Data-mining is helping Rio Salado College predict online students’ likely success and failure, reports the Chronicle of Higher Education. The Arizona community college is a pioneer in online education.
By the eighth day of class, Rio Salado College predicts with 70-percent accuracy whether a student will score a C or better in a course.
That’s possible because a Web course can be like a classroom with a camera rigged over every desk. The learning software logs students’ moves, leaving a rich “clickstream” for data sleuths to manipulate.
Running the algorithms, officials found clusters of behaviors that helped predict success. Did a student log in to the course homepage? View the syllabus? Open an assessment? When did she turn in an assignment? How does his behavior compare with that of previous students?
Instructors can see at a glance who’s green (likely to complete the course with a C or better), yellow (at risk of not earning a C), and red (highly unlikely to earn a C). That makes it possible to offer extra help before it’s too late.
The college is trying to intervene before the lights turn yellow or red.
For example, early data showed students in general-education courses who log in on Day 1 of class succeed 21 percent more often than those who don’t. So Rio Salado blasted welcome e-mails to students the night before courses began, encouraging them to log in.
The next step is a widespread rollout of the color-coded alerts, one that will put the technology in the hands of many more professors and students. The hope, (physical sciences instructor Shannon) Corona says, is that a yellow signal might prompt students to say to themselves: “Gosh, I’m only spending five hours a week in this course. Obviously students who have taken this course before me and were successful were spending more time. So maybe I need to adjust my schedule.”
In the future, colleges may ask students for an array of personal data in order to customize courses to fit each student’s abilities, interests and personalities, says George Siemens, an analytics expert at Canada’s Athabasca University.