预测开放教育能力水平:一种机器学习方法
Gerardo Ibarra-Vazquez1, María Soledad Ramírez-Montoya2, Mariana Buenestado-Fernández3
1School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey, Mexico.
Heliyon
|November 13, 2023
概括
机器学习模型有效地利用学生对知识,技能和价值观的认知预测开放式教育能力水平. 决策树和随机森林根据这些见解准确地对能力进行了分类.
科学领域:
- 教育技术的教育技术.
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 评估开放教育能力对于有效的学习至关重要.
- 学生的感知提供了对他们自己的能力水平有价值的见解.
- 现有的能力评估方法可能无法充分利用数据驱动的方法.
研究的目的:
- 调查构建机器学习模型以预测开放教育能力的可行性.
- 确定学生对知识,技能和态度的看法是否可以作为这些模型的特征.
- 使用衍生决策规则对学生的开放教育能力水平进行分类.
主要方法:
- 量化研究方法通过eOpen工具分析了来自26个国家的326名学生的数据.
- 应用决策树和随机森林的机器学习模型.
- 从学生的感知得出决策规则来预测能力水平,并分析偏差的预测错误.
主要成果:
- 学生对与开放教育相关的知识,技能和态度/价值观的看法为建模提供了令人满意的数据.
- 机器学习模型成功预测了参与者的能力水平.
- 决策树为能力预测提供了可解释的规则.
结论:
- 学生的感知是开放教育能力的可靠预测指标.
- 机器学习,特别是决策树和随机森林,可以有效地用于分类能力水平.
- 该研究验证了基于学生的数据可以在开放式教育中提供准确的能力评估的假设.
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