Education professor Phil Winne elected as Fellow to the Royal Society of Canada

Today it was announced that Dr. Phil Winne has been elected as a Fellow to the Academy of Social Sciences by the Royal Society of Canada (RSC). Dr. Winne’s induction into the Academy of Social Sciences comes in recognition of his contributions to research on self-regulated learning and learning analytics. Recognition by the RSC is Canada’s highest academic honour.

Dr. Winne's interest in self-regulated learning began while completing his PhD program at Stanford University. During this time, he began to conceptualize students as agents – self-directed decision makers – who held control over how they learned, a view that evolved into ‘self-regulated learning’. While collaborating with Dr. Ron Marx (Arizona State University, retired), Dr. Winne learned that upper elementary students tended to be very under-educated about learning strategies and challenged to describe how they learned. After purchasing his first MacIntosh computer in 1984, Dr. Winne had an idea about how to gather data about fine-grained details of learning: “If students used computers to do lots of their work, the computer could be a powerful instrument to record what they did and which information they worked with.” This idea became the basis for today’s nStudy software system. This system helps learners analyze how they learn and determine what learning strategies work for them. Collecting and analyzing this big data has greatly contributed to understanding how people learn and to research on self-regulated learning.

With his induction into the Royal Society of Canada, Dr. Winne hopes to continue partnering with schools, colleges, universities, and researchers around the world to continue exploring how big data can help learners learn. “I believe such partnerships can help learners progressively improve their self-regulated learning as learning science is exponentially accelerated by truly big data,” says Dr. Winne. As for the future, Dr. Winne and his research team continue to design software systems that process nStudy’s data to automatically generate guides for learners and their instructors to help them reach educational goals.