CN111667178A - Evaluation and recommendation method and device for teachers in training institutions, electronic equipment and medium - Google Patents
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Abstract
The application provides an evaluation and recommendation method and device for teachers in training institutions, electronic equipment and a medium. The method comprises the following steps: the method comprises the steps of obtaining information of multiple dimensions of at least one teacher, obtaining characteristic information of at least one student and requirement information of the teacher in each dimension; determining a teacher evaluation dimension matrix and determining a student evaluation dimension matrix; determining the grade value of each dimension of each teacher in the teacher evaluation dimension matrix according to the teacher evaluation rule tree in the rule base to obtain a teacher evaluation result matrix; according to the student evaluation rule tree in the rule base, determining expected values of all dimensions of the teacher in the student evaluation dimension matrix to obtain a student investigation result matrix; determining the similarity between the requirements of each student and each teacher; and showing the information of a plurality of teachers with higher similarity to the requirements of the students to each student. The method realizes intelligent evaluation and intelligent recommendation of the comprehensive qualification of the teacher.
Description
Technical Field
The application relates to the technical field of teacher recommendation, in particular to an evaluation and recommendation method, device, electronic equipment and medium for teachers in training institutions.
Background
At present, the education and training industry in China develops rapidly, the market scale is huge, and the number of education and training structures reaches hundreds of thousands of huge. However, the quality of teachers, which is a resource of the education core, is uneven, and training institutions too pursue economic benefits, so that the teaching quality is not high as a whole, and a professional and objective teacher evaluation system is not provided, the horizontal ability of teachers is not determined, corresponding teachers cannot be matched through the personal characteristics of students, the teaching effect is not good due to the fact that the students teach according to the nature, and the benefits of the students cannot be maximized.
The conventional teacher recruitment strategy basically only adopts an education background and work experience manual review mode, has single evaluation dimension on the qualification of teachers, and generally adopts manual judgment, namely resume and interview. Meanwhile, when the students select the teachers, the students lack quantitative and comprehensive evaluation on the aspects of the horizontal ability, the character characteristics, the personal preference and the like of the students, and the students passively select the teachers who give lessons, so that the students cannot intelligently match the teachers suitable for the students according to the characteristics of the students.
The defects lead to the fact that teaching resources can not be efficiently utilized, the teaching effectiveness is low due to the fact that the materials are used, the capacity of students is limited to be improved, and benefits of resource providers and resource consumers cannot be maximized.
Disclosure of Invention
The application provides an evaluation and recommendation method, device, electronic equipment and medium for teachers in training institutions, and aims to at least solve one of the technical defects. The technical scheme adopted by the application is as follows:
in a first aspect, an embodiment of the present application provides an evaluation and recommendation method for teachers of training institutions, including: the method comprises the steps of obtaining information of multiple dimensions of at least one teacher, obtaining characteristic information of at least one student and requirement information of the teacher in each dimension;
determining a teacher evaluation dimension matrix according to the information of multiple dimensions of each teacher; determining a student evaluation dimension matrix according to the feature information of each student and the requirement information of each dimension of the teacher;
determining the grade value of each dimension of each teacher in the teacher evaluation dimension matrix according to the teacher evaluation rule tree in the rule base to obtain a teacher evaluation result matrix; according to the student evaluation rule tree in the rule base, determining expected values of all dimensions of the teacher in the student evaluation dimension matrix to obtain a student investigation result matrix;
determining the similarity between the requirements of each student and each teacher according to the student investigation result matrix and the teacher evaluation result matrix; and showing the information of a plurality of teachers with higher similarity to the requirements of the students to each student.
In one embodiment of the present application, the teacher's information in the plurality of dimensions includes a combination of one or more of: the sex of the teacher, the age of the teacher, the working duration of the teacher, the NPS value of the teacher, the commenting ratio of the teacher, the proficiency subject of the teacher, the teaching style of the teacher, the academic course of the teacher and the graduation colleges of the teacher; and the teacher evaluates each row element of each row in the dimension matrix, and the row elements are information of each dimension of the corresponding teacher.
In one embodiment of the application, the teacher evaluation rule tree takes the teacher as a root node, each dimensionality of the teacher serves as a second-layer node, leaf nodes of each dimensionality are a plurality of evaluation rule key value pairs, and each evaluation rule key value pair comprises an evaluation item and a quantitative score corresponding to the evaluation item;
determining the scoring value of each dimension of each teacher in the teacher evaluation dimension matrix according to the teacher evaluation rule tree in the rule base to obtain a teacher evaluation result matrix, wherein the teacher evaluation result matrix comprises the following steps: traversing the information of each dimension of each teacher in the teacher evaluation dimension matrix; when the information of each determined dimension is the same as one evaluation item in the teacher evaluation rule tree, taking the corresponding quantitative score of the evaluation item as the score of the dimension; and when the scoring values of all dimensions in the teacher evaluation dimension matrix are determined, the teacher evaluation dimension matrix is converted into a teacher evaluation result matrix.
