Fully Adaptive Framework: Neural Computerized Adaptive Testing for Online Education
Authors: Yan Zhuang, Qi Liu, Zhenya Huang, Zhi Li, Shuanghong Shen, Haiping Ma4734-4742
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world datasets demonstrate the effectiveness and robustness of NCAT compared with several state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Anhui Province Key Laboratory of Big Data Analysis and Application, School of Data Science & School of Computer Science and Technology, University of Science and Technology of China 2Anhui University |
| Pseudocode | No | The paper describes the methodology using text and diagrams but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/bigdata-ustc/NCAT |
| Open Datasets | Yes | We use three real-world educational datasets, namely ASSIST, EXAM, and NIPS-EDU. ... NIPS-EDU (Wang et al. 2020b) refers to the dataset in Neur IPS 2020 Education Challenge. ... the datasets can be found in https://github.com/bigdata-ustc/Edu Data. |
| Dataset Splits | Yes | We perform 5fold cross validation for all datasets; for each fold, we use 60%-20%-20% students for training1, validation, and testing respectively. Furthermore, we partition the questions responded to by each student into the support set (Di s 70%) and query set (Di u, 30%). |
| Hardware Specification | Yes | All methods are developed and trained on a Tesla K20m GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies such as libraries or programming languages used for implementation. |
| Experiment Setup | Yes | We set the embedding size d = 128 and the learning rate in RL algorithm to 0.001. The temperature parameter ν in Eq.(6) is set to 2 0.1t which is slowly reduced during test. The capacity of the replay buffer for Q-learning is set to 10000 in experiments. The exploration factor ϵ decays from 1 to 0 during training. |