摘要
研究将PNN和曼哈顿距离、贝叶斯定理相结合,提出了一种相对简洁的可融入额外信息的认知诊断法MB-PNN,通过模拟和实证研究考察了MB-PNN的有效性和适宜性,得到以下结论:(1)M-PNN的判准率高于PNN,表明将PNN中的ED修改为MD是适宜的;(2)MB-PNN的判准率较M-PNN和PNN高,表明基于多种信息的判别较基于单一信息的判别更为精准;(3)MB-PNN保留了PNN原有的非参数优势,基本不受知识状态分布和样本容量影响;(4)MB-PNN最能区分不同类型的学生,在认知诊断评估实践中更为适宜。
In the era of big data, cognitive diagnostic assessment(CDA) can no longer be limited to the mining of single test information, but should acquire a large amount of information of students through various methods, which will aid in distinguishing the knowledge state of students synthetically. Existing preliminary research on is based on parametric models incorporating process information, which require additional parameters to be included in the original model. As a result, the model becomes more complex and less universal. Moreover, to deal with different types of data,such a model needs to be refactored. Therefore, how to simplify the method to combine process information is a topic worthy of extensive research.Among machine-learning-based diagnostic methods, the probabilistic neural network(PNN) diagnostic method combines the advantages of neural networks and non-parametric methods. The existing algorithm of the PNN uses Euclidean distance discrimination(EDD). Nevertheless, Kang and Yang(2019) found that the Manhattan distance discrimination(MDD) method has a higher accuracy rate than that of EDD. Based on this, this study has the following objectives:(1) The Euclidean distance in the PNN algorithm is modified to the Manhattan distance to improve the accuracy of the PNN diagnostic method.(2) In the second layer of the PNN algorithm, Bayes’ theorem is added such that additional information can be fused in the model and the state of students’ knowledge can be comprehensively identified. Accordingly, the study proposes a relatively concise cognitive diagnostic method(termed MB-PNN) that can incorporate additional information. Further, the effectiveness and suitability of the MB-PNN are examined through simulation and empirical research. The results demonstrate that:(1) Under the same conditions, the accuracy rate of the M-PNN is higher than that of the PNN, indicating that replacing the Euclidean Distance in the PNN with the Manhattan Distance is effective.(2) Under the same conditions, the accuracy rate of t
作者
康春花
朱仕浩
宫皓明
曾平飞
Kang Chunhua;Zhu Shihao;Gong Haoming;Zeng Pingfei(Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province,Zhejiang Normal University,Jinhua,321004)
出处
《心理科学》
CSCD
北大核心
2023年第1期212-220,共9页
Journal of Psychological Science
基金
全国教育科学规划教育部重点课题(DCA160262)的资助。
关键词
额外信息
贝叶斯定理
机器学习诊断法
判准率
additional information
Bayes’theorem
machine learning diagnosis method
accuracy rate
