关键词：误差度量；知识追踪模型；贝叶斯；对数似然比；特征曲线；均方误差
摘 要：We compare several metrics, including log likelihood (LL), root mean squared error (RMSE), and area under the receiver operating characteristic curve (AUC), to evaluate which metric is most suited for this purpose. LL is commonly used as an error metric in Expectation Maximization (EM) to perform parameter estimation. RMSE and AUC have been suggested but have not been explored in depth. In order to examine the effectiveness of using each metric, we measure the correlations between the values calculated by each and the distances from the corresponding points to the ground truth. Additionally, we examine how each metric compares to the others. Our findings show that RMSE is significantly better than LL and AUC.