Dr. Phil Winne is Distinguished 丁香园AV Professor of Education and a Fellow of the Royal Society of Canada, the American Educational Research Association, the American Psychological Association, the Association for Psychological Science, and the Canadian Psychological Association. Formerly a 2-term Tier I Canada Research Chair, he researches self-regulated learning, metacognition and learning analytics; and develops software technologies to support learners and gather big data for learning science. Author of more than 195 scholarly publications, he has been honored to receive the Robbie Case Memorial Award, the Barry J. Zimmerman Award, and the Canadian Society for the Study of Education Mentorship Award. In this interview, Dr. Winne talks about his current research and his experiences as a researcher and educator.
Please tell us a little bit about yourself including your academic and professional backgrounds.
I grew up an only child with many close friends in a small town in the beautiful Hudson River valley, about 170 kilometers north of New York City. My graduating class was small, 42 peers. In high school, I lettered in three sports, but that was not much to crow about. I often joke, 鈥淵ou had a good chance to make the team if you could spit straight.鈥� I took chances in my undergraduate education to explore who I was and what I thought, finally earning a Bachelor鈥檚 degree at Bucknell University in Education and Mathematics after sampling three universities. At Bucknell, my immense good fortune was to enjoy a semester-long debate with the professor leading my student teaching seminar. He gently guided me through a century鈥檚 literature of research about learning concepts. I discovered deep and magnetic interest in educational psychology. I travelled to Stanford University for my PhD, partly because of its reputation and partly because it required travel to, for me, a faraway place. Mentors and opportunities afforded me were exceptional at Stanford, and jobs in the U.S. National Institute of Education in Washington, DC and the Far West Laboratory for Educational Research and Development in San Francisco.
I arrived at 丁香园AV in 1975. I raced through the ranks, reaching Full Professor at the beginning of my ninth year here. I realized 鈥渨ork鈥� is my hobby. Now, 45 years later, I still believe that. I held a variety of posts in university governance and scholarly societies. These revealed and helped me appreciate colleagues鈥� diversity and energies, and the labyrinths of practices within policies. I became a proud Canadian in 1994 and, four years later, married my wife, Dr. Nancy Perry, Dorothy Lam Chair in Special Education at the University of British Columbia. She playfully reminds me (when it suits her) that I did not hire her as a Research Assistant more than a decade earlier when she was an undergraduate at 丁香园AV. I will retire at the end of August 2023. I think that means I will continue 鈥渨orking鈥� without salary. It is quite appealing.
It is great to know about your childhood and your university days. Can you please share your current research with us?
With much more emphasis on learning as a pursuit than as an accomplishment, the Greek-rooted word polymath describes me. My current research projects include: (a) investigating how to guide inquiry learning about complex systems (e.g., electric circuits), (b) probing the nature of error when statistically estimating properties of a parameter in a population (e.g., the mean) using a randomly drawn sample, (c) designing software to be an instrument for researching how high school students develop skills in computational thinking while simultaneously scaffolding their learning, (d) exploring factors relating to undergraduates鈥� substance misuse, (e) algorithms for analyzing how information in online sources is selected and transformed when students research and write term papers, and (f) conceptualizing big data and developing tools to use big data in ways to transform education.
Although it may not appear so, all these projects and those deeper in my history have a common thread, now a bit more tightly braided after decades of spinning. I consider learners to be learning scientists. I believe they are investigating as best they can how to make learning work well for them. In other words, self-regulating learners experiment with how to learn. I recently conducted a webinar on self-regulated learning titled Learning analytics for self-regulated learning: Frameworks, methods & future work for the American Psychological Association. Here is the link to the Webinar, .
Unlike 鈥減rofessional鈥� learning scientists who involve a large number of learners in a single experiment, learners pursuing an 鈥�狈=尘别鈥� program of life-long self-regulated learning are tremendously disadvantaged. One significant handicap is a very human characteristic 鈥� imperfect memory. Learners self-regulate learning based on quite incomplete and distorted recollections about which learning skills and strategies they used. Data about how they actually 鈥渄o鈥� learning is very fuzzy. A second obstacle to working as a learning scientist is analyzing 鈥渄ata鈥� to identify effects of learning skills and strategies. As a 鈥減rofessional鈥� learning scientist, I have years of training and experience to apply in scrutinizing and analyzing data when I judge whether a particular treatment has a particular effect. Students have none of this training, no access to sophisticated tools I can use to analyze data. And, they do not have as much opportunity to shape their research studies as I do. Their attempts to figure out 鈥渨hat works鈥� are greatly hampered.
