摘要
针对现有的基于认知诊断的习题推荐建模角度单一以及习题推荐结果不够合理的问题,提出结合认知诊断和深度因子分解机的个性化习题推荐方法。首先,设计一种知识点关系计算方法构建课程知识树,并提出增强Q矩阵准确表示习题所含知识点的关系的概念;其次,提出基于知识点关系和习题表征的认知诊断(NeuralCD-KD)模型,该模型计算增强Q矩阵,利用特征二阶交叉和注意力机制融合习题难度的内外因素,并模拟学生的认知状态。在私有数据集和公开数据集上验证了提出的认知诊断模型的有效性,并且该方法能对学生的认知状态做出合理解释。为了个性化习题推荐,结合诊断模型和深度双线性因子分解机,提出结合认知诊断和深度因子分解机(NKD-DBFM)方法,在私有数据集上验证了所提习题推荐方法的有效性,在曲线下面积(AUC)上相较于最优基线模型神经认知诊断模型(NeuralCDM)提升了3.7个百分点。
A personalized exercise recommendation method that combines cognitive diagnosis and deep factorization machine was proposed to address the problems of single modeling angle and unreasonable exercise recommendation results of the existing exercise recommendation based on cognitive diagnosis.Firstly,a new method for calculating the relationship between knowledge points was designed to construct a course knowledge tree,and the concept of enhanced Q matrix to accurately represent the relationship between knowledge points contained in exercises was proposed.Secondly,the Neural Cognitive Diagnosis with Knowledge-based Discernment(NeuralCD-KD)model was proposed to calculate the enhanced Q matrix.In the model,the feature second-order cross and attention mechanism were used to fuse internal and external factors of exercise difficulty,and the students’cognitive states were simulated.The effectiveness of the proposed cognitive diagnosis model was verified on private and public datasets,and this method was able to give reasonable explanations for students’cognitive states.To personalize exercise recommendation,a Neural Knowledge-based Cognitive Diagnosis with Deep Bilinear Factorization Machine(NKD-DBFM)method was proposed by combining the diagnostic model with deep bilinear factorization machine,and the effectiveness of this proposed exercise recommendation method was verified on the private dataset.Compared with the optimal baseline model Neural Cognitive Diagnosis Model(NeuralCDM),the proposed method improves the Area Under Curve(AUC)by 3.7 percentage points.
作者
韩祎珂
徐彬
张硕
HAN Yike;XU Bin;ZHANG Shuo(School of Computer Science and Technology,Northeastern University,Shenyang Liaoning 110169,China;College of Information Science and Technology,Northeastern University,Shenyang Liaoning 110169,China)
出处
《计算机应用》
CSCD
北大核心
2024年第8期2351-2356,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(72271048)
辽宁省自然科学基金资助项目(2022-MS-119)
中国高校产学研创新基金-云中大学项目(2022MU017)。
关键词
认知诊断
习题推荐
知识点关系
Q矩阵
习题表征
cognitive diagnosis
exercise recommendation
knowledge point relationship
Q matrix
exercise representation
