CN114820248A - Work interpolation method based on student ability portrait - Google Patents
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Abstract
The application provides a work interpolation method based on student ability portrait, which comprises the following steps: acquiring basic data of students, and extracting capability evaluation index data; judging whether to obtain a certificate or not according to the capability evaluation index data and carrying out differentiation labeling; constructing a capacity evaluation model according to the capacity evaluation index data; analyzing the learning ability of students with the same ability according to the ability evaluation model and the course learning data, and analyzing the learning ability of students with the same ability according to the ability evaluation model and the course learning data specifically comprises the following steps: analyzing the learning input degree based on the course learning data, analyzing the learning willingness based on the learning steps, and analyzing the potential problems of the students based on wrong questions; adjusting the ability evaluation model according to the learning ability of the student; constructing a matching model based on the capability evaluation model and the job hunting intention; based on the capability deviation, personalized courses are recommended.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the technical field of information, in particular to a work interpolation method based on student capability portrayal.
[ background of the invention ]
In recent years, the realization of post matching for talent recruitment in colleges and universities has become a problem of intensive research by many scholars. The personnel post matching is a bidirectional dynamic process, and currently, the personnel post matching system or method related to talent recruitment in colleges and universities in the market cannot evaluate the comprehensive capacity of students from multiple dimensions, and is difficult to help the students find proper work. For example, there are many laboratories in schools, and after training, some students pass an examination and get certificates at the same time, and some students do not pass the examination and get certificates. Generally, the number of students who obtain the same kind of school calendar and the same certificate is large, and it is difficult to accurately judge the ability of each student. In addition, the students have different bottom salary requirements for work, and if the consideration in multiple aspects cannot be integrated, the problems of high post, low energy, high energy, low post and the like can be caused. On one hand, students cannot effectively recognize own abilities, and meanwhile, enterprises are difficult to find suitable talents. If the problem of people post matching cannot be solved well, strong employment competition pressure can be brought to students, and meanwhile, the human resource cost of enterprises is increased.
[ summary of the invention ]
The invention provides a work interpolation method based on student ability portrait, which mainly comprises the following steps:
acquiring basic data of students, and extracting capability evaluation index data; judging whether to obtain a certificate or not according to the capability evaluation index data and carrying out differentiation labeling; constructing a capacity evaluation model according to the capacity evaluation index data; analyzing the learning ability of students with the same ability according to the ability evaluation model and the course learning data; adjusting the ability evaluation model according to the learning ability of the student; constructing a matching model based on the capability evaluation model and the job hunting intention; recommending personalized courses based on the capability deviation;
further optionally, the acquiring basic data of the student, and the extracting capability evaluation index data includes:
the student basic data comprises student calendar data, acquired certificate data and on-line course learning data; wherein, the scholarly data and the acquired certificate data are acquired by a school educational administration management system; the online course learning data is obtained through a school network course platform; performing data preprocessing according to the student basic data, classifying the data and extracting the capability evaluation index data, namely index ═ academic calendar, acquired certificate and course learning }, wherein the index represents the capability evaluation index; and (3) carrying out data cleaning on the acquired data by using an Extract-Transform-Load (ETL) mode, and deleting missing data, repeated data and error data.
Further optionally, the determining whether to obtain the certificate and perform differentiated labeling according to the capability evaluation index data includes:
firstly, a certificate list is constructed, wherein the certificate list is a list for recording all certificate types; and comparing and analyzing the capability evaluation index data with the data of the certificate list, screening out students with certificates and students without the certificates, marking the information obtained by the certificates in a differentiation way, and finally recording the result in the certificate grade state list.
Further optionally, the constructing a capability evaluation model according to the capability evaluation index data includes:
constructing a linear ability evaluation model according to the ability evaluation index data, wherein M is (E + S) multiplied by W, M represents student ability, E represents a course, wherein 0 represents a subject course, 1 represents a subject course, 2 represents a master research student course, 3 represents the above of the master research student course, S represents a course score sum, W represents a certificate level weight, and the certificate level weight is obtained from the certificate level state list; and evaluating the ability of the student through the linear ability evaluation model, wherein if the M value is larger, the ability of the student is stronger.
