可持续环境教育:一些机器学习算法用于对可持续环境态度的分类
Semra Benzer1, Farid Hassanbaki Garabaghi2, Recep Benzer3
1Gazi University, Faculty of Education, Teknikokullar, Ankara 06500, Turkey.
Evaluation and program planning
|July 15, 2025
概括
这项研究评估了学生对可持续环境的态度,并使用机器学习对其进行了分类. 支持向量机器-序列最小优化 (SVM-SMO) 分类器表现最好,特别是在有限的数据.
科学领域:
- 环境科学 环境科学
- 教育教育教育教育教育教育.
- 计算机科学 计算机科学
背景情况:
- 人类的工业活动对环境产生了负面影响,需要可持续发展.
- 可持续的环境教育对于培养负责任的态度和技能至关重要.
- 了解学生的环境态度是有效教育策略的关键.
研究的目的:
- 评估学生对可持续环境的态度.
- 使用机器学习根据学生的环境态度对学生进行分类.
- 为了比较不同机器学习分类器对此任务的有效性.
主要方法:
- 基于5分利克特尺度的加权评分系统被用于评估态度.
- 机器学习算法包括SVM-SMO,MLPNN,RBF网络和物流回归被用于分类.
- 评估了绩效,特别是在训练数据有限的情况下.
主要成果:
- 与MLPNN,RBF网络和物流回归相比,SVM-SMO分类器表现出更高的性能.
- 这种优异的表现在训练数据数量有限时最为显著.
- 该研究成功地根据学生对可持续环境的态度对学生进行了分类.
结论:
- 机器学习,特别是SVM-SMO,是对学生环境态度进行分类的有效工具.
- 这些发现突显了SVM-SMO在教育研究中的潜力,特别是在数据限制的情况下.
- 这种分类可以为有针对性的可持续环境教育倡议提供信息.
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