CN109902128A - Learning path planing method, device, equipment and storage medium based on big data - Google Patents
Learning path planing method, device, equipment and storage medium based on big data Download PDFInfo
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Abstract
This application involves learning path planing method, device, equipment and storage medium based on big data, method includes: the characteristic information for obtaining multiple learners；Classified according to characteristic information to each learner, obtains multiple learner's monoids；The number that each course in Statistics Course library was learnt by first learner's monoid respectively；According to the number learnt from more to few sequence, the course in course library is ranked up, obtains the corresponding learning route of first learner's monoid；Obtain the corresponding learning route of each learner's monoid.Learner's monoid is established by the characteristic information of learner, and the number that each course in course library is learnt according to learner's monoid, the corresponding learning route of learner's monoid is established, suitable learning path is planned for all kinds of learners, improves the study specific aim and learning efficiency of learner.
Description
Technical field
This application involves field of computer technology, more particularly to the learning path planing method based on big data, device, set
Standby and storage medium.
Background technique
With the popularity of the internet with the extensive use of computer technology, traditional educational mode is gradually to online religion
Educate direction transformation.Many existing online educations automatically can provide learning path planning service for learner, be learner
The good learned lesson of reasonable arrangement provides reference, improves the learning efficiency of learner.
However, current learning path planing method is mainly according to existing learner to the Grasping level of knowledge point, planning
Popular learning route out does not account for the individual difference between learner but, does not classify to learner, not
Suitable learning path planning is made for inhomogeneous learner.In this way, the learning efficiency of learner is lower and lacks study
Specific aim.
Summary of the invention
The individual difference between learner is not considered for current learning path planing method, leads to the study road of planning
Diameter is unable to satisfy in all kinds of learner's demands the problem of, and this application provides a kind of learning path planing methods, pass through learner
Characteristic information establish learner's monoid, and each course in course library is learnt according to all learners in learner's monoid
Number establishes the corresponding learning route of learner's monoid, plans suitable learning path for all kinds of learners, accomplished because
Material teaching, improves the study specific aim and learning efficiency of learner.
A kind of learning path planing method based on big data, comprising: obtain the characteristic information of multiple learners；Each
The characteristic information of habit person includes at least one in gender, age and lesson test achievement；By the characteristic information of each learner
Clustering Model is inputted, is classified according to the characteristic information of learner to each learner, obtains multiple learner's monoids；Respectively
The number that each course in Statistics Course library was learnt by first learner's monoid；The first learner monoid is described
Any learner's monoid in multiple learner's monoids；The number learnt by the first learner monoid according to course from
More to few sequence is ranked up the course in the course library, obtains the corresponding course of the first learner monoid
Practise path；Finally obtain the corresponding learning route of each learner's monoid.
Optionally, the multiple learner includes the first learner.The corresponding class of each learner's monoid is obtained described
After journey learning path, the method also includes: it is first learner according to the characteristic information of first learner
With affiliated learner's monoid；Obtain target course learning road corresponding to learner's monoid belonging to first learner
Diameter；Recommend the target learning route to first learner.
Optionally, the characteristic information according to learner classifies to each learner, obtains multiple learner's classes
Group, comprising: classified according to any two in the gender, age and lesson test achievement to each learner, established
Father learner's monoid and sub- learner's monoid.
Optionally, any two according in the gender, age and lesson test achievement to each learner into
Row classification, establishes father learner's monoid and sub- learner's monoid, comprising: divide multiple age brackets and multiple lesson test achievements
Section；Classified according to age bracket belonging to the age of learner to each learner, establishes multiple father learner's monoids；
According to lesson test achievement section belonging to the lesson test achievement of learner, to each in each father learner's monoid
Habit person classifies, and establishes multiple sub- learner's monoids of father learner's monoid.
Optionally, any two according in the gender, age and lesson test achievement to each learner into
Row classification, establishes father learner's monoid and sub- learner's monoid, comprising: divide multiple age brackets；According to the gender pair of learner
Each learner classifies, and establishes two father learner's monoids；According to age bracket belonging to the age of learner to every
Each learner in a father learner's monoid classifies, and establishes multiple sub- study of father learner's monoid
Person's monoid.
