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校准结构缺失的多维评估:多组高阶IRT模型的应用.

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概括
此摘要是机器生成的。

本研究引入了多组层次顺序对象响应理论 (HO-IRT) 模型在教育评估中的新应用. 这些发现表明,使用非代表性测试仍然可以为复杂的教育构造提供准确的分数.

关键词:
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科学领域:

  • 教育测量教育的测量
  • 心理测量 心理测量 心理测量
  • 项目响应理论.

背景情况:

  • 教育构造越来越复杂,需要在一般和子域级别进行测量.
  • 目前的方法通常需要大型项目库或单独报告得分,从而限制了实际评估.
  • 同时报告一般和子域分数是可取的,但具有挑战性.

研究的目的:

  • 提出和评估一个多组层次性订单项响应理论 (HO-IRT) 模型,用于同时报告分数的结构性缺失.
  • 通过使用NEAT (北,东,南,西) 设计,研究一种新的应用场景,包括代表性和非代表性测试.
  • 在使用非代表性测试时探索HO-IRT模型的参数恢复.

主要方法:

  • 使用多组HO-IRT模型与结构缺失.
  • 采用NEAT设计,包括代表性和非代表性测试.
  • 进行蒙特卡洛模拟以评估参数恢复和根平均平方误差 (RMSE).
  • 使用全信息最大概率方法解决缺失的数据.

主要成果:

  • 该研究表明,非代表性测试可以产生与代表性测试相比的RMSE.
  • 参数恢复被发现是强大的,即使在较高和较低级别的因素之间存在温和的相关性.
  • 拟议的HO-IRT模型有效地控制了评估长度,同时报告了一般和子域分数.

结论:

  • 多组HO-IRT模型为复杂的教育构造提供了一个可行的解决方案,用于同时报告通用和子域分数.
  • 在NEAT设计中使用非代表性 anchor测试是一个实际的替代方案,当构造定义演变时.
  • 这种方法提高了教育测量的效率,而不会影响得分的准确性.