用智能决策系统应用数学学生的自我评估:在数字教育中的问卷,设计和实施
Vasiliki Matzavela1, Efthimios Alepis1
1Department of Informatics, University of Piraeus, Piraeus, Greece.
概括
本研究提出了一个应用程序,用于准确预测在线学习环境中的学生表现. 这个工具有助于教育工作者开发有效的学习干预措施,以改善学术成果.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 学习分析学习分析
背景情况:
- 在线教育中对可靠的智能m-learning环境的需求日益增加.
- 需要制定决策支持系统,以提高学习的灵活性和有效性.
- 难以准确预测学生在期末考试中的成绩.
研究的目的:
- 为在学术环境中准确预测学生成绩提出申请.
- 帮助教育工作者和学习专家在知识提取.
- 促进学习干预措施的设计,以改善结果.
主要方法:
- 开发一个预测学生成绩的应用程序.
- 使用数据分析来确定关键绩效指标.
- 机器学习算法用于预测准确性的应用.
主要成果:
- 该应用程序提供了学生表现的准确预测.
- 确定了影响学生在线学习成功的关键因素.
- 证明了应用程序在支持教育决策方面的实用性.
结论:
- 精确的学生绩效预测是可以通过智能系统实现的.
- 开发的应用程序为教育工作者提供了宝贵的工具.
- 数据驱动的洞察力可以提高学习干预措施的设计和有效性.
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