Optionally, it is described obtain the corresponding learning route of each learner's monoid after, the method also includes:
Obtain multiple mark values corresponding to each course in the course library；The mark value is that a learner passes through terminal
The fractional value that course learning interface evaluates the significance level of any course；It calculates separately described corresponding to each course
The average value of multiple mark values；The number that was learnt according to each course by the first learner monoid and described average
Value, obtains weighted value of each course relative to the first learner monoid；According to the sequence of the weighted value from big to small,
Course in the course library is ranked up, the corresponding course learning path optimizing of the first learner monoid is obtained；Most
The corresponding course learning path optimizing of each learner's monoid is obtained eventually.
Optionally, the expression formula of the weighted value are as follows:
wi=ami·exp(b·ni)
Wherein, wiFor the weighted value corresponding to i-th of course in the course library；miIt is described for i-th of course
The number that first learner's monoid learnt；niFor the average value corresponding to i-th of course；lijFor i-th of course institute
Corresponding j-th of mark value；J is the integer more than or equal to 1, indicates the quantity of learner；A, b is respectively the constant for being greater than 0；
A indicates miIn the weight of the expression formula；B indicates niIn the weight of the expression formula.
Based on the same technical idea, the present invention also provides a kind of learning path device for planning based on big data, packet
It includes:
Transceiver module, for obtaining the characteristic information of multiple learners；Including at least property of the characteristic information of each learner
Not, one in age and lesson test achievement.
Processing module, for the characteristic information of each learner to be inputted Clustering Model, according to the characteristic information of learner
Classify to each learner, obtains multiple learner's monoids；Each course in Statistics Course library is by the first study respectively
The number that person's monoid learnt；The first learner monoid is any learner's class in the multiple learner's monoid
Group；The number learnt according to course by the first learner monoid is from more to few sequence, in the course library
Course is ranked up, and obtains the corresponding learning route of the first learner monoid；Finally obtain each learner's monoid
Corresponding learning route.
Optionally, the multiple learner includes the first learner.The processing module is also used to learn according to described first
The characteristic information of habit person, for learner's monoid belonging to first learner matching；It obtains belonging to first learner
Target learning route corresponding to learner's monoid；Recommend the target learning route to first learner.
Optionally, the processing module is specifically used for according to any two in the gender, age and lesson test achievement
Item classifies to each learner, establishes father learner's monoid and sub- learner's monoid.
Optionally, the processing module is specifically used for dividing multiple age brackets and multiple lesson test achievement sections；According to
Age bracket belonging to the age of learner classifies to each learner, establishes multiple father learner's monoids；According to
Lesson test achievement section belonging to the lesson test achievement of habit person, to each learner in each father learner's monoid into
Row classification, establishes multiple sub- learner's monoids of father learner's monoid.
Optionally, the processing module is specifically used for dividing multiple age brackets；According to the gender of learner to each study
Person classifies, and establishes two father learner's monoids；Each father is learnt according to age bracket belonging to the age of learner
Each learner in person's monoid classifies, and establishes multiple sub- learner's monoids of father learner's monoid.
Optionally, the processing module is also used to obtain multiple labels corresponding to each course in the course library
Value；The mark value evaluates the significance level of any course by the course learning interface of terminal by a learner
Fractional value；Calculate separately the average value of the multiple mark value corresponding to each course；According to each course by described first
The number and the average value that learner's monoid learnt obtain each course relative to the first learner monoid
Weighted value；According to the sequence of the weighted value from big to small, the course in the course library is ranked up, obtains described first
The corresponding course learning path optimizing of learner's monoid；Finally obtain each learner's monoid corresponding course learning optimization road
Diameter.
Optionally, the expression formula of the weighted value are as follows:
wi=ami·exp(b·ni)
Wherein, wiFor the weighted value corresponding to i-th of course in the course library；miIt is described for i-th of course
The number that first learner's monoid learnt；niFor the average value corresponding to i-th of course；lijFor i-th of course institute
Corresponding j-th of mark value；J is the integer more than or equal to 1, indicates the quantity of learner；A, b is respectively the constant for being greater than 0；
A indicates miIn the weight of the expression formula；B indicates niIn the weight of the expression formula.
