Patent AU-2021104571-A4 (AU)
Title: Intelligent adaptive recommendation method based on MCM model
Inventor: LI HAOYANG
IPC: G09B7/04
Filed: 2021-07-26
Granted: 2021-10-14
Abstract: Disclosed is an intelligent adaptive recommendation method based on an MCM model. The 
method includes acquiring historical data of errors on knowledge points of all students, 
acquiring a plurality of error-cause labels of a current student, calculating an error-cause priority 
value P(E) for each error-cause label of the current student, and extracting, according to at 
least one of MCM labels corresponding to the each error-cause label, MCM learning resources 
corresponding to the at least one of MCM labels from a preset content management system, 
sorting the plurality of error-cause labels according to a descending order of the error-cause 
priority value P(E), extracting part or all of MCM learning resources from the MCM learning 
resources corresponding to the at least one error-cause label according to a sorting result and 
pushing the part or all of the MCM learning resources to the current student, and in a case 
where the current student finishes learning MCM learning resources corresponding to each 
MCM label, pushing errors on knowledge points corresponding to each MCM label to the 
current student.
1/1 
Historical data of errors on knowledge points of all students is 
acquired, where historical data of errors on knowledge points of a 
student includes data of errors on a plurality of knowledge points, 
and data of errors on a knowledge point includes errors on a S110 knowledge point, an error-cause label corresponding to the errors 
on the knowledge point and an MCM label corresponding to the 
errors on the knowledge point 
A plurality of error-cause labels of a current student are 
acquired, and a first error-cause label is screened out from the S120 plurality of error-cause labels, where the first error-cause is an 
intelligence factor 
An error-cause priority value is calculated for each first error- S130 
cause label of the current student 
MCM learning resources corresponding to the MCM labels are extracted 
from a preset content management system according to MCM labels 
corresponding to the each first error-cause label, at least one first error
cause label is sorted according to a descending order of the error-cause 
priority value , part or all of MCM learning resources are extracted S140 
according to a sorting result and the part or all of the MCM learning 
resources are pushed to the current student, and in a case where the current 
student finishes learning MCM learning resources corresponding to each 
MCM label, errors on knowledge points corresponding to the MCM labels 
are pushed to the current student 
A plurality of error-cause labels of the current student are acquired, a 
second error-cause label is screened out from the plurality of error-cause 
labels, the second error-cause is a non-intelligence factor, and intervention 
resources for error-cause of non-intelligence factor are pushed to the S150 
current student according to a content of the second error-cause label 
A learning scope of the student is acquired, and MCM test questions in the 
part or all of the MCM learning resources are composed into a test paper S160 
according to the learning scope 
A learning scope of the student is acquired, and part or all of errors on 
knowledge points pushed are composed into a test paper according to the S170 
learning scope 
FIG. 1