Thank you for your quick feedback and for contributing to our improved score. We greatly appreciate your efforts. We apologize that some issues still need improvement, and we would like to resolve them as follows.
## Response to Weakness 1
To investigate the computational efficiency of the proposed approach, we have compared it with RCD on ASSISTments and Math datasets in terms of model inference time and training time. **Table III** in ***global.pdf*** presents the overall comparison, where the time of the proposed DisenGCD under different hyperparameters *P* is also reported.
As can be seen, DisenGCD's inference time is better than RCD's. This indicates that the proposed DisenGCD is more efficient than RCD, further signifying that it is promising to extend DisenGCD to dynamic CD.
It can be seen from **Table III:Lower**: although a larger *P* will make DisenGCD take more time to train the model, the proposed DisenGCD achieves the best performance on both two datasets when *P*=5 and its runtime does not increase too much, which is much better than RCD.
In summary, the above comparison shows the model efficiency superiority of the proposed DisenGCD.
## Response to Weakness 2
We have to argue that the diagnostic function in the proposed DisenGCD is interpretable.
Firstly, $\mathbf{h}\_{si} = F_{si}(\overline{\mathbf{S}_i}+ \overline{\mathbf{C}_k})$ denotes the combination of the learned student representation and the learned concept representations, which is similar to the combination in RCD, i.e., $Concat(\mathbf{S}\_i, \mathbf{C}_k)$. As a result, $\mathbf{h}\_{si}$ can be seen as the student's mastery of each knowledge concept.
Secondly $\mathbf{h}\_{ej} = F\_{ej}(\overline{\mathbf{E}_j}+\overline{\mathbf{C}_k})$ denotes the combination of the learned exercise representation and the learned concept representations, which is also similar to the combination in RCD for exercises, i.e., $Concat(\mathbf{E}\_j, \mathbf{C}_k)$. Therefore, $\mathbf{h}\_{ej}$ can represent the exercise difficulty of each concept.
Thirdly, $\mathbf{h}\_{simi} = \sigma(F\_{simi}(\mathbf{h}\_{si}\cdot\mathbf{h}\_{ej} ))$ is mainly used to measure the similarity between $\mathbf{h}\_{si}$ and $\mathbf{h}\_{ej}$ via a dot-product followed by an FC layer and a Sigmoid function. Therefore, a higher value in each bit of $\mathbf{h}\_{simi}$ represents a higher mastery on each concept, further indicating a higher probability of answering the related exercises.
Finally, $\hat{r\_{ij}} = (\sum Q_k\cdot \mathbf{h}_{simi})/\sum Q_k$ has a similar idea to NCD to compute the overall mastery averaged over all concepts contained in exercise $e_j$.
In summary, the proposed model is well-interpretable, as agreed by **Reviewer 2m1M**.
## Response to Weakness 3
We have to admit that data privacy and security are indeed a crucial problem in intelligent education.
However, existing cognitive diagnosis models did not explore this. To the best of our knowledge, we think the following two potential techniques can be applied to CD to solve data privacy and security issues.
1. The first is to apply the differential privacy technique. Differential privacy [R1] protects individual data by adding noise, ensuring personal information can’t be easily inferred from data analysis. Differential privacy is widely used in fields like computer vision [R2], recommender systems [R3,R4], and many others [R5].
Among these, the recommendation task is most related to cognitive diagnosis (CD), and thus the differential privacy can be easily extended to CD by summarizing the experiences of these approaches. A successful example of extending techniques in the recommendation to CD is the MF [R6] model.
2. The second is to apply the Federated Learning technique. It trains a model across multiple devices without sharing data, improving privacy and efficiency. It has succeeded in many domains, including recommender systems [R7].
According to its successful experiences in the recommendation, federated learning can also be extended to CD. Moreover, a few studies [R8] have applied federated learning to knowledge tracing (KT), making good progress. KT is the most related task to CD in intelligent education; thus, their experiences can be brought to CD to solve data privacy and security problems.
To sum up, the proposed DisenGCD and existing CDMs could be combined with differential privacy and federated learning to solve the data privacy and security problem in intelligent education. We will discuss this problem in revised paper.
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[R1] Dwork, Differential privacy.
[R2] Mixed differential privacy in computer vision.
[R3] A differential privacy framework for matrix factorization recommender systems.
[R4] Applying differential privacy to matrix factorization.
[R5] Differential privacy: A survey of results.
[R6] Matrix factorization techniques for recommender systems.
[R7] Federated recommendation systems.
[R8] Federated deep knowledge tracing.
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