一个基于DeepEnrollNet的教学皇帝子优化 (IEPO) 用于大学生入学预测和留学生推
1Department of Information Systems, College of Computer and Information Sciences, Majmaah University, Majmaah, 11952, Saudi Arabia. s.sharma@mu.edu.sa.
Scientific reports
|December 27, 2024
概括
学术机构现在可以通过新的AI框架预测学生入学率,并防止学生退学. 这种方法集成了深度学习和推系统,分析文本和数字数据,以制定个性化的保留策略.
科学领域:
- 人工智能的人工智能
- 教育数据挖掘教育数据挖掘
- 机器学习 机器学习
背景情况:
- 学术机构在准确预测学生入学率和管理留学生数量方面面临挑战.
- 目前的方法难以整合多种数据类型 (数字和文本),限制了个性化干预.
- 需要一个统一的框架来提高预测准确性和积极的学生支持.
研究的目的:
- 提出一个创新的框架,结合深度学习和推系统,用于学生入学预测和流失预防.
- 开发一种有效处理学生数字和文本数据的方法,以获得更好的洞察力.
- 增强学生留学和学术成功的机构策略.
主要方法:
- 使用数字和文本数据的先进预处理,包括GloVe嵌入,LDA和SentiWordNet.
- 通过使用Pythagorean fuzzy AHP与教学皇帝子优化 (IEPO) 实现了加权特征融合和选择.
- 开发了DeepEnrollNet (CNN-GRU-Attention QCNN) 用于入学预测和深度Q网络 (DQN) 用于保留建议.
主要成果:
- DeepEnrollNet 模型实现了高精度,平均平方误差 (MSE) 的最小值为 0.218978.8.
- 该框架在整合各种数据以进行整体学生分析方面表现出有效性.
- 制定了可操作的留学生建议,以解决潜在的学生流失问题.
结论:
- 拟议的框架在预测学生入学率和防止退学方面取得了重大进展.
- 整合深度学习和推系统为个性化留学生提供了一个强大的工具.
- 这种统一的方法提高了机构在学生支持中以数据为导向的决策能力.
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