实证研究大学生课外阅读偏好通过功能数据分析图书馆的图书借书行为
Fan Zhang1, Yuling Liu2, Chao Song3
1The Library of Guangzhou Huashang College, Guangzhou, China.
PloS one
|January 26, 2024
概括
这项研究使用图书馆借用数据来预测学生的兴趣. 结合神经网络和部分微分方程的智能框架揭示了课外阅读习惯.
科学领域:
- 计算机科学 计算机科学
- 信息科学 信息科学 信息科学
- 教育教育教育教育教育教育.
背景情况:
- 图书馆的学生阅读记录提供了对一般知识获取的见解.
- 从图书馆数据预测课外兴趣需要先进的分析方法.
- 现有的方法可能无法完全捕捉阅读行为的细微差别.
研究的目的:
- 为了深入挖掘图书馆的书借数据,预测学生的课外兴趣.
- 开发一种智能计算框架,用于分析各种图书馆数据集.
- 为了提高学生与不同书籍目录和属性的接触的理解.
主要方法:
- 提出了一个智能计算框架,将神经网络架构与部分微分方程 (PDE) 函数模块融合在一起.
- 设计了一个自适应式学习反向传播神经网络 (BPNN),具有自动超参数调.
- 将PDE模块嵌入网络以增强神经感知器损失功能,并利用信息进行评估.
主要成果:
- 在图书馆借用记录的多模式时间序列数据集上的实证实验证明了该框架的有效性.
- 拟议的框架成功地揭示了学生的课外阅读兴趣.
- 验证了处理相关账本借款记录以预测利息的能力.
结论:
- 开发的智能计算框架准确地从图书馆借用数据中预测学生的课外兴趣.
- 该方法对于分析多样化和多模式时间序列图书馆数据集是有效的.
- 该框架在各种图书馆设置的"大数据"分析中具有潜在的应用.
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