通过知识图和KNN模型促进意识形态和政策教育:强调积极心理学
Shuangquan Chen1, Yu Ma1, Wanting Lian2
1School of Marxism, Dalian Maritime University, Liaoning, Dalian, 116000, China.
BMC psychology
|March 26, 2024
概括
这项研究引入了一个预测系统,将教育心理学与大学的意识形态和政治教育相结合. 该系统准确地预测关系,增强科学和有针对性的教学方法.
科学领域:
- 教育心理学教育心理学
- 高等教育教学的教学.
- 知识图应用知识图应用
背景情况:
- 意识形态和政治课程是高等教育的核心,需要与心理和教育原则保持一致.
- 整合教育心理学增强了这种教育的科学,有针对性和前性方面.
- 知识图形技术提供了超越传统文本匹配的高级信息获取.
研究的目的:
- 开创一个预测系统,以了解教育心理学和大学的意识形态/政治教育之间的关系.
- 构建学生心理映射关系的知识图.
- 使用机器学习预测教育心理学和意识形态/政治教育之间的相互作用.
主要方法:
- 提取各种学生心理映射关系的提取.
- 构建一个知识图表来表示这些关系.
- 应用K-最近邻居 (KNN) 算法进行预测分析.
主要成果:
- 该系统实现了高精度,回忆和F1分数,F1分数达到0.95,用于精确的样本分类.
- 平均系统响应时间小于3秒,满足实际在线教学需求.
- 该系统有效地预测了教育心理学与意识形态/政治教育之间的关系.
结论:
- 开发的系统增强了意识形态和政治教学在高等教育的科学和预测能力.
- 通过知识图表和KNN促进的教育心理学原则的整合,提高了教学效率.
- 该系统的性能符合在线教育交付的实际要求.
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