CN112948684A - Intelligent course recommendation method for online learning platform based on big data analysis and cloud computing - Google Patents
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Abstract
The invention discloses an online learning platform course intelligent recommendation method based on big data analysis and cloud computing, which is characterized in that each course learned in a student platform account is counted, course subjects in detailed information of each course learned in the student platform account are extracted, each course learned in the student platform account is counted, the number of courses in each subject is analyzed, the learning duration of each chapter subject in each course in each subject is obtained, and the course recommendation prediction coefficient of a student in an online learning platform is calculated; meanwhile, acquiring all basic information of the student in the student platform account, screening other students which accord with all basic information of the student in the online learning platform, counting course recommendation estimation coefficients corresponding to other students which accord with conditions, calculating comprehensive course recommendation estimation coefficients of the student in the online learning platform, comparing and counting all courses which accord with recommendation requirements in the online learning platform, and sequentially recommending to the student platform account.
Description
Technical Field
The invention relates to the technical field of intelligent course recommendation, in particular to an intelligent course recommendation method for an online learning platform based on big data analysis and cloud computing.
Background
In the online learning platform, course recommendation is one of the key links. The effective course recommendation can not only improve the class attendance rate and satisfaction of students, but also promote the sales of learning courses and improve the income of an online learning platform.
At present, most course recommendation methods for online learning platforms generally recommend according to interest preferences of students, and influence of learning behaviors of the students and learning course effects is not comprehensively considered, so that the interpretability and the rationality of recommendation results are poor, the learning requirements of the students cannot be met, the satisfaction and the interest of the students are reduced, meanwhile, the existing course recommendation methods cannot comprehensively analyze according to other students with similar conditions, the accuracy of course recommendation is not high, the students cannot be guaranteed to be suitable for learning and recommending courses, and course selection rate of the recommended courses by the students is influenced.
Disclosure of Invention
The invention aims to provide an online learning platform course intelligent recommendation method based on big data analysis and cloud computing, which is characterized in that the method comprises the steps of counting all courses learned in a student platform account, obtaining detailed information of all the courses learned in the student platform account, extracting course subjects in the detailed information of all the courses, counting all the courses learned in the student platform account, analyzing the number of the courses learned in the student platform account, calculating the number ratio of the courses of all the subjects, obtaining the learning duration of all the chapters and themes in all the courses of all the subjects learned in the student platform account, comparing and analyzing the learning duration difference of all the chapters and themes in all the courses of all the subjects, calculating the course recommendation estimation coefficient of a student in the online learning platform, and storing; meanwhile, each piece of basic information of the student in the student platform account is obtained, other students which accord with each piece of basic information of the student in the online learning platform are screened, course recommendation estimation coefficients which correspond to other students which accord with conditions in the online learning platform are counted, comprehensive course recommendation estimation coefficients of the student in the online learning platform are calculated, courses which accord with recommendation requirements in the online learning platform are compared and counted, and are sequentially recommended to the student platform account, and the problems in the background technology are solved.
The purpose of the invention can be realized by the following technical scheme:
an online learning platform course intelligent recommendation method based on big data analysis and cloud computing comprises the following steps:
s1, counting the courses learned in the student platform account, acquiring the detailed information of the courses learned in the student platform account, and extracting the course subjects in the detailed information of the courses;
s2, counting the courses in each subject learned in the student platform account, analyzing the number of the courses in each subject learned in the student platform account, and calculating the ratio of the number of the courses in each subject;
s3, acquiring the learning duration of each chapter theme in each course of each subject learned in the student platform account, and comparing and analyzing the learning duration difference of each chapter theme in each course of each subject;
s4, calculating course recommendation estimation coefficients of the student in the online learning platform, and storing the course recommendation estimation coefficients;
s5, simultaneously acquiring each basic information of the student in the student platform account, and screening other students in the online learning platform according with each basic information of the student;
s6, counting course recommendation estimation coefficients corresponding to other students meeting the conditions in the online learning platform;
s7, calculating the comprehensive course recommendation estimation coefficient of the student in the online learning platform, comparing and counting courses meeting recommendation requirements in the online learning platform, and sequentially recommending the courses to the account of the student platform;
the online learning platform course intelligent recommendation method based on big data analysis and cloud computing uses an online learning platform course intelligent recommendation system based on big data analysis and cloud computing, and comprises a course counting module, a course information extraction module, a course subject counting module, a subject quantity counting module, a subject proportion analysis module, a learning duration counting module, a learning duration analysis module, an analysis server, a storage database, a basic information acquisition module, a basic information screening module and a cloud recommendation platform;
