In the information age, data will follow us from the time we first walk into kindergarten to well past retirement. As data is used to guide us in making all kinds of decisions, from what we consume to what health plan we follow, it's also becoming a powerful tool in education.
As more schools and colleges use algorithms to determine a student's path, the Amazon- and Netflix-style practice of data mining will soon be the norm in how schools and students operate.
But that might not be such a bad thing. Just as the two online behemoths -- Amazon and Netflix -- are able to use software to predict books, music, and movies you might like based on your past preferences, schools are using data to place students not only in their appropriate learning level, but even to recommend what subject to major in.
In K-12 education, it's happening in classrooms and computer labs in both rich and blue-collar schools. In Covington Elementary, for example, the affluent Silicon Valley community where each fifth-grade student has a laptop and is learning math using Khan Academy videos and quizzes, teachers can track each student's progress in real time on their iPads. When a student is stuck in one subject area, teachers can help the student one-on-one.
Likewise, at Rocketship's Los Suenos Elementary school in a working class neighborhood in San Jose, teacher Alana Mednick can track her students' progress based on how they score on their online computer games in their Learning Lab. And these examples are hardly rare these days.
On the college level, student data is being used for everything from recommending courses to picking majors. Austin Peay State University in Clarksville, Tenn., rolled out a program last year that uses data based on students' majors, class history, grades, and similar student performance to help students decide on courses. (Students also still get advice from guidance counselors.) And according to the university's provost, students who took the software-recommended classes received a half-point higher GPA than those who didn't.
This spring, Austin Peay will take the experiment on a larger scale and use the computer algorithm to recommend a major for students who are undecided and for those who might choose one that's not "right" for them.
But even before students apply to college, a company called Parchment will help them figure out which schools they'll have the best chance to get accepted to. Parchment uses vast amounts of users’ data — GPA, SAT scores, extracurricular activities and so on — to assess whether former applicants with similar profiles gained admission into certain schools. Parchment also says it can help point students towards schools that match their profiles, helping them find schools that are a good fit.
Getting that granular level of information to help guide decisions can help students bypass mistakes -- but what happens then to serendipity, to the path that follows curiosity and experimentation what educators called "passion-based learning" that's the antithesis to the data-driven definition of achievement and success?
Capturing data has turned into an expensive and convoluted proposition, he said. Schools, whether they're K-12 or higher ed, will collect data, then share that information with certain faculty but not others (the latter of whom are then upset they weren't included), then bring in administrators who are uncomfortable with the data they've seen and want to "make sure it's clean," at which point they hire auditors, then compare the numbers to other institutions to see how they rate against them. The auditors will make recommendations, like buying more software, which requires hiring consultants, sending out requests for proposals, then implementing the software.
"So what happened to the kids?" Milliron said.
If Amazon's high-powered data engines can take just one split second to process data about a person that will allow them to make a good decision, why can't that same power be applied towards education? he asked.
"What we’ve seen in the consumer world and healthcare world that's made such a huge impact is what happens when you get data to the front lines," he said.
To that end, new initiatives are being launched in different colleges to help students. A program called Course Signals at Purdue warns students who are at risk of poor grades and "facilitates intervention and support" that can help improve student grades by an average of one letter, according to the school.
Here's how it works: The program uses information already available about each student to determine whether he or she is "at risk of failing or withdrawing from a course as early as the second week of the semester or quarter. Based on the data, the solution displays a red, yellow or green signal to students and faculty, indicating a students status in a course in real time. A red light indicates a high likelihood of failing; yellow indicates a potential problem of succeeding; and green signals a high likelihood of succeeding." Students receive an email with the progress report, along with suggested resources and recommendations from faculty on what to do next.
At WGU Texas, where Milliron is chancellor, the non-traditional online learning institution uses the same conceit for students, allowing them to create what he calls their own "learning journeys." Though the school is technically based in Texas, only 1,600 of its 25,000 students are located in the state. What also makes this school different from others is that it's "competency-based advancement," which means students don't have to take classes in subjects they're already proficient in and progress at exactly their own level.
"The average student at WGU finishes in about 30 months as opposed to 60 months. So, they can get through in about half the time because many of them already have significant experience in their field and they can test out on competencies," Milliron said in a recent Texas Tribune article. "They’d have to sit through classes that they could be teaching, which is often the challenge with adult learners."
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