Yesterday UO’s AVP for Student Success Doneka Scott and Director of Academic Advising Kimberly Johnson talked to the Senate about UO’s efforts to use predictive analytics to help students graduate on time. Today the NYT has a report on similar national efforts – which include using data on social interactions – here:
… Different courses at different universities have proved to be predictors of success, or failure. The most significant seem to be foundational courses that prepare students for higher-level work in a particular major. Across a dozen of its clients, the data analysts Civitas Learning found that the probability of graduating dropped precipitously if students got less than an A or a B in a foundational course in their major, like management for a business major or elementary education for an education major. El Paso Community College’s nursing hot spot was a foundational biology course. Anyone who got an A had a 71 percent chance of graduating in six years; those with a B had only a 53 percent chance.
At the University of Arizona, a high grade in English comp proved to be crucial to graduation. Only 41 percent of students who got a C in freshman writing ended up with a degree, compared with 61 percent of the B students and 72 percent of A students.
“We always figured that if a student got a C, she was fine,” said Melissa Vito, a senior vice provost. “It turns out, a C in a foundation course like freshman composition can be an indicator that the student is not going to succeed.” The university now knows it needs to throw more resources at writing, specifically at those C students.
… At the University of Arizona, Sudha Ram, the director of Insite: Center for Business Intelligence and Analytics, has been experimenting with tracking freshmen — the category of students most likely to drop out — as they swipe their identification cards to go to the library or gym, pay for a meal in the cafeteria or buy a sweatshirt in the bookstore.
“We are measuring social interaction,” Dr. Ram said. “How many people do they tend to hang out with for different activities, and is their hanging out dropping off week by week or getting stronger? A lot of theoretical work has been done on this.”
The findings are put into algorithms to predict who is in danger of not making it to sophomore year.
“Most of the predictive-analytics people are looking at grades,” Dr. Ram said. “A lot of times it’s not the grades but whether they feel comfortable and socially integrated. If they are not socially integrated, they drop out.” ..
So an A student in freshman bio has a better chance of graduating than a B student, who has a better chance of graduating than a C student. Duh!
Any professor or dept head could tell you that. How much money is being spent on these “analytics”? Is there any data, not fake “metrics,” showing whether it does any good? Or is just another way to bloat the “administration”?
Did read the article or do you think it’s fake news?
I read the excerpt from UOM. It tells me nothing about the question I raise. It has nothing to do with “fake news,” sorry.
OA, you may be an example of the problem I refer to.
Predicative analytics with respect to college students is not so much about grades, but about the course trajectory that they are on and this can lead to more efficiency and less student wandering in the curriculum and can lead to more intelligent advising.
This is just in its early stages so predicting success or failure is premature. See for example this article
http://time.com/3621228/college-data-tracking-graduation-rates/
The overall goal is to lower time to degree, something that most big Universities struggle with
dog, fine, I would like grad rates to go up too. What I want to know is how much this stuff costs and how well it works. I’ve lived through a vast number of educational fads that were ineffectual or damaging or a waste of money or all three.
Higher ed is facing lean times and growing public skepticism. It can hardly afford to waste still morr money on schemes dreamed up by administrators and higher ed “professionals.”
Not least at UO, which faces soon a veritable financial shitstorm.
I believe I specifically said that all of this is just starting and its too soon to tell on its effectiveness.
As for me, I haven’t lived through any educational fads, let alone a vast number of them.
Here’s to the shitstorm …
The post oversimplifies the effort and the comment is an oversimplified response to the oversimplified post.
There are dozens, if not hundreds, of factors that contribute to a student’s time to degree. Incoming academic preparation, grades in gateway courses, inadequate advising,poor student choices, academic policies, poorly designed degree paths and on and on. Certainly we can imagine that by examining those factors, we might understand patterns that could be changed by changing how we do things as an institution.
Our effort, like those on campuses across the country, is focused on using data we have to remove barriers, advise students better and provide clearer paths to graduation in 4 years. Evidence is emerging across institutions that these efforts make a real difference in students’ time to degree and academic performance. The many moving parts make it much more complicated than what has been presented here.
There have been many public conversations and presentations on the effort that provide members of our university a chance to learn more about the effort.