基于大学生复杂思维能力的性别预测:来自机器学习方法的分析
Gerardo Ibarra-Vazquez1, María Soledad Ramí Rez-Montoya1, Hugo Terashima2
1Institute for the Future of Education, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501, Monterrey, 64849 Nuevo León Mexico.
机器学习模型根据复杂的思维感知准确地分类了学生的性别,在培训和测试中实现了高准确性. 然而,模型显示了偏见,经常将男性错误地归类为女性,强调了在教育技术方面需要进一步研究的需要.
科学领域:
- 教育心理学教育心理学
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
背景情况:
- 学生的性别分类对于个性化教育至关重要.
- 对复杂思维能力的看法在性别之间可能有所不同.
- 机器学习为分析教育数据提供了新的方法.
研究的目的:
- 评估机器学习模型,以基于复杂思维感知来对学生性别进行分类.
- 分析模型性能和预测偏差.
- 探索适应性教育策略中的应用.
主要方法:
- 使用eComplexity仪器,利用了来自墨西哥605名大学生的数据.
- 应用了四种机器学习模型:随机森林,支持矢量机器,多层感知和1D卷积神经网络.
- 进行培训/测试分析和混矩阵分析,包括数据集不平衡的过量抽样.
主要成果:
- 模型实现了高分类精度:96.94% (培训) 和82.14% (测试).
- 混矩阵分析显示了所有模型的预测偏差.
- 最常见的错误是错误地将男性学生归类为女性.
结论:
- 机器学习模型可以有效地根据复杂的思维感知数据来区分性别.
- 预测偏差需要仔细考虑和进一步的缓解策略.
- 这项研究支持在调查分析中使用机器学习来分析新的教育实践,以减少基于性别的社会差距.
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