扩展机器学习来预测不平衡的物理课程结果
Extending Machine Learning to Predict Unbalanced Physics Course Outcomes
摘要：
机器学习算法最近被用于将学生分类为可能在物理课上获得A或B的学生或可能获得C，D或F的学生。当结果变量基本上不平衡时，该研究中使用的性能指标变得不可靠。本研究旨在进一步探讨学生将获得C，D和F的分类，并将这些方法扩展到预测学生是否会获得D或F。这项工作所使用的样本（$N=7184$）是相当不平衡的，只有12%的学生获得D或F。应用与先前研究相同的方法产生了非常不准确的分类器，仅正确分类了20%的D或F病例。本文将重点研究随机森林机器学习算法。通过调整随机森林决策阈值，D或F结果的正确分类率提高到46%。本研究还调查了以前的发现，人口统计学变量，如性别，代表性不足的少数民族地位，第一代的地位有低变量的重要性预测类的结果。向下抽样显示，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，其预测D和F结果的准确度为46%，C，D和F结果的准确度为69%;这些结果的预测准确度在机器学习文献中被称为“敏感性”。当应用该分类模型预测代表性不足的人口统计学群体的C、D或F结果时，检测到了实质性变化，其中女性敏感性为61%，代表性不足的少数民族学生为67%，第一代学生为78%。在D和F结局中观察到类似的变化。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