the course information extraction module is respectively connected with the course counting module and the course subject counting module, the course subject counting module is respectively connected with the subject number counting module and the learning duration counting module, the subject proportion analysis module is respectively connected with the subject number counting module and the analysis server, the learning duration analysis module is respectively connected with the learning duration counting module, the analysis server and the storage database, the basic information screening module is respectively connected with the basic information acquisition module and the storage database, and the analysis server is connected with the cloud recommendation platform;
the course counting module is used for counting courses which are learned in a student platform account, inputting personal account information to an online learning platform through students for logging in, acquiring each course which is learned in the student platform account, counting each course which is learned in the student platform account, and sending each course which is learned in the student platform account to the course information extraction module;
the course information extraction module is used for receiving each course which is learned in the student platform account and sent by the course counting module, acquiring detailed information of each course which is learned in the student platform account, extracting course subjects in the detailed information of each course which is learned in the student platform account, counting subjects corresponding to each course which is learned in the student platform account, and sending the subjects corresponding to each course which is learned in the student platform account to the course subject counting module;
the course subject counting module is used for receiving subjects corresponding to the learned courses in the student platform account sent by the course information extraction module, counting the courses in the learned courses in the student platform account, and forming a course set P in each subject learned in the student platform accounti(pi1,pi2,...,pij,...,pim)，pij represents the jth course in the ith subject learned in the student platform account, and all course sets in all subjects learned in the student platform account are respectively sent to the subject quantity counting module and the learning duration counting module;
the subject quantity counting module is used for receiving each course set in each subject learned in the student platform account sent by the course subject counting module, counting the number of courses of each subject learned in the student platform account, and forming courses of each subject learned in the student platform accountSet of program numbers X (X)1,x2,...,xi,...,xn)，xiThe method comprises the steps of representing the number of courses for learning the ith subject in a student platform account, and sending a set of the number of courses for learning each subject in the student platform account to a subject proportion analysis module;
the subject proportion analysis module is used for receiving the number set of courses which have been learned in each subject in the student platform account and are sent by the subject quantity counting module, calculating the proportion of the number of the courses which have been learned in each subject in the student platform account, counting the proportion of the number of the courses which have been learned in each subject in the student platform account, and forming a number proportion set k (k) of the number of the courses which have been learned in each subject in the student platform account1,k2,...,ki,...,kn)，kiThe method comprises the steps that the proportion of the number of courses for learning the ith subject in a student platform account is expressed, and a set of the proportion of the number of courses for learning each subject in the student platform account is sent to an analysis server;
the learning duration counting module is used for receiving each course set in each subject learned in the student platform account sent by the course subject counting module, acquiring the learning duration of each chapter subject in each course of each subject learned in the student platform account, counting the learning duration of each chapter subject in each course of each subject learned in the student platform account, and forming the learning duration set of each chapter subject in each course of each subject learned in the student platform account
The learning duration representing the learning duration of the r chapter subject in the j course of the ith subject learned in the student platform account is sent to the learning duration analysis module;
the learning duration analysis module is used for receiving a learning duration set of each chapter theme in each course of each subject, which is learned in a student platform account and sent by the learning duration statistics module, extracting recommended learning durations of each chapter theme in each course of each subject, which are learned in a student platform account and stored in a storage database, calculating a learning duration difference of each chapter theme in each course of each subject, which is learned in a student platform account, counting the learning duration difference of each chapter theme in each course of each subject, which is learned in the student platform account, and sending the learning duration difference of each chapter theme in each course of each subject, which is learned in the student platform account, to the analysis server;
the analysis server is used for receiving the subject proportion analysis module and the student platform account, sending the subject proportion analysis module to the student platform account, receiving the learning time length difference value of each section theme in each subject class learned in the student platform account, sending the learning time length difference value to the analysis server, extracting the recommendation proportion influence coefficient of the course learning time length stored in the storage database, calculating the course recommendation estimation coefficient of the student in the online learning platform, and sending the course recommendation estimation coefficient of the student in the online learning platform to the storage database;
the storage database is used for receiving the course recommendation estimation coefficient of the student in the online learning platform sent by the analysis server, storing the recommended learning duration of each chapter theme in each course of each subject learned in the account of the student platform, and storing the recommendation proportion influence coefficient lambda of the course learning durationtStoring basic information and course recommendation estimation coefficients of each student in the online learning platform, storing weight proportion influence coefficients of other students and the student on course recommendation and respectively recording the weight proportion influence coefficients as alpha and beta, wherein alpha + beta is 1, and storing standard recommendation estimation coefficients of each course in the online learning platform;
the basic information acquisition module is used for acquiring the basic information of the student in the student platform account, acquiring each piece of basic information of the student in the student platform account, extracting the age, sex and grade of the student in the student platform account, counting each piece of basic information of the student in the student platform account, and sending each piece of basic information of the student in the student platform account to the basic information screening module;
