[on behalf of Joseph Jay Williams]
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 <http://www.josephjaywilliams.
com/research-overview>, 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 <https://uoftcsed.github.io/>, the Machine Learning group
<http://www.cs.toronto.edu:40292/>, and HCI people at DGP
<http://www.dgp.toronto.edu/home/>. Examples of other faculty the postdoc
can collaborate with are Ashton Anderson
<http://www.cs.toronto.edu/~ashton/>, David Duvenaud
<https://www.cs.toronto.edu/~duvenaud/>, Tovi Grossman
<http://www.tovigrossman.com/>, and Fanny Chevalier
<http://fannychevalier.net/>.
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:
Computer Science Education, research into enhancing teaching ofintroductory programming, motivating broader involvement, end-user
Developing new systems for crowdsourcing the design of online problemsand lessons, using multi-stage workflows that incorporate input from
Creating and evaluating tools that enable collaboration betweeninstructors and researchers, such as co-design of interventions and
Investigating why and when prompting students to explain text/videolectures promotes learning, and understanding the effect of multi-modal
Enhancing student wellness and mental health by testing interventions forencouraging people to exercise, monitor stress, apply principles from
Interpretable and Interactive Machine Learning Systems for dynamicallyenhancing and personalizing instruction, especially from the perspective of