对物品响应理论模型的比较研究,用于混合离散连续响应
Cengiz Zopluoglu1, J R Lockwood2
1College of Education, University of Oregon, Eugene, OR 97403, USA.
Journal of Intelligence
|March 27, 2024
概括
人工智能驱动的语言能力测试的新测量模型显示出有希望. 贝塔项目响应模型为 dikta 任务提供了卓越的预测准确性,尽管需要适合模型的基准.
科学领域:
- 教育测量教育的测量
- 教育中的人工智能
- 心理测量 心理测量 心理测量
背景情况:
- 语言能力评估对于教育和职业生涯至关重要.
- 人工智能集成可以实现复杂的项目类型,例如带有混合响应分布的命令任务.
- 现有的测量模型可能无法充分捕捉这些独特的响应特征.
研究的目的:
- 评估用于人工智能驱动的语言评估的新型测量模型,具有混合离散连续响应特征.
- 评估Beta,Simplex和Samejima的连续物品响应模型的零和一个膨胀扩展的性能.
- 通过使用隐性回归来改进参数估计,将抵押信息纳入.
主要方法:
- 对扩展的Beta,Simplex和Samejima的连续项目响应模型的评估.
- 隐性回归的应用,以结合抵押信息.
- 使用项目和人参数以及样本之外的预测准确度来比较模型性能.
主要成果:
- 所有评估的模型都产生了高度相关的项目和人参数.
- 贝塔项目响应模型表现出优异的样本外预测准确性.
- 发现的一个重大挑战是缺乏适合这些新型模型的模型和项目的确立基准.
结论:
- 新的测量模型,特别是Beta项目响应模型,对于混合响应分布的AI驱动语言评估是有效的.
- 进一步的研究是必不可少的,以制定基准来评估这些创新模式的适应性.
- 建立可靠性和有效性基准对于这些先进的评估工具的实际应用至关重要.
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