CN115330358B - Education and training management system and education and training method - Google Patents
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Abstract
The invention belongs to the field of education management, relates to a data processing technology, and is used for solving the problem that the existing education training management system cannot comprehensively analyze the learning condition of students and the state of teachers when the learning state of the students is abnormal, in particular to the education training management system and an education training method, wherein the education training management system comprises a management platform, and the management platform is in communication connection with a student management module, a distribution analysis module, a teacher management module and a storage module; the student management module is used for managing and analyzing students: setting a management period, marking students of an education and training institution as analysis objects, and carrying out numerical calculation on score data CJ, attendance data KQ and operation data ZY to obtain the expression coefficients of the analysis objects in the management period; the invention can manage and analyze the integral performance of students in education institutions, thereby feeding back the performance of the students through the performance coefficients.
Description
Technical Field
The invention belongs to the field of education management, relates to a data processing technology, and particularly relates to an education training management system and an education training method.
Background
The training institution, the education and training institution aiming at improving ability, training skill, education, certification and training, etc. needs to have site requirement and teacher and resource requirement, needs the education administration department to give certification and get social strength to handle the qualification for business, and the training course price needs to be calculated and reported to the education department for approval, and the course is increased or the charge is changed after applying to the education department and obtaining approval.
The existing education training management system can only monitor the learning state of students, cannot comprehensively analyze the learning condition of the students and the state of teachers when the learning state of the students is abnormal, and further cannot investigate reasons causing the abnormal learning state of the students, so that the students and the teachers cannot be effectively managed.
In view of the above technical problem, the present application proposes a solution.
Disclosure of Invention
The invention aims to provide an education training management system and an education training method, which are used for solving the problem that the existing education training management system cannot comprehensively analyze the learning condition of students and the state of teachers when the learning state of the students is abnormal;
the technical problems to be solved by the invention are as follows: provided are an education training management system and an education training method capable of comprehensively analyzing the learning condition of a student and the state of a teacher when the learning state of the student is abnormal.
The purpose of the invention can be realized by the following technical scheme:
the education training management system comprises a management platform, wherein the management platform is in communication connection with a student management module, a distribution analysis module, a teacher management module and a storage module;
the student management module is used for managing and analyzing students: setting a management period, marking students of an education and training institution as analysis objects, acquiring grade data CJ, attendance data KQ and operation data ZY of the analysis objects in the management period, and performing numerical calculation on the grade data CJ, the attendance data KQ and the operation data ZY to obtain an expression coefficient BX of the analysis objects in the management period; acquiring an expression threshold BXmin through a storage module, comparing an expression coefficient BX of an analysis object with the expression threshold BXmin, and judging whether the overall expression of the analysis object in a management period meets requirements or not according to a comparison result;
the distribution analysis module is used for carrying out distribution analysis on the abnormal objects and setting the abnormal period of the management period as an integral abnormal or a prominent abnormal;
the teacher management module is used for performing management analysis on the prominent teacher after receiving the prominent teacher: acquiring abnormal values of classes carried by the history of the outstanding teachers, summing the abnormal values of the classes carried by the history of the outstanding teachers, taking an average value to obtain abnormal analysis values, acquiring abnormal analysis threshold values through a storage module, and comparing the abnormal analysis values with the abnormal analysis threshold values: if the abnormal analysis value is smaller than the abnormal analysis threshold value, judging that the analysis result of the outstanding teacher is qualified; if the abnormal analysis value is larger than or equal to the abnormal analysis threshold value, judging that the analysis result of the prominent teacher is unqualified, and marking the prominent teacher with the unqualified analysis result as a mark teacher; and carrying out deep analysis on the marking teacher and obtaining the unqualified characteristics of the marking teacher, and sending the unqualified characteristics of the marking teacher to a mobile phone terminal of a manager through a management platform.
As a preferred embodiment of the present invention, the acquiring process of the achievement data CJ of the analysis subject includes: acquiring the scores of all the departments of the examinations of the analysis object in the management period, and marking the sum of the scores of all the departments of the examinations of the analysis object in the management period as score data CJ with the unit of percentage; the process of acquiring the attendance data KQ comprises the following steps: acquiring the times of class opening and the times of late arrival and absence of attendance of an analysis object in a management period, and marking the ratio of the times of late arrival and absence of attendance to the times of class opening as attendance data KQ; the process of acquiring the job data ZY includes: and acquiring the number of times of job arrangement and the number of times of job non-intersection of the analysis object in the management period, and marking the ratio of the number of times of job non-intersection to the number of times of job arrangement as job data ZY.
