Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling

Summary

This paper introduces Coral, a model that enhances cognitive diagnosis by integrating collaborative signals among learners with disentangled representation learning. Coral addresses the importance of leveraging collaborative connections among learners to better understand human learning processes. Traditional cognitive diagnosis methods focus on individual attributions and explicit practice records, but they often overlook the potential of collaborative signals. The proposed Coral model aims to fill this gap by integrating collaborative and disentangled cognitive states. The disentangled state encoder separates the cognitive states of learners and reconstructs their practice performance from an inner-learner perspective. The collaborative representation learning dynamically constructs a collaborative graph by iteratively searching for optimal neighbors in a context-aware manner, effectively capturing collaborative signals. The model is evaluated on three real-world datasets: ASSIST, Junyi, and NIPS2020EC. The results show that Coral outperforms existing methods in terms of ACC, AUC, F1-score, and RMSE. Coral also performs well in sparse and cold-start scenarios, demonstrating its robustness and adaptability.

Strengths

1. Coral introduces an integration of collaborative signals with disentangled representation learning, addressing a gap in existing cognitive diagnosis methods. 2. Experimental results demonstrate performance improvements over state-of-the-art methods across various datasets and scenarios. 3. The disentangled representation provides better explainability and controllability of cognitive states, which is crucial for personalized education.

Weaknesses

1. The approach in this paper is not very innovative, and the motivation is not very convincing. 2. Although visualizing the embeddings provides some level of interpretability, it still does not yield any explicit conclusions or observable phenomena. Rather than visualizing the embeddings (t-SNE has a certain degree of randomness), I would prefer to see some interesting conclusions based on the dataset. 3. The paper mentions computational inefficiencies, which could limit the model's applicability in large-scale or real-time settings. 4. The model requires careful tuning of hyperparameters, which may not be straightforward for all datasets and might require extensive experimentation. 5. The paper could benefit from a more extensive comparison with a broader range of alternative approaches to strengthen its contributions.

Questions

Please see the weaknesses

Rating

5

Confidence

3

Soundness

3

Presentation

3

Contribution

2

Limitations

n/a

Summary

This paper presents a Collaborative Cognitive Diagnosis model, named Coral, which incorporates a disentangled state encoder, collaborative graphs, and a state decoder. The goal of Coral is to model collaborative and disentangled cognitive states using representation learning techniques.

Strengths

1. The introduction of collaborative connections to cognitive diagnosis is well-motivated and logical. 2. The paper is well-structured, and the methodology is explained in detail. 3. The release of the code enhances the reproducibility of the research.

Weaknesses

1. While the authors identify the challenge of obtaining explicit collaborative connections, they do not provide clear solutions to address this issue. 2. The heavy use of theoretical notations makes the paper less readable. 3. The detailed presentation of the methodology makes the experimental section appear crowded. 4. The performance improvements demonstrated by Coral are moderate.

Questions

1. What is the performance impact if the concepts are not disentangled? Currently, there is insufficient motivation for integrating collaborative signals into the disentangled representations.

Rating

5

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

There is no significant negative societal impact identified in this work. The authors acknowledge an inefficiency issue within the model.

Summary

This paper explores the Coral model, which combines disentangled representation learning with collaborative signals to enhance cognitive diagnosis in intelligent education. It focuses on identifying implicit collaborative links among learners to improve understanding of their cognitive states. The model integrates a context-aware collaborative graph learning mechanism to simulate the collaborative relationships between learners based on their cognitive states. By incorporating collaborative information into the disentangled cognitive states of learners, the model achieves collaborative disentanglement. Experiments on real datasets demonstrate Coral's performance compared to existing methods.

