可解释性
计算机科学
人工智能
机器学习
认知
领域(数学)
图形
维数(图论)
过程(计算)
任务(项目管理)
决策树
理论计算机科学
心理学
数学
操作系统
经济
神经科学
管理
纯数学
作者
Xinping Wang,Caidie Huang,Jinfang Cai,Liangyu Chen
标识
DOI:10.1145/3459637.3482311
摘要
Cognitive diagnosis is a crucial task in the field of educational measurement and psychology, which is aimed to mine and analyze the level of knowledge for a student in his or her learning process periodically. While a number of approaches and tools have been developed to diagnose the learning states of students, they do not fully learn the relationship between students, exercises and knowledge concepts in the learning system, or do not consider the traits that it is easier to complete diagnosis when focusing on a small part of knowledge concepts rather than all knowledge concepts. To address these limitations, we develop CDGK, a model based artificial neural network to deal with cognitive diagnosis. Our method not only captures non-linear interactions between exercise features, student scores, and their mastery on each knowledge concept, but also performs an aggregation of the knowledge concepts via converting them into graph structure, and only considering the leaf node in the knowledge concept tree, which can reduce the dimension of the model without accuracy loss. In our evaluation on two real-world datasets, CDGK outperforms the state-of-the-art related approaches in terms of accuracy, reasonableness and interpretability.
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