As a preferred embodiment of the present invention, the specific process of comparing the performance coefficient BX of the analysis target with the performance threshold BXmin includes: if the expression coefficient BX is smaller than the expression threshold BXmin, judging that the expression of the analysis object in the management period does not meet the requirement, and marking the corresponding analysis object as an abnormal object; if the expression coefficient BX is larger than or equal to the expression threshold BXmin, judging that the expression of the analysis object in the management period meets the requirement, and marking the corresponding analysis object as a normal object; the quantity ratio of the abnormal objects to the analysis objects is marked as an abnormal ratio, an abnormal threshold value is obtained through a storage module, and the abnormal ratio is compared with the abnormal threshold value: if the abnormal ratio is smaller than the abnormal threshold, judging that the overall performance of the analysis object in the management period meets the requirement; if the abnormal ratio is larger than or equal to the abnormal threshold, the integral performance of the analysis object in the management period is judged to be not met, the student management module sends an integral unqualified signal to the management platform, and the management platform sends the integral unqualified signal to the distribution analysis module after receiving the integral unqualified signal.
As a preferred embodiment of the present invention, a specific process of the distribution analysis module performing distribution analysis on the abnormal object includes: acquiring the number of abnormal objects of each class and marking the abnormal objects as abnormal values, establishing an abnormal set of the abnormal values of all the classes, carrying out variance calculation on the abnormal set to obtain an abnormal expression value, acquiring an abnormal expression threshold value through a storage module, comparing the abnormal expression value with the abnormal expression threshold value, and marking the abnormal characteristics of the management period as integral abnormality or salient abnormality through a comparison result.
As a preferred embodiment of the present invention, the specific process of comparing the abnormal performance value with the abnormal performance threshold includes: if the abnormal performance value is smaller than the abnormal performance threshold value, judging that the abnormal characteristic of the management period is overall abnormal, sending an overall abnormal signal to a management platform by a distribution analysis module, and sending the overall abnormal signal to a mobile phone terminal of a manager after the management platform receives the overall abnormal signal; if the abnormal performance value is larger than or equal to the abnormal performance threshold value, judging that the abnormal characteristic of the management period is a prominent abnormality, sequencing the classes according to the sequence of the abnormal value values from large to small, marking L1 classes which are sequenced at the front as prominent classes, marking teachers of the prominent classes as prominent teachers, sending the prominent teachers to the management platform by the distribution analysis module, and sending the prominent teachers to the teacher management module after the management platform receives the prominent teachers.
As a preferred embodiment of the invention, the specific process of deep analysis for the marking teacher comprises the following steps: establishing a rectangular coordinate system by taking the starting time of a management period as an X axis and taking the abnormal value of the class carried by the teacher marked in the management period as a Y axis, marking a plurality of analysis points in the rectangular coordinate system by taking the starting time of the management period as an abscissa and taking the abnormal value of the class carried by the teacher marked in the management period as an ordinate, making a distinguishing ray parallel to the X axis in a first quadrant of the rectangular coordinate system, wherein the endpoint coordinate value of the distinguishing ray is (0) and QFy, wherein QFy is a preset distinguishing threshold, marking the numerical value of the abscissa of the analysis point positioned below the distinguishing ray as a distinguishing value, establishing a distinguishing set by all the distinguishing values, carrying out variance calculation on the distinguishing set to obtain a distinguishing expression value, obtaining the distinguishing expression threshold through a storage module, and comparing the distinguishing expression value with the expression distinguishing threshold: if the distinguishing performance value is smaller than the distinguishing performance threshold value, the unqualified characteristic of the marking teacher is marked as stage unqualified; if the distinguishing expression value is larger than or equal to the distinguishing expression value, the unqualified characteristic of the marking teacher is marked as the sustainable unqualified characteristic; and sending the unqualified features of the marking teacher to a management platform, and sending the unqualified features of the marking teacher to a mobile phone terminal of a manager after the management platform receives the unqualified features of the marking teacher.
