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. Our findings have implications for the design of computer-administered growth mindset interventions, especially in relation to students who experience procedural difficulties completing the intervention.

成长心态干预培养学生的信念，即他们的能力可以通过努力和适当的策略来增长。然而，并非每个学生都能从此类干预中受益——但确定哪些学生因素支持成长型思维干预的研究很少。在这项研究中，我们利用机器学习方法在美国进行的一项全国性实验中预测了成长心态的有效性，该实验有超过 10,000 名学生。这些方法能够分析学生水平预测变量组合和干预结果之间的任意复杂相互作用，定义为过渡到高中期间平均绩点 (GPA) 的提高。我们使用了两种独立的机器学习模型：一种用于控制 51 个学生级别的预测变量与 GPA 之间的复杂关系，和一个预测由于干预导致的 GPA 变化。我们分析了经过训练的模型，以发现哪些特征对模型预测的影响最大，发现之前的学业成绩、导航受阻（试图通过干预软件过快地导航）、自我报告的学习原因和种族/民族是最重要的预测因素在预测干预效果的模型中。与之前的研究一样，我们发现干预对先前学业成绩低的学生最有效。这项研究的独特之处在于，我们发现阻塞导航预测的干预效果比平均值低 0.185 GPA 点（0-4 级）。这是一个显着的负面预测，因为我们样本中的平均干预效果仅为 0.026 GPA 分，尽管学生很少（4. 4%）经历了大量的导航阻塞事件。我们还发现，预计一些少数族裔学生从干预中受益较少（甚至根本没有）。我们的研究结果对计算机管理的成长心态干预的设计有影响，特别是与在完成干预时遇到程序困难的学生有关。