CN115511677A - Intelligent education resource platform allocation method and system - Google Patents
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Abstract
The invention belongs to the technical field of a method and a system applied to educational resource platform allocation, and particularly relates to a method and a system for intelligent educational resource platform allocation; according to the invention, the required content is selected in the user selection module by the user, the education resource allocation platform learns according to the education resource allocated by the selected content, the user interaction module automatically jumps out the questions related to the course during the interval of the user learning and after the user finishes learning, and the wrong questions of the user answering the courses are collected into the wrong question set database; the user can achieve a fitting learning effect according to requirements, and an elastic pushing mechanism is performed for the user; the memory enhancing module rearranges corresponding courses and questions related to the courses for the user to learn according to the progress of the user learning the courses, the correct rate of answering the courses and the time intervals of the learning courses; the method not only saves a great amount of time for the user to learn on the education resource platform, but also achieves the desired learning effect, and scientifically reviews the courses learned by the user.
Description
Technical Field
The invention belongs to the technical field of methods and systems applied to educational resource platform allocation, and particularly relates to a method and a system for intelligent educational resource platform allocation.
Background
The educational resource platform is frequently appeared in daily life of people, and plays an important role in learning courses for people; however, the content pushed by the education resource platform is not pushed in combination with the actual learning condition of the user, the pushed content is too stiff, and the content is not adjusted according to the feedback condition; if the pushing mechanism is not adjusted, the learning effect of the user is greatly reduced.
On one hand, the existing education resource platform cannot achieve a fitting learning effect by combining with the requirements of users, and an elastic pushing mechanism for the users is not provided; not only does the user waste a lot of time in learning of the education resource platform, but also does not achieve the desired effect; on the other hand, the education resource platform does not scientifically review the courses learned by the user, does not consider the law of memory curves after the user learns, and does not effectively prompt review contents after the user finishes learning.
Disclosure of Invention
The invention is based on the technical problems, and provides a method and a system for intelligently allocating an education resource platform aiming at the method and the system for allocating the education resource platform; the method not only can carry out an elastic pushing mechanism according to the content selection requirements of the user, but also can scientifically review the courses learned by the users of the education resource platform, so that the learned contents are enhanced.
The invention is realized by the following steps:
the invention provides a method and a system for allocating an intelligent education resource platform, wherein the method applies a user selection module, the education resource allocation platform, a memory enhancement module, a user interaction module and an error question set database; characterized in that the method comprises the following steps:
step 1, a user selects required contents in a user selection module and clicks and submits the contents;
step 2: the content submitted by the user selection module is uploaded to an education resource allocation platform, and the platform allocates education resources according to the selected content of the user;
step 3, the user learns according to the educational resources allocated by the educational resource allocation platform;
step 4, the user interaction module automatically jumps out the questions related to the courses after the user learns at intervals and finishes learning, and the user has to answer and count the scores;
step 5, collecting wrong questions related to the course answered by the user into a wrong question set database;
and 6, the memory enhancing module rearranges corresponding courses and questions related to the courses for the user to learn according to the progress of the user learning the courses, the correct rate of answering the courses and the time intervals of the learning courses.
According to an implementation manner of the aspect of the present invention, the specific operation method for the user to select the required content in the user selection module in step 1 includes:
the user selects the required content in the user selection module, and the content comprises the following contents: the subjects selected by the user, the percentage of learning degree reached, the total learning duration, the learning mode and the like; marking the content needed by the user selection module
Wherein β =0,1,2, · n; n is a positive integer and represents the maximum value of the value of beta in the content selected by the user selection module; the learning mode comprises the following steps: new course learning and old course review.
