Using machine learning to identify the most at-risk students in physics classes
Using machine learning to identify the most at-risk students in physics classes
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DOI：
10.1103/physrevphyseducres.16.020130
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Jie Yang;Seth DeVore;D. Hewagallage;Paul Miller;Qing X. Ryan;John Stewart
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文献类型：
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作者：
Jie Yang;Seth DeVore;D. Hewagallage;Paul Miller;Qing X. Ryan;John Stewart
机器学习算法最近被用来预测学生在物理入门课上的表现。预测模型将学生分类为可能获得A或B的学生或可能获得C、D、F或退出班级的学生。早期预测可以更好地指导教育干预和分配教育资源。然而，在该研究中使用的表现指标变得不可靠时，用于分类学生是否会收到A，B或C（ABC结果），或者如果他们会收到D，F或退出（W）从类（DFW结果），因为结果基本上是不平衡的，有10%到20%的学生收到D，F或W。这项工作提出了调整预测模型和替代模型性能指标更适合不平衡的结果变量的技术。这些技术被应用到三个样本从介绍力学类在两个机构（$N=7184$，$1683$，和$926$）。应用与早期研究相同的方法产生了一个非常不准确的分类器，仅正确分类了16%的DFW病例;调整模型将DFW分类准确率提高到43%。使用机构和类内数据的组合提高DFW的准确性到53%的类的第二周。与之前的研究一样，性别、代表性不足的少数民族地位、第一代大学生地位和低社会经济地位等人口统计学变量在最终预测模型中并不是重要变量。
Machine learning algorithms have recently been used to predict students' performance in an introductory physics class. The prediction model classified students as those likely to receive an A or B or students likely to receive a grade of C, D, F or withdraw from the class. Early prediction could better allow the direction of educational interventions and the allocation of educational resources. However, the performance metrics used in that study become unreliable when used to classify whether a student would receive an A, B or C (the ABC outcome) or if they would receive a D, F or withdraw (W) from the class (the DFW outcome) because the outcome is substantially unbalanced with between 10\% to 20\% of the students receiving a D, F, or W. This work presents techniques to adjust the prediction models and alternate model performance metrics more appropriate for unbalanced outcome variables. These techniques were applied to three samples drawn from introductory mechanics classes at two institutions ($N=7184$, $1683$, and $926$). Applying the same methods as the earlier study produced a classifier that was very inaccurate, classifying only 16\% of the DFW cases correctly; tuning the model increased the DFW classification accuracy to 43\%. Using a combination of institutional and in-class data improved DFW accuracy to 53\% by the second week of class. As in the prior study, demographic variables such as gender, underrepresented minority status, first-generation college student status, and low socioeconomic status were not important variables in the final prediction models.