学生在交互式环境中的表现:一个智能模型
Doaa Mohamed Elbourhamy1, Ali Hassan Najmi2, Abdellah Ibrahim Mohammed Elfeky1,3
1Kafrelsheikh University, Kafrelsheikh, Egypt.
PeerJ. Computer science
|June 22, 2023
概括
这项研究表明,信息图表有效地教授计算机网络,教育数据挖掘 (EDM) 模型可以预测学生的表现. 这些方法增强学习,特别是在远程教育挑战期间.
科学领域:
- 教育技术的教育技术
- 计算机科学 计算机科学
- 数据挖掘 数据挖掘
背景情况:
- 由于COVID-19大流行,需要远程学习,这凸显了对有效教育技术的需求.
- 追踪学生表现对于识别和解决学习障碍至关重要.
- 教育数据挖掘 (EDM) 提供了预测学生学业成功的方法.
研究的目的:
- 开发一个模型来预测学生在计算机网络课程中的表现.
- 评估信息图形作为计算机网络教学工具的有效性.
- 为了确定最佳的数据挖掘算法,以预测学生的成绩.
主要方法:
- 使用了Moodle (模块化面向对象的动态学习环境) 平台进行课程交付.
- 使用问卷来评估信息图表在计算机网络教学中的有效性.
- 在Weka程序中应用各种数据挖掘分类算法来预测学生的成绩.
主要成果:
- 信息图表被证明是教计算机网络的有效方法.
- 几种机器学习算法,包括多层感知子,随机森林和随机树,在预测学生表现方面表现出了有效性.
- 该研究确定了特定的算法,这些算法在分类学生成功方面表现最好.
结论:
- 教育技术,特别是信息图表和EDM,可以显著改善学习体验和学习成果.
- 开发的EDM模型和确定的算法为监测和增强学生学术进步提供了有价值的工具.
- 集成像Moodle这样的平台和有效的数字资源是成功远程教育的关键.
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