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.

翻译：学生成长的心态干预培养学生认为,他们的能力可以通过努力和适当的战略而增长。然而,并不是每个学生都能从这种干预中受益,而是研究确定哪些学生因素支持增长的心态干预,研究发现哪些学生因素支持增长的心态干预是稀少的。在本研究中,我们利用机器学习方法预测美国全国范围内有超过10,000名学生的实验中的增长心态效果。这些方法可以分析学生水平预测变量和干预结果相结合之间的任意复杂互动,被定义为在向高中过渡期间,年级平均水平的提高(GPA)是预测干预有效性的最重要预测因素。我们利用了两个不同的机器学习模式:一个是控制51个学生级预测器和GPA之间的复杂关系,另一个是预测GPA因干预而发生的变化。我们分析了经过培训的模型,发现模型对模型预测效果的影响最大,而模型预测的特征影响最大,在学前成绩中发现,导航受阻(试图通过干预速度过快),种族/种族是模型中最重要的预测因素。我们发现,从以往的研究发现,干预对于学术成就较低的学生最为有效。我们发现,在0.8的预测中发现,在0.8比预估值低的预测中发现,我们预测效果为低。