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Knowledge point learning duration prediction method suitable for adaptive learning and application thereof

A technology of adaptive learning and prediction method, applied in the field of knowledge point learning duration prediction of adaptive learning, it can solve the problems of rare, reflected, and low degree of personalization in learning cost prediction, so as to ensure scientificity and rationality. , the effect of improving accuracy and sensitivity

Active Publication Date: 2019-07-26
SHANGHAI SQUIRREL CLASSROOM ARTIFICIAL INTELLIGENCE TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the prediction of learning cost is rare, such as the prediction of the core learning duration. Even if there is prediction information, it is mostly based on the content itself, not the individual situation of the students.
This method may be relatively simple to model according to the characteristics of the content, and the characteristics of people are not reflected in the model, so the degree of personalization of the prediction is not high

Method used

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  • Knowledge point learning duration prediction method suitable for adaptive learning and application thereof
  • Knowledge point learning duration prediction method suitable for adaptive learning and application thereof
  • Knowledge point learning duration prediction method suitable for adaptive learning and application thereof

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0059] 1. Overall introduction

[0060] This embodiment mainly includes the pre-test stage and the learning stage. In the learning stage, the learning pre-test stage is judged as a weak knowledge point. The final output of this program will be to predict the different time spent by students in the learning stage and learning different knowledge points. The overall idea is: use the method of linear regression, based on the data buried points and existing data of the current product process, take some dimensions highly related to learning time as independent variables (predictive variables), and the learning time of knowledge points as dependent variables (result variables ), from which the parameters in the predictive regression model are derived.

[0061] 2. Variable description

[0062] 1) Outcome variable

[0063] User knowledge point learning time (spent_time): This dimension represents the learning time for student A to complete knowledge point B.

[0064] 2) predictor ...

Embodiment 2

[0097] This embodiment corresponds to Embodiment 1, and provides an adaptive learning method based on learning duration prediction, including the following steps:

[0098] S1: Using the learning duration prediction method as described in Example 1 to obtain the predicted learning duration of each knowledge point of the user;

[0099] S2: Predict the learning time based on each knowledge point of the user, and combine the user's choice to obtain a personalized learning path;

[0100] S3: Push learning content based on the personalized learning path.

[0101] Among them, user selection is obtained based on time cost.

Embodiment 3

[0103] This embodiment corresponds to Embodiment 2, and provides an adaptive learning computer system based on learning duration prediction, including the following modules:

[0104] The learning duration prediction module is used to obtain the predicted learning duration of each knowledge point of the user by using the learning duration prediction method as claimed in claim 1;

[0105] The learning path acquisition module is used to predict the learning time based on each knowledge point of the user, combined with the user's choice, to obtain a personalized learning path;

[0106] A push module, configured to push learning content based on the personalized learning path.

[0107] Among them, user selection is obtained based on time cost.

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Abstract

The invention relates to a knowledge point learning duration prediction method suitable for adaptive learning and application thereof. The knowledge point learning duration prediction method comprisesthe following steps: collecting user learning data, wherein the user learning data comprises a pre-test capability value of a user knowledge point, an average use time of other learned knowledge points of a user, a user check analysis rate, a knowledge point mastery rate, an average use time of the knowledge points learned by other students, and a learning mode of the user; performing data preprocessing on the obtained data; constructing a prediction regression model, and obtaining parameters in the prediction regression model by adopting a linear regression method based on the preprocessed data; performing model diagnosis; and obtaining the user knowledge point prediction learning duration based on the prediction regression model using the obtained parameters. Compared with the prior art, the method has the advantages of more individualized learning process, high prediction accuracy, high scientific rationality and the like.

Description

technical field [0001] The invention relates to the technical field of learning devices, in particular to a method for predicting the learning time of knowledge points suitable for adaptive learning and its application. Background technique [0002] Nowadays, with the development of artificial intelligence technology, the rise of self-adaptive learning mode can be clearly seen in educational products. Such products give different feedback to students on different learning trajectories in the system, mainly including the evaluation of learning effect and personalized content recommendations. However, the prediction of learning cost is rare. For example, the prediction of the core learning duration, even if there is prediction information, is mostly based on the content itself, not the individual situation of the students. This method may be relatively simple to model according to the characteristics of the content, but the characteristics of people are not reflected in the m...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/18
CPCG06F17/18
Inventor 崔炜贾静
Owner SHANGHAI SQUIRREL CLASSROOM ARTIFICIAL INTELLIGENCE TECH CO LTD
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