According to an aspect of the present invention, in an implementation manner, the method for the educational resource allocation platform to allocate the educational resources according to the content selected by the user in step 2 includes:
the education resource allocation platform divides courses according to subjects selected by the user, the percentage of learning degree and the total learning duration; taking the total learning time length as a determined time length, and filling courses in the time length; the course content comprises courses selected by the user to reach the learning degree percentage within the total subject time selected by the user;
push course content = user selects courses within the total subject duration up to a learning degree percentage and a selection;
performing algorithm selection on the pushed course content with the determined learning percentage, and if a new user selects courses, selecting the courses to be learned from front to back of the learning courses to play the courses to be learned in order to combine the actual learning effect; if the user finishes learning, the user can randomly select a review course of learning to learn, in order to enable the selected course to be more representative, the user can calculate and judge according to the selected course, and then pushes a more humanized course;
the educational resource learning course subject has structured information, and the resource learning library has dimension data such as title, exercise subject, unit, extended course, score and the like for each course; labeling each course tag value as B g G =1,2, ·, n; n is a positive integer and represents the maximum value of the value of g in the course label;
in this kind of data, a field (also a feature) is used as a dimension, and then vectorization represents that the value of each dimension is not necessarily a numerical value, but the form is also vectorized, namely the requested vector space model; at this time, the similarity between the two subject matters is calculated in the following manner;
suppose the vector representations of the subject matter of course 1 are P k K =1,2, ·, n; n is a positive integer and represents the maximum value of the vector K of the subject matter of the course 1; the vector notation of course 2's subject matter is q k K =1,2, ·, n; n is a positive integer representing the maximum value of the vector K of the subject matter of course 2;
according to the calculation formula:
course 1: v 1 ＝(P 1 ，P 2 ,P 3 ,...P k )
Course 2: v 2 ＝(q 1 ，q 2 ，q 3 ,...q k )
sim(P k ，q k ) Representing two components P of a vector k ，q k Similarity between them; w is a group of k The representation is the weight value of the kth subject matter, and different weight values can be adopted for different courses; the pushed course content learning time is determined to be within the range of the total learning time selected by the user;
sim(P k ，q k ) The closer the value approaches 1, the closer the directions representing the two vectors are; sim (P) k ，q k ) The more the value approaches-1, the more opposite the two vector directions are represented; close to 0, meaning that the two vectors are nearly orthogonal;
with the result sim (P) k ，q k ) Value 0 bound, sim (P) k ，q k ) The value range is (0,1)]The typical course selection has high acquaintance, which is mainly used for strengthening the learning effect and repeatedly training courses with high acquaintance; sim (P) k ，q k ) The value range of [ -1,0) represents that the selected course is low in acquaintance, mainly used for learning new courses, and new course contents are learned on the basis of reviewing a small amount of old knowledge.
According to an aspect of the present invention, in an implementation manner, the method for operating the user interaction module in step 4 includes:
the user interaction module is used for automatically jumping out the questions related to the courses according to the courses watched by the user when the user watches videos for learning, and the user needs to answer and count scores; the questions related to the courses appear at intervals when the user learns and after the learning is finished; when a user learns the course video, the user can set questions within 3 times that the video length is 1/3 and 2/3 and the video is completely played; and the user can carry out the learning course video after the user has finished answering; the scores of the user answers are recorded and used as a basis for judging the familiarity degree of the user to the courses, and wrong questions are collected into a wrong question set database.
According to an implementation manner of the aspect of the present invention, the method for specifically operating the error problem set database in step 5 includes:
questions with errors related to the course answered by the user can be automatically stored in an error question set database, and the error question set database can be continuously changed and updated along with the accuracy rate of the user answer;
the wrong question set database can grade the wrong questions into 3 types: strange subject types, slightly recognized subject types and skilled subject types; redistributing the question types according to the ratio of the user response times to the occurrence times;
in order to increase the randomness of the questions output from the wrong question set database, the proportion of the output questions to strange question types, slightly known question types and skilled question types is fixed to be 50%, 30% and 20% besides the input answer pair percentage;
the wrong question set database is composed of strange question types (1-answer pair percentage) 50% + slightly recognized question types (1-answer pair percentage) 30% + skilled question types (1-answer pair percentage) 20%; the number of times of answering questions and the number of times of answering pairs are equal for 3 times continuously, and the wrong question set database is automatically removed from the questions.