Strengths

1. This paper will undoubtedly attract attention and excitement from researchers in various fields, e.g., personalized learning, cognitive diagnosis, knowledge tracing, and performance prediction. It represents an exploration of modeling user collaboration in educational settings. While the modeling of collaborative relationships among users is well-established in other fields (e.g., social networks, recommendations), the technological development in online education has lagged. The research line on modeling learner relationships is almost non-existent. I hope this work will advance the intelligent education community. 2. Compared to previous cognitive diagnosis research, Coral introduces a new method that combines disentangled representation learning with collaborative signals, holding great potential for enhancing cognitive diagnosis. 3. Theoretical Contribution: The authors have meticulously derived the optimization form of the lower bound of cognitive representation encoders, the approximate optimization objective of collaborative relationship modeling, and the model convergence. The theoretical proofs are reasonable and solid. 4. The experimental results on real datasets in normal, data-sparse, and cold-start scenarios indicate that Coral outperforms existing methods, demonstrating its effectiveness in improving diagnostic accuracy. 5. The writing is good. The proposed problem and model are described clearly.

Weaknesses

I expect that this paper will comfortably clear the bar for acceptance. However, I believe two main issues should be addressed first. Due to these reasons, I have set my score at a relatively borderline level but anticipate increasing it after the authors' rebuttal. 1. If two students have fewer interaction records, leading to increased similarity in their interaction patterns, they might exhibit different cognitive levels. Using the method outlined in the paper to learn their representations may yield similar results, but this might not accurately reflect the true situation. 2. The paper employs GNN to learn network representations, but the graph-based baseline comparison only includes RCD and does not include some recent graph-based cognitive diagnosis methods, such as [1]. Other/Minor issues (Optional): 1. In the second paragraph, the authors describe an example: experienced teachers can summarize students' similarities. Similarly, could large language models be used instead of real human teachers to construct collaborative graphs? I look forward to the authors discussing the integration with LLMs. Although this does not affect the readability and acceptance of the paper, I believe readers will be very interested. 2. In Table 6, "w/ full-kit (m,m)" should be "w/ full-kit (n,m)." 3. In Table 1, why are there two results for DCD on the NIPS2020EC dataset? It seems like results from two different runs. ``` [1] Wang, Shanshan, et al. "Self-supervised graph learning for long-tailed cognitive diagnosis." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37. No. 1. 2023. ```

Questions

1. The motivation of this paper is to consider collaborative information among learners. What are the specific differences between the collaborative information proposed in this paper and the collaborative information in recommendation systems? 2. Do the ASSIST and NIPS2020EC datasets have similarities and prerequisite relationships among the knowledge concepts that support RCD? 3. In the example of question recommendation in Appendix E, are these five questions answered by learners in comparable situations (i.e., without acquiring new knowledge during the answering process)?

Rating

7

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

The authors discuss the limitations of the computational efficiency of the paper. Meanwhile, they also give 2 simple yet effective solutions to address current limitations in the appendix.

Summary

The authors propose Coral, a Collaborative Cognitive Diagnosis model with Disentangled Representation Learning, aimed at improving our understanding of human learning by leveraging collaborative signals among learners. By disentangling cognitive states and dynamically constructing a collaborative graph, Coral captures implicit connections between learners, leading to more accurate cognitive diagnoses. The model's innovative approach involves an iterative process of encoding, collaborative representation learning, and decoding, which significantly enhances its performance over existing methods. Extensive experiments demonstrate Coral's superior effectiveness in real-world datasets, making it a promising tool for intelligent education systems.

Strengths

1. Coral’s use of disentangled representation learning and dynamic graph construction is a novel approach in the field. This allows the model to capture implicit collaborative signals among learners, leading to more accurate and insightful cognitive diagnoses. 2. The extensive experiments and superior performance across multiple real-world datasets highlight Coral’s practical utility. By disentangling cognitive states and integrating collaborative information, Coral offers greater explainability and controllability in cognitive diagnosis. This may make it easier for educators and researchers to understand and interpret the learning patterns and cognitive states of students.

