智能分类和预测在线学习环境中学生的心理健康,使用增强算法和LIWC功能
1Hangzhou City University, Huzhou Street, Hangzhou, Zhejiang, China.
Scientific reports
|July 2, 2025
概括
这项研究引入了一个智能模型,用于在线学习中对学生心理健康进行分类. 它在识别压力和焦虑方面达到98-99%的准确性,有助于及时进行心理干预.
科学领域:
- 教育心理学教育心理学
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 在线学习环境为监测学生心理健康带来了独特的挑战.
- 准确及时识别学生的心理困扰对于有效的干预至关重要.
研究的目的:
- 开发和验证一种智能模型,用于在线学习中对学生的心理健康状况进行分类.
- 通过使用先进的算法和语言特征,提高心理健康分类的准确性和稳定性.
主要方法:
- 利用语言调查和词汇计数 (LIWC) 字典从在线学习平台中提取情感和心理特征.
- 实现了一个增强算法来整合多个弱分类器.
- 使用Antlion优化算法优化模型性能.
主要成果:
- 实现了分类准确度在98%至99%之间.
- 显著降低了学生心理健康状况的错误分类率.
- 在识别经历高压力和焦虑的学生方面表现出很高的准确性.
结论:
- 开发的智能模型有效地提高了在线学习中心理健康状况分类和实时监控的准确性.
- 该模型为学生实施有针对性的心理干预提供了关键支持.
- 这种方法为在数字教育环境中积极支持心理健康提供了一个有前途的工具.
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