the basic information screening module is used for receiving the basic information of the student in the student platform account sent by the basic information acquisition module, extracting and storingComparing each basic information of each student in the online learning platform stored in the database, comparing each basic information of the student in the student platform account with the corresponding basic information of other students, screening other students meeting each basic information of the student in the online learning platform, counting other students meeting the conditions in the online learning platform, extracting course recommendation estimation coefficients corresponding to other students meeting the conditions in the online learning platform stored in the storage database, and forming course recommendation estimation coefficient sets xi ' (xi ') corresponding to other students meeting the conditions in the online learning platform '1,ξ′2,...,ξ′p,...,ξ′q)，ξ′pThe course recommendation estimation coefficients corresponding to the other pth students meeting the conditions in the online learning platform are represented, and course recommendation estimation coefficient sets corresponding to the other students meeting the conditions in the online learning platform are sent to the analysis server;
the analysis server is used for receiving the course recommendation estimation coefficient sets corresponding to other students meeting the conditions in the online learning platform and sent by the basic information screening module, extracting the weight proportion influence coefficients of the other students and the students on course recommendation stored in the storage database, calculating the comprehensive course recommendation estimation coefficients of the students in the online learning platform, meanwhile, extracting standard recommendation estimation coefficients of all courses in an online learning platform stored in a storage database, comparing the comprehensive course recommendation estimation coefficient of the student in the online learning platform with the standard recommendation estimation coefficients of all the courses, counting all the courses meeting the recommendation requirement in the online learning platform if the standard recommendation estimation coefficient of a course is greater than or equal to the comprehensive course recommendation estimation coefficient of the student, and sending all the courses meeting the recommendation requirement in the online learning platform to a cloud recommendation platform;
the cloud recommendation platform is used for receiving all courses which meet recommendation requirements in the online learning platform and are sent by the analysis server, and recommending all the courses which meet the recommendation requirements to the account of the student platform in sequence.
Further, the course detailed information respectively comprises course subjects, course sections, course section subjects and course section contents.
Further, the calculation formula of the number of courses which have been learned in each subject in the student platform account is
kiExpressed as the ratio of the number of courses in the student platform account that learn the ith subject, xiExpressed as the number of courses in the student platform account that learned the ith subject.
Further, the learning duration difference value calculation formula of each section theme in each course of each subject learned in the student platform account is
Showing the learning time length difference value of the subjects of the r < th > chapter in the j < th > course for learning the ith subject in the student platform account,
showing the learning duration of the subjects of the r < th > chapter in the j < th > course of the ith subject learned in the student platform account,
the recommended learning duration is expressed as the recommended learning duration of the r chapter subject in the j course of the ith subject learned in the student platform account.
Further, the course recommendation estimation coefficient calculation formula of the student in the online learning platform is
Xi is expressed as the course recommendation estimation coefficient, k, of the student in the online learning platformiExpressed as the ratio of the number of courses in the student platform account that learn the ith subject, λtThe recommended proportional influence coefficient expressed as the lesson learning time period,
is shown as a studentThe learning duration difference value of the r chapter subject in the j course of the ith subject is learned in the platform account,
the recommended learning duration is expressed as the recommended learning duration of the r chapter subject in the j course of the ith subject learned in the student platform account.
Further, the calculation formula of the comprehensive course recommendation estimation coefficient of the student in the online learning platform is
ξGeneral assemblyExpressed as the comprehensive course recommendation estimation coefficient of the student in the online learning platform, alpha and beta are respectively expressed as other students and the weight proportion influence coefficient of the student on the course recommendation, wherein alpha + beta is 1, xipThe prediction coefficient is expressed as the course recommendation prediction coefficient corresponding to the pth student meeting the condition in the online learning platform, q is expressed as the number of other students meeting the condition in the online learning platform, and xi is expressed as the course recommendation prediction coefficient of the student in the online learning platform.
Has the advantages that:
(1) the invention provides an online learning platform course intelligent recommendation method based on big data analysis and cloud computing, which is characterized in that the detailed information of each course learned in a student platform account is acquired by counting each course learned in the student platform account, the course subjects in the detailed information of each course are extracted, each course in each subject learned in the student platform account is counted, the number of courses learned in each subject in the student platform account is analyzed, the number ratio of the courses in each subject is calculated, reliable reference data are provided for later-stage calculation of the course recommendation prediction coefficient of a student in an online learning platform, the learning time length of each subject in each course learned in each subject in the student platform account is acquired, the learning time length difference value of each subject of each chapter in each course in each subject is contrastively analyzed, the course recommendation prediction coefficient of the student in the online learning platform is calculated, and storing is carried out, so that the interpretability and the rationality of the recommendation result are increased, the learning requirements of students are met, and the satisfaction and the interestingness of the students are improved.