According to one implementation manner of the aspect of the present invention, the specific operation method of the memory enhancing module in step 6 includes:
the memory enhancing module rearranges corresponding courses and questions related to the courses for the user to learn according to the progress of the user learning the courses, the correct rate of answering the courses and the time intervals of the learning courses;
the brain needs to be repeated; the shorter the time of memory interval is during each review, the better the memory effect is, because the impression can be deepened when the user looks at the same thing for a plurality of times, but the user often forgets to look at the thing only once; the memory rule is that when learning a new knowledge and rapidly mastering it, except for frequent application, a certain frequency is needed to review the learned course for keeping permanent memory in the brain;
the memory enhancing module pushes courses and course titles to form 20% of an error database, 30% of the progress of the combined learned course and 50% of the content of the newly learned course; the study course time interval is in units of days, the corresponding course is rearranged for 1 time within 3 days of continuous study, the corresponding course is rearranged for 2 times within 5 days of continuous study, and 7 days of continuous study is taken as a study period.
A cloud system is characterized in that education resources allocated by an allocation platform are learned according to contents required by selection in a user selection module, and users answer questions related to courses and collect wrong questions in a wrong question set database; and allocating the education resource platform through cloud computing and analysis.
Based on any one of the aspects, the invention has the beneficial effects that:
1. according to the invention, the required content is selected in the user selection module by the user, the education resource allocation platform learns according to the education resource allocated by the selected content, the user interaction module automatically jumps out the questions related to the course during the interval of the user learning and after the user finishes learning, and the wrong questions of the user answering the courses are collected into the wrong question set database; the user can achieve a fitted learning effect according to the requirements, and an elastic pushing mechanism is carried out aiming at the user; the method not only saves a great amount of time for the user to learn on the education resource platform, but also achieves the desired learning effect.
2. After the user finishes learning the course, the memory enhancing module rearranges the corresponding course and the questions related to the course according to the progress of the user learning the course, the correct rate of answering the course and the time interval of the learning course for the user to learn; the education resource platform scientifically reviews the courses learned by the user, and effectively prompts review contents after the user finishes learning by considering the law of the memory curve after the user learns.
Drawings
The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a flow chart of the method steps of the present invention.
Detailed Description
While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
4. With reference to fig. 1, a method and system for intelligently deploying an educational resource platform, the method employs a user selection module, an educational resource deployment platform, a memory enhancement module, a user interaction module, and a wrong-question-set database; the method comprises the following steps:
step 1, the user selects the needed content in the user selection module and clicks to submit
In a specific embodiment of the present invention, the specific operation method for the user to select the required content in the user selection module in step 1 includes:
the user selects the required content in the user selection module, and the content comprises: the subjects selected by the user, the percentage of learning degree reached, the total learning duration, the learning mode and the like; marking the content needed by the user selection module
Wherein β =0,1,2, ·, n; n is a positive integer and represents the maximum value of the value of beta in the content selected by the user selection module; the learning mode comprises the following steps: new course learning and old course review.
Step 2: the content submitted by the user selection module is uploaded to an education resource allocation platform, and the platform allocates education resources according to the selected content of the user;
in a specific embodiment of the present invention, the operation method for allocating educational resources by the educational resource allocation platform in step 2 according to the content selected by the user includes:
the education resource allocation platform divides courses according to subjects selected by the user, the percentage of learning degree and the total learning duration; taking the total learning time length as a determined time length, and filling courses in the time length; the course content comprises courses selected by the user to reach the learning degree percentage within the total subject time selected by the user;
pushing course content = user selecting courses within the total subject time length up to learning degree percentage and selection;
performing algorithm selection on the pushed course content with the determined learning percentage, and if a new user selects courses, selecting the courses to be learned from front to back of the learning courses to play the courses to be learned in order to combine the actual learning effect; if the user finishes learning, the user can randomly select a review course of learning to learn, in order to enable the selected course to be more representative, the user can calculate and judge according to the selected course, and then pushes a more humanized course;
the educational resource learning course subject matter has structured information, and the resource learning library has dimension data such as title, exercise subject, unit, extension course, score and the like for each course; labeling each curriculum label value as B g G =1,2, ·, n; n is a positive integer and represents the maximum value of g which can be taken as a value in the course label;
