Multiple postdoctoral positions are immediately available for research that designs interventions and experiments to dynamically enhance and personalize real-world educational technologies, spanning K12, university courses, MOOCs, and learning by crowd workers.
The postdoc 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 postdoc will be based at University of Toronto’s Computer Science department, working with Joseph Jay Williams, and with opportunities to collaborate with faculty in U of T’s Computer Science Education research group, the Machine Learning group, and HCI people at DGP. Examples of other faculty the postdoc can collaborate with are Ashton Anderson, David Duvenaud, Tovi Grossman, and Fanny Chevalier.
The appointment is for one year, with the possibility of renewal based on mutual interest.
The postdoc will play a key role in deciding which projects are pursued, but illustrative examples of potential research directions are:
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Computer Science Education, research into enhancing teaching of introductory programming, motivating broader involvement, end-user programming.
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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.
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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.
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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.
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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.
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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.
Application Instructions
If you might be interested, provide your email and other information using this form.
Then, to apply for the position, please email Joseph Jay Williams (williams -at- cs.toronto.edu) with the subject line “Postdoc in Dynamically Enhancing & Personalizing Educational Technology – [Your name]”, with the following as attachments:
(1) Curriculum Vitae.
(2) Names and contact information of 3 references who are familiar with your research. We will follow up with your references to secure their recommendation letters on your behalf.
In your email application, please include a brief explanation of your research interests and how they fit with this position (in lieu of a formal cover letter). Applications will be accepted on a rolling basis until the position is filled.