# ContextKT: A Context-Based Method for Knowledge Tracing
## 5 Experiments
### Comparison with Existing Methods  
In this subsection, we compared ContextKT with baseline methods. In the experiments, we evaluated the performance of ContextKT on all five evaluate indices: AUC, ACC, PRE, MAE, and RMSE. The results are shown in Tables 5 and 6. The results on HDU are shown in Table 5. In the classification task, ContextKT outperformed all the baseline methods. In addition, in the regression task, the ContextKT model achieved the highest performance of other methods on all evaluation indices except MAE. Compared with MAE, RMSE can better reflect whether there is a serious deviation value in the process of prediction. Therefore, ContextKT will not make serious mistakes in the prediction of students' performance. We can see the deep-learning-based methods outperform the IRT-based methods. This is because the ability of a student is usually modeled as a constant in the IRT-based methods., which is handled dynamically in the deep learning-based models. Such as, in the IRT, they treat students' abilities as a one-dimensional vector and they do not consider the connection between questions and knowledge concepts. Therefore, they do not achieve a good performance in the experiments. DKT into the study records of a student into RNN to obtain their knowledge states, which can effectively capture the current ability of the student. Thus DKT outperforms IRT in the experiments. Our method utilizes deep learning components to capture the multiple factors in the study process of students, including both the behaviors and changing of memory. Thus, ContextKT achieves the best performance in the evaluations. Table 6 shows the comparison results on Algebra08, which is similar to the ones on HDU. To be specific, we can see ContextKT achieved the highest performance on all evaluation indices than other methods in both classification and regression tasks. Then DKT, DKVMN and SAKT are better than others. This is because deep learning-based models, including ContextKT, DKT, DKVMN, and SAKT, have better performances than models based on traditional machine learning. For example, the AUC of deep learning-based \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **AUC** & **ACC** & **PRE** & **MAE** & **RMSE** \\ \hline ContextKT & **0.751075** & **0.813716** & **0.825841** & **0.271567** & **0.370025** \\ DKT & 0.738981 & 0.812905 & 0.824963 & 0.27309 & 0.371288 \\ DKVMN & 0.739936 & 0.810656 & 0.814686 & 0.277212 & 0.371782 \\ SAKT & 0.721765 & 0.808951 & 0.814686 & 0.276537 & 0.375799 \\ KTM & 0.708104 & 0.808217 & 0.812423 & 0.286546 & 0.378445 \\ AFM & 0.548704 & 0.732023 & 0.809430 & 0.330467 & 0.465264 \\ DAS3H & 0.710522 & 0.808474 & 0.812075 & 0.285311 & 0.377648 \\ IRT & 0.65186 & 0.805118 & 0.807628 & 0.301519 & 0.386675 \\ MIRT & 0.650422 & 0.804997 & 0.806328 & 0.311088 & 0.387262 \\ PFA & 0.590877 & 0.741067 & 0.816862 & 0.309607 & 0.452905 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance Comparison of Methods on Algebra08 Dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **AUC** & **ACC** & **PRE** & **MAE** & **RMSE** \\ \hline ContextKT & **0.707121** & **0.685405** & **0.657779** & 0.421046 & **0.459389** \\ DKT & 0.688482 & 0.662843 & 0.631488 & 0.420853 & 0.464716 \\ DKVMN & 0.693316 & 0.668589 & 0.634472 & **0.415228** & 0.463205 \\ SAKT & 0.683009 & 0.661207 & 0.633761 & 0.426612 & 0.463929 \\ KTM & 0.67722 & 0.657631 & 0.612784 & 0.430281 & 0.466044 \\ AFM & 0.52354 & 0.548486 & 0.421101 & 0.472343 & 0.554354 \\ DAS3H & 0.68344 & 0.659335 & 0.608092 & 0.426824 & 0.463968 \\ IRT & 0.642549 & 0.641442 & 0.600058 & 0.441641 & 0.473463 \\ MIRT & 0.642874 & 0.641358 & 0.590304 & 0.451555 & 0.473396 \\ PFA & 0.563187 & 0.570269 & 0.456779 & 0.449813 & 0.559884 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance Comparison of Methods on HDU Dataset. models usually are higher than 0.7, while traditional models, such as IRT, are around 0.6.