in this kind of data, a field (also a feature) is taken as a dimension of vectorization, and then vectorization represents that the value of each dimension is not necessarily a numerical value, but the form is also vectorized, namely the requested vector space model; the similarity between the two subject matters is calculated in the following way;
suppose the vector representations of the subject matter of course 1 are P k K =1,2, ·, n; n is a positive integer and represents the maximum value of the vector K of the subject matter of the course 1; the vector notation of the subject matter for curriculum 2 is q k K =1,2, ·, n; n is a positive integer representing the maximum value of the vector K of the subject matter of course 2;
according to the calculation formula:
course 1: v 1 ＝(P 1 ，P 2 ,P 3 ,...P k )
Course 2: v 2 ＝(q 1 ，q 2 ，q 3 ,...q k )
sim(P k ，q k ) Representing two components P of a vector k ，q k The similarity between them; w is a group of k The representation is the weight value of the kth subject matter, and different weight values can be adopted for different courses; the pushed course content learning time is determined to be within the range of the total learning time selected by the user;
sim(P k ，q k ) The closer the value approaches 1, the closer the directions representing the two vectors are; sim (P) k ，q k ) The more the value approaches-1, the more opposite the two vector directions are represented; close to 0, meaning that the two vectors are nearly orthogonal;
with the result sim (P) k ，q k ) Value 0 bound, sim (P) k ，q k ) The value range is (0,1)]The typical course selection has high acquaintance, which is mainly used for strengthening the learning effect and repeatedly training courses with high acquaintance; sim (P) k ，q k ) The value range of [ -1,0) represents that the selected course is low in acquaintance, mainly used for learning new courses, and new course contents are learned on the basis of reviewing a small amount of old knowledge.
Step 3, the user learns according to the educational resources allocated by the educational resource allocation platform;
step 4, the user interaction module automatically jumps out the questions related to the courses after the user learns at intervals and finishes learning, and the user has to answer and count the scores;
in a specific embodiment of the present invention, the user interaction module operation method in step 4 includes:
the user interaction module is used for automatically jumping out the questions related to the courses according to the courses watched by the user when the user watches videos for learning, and the user needs to answer and count scores; the questions related to the courses appear at intervals when the user learns and after the learning is finished; when a user learns the course video, the user can set questions within 3 times that the video length is 1/3 and 2/3 and the video is completely played; and the user can carry out the learning course video after the user has finished answering; the scores of the user answers are recorded and used as a basis for judging the familiarity degree of the user to the courses, and wrong questions are collected into a wrong question set database.
Step 5, collecting all questions related to the course answered by the user and wrong questions into a wrong question set database;
in an embodiment of the present invention, the method for specifically operating the error problem set database in step 5 includes:
questions which are answered by the user and have errors related to the courses are automatically stored in an error question set database, and the error question set database is continuously changed and updated along with the accuracy rate of the answers of the user;
the wrong question set database can grade the wrong questions into 3 types: strange question type, slightly known question type and skilled question type; redistributing the question types according to the ratio of the user response times to the occurrence times;
in order to increase the randomness of the questions in the wrong question set database, in addition to introducing answer pair percentages, the fixed question ratio of strange question types, slightly recognized question types and skilled question types is 50%, 30% and 20%;
wrong question set database questions constitute = strange question type (1-answer pair percentage) · 50% + slightly recognized question type (1-answer pair percentage) · 30% + skilled question type (1-answer pair percentage) · 20%; the number of times of answering questions and the number of times of answering pairs are equal for 3 times continuously, and the wrong question set database is automatically removed from the questions.
And 6, the memory enhancing module rearranges corresponding courses and questions related to the courses for the user to learn according to the progress of the user learning the courses, the correct rate of answering the courses and the time intervals of the learning courses.
In an embodiment of the present invention, the specific operation method of the memory enhancing module in step 6 includes:
the memory enhancing module rearranges corresponding courses and questions related to the courses for the user to learn according to the progress of the user learning the courses, the correct rate of answering the courses and the time intervals of the learning courses;
the brain needs to be repeated; the shorter the time of memory interval is during each review, the better the memory effect is, because the impression can be deepened when the user looks at the same thing for a plurality of times, but the user often forgets to look at the thing only once; the memory law is that when learning a new knowledge and rapidly mastering the knowledge, except for frequent application, a certain frequency is needed to review the learned course in order to keep permanent memory in the brain;
the memory enhancing module pushes courses and course titles to form 20% of an error database, 30% of the progress of the combined learned course and 50% of the content of the newly learned course; the study course time interval is in units of days, the corresponding course is rearranged for 1 time within 3 days of continuous study, the corresponding course is rearranged for 2 times within 5 days of continuous study, and 7 days of continuous study is taken as a study period.
