Knowledge tracking system and method based on hierarchical memory network

Abstract

The invention discloses a knowledge tracking system based on a hierarchical memory network. The system comprises a controller assembly, a hierarchical memory assembly, a read head and write head assembly, the read head and write head assembly is arranged between the controller assembly and the hierarchical memory assembly, and the read head and write head assembly is used for writing input information processed by the controller assembly into the hierarchical memory assembly to be stored and updated; and the hierarchical memory matrix assembly comprises a working storage unit, a long-term storage unit, a segmentation module and an attenuation module, wherein the segmentation module is used for dividing input information into working memory information and long-term memory information and storing the working memory information and the long-term memory information into the working storage unit and the long-term storage unit respectively, and the attenuation module is used for attenuatingthe long-term memory information stored in the long-term storage unit and then storing the attenuated long-term memory information into the long-term storage unit. According to the knowledge trackingsystem based on the hierarchical memory network, the modes of long-term memory and short-term memory of human beings are simulated, input knowledge information is classified, attenuated and stored, and prediction is more accurate.

Description

technical field [0001] The invention belongs to the technical field of knowledge tracking, and more specifically relates to a knowledge tracking system and method based on a hierarchical memory network. Background technique [0002] The goal of knowledge tracking is to model students' knowledge status by analyzing their historical learning situation, which represents the degree of students' mastery of skills in the learning process. The difficulty of knowledge tracking is that the learning process is affected by many cognitive factors, especially the complexity of human memory. Starting from the characteristics of simulating human memory, we can divide the existing knowledge tracking models into three categories. [0003] The first type is the static knowledge tracking model, which is characterized by using the method of logistic regression to model the connection between students and skills, so as to estimate the degree of students' mastery of skills in the learning proces...

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Problems solved by technology

[0005] However, the first two types of models do not directly simulate human memory, the human memory model (Atkinson-Shiffrin memory model, ASMM), ASMM divides human memory into three parts, namely sensory memory (Sensory memory), working memory (Working memory) and Long-term memory (Long-te

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