Further optionally, the analyzing the learning ability of the student with the same ability as the curriculum learning data according to the ability evaluation model comprises:
the course learning data comprises the course to be learned, the sign-in times of the course, the learning duration of the course, the work submission times, the learning step records, the interaction records, the wrong question records and the course scores; if the abilities of a plurality of students are the same through the analysis and calculation of the ability evaluation model, the learning abilities of the students are further analyzed, wherein the judgment indexes of the learning abilities of the students comprise course scores, learning enrollment degrees and learning willingness; firstly, establishing a student learning ability evaluation system, namely K-C + E + T)/3, wherein K represents the learning ability of the student, C represents the class achievement characteristics, if the class achievement is higher than the overall average achievement, C-1, otherwise C-0; e represents a learning input degree characteristic, if the learning input degree is higher than the overall average learning input degree, E is equal to 1, otherwise, E is equal to 0; t represents a learning intention value; analyzing and predicting the learning ability of the student according to a BP (Back-Propagation) neural network; determining a training sample and a testing sample according to the course learning data, wherein the training sample is used for neural network training, and the testing sample is used for detecting the relative error between an actual value and a predicted value; the method comprises the following steps: analyzing the learning input degree based on the course learning data; analyzing learning willingness based on the learning step; analyzing potential problems of students based on wrong problems;
the analyzing of the learning input degree based on the course learning data specifically comprises:
the learning input degree comprises the course learning duration analysis and the learning behavior analysis, the learning behavior is calculated and analyzed according to the classroom attendance times and the work completion times, and the learning input degree model is established, namely E is W1 multiplied by L + W2 multiplied by S + W3 multiplied by H, wherein E represents the input degree, W1, W2 and W3 represent different weights, W1+ W2+ W3 is 1, L represents the average learning duration of one-bit students, S represents the average check-in number of one-bit students, and H represents the work completion number of one-bit students. And calculating the learning input degrees of all students and calculating the average learning input degree of the whole. If the learning input degree of a certain student is higher than the average input degree, the student is indicated to be more input than other students.
The analyzing learning will based on the learning step specifically comprises:
and analyzing learning willingness according to the learning steps. The learning step refers to a learning sequence that the students finally reach qualified achievement through video learning, question-answer interaction and test. The video learning comprises course learning time length and course learning quantity. And classifying and screening the students by utilizing the learning step, wherein the students are classified into an autonomous type, an instructive type and a cooperative type. The self-contained mode means that the students independently complete video learning with more than the number of courses arranged by teachers in a specified time and pass tests; the cooperative type means that the students finish video learning of the number of courses arranged by a teacher through question and answer interaction in a specified time and pass a test; the guidance type means that the class of students do not complete the teacher's video learning of the scheduled lesson and do not pass the quiz. A learning intention model is constructed, namely a ═ Wi x (1-P), wherein a represents learning intention, i ═ {1,2,3}, W1 represents autonomous weight and W1 ═ 1, W2 represents cooperative weight and W2 ═ 0.5, W3 represents guidance weight and W3 ═ 0.1, and P represents ranking proportion of learning intention of a certain student in a certain class.
The analysis of the potential problems of the students based on the wrong questions specifically comprises the following steps:
obtaining student wrong question records according to the course learning data, wherein the student wrong question records comprise a question score table and a wrong question and knowledge point corresponding relation table, mining the relation between all wrong question and knowledge points by using an association rule mining algorithm Apriori algorithm, judging the strength of the association of the knowledge points by comparing the support degree and the confidence coefficient, drawing a knowledge point association network diagram according to the association rule, and the knowledge point association network diagram shows the potential problems of students.
Further optionally, the adjusting, by the students who obtain the same ability certificate, the ability evaluation model according to the learning ability of the students includes:
adjusting the capability evaluation model variables according to the learning capability of the student to construct a new capability evaluation model; the new ability evaluation model comprises student specials and student comprehensive abilities, wherein the student specials are analyzed by the course achievement Top-N related courses to obtain related abilities; the student comprehensive ability, namely M ═ E + S x W + K, M represents the student comprehensive ability, E represents the academic calendar, S represents the school score sum, W represents the certificate level weight, (E + S) x W represents the student objective professional ability, and K represents the student learning ability; and if the objective professional abilities of the students of a plurality of students are the same through analysis and calculation, the abilities of the students are determined by the learning ability values of the students.