A cloud system is characterized in that education resources allocated by an allocation platform are learned according to contents required by selection in a user selection module, and users answer questions related to courses and collect wrong questions in a wrong question set database; and allocating the education resource platform through cloud computing and analysis.
According to the invention, the required content is selected in the user selection module by the user, the education resource allocation platform learns according to the education resource allocated by the selected content, the user interaction module automatically jumps out the questions related to the course during the interval of the user learning and after the user finishes learning, and the wrong questions of the user answering the courses are collected into the wrong question set database; the user can achieve a fitting learning effect according to requirements, and an elastic pushing mechanism is performed for the user; the memory enhancing module rearranges corresponding courses and questions related to the courses for the user to learn according to the progress of the user learning the courses, the correct rate of answering the courses and the time intervals of the learning courses; the method not only saves a great amount of time for the user to learn on the education resource platform, but also achieves the desired learning effect, and scientifically reviews the courses learned by the user.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (8)
1. A method and system for intelligent education resource platform allocation, the method uses user selection module, education resource allocation platform, memory enhancing module, user interaction module and wrong question set database; characterized in that the method comprises the steps of:
step 1, a user selects required contents in a user selection module and clicks and submits the contents;
and 2, step: the content submitted by the user selection module is uploaded to an education resource allocation platform, and the platform allocates education resources according to the selected content of the user;
step 3, the user learns according to the educational resources allocated by the educational resource allocation platform;
step 4, the user interaction module automatically jumps out the questions related to the courses after the user learns at intervals and finishes learning, and the user has to answer and count the scores;
step 5, collecting all questions related to the course answered by the user and wrong questions into a wrong question set database;
and 6, the memory enhancing module rearranges the corresponding courses and the questions related to the courses for the user to learn according to the progress of the user learning the courses, the correct rate of answering the courses and the time interval of the learning courses.
2. The method and system for platform deployment of intelligent educational resources according to claim 1, wherein: the specific operation method for selecting the required content in the user selection module by the user in the step 1 comprises the following steps:
user selection within user selection moduleSelecting required contents, wherein the contents comprise: subject selected by the user, percentage of learning degree, total learning duration, learning mode and the like; marking the content required by the selection in the user selection module
Wherein β =0,1,2, · n; n is a positive integer and represents the maximum value of the value of beta in the content selected by the user selection module; the learning mode comprises the following steps: new course learning and old course review.
3. The method and system for platform deployment of intelligent educational resources according to claim 1, wherein: the operation method for allocating the educational resources by the educational resource allocation platform according to the content selected by the user in the step 2 comprises the following steps:
the education resource allocation platform divides courses according to subjects selected by the user, the percentage of learning degree and the total learning duration; taking the total learning time length as a determined time length, and filling courses in the time length; the course content comprises courses selected by the user to reach the learning degree percentage within the total subject time selected by the user;
pushing course content = user selection of courses up to a learning degree percentage and within a total subject duration selected by the user;
performing algorithm selection on the pushed course content with the determined learning percentage, and if a new user selects courses, selecting the courses to be learned from front to back of the learning courses to play the courses to be learned in order to combine the actual learning effect; if the user finishes learning, the user can randomly select a review course of learning to learn, in order to enable the selected course to be more representative, the user can calculate and judge according to the selected course, and then pushes a more humanized course;
the educational resource learning course subject matter has structured information, and the resource learning library has dimension data such as title, exercise subject, unit, extension course, score and the like for each course; labeling each course tag value as B g ，g＝1,2,...,n；n is a positive integer and represents the maximum value of g which can be taken as a value in the course label;
in this kind of data, a field (also a feature) is taken as a dimension of vectorization, and then vectorization represents that the value of each dimension is not necessarily a numerical value, but the form is also vectorized, namely the requested vector space model; at this time, the similarity between the two subject matters is calculated in the following manner;