The educational training method comprises the following steps:
the method comprises the following steps: and (3) performing management analysis on students: setting a management period, marking students of an education and training institution as analysis objects, acquiring score data CJ, attendance data KQ and job data ZY of the analysis objects in the management period, carrying out numerical calculation to obtain an expression coefficient of the analysis objects, and marking the analysis objects as normal objects or abnormal objects through the expression coefficient of the analysis objects;
step two: the quantity ratio of the abnormal objects to the analysis objects is marked as an abnormal ratio, whether the overall performance of the analysis objects in the management period meets the requirements or not is judged according to the numerical value of the abnormal ratio, and an overall unqualified signal is sent to the distribution analysis module through the management platform when the overall performance of the analysis objects in the management period does not meet the requirements;
step three: and (3) carrying out distribution analysis on abnormal objects: acquiring the number of abnormal objects of each class and marking the abnormal objects as abnormal values, establishing an abnormal set of the abnormal values of all the classes, carrying out variance calculation on the abnormal set to obtain abnormal representation values, and marking the abnormal features of the management period as integral abnormal or outstanding abnormal according to the numerical value of the abnormal representation values;
step four: and performing management analysis on the prominent teachers, marking the prominent teachers with unqualified analysis results as marking teachers, performing deep analysis on the marking teachers, and marking unqualified features of the marking teachers as persistent unqualified or stage unqualified features.
The invention has the following beneficial effects:
1. the student management module can manage and analyze the integral performance of students of the education institution, and the performance coefficients are obtained by carrying out numerical calculation in combination with various daily parameters of the students, so that the performance conditions of the students are fed back through the performance coefficients, and the integral management state of the education institution can be reflected in combination with the performance coefficients of all the students in the institution;
2. the abnormal object distribution analysis can be carried out on the integrally unqualified education institutions through the distribution analysis module, so that the concentration of the abnormal objects is searched, different measures can be taken for processing according to the distribution analysis result, for example, when the abnormal objects are scattered, the adjustment is carried out in a mode of converting a teaching method, when the abnormal objects are concentrated, the evaluation analysis is carried out on a prominent teacher, and the processing efficiency when the integral expression is abnormal is further improved;
3. can manage the analysis to outstanding mr through mr management module, through carrying out the analysis to the whole abnormal value of the historical class that the mark mr takes, judge mark mr's whole performance, when whole performance is unqualified, carry out the analysis of performance rule to mark mr to take different measures according to different analysis results, retrain mr with different measures, improve the teaching quality.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a block diagram of a system according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a method according to a second embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
As shown in fig. 1, the educational training management system includes a management platform, and the management platform is communicatively connected with a student management module, a distribution analysis module, a teacher management module, and a storage module.
The student management module is used for managing and analyzing students: setting a management period, marking students of an education and training institution as analysis objects, and acquiring score data CJ, attendance data KQ and operation data ZY of the analysis objects in the management period, wherein the acquisition process of the score data CJ of the analysis objects comprises the following steps: acquiring the scores of all the departments of the examination of the analysis object in the management period, and marking the sum of the scores of all the departments of the examination of the analysis object in the management period as score data CJ with the unit of percentage; the process of acquiring the attendance data KQ comprises the following steps: acquiring the times of class opening and the times of late arrival and absence of attendance of an analysis object in a management period, and marking the ratio of the times of late arrival and absence of attendance to the times of class opening as attendance data KQ; the process of acquiring the job data ZY includes: acquiring the number of times of job arrangement and the number of times of job non-intersection of an analysis object in a management period, marking the ratio of the number of times of job non-intersection to the number of times of job arrangement as job data ZY, and obtaining an expression coefficient BX of the analysis object in the management period through a formula BX = alpha 1 x CJ-alpha 2 x KQ-alpha 3 x ZY, wherein the expression coefficient is a numerical value reflecting the good and bad performance of the analysis object in the management period, and the larger the numerical value of the expression coefficient is, the better the performance of the analysis object in the management period is; wherein alpha 1, alpha 2 and alpha 3 are all proportionality coefficients, and alpha 3 is more than alpha 2 and more than alpha 1 and more than 1; obtaining an expression threshold BXmin through a storage module, and comparing an expression coefficient BX of an analysis object with the expression threshold BXmin: if the performance coefficient BX is smaller than the performance threshold BXmin, judging that the performance of the analysis object in the management period does not meet the requirement, and marking the prominent teacher with the unqualified analysis result as a mark teacher; if the expression coefficient BX is larger than or equal to the expression threshold BXmin, judging that the expression of the analysis object in the management period meets the requirement, and marking the corresponding analysis object as a normal object; the number ratio of the abnormal objects to the analysis objects is marked as an abnormal ratio, an abnormal threshold value is obtained through a storage module, and the abnormal ratio is compared with the abnormal threshold value: if the abnormal ratio is smaller than the abnormal threshold, judging that the overall performance of the analysis object in the management period meets the requirement; if the anomaly ratio is larger than or equal to the anomaly threshold value, judging that the overall performance of the analysis object in the management period does not meet the requirement, sending an overall unqualified signal to a management platform by a student management module, and sending the overall unqualified signal to a distribution analysis module after the management platform receives the overall unqualified signal; the method comprises the steps of managing and analyzing the integral performance of students of an education institution, carrying out numerical calculation by combining various daily parameters of the students to obtain performance coefficients, feeding back the performance conditions of the students through the performance coefficients, and reflecting the integral management state of the education institution by combining the performance coefficients of all the students in the institution.
