Disentangling Cognitive Diagnosis with Limited Exercise Labels
Authors: Xiangzhi Chen, Le Wu, Fei Liu, Lei Chen, Kun Zhang, Richang Hong, Meng Wang
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on widely used benchmarks demonstrate the superiority of our proposed model. |
| Researcher Affiliation | Academia | Hefei University of Technology Tsinghua University Institute of Dataspace, Hefei Comprehensive National Science Center Institute of Artificial Intelligence, Hefei Comprehensive National Science Center |
| Pseudocode | Yes | Algorithm 1 Filling Missing Q-matrix for Interpretable Baselines |
| Open Source Code | Yes | Our code is available at https://github.com/kervias/DCD |
| Open Datasets | Yes | Our experiments are conducted on three real-world datasets, i.e., Matmat2, Junyi [4] and NIPS2020EC [42], all of which contain knowledge concepts of the tree structure. 2https://github.com/adaptive-learning/matmat-web 3Our code is available at https://github.com/kervias/DCD |
| Dataset Splits | Yes | We adopt five-fold cross-validation to avoid randomness. |
| Hardware Specification | Yes | We train our model with Python 3.9 and Py Torch 1.12.1 on NVIDIA RTX A5000. |
| Software Dependencies | Yes | We train our model with Python 3.9 and Py Torch 1.12.1 on NVIDIA RTX A5000. |
| Experiment Setup | Yes | We set different hyperparameters to balance each loss function. The final object function can be summarized as follow: arg min Θ=[ϕu,ϕdv,ϕrv] L = Lm + αLl + Lul + z {zu,zdv,zrv} Li d(z)) + X z {zu,zdv,zrv} Lp(z), where Θ = [ϕu, ϕd v, ϕr v] is the parameter set in the whole model, α is the hyperparameter for alignment of labeled exercises, and βi denotes the weight for disentanglement term corresponding to the i-th level in the knowledge concept tree. We set a prior Gaussian distribution with N(0, 1) for each latent factor in µu and µd v, and a prior Bernoulli distribution with Bernoulli(0.2) for each latent factor in µr v. The default margin is 0.5. |