(2) According to the invention, through acquiring each basic information of the student in the student platform account, screening other students in the online learning platform which accord with each basic information of the student, counting course recommendation estimation coefficients corresponding to other students which accord with conditions in the online learning platform, calculating the comprehensive course recommendation estimation coefficients of the student in the online learning platform, comparing and counting courses which accord with recommendation requirements in the online learning platform, and sequentially recommending to the student platform account, the accuracy of course recommendation is improved, the student is ensured to be suitable for learning recommended courses, the course selection rate of the student for recommended courses is increased, the sales of the learning courses is promoted, and the income of the online learning platform is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the method steps of the present invention;
fig. 2 is a schematic view of a module connection structure according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the online learning platform course intelligent recommendation method based on big data analysis and cloud computing includes the following steps:
s1, counting the courses learned in the student platform account, acquiring the detailed information of the courses learned in the student platform account, and extracting the course subjects in the detailed information of the courses;
s2, counting the courses in each subject learned in the student platform account, analyzing the number of the courses in each subject learned in the student platform account, and calculating the ratio of the number of the courses in each subject;
s3, acquiring the learning duration of each chapter theme in each course of each subject learned in the student platform account, and comparing and analyzing the learning duration difference of each chapter theme in each course of each subject;
s4, calculating course recommendation estimation coefficients of the student in the online learning platform, and storing the course recommendation estimation coefficients;
s5, simultaneously acquiring each basic information of the student in the student platform account, and screening other students in the online learning platform according with each basic information of the student;
s6, counting course recommendation estimation coefficients corresponding to other students meeting the conditions in the online learning platform;
and S7, calculating the comprehensive course recommendation estimation coefficient of the student in the online learning platform, comparing and counting courses meeting the recommendation requirement in the online learning platform, and sequentially recommending to the account of the student platform.
Referring to fig. 2, the online learning platform course intelligent recommendation method based on big data analysis and cloud computing uses an online learning platform course intelligent recommendation system based on big data analysis and cloud computing, and includes a course counting module, a course information extraction module, a course subject counting module, a subject number counting module, a subject proportion analysis module, a learning duration counting module, a learning duration analysis module, an analysis server, a storage database, a basic information acquisition module, a basic information screening module, and a cloud recommendation platform.
The course information extraction module is respectively connected with the course counting module and the course subject counting module, the course subject counting module is respectively connected with the subject number counting module and the learning duration counting module, the subject proportion analysis module is respectively connected with the subject number counting module and the analysis server, the learning duration analysis module is respectively connected with the learning duration counting module, the analysis server and the storage database, the basic information screening module is respectively connected with the basic information acquisition module and the storage database, and the analysis server is connected with the cloud recommendation platform.
The course statistics module is used for counting courses which are learned in the student platform account, inputting personal account information into the online learning platform through students for logging in, acquiring each learned course in the student platform account, counting each learned course in the student platform account, and sending each learned course in the student platform account to the course information extraction module.
The course information extraction module is used for receiving each learned course in the student platform account sent by the course counting module, obtaining the detailed information of each learned course in the student platform account, wherein the course detailed information respectively comprises a course subject, a course section subject and course section content, extracting the course subject in the detailed information of each learned course in the student platform account, counting the subjects corresponding to each learned course in the student platform account, and sending the subjects corresponding to each learned course in the student platform account to the course subject counting module.
The course subject counting module is used for receiving subjects corresponding to the learned courses in the student platform account sent by the course information extraction module, counting the courses in the learned courses in the student platform account, and forming a course set P in each subject learned in the student platform accounti(pi1,pi2,...,pij,...,pim)，pij represents the jth course in the ith subject learned in the student platform account, and all course sets in all subjects learned in the student platform account are respectively sent to the subject quantity counting module and the learning duration counting module.
The subject quantity counting module is used for receiving each course set in each subject learned in the student platform account sent by the course subject counting module, counting the number of courses of each subject learned in the student platform account, and forming a course number set X (X) of each subject learned in the student platform account1,x2,...,xi,...,xn)，xiThe number of courses for the ith subject learned in the student platform account is represented, and the number of courses for each subject learned in the student platform account is sent to the subject proportion analysis module in a set mode.
