Abstracts & Bios



Fiona Rawle


Building Academic Resilience: Evidence-based practices for teaching students to embrace and learn from failure

Failure is a critical component of learning, however students often view failure in a negative light. How can we encourage our students to see the value of failure, learn how to fail well, and become resilient learners? This session will start with an exploration of the literature on productive failure and resiliency-building approaches in academia. We will then dive into a recent novel course reframing called FLIP (Failure: Learning in Progress). The purpose of this FLIP course reframing project was to (1) establish instructional tools and approaches for teaching students to embrace and learn from failure; (2) develop both formative and summative assessment methods for failure bounceback; and (3) create a resource of failure narratives that can be used in courses across disciplines.


Fiona Rawle has a Ph.D in Pathology and Molecular Medicine and is the Associate Dean, Undergraduate, at the University of Toronto Mississauga, and an Associate Professor, Teaching Stream, in the Dept. of Biology. Her research focuses on failure-driven learning, the science of learning, and public communication of science. She has received numerous awards focused on teaching, including the University of Toronto’s President’s Teaching Award. Dr. Rawle is also a member of the University of Toronto’s TIDE group (Toronto Initiative for Diversity & Excellence), through which she gives lectures and workshops on unconscious bias, equity, and diversity.




tullis jonathan SQ

Jonathan Tullis


Learning from examples: The consequences of comparison, generation, and reminding

Generalizing appropriately across exemplars of declarative concepts is difficult for students. Students sometimes generalize ideas too broadly and other times fail to generalize. Here, we examine how three common pedagogical techniques support or inhibit appropriate generalization of declarative concepts. First, comparison requires learners to explicitly identify the deep structure of a concept and consequently may prompt the broadest transfer. Second, generating an example requires learners to connect a concept to their existing knowledge, boost memory for the concept, and enable transfer. Finally, remindings may bring two distinct episodes together and cause learners to abstract the common deep structure from the superficial features. Across several experiments, we examine whether and how each instructional strategy supports the generalization and transfer of conceptual knowledge. Evidence reveals that generation does not bolster learner, but comparison and reminding can support near and far transfer.


Jonathan Tullis is the principal investigator of the Cognition And Memory in Education and Learning (CAMEL) Lab in the Department of Educational Psychology at the University of Arizona. Jonathan earned his PhD in cognitive psychology at the University of Illinois, Urbana-Champaign and completed a post-doc at Indiana University. He investigates how to structure and adapt learning environments to match the characteristics and quirks of memory and cognition. To that end, he examines how ideas and examples should be designed and organized to support memory and appropriate transfer of concepts. He also examines how students monitor and control their own learning. His work has been funded by the National Science Foundation.




Ji Son SQ

Ji Y. Son


Practicing Connections, A Practical Theory for Teaching Hard Things to All Students

Despite the valuable research into the cognitive, affective, and neurological effects of learning experiences, there is still a research-to-practice gap: many educational materials are designed  independently of what we know from research to produce deep learning, and deep learning is still viewed as an elusive outcome of our educational system (Biesta, 2007; Canole, Dyke, Oliver, & Seale, 2004; Levin, 2004; Smeyers & Depaepe, 2013; Vanderlinde & van Braak, 2010). Instruction is an undoubtedly multivariate system so it is highly unlikely that any particular variable - even your favorite one! - is going to carry much weight in and of itself when implemented in an authentic educational setting. This talk will focus on how developing a new model of collaboration among researchers, instructors, and developers necessitates a practical theory of how deep learning occurs in an authentic discipline (e.g., statistics and data science) over time.


Ji Y. Son is Professor of Psychology at California State University at Los Angeles and director of the Learning Lab at Cal State LA. She is a co-author of the interactive textbook "Introduction to Statistics: A Modeling Approach" published using CourseKata.org. Her PhD in Cognitive Science and Psychology is from Indiana University, Bloomington. She is interested in how basic cognitive and perceptual processes foster rich and transferable learning. Her work examines methods of applying these psychological insights at scale to issues like mathematics remediation and student success. The central idea behind Ji’s work is that learning changes the way we see the world.



Michelle Cadieux SQ

Michelle Cadieux


Lessons learned from teaching a large introductory psychology course: What works and what doesn’t

After 7 years of helping to teach and coordinate a large introductory psychology class, I have learned that some things work well with big classes and some things can be a total disaster. In this talk, I will discuss the projects, assignments, and technologies that we have implemented successfully and unsuccessfully throughout the years. This will include writing projects, practice tests, iclickers, gamification, learning portfolios, peer-created practice questions, and student presentations. We will examine student feedback to see what students enjoyed and what they found frustrating. We will also look at the effects these practices had on final grades. The goal of the presentation will be to explore what worked, what didn’t, and what simply wasn’t worth the effort.


Michelle Cadieux is the course coordinator for Introductory Psychology at McMaster University. She helps to teach over 4,500 students every year. Her primary research involves investigating the best methods and practices for instructing the largest class at McMaster. Dr. Cadieux’s position puts her on the front lines of exploring what can be done to help students transition to university life and achieve their academic goals. She examines how different course structures can promote or hinder learning, while paying particular interest to the role that online learning now plays in many large classrooms. She is most interested in how to improve overall student engagement to prevent students from being lost in a sea of first year undergraduates.