In one embodiment of the present application, the characteristic information of the student includes a combination of one or more of: basic information, hobbies, character characteristics and at least one subject level of ability;
the requirement information of the students for each dimension of the teacher comprises one or more of the following items: the method comprises the following steps of requiring sex of a teacher, requiring age of the teacher, requiring working time of the teacher, requiring an NPS value of the teacher, requiring a good comment proportion of the teacher, requiring proficiency subjects of the teacher, requiring teaching style of the teacher, requiring a academic calendar of the teacher and requiring college colleges of graduates of the teacher;
the students evaluate the row elements of each row in the dimension matrix, and the row elements are the characteristic information of the corresponding students and the requirement information of the teacher in each dimension.
In one embodiment of the application, the student evaluation rule tree takes students as root nodes, each dimensionality of a teacher is taken as a second-layer node, leaf nodes of each dimensionality are a plurality of evaluation rule key value pairs, and the evaluation rule key value pairs comprise evaluation items and quantitative scores corresponding to the evaluation items;
according to the student evaluation rule tree in the rule base, determining expected values of all dimensions of the teacher in the student evaluation dimension matrix to obtain a student investigation result matrix, wherein the method comprises the following steps: traversing the demand information of each student in the student evaluation dimension matrix for each dimension of the teacher; when the condition that the requirement information of each dimensionality of the teacher is the same as one evaluation item in the student evaluation rule tree is determined, taking the corresponding quantitative score of the evaluation item as an expected value of each dimensionality of the teacher; and when all expected values of the dimensionality of the teacher in the student evaluation dimensionality matrix are determined, the student evaluation dimensionality matrix is converted into a student investigation result matrix.
In one embodiment of the present application, determining the similarity between the requirement of each student and each teacher according to the student investigation result matrix and the teacher evaluation result matrix includes: taking row elements of a row corresponding to each student in the student investigation result matrix as a first row vector; each vector element in the first row of vectors is the expected value of each dimension of the teacher for the student; taking row elements of each teacher corresponding row in the teacher evaluation result matrix as second row vectors; each vector element in the second row vector is the scoring value of each dimensionality of the teacher; and solving the similarity between each first row vector and each second row vector in a cosine similarity calculation mode, and taking each similarity obtained by the solving as the similarity between the requirement of each student and the corresponding teacher.
In one embodiment of the application, the information of a plurality of teachers with higher similarity to the requirements of the student is presented to each student, and the method comprises the following steps: sequencing the similarity between the requirements of the students and the teachers from high to low, and displaying the information of the teachers with the similarity ranking N at the top to the students; n is more than or equal to 1 and is a positive integer.
In a second aspect, an embodiment of the present application provides an evaluation and recommendation device for teachers of training institutions, including: the terminal information input module is used for acquiring information of multiple dimensions of at least one teacher, and acquiring characteristic information of at least one student and demand information of each dimension of the teacher;
the data regularization processing module is used for determining teacher evaluation dimension matrixes according to the information of multiple dimensions of each teacher; determining a student evaluation dimension matrix according to the feature information of each student and the requirement information of each dimension of the teacher;
the rule base module is used for providing a teacher evaluation rule tree and a student evaluation rule tree;
the teacher qualification evaluation module is used for determining the grade value of each dimension of each teacher in the teacher evaluation dimension matrix according to the teacher evaluation rule tree in the rule base to obtain a teacher evaluation result matrix;
the student condition investigation module is used for determining expected values of all dimensions of the teacher in the student evaluation dimension matrix according to the student evaluation rule tree in the rule base to obtain a student investigation result matrix;
the teacher-student matching recommendation module is used for determining the similarity between the requirements of each student and each teacher according to the student investigation result matrix and the teacher evaluation result matrix; and showing the information of a plurality of teachers with higher similarity to the requirements of the students to each student.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor and a memory; the memory is configured to store machine readable instructions that, when executed by the processor, cause the processor to perform the method for evaluation and recommendation by a teacher of a training institution provided in embodiments of the present application.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having a computer program stored thereon, where the computer program, when executed by a processor, implements the method for evaluating and recommending a teacher of a training institution provided by embodiments of the present application.
The technical scheme provided by the embodiment of the application has the following beneficial technical effects:
in the embodiment of the application, the limitation of a traditional evaluation mode is broken through aiming at evaluation of teachers and students and pain points matched with teachers and students in the education and training industry, and various characteristics of the teachers are quantified in a digital form on the basis of information of multiple dimensions of the teachers, so that intelligent evaluation of comprehensive qualification of the teachers is realized; meanwhile, multiple kinds of information of students are quantized, and intelligent recommendation of teachers is achieved in the mode. The essence of the system applied to the evaluation and recommendation method is based on big data technology, dynamic acquisition and management of multi-dimensional information of teachers are achieved, a permanent file (knowledge base) of the teachers is built in the system after new teacher information is input into the system, the file can be continuously updated and perfected, a nationwide teacher information knowledge base can be finally built, teacher information is recorded in an all-around mode, and qualification certification service can be provided for third-party institutions.