Based on the same technical idea, the present invention also provides a kind of computer equipments, including transceiver, memory and place
Device is managed, be stored with computer-readable instruction in the memory makes when the computer-readable instruction is executed by the processor
The processor is obtained to execute such as the step in the above-mentioned learning path planing method based on big data.
Based on the same technical idea, the present invention also provides a kind of storage medium for being stored with computer-readable instruction,
When the computer-readable instruction is executed by one or more processors, so that one or more processors execute such as above-mentioned base
Step in the learning path planing method of big data.
The application's the utility model has the advantages that establishes learner's monoid by the characteristic information of learner, and according to learner's monoid
In all learners number that each course in course library is learnt, establish the corresponding learning route of learner's monoid, be
All kinds of learners plan suitable learning path, have accomplished to teach students in accordance with their aptitude, and improve the study specific aim and study effect of learner
Rate.
Detailed description of the invention
Fig. 1 is the flow diagram of the learning path planing method based on big data in the embodiment of the present application.
Fig. 2 is the flow diagram for recommending learning route step in the embodiment of the present application to learner.
Fig. 3 is the flow diagram of optimization aim learning route step in the embodiment of the present application.
Fig. 4 is the structural schematic diagram of the learning path device for planning based on big data in the embodiment of the present application.
Fig. 5 is the structural schematic diagram of computer equipment in the embodiment of the present application.
Specific embodiment
It should be appreciated that specific embodiment described herein is not used to limit the application only to explain the application.
Those skilled in the art of the present technique are appreciated that unless expressly stated, singular " one " used herein, " one
It is a ", " described " and "the" also may include plural form.It is to be further understood that used in the description of the present application
Wording " comprising " refers to that there are the feature, program, step, operation, element and/or component, but it is not excluded that in the presence of or add
Add other one or more features, program, step, operation, element, component and/or their group.
Fig. 1 is a kind of flow chart of the learning path planing method based on big data in some embodiments of the application, such as
Shown in Fig. 1, S1-S3 may comprise steps of:
S1, the characteristic information for obtaining multiple learners.
In some embodiments, the characteristic information of each learner is included at least in gender, age and lesson test achievement
One.
Learner inputs the information such as personal gender and age in the login interface login account of terminal, and backstage takes
Business device obtains the gender and age information of learner transmitted by terminal.Terminal can be the smart machines such as mobile phone or computer.
In addition, background server recommends the exercise of course to the answer interface of terminal, to test learner to the grasp situation of course；
Also, background server counts the course and lesson test achievement of each learner study of Confucian classics.
It is understood that, feature letter that background server obtain more using the learner of learning path planning system
It ceases also more.
S2, the characteristic information of each learner is inputted into Clustering Model, according to the characteristic information of learner to each study
Person classifies, and obtains multiple learner's monoids.
In reality, the learner at different sexes or age is different to the learning sequence of each course.In general, being learned in colleges and universities
In the student of literal arts, schoolgirl's proportion is higher, and in the student of scientific principle section, boy student's proportion is higher, this makes in maximum probability
It is different to obtain male, schoolgirl study habit.In civil servant examination, often integrated survey examinee is to the palm that is literary, managing knowledge
Hold ability；Male, female examinee are in the correlated curriculum of study and civil servant examination, because of the difference of study habit, so to course
Learning sequence is different.
It is different because interest or grasping the differences such as speed to knowledge point likewise, during learning identical course library
The learner of age bracket is also different to the learning sequence of course.
In addition, learner is different to the Grasping level of the knowledge point of each course, learner can be to each course
Habit sequence is arranged, preferably to grasp the knowledge point of each course.Lesson test achievement can reflect learner to each class
The Grasping level of the knowledge point of journey.
According to the difference in terms of gender, age and lesson test achievement between learner, classify to learner, it will
The similar learner of study habit is classified as one kind, and then releases the learning path for being suitble to all kinds of learners.
In one embodiment, the Clustering Model divides each learner according to the characteristic information of each learner
Class obtains multistage learner's monoid.
In one embodiment, step S2 is the following steps are included: by the Clustering Model to the gender, age and class
Any two in journey test result are classified, and father learner's monoid and sub- learner's monoid are established.