The subject proportion analysis module is used for receiving the number set of courses which have been learned in each subject in the student platform account and are sent by the subject number statistics module and calculating the proportion of the number of the courses which have been learned in each subject in the student platform account
kiExpressed as the ratio of the number of courses in the student platform account that learn the ith subject, xiThe number of courses for the ith subject learned in the student platform account is represented, the number ratio of the courses for each subject learned in the student platform account is counted, and a set k (k) of the number ratio of the courses for each subject learned in the student platform account is formed1,k2,...,ki,...,kn) And sending the number proportion set of the courses which are learned in the student platform account and belong to each subject to the analysis server, and providing reliable reference data for the course recommendation estimation coefficients of the student in the later-stage calculation online learning platform.
The learning duration counting module is used for receiving each course set in each subject learned in the student platform account sent by the course subject counting module, acquiring the learning duration of each chapter subject in each course of each subject learned in the student platform account, counting the learning duration of each chapter subject in each course of each subject learned in the student platform account, and forming the learning duration set of each chapter subject in each course of each subject learned in the student platform account
The learning duration representing the learning duration of the r chapter subject in the j course of the ith subject learned in the student platform account is sent to the learning duration analysis module.
The learning duration analysisThe module is used for receiving the learning time length set of each chapter theme in each course of each subject in the learning in the student platform account sent by the learning time length counting module, extracting the recommended learning time length of each chapter theme in each course of each subject in the student platform account stored in the storage database, and calculating the learning time length difference value of each chapter theme in each course of each subject in the student platform account
Showing the learning time length difference value of the subjects of the r < th > chapter in the j < th > course for learning the ith subject in the student platform account,
showing the learning duration of the subjects of the r < th > chapter in the j < th > course of the ith subject learned in the student platform account,
the recommended learning time length of the ith chapter theme in the jth course for learning the ith subject in the student platform account is represented, the learning time length difference of each chapter theme in each course for which each subject is learned in the student platform account is counted, and the learning time length difference of each chapter theme in each course for which each subject is learned in the student platform account is sent to the analysis server.
The analysis server is used for receiving the number proportion set of courses which have been learned in each subject from the student platform account sent by the subject proportion analysis module, receiving the learning time length difference value of each chapter theme in each course which has been learned in each subject from the student platform account sent by the learning time length analysis module, extracting the recommendation proportion influence coefficient of the course learning time length stored in the storage database, and calculating the course recommendation estimation coefficient of the student in the online learning platform
Xi is expressed as the course recommendation estimation coefficient, k, of the student in the online learning platformiCourse expressed as learning ith subject in student platform accountNumber ratio, λtThe recommended proportional influence coefficient expressed as the lesson learning time period,
showing the learning time length difference value of the subjects of the r < th > chapter in the j < th > course for learning the ith subject in the student platform account,
the recommendation learning duration of the subjects of the r chapter in the j course for learning the ith subject in the student platform account is represented, and the course recommendation estimation coefficient of the student in the online learning platform is sent to the storage database, so that the interpretability and the rationality of a recommendation result are improved, the learning requirements of the student are met, and the satisfaction degree and the interestingness of the student are improved.
The storage database is used for receiving the course recommendation estimation coefficient of the student in the online learning platform sent by the analysis server, storing the recommended learning duration of each chapter theme in each course of each subject learned in the account of the student platform, and storing the recommendation proportion influence coefficient lambda of the course learning durationtAnd storing each basic information and course recommendation estimation coefficient of each student in the online learning platform, storing the weight proportion influence coefficients of other students and the students on course recommendation, respectively recording the weight proportion influence coefficients as alpha and beta, wherein alpha + beta is 1, and storing the standard recommendation estimation coefficient of each course in the online learning platform.
The basic information acquisition module is used for acquiring the basic information of the student in the student platform account, acquiring each piece of basic information of the student in the student platform account, extracting the age, the sex and the grade of the student in the student platform account, counting each piece of basic information of the student in the student platform account, and sending each piece of basic information of the student in the student platform account to the basic information screening module.
The basic information screening module is used for receiving the basic information of the student in the student platform account sent by the basic information acquisition module, extracting the basic information of the student in the online learning platform stored in the storage database, and enabling the student platform to be used as the platform for the studentComparing each basic information of the student in the account with the corresponding basic information of other students, screening other students meeting each basic information of the student in the online learning platform, counting the other students meeting the conditions in the online learning platform, extracting course recommendation estimation coefficients corresponding to the other students meeting the conditions in the online learning platform stored in the storage database, and forming course recommendation estimation coefficient sets xi ' (xi ') corresponding to the other students meeting the conditions in the online learning platform '1,ξ′2,...,ξ′p,...,ξ′q)，ξ′pAnd the course recommendation estimation coefficients corresponding to the other p-th students meeting the conditions in the online learning platform are expressed, and the course recommendation estimation coefficient sets corresponding to the other students meeting the conditions in the online learning platform are sent to the analysis server.