The assessment and recommendation method is based on the assessment mechanism and the recommendation mechanism of the knowledge base and the rule base, the system assesses the qualification of the teachers from multiple dimensions and fine granularity levels relatively, accurately and objectively, and due to the fact that the qualification of the teachers can be automatically assessed, assessment efficiency of the qualification of the teachers is greatly improved, the practice level of the teachers is effectively mastered, and the phenomenon that the teachers are unsmooth in resources is avoided. Meanwhile, the intelligent matching is realized by combining the characteristics of students, the teacher resource is efficiently utilized, the purpose of teaching according to the nature can be realized, the capability level of the students can be more quickly improved, the benefits of the students can be maximized, and the win-win situation of institutions, teachers and students can be realized.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow chart of a method for evaluating and recommending teachers for a training institution according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an architecture of a teacher evaluation rule tree and a student evaluation rule tree according to an embodiment of the present application;
FIG. 3 is a block diagram of an evaluation and recommendation device for teachers of a training institution according to an embodiment of the present application;
FIG. 4 is a schematic diagram illustrating steps performed by modules of an evaluation and recommendation device for teachers of a training institution according to an embodiment of the present disclosure;
fig. 5 is a module schematic diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to the present application, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar parts or parts having the same or similar functions throughout.
It will be understood by those within the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
The embodiment of the application provides an evaluation and recommendation method for teachers in training institutions, a flow chart of the method is shown in fig. 1, and the method comprises the following steps:
s101: the method comprises the steps of obtaining information of multiple dimensions of at least one teacher, obtaining characteristic information of at least one student and requirement information of the teacher in each dimension.
In one embodiment of the present application, the teacher's information in the plurality of dimensions includes a combination of one or more of: the sex of the teacher, the age of the teacher, the working duration of the teacher, the NPS value of the teacher, the goodness proportion of the teacher, the subjects good at the teacher, the teaching style of the teacher, the academic course of the teacher and the graduation colleges of the teacher.
It should be noted that the information of the dimension is the lower level of the dimension, and each dimension includes a plurality of kinds of information. For example, the sex is a dimension, the teacher's sex is information that a male and the teacher's sex is a female, or the age is a dimension, and the teacher's age is information that 25 years is an age.
And acquiring information of a plurality of dimensionalities of the teacher for establishing a teacher information base. The method for acquiring the information of the teacher with multiple dimensions can be divided into two modes of terminal entry and network information acquisition. The terminal input adopts a standard format information table provided by the system to acquire information, and the information table comprises basic information acquisition, education background information acquisition, work experience information acquisition, academic certificate information acquisition, honor certificate information acquisition and the like. And the network information acquisition adopts a network crawler algorithm to acquire relevant information of the teacher from the network, including network evaluation, network popularity and the like. The collection of the network information can be executed regularly through a timing task and updated to a teacher information base.
In one embodiment of the present application, the characteristic information of the student includes a combination of one or more of: basic information, hobbies, character characteristics, and at least one level of capability level for a subject.
In one embodiment of the present application, the student's need information for each of the teacher's dimensions includes a combination of one or more of: the method comprises the following steps of requiring sex of a teacher, requiring age of the teacher, requiring working time of the teacher, requiring an NPS value of the teacher, requiring a good comment proportion of the teacher, requiring proficiency subjects of the teacher, requiring teaching style of the teacher, requiring a academic calendar of the teacher and requiring college colleges of graduates of the teacher.
And acquiring characteristic information of students and demand information of teachers in all dimensions for establishing a student information base. The characteristic information of the students and the requirement information of the teacher in each dimension are acquired by adopting a terminal entry mode, and the form with the standard format provided by the system is used for the students to fill in related information, such as basic information Hell interest and hobbies. Meanwhile, the system also provides a character test and a capability level test, and obtains character characteristics and at least one subject capability level hierarchy according to the test result, wherein the capability level test can be carried out according to the corresponding subject selected by the student. Meanwhile, a questionnaire is made for a plurality of dimensions of the teacher, so that the teacher type required by the students is filled, and the requirement information of the students for each dimension of the teacher is obtained through a specific form chart 1 of the questionnaire.
TABLE 1
Optionally. In order to distinguish the teacher information from the student information, the student information can be marked with a flag.
S102: determining a teacher evaluation dimension matrix according to the information of multiple dimensions of each teacher; and determining a student evaluation dimension matrix according to the feature information of each student and the requirement information of each dimension of the teacher, and then executing the step S103 and the step S104.
Step S102 is mainly used for cleaning the collected original information and converting the data into structured data, wherein the data are divided into two parts according to different marks of student information and teacher information and respectively stored as a teacher evaluation dimension matrix and a student evaluation dimension matrix.
In one embodiment of the present application, the teacher evaluates the row elements of each row in the dimension matrix as information of the corresponding teacher's respective dimensions. For example, teacher evaluates each row element of ith row of dimension matrix as teacher JiGender, age, duration of practice, NPS value, goodness proportion, subjects adept, style of lectures, academic calendar and graduate colleges. i is a positive integer greater than or equal to 1, and the maximum value of i is the total number of teachers in the system.
