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在智能教学中基于IPSO-LSTM的创新能力评估模型的设计

¹School of Education, Guangzhou University, Guangzhou, Guangdong, China.

PeerJ. Computer science

|December 11, 2023

概括

本研究引入了一个智能系统,通过整合心理状态和绩效指标来评估学生的创新能力. 开发的模型达到95%以上的准确性,突出了心理因素在培养创造力方面的关键作用.

科学领域:

教育技术的教育技术
心理学心理学心理学
人工智能的人工智能

背景情况:

对创新经济日益增长的需求需要关注学生的创新能力.
当前的人才培训计划需要有效的方法来评估和增强学生的创造力.
将心理见解与绩效指标相结合,为创新评估提供了一种新的方法.

研究的目的:

开发一个智能框架来评估学生的创新能力.
为客观评估量化定性心理数据.
提出一种高精度的模型来对创新能力进行分类.

主要方法:

使用德尔菲方法量化定性心理数据.
开发了一种改进的粒子群优化长期短期记忆 (IPSO-LSTM) 模型用于分类.
特性贡献分析以确定关键影响因素.

主要成果:

IPSO-LSTM模型在优秀,一般和失败的创新能力方面实现了95.3%的分类准确率.
心理方面,特别是创造能力评估,占优秀学生贡献的50%以上.
这项研究证实了心理状态和创造力之间存在显著联系.

结论:

拟议的智能评估框架有效评估学生的创新能力.
心理状态是学生创造性潜力的关键决定因素.
结果为整合创新和心理教育提供了理论基础.

关键词:

在IPSO-LSTM中使用.创新能力评估创新能力评估智能评估是一种智能评估.心理分析就是心理分析.

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