The analysis server is used for receiving the course recommendation estimation coefficient sets corresponding to other students meeting the conditions in the online learning platform and sent by the basic information screening module, extracting the weight proportion influence coefficients of the other students and the students on course recommendation stored in the storage database, and calculating the comprehensive course recommendation estimation coefficient set of the students in the online learning platform
ξGeneral assemblyExpressed as the comprehensive course recommendation estimated coefficient of the student in the online learning platform, and alpha and beta are respectively expressed as other students and the weight proportion influence coefficient of the student on the course recommendation, wherein alpha + beta is 1 and xi'pExpressing the course recommendation estimation coefficients corresponding to other pth students meeting the conditions in the online learning platform, expressing q as the number of other students meeting the conditions in the online learning platform, expressing xi as the course recommendation estimation coefficients of the student in the online learning platform, simultaneously extracting the standard recommendation estimation coefficients of all courses in the online learning platform stored in the storage database, comparing the comprehensive course recommendation estimation coefficients of the student in the online learning platform with the standard recommendation estimation coefficients of all courses, and if the standard recommendation estimation coefficients of a course are more than or equal to the comprehensive course recommendation estimation coefficients of the student, tabulatingAnd when the course in the online learning platform meets the recommendation requirement, counting the courses meeting the recommendation requirement in the online learning platform, and sending the courses meeting the recommendation requirement in the online learning platform to the cloud recommendation platform.
The cloud recommendation platform is used for receiving all courses meeting recommendation requirements in the online learning platform sent by the analysis server, and recommending all the courses meeting the recommendation requirements to the student platform account in sequence, so that the accuracy of course recommendation is improved, students are guaranteed to be suitable for learning recommended courses, the course selection rate of the recommended courses is increased, learning course sale is promoted, and the income of the online learning platform is improved.
The foregoing is merely exemplary and illustrative of the principles of the present invention and various modifications, additions and substitutions of the specific embodiments described herein may be made by those skilled in the art without departing from the principles of the present invention or exceeding the scope of the claims set forth herein.
Claims (6)
1. An online learning platform course intelligent recommendation method based on big data analysis and cloud computing is characterized by comprising the following steps: the method comprises the following steps:
s1, counting the courses learned in the student platform account, acquiring the detailed information of the courses learned in the student platform account, and extracting the course subjects in the detailed information of the courses;
s2, counting the courses in each subject learned in the student platform account, analyzing the number of the courses in each subject learned in the student platform account, and calculating the ratio of the number of the courses in each subject;
s3, acquiring the learning duration of each chapter theme in each course of each subject learned in the student platform account, and comparing and analyzing the learning duration difference of each chapter theme in each course of each subject;
s4, calculating course recommendation estimation coefficients of the student in the online learning platform, and storing the course recommendation estimation coefficients;
s5, simultaneously acquiring each basic information of the student in the student platform account, and screening other students in the online learning platform according with each basic information of the student;
s6, counting course recommendation estimation coefficients corresponding to other students meeting the conditions in the online learning platform;
s7, calculating the comprehensive course recommendation estimation coefficient of the student in the online learning platform, comparing and counting courses meeting recommendation requirements in the online learning platform, and sequentially recommending the courses to the account of the student platform;
the online learning platform course intelligent recommendation method based on big data analysis and cloud computing uses an online learning platform course intelligent recommendation system based on big data analysis and cloud computing, and comprises a course counting module, a course information extraction module, a course subject counting module, a subject quantity counting module, a subject proportion analysis module, a learning duration counting module, a learning duration analysis module, an analysis server, a storage database, a basic information acquisition module, a basic information screening module and a cloud recommendation platform;
the course information extraction module is respectively connected with the course counting module and the course subject counting module, the course subject counting module is respectively connected with the subject number counting module and the learning duration counting module, the subject proportion analysis module is respectively connected with the subject number counting module and the analysis server, the learning duration analysis module is respectively connected with the learning duration counting module, the analysis server and the storage database, the basic information screening module is respectively connected with the basic information acquisition module and the storage database, and the analysis server is connected with the cloud recommendation platform;
the course counting module is used for counting courses which are learned in a student platform account, inputting personal account information to an online learning platform through students for logging in, acquiring each course which is learned in the student platform account, counting each course which is learned in the student platform account, and sending each course which is learned in the student platform account to the course information extraction module;
the course information extraction module is used for receiving each course which is learned in the student platform account and sent by the course counting module, acquiring detailed information of each course which is learned in the student platform account, extracting course subjects in the detailed information of each course which is learned in the student platform account, counting subjects corresponding to each course which is learned in the student platform account, and sending the subjects corresponding to each course which is learned in the student platform account to the course subject counting module;
the course subject counting module is used for receiving subjects corresponding to the learned courses in the student platform account sent by the course information extraction module, counting the courses in the learned courses in the student platform account, and forming a course set P in each subject learned in the student platform accounti(pi1,pi2,...,pij,...,pim)，pij represents the jth course in the ith subject learned in the student platform account, and all course sets in all subjects learned in the student platform account are respectively sent to the subject quantity counting module and the learning duration counting module;
