In this study, we examine a set of primary data collected from 484 students enrolled in a large public university in the Mid-Atlantic United States region during the early stages of the COVID-19 pandemic. The data, called Ties data, included students' demographic and support network information. The support network data comprised of information that highlighted the type of support, (i.e. emotional or educational; routine or intense). Using this data set, models for predicting students' academic achievement, quantified by their self-reported GPA, were created using Chi-Square Automatic Interaction Detection (CHAID), a decision tree algorithm, and cforest, a random forest algorithm that uses conditional inference trees. We compare the methods' accuracy and variation in the set of important variables suggested by each algorithm. Each algorithm found different variables important for different student demographics with some overlap. For White students, different types of educational support were important in predicting academic achievement, while for non-White students, different types of emotional support were important in predicting academic achievement. The presence of differing types of routine support were important in predicting academic achievement for cisgender women, while differing types of intense support were important in predicting academic achievement for cisgender men.
翻译:在这项研究中,我们检查了在COVID-19大流行早期美国中大西洋地区一所大型公立大学注册的484名学生收集的一套初级数据,该数据称为“铁线数据”,包括学生人口和支助网络信息。支持网络数据包括突出支持类型(即情感或教育;常规或密集)的信息。利用这一数据集,用自己报告的GPPA量化的学生学业成绩预测模型,使用Chi-Square自动互动检测(CHAID)、决策树算法和森林(一种使用有条件推断树的随机森林算法)。我们比较了方法的准确性和每种算法所建议的一系列重要变量的变异性。每种算法都发现对不同学生人口构成的重要变量和一些重叠。对于白人学生来说,不同类型的教育支持对于预测学业成绩很重要,而对非白人学生来说,不同类型的情感支持对于预测学业成绩十分重要。存在不同种类的常规支持对于预测精准性女性的学术成就十分重要,而不同种类的坚定支持对于预测性别成就也很重要。