教育中的可解释人工智能:将教育领域的知识集成到深度学习模型中,以改善学生绩效预测
Ming Qiang1, Ziyang Liu2, Ru Zhang3
1Centre of International Education, Fuzhou Polytechnic, Fuzhou, 350108, Fujian, China.
Scientific reports
|February 17, 2026
概括
这项研究通过整合教育领域知识,提高准确性和可信度,改进了人工神经网络 (ANN) 模型用于学生绩效预测. 开发的学生绩效预测解释 (SPPE) 算法为教育应用提供了宝贵的见解.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 机器学习用于学习分析.
背景情况:
- 像人工神经网络 (ANN) 这样的深度学习模型对于预测学生表现很普遍.
- 这是一个很棒的节目,这是一个很棒的节目.
- 一个黑盒子.
- 由于ANN的性质往往会产生不可靠的见解,与教育领域的知识不一致.
- 这种缺乏可解释性阻碍了模型可靠性和性能优化.
研究的目的:
- 为学生绩效预测开发一个可解释的ANN.
- 解决ANN学到的关系和既定的教育领域知识之间的不一致性.
- 提高学生绩效预测模型的准确性和可靠性.
主要方法:
- 使用Shapley增量解释 (SHAP) 来分析受过葡萄牙高中学生数学绩效数据训练的ANN.
- 开发了学生绩效预测解释 (SPPE) 算法,通过结合教育领域知识来优化ANN.
- 进行全球和本地可解释性分析以追踪特征贡献变化.
主要成果:
- 确定了影响学生数学表现的关键特征.
- 原来的ANN模型的学习相关性与教育领域的知识相矛盾.
- 与原始模型相比,SPPE优化的ANN在预测准确度上得到了26.9%的改进.
- 优化的ANN超越了传统的机器学习算法.
- 在不同的ANN架构中,SPPE策略显示出强度.
结论:
- 整合教育领域的知识显著提高了ANN模型的准确性和可解释性,用于学生绩效预测.
- 拟议的SPPE算法为开发可靠的教育人工智能提供了一种实用和可通用的方法.
- 结果为改善教育应用和开发可解释的神经网络框架提供了可操作的见解.
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