the subject quantity counting module is used for receiving each course set in each subject learned in the student platform account sent by the course subject counting module, counting the number of courses of each subject learned in the student platform account, and forming a course number set X (X) of each subject learned in the student platform account1,x2,...,xi,...,xn)，xiThe method comprises the steps of representing the number of courses for learning the ith subject in a student platform account, and sending a set of the number of courses for learning each subject in the student platform account to a subject proportion analysis module;
the subject proportion analysis module is used for receiving the number set of courses which have been learned in each subject in the student platform account and are sent by the subject quantity counting module, calculating the proportion of the number of the courses which have been learned in each subject in the student platform account, counting the proportion of the number of the courses which have been learned in each subject in the student platform account, and forming a number proportion set k (k) of the number of the courses which have been learned in each subject in the student platform account1,k2,...,ki,...,kn)，kiThe method comprises the steps that the proportion of the number of courses for learning the ith subject in a student platform account is expressed, and a set of the proportion of the number of courses for learning each subject in the student platform account is sent to an analysis server;
the learning duration counting module is used for receiving each course set in each subject learned in the student platform account sent by the course subject counting module, acquiring the learning duration of each chapter subject in each course of each subject learned in the student platform account, counting the learning duration of each chapter subject in each course of each subject learned in the student platform account, and forming the learning duration set of each chapter subject in each course of each subject learned in the student platform account
The learning duration representing the learning duration of the r chapter subject in the j course of the ith subject learned in the student platform account is sent to the learning duration analysis module;
the learning duration analysis module is used for receiving a learning duration set of each chapter theme in each course of each subject, which is learned in a student platform account and sent by the learning duration statistics module, extracting recommended learning durations of each chapter theme in each course of each subject, which are learned in a student platform account and stored in a storage database, calculating a learning duration difference of each chapter theme in each course of each subject, which is learned in a student platform account, counting the learning duration difference of each chapter theme in each course of each subject, which is learned in the student platform account, and sending the learning duration difference of each chapter theme in each course of each subject, which is learned in the student platform account, to the analysis server;
the analysis server is used for receiving the subject proportion analysis module and the student platform account, sending the subject proportion analysis module to the student platform account, receiving the learning time length difference value of each section theme in each subject class learned in the student platform account, sending the learning time length difference value to the analysis server, extracting the recommendation proportion influence coefficient of the course learning time length stored in the storage database, calculating the course recommendation estimation coefficient of the student in the online learning platform, and sending the course recommendation estimation coefficient of the student in the online learning platform to the storage database;
the storage database is used for receiving the course recommendation estimation coefficient of the student in the online learning platform sent by the analysis server, storing the recommended learning duration of each chapter theme in each course of each subject learned in the account of the student platform, and storing the recommendation proportion influence coefficient lambda of the course learning durationtStoring basic information and course recommendation estimation coefficients of each student in the online learning platform, storing weight proportion influence coefficients of other students and the student on course recommendation and respectively recording the weight proportion influence coefficients as alpha and beta, wherein alpha + beta is 1, and storing standard recommendation estimation coefficients of each course in the online learning platform;
the basic information acquisition module is used for acquiring the basic information of the student in the student platform account, acquiring each piece of basic information of the student in the student platform account, extracting the age, sex and grade of the student in the student platform account, counting each piece of basic information of the student in the student platform account, and sending each piece of basic information of the student in the student platform account to the basic information screening module;
the basic information screening module is used for receiving the basic information of each student in the student platform account sent by the basic information acquisition module, extracting the basic information of each student in the online learning platform stored in the storage database, comparing the basic information of each student in the student platform account with the basic information corresponding to other students, screening other students meeting the basic information of each student in the online learning platform, counting the other students meeting the conditions in the online learning platform, extracting course recommendation prediction coefficients corresponding to the other students meeting the conditions in the online learning platform stored in the storage database, and forming a course recommendation prediction coefficient set xi ' (xi ') corresponding to the other students meeting the conditions in the online learning platform '1,ξ′2,...,ξ′p,...,ξ′q)，ξ′pExpressing the course recommendation estimation coefficients corresponding to the other p-th students meeting the conditions in the online learning platform, and recommending other students meeting the conditions in the online learning platform to the other studentsGenerating a corresponding course recommendation estimation coefficient set and sending the corresponding course recommendation estimation coefficient set to an analysis server;
the analysis server is used for receiving the course recommendation estimation coefficient sets corresponding to other students meeting the conditions in the online learning platform and sent by the basic information screening module, extracting the weight proportion influence coefficients of the other students and the students on course recommendation stored in the storage database, calculating the comprehensive course recommendation estimation coefficients of the students in the online learning platform, meanwhile, extracting standard recommendation estimation coefficients of all courses in an online learning platform stored in a storage database, comparing the comprehensive course recommendation estimation coefficient of the student in the online learning platform with the standard recommendation estimation coefficients of all the courses, counting all the courses meeting the recommendation requirement in the online learning platform if the standard recommendation estimation coefficient of a course is greater than or equal to the comprehensive course recommendation estimation coefficient of the student, and sending all the courses meeting the recommendation requirement in the online learning platform to a cloud recommendation platform;
the cloud recommendation platform is used for receiving all courses which meet recommendation requirements in the online learning platform and are sent by the analysis server, and recommending all the courses which meet the recommendation requirements to the account of the student platform in sequence.
