  Growth mindset interventions foster students' beliefs that their abilities
can grow through effort and appropriate strategies. However, not every student
benefits from such interventions - yet research identifying which student
factors support growth mindset interventions is sparse. In this study, we
utilized machine learning methods to predict growth mindset effectiveness in a
nationwide experiment in the U.S. with over 10,000 students. These methods
enable analysis of arbitrarily-complex interactions between combinations of
student-level predictor variables and intervention outcome, defined as the
improvement in grade point average (GPA) during the transition to high school.
We utilized two separate machine learning models: one to control for complex
relationships between 51 student-level predictors and GPA, and one to predict
the change in GPA due to the intervention. We analyzed the trained models to
discover which features influenced model predictions most, finding that prior
academic achievement, blocked navigations (attempting to navigate through the
intervention software too quickly), self-reported reasons for learning, and
race/ethnicity were the most important predictors in the model for predicting
intervention effectiveness. As in previous research, we found that the
intervention was most effective for students with prior low academic
achievement. Unique to this study, we found that blocked navigations predicted
an intervention effect as low as 0.185 GPA points (on a 0-4 scale) less than
the mean. This was a notable negative prediction given that the mean
intervention effect in our sample was just 0.026 GPA points, though few
students (4.4%) experienced a substantial number of blocked navigation events.
We also found that some minoritized students were predicted to benefit less (or
even not at all) from the intervention.