In one embodiment of the present application, the students evaluate the individual row elements of each row in the dimension matrix as the individual characteristic information of the corresponding student and the demand information for the teacher in the individual dimensions. For example, the student evaluates the row elements of the ith row of the dimension matrix for student XiCharacteristic information of the teacher and requirement information of each dimension of the teacher. For example, it may be student XiThe basic information, the interests and hobbies, the character characteristics and at least one level of subject ability level, the sex requirement of the teacher, the age requirement of the teacher, the working duration requirement of the teacher, the NPS value requirement of the teacher, the rating requirement of the teacher, the subject excellence requirement of the teacher, the teaching style requirement of the teacher, the academic requirement of the teacher and the graduation requirement of the teacher. i is a positive integer greater than or equal to 1, and the maximum value of i is the total number of students in the system.
S103: and determining the scoring value of each dimension of each teacher in the teacher evaluation dimension matrix according to the teacher evaluation rule tree in the rule base to obtain a teacher evaluation result matrix.
In one embodiment of the application, the teacher evaluation rule tree takes the teacher as a root node, each dimensionality of the teacher serves as a second-level node, leaf nodes of each dimensionality serve as a plurality of evaluation rule key value pairs, and each evaluation rule key value pair comprises an evaluation item and a quantitative score corresponding to the evaluation item.
Step S103 specifically includes: and traversing the information of each dimension of each teacher in the teacher evaluation dimension matrix, and taking the corresponding quantitative score of each evaluation item as the score of each dimension when the information of each determined dimension is the same as the information of one evaluation item in the teacher evaluation rule tree. And when the scoring values of all dimensions in the teacher evaluation dimension matrix are determined, the teacher evaluation dimension matrix is converted into a teacher evaluation result matrix.
S104: and determining expected values of all dimensions of the teacher in the student evaluation dimension matrix according to the student evaluation rule tree in the rule base to obtain a student investigation result matrix.
In one embodiment of the application, the student evaluation rule tree takes students as root nodes, each dimensionality of a teacher as a second-level node, and leaf nodes of each dimensionality are a plurality of evaluation rule key-value pairs, wherein each evaluation rule key-value pair comprises an evaluation item and a quantitative score corresponding to the evaluation item.
Step S104 specifically includes: and traversing the demand information of each student in the student evaluation dimension matrix for each dimension of the teacher, and taking the corresponding quantitative score of each evaluation item as the expected value of each dimension of the teacher when determining that the demand information of each dimension of the teacher is the same as one evaluation item in the student evaluation rule tree. And when all expected values of the dimensionality of the teacher in the student evaluation dimensionality matrix are determined, the student evaluation dimensionality matrix is converted into a student investigation result matrix.
In step S103 and step S104, the rule base is the most core structure of the system, the design of the rule directly relates to the accuracy and objectivity of the evaluation result, and the finer and more comprehensive the rule is defined, the higher the accuracy of the evaluation result is.
The evaluation rule adopts "key: value ' format is formally stored, key ' represents an evaluation item, value ' represents a quantization score corresponding to the item, taking the dimension of graduation institution of teacher as an example, such as: "universities and universities 985 colleges: 100 "," non-standard 985 colleges: 95 "," model general subject: 85' and the like, and respectively and uniformly managing the different types of rule key value pairs, and particularly adopting an innovative tree-shaped storage structure which is a teacher evaluation rule tree and a student evaluation rule tree. Teacher evaluation rule tree and student evaluation rule tree as shown in fig. 2, the left part in fig. 2 is the teacher evaluation rule tree, and the right part in fig. 2 is the student evaluation rule tree.
The scoring rules for the dimensional information are described below in several dimensions of the teacher.
a. For example, the dimension of graduation colleges and academic calendars, "universities and professions 985 colleges: 100 points, non-standard 985 and standard 211 colleges: 95 points of the general subject of the scope, non-scope 211 and scope class: and 85 minutes.
b. Taking the dimension of the working duration as an example, "> 30 years: 100 minutes, 20-30 years 90 minutes and 15-20 years; 85 minutes "," 10-15 years: 80 minutes "," 8-10 years: 75 minutes, 5-8 years: 70 minutes, 3-5 years: 65 minutes, 2-3 years: 60 minutes, 1-2 years: 55 minutes "," 0.5-1 year: 50 minutes "," <0.5 years: score 45 "," inexperienced: and 40 minutes ".
c. Taking the dimension of the total duration of the example lectures as an example, "> 50000 h: 100 points "," 40000 times 50000 h: 90 minutes and 20000-one 30000 h: 80 min "," 10000-: 70 min "," 5000 + 10000 h: 60 minutes "," 3000-: 50 minutes "," 1000-3000 h: 40 minutes "," <1000 h: 30 minutes ".
d. Taking the dimension of example teaching style as an example, different teaching styles are coded, various teaching style options are set, and teachers need to select when filling personal basic information, and more options can be selected but can not exceed 3 items. Specifically, "intellectual education style: 1 "," emotional education style: 2 "," natural type teaching style: 3 "," humor type teaching style: 4 "," skill type teaching style: 5 "(value of 1).
e. Taking the dimension of example lecture style as an example, "male: 0 "," female: 1".
f. Taking the age group dimension as an example, "25-30 years old: 00 "," 30-35 years old: 01 "," 40-45 years old: 02 "," 50-55 years old: 04 ", and so on.
g. Taking this dimension of the adept subject as an example, encoding is performed according to different subjects, so as to ensure that values corresponding to different subjects (keys) are different, and details are not repeated here.
