I am a Learning/Data Scientist at Enuma, a Co-Winner of the $15M Global Learning XPRIZE competition in 2019.
At Enuma, I strive to increase K-2 children’s motivation, engagement, and learning at scale using learning/data science and education technology.
During my PhD and postdoc, I used web-based cognitive tasks, brain imaging, eye-tracking, and machine learning to study human decision-making. For example, I developed The Choose-And-Solve Task to show how some individuals with math anxiety choose to avoid math and published my work in Science Advances (Choe et al., 2019).
These tasks are based on the jsPsych library and have been used in my research with Qualtrics. You can try these right now! NOTE: These tasks are NOT designed to work on mobile phones and tablets.
Have you wondered how to use jsPsych with Qualtrics? Here is the jsPsych in Qualtrics Tutorial Series!
The Choose-And-Solve Task (CAST) is a novel effort-based decision-making task in which participants chose between solving easy, low-reward problems and hard, high-reward problems in both math and nonmath contexts.
Higher levels of math anxiety were associated with a tendency to select easier, low-reward problems over harder, high-reward math (but not word) problems, suggesting that we cannot even pay math-anxious people to do hard math. Addressing math avoidance behaviors can help break the vicious cycle of math anxiety and increase interest and success in STEM fields. Please see the paper (Choe et al., 2019, Science Advances).