通过模糊系统结合优化算法和人工智能的创新策略来评估高等教育中的学生失败情况
Junting Nie1, Hossein Ahmadi Dehrashid2
1Xinyang Vocational and Technical College, Xinyang 464000, Henan Province, China.
Heliyon
|June 13, 2024
概括
两个新的算法,哈里斯·霍克的优化器 (HHO) 和地球优化算法 (EWA),被用来改善学生的绩效预测. 与EWA-ANFIS相比,HHO-ANFIS在识别学生失败方面表现出更高的准确性.
科学领域:
- 教育数据挖掘教育数据挖掘
- 教育中的人工智能
- 超启发式优化优化
背景情况:
- 预测学生的成绩对于教育干预至关重要.
- 传统的模型经常与复杂的,多变量学生数据作斗争.
- 超启发式算法提供了提高预测模型准确性的潜力.
研究的目的:
- 介绍和评估两种新的元启发算法,哈里斯·霍克的优化器 (HHO) 和地球优化算法 (EWA),以提高学生的绩效预测.
- 为了比较HHO和EWA在与自适应神经模糊推理系统 (ANFIS) 模型集成时的疗效.
- 用各种性能指标评估开发模型的预测准确性.
主要方法:
- 使用哈里斯·霍克的优化器 (HHO) 和地球优化算法 (EWA) 训练自适应神经模糊推理系统 (ANFIS) 模型.
- 利用十四个与学生相关的过程变量进行模型开发,包括人口统计,学术历史和社会经济因素.
- 使用平均绝对误差 (MAE),平均平方误差 (MSE) 和曲线下的面积 (AUC),以及自定义评分系统来评估模型性能.
主要成果:
- 哈里斯·霍克的优化器-ANFIS (HHO-ANFIS) 模型在预测学生失败方面显著超过了地球优化算法-ANFIS (EWA-ANFIS).
- HHO-ANFIS获得了更高的准确性,AUC = 0.8004,MSE = 0.62689,MAE = 0.64105. 这两种方法都获得了更高的准确性.
- EWA-ANFIS的准确性较低,AUC=0.7565,MSE=0.71543和MAE=0.70819.这些数据都显示出较低的准确性.
结论:
- 听优化算法大大提高了传统ANFIS模型的准确性和性能,以预测学生的成绩.
- HHO-ANFIS模型显示出高能力,能够准确识别面临失败风险的学生.
- 这些发现表明,在教育系统管理和学术课程质量方面,有可能有显著的改进.
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