Further optionally, the constructing a matching model based on the ability evaluation model and the intention to seek employment includes:
analyzing the matching degree between the student and the enterprise recruitment key index by utilizing the capability evaluation model and the job hunting intention, and establishing a matching model; wherein the intention to seek the job comprises intention position and bottom salary requirements, and data of the intention position and the bottom salary requirements are obtained from the resume of the student; if the enterprise recruitment key index appears in the resume text of the student, the text is differentially represented by a color; meanwhile, standardizing the employment indexes of students and enterprises to construct the characteristic attributes of the students; if the students have the enterprise employment index, marking the enterprise employment index by using a specific numerical value; if the student does not have the enterprise employment index, marking the student with another specific numerical value; calculating the similarity of the two groups according to a K-means clustering recommendation algorithm, wherein if the similarity is higher, the matching degree of the student and the employment unit is higher; finally, recording the numerical value of the student ability in a resume and recommending Top-N students with high matching degree to an enterprise; and if the enterprise finds that a plurality of students obtain the same certificate and similar evaluation through resume judgment, performing comprehensive judgment according to the student capability values marked by the student resumes.
Further optionally, the recommending personalized courses based on the capability deviation includes:
the personalized course recommendation module comprises a data processing module, a neural network model construction module and a personalized course generation module; judging whether capacity deviation analysis is needed or not according to the resume refund times, and if the resume refund times are higher than a specified threshold value, analyzing the capacity deviation; the data processing module comprises a data processing module and a neural network model building module, wherein the data processing module is used for obtaining student course learning data, analyzing student resumes and enterprise recruitment indexes by utilizing a deep learning RNN algorithm, finding out the capability deviation and building student capability deviation vectors, namely LA (LA) { c1, c2, c3, … and cn }, and the neural network model building module is used for building and training a Convolutional Neural Network (CNN) model by utilizing the data of the data processing module; and the personalized course generation module carries out personalized course prediction by utilizing the trained convolutional neural model.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
in order to solve the problems, the invention provides a work interpolation method and system based on student ability portraits, which analyzes the abilities of students by establishing the student portraits and an ability evaluation model, realizes classmates with the same apparent abilities, obtains which classmates are more excellent under the same performances after carrying out fine analysis, and simultaneously enables the abilities of students to be matched with employment posts. And personalized course training is provided by analyzing the capability deviation of the students, the students are helped to improve professional skills, employment is better realized, and the cost of human resources of enterprises is saved.
[ description of the drawings ]
FIG. 1 is a flow chart of a work interpolation method based on student ability portrayal according to the present invention.
FIG. 2 is a diagram illustrating a work interpolation method based on student ability portrayal according to the present invention.
[ detailed description ] embodiments
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1 and fig. 2, the work interpolation method based on student ability portraits of the present embodiment may specifically include:
step 101, acquiring basic data of students and extracting capability evaluation index data.
The student basic data includes the study data, the acquired certification data and the on-line course learning data. Wherein, the scholarly data and the acquired certificate data are acquired by a school educational administration management system; the online course learning data is obtained through a school network course platform. And performing data preprocessing according to the student basic data, classifying the data and extracting the capability evaluation index data, namely index ═ academic calendar, acquired certificate and course learning, wherein the index represents the capability evaluation index. The acquired data is subjected to data cleaning in an Extract-Transform-Load (ETL) mode, and missing data, repeated data and error data are deleted.
And 102, judging whether to obtain the certificate or not according to the capability evaluation index data and carrying out differentiation labeling.
Firstly, a certificate list is constructed, wherein the certificate list is a list for recording all certificate types. And comparing and analyzing the capability evaluation index data with the data of the certificate list, screening out students with certificates and students without the certificates, marking the information obtained by the certificates in a differentiation way, and finally recording the result in the certificate grade state list.
And 103, constructing a capacity evaluation model according to the capacity evaluation index data.
And constructing a linear ability evaluation model according to the ability evaluation index data, wherein M is (E + S) multiplied by W, M represents student ability, E represents a course, wherein 0 represents a subject course, 1 represents a subject course, 2 represents a master research student course, 3 represents the above of the master research student course, S represents a course score sum, W represents a certificate level weight, and the certificate level weight is obtained from the certificate level state list. And evaluating the ability of the student through the linear ability evaluation model, wherein if the M value is larger, the ability of the student is stronger.
