Exploiting Non-Interactive Exercises in Cognitive Diagnosis
Authors: Fangzhou Yao, Qi Liu, Min Hou, Shiwei Tong, Zhenya Huang, Enhong Chen, Jing Sha, Shijin Wang
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on realworld datasets demonstrate the supremacy of our framework in long-tailed data. In this section, we first introduce the datasets and our experimental setups. Then, we conduct extensive experiments to answer the following questions: |
| Researcher Affiliation | Collaboration | 1Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China 2State Key Laboratory of Cognitive Intelligence 3i FLYTEK AI Research (Central China), i FLYTEK Co., Ltd. |
| Pseudocode | Yes | Algorithm 1 Exercise-aware Informative Response Sampling |
| Open Source Code | Yes | Our code is available at https://github.com/fannazya/EIRS. |
| Open Datasets | Yes | We conduct experiments on two realworld datasets, i.e., ASSISTments1 and Junyi dataset 2. 1https://sites.google.com/site/assistmentsdata/home/2009-2010assistment-data 2https://pslcdatashop.web.cmu.edu/Files?datasetId=1198 |
| Dataset Splits | No | The paper states, "The number 5 is to ensure that the dataset can be split into train and test sets at an 8:2 ratio." and "all the hyper-parameters are tuned in the validation datasets.", but it does not specify the exact split percentage for the validation set, only the train/test split. |
| Hardware Specification | No | The paper does not specify any particular hardware used for experiments, such as GPU or CPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions using the "Adam algorithm" for optimization but does not list specific software dependencies with their version numbers (e.g., Python, PyTorch, TensorFlow versions, or other libraries). |
| Experiment Setup | Yes | In our framework, we set the sample number from [1,2,3,4,5]. For the curriculum coefficient λ, its initial value λ0 is chosen from the interval (0, 1], and λ linearly increases from λ0 to 1 as the number of epochs increases. We employ the Adam algorithm [Kingma and Ba, 2015] for optimization, and all the hyper-parameters are tuned in the validation datasets. |