An article in the Chronicle of Higher Education discusses using data-mining techniques for better student advising.

One way to ensure that students will reach the finish line is to quickly figure out if they’ve selected a suitable track. So the Arizona State system front-loads key courses. For example, to succeed in psychology, a student must perform well in statistics.

“Kids who major in psych put that off, because they don’t want to take statistics,” Ms. Capaldi says. “They want to know, Does their boyfriend love them? Are they nuts? They take all those courses, then they hit statistics and they say, ‘Oh, God, I can’t do this. I can’t do experimental design.’ And so they’re in the wrong major. By putting those courses first, you can see if a student is going to succeed in that major early.”

Arizona State’s retention rate rose to 84 percent from 77 percent in recent years, a change that the provost credits largely to eAdvisor.

It might work.

My university has tried all the usual approaches:

• Every faculty member must advise some undergraduates (particularly in their first year).
• Certain trained faculty advisers will do it and be rewarded with release time.
• Specialist staff advisers will do it, freeing up professors to teach.

I think I saw the whole cycle go around and start over again.

As a freshman at Reed College years ago, I was the victim of ineffectual advising. I was assigned to a professor of foreign languages. She was not informed about things outside her department. She just said OK to whatever courses I chose and signed the forms. It was not until my second year that I began to realize that I had made some bad choices in terms of meeting requirements and taking things in sequence. It took me another year to get straightened out and on track.

A more aware human adviser could have helped me sooner. For a university with complicated “distribution requirements,” a human adviser plus the data-mining might actually help a lot.