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通过混合集群和进化神经网络在多阶段框架中提高教育质量

Saleem Malik¹, Chandrakanta Mahanty², Jnanaranjan Mohanty³

¹CSE Department, P A College of Engineering, Mangalore, 574153, India. baronsaleem@gmail.com.

Scientific reports

|July 2, 2025

概括

本研究引入了使用HPEFCM-FSP和NeuroEvoClass算法进行有效的学生绩效评估和在教育环境中早期识别有风险的学生的两阶段数据挖掘方法.

科学领域:

教育数据挖掘 (EDM) 是一个技术.
机器学习在教育中的应用
用于学术分析的人工智能

背景情况:

有效的学生绩效评估对于教育改进至关重要,特别是在高等教育和技术教育中.
传统的数据挖掘方法面临着大型,复杂的教育数据集的挑战,造成研究差距.
预测学生表现是教育数据挖掘 (EDM) 中的一个关键领域.

研究的目的:

提出一个综合的,多阶段的战略,解决学生绩效评估和预测方面的挑战.
通过学业绩集群识别需要额外支持的学生.
为了提高预测学生学业表现不佳的准确性和可靠性,用于早期干预.

主要方法:

第一个阶段:用于学生集群的混合概率集团模糊C-Medoids与特征选择 (HPEFCM-FSP).
第二阶段:NeuroEvoClass算法,将群体智能启发的进化策略和人工神经网络 (ANN) 结合起来,用于性能预测.
粒子集群优化 (PSO) 用于优化ANN权重和网络拓,以动态适应复杂的学生数据.

主要成果:

拟议的HPEFCM-FSP算法在聚类教育数据方面表现优于K-means和Fuzzy C-means,实现更高的轮分数和Dunn指数值.
通过渐进的融合,NeuroEvoClass表现出卓越的预测能力,在准确性,精度,回忆和F1得分方面超过传统模型.
综合方法有效地识别了面临学业表现不佳风险的学生,这对于早期预警系统 (EWS) 来说是有希望的.

结论:

多阶段战略为教育工作者和政策制定者提供数据驱动的洞察力,以提高学生的学业成绩.
通过揭示学习行为,HPEFCM-FSP强大的特征选择和集群能力使有针对性的教育干预成为可能.
开发的算法为处理大型,异质的教育数据集提供了高效和强大的解决方案,促进了个性化的学生支持和机构决策.

关键词:

人工神经网络的人工神经网络教育数据挖掘教育数据挖掘高等技术教育高等技术教育学生的表现学生的表现

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