推进教育数据挖掘以提高学生绩效预测:功能选择算法和分类技术的融合与动态功能合奏演变的融合
Saleem Malik1, S Gopal Krishna Patro2, Chandrakanta Mahanty3
1CSE Department, P A College of Engineering, Mangalore, India. baronsaleem@gmail.com.
Scientific reports
|March 14, 2025
概括
一个新的模型,用于增强特征选择 (DE-FS) 的动态特征合奏演变,使用自适应值来改善学生绩效预测. 这种方法通过动态调整数据模式来提高教育数据挖掘的准确性和灵活性.
科学领域:
- 教育数据挖掘 (EDM) 技术
- 机器学习在教育中的应用
- 学习分析学习分析
背景情况:
- 教育机构产生大量的数据,为EDM提供了提高学习成果的机会.
- 传统的特征选择方法通常依赖于静态值,这对于不断发展的教育数据集可能是不够的.
- 过度装配和不足装配是教育背景下的预测建模中常见的挑战.
研究的目的:
- 为了引入一个新的特征选择模型,动态特征合奏演化用于增强特征选择 (DE-FS).
- 通过动态和自适应值来解决静态特征选择方法的局限性.
- 提高预测学生成绩的准确性和灵活性.
主要方法:
- DE-FS将传统方法 (相关性矩阵分析,信息获取,Chi-square) 与热图用于特征选择相结合.
- 一个核心创新是动态和适应性值机制,根据不断变化的数据模式进行调整.
- 该模型的预测性能在各种教育数据集中进行了评估.
主要成果:
- 与传统方法相比,DE-FS表现出优越的预测性能.
- 动态值机制有效地适应了波动的数据模式.
- 该模型实现了学生表现的准确和可靠的预测.
结论:
- DE-FS为教育数据挖掘提供了基于集体的高级特征选择方法.
- 适应性值可以提高模型的准确性,灵活性和稳定性.
- DE-FS支持有针对性的干预和改进资源分配,以提供个性化的学习体验.
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