Weaknesses

1. Coral's effectiveness relies heavily on the quality and quantity of learner data available. In scenarios where such data is sparse or noisy, the model's performance may not be as robust. 2. While the model shows great promise, its computational requirements for dynamically constructing collaborative graphs and learning representations might pose scalability issues, especially in very large educational environments. 3. There may lack some highly related baselines/papers for the cognitive diagnosis. [1] https://arxiv.org/pdf/2002.00276 [2] https://ieeexplore.ieee.org/document/10027634 [3] https://arxiv.org/pdf/2005.13107

Questions

See weaknesses.

Rating

7

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

No

Summary

This paper introduces Coral, a novel model for collaborative cognitive diagnosis with disentangled representation learning, that can help in developing intelligent education systems. The key contributions are incorporating collaborative information among learners to improve cognitive state diagnosis by using disentangled representation learning to model cognitive states, and then using context-aware graph construction method to identify implicit collaborative connections. Coral demonstrates superior performance over state-of-the-art methods across several real-world datasets, showing particular strength in sparse and cold-start scenarios. The approach offers improved interpretability and adaptability in modeling learners' cognitive states, potentially advancing personalized tutoring and understanding of human learning processes.

Strengths

* The paper introduces an intriguing dual-perspective approach to cognitive diagnosis. By examining both inner-learner and inter-learner aspects, it offers a more comprehensive view of cognitive states. The integration of context-aware collaborative graph learning with disentangled representation learning is particularly noteworthy, as it potentially allows for more nuanced insights into learner interactions. * The empirical validation using three diverse datasets (ASSIST, Junyi, NIPS2020EC) lends credibility to the model's effectiveness. Its superior performance across various metrics compared to existing models is encouraging. Moreover, the model's resilience in scenarios with sparse data and cold start environments, suggests practical applicability in real-world educational settings. * From a structural standpoint, the paper presents its ideas coherently. The authors have taken care to provide detailed explanations and mathematical formulations for each component of their model. The inclusion of key equations and visual aids, such as t-SNE plots, effectively illustrates the model's inner workings, particularly in terms of disentanglement and neighbor selection processes. * This research makes a valuable contribution to the field of cognitive diagnosis. By enhancing both the accuracy and interpretability of learner cognitive state assessments, it opens up new avenues for personalized education. The model's broad applicability across various educational datasets, coupled with its novel methodologies like context-aware graph learning and co-disentanglement, provides a solid foundation for future research in educational data mining and related fields.

Weaknesses

* The model suffers from computational inefficiencies, which can limit its practical applicability, especially in large-scale educational settings. This issue is transparently acknowledged by the authors too, which is positive. * While the paper aims to achieve disentangled representations, the evaluation of disentanglement quality remains simple and limited. It will be valuable to add more ablation studies related to disentanglement quality and run qualitative analysis of how individual latent dimensions correspond to interpretable knowledge concepts across different datasets. * The paper doesn't adequately address potential limitations of the collaborative approach, such as negative transfer or performance in edge cases. It will be valuable to discuss scenarios where the collaborative approach might not be beneficial or could lead to negative transfer. E.g., a highly advanced learner who struggles in one specific area may be grouped with generally low-performing learners

Questions

* How sensitive is the model performance to changes in key hyperparameters such as the number of neighbors (K) and the beta value? Adding a sensitivity analysis for these hyperparameters in the appendix can help assess the model's robustness and guide practitioners in tuning it for different datasets. * Can you provide a breakdown of Coral's performance as the percentage of questions answered by new learners varies (e.g., 0%, 5%, 10%, 20%)? This would give a clearer picture of the model's efficacy in true cold-start scenarios. * Is it possible to show how disentangled factors correspond to specific knowledge areas (since mapping to interpretable cognitive factors are not easy) across different datasets? This would strengthen the claim of improved interpretability over existing methods.