In one embodiment, the Clustering Model is according to the age and lesson test achievement of each learner to each
Learner classifies, and establishes father learner's monoid and sub- learner's monoid.
Optionally, step S2 specifically includes following steps S211-S213:
S211, the Clustering Model divide multiple age brackets and multiple lesson test achievement sections.
S212, Clustering Model age bracket according to belonging to the age of learner classify to each learner, build
Found multiple father learner's monoids.
S213, the Clustering Model lesson test achievement section according to belonging to the lesson test achievement of learner, to each
Each learner in father learner's monoid classifies, and establishes multiple sub- learners of father learner's monoid
Monoid.
In one embodiment, the Clustering Model carries out each learner according to the gender and age of each learner
Classification, establishes father learner's monoid and sub- learner's monoid.
Optionally, step S2 specifically includes following steps S221-S223:
S221, the Clustering Model divide multiple age brackets.
S222, the Clustering Model classify to each learner according to the gender of learner, establish two fathers
Learner's monoid.
S223, Clustering Model age bracket according to belonging to the age of learner are in each father learner's monoid
Each learner classify, establish multiple sub- learner's monoids of father learner's monoid.
In one embodiment, the Clustering Model is according to the gender and lesson test achievement of each learner to each
Habit person classifies, and establishes father learner's monoid and sub- learner's monoid.
Optionally, step S2 specifically includes following steps S231-S233:
S231, the Clustering Model divide multiple lesson test achievement sections.
S232, the Clustering Model classify to each learner according to the gender of learner, establish two fathers
Learner's monoid.
S233, the Clustering Model lesson test achievement section according to belonging to the lesson test achievement of learner are to each institute
The each learner stated in father learner's monoid classifies, and establishes multiple sub- learner's classes of father learner's monoid
Group.
Learner is divided into multistage learner's monoid according to multiple characteristic informations, learner is carefully divided, is mentioned
The similarity of the characteristic information of learner in high same learner's monoid.
S3, the number that each course in Statistics Course library was learnt by first learner's monoid respectively；According to course
The number learnt by the first learner monoid arranges the course in the course library from more to few sequence
Sequence obtains the corresponding learning route of the first learner monoid.Finally obtain the corresponding course of each learner's monoid
Learning path.
The first learner monoid is any learner's monoid in the multiple learner's monoid.
The learning route is using course as basic unit.
When learner carries out course learning by the course learning interface of terminal, learner learnt current course a it
Afterwards, next course b that will learn can be selected, thus will form a course selection path from course a to course b.
Background server obtains the course of each learner study of Confucian classics in any learner's monoid, has also just obtained every in course library
The number that a course was learned by all learners in learner's monoid.The number that was learnt according to course is from big to small
Sequentially, each course is ranked up, to obtain learning route corresponding with learner's monoid.
As shown in Fig. 2, after step s 3, the method also includes step S411-S413 in some embodiments:
S411, according to the characteristic information of the first learner, for learner's monoid belonging to first learner matching.Institute
Stating the first learner is any learner in the multiple learner.
S412, target learning route corresponding to learner's monoid belonging to first learner is obtained.
The first learner of S413, Xiang Suoshu recommends the target learning route.
The present embodiment, when there is learner to enter course learning interface in terminal, when carrying out course learning, background server root
According to the characteristic information of learner, learner's monoid belonging to the learner is judged, according to corresponding to affiliated learner's monoid
Learning route is that the learner recommends course.
As shown in figure 3, after step s 3, the method also includes step S421-S424 in some embodiments:
Multiple mark values corresponding to each course in S421, the acquisition course library；The mark value is
The fractional value that habit person evaluates the significance level of any course by the course learning interface of terminal.
Learner can learn selected course by the course learning interface of terminal, and it is possible in course
The importance for practising course of the interface to be learned is marked.For example, course learning interface is provided with label column, marks and be arranged in column
There is marker character, marker character can be five-pointed star image.Learner passes through the quantity of the five-pointed star image in radio check mark column, to class
The significance level of journey is evaluated.The quantity for the five-pointed star image that learner chooses is calculated as corresponding score by background server
Value or score section to get arrive the mark value.