As can be seen from the above, in the student survey result matrix, each student corresponds to a row element in a row, as shown in table 2. The row elements of each row are the expected values of the corresponding student for the various dimensions of the teacher. As shown in table 2, in the teacher evaluation result matrix, each teacher corresponds to a row element of one row. The row elements of each row are the values of the scores of the various dimensions of the corresponding teacher.
TABLE 2
TABLE 3
S105: and determining the similarity between the requirements of each student and each teacher according to the student investigation result matrix and the teacher evaluation result matrix.
In an embodiment of the present application, step S105 specifically includes:
(a1) and taking the row element of the row corresponding to each student in the student investigation result matrix as a first row vector s. Each vector element in the first row vector s is the expected value of the student for each dimension of the teacher.
(a2) And taking the row element of the row corresponding to each teacher in the teacher evaluation result matrix as a second row vector t. Each vector element in the second row vector t is a scoring value of each dimension of the teacher.
(a3) And solving the similarity between each first row vector s and each second row vector t in a cosine similarity calculation mode, and taking each similarity obtained by the solving as the similarity between the requirement of each student and the corresponding teacher.
In the present embodiment, each similarity may be calculated as a similarity between the requirement of each student and the corresponding teacher using the following formula (1). Of course, the formula (1) can also be specifically expressed as the formula (2).
Wherein, Cs,tSet of dimensions i, r, representing the teachers,iExpected value, r, representing i-dimension of student teachert,iThe i-dimension score value of the teacher is represented. i is a positive integer greater than or equal to 1, and the maximum value of i is the number of dimensions of the teacher.
S106: and showing the information of a plurality of teachers with higher similarity to the requirements of the students to each student.
In an embodiment of the present application, step S106 specifically includes: sequencing the similarity between the requirements of the students and the teachers from high to low, and displaying the information of the teachers with the similarity ranking N at the top to the students; n is more than or equal to 1 and is a positive integer.
Taking N to 10 as an example, the similarity between the requirements of the students and the teachers is ranked from high to low, the information of the teachers with the similarity ranking of 10 is displayed to the students, and the students select proper teachers according to the information of the 10 teachers.
The assessment and recommendation method for teachers in training institutions breaks through the limitation of the traditional assessment mode aiming at the assessment of teachers and students in the education and training industry and pain points matched with teachers and students, quantifies all the characteristics of the teachers in a digital form on the basis of information of multiple dimensionalities of the teachers, and achieves intelligent assessment of comprehensive qualifications of the teachers; meanwhile, multiple kinds of information of students are quantized, and intelligent recommendation of teachers is achieved in the mode. The essence of the system applied to the evaluation and recommendation method is based on big data technology, dynamic acquisition and management of multi-dimensional information of teachers are achieved, a permanent file (knowledge base) of the teachers is built in the system after new teacher information is input into the system, the file can be continuously updated and perfected, a nationwide teacher information knowledge base can be finally built, teacher information is recorded in an all-around mode, and qualification certification service can be provided for third-party institutions.
The assessment and recommendation method is based on the assessment mechanism and the recommendation mechanism of the knowledge base and the rule base, the system assesses the qualification of the teachers from multiple dimensions and fine granularity levels relatively, accurately and objectively, and due to the fact that the qualification of the teachers can be automatically assessed, assessment efficiency of the qualification of the teachers is greatly improved, the practice level of the teachers is effectively mastered, and the phenomenon that the teachers are unsmooth in resources is avoided. Meanwhile, the intelligent matching is realized by combining the characteristics of students, the teacher resource is efficiently utilized, the purpose of teaching according to the nature can be realized, the capability level of the students can be more quickly improved, the benefits of the students can be maximized, and the win-win situation of institutions, teachers and students can be realized.
Based on the same inventive concept, the embodiment of the application also provides an evaluation and recommendation device for teachers of training institutions, and as shown in fig. 3, the evaluation and recommendation device comprises a terminal information entry module 201, a data regularization processing module 202, a rule base module 203, a teacher qualification evaluation module 204, a student condition investigation module 205 and a teacher-student matching recommendation module 206.
The function and execution sequence of the above modules are shown in fig. 3 and 4, specifically:
the terminal information entry module 201 is configured to obtain information of multiple dimensions of at least one teacher, and obtain feature information of at least one student and requirement information of each dimension of the teacher.
The data regularization processing module 202 is configured to determine a teacher evaluation dimension matrix according to the information of the plurality of dimensions of each teacher; and determining a student evaluation dimension matrix according to the characteristic information of each student and the requirement information of each dimension of the teacher.
The rule base module 203 is used for providing a teacher evaluation rule tree and a student evaluation rule tree.
