Graduate Employment Prediction with Bias
Authors: Teng Guo, Feng Xia, Shihao Zhen, Xiaomei Bai, Dongyu Zhang, Zitao Liu, Jiliang Tang670-677
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework. |
| Researcher Affiliation | Collaboration | 1Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian University of Technology, Dalian 116620, China 2School of Science, Engineering and Information Technology, Federation University Australia, Ballarat, VIC 3353, Australia 3Computing Center, Anshan Normal University, Anshan 114007, China 4TAL AI Lab, TAL Education Group, Beijing 100080, China 5Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA |
| Pseudocode | No | The paper does not include pseudocode or a clearly labeled algorithm block. It describes the model components and provides mathematical equations. |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The dataset used in this experiment includes 2,133 students from a Chinese university. They all enrolled in 2013 and graduated in 2017. ... This dataset consists of three types of information... No information is provided about the public availability of this dataset, suggesting it is proprietary or internal. |
| Dataset Splits | No | The paper mentions dividing data into a 'training set a and testing set b by stratiļ¬ed sampling' but does not specify a validation set split or cross-validation details for reproduction. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used to run the experiments. It only mentions 'extensive experiments' but no system specifications. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | We test the different dimensions including 3, 6, 12, 24, 32, 64, 80, 96 and the performance is shown in Figure 4. The change of dropout proportions generates a slight impact and 0.3 is the best. ... the model can achieve the best prediction performance when the learning rate is set to 0.01. |