The distribution analysis module is used for carrying out distribution analysis on the abnormal object after receiving the integral unqualified signal: acquiring the number of abnormal objects of each class, marking the abnormal objects as abnormal values, establishing an abnormal set of the abnormal values of all classes, carrying out variance calculation on the abnormal set to obtain an abnormal expression value, acquiring an abnormal expression threshold value through a storage module, and comparing the abnormal expression value with the abnormal expression threshold value: if the abnormal performance value is smaller than the abnormal performance threshold value, judging that the abnormal characteristic of the management period is overall abnormal, sending an overall abnormal signal to a management platform by a distribution analysis module, and sending the overall abnormal signal to a mobile phone terminal of a manager after the management platform receives the overall abnormal signal; if the abnormal performance value is larger than or equal to the abnormal performance threshold value, judging that the abnormal characteristic of the management period is a prominent abnormality, sequencing the classes according to the sequence of the abnormal value values from large to small, marking L1 classes which are sequenced at the front as prominent classes, marking teachers of the prominent classes as prominent teachers, sending the prominent teachers to the management platform by the distribution analysis module, and sending the prominent teachers to the teacher management module after the management platform receives the prominent teachers; abnormal object distribution analysis is carried out on the integrally unqualified education institutions, so that the concentration of the abnormal objects is searched, different measures can be taken for processing according to the distribution analysis result, for example, when the abnormal objects are scattered, the teaching method is converted for adjustment, when the abnormal objects are concentrated, the outstanding teachers are evaluated and analyzed, and the processing efficiency when the integral performance is abnormal is improved.
The teacher management module is used for performing management analysis on the prominent teacher after receiving the prominent teacher: acquiring abnormal values of classes in the history of the prominent teachers, summing the abnormal values of the classes in the history of the prominent teachers, taking an average value to obtain abnormal analysis values, acquiring abnormal analysis threshold values through a storage module, and comparing the abnormal analysis values with the abnormal analysis threshold values: if the abnormal analysis value is smaller than the abnormal analysis threshold value, judging that the analysis result of the outstanding teacher is qualified; if the abnormal analysis value is larger than or equal to the abnormal analysis threshold value, judging that the analysis result of the prominent teacher is unqualified, and marking the corresponding prominent teacher as a mark teacher; performing deep analysis on the marking teachers: establishing a rectangular coordinate system by taking the starting time of a management period as an X axis and taking an abnormal value for marking the class carried by a teacher in the management period as a Y axis, marking a plurality of analysis points in the rectangular coordinate system by taking the starting time of the management period as an abscissa and taking the abnormal value for marking the class carried by the teacher in the management period as an ordinate, making a distinguishing ray parallel to the X axis in a first quadrant of the rectangular coordinate system, wherein the endpoint coordinate value of the distinguishing ray is (0, QFy), the QFy is a preset distinguishing threshold, marking the numerical value of the abscissa of the analysis point positioned at the lower side of the distinguishing ray as the distinguishing value, establishing a distinguishing set with all the distinguishing values, carrying out variance calculation on the distinguishing set to obtain a distinguishing expression value, acquiring the distinguishing expression threshold through a storage module, and comparing the distinguishing expression value with the expression threshold: if the distinguishing performance value is smaller than the distinguishing performance threshold value, marking the unqualified characteristic of the marking teacher as stage unqualified; if the distinguishing expression value is larger than or equal to the distinguishing expression value, the unqualified characteristic of the marking teacher is marked as the sustainable unqualified characteristic; the unqualified features of the marking teacher are sent to a management platform, and the management platform sends the unqualified features of the marking teacher to a mobile phone terminal of a manager after receiving the unqualified features of the marking teacher; the method comprises the steps of managing and analyzing the prominent teachers, analyzing the overall abnormal values of historical classes carried by the marked teachers, judging the overall performance of the marked teachers, and analyzing the performance rules of the marked teachers when the overall performance is unqualified, so that different measures are taken according to different analysis results, the teachers are restrained by the different measures, and the teaching quality is improved.