The teacher qualification evaluation module 204 is configured to determine a scoring value of each dimension of each teacher in the teacher evaluation dimension matrix according to the teacher evaluation rule tree in the rule base, so as to obtain a teacher evaluation result matrix.
The student condition investigation module 205 is configured to determine expected values of each dimension of the teacher in the student evaluation dimension matrix according to the student evaluation rule tree in the rule base, so as to obtain a student investigation result matrix.
The teacher-student matching recommendation module 206 is configured to determine similarity between the requirement of each student and each teacher according to the student investigation result matrix and the teacher evaluation result matrix; and showing the information of a plurality of teachers with higher similarity to the requirements of the students to each student.
In one embodiment of the present application, the teacher's information in the plurality of dimensions includes a combination of one or more of: the sex of the teacher, the age of the teacher, the working duration of the teacher, the NPS value of the teacher, the goodness proportion of the teacher, the subjects good at the teacher, the teaching style of the teacher, the academic course of the teacher and the graduation colleges of the teacher.
In one embodiment of the present application, the characteristic information of the student includes a combination of one or more of: basic information, hobbies, character characteristics, and at least one level of capability level for a subject.
In one embodiment of the present application, the student's need information for each of the teacher's dimensions includes a combination of one or more of: the method comprises the following steps of requiring sex of a teacher, requiring age of the teacher, requiring working time of the teacher, requiring an NPS value of the teacher, requiring a good comment proportion of the teacher, requiring proficiency subjects of the teacher, requiring teaching style of the teacher, requiring a academic calendar of the teacher and requiring college colleges of graduates of the teacher.
In one embodiment of the present application, the teacher evaluates the row elements of each row in the dimension matrix as information of the corresponding teacher's respective dimensions.
In one embodiment of the present application, the students evaluate the individual row elements of each row in the dimension matrix as the individual characteristic information of the corresponding student and the demand information for the teacher in the individual dimensions.
In one embodiment of the application, the teacher evaluation rule tree takes the teacher as a root node, each dimensionality of the teacher serves as a second-level node, leaf nodes of each dimensionality serve as a plurality of evaluation rule key value pairs, and each evaluation rule key value pair comprises an evaluation item and a quantitative score corresponding to the evaluation item.
The teacher qualification evaluation module 204 is specifically configured to: traversing the information of each dimension of each teacher in the teacher evaluation dimension matrix; when the information of each determined dimension is the same as one evaluation item in the teacher evaluation rule tree, taking the corresponding quantitative score of the evaluation item as the score of the dimension; and when the scoring values of all dimensions in the teacher evaluation dimension matrix are determined, the teacher evaluation dimension matrix is converted into a teacher evaluation result matrix.
In one embodiment of the application, the student evaluation rule tree takes students as root nodes, each dimensionality of a teacher as a second-level node, and leaf nodes of each dimensionality are a plurality of evaluation rule key-value pairs, wherein each evaluation rule key-value pair comprises an evaluation item and a quantitative score corresponding to the evaluation item.
The student status investigation module 205 is specifically configured to: traversing the demand information of each student in the student evaluation dimension matrix for each dimension of the teacher; when the condition that the requirement information of each dimensionality of the teacher is the same as one evaluation item in the student evaluation rule tree is determined, taking the corresponding quantitative score of the evaluation item as an expected value of each dimensionality of the teacher; and when all expected values of the dimensionality of the teacher in the student evaluation dimensionality matrix are determined, the student evaluation dimensionality matrix is converted into a student investigation result matrix.
In an embodiment of the present application, the teacher-student matching recommendation module 206 is specifically configured to: taking row elements of a row corresponding to each student in the student investigation result matrix as a first row vector; each vector element in the first row of vectors is the expected value of each dimension of the teacher for the student; taking row elements of each teacher corresponding row in the teacher evaluation result matrix as second row vectors; each vector element in the second row vector is the scoring value of each dimensionality of the teacher; and solving the similarity between each first row vector and each second row vector in a cosine similarity calculation mode, and taking each similarity obtained by the solving as the similarity between the requirement of each student and the corresponding teacher.
In an embodiment of the present application, the teacher-student matching recommendation module 206 is specifically configured to: sequencing the similarity between the requirements of the students and the teachers from high to low, and displaying the information of the teachers with the similarity ranking N at the top to the students; n is more than or equal to 1 and is a positive integer.
It is understood that the above modules of the evaluation and recommendation device for teachers of training institutions in the present embodiment have the functions of the corresponding steps of the evaluation and recommendation method for teachers of training institutions in the above embodiments. The function can be realized by hardware, and can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules corresponding to the functions described above. The modules can be software and/or hardware, and each module can be implemented independently or by integrating a plurality of modules. The functional description of each module of the evaluation and recommendation device for teachers in training institutions may specifically be the corresponding description of the evaluation and recommendation method for teachers in training institutions in the above embodiments, and will not be described herein again.
Based on the same inventive concept, the embodiment of the present application provides an electronic device, as shown in fig. 5, the electronic device includes a processor and a memory.
The memory is configured to store machine readable instructions that, when executed by the processor, cause the processor to perform the method for evaluation and recommendation of a teacher of a training institution as provided in the various embodiments of the present application described above.
