# EduModal

**Repository Path**: eason_0212/edu-modal

## Basic Information

- **Project Name**: EduModal
- **Description**: Edu-Modal.
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-07-16
- **Last Updated**: 2023-07-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# EduModal - 多模态学习分析在教育研究中应用的文献索引数据库

## 索引更新中，当前仅显示部分内容

## 介绍

### *bib 文献元信息的BibTeX文件
示例：

```
@inproceedings{ye_college_2016,
	title = {College {Student} {Scholarships} and {Subsidies} {Granting}: {A} {Multi}-modal {Multi}-label {Approach}},
	shorttitle = {College {Student} {Scholarships} and {Subsidies} {Granting}},
	abstract = {Scholarships and financial aids in modern universities are the basic administrative plans to ensure and promote the completion of academic training and studies for students. Traditional grants allocation procedures are based on manual determination, which costs lots of human resources. In this paper, we investigate an assistance model for helping improve the scheme of granting. We first collect students information from multi-modal channels, including their behaviors of campus consumption, internet usage, daily trajectory together with their enrollment information. The approval status and amount of funds granted are converted as labels. We propose the College Student Scholarships and Subsidies Granting (CS3G) approach to address the concrete problem. CS3G approach overcomes 3 obstacles, i.e., complicated multi-label influences, private modal information protection and difficulties in label collection. In detail, based on the facts that scholarships mainly depend on academic achievements, subsidies granting is generally based on students financial hardships as well as credits, and there are implicit influences among scholarships and subsidies, the CS3G approach handles types of interactions between multiple labels, it is notable that data from different modalities are collected by different divisions of a university, privacy protection is considered in CS3G, i.e., no interaction between features from different modalities in the model training phase. Besides, due to the confidentiality of the concrete types/amounts of granting, only a portion of labels is collected in this application, CS3G is trained in a semi-supervised style. Empirical investigations show good generalization ability of CS3G on benchmark datasets, and a real assessment of a university also validates the power of our approach for tackling this type of problem well.},
	booktitle = {2016 {IEEE} 16th {International} {Conference} on {Data} {Mining} ({ICDM})},
	author = {Ye, Han-Jia and Zhan, De-Chuan and Li, Xiaolin and Huang, Zhen-Chuan and Jiang, Yuan},
	month = dec,
	year = {2016},
	note = {ISSN: 2374-8486},
	keywords = {Concrete, Feature extraction, Internet, Multi-Label Learning, Multi-Modal Learning, Privacy-Preserving, Resource management, Scholarships, Student Scholarships and Subsidies Granting, Training, Trajectory},
	pages = {559--568},
}
```

### *.json 文献标签信息的JSON文件
示例：

```
{
    "Question": "助学金评定",
    "Task": "LF"
}
```
其中，Question为研究问题编码，Task为数据任务编码。
可能的数据任务编码包括：
| 编码 | 内容   |
|----|------|
| JP | 联合表示 |
| CP | 协同表示 |
| EA | 显式对齐 |
| IA | 隐式对齐 |
| ET | 示例转译 |
| GT | 生成转译 |
| EF | 特征融合 |
| LF | 决策融合 |
| HF | 混合融合 |