MHA
“A digital intervention focused on improving mental health”
Mental health is an area where personalization and user-focused design can be especially beneficial for people. With collaborators from Northwestern University, we are working on a digital intervention for Mental Health America which consists of treatment modules that involve psychoeducation material, interactive activities, and supportive messaging. Using machine learning, we will personalize content by determining which messages/prompts are most effective and engaging for users. Additionally, we are working on deploying and measuring the effects of TenQ, a condensed ten-question survey informed by cognitive behavioural therapy meant to help people reflect on their mental health, with the goal of lowering the barrier to accessing mental health resources.
Students OnTrack
“A digital intervention focused on improving homework completion”
Often students, especially first year students, can become overwhelmed with academic responsibilities and fall behind on weekly homework. In a similar fashion, sometimes students convince themselves they don’t need to do homework because they already know the material - then they find themselves struggling to cram information before a test/exam. OnTrack simply encourages 1st year computer science students to complete their online homework on time. This is accomplished by sending personalised reminder emails with different bodies, at different times, from different people (instructor vs. TA). A/B comparisons are used to determine which emails are most effective.
Personalised Explanations/PCRS DropDowns
“A digital intervention focused on improving student learning”
Different students are likely to get different levels of learning outcome even if they receive the same prompt or message. For example, although there is a large body of academic literature showing that students learn better when they write explanations of the concepts they are learning, some of them might not be affected in the real world because they rush to finish their problem set without time to reflect. In the personalized explanations project, we design randomized experiments to examine what educators should tell students in what context to improve their learning and engagement. We then apply contextual bandits to tailor students’ experience on online education systems. We embed this system in the University of Toronto’s PCRS online learning system.
Statistically Considerate Bandits
“hypothesis testing for adaptive experiments”
The IAI group uses bandit algorithms - a type of statistical tool - to complete our research. We want to reduce the rate of false positives that bandit algorithms produce, and make them more accurate. The Lab attempts to accomplish this by using a variety of statistical approaches to improve bandit algorithms.
DIAMANTE
“A digital intervention focused on improving Diabetic health and mental wellbeing”
The DIAMANTE project focuses on the intersection of physical and mental health in patients with diabetes and depression. The project takes the form of an app in which participants keep track of a target number of steps walked daily and fill out a survey. We then apply contextual bandits to determine which factors are effective in helping users reach their step goals and improve their mental health. The app sends both motivational messages and requests for feedback to the users. For the first six days, it also sends one message per day with advice on managing diabetes and depression and asks the participant to rate their mood.