来自大学电子政府分析平台的学术数据:教育数据挖掘方法
Konstantinos Chytas1, Anastasios Tsolakidis1, Evangelia Triperina1
1University of West Attica, Agiou Spiridonos 28, Egaleo 122 43, Greece.
Data in brief
|July 17, 2023
概括
这项研究分析了希腊大学电子政府平台的学术数据,重点关注COVID-19期间的混合学习. 调查结果为高等教育机构的教育数据挖掘和服务质量提升提供了洞察力.
科学领域:
- 教育数据挖掘教育数据挖掘
- 电子政府服务 电子政府服务
- 高等教育的分析.
背景情况:
- 由于COVID-19的流行,大学需要快速采用混合学习模式.
- 大学电子政府 (U-EGOV) 服务是支持教育,研究和管理功能的关键信息系统.
- 现有的U-EGOV框架概述了从存在到转型的阶段,影响了服务交付.
研究的目的:
- 描述和分析来自大学电子政府分析平台的学术数据.
- 支持在COVID-19爆发期间和之后在希腊大学促进和评估混合学习.
- 建立可重复使用的数据集来衡量学术质量,并促进其他高等教育机构 (HEI) 的类似分析.
主要方法:
- 来自大学电子政府服务 (U-EGOV) 的自动数据聚合.
- 教育数据挖掘方法应用于收集的学术数据.
- 分析跨越三个学期的数据 (2019-2020年春季,2020-2021年冬季,2020-2021年春季).
主要成果:
- 从预处理的数据中创建了一个由4765个记录 (1661个独特的学生,20个课程) 组成的数据集.
- 数据集包括全面的学术元素,如课程,作业,成绩和考试.
- 这些数据支持对同步和异步教育组件的分析.
结论:
- 开发的数据集可以作为评估学术成果质量和进展的宝贵参考.
- 该方法为其他高等教育机构的比较研究提供了基础.
- 该研究强调了电子政府平台在增强混合学习和教育服务方面的潜力.
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