Rating

6

Confidence

3

Soundness

3

Presentation

3

Contribution

4

Limitations

* The authors briefly mention computational inefficiencies as a limitation in Section 5. They also provide some preliminary optimization strategies in Appendix F.2, which is positive. However, it would be valuable to also expand it to other concerns like any potential bias in the modeling process, limitations in the datasets used or generalizability to other educational contexts, any limitations in the disentanglement approach to capture all relevant cognitive factors, etc * Also the authors do not fully address potential ethical concerns and mitigation strategies related to privacy and fairness in cognitive modeling. E.g., privacy concerns related to modeling learner cognitive states, potential bias in the model that could disadvantage certain groups of learners, etc

Summary

The paper presents a novel model called Coral, aimed at enhancing cognitive diagnosis in educational contexts by leveraging collaborative signals among learners. The authors argue that learners with similar cognitive states often exhibit comparable problem-solving performances, and thus, understanding these collaborative connections can significantly improve the diagnosis of individual cognitive states. Key Contributions - 1. Disentangled Representation Learning: Coral introduces a disentangled state encoder to effectively separate the cognitive states of learners. This approach allows for clearer interpretations of individual knowledge proficiencies by disentangling the various cognitive factors that influence learning performance. 2. Collaborative Graph Construction: The model incorporates a context-aware collaborative representation learning mechanism that dynamically constructs a graph of learners. This graph is built by identifying optimal neighbors based on their cognitive states, facilitating the extraction of collaborative information. 3. Co-disentanglement Process: Coral achieves co-disentanglement by aligning the initial cognitive states with collaborative states through a decoding process. This integration enhances the model's ability to reconstruct practice performance while providing a more nuanced understanding of learners' cognitive states. 4. Empirical Validation: The paper reports extensive experiments demonstrating that Coral outperforms state-of-the-art methods across several real-world datasets, highlighting its effectiveness in accurately diagnosing cognitive states through collaborative learning. Overall, this work addresses significant gaps in existing cognitive diagnosis methods by simultaneously modeling both individual and collaborative learning information, paving the way for more personalized educational interventions.

Strengths

Originality The paper introduces a unique approach to cognitive diagnosis by integrating collaborative signals among learners, which has not been extensively explored in existing literature. The concept of using a disentangled representation to model cognitive states while simultaneously capturing collaborative learning dynamics is innovative. Prior models either focused solely on individual cognitive factors or did not adequately account for collaborative interactions. I think it reflects a creative synthesis of ideas from domains of educational psychology and collaborative filtering. Quality The authors provide a comprehensive framework that includes a disentangled state encoder, a context-aware collaborative graph learning mechanism, and a co-disentanglement process. The empirical validation through extensive experiments on real-world datasets demonstrates the robustness of the model. The results indicate significant improvements over state-of-the-art methods, showcasing the effectiveness of the proposed approach. The paper also includes detailed mathematical formulations and algorithms, which enhance the reproducibility of the research. Clarity The paper is well-structured, with a logical progression from the introduction of the problem to the presentation of the model and its components. The use of clear diagrams and figures aids in the understanding of complex concepts, such as the collaborative graph construction and the disentanglement process. The performance comparison table is extremely effective in showcasing how the contribution is significantly better than the state-of-the-art methods on multiple datasets. Significance The work definitely shows potential impact on the field of intelligent education. By improving the accuracy of cognitive diagnosis, the Coral model can facilitate more personalized learning experiences. The integration of collaborative learning dynamics addresses a critical gap in existing models and could influence the design of educational technologies and interventions, making this research valuable not only academically but also practically in educational settings.

