.png){kind=icon}
返回
Artificial neural network-based performance assessments
.png){kind=icon}
PRE
AI
DOI：10.1016/S0747-5632(99)00025-4.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We have explored the ability of artificial neural network technologies to generate performance models of complex problem-solving tasks without the detailed a priori knowledge of the nature of the task. To test the generalizibility of this approach we applied this analysis to two diverse content domains-high school genetics and clinical patient management. In both domains, the artificial neural networks, using only the sequence of actions taken while performing the task, generated multiple classification groups defining different levels of competence. The validity of these neural network performance groupings was further established by the good concordance of these classifications with independently derived expert ratings. (C) 1999 Elsevier Science Ltd. All rights reserved.
Keyword:
EXPERTS
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
9.1K
被引数:
5.8W
机构
暂无机构信息
引用论文
SEARCH PATH MAPPING - A VERSATILE APPROACH FOR VISUALIZING PROBLEM-SOLVING BEHAVIOR.png)
ACADEMIC MEDICINE
OAAI
ACADEMIC MEDICINEIF5.2
OAAI分享收藏分享收藏
Large scale micro-macro bilateral control using piezoelectric cantilever with plant nominalization
PREAI
分享收藏
查看更多内容