On June 20, Timothy A. McKay visited UBC to share his experiences using learning analytics to explore equity and improve learning at the University of Michigan. More than 60 graduate students, faculty, and staff attended McKay’s keynote, which kicked off UBC’s Afternoon of Learning Analytics event.
A professor of physics, astronomy, and education, McKay was working on big data astronomy projects — such as the Sloan Digital Sky Survey — long before diving into the world of learning data. In fact, it was teaching large introductory physics courses that first got him thinking about the trends he might find in his own courses. “When you teach a course with 700 students, you have the opportunity to learn a lot about what students go through when they’re learning physics and to do pretty precise measurements,” McKay explained.
First, McKay found it was possible to predict students’ grades in a particular course by looking at their grades in other classes. However, as part of that analysis, he found that students were getting considerably lower grades in STEM courses across the board — including McKay’s own physics courses — regardless of their performance in their other courses. Significantly, McKay found that this STEM “grade penalty” tends to be larger for certain subcategories of students, such as women and first-generation college students.
This is something McKay wants instructors and universities to pay attention to. “I think there’s strong evidence that suggests our students can learn these introductory science topics, but we’ve created environments in which some people learn them and other people don’t,” said McKay.
After exploring this topic with other institutions through collaborations such as Sloan Equity and Inclusion in STEM Introductory Courses (SEISMIC), McKay has found the issue of inequity in STEM to be profuse. “It’s not just a problem for my class or yours or for physics or chemistry,” said McKay. “It’s [a problem] for all of us, so we really need to work together to counter it.”
Learning analytics, McKay says, can also be part of the solution. “Data opens up the possibility of understanding each individual in the context of everyone else who is or has ever taken this class.” For example, he suggests that if the data shows that students who get started on homework well in advance tend to do better in a particular course, then that information should be shared with students to help them make their own evidence-informed decisions about how to approach their studies.
For McKay, the point of honing in on equity issues is to change practice. “I want to design courses and deliver them so that they are equitable and inclusive, and also very effective,” said McKay. “I want students to learn a lot.”
Learn more about learning analytics at UBC
UBC’s Learning Analytics Project is taking a data-driven approach to improving teaching and learning by investigating how data and learning analytics tools can support students, instructors, and advisors.
Want to find out more about opportunities to get involved with learning analytics at UBC? See the opportunities available to instructors and students, or get in touch with the learning analytics project team.