基于阿凡达的学习系统的评估和改进:从语言结构调整到情感驱动的表达
Aru Ukenova1, Gulmira Bekmanova1, Nazar Zaki2
1Faculty of Information Technologies, L.N.Gumilyov Eurasian National University, Astana 010000, Kazakhstan.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
这项研究通过结合情感驱动的交互来增强化身影学习系统. 虽然最初的结果有利于传统方法,但avatar中的情感智能显示出更深入的学习体验的前景.
科学领域:
- 教育技术的教育技术
- 人与计算机的交互
- 教育中的人工智能
背景情况:
- 传统的学习系统往往缺乏引人入胜和个性化的元素.
- 基于阿凡达的学习系统为增强学生参与提供了潜力.
- 在教育化身中整合情绪智能是一个新兴的领域.
研究的目的:
- 调查基于化身的学习系统中情感驱动的交互的有效性.
- 在avatar学习环境中比较不同教学策略的有效性.
- 通过教育化身识别影响学生参与和学习成果的因素.
主要方法:
- 对各种教学方法进行比较分析.
- 单向ANOVA用于测试后结果差异.
- 图基的HSD用于对对组比较.
- 传统与基于avatar的方法的效果大小评估.
主要成果:
- 单向ANOVA显示了教学策略中测试后得分的显著差异.
- 使用Tukey的HSD进行对对比并没有产生显著的群体差异.
- 效果大小偏爱传统方法,而不是以化身为基础的方法,视频课程显示了适度的区别.
- 学生强调了需要情感真实性和文化适应,如哈萨克语的口音.
结论:
- 情感驱动的化身有潜力培养更深层次的学习体验.
- 头像系统最初效率较低的原因可能是新性和调整时间.
- 对情感真实性和文化相关性的系统改进对于广泛采用至关重要.
- 需要对更大的样本进行进一步的研究,以加强统计能力.
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