计算机科学 ›› 2023, Vol. 50 ›› Issue (11): 241-247.doi: 10.11896/jsjkx.221100169
苑冬雪, 孙权森, 傅鹏
YUAN Dongxue, SUN Quansen, FU Peng
摘要： 认知诊断是智能教育系统中的一个基础问题,旨在评估学生对不同知识概念的掌握程度。虽然目前基于深度学习的认知诊断方法相比传统方法有了较大改进,但是其无法充分利用概念之间的潜在相关性。为此,提出一种基于注意力机制的概念增强认知诊断模型(ACECD),通过建模相关概念之间的关系来获得更准确的认知诊断结果。具体来说,首先将学生、练习和概念投影到因子向量来执行复杂交互;然后把概念因子输入自注意力网络中捕获概念之间存在的隐式相关性关系,并用捕获到的隐式关系增强概念因子向量;最后把增强过的概念因子与学生因子和练习因子进行交互,将交互结果输入诊断模块得到最终诊断结果。此外,利用练习因子与概念因子之间的交互修正人为标定Q矩阵的误差。在两个真实世界数据集上与其他方法进行比较,实验结果表明基于注意力机制的概念增强认知诊断模型有效地改善了诊断结果。
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