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Knowledge tracking method

A technology of knowledge and knowledge status, which is applied in the field of knowledge tracking, can solve the problems of knowledge tracking error accumulation and the need for a lot of time, and achieve the effect of reducing cumulative deviation and improving robustness and

Active Publication Date: 2019-11-08
UNIV OF SCI & TECH OF CHINA
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Problems solved by technology

[0010] The above methods, especially those based on neural networks, have potential error accumulation problems in the process of knowledge tracking
In addition, when the above method has a long learning sequence for students, due to the characteristics of the sequence model, the process of knowledge tracking takes a lot of time

Method used

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Embodiment Construction

[0024] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0025] An embodiment of the present invention provides a knowledge tracking method, Figure 1 ~ Figure 2 The corresponding flow and network structure are shown.

[0026] See figure 1 , the knowledge tracking method mainly includes the following steps:

[0027] Step 11. Obtain the student's learning sequence and corresponding exercise sequence.

[0028] All relevant sequences to be obtained in this step can be obtained from public datasets on the...

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Abstract

The invention discloses a knowledge tracking method. The method comprises the following steps: converting a learning sequence and an exercise sequence into dense vector representation by utilizing a feedforward neural network, obtaining a correlation relationship and a relative position relationship of exercises by utilizing an attention mechanism and absolute positions of the exercises respectively, and initially summarizing knowledge states of students at each learning node of the learning sequence according to the two relationships; then, carrying out deep fusion on the preliminarily summarized historical knowledge states of the students by utilizing a convolutional neural network, and tracking continuously changing knowledge states of the students in a learning process; compared with the prior art, the problem of accumulated deviation in the knowledge tracking process can be reduced, the accuracy of knowledge tracking can be improved, and the efficiency of knowledge tracking can beeffectively improved; meanwhile, the method also greatly improves the robustness of a student knowledge state tracking process and the accuracy of a tracking result.

Description

technical field [0001] The invention relates to the technical field of deep learning and educational data mining, and in particular to a knowledge tracking method. Background technique [0002] With the emergence of more and more smart education systems and massive online open courses on the Internet, students' individualized education has received more attention and practice. Personalized education can customize the most suitable learning plan for different students, greatly improving their learning efficiency. The most critical part of personalized education is to assess students' current state of knowledge based on their historical learning sequences, also known as knowledge tracking. The knowledge status reflects the degree of students' mastery of different knowledge content. After correctly evaluating the knowledge status of students at different moments, the smart education system can carry out targeted training for students who have weaker knowledge content and help ...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06Q50/20
CPCG06Q50/20G06N3/045G06N3/044G06F18/214G06F18/253
Inventor 陈恩红刘淇沈双宏赵伟豪黄振亚
Owner UNIV OF SCI & TECH OF CHINA
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