Example two
As shown in fig. 2, the educational training method includes the steps of:
the method comprises the following steps: and (3) performing management analysis on students: setting a management period, marking students of an education and training institution as analysis objects, acquiring grade data CJ, attendance data KQ and operation data ZY of the analysis objects in the management period, carrying out numerical calculation to obtain an expression coefficient of the analysis objects, and marking the analysis objects as normal objects or abnormal objects through the expression coefficient of the analysis objects;
step two: the quantity ratio of the abnormal objects to the analysis objects is marked as an abnormal ratio, whether the overall performance of the analysis objects in the management period meets the requirements or not is judged according to the numerical value of the abnormal ratio, and an overall unqualified signal is sent to the distribution analysis module through the management platform when the overall performance of the analysis objects in the management period does not meet the requirements;
step three: and (3) carrying out distribution analysis on abnormal objects: acquiring the number of abnormal objects of each class and marking the abnormal objects as abnormal values, establishing an abnormal set of the abnormal values of all the classes, carrying out variance calculation on the abnormal set to obtain abnormal representation values, and marking the abnormal features of the management period as integral abnormal or outstanding abnormal according to the numerical value of the abnormal representation values;
step four: and performing management analysis on the prominent teachers, marking the prominent teachers with unqualified analysis results as marking teachers, performing deep analysis on the marking teachers, and marking unqualified characteristics of the marking teachers as non-sustainable or non-periodic.
The education training management system and the education training method are used for carrying out management analysis on students during working: setting a management period, marking students of an education and training institution as analysis objects, acquiring grade data CJ, attendance data KQ and operation data ZY of the analysis objects in the management period, carrying out numerical calculation to obtain an expression coefficient of the analysis objects, and marking the analysis objects as normal objects or abnormal objects through the expression coefficient of the analysis objects; the quantity ratio of the abnormal objects to the analysis objects is marked as an abnormal ratio, whether the overall performance of the analysis objects in the management period meets the requirements or not is judged according to the numerical value of the abnormal ratio, and an overall unqualified signal is sent to the distribution analysis module through the management platform when the overall performance of the analysis objects in the management period does not meet the requirements; and (3) carrying out distribution analysis on abnormal objects: acquiring the number of abnormal objects of each class and marking the abnormal objects as abnormal values, establishing an abnormal set of the abnormal values of all the classes, carrying out variance calculation on the abnormal set to obtain an abnormal representation value, and marking the abnormal characteristics of the management period as integral abnormality or salient abnormality according to the numerical value of the abnormal representation value.
The foregoing is merely illustrative and explanatory of the present invention and various modifications, additions or substitutions may be made to the specific embodiments described by those skilled in the art without departing from the scope of the invention as defined in the accompanying claims.
The formulas are obtained by acquiring a large amount of data and performing software simulation, and the coefficients in the formulas are set by the technicians in the field according to actual conditions; such as: formula BX = α 1 × cj- α 2 × kq- α 3 × zy; collecting multiple groups of sample data and setting corresponding expression coefficient for each group of sample data by technicians in the field; substituting the set expression coefficient and the acquired sample data into formulas, forming a ternary linear equation set by any three formulas, screening the calculated coefficients and taking the mean value to obtain values of alpha 1, alpha 2 and alpha 3 which are respectively 2.19, 3.48 and 5.14;
the size of the coefficient is a specific numerical value obtained by quantizing each parameter, so that the subsequent comparison is convenient, and regarding the size of the coefficient, the corresponding expression coefficient is preliminarily set for each group of sample data by a person skilled in the art according to the number of the sample data; it is sufficient that the proportional relationship between the parameter and the quantized numerical value is not affected, for example, the expression coefficient is proportional to the numerical value of the achievement data.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (4)
1. The education training management system comprises a management platform and is characterized in that the management platform is in communication connection with a student management module, a distribution analysis module, a teacher management module and a storage module;
the student management module is used for managing and analyzing students: setting a management period, marking students of an education and training institution as analysis objects, acquiring score data CJ, attendance data KQ and operation data ZY of the analysis objects in the management period, and carrying out numerical calculation on the score data CJ, the attendance data KQ and the operation data ZY to obtain an expression coefficient BX of the analysis objects in the management period; acquiring an expression threshold BXmin through a storage module, comparing an expression coefficient BX of an analysis object with the expression threshold BXmin, and judging whether the overall expression of the analysis object in a management period meets requirements or not according to a comparison result;