Weaknesses

1. Limited Generalizability of Experiments While the paper demonstrates the effectiveness of the Coral model through extensive experiments on several real-world datasets, the choice of datasets may limit the generalizability of the findings. The experiments primarily focus on specific educational contexts, which may not capture the diverse learning environments and learner behaviors present in broader educational settings. Actionable Insight: Future work should consider testing the model on a wider variety of datasets, including those from different educational domains (e.g., K-12, higher education, online learning platforms) and varying learner demographics. This would help validate the robustness of the model and its applicability across different contexts. 2. Lack of Comparison with Baseline Models The paper claims significant improvements over state-of-the-art methods, but it does not provide a comprehensive comparison with a more wider range of baseline models. Especially collaborative filtering methods and other machine learning approaches that could serve as alternatives. Actionable Insight: The authors should include a more diverse set of baseline models in their experiments to provide a clearer picture of Coral's performance. Additionally, discussing the strengths and weaknesses of these models in comparison to Coral would enhance the reader's understanding of the model's contributions. 3. Insufficient Exploration of Hyper-parameter Sensitivity The model's performance could be sensitive to various hyper-parameters, such as the number of neighbors in the collaborative graph or the dimensions of the disentangled representations. However, the paper does not provide a thorough analysis of how these hyper-parameters impact the model's performance. Actionable Insight: Conducting a sensitivity analysis on key hyper-parameters would help identify optimal settings and provide insights into the model's robustness. This could involve systematic experiments varying one parameter at a time while keeping others constant, followed by a discussion of the results. 4. Explainability and Interpretability While the paper emphasizes the importance of disentangled representations for explainability, it lacks a detailed exploration of how the model's outputs can be interpreted in practical educational contexts. Actionable Insight: The authors should include case studies or examples demonstrating how the model’s outputs can be interpreted and applied in real educational settings. This could involve visualizing the relationships between cognitive states and learner performance or providing guidelines for educators on how to utilize the model's insights. 5. Potential Overfitting Concerns The complexity of the Coral model, with its multiple components and parameters, raises concerns about potential overfitting, especially if the training datasets are not sufficiently large or diverse. The paper does not address how the model's complexity is managed or validated against overfitting. Actionable Insight: Providing validation metrics would strengthen the paper's claims regarding the model's generalization capabilities. 6. Limited Discussion on Ethical Considerations The paper does not address potential ethical implications of using collaborative cognitive diagnosis models in educational settings, such as data privacy concerns or the impact on learner autonomy and agency. Actionable Insight: Including a section on ethical considerations would enhance the paper's comprehensiveness and relevance. This could involve discussing how to ensure data privacy, the implications of algorithmic bias, and strategies for maintaining learner agency in personalized educational interventions.

Questions

Dataset Diversity: What criteria did you use to select the datasets for your experiments? How do you ensure that the datasets adequately represent the diversity of learning environments and learner behaviors? Baseline Comparisons: Can you provide more details on the baseline models you compared Coral against? Specifically, how do these models differ in their approach to cognitive diagnosis, and why were they chosen as benchmarks? Hyper-parameter Sensitivity: Did you conduct a sensitivity analysis on the hyper-parameters of the Coral model? If so, what were the findings, and how do hyper-parameter choices impact the model's performance? Interpretability of Results: How can educators and practitioners interpret the outputs of Coral in practical settings? Are there specific examples or case studies that illustrate how the model's insights can be applied in educational contexts? Overfitting Concerns: What measures did you take to prevent overfitting in your model? Can you provide validation metrics or results from cross-validation to support the generalization capabilities of Coral? In your "data_loader.py" script you have provided a check for validation set on line 60 ("val_set.json") and also in the paper on line 279 under the Experimental setup --> Settings section you claim "split all the datasets with a 7:1:2 ratio into training sets, validation sets, and testing sets". However, no validation metrics or validation sets seem to be used. Ethical Considerations: Have you considered the ethical implications of using collaborative cognitive diagnosis models in educational settings? How do you address potential concerns regarding data privacy and algorithmic bias?

Rating

7

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

The authors have not addressed the limitations and potential negative societal impact of their work, but there are still areas that could be improved. They could further elaborate on the generalizability of their findings beyond the specific educational contexts covered in the experiments. The authors do not explicitly discuss the potential negative societal impact of their work. This is an important aspect that should be addressed, as cognitive diagnosis models can have significant implications for learners, educators, and educational institutions. An in depth explanation should be provided pertinent to limitations on dimensions like : Data Privacy, Algorithmic Bias, Misuse of Diagnostic Information etc.