suppose the vector representations of the subject matter of course 1 are P k K =1,2, ·, n; n is a positive integer and represents the maximum value of the vector K of the subject matter of the course 1; the vector notation of course 2's subject matter is q k K =1,2, ·, n; n is a positive integer representing the maximum value of the vector K of the subject matter of course 2;
according to the calculation formula:
course 1: v 1 ＝(P 1 ，P 2 ,P 3 ,...P k )
Course 2: v 2 ＝(q 1 ，q 2 ，q 3 ,...q k )
sim(P k ，q k ) Representing two components P of a vector k ，q k The similarity between them; w is a group of k The representation is the weight value of the kth subject matter, and different weight values can be adopted for different courses; the pushed course content learning time is determined to be within the range of the total learning time selected by the user;
sim(P k ，q k ) The closer the value approaches 1, the closer the directions representing the two vectors are; sim (P) k ，q k ) The more the value approaches-1, the more opposite the two vector directions are represented; close to 0, meaning that the two vectors are nearly orthogonal;
with the result sim (P) k ，q k ) Value 0 bound, sim (P) k ，q k ) The value range is (0,1)]The representative course selection has higher degree of recognition and higher priorityThe learning effect is enhanced for the old course review, and the course with high acquaintance degree is repeatedly trained; sim (P) k ，q k ) The value range of [ -1,0) represents that the selected course has low acquaintance, is mainly used for learning new courses, and learns new course contents on the basis of reviewing a small amount of old knowledge.
4. The method and system for platform deployment of intelligent educational resources according to claim 1, wherein: the user interaction module operation method in the step 4 comprises the following steps:
the user interaction module is used for automatically jumping out the questions related to the courses according to the courses watched by the user when the user watches videos for learning, and the user has to answer and count scores; the questions related to the courses appear at intervals when the user learns and after the learning is finished; when a user learns the course video, the user can set questions within 3 times that the video length is 1/3 and 2/3 and the video is completely played; and the user can do learning course video only after answering; the scores of the user answers are recorded and used as a basis for judging the familiarity degree of the user to the courses, and wrong questions are collected into a wrong question set database.
5. The method and system for platform deployment of intelligent educational resources according to claim 1, wherein: the specific operation method of the error set database in the step 5 comprises the following steps:
questions which are answered by the user and have errors related to the courses are automatically stored in an error question set database, and the error question set database is continuously changed and updated along with the accuracy rate of the answers of the user;
the wrong question set database can grade the wrong questions into 3 types: strange subject types, slightly recognized subject types and skilled subject types; redistributing the question types according to the ratio of the user response times to the occurrence times;
in order to increase the randomness of the questions in the wrong question set database, in addition to introducing answer pair percentages, the fixed question ratio of strange question types, slightly recognized question types and skilled question types is 50%, 30% and 20%;
the wrong question set database is composed of strange question types (1-answer pair percentage) 50% + slightly recognized question types (1-answer pair percentage) 30% + skilled question types (1-answer pair percentage) 20%; the number of times of answering questions and the number of times of answering pairs are equal for 3 times continuously, and the wrong question set database is automatically removed from the questions.
6. The method and system for platform deployment of intelligent educational resources according to claim 1, wherein: the specific operation method of the memory enhancing module in the step 6 comprises the following steps:
the memory enhancing module rearranges corresponding courses and questions related to the courses for the user to learn according to the progress of the user learning the courses, the correct rate of answering the courses and the time intervals of the learning courses;
the memory enhancing module pushes courses and course titles to form 20% of an error database, 30% of the progress of the combined learned course and 50% of the content of the newly learned course; the study course time interval is in units of days, the corresponding course is rearranged for 1 time within 3 days of continuous study, the corresponding course is rearranged for 2 times within 5 days of continuous study, and 7 days of continuous study is taken as a study period.
7. A cloud system, characterized in that: learning the education resources allocated by the allocation platform according to the selected required content in the user selection module, and collecting wrong questions related to the user response course into a wrong question set database; the method and system for intelligent platform deployment of education resources according to any one of claims 1-6, wherein the method and system are implemented by cloud computing and analysis.
8. A cloud system, characterized in that: the method and the system for intelligently allocating the education resource platform, which are disclosed by any one of claims 1-6, are realized by allocating the education resource platform by the cloud computing and analyzing service program under the network by the cloud.
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