预测和解释模型可能会遗漏教育数据中的信息特征
Predictive and explanatory models might miss informative features in educational data
摘要：
由于高等教育的人口统计和我们提出的问题，我们在教育数据挖掘（EDM）中经常遇到变化很小的变量。然而，很少有人研究如何分析这些数据。因此，我们使用逻辑斯谛回归、惩罚回归和随机森林进行了模拟研究。我们系统地改变了积极结果的比例、特征不平衡和比值比。我们发现算法根据特征的不平衡和结果的不平衡来不同地处理具有相同优势比的特征。虽然没有一种算法完全解决了如何处理不平衡数据的问题，但Firth和Log-F等惩罚方法减少了内置比值比与算法确定的值之间的差异。我们的研究结果表明，EDM研究在确定哪些变量与结果相关时可能包含假阴性。然后，我们将我们的研究结果应用于研究生入学数据集。最后，我们提出建议，研究人员应该考虑对数百例数据集进行惩罚性回归，并应在出版物中包括更多关于其数据的背景，如结果和特征不平衡。
英文摘要：
We encounter variables with little variation often in educational data mining (EDM) due to the demographics of higher education and the questions we ask. Yet, little work has examined how to analyze such data. Therefore, we conducted a simulation study using logistic regression, penalized regression, and random forest. We systematically varied the fraction of positive outcomes, feature imbalances, and odds ratios. We find the algorithms treat features with the same odds ratios differently based on the features' imbalance and the outcome imbalance. While none of the algorithms fully solved how to handle imbalanced data, penalized approaches such as Firth and Log-F reduced the difference between the built-in odds ratio and value determined by the algorithm. Our results suggest that EDM studies might contain false negatives when determining which variables are related to an outcome. We then apply our findings to a graduate admissions data set. We end by proposing recommendations that researchers should consider penalized regression for data sets on the order of hundreds of cases and should include more context about their data in publications such as the outcome and feature imbalances.
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