相关实验视频
使用黑洞优化和高斯过程回归预测学生的学业成绩
1School of Education, Durham University, Leazes Road, Durham, DH1 1TA, UK. yanyu02862346@163.com.
Scientific reports
|March 29, 2025
概括
本研究介绍了一种结合黑洞优化 (BHO) 和高斯过程回归 (GPR) 的新方法,用于预测高等教育学术成绩. 该方法准确地识别了关键因素并预测了学生的成功,为教育工作者提供了宝贵的见解.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 机器学习用于学术预测
背景情况:
- 学术成绩对于评估教育机构和学生学习至关重要.
- 诸如不良的学习环境和家庭问题等因素可能会对学生的成绩产生负面影响.
- 教师寻求有效的策略来解决和预测学术挑战.
研究的目的:
- 开发和评估一种用于预测高等教育学术成功的混合算法.
- 确定影响学生学业成绩的重要指标.
- 为教育工作者提供分析学生行为和改善学习监督的工具.
主要方法:
- 一种三阶段的方法,涉及数据预处理,使用黑洞优化 (BHO) 进行特征选择,以及学术绩效预测.
- 使用高斯过程回归 (GPR) 进行特征选择和加权GPR模型进行预测.
- 使用GPR来确定组合预测模型中的最佳权重.
主要成果:
- 拟议的BHO-GPR混合方法实现了较低的错误率,RMSE为0.95和MAE为0.81.
- 与现有方法相比,在预测学业成绩方面表现优越.
- 成功确定了影响学生学业成功的有效指标.
结论:
- 混合BHO-GPR模型为预测学生学业成绩提供了强大而准确的方法.
- 这种方法可以帮助教育工作者了解学生的行为模式和学术成果.
- 这些发现支持制定有针对性的干预措施和加强学习监督策略.
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