Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Collaborative Cognitive Diagnosis with Disentangled Representation Learning for Learner Modeling
Authors: Weibo Gao, Qi Liu, Linan Yue, Fangzhou Yao, Hao Wang, Yin Gu, zheng zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the superior performance of Coral, showcasing significant improvements over state-of-the-art methods across several real-world datasets. |
| Researcher Affiliation | Academia | State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China |
| Pseudocode | Yes | Algorithm 1 Coral Model |
| Open Source Code | Yes | Our code is available at https://github.com/bigdata-ustc/Coral. |
| Open Datasets | Yes | We conduct experiments on three real-world datasets: ASSIST [11], Junyi [5] and Neur IPS2020EC [43]. |
| Dataset Splits | Yes | In this setting, we split all the datasets with a 7:1:2 ratio into training sets, validation sets, and test sets. |
| Hardware Specification | Yes | All experiments are conducted on a Linux server equipped with two 3.00GHz Intel Xeon Gold 5317 CPUs and two Tesla A100 40G GPUs. |
| Software Dependencies | No | Each model is implemented by Py Torch [37] and optimized by Adam optimizer [19]. (No version numbers specified for PyTorch or Adam) |
| Experiment Setup | Yes | We set the dimension size d as 20, the layer of graph modeling as 2, and the mini-batch size as 512. In the training stage, we select the learning rate from {0.002, 0.005, 0.01, 0.02, 0.05}, select α from {0.05, 0.1, 0.5, 1} and β from {0.25, 0.5, 1}, and select neighboring number K from {1, 2, 3, 4, 5, 10, 15, 20, 15, 30, 25, 40, 45, 50}. All network parameters are initialized with Xavier initialization [15]. |