MCM-based intelligent adaptive review resource pushing method

Abstract

The invention discloses an MCM-based intelligent adaptive review resource pushing method. The method comprises the steps of obtaining historical knowledge point wrong question data of all students; obtaining a plurality of error cause labels of the current student, and screening out a first error cause label belonging to the intelligence factor; calculating an error cause importance degree value P(E) of each first error cause label of the current student; according to the MCM label corresponding to each first error factor label, obtaining a second error factor label, extracting MCM learning resources corresponding to the MCM label from a preset content management system; and performing power reduction sorting according to the error cause importance degree value P (E), extracting part or all of the MCM learning resources according to the sorting, pushing the MCM learning resources to the students, and pushing knowledge point errors corresponding to the MCM tags to the students after the MCM learning resources corresponding to each MCM tag are learned by the students. According to the invention, the problems that the symptoms are not treated during reviewing and the learning level of students is difficult to truly improve are solved.

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...

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.

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