And step 104, analyzing the learning ability of students with the same ability according to the ability evaluation model and the course learning data.
The course learning data comprises the course to be learned, the number of times the course is signed in, the learning duration of the course, the number of times of job submission, the records of learning steps, interactive records, wrong subject records and the score of the course. And if the abilities of a plurality of students are the same through the analysis and calculation of the ability evaluation model, further analyzing the learning abilities of the students, wherein the judgment indexes of the learning abilities of the students comprise the class achievement, the learning enrollment and the learning willingness. Firstly, establishing a student learning ability evaluation system, namely K-C + E + T)/3, wherein K represents the learning ability of the student, C represents the class achievement characteristics, if the class achievement is higher than the overall average achievement, C-1, otherwise C-0; e represents a learning input degree characteristic, if the learning input degree is higher than the overall average learning input degree, E is equal to 1, otherwise, E is equal to 0; t represents a learning intention value. And analyzing and predicting the learning ability of the student according to a BP (Back-Propagation) neural network. And determining a training sample and a testing sample according to the course learning data, wherein the training sample is used for neural network training, and the testing sample is used for detecting the relative error between the actual value and the predicted value.
And analyzing the learning input degree based on the course learning data.
The learning input degree comprises the course learning duration analysis and the learning behavior analysis, the learning behavior is calculated and analyzed according to the classroom attendance times and the work completion times, and the learning input degree model is established, namely E is W1 multiplied by L + W2 multiplied by S + W3 multiplied by H, wherein E represents the input degree, W1, W2 and W3 represent different weights, W1+ W2+ W3 is 1, L represents the average learning duration of one-bit students, S represents the average check-in number of one-bit students, and H represents the work completion number of one-bit students. And calculating the learning input degrees of all students and calculating the average learning input degree of the whole. If the learning input degree of a certain student is higher than the average input degree, the student is indicated to be more input than other students.
Based on the learning step, learning will is analyzed.
And analyzing learning willingness according to the learning steps. The learning step refers to a learning sequence that the students finally reach qualified achievement through video learning, question-answer interaction and test. The video learning comprises course learning duration and course learning number. And classifying and screening the students by utilizing the learning step, wherein the students are classified into an autonomous type, an instructive type and a cooperative type. The self-help mode means that the students independently complete video learning with more than the number of courses arranged by teachers in a specified time and pass tests; the cooperative type means that the students finish video learning of the number of courses arranged by a teacher through question and answer interaction in a specified time and pass a test; the guidance type means that the class of students do not complete the teacher's video learning of the scheduled lesson and do not pass the quiz. A learning intention model is constructed, namely a ═ Wi x (1-P), wherein a represents learning intention, i ═ {1,2,3}, W1 represents autonomous weight and W1 ═ 1, W2 represents cooperative weight and W2 ═ 0.5, W3 represents guidance weight and W3 ═ 0.1, and P represents ranking proportion of learning intention of a certain student in a certain class.
Potential problems of students are analyzed based on wrong questions.
Acquiring student wrong question records according to course learning data, wherein the student wrong question records comprise a question score table and a wrong question and knowledge point corresponding relation table, mining the association relation among all wrong question corresponding knowledge points by using an association rule mining algorithm Apriori algorithm, judging the strength of the association of the knowledge points by comparing support degree and confidence, drawing a knowledge point association network diagram according to association rules, and the knowledge point association network diagram shows potential problems of students.
And 105, adjusting the ability evaluation model for the students with the same ability certificate according to the learning ability of the students.
And adjusting the variables of the ability evaluation model according to the learning ability of the students, and constructing a new ability evaluation model. The new ability evaluation model comprises student specials and student comprehensive abilities, wherein the student specials are analyzed by the course achievement Top-N related courses to obtain related abilities; the student integrated ability, that is, M ═ E + S) × W + K, M represents student integrated ability, E represents the academic calendar, S represents the curriculum score sum, W represents the certificate level weight, (E + S) × W represents student objective professional ability, and K represents student learning ability. And if the objective professional abilities of the students of a plurality of students are the same through analysis and calculation, the abilities of the students are determined by the learning ability values of the students.
And 106, constructing a matching model based on the capability evaluation model and the job hunting intention.