The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
The electronic device includes: a memory and a processor, wherein the processor may be referred to as a processing device 301 described below, and the memory may include at least one of a Read Only Memory (ROM)302, a Random Access Memory (RAM)303, and a storage device 308, which are described below:
as shown in fig. 5, the electronic device may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 301 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)302 or a program loaded from a storage device 308 into a Random Access Memory (RAM) 303. In the RAM303, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing device 301, the ROM302, and the RAM303 are connected to each other via a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
Generally, the following devices may be connected to the I/O interface 305: input devices 306 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 307 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage devices 308 including, for example, magnetic tape, hard disk, etc.; and a communication device 309. The communication means 309 may allow the electronic device to communicate wirelessly or by wire with other devices to exchange data. While fig. 5 illustrates an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication means 309, or installed from the storage means 308, or installed from the ROM 302. The computer program, when executed by the processing device 301, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It is noted that the computer readable medium of the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText transfer protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: the method comprises the steps of receiving a selection operation of a user aiming at any one segment of multimedia information in the multimedia information to be processed, determining a target multimedia information segment based on the selection operation, determining a corresponding processing mode when receiving a trigger operation aiming at the target multimedia information segment, and carrying out corresponding processing on the target multimedia information segment based on the determined processing mode.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming 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).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules or units described in the embodiments of the present disclosure may be implemented by software or hardware. The name of a module or a unit does not in some cases form a limitation of the unit itself, for example, the receiving module may also be described as a module for receiving a selected operation of a user for any piece of multimedia information in the multimedia information to be processed.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The electronic device provided by the embodiment of the application has the same inventive concept and the same beneficial effects as the embodiments described above, and the contents not shown in detail in the electronic device may refer to the embodiments described above, and are not described again here.
Based on the same inventive concept, embodiments of the present application provide a computer-readable storage medium on which a computer program is stored, the program, when executed by a processor, implementing the method for evaluating and recommending a teacher of a training institution provided in the above-described embodiments of the present application.
The computer readable medium includes, but is not limited to, any type of disk including floppy disks, hard disks, optical disks, CD-ROMs, and magneto-optical disks, ROMs, RAMs, EPROMs (Erasable Programmable Read-Only Memory), EEPROMs, flash Memory, magnetic cards, or fiber optic cards. That is, a readable medium includes any medium that stores or transmits information in a form readable by a device (e.g., a computer).
The computer-readable storage medium provided in the embodiments of the present application has the same inventive concept and the same advantages as the embodiments described above, and contents not shown in detail in the computer-readable storage medium may refer to the embodiments described above, and are not described herein again.
Those of skill in the art will appreciate that the various operations, methods, steps in the processes, acts, or solutions discussed in this application can be interchanged, modified, combined, or eliminated. Further, other steps, measures, or schemes in various operations, methods, or flows that have been discussed in this application can be alternated, altered, rearranged, broken down, combined, or deleted. Further, steps, measures, schemes in the prior art having various operations, methods, procedures disclosed in the present application may also be alternated, modified, rearranged, decomposed, combined, or deleted.
The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless otherwise specified.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The foregoing is only a partial embodiment of the present application, and it should be noted that, for those skilled in the art, several modifications and decorations can be made without departing from the principle of the present application, and these modifications and decorations should also be regarded as the protection scope of the present application.
Claims (10)
1. A method for evaluating and recommending teachers for training institutions is characterized by comprising the following steps:
the method comprises the steps of obtaining information of multiple dimensions of at least one teacher, obtaining characteristic information of at least one student and requirement information of the teacher in each dimension;
determining a teacher evaluation dimension matrix according to the information of the plurality of dimensions of each teacher; determining a student evaluation dimension matrix according to the feature information of each student and the requirement information of each dimension of the teacher;
determining the score value of each dimension of each teacher in the teacher evaluation dimension matrix according to the teacher evaluation rule tree in the rule base to obtain a teacher evaluation result matrix; according to the student evaluation rule tree in the rule base, determining expected values of all dimensions of the teacher in the student evaluation dimension matrix to obtain a student investigation result matrix;
determining the similarity between the requirement of each student and each teacher according to the student investigation result matrix and the teacher evaluation result matrix; and showing the information of a plurality of teachers with higher similarity to the requirements of the students to each student.
2. The teacher's assessment and recommendation method for a training institution as recited in claim 1, wherein the teacher's information in multiple dimensions includes a combination of one or more of: the sex of the teacher, the age of the teacher, the working duration of the teacher, the NPS value of the teacher, the commenting ratio of the teacher, the proficiency subject of the teacher, the teaching style of the teacher, the academic course of the teacher and the graduation colleges of the teacher;
and the teacher evaluates each row element of each row in the dimension matrix, and the row elements are the information of each corresponding dimension of the teacher.