If pressed, I will position two goals at the top of a long list for my research program. First, I hope to conceptualize and distribute evidence about productive self-regulated learning; and, second, to create tools that will help learners and their teachers pull their N=me learning science up by the bootstraps. Climbing this staircase has required dogged persistence, willingness to step backwards on occasion and several infusions of serendipitous luck. Each step toward the summit has been very satisfying. I really enjoy it.
Your research population involves elementary, high school as well as university students. How is your experience of working with very different age groups? Do you have any suggestion for researchers interested in working with varied student populations?
While I have been fortunate to have learners ranging from grade 5 to undergraduates participate in my research projects, the vast majority have been undergraduates who visit a campus lab to participate in experiments. To be honest, I have found it challenging to involve students in public schools. My experiments so far required studying content outside the everyday curriculum, and that intrusion on an already overfilled curriculum was difficult to manage. However, I have high hopes the software my research group and I have developed can be a means to overcome this challenge. It provides a system that supports research when learners use any content they study online, and it does not require specialized equipment beyond a computer, an internet connection and the Chrome web browser.
My advice to researchers who hope to successfully invite students to participate in research is this: Find balance between the requirements of school and the requirements for well-designed research. On one hand, this will challenge needs for experimental control. On the other hand, genuine collaboration can boost authenticity of research. I have another suggestion in the section on suggestions to practitioners/educators.
What impact would you like to see your research have on communities and society at large?
I hope my research portfolio makes two differences. First, to support improvements in learning and teaching, I want to give away learning science. For me, this means striving to publicize the benefits learning science has to offer and enlightening people about misunderstandings that mislead educational practices and misdirect scarce resources. Second, to avoid fads and practices founded on myth, I hope my research honestly portrays current limits of learning science in ways that motivate surpassing those limits rather than rejecting the enterprise altogether.
What suggestion would you like to give to practitioners/educators?
Among many, many suggestions I might offer, one is primus inter pares (first among equals). It is: Do more research. I often hear protests: Research is inauthentic. Useful data are scarce. Experience, not research, is the font of expertise 鈥� .This list is long, and it undercuts reasons to do research. As a practicing learning scientist, I promote doing more and better research. How? I suggest setting aside 1% of human resources and time to do research. Every student, every teacher, every administrator, every technologist, every counsellor 鈥nd every researcher would allocate 1% of the school year to research. For the community in public schools, this asks for about 10 hours per year. With a partnership that doesn鈥檛 cut into requirements, I believe all the parties could learn more about one another, generate truly revealing results and, over time, smooth bumps that make 鈥渢ranslating鈥� research into practice too jittery.
What suggestion would you like to give to prospective graduate students interested in your field of research?
Nearly a half-century of experience leads me to offer five suggestions. 1. Read a wide range of scholarship. Some of my most motivating (and controversial) ideas germinated while reading in areas of psychology, science, and philosophy that most would consider quite beyond the boundaries of my research foci. 2. Collaborate. Peers working together lend multiple perspectives and talents to a project. This accelerates progress and helps avoid hidden traps. 3. Seek opportunities to administer and lead scholarly work. Trying to understand and promote peers鈥� work within a context of tradition, policy and regulations is a truly educative challenge. My experiences in these roles helped me perceive more clearly how colleagues think and what they value, and to develop a modicum of skill for advancing the greater good. 4. Search for a mentor (or several). Beyond deep friendships I have developed with my mentors, many now sadly passed, working at an expert鈥檚 elbow (to borrow Allan MacKinnon鈥檚 perceptive phrase) is the best way I know to better know yourself and develop as a scholar. 5. Accept challenge as a problem to solve rather than a barrier.