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通过可解释的教育数据挖掘方法提高高等学生的编程技能.

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科学领域:

  • 教育数据挖掘 (EDM) 技术
  • 计算机科学教育计算机科学教育
  • 机器学习 机器学习

背景情况:

  • 教育数据挖掘 (EDM) 提供了分析学生数据以预测和改善学业绩的潜力.
  • 高等教育在客观评估和提高学生的编程技能方面面临着挑战.
  • 可解释的人工智能 (XAI) 对于理解教育中预测模型的决策过程至关重要.

研究的目的:

  • 开发和评估一个先进的EDM系统,用于分类和提高高等学生的编程技能.
  • 整合有效的特征工程,分类技术和XAI以实现模型可解释性.
  • 确定表现不佳学生的编程技能差距,并提供有针对性的建议.

主要方法:

  • 功能工程和编程技能指标的选择.
  • 对六种机器学习算法进行分类任务的评估.
  • 开发和验证一种新型组合方法,堆叠-SRDA.
  • 应用可解释的人工智能 (XAI) 技术来实现模型透明度.

主要成果:

  • 拟议的堆叠-SRDA组合方法在准确性,精度,回忆,f1分数,ROC曲线和麦克纳马测试等方面明显优于其他评估的算法.
  • 严格的实验,包括一个废弃研究,证实了开发方法的有效性.
  • XAI工具为分类模型的可解释性提供了宝贵的见解.
  • 该系统成功地发现了技能差距,并为较弱的学生提供了量身定制的建议.

结论:

  • 先进的EDM系统,特别是Stacking-SRDA组合,提供了一个强大的解决方案,用于评估和增强高等学生的编程技能.
  • 集成XAI提高了对系统预测的信任和理解.
  • 技能差距分析和推系统为教育工作者提供了一种实际的工具,以支持学生的发展.