2. The online learning platform course intelligent recommendation method based on big data analysis and cloud computing as claimed in claim 1, wherein: the course detailed information respectively comprises course subjects, course sections, course section subjects and course section contents.
3. The online learning platform course intelligent recommendation method based on big data analysis and cloud computing as claimed in claim 1, wherein: the formula for calculating the number ratio of the courses which have been learned in each subject in the account of the student platform is
kiExpressed as the ratio of the number of courses in the student platform account that learn the ith subject, xiIs shown as schoolThe number of courses for the ith subject learned in the live platform account.
4. The online learning platform course intelligent recommendation method based on big data analysis and cloud computing as claimed in claim 1, wherein: the learning time difference value calculation formula of each chapter theme in each course of each subject learned in the student platform account is
Showing the learning time length difference value of the subjects of the r < th > chapter in the j < th > course for learning the ith subject in the student platform account,
showing the learning duration of the subjects of the r < th > chapter in the j < th > course of the ith subject learned in the student platform account,
the recommended learning duration is expressed as the recommended learning duration of the r chapter subject in the j course of the ith subject learned in the student platform account.
5. The online learning platform course intelligent recommendation method based on big data analysis and cloud computing as claimed in claim 1, wherein: the course recommendation estimation coefficient calculation formula of the student in the online learning platform is
Xi is expressed as the course recommendation estimation coefficient, k, of the student in the online learning platformiExpressed as the ratio of the number of courses in the student platform account that learn the ith subject, λtThe recommended proportional influence coefficient expressed as the lesson learning time period,
showing the learning time length difference value of the subjects of the r < th > chapter in the j < th > course for learning the ith subject in the student platform account,
the recommended learning duration is expressed as the recommended learning duration of the r chapter subject in the j course of the ith subject learned in the student platform account.
6. The online learning platform course intelligent recommendation method based on big data analysis and cloud computing as claimed in claim 1, wherein: the comprehensive course recommendation estimation coefficient calculation formula of the student in the online learning platform is
ξGeneral assemblyExpressed as the comprehensive course recommendation estimated coefficient of the student in the online learning platform, and alpha and beta are respectively expressed as other students and the weight proportion influence coefficient of the student on the course recommendation, wherein alpha + beta is 1 and xi'pThe estimation coefficient is expressed as the course recommendation estimation coefficient corresponding to the pth student meeting the condition in the online learning platform, q is expressed as the number of other students meeting the condition in the online learning platform, and xi is expressed as the course recommendation estimation coefficient of the student in the online learning platform.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116308913A (en) * | 2023-02-27 | 2023-06-23 | 北京思想天下教育科技有限公司 | Intelligent course management system based on cloud platform |
| CN116402391A (en) * | 2023-04-07 | 2023-07-07 | 长沙民政职业技术学院 | Comprehensive capability evaluation method and system based on big data |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116308913A (en) * | 2023-02-27 | 2023-06-23 | 北京思想天下教育科技有限公司 | Intelligent course management system based on cloud platform |
| CN116308913B (en) * | 2023-02-27 | 2023-09-08 | 北京思想天下教育科技有限公司 | Intelligent course management system based on cloud platform |
| CN116402391A (en) * | 2023-04-07 | 2023-07-07 | 长沙民政职业技术学院 | Comprehensive capability evaluation method and system based on big data |
| CN116402391B (en) * | 2023-04-07 | 2023-11-10 | 长沙民政职业技术学院 | Comprehensive capability evaluation method and system based on big data |
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