And analyzing the matching degree between the student and the enterprise recruitment key index by utilizing the capability evaluation model and the job hunting intention, and establishing a matching model. The job hunting intention comprises intention position and bottom salary requirements, and data of the intention position and the bottom salary requirements are obtained from student resumes. And if the enterprise recruitment key index appears in the resume text of the student, the text is differentially represented by a color. Meanwhile, the student and enterprise employment indexes are subjected to standardized processing, and student characteristic attributes are constructed. If the students have the enterprise employment index, marking the enterprise employment index by using a specific numerical value; if the student does not have the corporate employment indicator, the student is labeled with another specific numerical value. And calculating the similarity of the two groups according to a K-means clustering recommendation algorithm, wherein if the similarity is higher, the matching degree of the student and the employing unit is higher. And finally, recording the numerical value of the student ability in a resume and recommending Top-N students with high matching degree to an enterprise. And if the enterprise finds that a plurality of students obtain the same certificate and similar evaluation through resume judgment, performing comprehensive judgment according to the student capability values marked by the student resumes.
And step 107, recommending personalized courses based on the capability deviation.
The personalized course recommendation module comprises a data processing module, a neural network model building module and a personalized course generating module. And judging whether the capability deviation analysis is needed or not according to the resume refund times, and if the resume refund times are higher than a specified threshold, analyzing the capability deviation. The data processing module comprises a data processing module and a neural network model building module, wherein the data processing module is used for obtaining student course learning data, analyzing student resumes and enterprise recruitment indexes by utilizing a deep learning RNN algorithm, finding out the capability deviation and building student capability deviation vectors, namely LA (LA) { c1, c2, c3, … and cn }, and the neural network model building module is used for building and training a Convolutional Neural Network (CNN) model by utilizing the data of the data processing module; and the personalized course generation module carries out personalized course prediction by utilizing the trained convolutional neural model.
The above description is only an embodiment of the present invention, and is not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes performed by the present invention or directly or indirectly applied to other related technical fields are included in the scope of the present invention.
Programs for implementing the information governance of the present invention may be written in computer program code for carrying out operations of the present invention in one or more programming languages, including an object oriented programming language such as Java, python, C + +, or a combination thereof, as well as conventional procedural programming languages, such as the C language or similar programming languages.
The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present invention.
And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Claims (8)
1. A work interpolation method based on student ability portrayal, the method comprising:
acquiring basic data of students, and extracting capability evaluation index data; judging whether to obtain a certificate or not according to the capability evaluation index data and carrying out differentiation labeling; constructing a capacity evaluation model according to the capacity evaluation index data; analyzing the learning ability of students with the same ability according to the ability evaluation model and the course learning data, and analyzing the learning ability of students with the same ability according to the ability evaluation model and the course learning data specifically comprises the following steps: analyzing the learning input degree based on the course learning data, analyzing the learning willingness based on the learning steps, and analyzing the potential problems of the students based on wrong questions; for students who obtain the same ability certificate, adjusting an ability evaluation model according to the learning ability of the students; constructing a matching model based on the capability evaluation model and the job hunting intention; based on the capability deviation, personalized courses are recommended.
2. The method of claim 1, wherein the obtaining student base data and extracting capability evaluation index data comprises:
the student basic data comprises student calendar data, acquired certificate data and on-line course learning data; wherein, the scholarly data and the acquired certificate data are acquired by a school educational administration management system; the online course learning data is obtained through a school network course platform; performing data preprocessing according to the student basic data, classifying the data and extracting the capability evaluation index data, namely index ═ academic calendar, acquired certificate and course learning }, wherein the index represents the capability evaluation index; the acquired data is subjected to data cleaning in an Extract-Transform-Load (ETL) mode, and missing data, repeated data and error data are deleted.
3. The method according to claim 1, wherein the determining whether to obtain the certificate and perform differentiation labeling according to the capability evaluation index data includes:
firstly, a certificate list is constructed, wherein the certificate list is a list for recording all certificate types; and comparing and analyzing the capability evaluation index data with the data of the certificate list, screening out students with certificates and students without the certificates, marking the information obtained by the certificates in a differentiation way, and finally recording the result in the certificate grade state list.
