论文标题
扩展机器学习以预测不平衡的物理课程成果
Extending Machine Learning to Predict Unbalanced Physics Course Outcomes
论文作者
论文摘要
机器学习算法最近已被用来将可能在物理课中接受C，D或F的学生分类为可能接受C，D或F的学生。当结果变量基本不平衡时，该研究中使用的性能指标变得不可靠。 This study seeks to further explored the classification of students who will receive a C, D, and F and extend those methods to predicting whether a student will receive a D or F. The sample used for this work ($N=7184$) is substantially unbalanced with only 12\% of the students receiving a D or F. Applying the same methods as the previous study produced a classifier that was very inaccurate, classifying only 20\% of the D or F cases正确。这项研究将集中于随机的森林机器学习算法。通过调整随机森林决策阈值，D或F结果的正确分类率上升到46 \％。这项研究还调查了先前的发现，即性别，代表性不足的少数群体状况和第一代状态等人口统计学变量对于预测阶级成果的重要性较低。倒入采样表明，这不是这些学生代表性不足的结果。构建了一个优化的分类模型，该模型以46 \％的精度预测了D和F结果，并且C，D和F结果具有69 \％的精度；这些结果预测的准确性在机器学习文献中称为“灵敏度”。当应用此分类模型以预测代表性不足的人群群体的C，D或F结果时，检测到实质性差异，对女性的敏感性为61 \％\％，代表性不足的少数族裔学生为67％，第一代学生为78％。在D和F结果中观察到了类似的变化。
Machine learning algorithms have recently been used to classify students as those likely to receive an A or B or students likely to receive a C, D, or F in a physics class. The performance metrics used in that study become unreliable when the outcome variable is substantially unbalanced. This study seeks to further explored the classification of students who will receive a C, D, and F and extend those methods to predicting whether a student will receive a D or F. The sample used for this work ($N=7184$) is substantially unbalanced with only 12\% of the students receiving a D or F. Applying the same methods as the previous study produced a classifier that was very inaccurate, classifying only 20\% of the D or F cases correctly. This study will focus on the random forest machine learning algorithm. By adjusting the random forest decision threshold, the correct classification rate of the D or F outcome rose to 46\%. This study also investigated the previous finding that demographic variables such as gender, underrepresented minority status, and first generation status had low variable importance for predicting class outcomes. Downsampling revealed that this was not the result of the underrepresentation of these students. An optimized classification model was constructed which predicted the D and F outcome with 46\% accuracy and C, D, and F outcome with 69\% accuracy; the accuracy of prediction of these outcomes is called "sensitivity" in the machine learning literature. Substantial variation was detected when this classification model was applied to predict the C, D, or F outcome for underrepresented demographic groups with 61\% sensitivity for women, 67\% for underrepresented minority students, and 78\% for first-generation students. Similar variation was observed for the D and F outcome.
