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with 60 students. At Purdue, the course has a DFW (drop, fail, withdraw) rate of 25 to 40 percent, depending on the semester. Many students struggle in this course and then end up changing majors due to low grade point average. The mechanical engineering department had taken steps to try to improve success in the course, including opening a tutorial center and offering supplemental instruction. “Those things have been in place for several semesters and have had some impact, but not as much as we would have liked to see,” Holloway said.
“I wondered if we gave students a very specific way to track their time and were able to mirror that back to them and have them be able to compare vs. others in their class, perhaps that would help them calibrate to this course and the expectations of what they should be doing,” she added.
Holloway offered extra credit to students who used Pattern in the course. Students logged their study habits for a total of four weeks during the semester (three exams and a final exam). She and educational technologist Brandon Karcher began compiling the student data and combining it with test scores in the course. “I flashed up a slide in class that said, students who earned A’s vs. B’s vs. C’s studied this many hours,” she said. “We didn’t do statistical significance on pilot data, but you could definitely see there is a connection between hours studied and grades earned. Our hope was that reflecting that back to the class would provide guidance
and help change some behaviors.”
Because those initial results were promising, other engineering instructors participated in an expanded study in seven courses, with nearly 300 students involved. The data from those courses is now being evaluated to assess whether use of the app is having an impact in these difficult engineering courses. In designing how the app would be used, Karcher said he and Holloway had to think about how they needed to tailor Pattern to their purposes. “Pattern has a default set of things you can log that are more generic,” he said. “In this case it was more exam-specific. Instead of logging about reading a book or doing homework or going to class, this was much more focused. We had to narrow down
the specific activities for exams.”
Karcher noted that the widespread use of quantified-self
apps might help students grasp the value of using an app like Pattern. “When we talk to students about tracking this type of data, it is still a new concept applying it in education, but they do value being able to visualize what they are doing,” he said. “A fun and interesting thing about this project is that we are introducing the idea of quantified self in an area that they hadn’t thought about before.”
Because Pattern’s usage was so novel, Holloway and Karcher felt the extrinsic reward of extra credit was needed to get students to use it. “Long term, we would want there to be enough perceived value that they are intrinsically motivated to use it,” Karcher said. “That is one of the things we are moving toward. The tool is still under development.”
Pattern has been licensed to other universities (such as the University of Wisconsin-Madison), so another goal is
Pattern asks students to self-report time spent reading course materials, working on problems, attending help sessions, going to office hours and more.
CAMPUS TECHNOLOGY | November/December 2017

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