MCM-based intelligent adaptive review resource push method

A technology of learning resources and resources, which is applied in the field of MCM-based smart adaptive review resource push, can solve the problems of not mastering the content of knowledge points, reviewing the symptoms but not the root cause, and low review efficiency, so as to solve the problem of reviewing the symptoms but not the root cause and improve the review efficiency Effect

Active Publication Date: 2021-02-05
SHANGHAI SQUIRREL CLASSROOM ARTIFICIAL INTELLIGENCE TECH CO LTD
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The existing error-prone questions or review questions push methods for knowledge points only judge students’ weak links in knowledge points from the test results (wrong / correct) of the test questions, and do not really consider the reasons for causing wrong questions about knowledge points other than the lack of mastery In addition to the content of knowledge points, there may be deeper problems such as model of thinking, learning ability (Capacity) and learning method (Methodology), as well as other non-intellectual factors such as students' psychological factors or habit of doing questions.
[0004] This results in low review efficiency, resulting in a temporary solution to the review rather than the root cause, and it is difficult for students to really improve their learning level.

Method used

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  • MCM-based intelligent adaptive review resource push method
  • MCM-based intelligent adaptive review resource push method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0040] like figure 1 As shown, a MCM-based intelligent adaptive review resource pushing method includes:

[0041] Step 1. Obtain the historical knowledge point error data of all students. Each student's historical knowledge point error data contains multiple knowledge point error data. Each of the knowledge point error data includes knowledge point error data, corresponding The wrong cause label and MCM label of the knowledge point wrong question;

[0042] It should be noted that the test questions and MCM tags in the content management system are pre-bound. Because the label and the MCM label constitute the wrong question data of the knowledge point. The error label is a label that reflects the cause of the error when the student makes a wrong test question, including user-marked (marked by the student himself or by the teacher) and machine-marked (marked based on preset rules or machine learning algorithms) , such as fault label and fault code example:

[0043]

[004...

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PUM

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Abstract

The invention discloses an intelligent adaptive review resource pushing method based on MCM, which includes obtaining the historical knowledge point wrong question data of all students; obtaining multiple wrong cause labels of the current student, and screening out the first wrong cause label belonging to the intelligence factor; For each first error label of the current student, the error importance degree value P(E) is calculated; according to the MCM label corresponding to each first error label, the MCM learning corresponding to the MCM label is extracted from the preset content management system. The resources are sorted in descending order according to the P(E) value of the error cause, and some or all of the MCM learning resources are extracted and pushed to the students according to the order. The knowledge point corresponding to the MCM label is wrong. The invention solves the problems that the review can cure the symptoms but not the root cause, and the students' learning level is difficult to really improve.

Description

technical field [0001] The invention belongs to the technical field of online education, and in particular relates to an MCM-based intelligent adaptive review resource pushing method. Background technique [0002] MCM is a strategy that separates students' model of thinking (Model of thinking), learning ability (Capacity) and learning method (Methodology) by splitting each learning thinking. [0003] The existing error-prone questions or review questions push methods for knowledge points only judge students’ weak links in knowledge points from the test results (wrong / correct) of the test questions, and do not really consider the reasons for causing wrong questions about knowledge points other than the lack of mastery In addition to the content of knowledge points, there may be deeper problems such as model of thinking, learning ability (Capacity) and learning method (Methodology), as well as other non-intellectual factors such as students' psychological factors or habit of d...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/435G06F16/438G06F16/9536G06Q50/20
CPCG06F16/435G06F16/438G06F16/9536G06Q50/205Y02D10/00G09B7/08G09B19/00
Inventor 栗浩洋许昭慧
Owner SHANGHAI SQUIRREL CLASSROOM ARTIFICIAL INTELLIGENCE TECH CO LTD
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