4. The method of claim 1, wherein the constructing a competency evaluation model from competency evaluation index data comprises:
constructing a linear ability evaluation model according to the ability evaluation index data, wherein M is (E + S) multiplied by W, M represents student ability, E represents a course, wherein 0 represents a subject course, 1 represents a subject course, 2 represents a master research student course, 3 represents the above of the master research student course, S represents a course score sum, W represents a certificate level weight, and the certificate level weight is obtained from the certificate level state list; and evaluating the ability of the student through the linear ability evaluation model, wherein if the M value is larger, the ability of the student is stronger.
5. The method as claimed in claim 1, wherein said analyzing learning abilities of students of the same abilities as the lesson learning data according to the ability evaluation model comprises:
the course learning data comprises the course to be learned, the sign-in times of the course, the learning duration of the course, the work submission times, the learning step records, the interaction records, the wrong question records and the course scores; if the abilities of a plurality of students are the same through the analysis and calculation of the ability evaluation model, the learning abilities of the students are further analyzed, wherein the judgment indexes of the learning abilities of the students comprise course scores, learning enrollment degrees and learning willingness; firstly, establishing a student learning ability evaluation system, namely K-C + E + T)/3, wherein K represents the learning ability of the student, C represents the class achievement characteristics, if the class achievement is higher than the overall average achievement, C-1, otherwise C-0; e represents a learning input degree characteristic, if the learning input degree is higher than the overall average learning input degree, E is equal to 1, otherwise, E is equal to 0; t represents a learning intention value; analyzing and predicting the learning ability of the student according to a BP (Back-Propagation) neural network; determining a training sample and a testing sample according to the course learning data, wherein the training sample is used for neural network training, and the testing sample is used for detecting the relative error between an actual value and a predicted value; the method comprises the following steps: analyzing the learning input degree based on the course learning data; analyzing learning willingness based on the learning step; analyzing potential problems of students based on wrong problems;
the analyzing of the learning input degree based on the course learning data specifically comprises:
the learning input degree comprises the course learning duration analysis and the learning behavior analysis, the learning behavior is calculated and analyzed according to the classroom attendance times and the work completion times, and a learning input degree model is established, namely E is W1 xL + W2 xS + W3 xH, wherein E represents the input degree, W1, W2 and W3 represent different weights, W1+ W2+ W3 is 1, L represents the average learning duration of one-bit students, S represents the average check-in number of one-bit students, and H represents the work completion number of one-bit students; calculating the learning input degrees of all students, and meanwhile, calculating the average learning input degree of the whole; if the learning input degree of a certain student is higher than the average input degree, the student is indicated to be input more than other students for learning;
the analyzing learning will based on the learning step specifically comprises:
analyzing learning willingness according to the learning steps; the learning step refers to a learning sequence that the students finally reach qualified achievement through video learning, question-answer interaction and test; the video learning comprises course learning time length and course learning quantity; classifying and screening students by utilizing the learning step, wherein the students are classified into an autonomous type, a guiding type and a cooperative type; the self-contained mode means that the students independently complete video learning with more than the number of courses arranged by teachers in a specified time and pass tests; the cooperative type means that the students finish video learning of the number of courses arranged by a teacher through question and answer interaction in a specified time and pass a test; the guidance type means that the students do not finish the video learning of the teacher arrangement course and do not pass the test; building a learning intention model, namely a ═ Wi x (1-P), wherein a represents learning intention, i ═ {1,2,3}, W1 represents an autonomous weight and W1 ═ 1, W2 represents a cooperative weight and W2 ═ 0.5, W3 represents a guiding weight and W3 ═ 0.1, and P represents the ranking proportion of learning intention of a certain student in a certain class;
the analysis of the potential problems of the students based on the wrong questions specifically comprises the following steps:
obtaining student wrong question records according to the course learning data, wherein the student wrong question records comprise a question score table and a wrong question and knowledge point corresponding relation table, mining the relation between all wrong question and knowledge points by using an association rule mining algorithm Apriori algorithm, judging the strength of the association of the knowledge points by comparing the support degree and the confidence coefficient, drawing a knowledge point association network diagram according to the association rule, and the knowledge point association network diagram shows the potential problems of students.