the distribution analysis module is used for carrying out distribution analysis on the abnormal objects and enabling the abnormal period of the management period to be an integral abnormal period or a prominent abnormal period;
the teacher management module is used for performing management analysis on the prominent teacher after receiving the prominent teacher: acquiring abnormal values of classes carried by the history of the outstanding teachers, summing the abnormal values of the classes carried by the history of the outstanding teachers, taking an average value to obtain abnormal analysis values, acquiring abnormal analysis threshold values through a storage module, and comparing the abnormal analysis values with the abnormal analysis threshold values: if the abnormal analysis value is smaller than the abnormal analysis threshold value, judging that the analysis result of the outstanding teacher is qualified; if the abnormal analysis value is larger than or equal to the abnormal analysis threshold value, judging that the analysis result of the prominent teacher is unqualified, and marking the prominent teacher with the unqualified analysis result as a mark teacher; deeply analyzing the marked teacher and acquiring unqualified characteristics of the marked teacher, and sending the unqualified characteristics of the marked teacher to a mobile phone terminal of a manager through a management platform;
the specific process of comparing the performance coefficient BX of the analysis object with the performance threshold BXmin includes: if the performance coefficient BX is smaller than the performance threshold BXmin, judging that the performance of the analysis object in the management period does not meet the requirement, and marking the corresponding analysis object as an abnormal object; if the expression coefficient BX is larger than or equal to the expression threshold BXmin, judging that the expression of the analysis object in the management period meets the requirement, and marking the corresponding analysis object as a normal object; the quantity ratio of the abnormal objects to the analysis objects is marked as an abnormal ratio, an abnormal threshold value is obtained through a storage module, and the abnormal ratio is compared with the abnormal threshold value: if the abnormal ratio is smaller than the abnormal threshold, judging that the overall performance of the analysis object in the management period meets the requirement; if the abnormal ratio is larger than or equal to the abnormal threshold, judging that the overall performance of the analysis object in the management period does not meet the requirement, sending an overall unqualified signal to a management platform by the student management module, and sending the overall unqualified signal to a distribution analysis module by the management platform after receiving the overall unqualified signal;
the specific process of the distribution analysis module for carrying out distribution analysis on the abnormal object comprises the following steps: acquiring the number of abnormal objects of each class and marking the abnormal objects as abnormal values, establishing an abnormal set of the abnormal values of all the classes, carrying out variance calculation on the abnormal set to obtain an abnormal expression value, acquiring an abnormal expression threshold value through a storage module, comparing the abnormal expression value with the abnormal expression threshold value, and marking the abnormal characteristics of a management period as integral abnormality or outstanding abnormality through a comparison result;
the specific process of comparing the abnormal performance value with the abnormal performance threshold value comprises the following steps: if the abnormal performance value is smaller than the abnormal performance threshold value, judging that the abnormal characteristic of the management period is overall abnormal, sending an overall abnormal signal to a management platform by a distribution analysis module, and sending the overall abnormal signal to a mobile phone terminal of a manager after the management platform receives the overall abnormal signal; if the abnormal performance value is larger than or equal to the abnormal performance threshold value, judging that the abnormal characteristic of the management period is a prominent abnormality, sequencing the classes according to the numerical value of the abnormal value from large to small, marking L1 classes which are sequenced at the front as prominent classes, marking teachers of the prominent classes as prominent teachers, sending the prominent teachers to the management platform by the distribution analysis module, and sending the prominent teachers to the teacher management module after the management platform receives the prominent teachers.
2. The educational training management system according to claim 1, wherein the acquisition process of the score data CJ of the analysis subject comprises: acquiring the scores of all the departments of the examinations of the analysis object in the management period, and marking the sum of the scores of all the departments of the examinations of the analysis object in the management period as score data CJ with the unit of percentage; the process of acquiring the attendance data KQ comprises the following steps: acquiring the times of class opening and the times of late arrival and absence of attendance of an analysis object in a management period, and marking the ratio of the times of late arrival and absence of attendance to the times of class opening as attendance data KQ; the process of acquiring the job data ZY includes: and acquiring the number of times of job arrangement and the number of times of job non-intersection of the analysis object in the management period, and marking the ratio of the number of times of job non-intersection to the number of times of job arrangement as job data ZY.
