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Knowledge and capability binary tracking method based on continuous matrix decomposition

A matrix decomposition and knowledge technology, applied in the field of knowledge tracking models, can solve problems such as unreasonable models and low accuracy of model output results, and achieve high interpretability, model accuracy, and strong interpretability

Pending Publication Date: 2021-03-19
HUAZHONG NORMAL UNIV
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Problems solved by technology

Therefore, the model constructed in the prior art is not reasonable enough, and the accuracy of the output result of the model is not high

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  • Knowledge and capability binary tracking method based on continuous matrix decomposition
  • Knowledge and capability binary tracking method based on continuous matrix decomposition
  • Knowledge and capability binary tracking method based on continuous matrix decomposition

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[0039] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0040] In the following introduction, the terms "first" and "second" are only used for the purpose of description, and should not be understood as indicating or implying relative importance. The following description provides multiple embodiments of the present invention, and different embodiments can be replaced or combined in combination, so the present invention can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment contains features A, B, C, and another embodiment contains features B, D, then the invention should also be considered to include all other possible combinations containing one or more of A, B, C, D Although this embodiment may not be clearly written in the following content.

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Abstract

The invention discloses a knowledge and capability binary tracking method based on continuous matrix decomposition. The method comprises the steps of constructing a training set based on historical learning behaviors, and determining a first likelihood function and a first log-likelihood function of the training set; determining knowledge model parameters according to the first log-likelihood function, and constructing a knowledge model based on the knowledge model parameters; determining a second likelihood function of a to-be-constructed joint model based on the output data, and determininga target function of a capability model according to the second likelihood function; determining capability model parameters based on the target function, and constructing a capability model based onthe capability model parameters; and the knowledge model and the capability model are combined to obtain the joint model, and the joint model is an additive model or a multiplicative model. Accordingto the method, the implicit capability model is constructed on the basis of the continuous matrix decomposition model, the two models are fused and trained through the lifting algorithm, and comparedwith a traditional model, the method has higher interpretability and model accuracy.

Description

technical field [0001] This application relates to the technical field of knowledge tracking models, in particular, to a binary tracking method of knowledge and ability based on continuous matrix decomposition. Background technique [0002] In the traditional knowledge tracking model, the model tries to attribute the learner's right or wrong feedback to the change of the learner's knowledge mastery. However, in actual situations, knowledge mastery is often not the only factor affecting learners' feedback on questions. Taking mathematics as an example, in order to solve a mathematical problem, in addition to mastering relevant knowledge, you also need to have various problem-solving abilities, such as abstract thinking ability and spatial imagination ability. Therefore, the model constructed in the prior art is not reasonable enough, and the accuracy of the output result of the model is not high. Contents of the invention [0003] In order to solve the above problems, the...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/15G06F17/16G06Q50/20
CPCG06F17/15G06F17/16G06Q50/20
Inventor 刘三女牙沈筱譞孙建文周东波李卿
Owner HUAZHONG NORMAL UNIV
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