游戏化教育中的预测分析:识别有风险的学生的混合模型
Devanshu Sawarkar1, Latika Pinjarkar1, Pratham Agrawal1
1Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University) Pune, India.
MethodsX
|August 25, 2025
概括
这项研究引入了一种混合机器学习模型,以准确地识别可能在游戏化学习环境中失去参与的学生. 这种模式可以提前进行干预,改善学生的支持和资源分配.
科学领域:
- 教育技术
- 在教育中的机器学习
- 学生支持系统
背景情况:
- 在教育环境中,学生的脱离和学带来了重大挑战.
- 游戏化学习环境为学生分析提供丰富的行为数据.
- 传统的评估方法可能无法及早识别有风险的学生.
研究的目的:
- 开发和验证混合预测模型,以准确识别游戏化的教育中面临风险的学生.
- 用机器学习组合来加强学生的风险评估.
- 为教育工作者提供及时学术干预的有效工具.
主要方法:
- 将后勤回归,决策树和随机森林集成到一个组合模型中.
- 分析学生数据,包括学业成绩,参与和任务完成.
- 开发一个基于机器学习的学生监控系统.
主要成果:
- 混合组合模型在识别有风险的学生方面表现优异,
- 该模型准确地预测学生需要基于游戏化的学习数据进行干预.
- 早期发现有风险的学生,促进及时支持.
结论:
- 混合机器学习模型提供了一个强大的方法来识别有风险的学生.
- 开发的系统为教育工作者提供了可操作的洞察力,以实施有针对性的干预措施.
- 这种方法提高了学生支持服务的效率和有效性.
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