...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Interlending & document supply >Estimating student ability and problem difficulty using item response theory (IRT) and TrueSkill
【24h】

Estimating student ability and problem difficulty using item response theory (IRT) and TrueSkill

机译：使用项目响应理论（IRT）和TrueSkill估算学生能力和问题难度

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

PurposeThe purpose of this paper is to investigate an efficient means of estimating the ability of students solving problems in the computer-based learning environment.Design/methodology/approachItem response theory (IRT) and TrueSkill were applied to simulated and real problem solving data to estimate the ability of students solving homework problems in the massive open online course (MOOC). Based on the estimated ability, data mining models predicting whether students can correctly solve homework and quiz problems in the MOOC were developed. The predictive power of IRT- and TrueSkill-based data mining models was compared in terms of Area Under the receiver operating characteristic Curve.FindingsThe correlation between students ability estimated from IRT and TrueSkill was strong. In addition, IRT- and TrueSkill-based data mining models showed a comparable predictive power when the data included a large number of students. While IRT failed to estimate students ability and could not predict their problem solving performance when the data included a small number of students, TrueSkill did not experience such problems.Originality/valueEstimating students ability is critical to determine the most appropriate time for providing instructional scaffolding in the computer-based learning environment. The findings of this study suggest that TrueSkill can be an efficient means for estimating the ability of students solving problems in the computer-based learning environment regardless of the number of students.
机译：本文的目的是调查估计学生解决基于计算机的学习环境中的学生问题的能力的有效手段.Design/methodology/ApproChitem响应理论（IRT）和TrueSkill被应用于模拟和真正的问题解决数据以估计数据学生在大规模开放的在线课程中解决家庭作业问题的能力（MOOC）。基于估计能力，制定了数据挖掘模型，预测学生是否能够在MOOC中正确解决家庭作业和测验问题。基于IRT和Trueskill的数据挖掘模型的预测力量在接收器操作特征曲线下的区域下进行了比较。从IRT和Trueskill估计的学生能力之间的相关性强劲。此外，基于IRT-和Trueskill的数据挖掘模型显示了当数据包括大量学生时的可比预测力。虽然IRT未能估计学生的能力，并且无法预测他们的问题，但是当数据包括少数学生时，TrueSkill没有经历此类问题。历史/估计学生的能力至关重要，以确定提供教学脚手架最合适的时间基于计算机的学习环境。本研究的调查结果表明，Trueskill可以是估计学生解决基于计算机的学习环境中的问题的能力的有效手段，而不论学生的数量如何。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号