Graduate Student Applications


Graduate Students can apply to do Computer Science & Education research at University of Toronto CS


Graduate students are invited to do research in Computer Science and Education, involving the design of interventions and experiments that can dynamically enhance and personalize real-world educational technologies, spanning K12, university courses, MOOCs, and learning by crowd workers.


Graduate students will set the agenda for research questions in collaboration with Joseph Jay Williams, who is particularly interested in creating systems that combine rigorous randomized experiments with crowdsourcing and human computation, applications of statistical machine learning (e.g. bandits & reinforcement learning, NLP, recommender systems), and theories from cognitive, clinical and social psychology (e.g. self-explanation, analogical comparison, growth mindset, teaching cognitive behavior therapy).


The graduate program will be based at University of Toronto's Computer Science department, working with Joseph Jay Williams, and with opportunities to collaborate with U of T's Computer Science Education research group, the Machine Learning group, and HCI people at DGP.

 

Graduate students will play a key role in deciding which projects are pursued, but illustrative examples of potential research directions are:

> Developing new systems for crowdsourcing 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 behaviors, taking a user-centered 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 Behavior 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.