Suppr超能文献

识别支持心态干预的学生因素:一种双模型机器学习方法。

Identifying Supportive Student Factors for Mindset Interventions: A Two-model Machine Learning Approach.

作者信息

Bosch Nigel

机构信息

School of Information Sciences and Department of Educational Psychology University of Illinois Urbana-Champaign.

出版信息

Comput Educ. 2021 Jul;167. doi: 10.1016/j.compedu.2021.104190. Epub 2021 Mar 17.

Abstract

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.

摘要

成长型思维干预措施培养学生的信念,即他们的能力可以通过努力和适当的策略得到提升。然而,并非每个学生都能从这类干预措施中受益——但识别哪些学生因素有助于成长型思维干预措施的研究却很匮乏。在本研究中,我们利用机器学习方法,在美国一项针对1万多名学生的全国性实验中预测成长型思维的有效性。这些方法能够分析学生层面预测变量组合与干预结果之间任意复杂的相互作用,干预结果定义为向高中过渡期间平均绩点(GPA)的提高。我们使用了两个独立的机器学习模型:一个用于控制51个学生层面预测变量与GPA之间的复杂关系,另一个用于预测干预导致的GPA变化。我们分析了经过训练的模型,以发现哪些特征对模型预测影响最大,结果发现先前的学业成绩、受阻导航(试图过快浏览干预软件)、自我报告的学习原因以及种族/族裔是预测干预有效性模型中最重要的预测因素。正如先前的研究一样,我们发现该干预措施对先前学业成绩较低的学生最为有效。本研究的独特之处在于,我们发现受阻导航预测的干预效果比平均值低0.185个GPA点数(满分0至4分)。鉴于我们样本中的平均干预效果仅为0.026个GPA点数,这是一个显著的负面预测,尽管很少有学生(4.4%)经历大量受阻导航事件。我们还发现,一些少数族裔学生预计从干预中受益较少(甚至根本没有受益)。我们的研究结果对计算机管理的成长型思维干预措施的设计具有启示意义,特别是对于那些在完成干预过程中遇到程序困难的学生。

相似文献

9
The future of Cochrane Neonatal.考克兰新生儿协作网的未来。
Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12.

本文引用的文献

1
Machine Learning and Psychological Research: The Unexplored Effect of Measurement.机器学习与心理研究:测量的未知效应
Perspect Psychol Sci. 2020 May;15(3):809-816. doi: 10.1177/1745691620902467. Epub 2020 Apr 29.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验