is one of those workflows, and the patterns that emerged in the two studied universities are unlikely to be unique to them.
For administrators considering similar work, the researchers offer several practical suggestions. Measure response times at the level of individual senders rather than aggregate offices. Examine name-based and other demographic variation explicitly. Treat the results as institutional problems rather than individual failures, and address them through systemic changes rather than through individual reprimand.
What Students Can Do
For students whose emails are not being answered, the research suggests several practical strategies. Subject lines that signal urgency and topic produce faster responses than vague ones. Following up after three business days is appropriate and produces results without harming relationships. Visiting in person, when possible, often resolves matters that email cannot.
More broadly, the research is a reminder that the institutional surfaces of higher education, the routine interactions that constitute most of the student experience, often function less well than students assume. Universities can improve them when they choose to measure and act. Universities that do not measure are unlikely to improve.
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