在使用长短期记忆 (LSTM) 网络的在线英语教学中用于新媒体数据挖掘和分析的模型
1Department of Language and Literature, College of Technology, Hubei Engineering University, Xiaogan, China.
PeerJ. Computer science
|March 4, 2024
概括
这项研究使用深度学习模型,长期短期记忆 (LSTM) 来分析学生的学习数据. 该模型准确地识别学习状态,帮助教师改进教学并了解学生的进步.
科学领域:
- 教育技术的教育技术
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 有效的师生关系需要理解学生的进步.
- 目前监测学习状态的方法可能耗时且缺乏深度.
- 技术为客观和有洞察力的学习分析提供了潜力.
研究的目的:
- 开发一种智能分类方法来分析学生学习时间序列数据.
- 利用深度学习,特别是长期短期记忆 (LSTM) 网络,以识别学生的学习状态.
- 为教育工作者提供一个工具,以获得学生学习状态的智能掌握,以有针对性的教学改进.
主要方法:
- 通过网络平台从使用软件的学习者收集数据.
- 功能工程包括用户学习特征,屏幕照明时间,惯性传感器数据和网络强度.
- 基于长短期内存 (LSTM) 的深度网络模型的实施,用于时间序列分类.
主要成果:
- 拟议的LSTM模型实现了高度精确的时间序列识别.
- 该模型在识别学习状态方面显示了超过95%的准确性.
- 这种方法被证明是有效的,使用相对简单的功能.
结论:
- 基于LSTM的智能分类模型显示了学习状态识别的重大前景.
- 识别学习状态的高精度可以帮助教师理解学生的进步.
- 这项技术促进了有针对性的教学改进,并加强了教师与学生的关系.
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