6. The method of claim 1, wherein the adjusting, for students who obtain the same competency certificate, a competency evaluation model according to the student's learning ability comprises:
adjusting the capability evaluation model variables according to the learning capability of the student to construct a new capability evaluation model; the new ability evaluation model comprises student specialties and student comprehensive abilities, wherein the student specialties are analyzed by the class achievement Top-N related classes to obtain related abilities; the student comprehensive ability, namely M ═ E + S x W + K, M represents the student comprehensive ability, E represents the academic calendar, S represents the school score sum, W represents the certificate level weight, (E + S) x W represents the student objective professional ability, and K represents the student learning ability; and if the objective professional abilities of the students of a plurality of students are the same through analysis and calculation, the abilities of the students are determined by the learning ability values of the students.
7. The method of claim 1, wherein the building a matching model based on the competency evaluation model and the intent-to-seek employment comprises:
analyzing the matching degree between the student and the enterprise recruitment key index by utilizing the capability evaluation model and the job hunting intention, and establishing a matching model; wherein the intention to seek the job comprises intention position and bottom salary requirements, and data of the intention position and the bottom salary requirements are obtained from the resume of the student; if the enterprise recruitment key index appears in the resume text of the student, the text is differentially represented by a color; meanwhile, standardizing the employment indexes of students and enterprises to construct the characteristic attributes of the students; if the students have the enterprise employment index, marking the enterprise employment index by using a specific numerical value; if the student does not have the enterprise employment index, marking the student with another specific numerical value; calculating the similarity of the two according to a K-means clustering recommendation algorithm, wherein if the similarity is higher, the matching degree of the student and the personnel unit is higher; finally, recording the numerical value of the student ability in a resume and recommending Top-N students with high matching degree to an enterprise; and if the enterprise finds that a plurality of students obtain the same certificate and similar evaluation through resume judgment, performing comprehensive judgment according to the student capability values marked by the student resumes.
8. The method of claim 1, wherein recommending personalized lessons based on the capability deviation comprises:
the personalized course recommendation module comprises a data processing module, a neural network model construction module and a personalized course generation module; judging whether capacity deviation analysis is needed or not according to the resume refund times, and if the resume refund times are higher than a specified threshold value, analyzing the capacity deviation; the data processing module comprises a data processing module and a neural network model building module, wherein the data processing module is used for obtaining student course learning data, analyzing student resumes and enterprise recruitment indexes by utilizing a deep learning RNN algorithm, finding out the capability deviation and building student capability deviation vectors, namely LA (LA) { c1, c2, c3, … and cn }, and the neural network model building module is used for building and training a Convolutional Neural Network (CNN) model by utilizing the data of the data processing module; and the personalized course generation module carries out personalized course prediction by utilizing the trained convolutional neural model.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116431902A (en) * | 2023-03-10 | 2023-07-14 | 青软创新科技集团股份有限公司 | Post recommendation method, computing equipment and storage medium |
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| CN117236722A (en) * | 2023-11-13 | 2023-12-15 | 光合新知(北京)科技有限公司 | Online teaching auxiliary method and system |
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| CN117910994A (en) * | 2024-03-19 | 2024-04-19 | 浙江之科智慧科技有限公司 | Course recommendation method, system and storage medium based on deep learning |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116431902A (en) * | 2023-03-10 | 2023-07-14 | 青软创新科技集团股份有限公司 | Post recommendation method, computing equipment and storage medium |
| CN116523704A (en) * | 2023-04-03 | 2023-08-01 | 广州市德慷电子有限公司 | Medical practice teaching decision method based on big data |
| CN116523704B (en) * | 2023-04-03 | 2023-12-12 | 广州市德慷电子有限公司 | Medical practice teaching decision method based on big data |
| CN117236722A (en) * | 2023-11-13 | 2023-12-15 | 光合新知(北京)科技有限公司 | Online teaching auxiliary method and system |
| CN117689117A (en) * | 2024-01-30 | 2024-03-12 | 湖南破壳智能科技有限公司 | Intelligent chemical industry planning consultation method and system |
| CN117689117B (en) * | 2024-01-30 | 2024-05-03 | 湖南破壳智能科技有限公司 | Intelligent chemical industry planning consultation method and system |
| CN117910994A (en) * | 2024-03-19 | 2024-04-19 | 浙江之科智慧科技有限公司 | Course recommendation method, system and storage medium based on deep learning |
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