3. The educational training management system of claim 1, wherein the specific process of deep analysis of the mark-up teacher comprises: establishing a rectangular coordinate system by taking the starting time of a management period as an X axis and taking an abnormal value for marking the class carried by a teacher in the management period as a Y axis, marking a plurality of analysis points in the rectangular coordinate system by taking the starting time of the management period as an abscissa and taking the abnormal value for marking the class carried by the teacher in the management period as an ordinate, making a distinguishing ray parallel to the X axis in a first quadrant of the rectangular coordinate system, wherein the endpoint coordinate value of the distinguishing ray is (0, QFy), the QFy is a preset distinguishing threshold, marking the numerical value of the abscissa of the analysis point positioned at the lower side of the distinguishing ray as the distinguishing value, establishing a distinguishing set with all the distinguishing values, carrying out variance calculation on the distinguishing set to obtain a distinguishing expression value, acquiring the distinguishing expression threshold through a storage module, and comparing the distinguishing expression value with the expression threshold: if the distinguishing performance value is smaller than the distinguishing performance threshold value, marking the unqualified characteristic of the marking teacher as stage unqualified; if the distinguishing expression value is larger than or equal to the distinguishing expression value, the unqualified characteristic of the marking teacher is marked as the sustainable unqualified characteristic; and sending the unqualified features of the marking teacher to a management platform, and sending the unqualified features of the marking teacher to a mobile phone terminal of a manager after the management platform receives the unqualified features of the marking teacher.
4. The educational training method is characterized by comprising the following steps:
the method comprises the following steps: and (3) performing management analysis on students: setting a management period, marking students of an education and training institution as analysis objects, acquiring grade data CJ, attendance data KQ and operation data ZY of the analysis objects in the management period, carrying out numerical calculation to obtain an expression coefficient of the analysis objects, and marking the analysis objects as normal objects or abnormal objects through the expression coefficient of the analysis objects;
step two: the quantity ratio of the abnormal objects to the analysis objects is marked as an abnormal ratio, whether the overall performance of the analysis objects in a management period meets the requirement or not is judged according to the numerical value of the abnormal ratio, and an overall unqualified signal is sent to a distribution analysis module through a management platform when the overall performance of the analysis objects in the management period does not meet the requirement;
step three: and (3) carrying out distribution analysis on abnormal objects: acquiring the number of abnormal objects of each class and marking the abnormal objects as abnormal values, establishing an abnormal set of the abnormal values of all classes, carrying out variance calculation on the abnormal set to obtain abnormal representation values, and marking the abnormal features of the management period as integral abnormality or salient abnormality according to the numerical value of the abnormal representation values;
step four: and performing management analysis on the prominent teachers, marking the prominent teachers with unqualified analysis results as marking teachers, performing deep analysis on the marking teachers, and marking unqualified features of the marking teachers as persistent unqualified or stage unqualified features.
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Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2006319731A1 (en) * | 2005-12-02 | 2007-06-07 | Remarkspdf Pty Ltd | Assessment of educational services |
| CN107316161A (en) * | 2017-07-18 | 2017-11-03 | 晋江市第中学 | A kind of method and system of teaching effect examination |
| CN107705033A (en) * | 2017-10-24 | 2018-02-16 | 武汉览山科技股份有限公司 | A kind of quality of instruction evaluation system based on big data |
| CN108648542A (en) * | 2018-05-29 | 2018-10-12 | 黑龙江省经济管理干部学院 | A kind of internet database network-based classroom instruction system |
