关键词：贝叶斯；学习跟踪模型；参数估计；参数搜索算法
摘 要：In the knowledge-tracing model, error metrics are used to guide parameter estimation towards values that accurately represent students' dynamic cognitive state. 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 e ectiveness of using each metric, we measure the cor- relations 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 ndings show that RMSE is signi cantly better than LL and AUC. With more knowledge of e ective error metrics for estimating parameters in the knowledge-tracing model, we hope that better parameter searching algorithms can be created.