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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

Related citation:Lasheng Yu,Xiaopeng Zheng.Research on Deep Knowledge Tracking Incorporating Rich Features and Forgetting Behaviors[J].Journal of Harbin Institute Of Technology(New Series),2022,29(4):1-6.DOI:10.11916/j.issn.1005-9113.2020081.
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Research on Deep Knowledge Tracking Incorporating Rich Features and Forgetting Behaviors
Author NameAffiliation
Lasheng Yu School of Computer Science and Engineering, Central South University, Changsha 410083, China 
Xiaopeng Zheng School of Computer Science and Engineering, Central South University, Changsha 410083, China 
The individualization of education and teaching through the computer-aided education system provides students with personalized learning, so that each student can obtain the knowledge they need. At this stage, there are a lot of intelligent tutoring systems. In these systems, students learning actions are tracked in real-time, and there are a lot of available data. From these data, personalized education that suits each student can be mined. To improve the quality of education, some models for predicting students next practice have been produced, such as Bayesian Knowledge Tracing (BKT), Performance Factor Analysis (PFA), and Deep Knowledge Tracing (DKT) with the development of deep learning. However, the model only considers the knowledge component and correctness of the problem, ignoring the breadth of other characteristics of the information collected by the intelligent tutoring system, the lag time of the previous interaction, the number of past attempts to a problem, and situations that students have forgotten the knowledge. Although some studies consider forgetting and rich information when modeling student knowledge, they often ignore student learning sequences. The main contribution of this paper is in two aspects. One is to transform the input into a position feature vector by introducing an auto-encoding network layer and to carry out multiple sets of bad political combinations. The other is to consider repeated time intervals, sequence time intervals, and the number of attempts to simulate forgetting behavior. This paper proposes an adaptive algorithm for the original DKT model. By using the stacked auto-encoder network, the input dimension is reduced to half of the original and the original features are retained and consider the forgetting memory behavior according to the time sequence of students learning. The model proposed in this paper has been experimented on two public data sets to improve the original accuracy.
Key words:  LSTM  knowledge of tracking  DKT  stacked autoencoder  forgetting behavior  feature information
Clc Number:TP301.6
Descriptions in Chinese:



(中南大学 计算机学院, 长沙 410083)