3. The teacher evaluation and recommendation method according to claim 2, wherein the teacher evaluation rule tree has a teacher as a root node, each of the teacher's dimensions as a second level node, and leaf nodes of each of the dimensions are a plurality of evaluation rule key-value pairs, each of the evaluation rule key-value pairs including an evaluation item and a quantization score corresponding to the evaluation item;
the method for determining the score value of each dimension of each teacher in the teacher evaluation dimension matrix according to the teacher evaluation rule tree in the rule base to obtain the teacher evaluation result matrix comprises the following steps:
traversing information of each dimension of each teacher in the teacher assessment dimension matrix;
when the information of one dimension is determined to be the same as one evaluation item in the teacher evaluation rule tree, taking the corresponding quantitative score of the evaluation item as the score of the dimension;
and when the scoring values of all the dimensions in the teacher evaluation dimension matrix are determined, converting the teacher evaluation dimension matrix into the teacher evaluation result matrix is completed.
4. The teacher's assessment and recommendation method for training institutions according to claim 1, wherein the student's characteristic information includes one or more of the following in combination: basic information, hobbies, character characteristics and at least one subject level of ability;
the requirement information of the students for each dimensionality of the teacher comprises one or more of the following items: the method comprises the following steps of requiring sex of a teacher, requiring age of the teacher, requiring working time of the teacher, requiring an NPS value of the teacher, requiring a good comment proportion of the teacher, requiring proficiency subjects of the teacher, requiring teaching style of the teacher, requiring a academic calendar of the teacher and requiring college colleges of graduates of the teacher;
and the student evaluates each row element of each row in the dimension matrix, and the evaluation matrix is corresponding to each characteristic information of the student and the requirement information of each dimension of the teacher.
5. The evaluation and recommendation method for teachers in training institutions according to claim 4, wherein the student evaluation rule tree takes students as root nodes, each dimension of teachers as a second-level node, leaf nodes of each dimension are a plurality of evaluation rule key-value pairs, and the evaluation rule key-value pairs include evaluation items and quantized scores corresponding to the evaluation items;
the method for determining the expected value of each dimensionality of the teacher in the student assessment dimensionality matrix according to the student assessment rule tree in the rule base to obtain the student investigation result matrix comprises the following steps:
traversing the demand information of each student in the student evaluation dimension matrix for each dimension of the teacher;
when the condition that the requirement information of each dimensionality of the teacher is the same as the evaluation item in the student evaluation rule tree is determined, taking the corresponding quantitative score of the evaluation item as the expected value of each dimensionality of the teacher;
and when all expected values of the dimensionality of the teacher in the student evaluation dimensionality matrix are determined, converting the student evaluation dimensionality matrix into the student investigation result matrix is completed.
6. The teacher evaluation and recommendation method for a training institution of claim 5, wherein said determining similarity between the needs of each student and the teachers based on said student survey result matrix and said teacher evaluation result matrix comprises:
taking row elements of a row corresponding to each student in the student investigation result matrix as a first row vector; each vector element in the first row vector is an expected value of each dimensionality of the teacher by the student;
taking row elements of a row corresponding to each teacher in the teacher evaluation result matrix as second row vectors; each vector element in the second row vector is a scoring value of each dimension of the teacher;
and solving the similarity between each first row vector and each second row vector in a cosine similarity calculation mode, and taking each similarity obtained by the solving as the similarity between the requirement of each student and the corresponding teacher.
7. The teacher's evaluation and recommendation method for a training institution as claimed in claim 1, wherein said presenting to each student information of a plurality of teachers having a high similarity to the student's needs comprises:
sequencing the similarity between the requirements of the students and the teachers from high to low, and displaying the information of the teachers with the similarity ranking N at the top to the students; n is more than or equal to 1 and is a positive integer.
8. An evaluation and recommendation device for teachers of training institutions, comprising:
the terminal information input module is used for acquiring information of multiple dimensions of at least one teacher, and acquiring characteristic information of at least one student and demand information of each dimension of the teacher;
the data regularization processing module is used for determining a teacher evaluation dimension matrix according to the information of the plurality of dimensions of each teacher; determining a student evaluation dimension matrix according to the feature information of each student and the requirement information of each dimension of the teacher;
the rule base module is used for providing a teacher evaluation rule tree and a student evaluation rule tree;
the teacher qualification evaluation module is used for determining the grade value of each dimension of each teacher in the teacher evaluation dimension matrix according to the teacher evaluation rule tree in the rule base to obtain a teacher evaluation result matrix;
the student condition investigation module is used for determining expected values of all dimensions of the teacher in the student evaluation dimension matrix according to the student evaluation rule tree in the rule base to obtain a student investigation result matrix;
the teacher-student matching recommendation module is used for determining the similarity between the requirements of each student and each teacher according to the student investigation result matrix and the teacher evaluation result matrix; and showing the information of a plurality of teachers with higher similarity to the requirements of the students to each student.
9. An electronic device, comprising:
a processor; and
a memory configured to store machine readable instructions that, when executed by the processor, cause the processor to perform the method of any of claims 1-7 for evaluation and recommendation by a teachers of a training institution.
10. A computer-readable storage medium on which a computer program is stored, the program, when executed by a processor, implementing the method for evaluation and recommendation by a teacher of a training institution of any one of claims 1-7.
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