提高学生在高等教育中的成功预测,使用群体优化增强高效的Net注意力机制
1College of Computer Science and Engineering, University of Ha'il, Ha'il, Saudi Arabia.
PloS one
|June 30, 2025
概括
本研究介绍了EffiXNet,这是一个先进的机器学习模型,用于使用大数据集预测学生表现. 该模型实现了高精度,使得早期干预和有针对性的学术支持成为可能.
科学领域:
- 教育数据挖掘教育数据挖掘
- 机器学习在教育中的应用
- 学术成绩预测预测
背景情况:
- 预测学生表现对于个性化的教育和学术成功至关重要.
- 越来越多的教育数据需要先进的机器学习 (ML) 技术.
- 来自中国机构的大量数据集 (200,000条记录,80个特征) 用于强大的模型开发.
研究的目的:
- 使用大规模教育数据集开发可靠的学生绩效预测技术.
- 通过一种新的混合选择模型来识别具有影响力的特征.
- 引入和评估EffiXNet,一个增强的EfficientNet模型,以提高预测准确度.
主要方法:
- 数据预处理以处理异常值和缺失值.
- 一种混合特征选择模型,结合了相关性过,相互信息,交叉验证 (CV),递归特征消除 (RFE) 和稳定性选择.
- 开发了EffiXNet,结合了自我注意力,动态卷积,改进了规范化和Sparrow搜索优化算法,用于超参数调整.
主要成果:
- EffiXNet实现了曲线下的面积 (AUC) 为0.99.
- 与基线模型相比,逻辑损失减少了25%.
- 报告的高性能指标:97.8%的精度,98.1%的F1分数,具有优化的内存使用.
结论:
- 开发的EffiXNet模型擅长捕捉复杂的模式,以准确预测学生的表现.
- 强调早期干预和教育中的定制支持的重要性.
- EffiXNet为全球的学术机构提供了一个可扩展和高效的解决方案.
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