胡国平,张丹,苏喻,刘青文,李佳,王瑞. 试题知识点预测:一种教研知识强化的卷积神经网络模型[J]. 中文信息学报, 2018, 32(5): 137-146.
HU Guoping, ZHANG Dan, SU Yu, LI Jia, LIU Qingwen, WANG Rui. Predicting Knowledge Points of Questions: an Expertise-Enriched CNN Model. , 2018, 32(5): 137-146.
Predicting Knowledge Points of Questions: an Expertise-Enriched CNN Model
HU Guoping1, ZHANG Dan1,3, SU Yu1,2, LI Jia1, LIU Qingwen1, WANG Rui1
1.IFLYTEK Co., Ltd., Hefei, Anhui 230088, China; 2.School of Computer Science and Technology, Anhui University, Hefei, Anhui 230039, China; 3.School of Computer Science and Technology, University of Science and Technology of China, Hefei, Anhui 230027, China
Abstract：In online learning systems, to offer students better learning services, a fundamental task is predicting questions’ knowledge points, i.e., predicting the knowledge concepts or skills of a question. Existing methods for this task usually rely on human labeling or traditional machine learning methods, They are defected in either labor intensive or focusing only on shallow features without capturing the deep semantic relations between questions and knowledge points. In this paper, we propose an Expertise-enriched Convolutional Neural Network(ECNN)to predict questions’ knowledge points. Specifically, we first define and extract question features under the guidance of educational experience. Then, we leverage a convolutional neural network to exploit question representations from deep sematic perspective. After that, considering the relations between questions and expertise priors, we develop an attention based method for calculating the importance of expertise for questions. At last, we design an objective function for model learning that constrains both knowledge points and semantics. Extensive experiments on a large-scale dataset demonstrate the effectiveness of the proposed model, showing a good application value.
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2018, Vol. 32 
