Big Data Comes To Campus
Very interesting article in the New York Times about the use of “big data” collected on students as they engage in online learning:
“Thousands of Arizona State University 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.
That excites some educators. George Siemens, a data-mining expert at the Canadian distance-learning university Athabasca, calls the traditional approach an inefficient model ‘that generates a fair degree of dropouts.’
Knewton dismantles that model. [Freshman Katye Allisone's] 8:35 a.m. class is not a lecture. Although students are supposed to show up at a fixed time, and an instructor is there to work with them, the action is on screen. Knewton allows Ms. Allisone to skip past some concepts she gets, like factors and multiples. When she struggles with inverting linear functions, the software provides more online tutoring. Two students who complete the same lesson might see different recommendations as to what to do next, based on their proficiency.
As the company develops and works with more data and content — major universities like University of Nevada, Las Vegas, are adopting its technology, as is the publishing giant Pearson — it will tailor instruction more finely. What time of day does a student best learn math? What materials and delivery styles most engage the student? Say you have the same concept explained in a video, in a textbook-like format and in Socratic steps. Knewton will associate a student’s ‘engagement metrics’ with those styles and use that to help determine the next step.
But what sounds flashy may be based, at least in part, on flawed assumptions, warns Richard E. Clark, professor of educational psychology and technology at the University of Southern California. He says there is no evidence that there are ‘visual’ learners who benefit from video over text, as Knewton implies. Studies, he says, have shown that ‘learning styles’ are not effective for shaping instruction.
The broader problem with data mining, as Mr. Clark sees it, is that it is seldom done right. Data analysts often make ‘questionable assumptions’ about the meaning of keystrokes, he says. They assume students who are spending the most time on some learning material are most interested in that content, for example. ‘That assumption may be true when people choose to watch Netflix movies but is not at all the case in many university courses where few choices are available,’ Mr. Clark says.” Read more here.
I appreciate the note of caution sounded by Clark. Given the rapidly advancing pace of technology and the tightening of university budgets, I have no doubt that this is the way higher education is headed. But we don’t have to accept it without reservation—we need to apply a healthy dose of skepticism and critical analysis.