S422, the average value for calculating separately the multiple mark value corresponding to each course.
The summation of the multiple mark value corresponding to each course is calculated, then the summation to the multiple mark value and institute
The quantity for stating multiple mark values does division arithmetic, obtains the average value.
S423, the number and the average value learnt according to each course by the first learner monoid, obtain
Weighted value to each course relative to the first learner monoid.
The weighted value is for measuring importance of the course relative to the first learner monoid.
S424, the sequence according to the weighted value from big to small are ranked up the course in the course library, obtain institute
State the corresponding course learning path optimizing of first learner's monoid.It is excellent to finally obtain the corresponding course learning of each learner's monoid
Change path.
The present embodiment, according to learner to the feedback information of the significance level of course, to the course learning of learner's monoid
Path optimizes, and obtains the course learning path optimizing for being more suitable for learner's monoid, more reliable course is provided for learner
Recommend, improves the learning efficiency of learner.
In some embodiments, the expression formula of the weighted value are as follows:
wi=ami·exp(b·ni)
Wherein, wiFor the weighted value corresponding to i-th of course in the course library；miIt is described for i-th of course
The number that first learner's monoid learnt；niFor the average value corresponding to i-th of course；lijFor i-th of course institute
Corresponding j-th of mark value；J is the integer more than or equal to 1, indicates the quantity of learner；A, b is respectively the constant for being greater than 0；
A indicates miIn the weight of the expression formula；B indicates niIn the weight of the expression formula.
The weighted value wiThe number m learnt with course by the first learner monoidiAnd the average value ni
It is positively correlated.
Above-described embodiment is established learner's monoid by the characteristic information of learner, and is owned according in learner's monoid
The number that learner learns each course in course library establishes the corresponding learning route of learner's monoid, is all kinds of
Habit person plans suitable learning path, has accomplished to teach students in accordance with their aptitude, has improved the study specific aim and learning efficiency of learner.
Based on the same technical idea, the present invention also provides a kind of learning path device for planning based on big data, such as
Shown in Fig. 4, which includes transceiver module 1 and processing module 2.The processing module 2 is used to control the receipts of the transceiver module 1
Hair operation.
The transceiver module 1, for obtaining the characteristic information of multiple learners；The characteristic information of each learner at least wraps
Include one in gender, age and lesson test achievement.
The processing module 2, for the characteristic information of each learner to be inputted Clustering Model, according to the feature of learner
Information classifies to each learner, obtains multiple learner's monoids；Each course in Statistics Course library is by first respectively
The number that learner's monoid learnt；The first learner monoid is any learner in the multiple learner's monoid
Monoid；The number learnt according to course by the first learner monoid is from more to few sequence, in the course library
Course be ranked up, obtain the corresponding learning route of the first learner monoid；Finally obtain each learner's class
The corresponding learning route of group.
In some embodiments, the multiple learner includes the first learner.The processing module 2 is also used to according to institute
The characteristic information of the first learner is stated, for learner's monoid belonging to first learner matching；Obtain first study
Target learning route corresponding to learner's monoid belonging to person；Recommend the target course to first learner
Practise path.
In some embodiments, the processing module 2 is specifically used for according in the gender, age and lesson test achievement
Any two classify to each learner, establish father learner's monoid and sub- learner's monoid.
In some embodiments, the processing module 2 be specifically used for dividing multiple age brackets and multiple lesson tests at
Achievement section；Classified according to age bracket belonging to the age of learner to each learner, establishes multiple father learner's classes
Group；According to lesson test achievement section belonging to the lesson test achievement of learner, to every in each father learner's monoid
A learner classifies, and establishes multiple sub- learner's monoids of father learner's monoid.
In some embodiments, the processing module 2 is specifically used for dividing multiple age brackets；According to the gender pair of learner
Each learner classifies, and establishes two father learner's monoids；According to age bracket belonging to the age of learner to every
Each learner in a father learner's monoid classifies, and establishes multiple sub- learner's classes of father learner's monoid
Group.
