一种基于机器学习的方法,用于构建大学学生的群组配置文件
Ran Song1,2, Fei Pang3, Hongyun Jiang1
1School of Mathematics, Physics and Information, Shaoxing University, Shaoxing, Zhejiang, 312000, China.
Heliyon
|April 11, 2024
概括
本研究开发了一种使用K-means集群和反向传播神经网络的新型学生概况模型,在分类四个不同的学生概况方面达到90.22%的准确性,以增强教育发展.
科学领域:
- 教育技术的教育技术
- 教育中的数据科学教育中的数据科学
- 高等教育研究 高等教育研究
背景情况:
- 高等教育的数字化转型需要先进的学生概况.
- 现有的学生分析方法往往缺乏全面性,依赖于单一的数据来源.
- 大数据技术使学生教育发展的复杂分析成为可能.
研究的目的:
- 使用问卷数据构建一个预测性的学生分析模型.
- 通过改进学生分类,提高教育问卷的有效性.
- 通过采用全面的多属性方法来解决先前研究的局限性.
主要方法:
- 利用问卷调查收集各种学生数据.
- 应用K-means集群算法用于初始学生数据分组.
- 开发了一种使用反向传播神经网络进行分类的分类预测模型.
主要成果:
- 确定了四种不同的学生个人资料:勤奋的学习者,认真的个人,有洞察力的成就者和道德倡导者.
- 根据已识别的个人资料,成功标记学生组.
- 为预测模型实现了高分类准确率90.22%.
结论:
- 开发的模型提供了一种新且有效的方法,用于大学生简介.
- 这些发现为教育研究人员和机构提供了有价值的方法参考.
- 这项研究增强了基于问卷的方法在高等教育中的有用性.
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