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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Self-Supervised Graph Learning for Long-Tailed Cognitive Diagnosis
Authors: Shanshan Wang, Zhen Zeng, Xun Yang, Xingyi Zhang
AAAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 16,948 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,978 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world datasets show the effectiveness of our approach, especially on the students with much sparser interaction records. |
| Researcher Affiliation | Academia | 1Anhui University, He Fei, China 2Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, He Fei, China 3University of Science and Technology of China, He Fei, China |
| Pseudocode | No | The paper describes the methodology in text and uses figures (e.g., Figure 2) to illustrate the model, but it does not include a dedicated pseudocode block or algorithm listing. |
| Open Source Code | Yes | Our code is available at https://github.com/zeng-zhen/SCD. |
| Open Datasets | Yes | We conduct experiments on two real-world datasets: junyi 1 and ASSIST 2. 1https://pslcdatashop.web.cmu.edu/Dataset Info?dataset Id= 1198 2https://sites.google.com/site/assistmentsdata/home/20092010-assistment-data/skill-builder-data-20092010 |
| Dataset Splits | Yes | To explore the effect of different sparse data on the experimental results, we divided the data set into different proportions. train:test 5:5 6:4 7:3 8:2 methods acc rmse acc50 rmse50 acc rmse acc50 rmse50 acc rmse acc50 rmse50 acc rmse acc50 rmse50 (Table 2 header) |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper states "We implement our SCD with Py Torch." but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For each model we set the batch size to 256. As for graph-based models, i.e. RCD and SCD, we set the layers of the graph network to 2. |