To understand what the lab does, please look at one of the recordings at www.josephjaywilliams.com/prospectivestudents and/or the papers at www.josephjaywilliams.com/papers.

We especially encourage undergraduates to apply early on, in their first or second year, and to apply to ROP (Research Opportunity Program) and NSERC/UTEA (we consider 1st/2nd/3rd year students to those). We post these opportunities for every Summer & Fall/Winter. We only take 4th year students in exceptional circumstances, as often they are only with the lab for a year before they leave. If you can learn to work hard and tackle ill-defined problems and acquire new skills and be an excellent researcher, you can start doing that even from high school or your first year.

We ask that undergraduates seriously participate in "lab citizenship" tasks like project management, assisting on many tasks like taking notes, organizing meetings. This helps the lab run more effectively and save people time, to then help you, and it also teaches you leadership skills you might not otherwise have an opportunity to learn.

You can also look at www.josephjaywilliams.com/research-overview or choose papers to read about at www.josephjaywilliams.com/papers

The Intelligent Adaptive Interventions Research Group aims to create intelligent self-improving systems – websites, apps and technology that help people learn new concepts and change habitual behavior, in areas from education to mental health to physical health like exercise and nutrition. Our main methodology is to conduct dynamic experiments to discover how to optimize and personalize technology, by integrating statistical machine learning, cognitive science, crowdsourcing, and human-computer interaction.

Illustrative examples of potential research directions are:

Developing new systems to crowdsource the design of online problems and lessons, using multi-stage workflows that incorporate input from students, crowd workers, instructors, and learning scientists.

Creating and evaluating tools that enable collaboration between instructors and researchers, such as co-design of interventions and personalized lessons, and coordinated analysis of data about learning outcomes for students with different characteristics.

Investigating why and when prompting students to explain text/video lectures promotes learning, and understanding the effect of multi-modal interfaces that incorporate writing, speaking, and video creation. Teaching metacognitive skills and self-regulated learning of study behaviours, taking a user-centred approach to designing social-psychological interventions for enhancing motivation such as Growth Mindset and Wise Feedback.

Enhancing student wellness and mental health by testing interventions for encouraging people to exercise, monitor stress, apply principles from Cognitive Behaviour Therapy to managing emotions. Investigating how to support online peer-to-peer interactions for having discussions around issues like managing anxiety or developing socio-emotional skills.

Interpretable and Interactive Machine Learning Systems for dynamically enhancing and personalizing instruction, especially from the perspective of combining human computation with techniques from multi-armed bandits/reinforcement learning, Bayesian optimization, applications of deep learning to natural language processing.

We also collaborate with U of T’s Computer Science Education research group (https://uoftcsed.github.io), the Machine Learning group (http://learning.cs.toronto.edu), and HCI people at DGP (http://www.dgp.utoronto.ca).