The project is the work of Tristan Denley, a programmer turned math professor turned provost. Mr. Denley’s software borrows a page from Netflix. It melds each student’s transcript with thousands of past students’ grades and standardized test scores to make suggestions for every student. When students log into their online portal, they see 10 “Course Suggestions for You” ranked on a five-star scale. For, say, a health and human performance major, kinesiology might get five stars (as the next class needed for her major). Physics might also top the list (to satisfy a science requirement in the core curriculum).

Behind those recommendations is a complex algorithm, but the basics are simple enough. Degree requirements figure in the calculations. So do classes that can be used in many programs, like freshman writing. And it bumps up courses for which a student might have a talent, by mining their records — grades, high school grade-point average, ACT scores — and those of others who walked this path before.

“We’re steering students toward the classes where they are predicted to make better grades,” Mr. Denley says. The predictions, he adds, are within about half a letter grade, on average.

The prediction process is more subtle than getting a suggestion to watch “Goodfellas” because you liked “The Godfather.” Take the hypothetical health major encouraged to take physics. The software sifts through a database of hundreds of thousands of grades other students have received. It analyzes the historical data to figure out how much weight to assign each piece of the health major’s own academic record in forecasting how she will do in a particular course. Success in math is heavily predictive of success in physics, for example. So if her transcript and ACT score indicate a history of doing well in math, physics would likely be recommended over biology, though both satisfy the same core science requirement.

Mr. Denley points to a spate of recent books by behavioral economists, all with a common theme: When presented with many options and little information, people find it difficult to make wise choices. The same goes for college students trying to construct a schedule, he says. They know they must take a social science class, but they don’t know the implications of taking political science vs. psychology vs. economics. They choose based on course descriptions or to avoid having to wake up for an 8 a.m. class on Monday. Every year, students in Tennessee lose their state scholarships because they fall a hair short of the G.P.A. cutoff, Mr. Denley says, a financial swing that “massively changes their likelihood of graduating.”

“When students do indeed take the courses that are recommended to them, they actually do substantially better,” he says. And take them they do. Last fall, 45 percent of classes on student schedules were from top-10 recommendations, 57 percent from their top 15. Though these systems are in their infancy, the concept is taking hold. Three other Tennessee colleges have adopted Mr. Denley’s software, and some institutions outside the state are developing their own spins on the idea.

Some express concerns about deferring such important decisions to algorithms, which have already come to dictate — and limit — so much of what we see and do online. Mr. Zimmer, the Milwaukee information-studies professor, sees the value in preventing students from going down paths that may frustrate them or cause them to quit college. But as higher education gets more efficient, he fears the loss of the unanticipated discovery.