In some embodiments, the processing module 2 is also used to obtain corresponding to each course in the course library
Multiple mark values；The mark value passes through significance level institute of the course learning interface to any course of terminal for a learner
The fractional value evaluated；Calculate separately the average value of the multiple mark value corresponding to each course；According to each course quilt
The number and the average value that the first learner monoid learnt obtain each course relative to first study
The weighted value of person's monoid；According to the sequence of the weighted value from big to small, the course in the course library is ranked up, is obtained
The corresponding course learning path optimizing of the first learner monoid；Finally obtain the corresponding course learning of each learner's monoid
Path optimizing.
In some embodiments, the expression formula of the weighted value are as follows:
wi=ami·exp(b·ni)
Wherein, wiFor the weighted value corresponding to i-th of course in the course library；miIt is described for i-th of course
The number that first learner's monoid learnt；niFor the average value corresponding to i-th of course；lijFor i-th of course institute
Corresponding j-th of mark value；J is the integer more than or equal to 1, indicates the quantity of learner；A, b is respectively the constant for being greater than 0；
A indicates miIn the weight of the expression formula；B indicates niIn the weight of the expression formula.
The weighted value wiThe number m learnt with course by the first learner monoidiAnd the average value ni
It is positively correlated.
Above-described embodiment is established learner's monoid by the characteristic information of learner, and is owned according in learner's monoid
The number that learner learns each course in course library establishes the corresponding learning route of learner's monoid, is all kinds of
Habit person plans suitable learning path, has accomplished to teach students in accordance with their aptitude, has improved the study specific aim and learning efficiency of learner.
Based on the same technical idea, the present invention also provides a kind of computer equipments, as shown in figure 5, the computer is set
Standby includes transceiver 901, processor 902 and memory 903, is stored with computer-readable instruction in the memory 903, described
When computer-readable instruction is executed by the processor 902, so that described in processor execution the respective embodiments described above
The learning path planing method based on big data the step of.
The corresponding entity device of transceiver module 1 shown in Fig. 4 is transceiver 901 shown in fig. 5,901 energy of transceiver
It enough realizes all or part of function of transceiver module 1, or realizes and the same or similar function of transceiver module 1.
The corresponding entity device of processing module 2 shown in Fig. 4 is processor 902 shown in fig. 5,902 energy of processor
It enough realizes all or part of function of processing module 2, or realizes and the same or similar function of transceiver module 1.
Based on the same technical idea, the present invention also provides a kind of storage medium for being stored with computer-readable instruction,
When the computer-readable instruction is executed by one or more processors, so that one or more processors execute above-mentioned each implementation
The step of learning path planing method based on big data in mode.
Through the above description of the embodiments, those skilled in the art can be understood that above-described embodiment side
Method can be realized by means of software and necessary general hardware platform, naturally it is also possible to by hardware, but in many cases
The former is more preferably embodiment.Based on this understanding, the technical solution of the application substantially in other words does the prior art
The part contributed out can be embodied in the form of software products, which is stored in a storage medium
In (such as ROM/RAM), including some instructions are used so that a terminal (can be mobile phone, computer, server or network are set
It is standby etc.) execute method described in each embodiment of the application.
Embodiments herein is described above in conjunction with attached drawing, but the application be not limited to it is above-mentioned specific
Embodiment, the above mentioned embodiment is only schematical, rather than restrictive, those skilled in the art
Under the enlightenment of the application, when not departing from the application objective and scope of the claimed protection, can also it make very much
Form, it is all using equivalent structure or equivalent flow shift made by present specification and accompanying drawing content, directly or indirectly
Other related technical areas are used in, these are belonged within the protection of the application.
Claims (10)
1. a kind of learning path planing method based on big data characterized by comprising
Obtain the characteristic information of multiple learners；The characteristic information of each learner includes at least gender, age and lesson test
One in achievement；
The characteristic information of each learner is inputted into Clustering Model, each learner is divided according to the characteristic information of learner
Class obtains multiple learner's monoids；
The number that each course in Statistics Course library was learnt by first learner's monoid respectively；The first learner class
Group is any learner's monoid in the multiple learner's monoid；Learnt according to course by the first learner monoid
Number from more to few sequence, the course in the course library is ranked up, it is corresponding to obtain the first learner monoid
Learning route；Finally obtain the corresponding learning route of each learner's monoid.