| CN109118091A (en) * | 2018-08-15 | 2019-01-01 | 南京光辉互动网络科技股份有限公司 | A kind of artistic accomplishment evaluation system |
| CN110765181A (en) * | 2019-10-24 | 2020-02-07 | 珍岛信息技术（上海）股份有限公司 | Intelligent educational administration analysis and management system based on big data |
| CN110807722A (en) * | 2019-11-22 | 2020-02-18 | 枣庄科技职业学院 | Achievement feedback statistical system for informatization teaching |
| CN112561749A (en) * | 2020-11-19 | 2021-03-26 | 安徽孺牛科技有限公司 | Education software management system for online and offline interaction |
| CN112561323A (en) * | 2020-12-14 | 2021-03-26 | 重庆科技学院 | Course teaching quality evaluation system and method based on student evaluation and output guidance |
| CN112686526A (en) * | 2020-12-25 | 2021-04-20 | 贵州树精英教育科技有限责任公司 | Teaching evaluation diagnosis analysis system |
| CN112884304A (en) * | 2021-02-02 | 2021-06-01 | 温州洪启信息科技有限公司 | Online distance education system based on big data |
| CN113034319A (en) * | 2020-12-24 | 2021-06-25 | 广东国粒教育技术有限公司 | User behavior data processing method and device in teaching management, electronic equipment and storage medium |
| CN113393095A (en) * | 2021-05-31 | 2021-09-14 | 广东马上信息科技有限公司 | Teacher teaching quality assessment method |
| CN113658022A (en) * | 2021-08-20 | 2021-11-16 | 青软创新科技集团股份有限公司 | Big data based teaching mode analysis method |
| CN114565206A (en) * | 2021-12-23 | 2022-05-31 | 上海新魁教育科技有限公司 | Teaching quality evaluation method based on teaching big data |
| CN115082266A (en) * | 2022-06-27 | 2022-09-20 | 武汉市汉阳区爱萌托育服务有限公司 | Student education subject comprehensive development analysis and evaluation system based on deep learning |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7512627B2 (en) * | 2005-12-30 | 2009-03-31 | Ecollege.Com | Business intelligence data repository and data management system and method |
| CN112258090B (en) * | 2020-11-16 | 2021-08-03 | 江苏子信科技创新研究院有限公司 | Online education management system based on Internet of things |
| CN114911209B (en) * | 2022-07-18 | 2022-09-30 | 巨野恒丰果蔬有限公司 | Garlic processing wastewater treatment management system based on data analysis |
-
2022
- 2022-10-11 CN CN202211237352.XA patent/CN115330358B/en active Active
Patent Citations (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2006319731A1 (en) * | 2005-12-02 | 2007-06-07 | Remarkspdf Pty Ltd | Assessment of educational services |
| CN107316161A (en) * | 2017-07-18 | 2017-11-03 | 晋江市第中学 | A kind of method and system of teaching effect examination |
| CN107705033A (en) * | 2017-10-24 | 2018-02-16 | 武汉览山科技股份有限公司 | A kind of quality of instruction evaluation system based on big data |
| CN108648542A (en) * | 2018-05-29 | 2018-10-12 | 黑龙江省经济管理干部学院 | A kind of internet database network-based classroom instruction system |
| CN109118091A (en) * | 2018-08-15 | 2019-01-01 | 南京光辉互动网络科技股份有限公司 | A kind of artistic accomplishment evaluation system |
| CN110765181A (en) * | 2019-10-24 | 2020-02-07 | 珍岛信息技术（上海）股份有限公司 | Intelligent educational administration analysis and management system based on big data |
| CN110807722A (en) * | 2019-11-22 | 2020-02-18 | 枣庄科技职业学院 | Achievement feedback statistical system for informatization teaching |
| CN112561749A (en) * | 2020-11-19 | 2021-03-26 | 安徽孺牛科技有限公司 | Education software management system for online and offline interaction |
| CN112561323A (en) * | 2020-12-14 | 2021-03-26 | 重庆科技学院 | Course teaching quality evaluation system and method based on student evaluation and output guidance |
| CN113034319A (en) * | 2020-12-24 | 2021-06-25 | 广东国粒教育技术有限公司 | User behavior data processing method and device in teaching management, electronic equipment and storage medium |
| CN112686526A (en) * | 2020-12-25 | 2021-04-20 | 贵州树精英教育科技有限责任公司 | Teaching evaluation diagnosis analysis system |
| CN112884304A (en) * | 2021-02-02 | 2021-06-01 | 温州洪启信息科技有限公司 | Online distance education system based on big data |
| CN113393095A (en) * | 2021-05-31 | 2021-09-14 | 广东马上信息科技有限公司 | Teacher teaching quality assessment method |
| CN113658022A (en) * | 2021-08-20 | 2021-11-16 | 青软创新科技集团股份有限公司 | Big data based teaching mode analysis method |
| CN114565206A (en) * | 2021-12-23 | 2022-05-31 | 上海新魁教育科技有限公司 | Teaching quality evaluation method based on teaching big data |
| CN115082266A (en) * | 2022-06-27 | 2022-09-20 | 武汉市汉阳区爱萌托育服务有限公司 | Student education subject comprehensive development analysis and evaluation system based on deep learning |
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