# Bridging the Vocabulary Gap: Using Side Information for Deep Knowledge Tracing
## 6 Results  
### Comparison with Baseline Models  
We compared the SIKT model with the DKT, DKVMN, SAKT, and SAINT models; the results are shown in Table 4. The results demonstrate that the SIKT model showed the most outstanding performance on the A09 and A12 datasets. Specifically, it achieved the highest AUC (80.73) and near-peak ACC (72.31) on the A09 dataset, while simultaneously obtaining the highest AUC (75.90) and ACC (73.20) on the A12 dataset, and the highest AUC on the Junyi (72.71) and Eedi (78.32) datasets. In comparison, although the DKT model achieved the highest ACC (72.72) on the A09 dataset, its AUC (79.87) was slightly lower than that of the SIKT model (80.73). On the EN dataset, the SAINT model exhibited the best performance, with the highest AUC (77.14) and ACC (70.13), indicating its ability to effectively capture learning patterns in this dataset.  
Regarding parameter size, the SIKT model stands out with only 57.24 k parameters, yet it delivered excellent results across multiple datasets. In particular, on the A09 and A12 test sets, it achieved the highest AUC and ACC, and the highest AUC on the Junyi and Eedi datasets, showcasing its efficiency with a smaller parameter footprint. In contrast, other models such as DKVMN (291.70 k), DKT (651.62 k), SAKT (847.36 k), and SAINT (71,184.90 k) have significantly larger parameter sizes, with the SAINT model having the largest. Despite its strong performance on the EN dataset, SAINT's substantial parameter size could introduce additional computational overhead.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Dataset** & **Students** & **Questions** & **Knowledge** & **Records** \\ \hline A09 & 8093 & 6902 & 542 & 602,956 \\ A09-train * & 5093 & 6282 & 534 & 385,806 \\ A09-test & 3000 & 5791 & 536 & 217,150 \\ A12 & 29,018 & 53,086 & 265 & 2,711,602 \\ A12-train & 26,018 & 52,697 & 265 & 2,434,816 \\ A12-test & 3000 & 40,040 & 255 & 276,786 \\ EN & 385,188 & 8339 & 1017 & 3,000,000 \\ EN-train & 335,188 & 8212 & 988 & 2,611,397 \\ EN-test & 50,000 & 6648 & 751 & 388,603 \\ Junyi & 19,323 & 705 & 39 & 2,879,392 \\ Junyi-train & 15,458 & 705 & 39 & 2,271,892 \\ Junyi-test & 3865 & 674 & 39 & 607,500 \\ Eedi & 20,000 & 27,612 & 282 & 3,354,687 \\ Eedi-train & 16,000 & 27,611 & 282 & 2,672,901 \\ Eedi-test & 4000 & 26,949 & 281 & 681,786 \\ \hline \end{tabular}  
* A09-train refers to the training set in the A09 dataset, while A09-test refers to the testing set in the A09 dataset, with other datasets following similarly. The students in the training set and testing set are mutually exclusive.  
\end{table}
Table 3: The number of students, questions, knowledge, and interaction records in all datasets.  
In summary, the SIKT model achieves a well-balanced trade-off between performance and resource utilization. It outperformed the DKT, DKVMN, and SAKT models on the A09, A12, Junyi, and Eedi datasets, while maintaining overall stability. Thanks to its significantly reduced parameter size, it was able to maintain high classification accuracy and AUC, making it particularly suitable for environments with limited computational resources. While the SAINT model showed the best performance on the EN dataset, its large parameter size may limit its practical applicability in resource-constrained scenarios.