2. the learning path planing method according to claim 1 based on big data, which is characterized in that
The multiple learner includes the first learner；
It is described obtain the corresponding learning route of each learner's monoid after, the method also includes:
According to the characteristic information of first learner, for learner's monoid belonging to first learner matching；
Obtain target learning route corresponding to learner's monoid belonging to first learner；
Recommend the target learning route to first learner.
3. the learning path planing method according to claim 1 based on big data, which is characterized in that
The characteristic information according to learner classifies to each learner, obtains multiple learner's monoids, comprising:
Classified according to any two in the gender, age and lesson test achievement to each learner, establishes father
Habit person's monoid and sub- learner's monoid.
4. the learning path planing method according to claim 3 based on big data, which is characterized in that
Any two according in the gender, age and lesson test achievement classify to each learner, establish
Father learner's monoid and sub- learner's monoid, comprising:
Divide multiple age brackets and multiple lesson test achievement sections；
Classified according to age bracket belonging to the age of learner to each learner, establishes multiple father learner's classes
Group；
According to lesson test achievement section belonging to the lesson test achievement of learner, to every in each father learner's monoid
A learner classifies, and establishes multiple sub- learner's monoids of father learner's monoid.
5. the learning path planing method according to claim 3 based on big data, which is characterized in that
Any two according in the gender, age and lesson test achievement classify to each learner, establish
Father learner's monoid and sub- learner's monoid, comprising:
Divide multiple age brackets；
Classified according to the gender of learner to each learner, establishes two father learner's monoids；
Classified according to age bracket belonging to the age of learner to each learner in each father learner's monoid,
Establish multiple sub- learner's monoids of father learner's monoid.
6. the learning path planing method according to claim 1 based on big data, which is characterized in that
It is described obtain the corresponding learning route of each learner's monoid after, the method also includes:
Obtain multiple mark values corresponding to each course in the course library；The mark value is that a learner passes through end
The fractional value that the course learning interface at end evaluates the significance level of any course；
Calculate separately the average value of the multiple mark value corresponding to each course；
The number and the average value learnt according to each course by the first learner monoid, obtains each course
Weighted value relative to the first learner monoid；
According to the sequence of the weighted value from big to small, the course in the course library is ranked up, described first is obtained and learns
The corresponding course learning path optimizing of habit person's monoid；Finally obtain the corresponding course learning path optimizing of each learner's monoid.
7. the learning path planing method according to claim 6 based on big data, which is characterized in that
The expression formula of the weighted value are as follows:
wi=ami·exp(b·ni)
Wherein, wiFor the weighted value corresponding to i-th of course in the course library；miIt is i-th of course by described first
The number that learner's monoid learnt；niFor the average value corresponding to i-th of course；lijFor corresponding to i-th of course
J-th of mark value；J is the integer more than or equal to 1, indicates the quantity of learner；A, b is respectively the constant for being greater than 0；A table
Show miIn the weight of the expression formula；B indicates niIn the weight of the expression formula.
8. a kind of learning path device for planning based on big data characterized by comprising
Transceiver module, for obtaining the characteristic information of multiple learners；The characteristic information of each learner includes at least gender, year
One in age and lesson test achievement；
Processing module, for the characteristic information of each learner to be inputted Clustering Model, according to the characteristic information of learner to every
A learner classifies, and obtains multiple learner's monoids；Each course in Statistics Course library is by first learner's class respectively
The number that group learnt；The first learner monoid is any learner's monoid in the multiple learner's monoid；It presses
The number learnt by the first learner monoid according to course from more to few sequence, to the course in the course library into
Row sequence, obtains the corresponding learning route of the first learner monoid；It is corresponding to finally obtain each learner's monoid
Learning route.
9. a kind of computer equipment, which is characterized in that including transceiver, memory and processor, be stored in the memory
Computer-readable instruction, when the computer-readable instruction is executed by the processor, so that the processor executes such as right
It is required that the step in any learning path planing method based on big data in 1 to 7.
10. a kind of storage medium for being stored with computer-readable instruction, which is characterized in that the computer-readable instruction is by one
Or multiple processors are when executing so that one or more processors execute as described in any in claim 1 to 7 based on big
Step in the learning path planing method of data.
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