基于互联网的大学生焦虑的识别使用文本和面部情绪分析分析
Graciela Guerrero1,2, Daniel Avila2, Fernando José Mateus da Silva3
1Neurocognition and Emotion Unit, Instituto de Investigación en Informática de Albacete, Albacete, Spain.
Internet interventions
|October 12, 2023
概括
这项研究引入了人工智能驱动的方法,使用面部和文本情绪识别来预测学生的焦虑. 这些工具提供早期识别焦虑障碍,以防止学业失败.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 教育技术的教育技术
背景情况:
- 学生焦虑会对学业成绩和留学能力产生负面影响.
- 成人表现焦虑量表 (AMAS-C) 是评估大学生焦虑的标准工具.
研究的目的:
- 开发和验证基于互联网的,用于预测大学生焦虑的自动化方法.
- 提供替代传统焦虑评估工具,如AMAS-C.
主要方法:
- 面部情绪识别在技能测试期间分析六种基本情绪.
- 文字情绪识别分析Facebook帖子的情绪 (积极,消极,中立).
- 对AMAS-C测试焦虑,谎言和总焦虑等级的验证.
主要成果:
- 面部和文本情绪识别方法都在预测焦虑方面表现出高精度.
- 文本分析达到86.84%的精度,略高于面部分析的84.21%.
结论:
- 自动预测工具可以有效地识别因焦虑而面临学术困难风险的学生.
- 早期识别有助于教育工作者和机构及时进行干预.
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