Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning
Authors: Lu Jiang, Yibin Wang, Jianan Wang, Pengyang Wang, Minghao Yin
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments over real-world MOOC datasets to demonstrate the effectiveness of our proposed method. |
| Researcher Affiliation | Academia | 1School of Computer Science and Information Technology, Northeast Normal University, China 2Department of Computer and Information Science, University of Macau, China 3Key Laboratory of Applied Statistics of MOE, Northeast Normal University, Changchun, China {jiangl761, wangyb856,wangjn}@nenu.edu.cn, pywang@um.edu.mo, ymh@nenu.edu.cn |
| Pseudocode | No | The paper describes the proposed method using textual descriptions and mathematical formulas but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code for the methodology or a link to a code repository. |
| Open Datasets | No | The paper states: 'We evaluate the performance over real-world MOOC data. The data constitute a MOOC heterogeneous information network containing 4 types of entities and 3 types of relations.' However, it does not provide any specific link, DOI, repository name, or formal citation for public access to this dataset. |
| Dataset Splits | No | The paper states, 'we split the datasets into two nonoverlapping sets: 20% of the datasets as the testing set and the rest 80% as the training set.' It does not mention a separate validation set or its split. |
| Hardware Specification | Yes | The device we used was two RTX 6000 with 24Gi B memory and CUDA=11.2. |
| Software Dependencies | Yes | The device we used was two RTX 6000 with 24Gi B memory and CUDA=11.2. |
| Experiment Setup | Yes | For my model we set the learning rate = 0.001, the l2 = 0.001, the dropout = 0.1, the input feature = 128, and the out feature = 128. |