Identifying supportive student factors for mindset interventions: A two-model machine learning approach
Identifying supportive student factors for mindset interventions: A two-model machine learning approach
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DOI：
10.1016/j.compedu.2021.104190
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发表时间：
2021-03-23
影响因子：
12
通讯作者：
Bosch, Nigel
中科院分区：
文献类型：
--
作者：
Bosch, Nigel
成长心态干预措施培养学生的信念，他们的能力可以通过努力和适当的战略增长。然而，并不是每个学生都能从这些干预措施中受益--然而，确定哪些学生因素支持成长心态干预措施的研究却很少。在这项研究中，我们利用机器学习方法来预测美国全国范围内超过10，000名学生的成长心态有效性。这些方法使任意复杂的学生水平的预测变量和干预结果的组合之间的相互作用的分析，定义为在过渡到高中的平均成绩（GPA）的改善。我们使用了两个独立的机器学习模型：一个用于控制51个学生水平预测因子与GPA之间的复杂关系，另一个用于预测干预导致的GPA变化。我们分析了训练的模型，以发现哪些特征对模型预测影响最大，发现先前的学业成绩，受阻的导航（试图过快地浏览干预软件），自我报告的学习原因以及种族/民族是模型中预测干预有效性的最重要的预测因素。与以前的研究一样，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，受阻导航预测的干预效果低至0.185 GPA点（0-4级），低于平均值。这是一个值得注意的负面预测，因为我们样本中的平均干预效果仅为0.026 GPA点，尽管很少有学生（4.4%）经历了大量的导航受阻事件。我们还发现，一些少数民族学生预计受益较少（甚至根本没有）的干预。我们的研究结果对计算机管理的成长心态干预措施的设计有影响，特别是与经历程序性困难完成干预的学生有关。
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.