With 72,000 students, A.S.U. is both the
country’s largest public university and a hotbed of data-driven experiments.
One core effort is a degree-monitoring system that keeps tabs on how students
are doing in their majors. Stray off-course and a student may have to switch
fields, so this system suggests alternatives courses match with your interest
and field.
If they fail to sign up for a key course or do
well enough, the computer cracks a whip, marking them “off-track.” Wander
off-track two semesters in a row, and a student may have to change majors.
If that sounds harsh, there’s a rationale: 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 A.S.U. system front-loads key
courses. For example, to succeed in psychology, a student must perform well in
statistics.
At Arizona State, algorithms figure in course
content, too. Thousands of A.S.U. students now take math courses through a
system that mines performance and behavioral data, building a profile on each
user and delivering recommendations about what learning activity they should do
next. The system, created by the start-up company Knewton, has given the
university a fresh way of addressing the continuous problem of students being
unprepared for college math. But it also offers a glimpse into what many more
students will experience as teaching increasingly shifts from textbooks and lectures that feed the same
structure of information to a class of 300, regardless of individual expertise,
to machines that study their users’ learning patterns and adapt to them.
So do you think all these countless hours of
online Homework really worth it?
At Rio Salado, a community college with about
70,000 students, 43,000 of them online, Mr. Lange got excited about the
behavioral data students leave behind: the vast wake of clicks captured by
software that runs Web courses. Records of when they logged in opened a
syllabus, turned in homework.
Could you mine it to model patterns of students
who succeeded in the past? Use that to identify current ones likely to fail?
And then help those students? Many educators are now asking similar questions.
“Mr. Lange and his colleagues had found that by
the eighth day of class they could predict, with 70 percent accuracy, whether a
student would score a “C” or better. Mr. Lange built a system, rolled out in
2009, that sent professors frequently updated alerts about how well each
student was predicted to do, based on their course performance and online
behavior.”
Research shows that social ties can influence
academic success. If students are more integrated into campus life, they’re
more likely to stay in school. If a friend drops out, they’re more likely to as
well.
Do you
believe in these theories of social ties or the ability to predict a students grades
for the future?
-Parry,
Marc. "Please Be EAdvised." The New York Times. The New York
Times, 22 July 2012. Web. 03 Apr. 2013.
I feel like it does seem pretty obvious just by looking around in most classes within the first few weeks of who will pass and fail. It is interesting that the ability to compile this data might actually help at risk students. It probably would be very hard to stay in school when all your friends are leaving. Will this data mining help target students learning patterns as well? Say, track students who do well in classes under professors that provide many visual or other kinds of examples.
ReplyDeleteThe ASU tool used to help students determine a career path/major is pretty nice. I know a few folks that I've met that have no clue as to what they want to do, or should do, with their life and career. With this tool, seemingly, a student could take an array of classes and the tool could help direct them down the path in which they would most likely do well. Sounds pretty straight-forward to me, although I know that there are some out there that buck (or would buck) the suggestions and insist on doing whatever they thought was best for themselves, whether they were good at it or not. I know that because there are some that are doing that currently at UNCC; folks I know that are not good at what they're attempting to do for a career, yet they stick unwavering to that path at all costs.
ReplyDeleteThe grade prediction is pretty spot on. There are a few instances where students score lower than they're capable of just out of sheer laziness, though. I wonder if that's taken into consideration in the algorithms.
At Rio Salado,43,000 out of 70,000 students are online student. "the vast wake of clicks captured by software that runs Web courses. Records of when they logged in opened a syllabus, turned in homework." The systems knows how's the students doing through the data sources. Like ours, We can get grade info by E-mail, and get advice from major department. Just like I get in here through Moodle, everything is connected. And in some class, they do clickers, checking attendance, taking quizs, taking exams. And I get feedback just right after class. It is amazing.
ReplyDeleteby wenyi chen
I think the system at ASU could be really helpful for some students. For some students, its not as clear cut as declare a major and follow a cookie cutter pathway. Many students are still trying to figure out what they would like to do, and may take courses in several different fields. I feel the system could be used to aid these students with advising, suggestions based on interests, and possible career goals. The system could be used to give guidance to a lot of students, not just to watch out for those who stray.
ReplyDelete