CN112200699A - Subject professional optimization evaluation method and system - Google Patents
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- CN112200699A CN112200699A CN202011062681.6A CN202011062681A CN112200699A CN 112200699 A CN112200699 A CN 112200699A CN 202011062681 A CN202011062681 A CN 202011062681A CN 112200699 A CN112200699 A CN 112200699A
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- 238000005457 optimization Methods 0.000 title claims abstract description 41
- 238000011156 evaluation Methods 0.000 title claims abstract description 38
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- 238000012552 review Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000009960 carding Methods 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 claims description 3
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Abstract
The invention discloses a subject professional evaluation method, which can be used for analyzing the whole process data of similar professional evaluation according to professional names and professional index data through a correlation comparison algorithm, finding out valuable information, carrying out comparison analysis according to key dimensions and the professional names, outputting similar professional evaluation courses and prompting key points; the key points and strategies for outputting the development of professional optimization work comprise ranking of various indexes of the current professional on the national scale, the whole province scale and the whole city scale, and the strategies comprise how to adjust and improve the aspects of the current professional optimization work and how to develop professional construction. The automatic optimization of the major is realized, so that the major optimization work is simple and rapid, and time and labor are saved.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a subject professional optimization evaluating method and system.
Background
"ten years raise trees, the person is raised in a century", education quality is the key to guarantee the quality of talents, in order to realize the idea that talents serve the society, various disciplines and specialities are established in various colleges at present to cultivate talents that can meet the needs of various industries.
To evaluate the quality of education, evaluation of each discipline is required. The current links of professional evaluation comprise indexes such as self-inspection and evaluation, basic information, professional orientation, culture targets and specifications, course setting and requirements, teaching process and arrangement, implementation guarantee and the like. At present, professional evaluation is not only performed in a plurality of links, but also needs more personnel to participate, and more time and labor are consumed.
For the above reasons, it is desirable to provide a method that can automatically perform professional optimization.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method capable of automatically performing professional optimization.
In order to achieve the purpose, the invention provides the following technical scheme:
a discipline professional optimization method comprises the following steps:
s1, analyzing the overall process data of similar professional evaluation by a correlation comparison algorithm based on the evaluation data domain of the same profession of the data system according to the professional name and the professional index data, finding out valuable information, and organizing the information based on a certain form;
s2, comparing and associating the professional name and the key dimension with the domain data of the same profession in the big data system, analyzing and outputting the professional optimization process of the same kind, and prompting the key point;
s3, outputting data support and suggestion strategies developed by professional optimization work, and generating professional optimization work guidance and evaluation reports, wherein the report contents comprise a basic data report and an optimization work analysis report, the basic data report contents comprise the current national or regional overview of a professional, the ranking condition of each index data of the professional and the gap condition of each index of the professional and a first-class professional, and the optimization work analysis report contents comprise course display and key point reminding of the optimization work of the same professional, the work suggestion and promotion guidance for the professional.
As a preferable scheme: the specific steps analyzed in the step S2 are:
(1) based on the professional name, positioning the associated professional data and associating based on the index dimension;
(2) traversing the associated data, and extracting the attention index data for summary and carding;
(3) and extracting data of corresponding fields for calculation based on the optimization algorithm, and outputting a calculation result.
As a preferable scheme: the professional index data comprises the education course scores of teachers, the education practice time, the number of education practice bases, the student ratio, the proportion of teachers with high-class titles to teachers with special purposes, the proportion of teachers with large doctor degrees to teachers with special purposes, the average education practice expenses, the employment rate, the number of recruits, the new-born report rate, the age structure of teachers with special purposes and the course structure.
As a preferable scheme: the method also comprises a step of displaying the evaluation results in a report and map mode.
As a preferable scheme: the method also comprises a step of displaying professional evaluation work courses of all ranking levels in a graph-context manner.
As a preferable scheme: the key dimensions comprise key index dimensions of areas where schools are located and people want to know.
A system for discipline expertise review, comprising:
the data clustering module is used for acquiring subject professional data from a data system and classifying and gathering the data;
the data analysis module is used for carrying out comparison and analysis according to the professional names and the key dimensions, outputting professional optimization courses of the same type and prompting key points;
and the result evaluation module is used for outputting key points and strategies developed by professional evaluation work.
As a preferable scheme: the system also comprises a visualization module which is used for visually outputting the evaluation result.
A computer storage medium having stored thereon an executable program which, when executed by a processor, performs the steps of the method for discipline assessment.
Compared with the prior art, the invention has the advantages that: by using the method, the whole process data of similar professional evaluation can be analyzed through a correlation comparison algorithm according to the professional name and the professional index data, valuable information is found out, comparison analysis is carried out according to the professional name and the key dimension, a similar professional evaluation course is output, and a key point is prompted; the key points and strategies for outputting the development of professional optimization work comprise ranking of various indexes of the current professional on the national scale, the whole province scale and the whole city scale, and the strategies comprise how to adjust and improve the aspects of the current professional optimization work and how to develop professional construction. The automatic optimization of the major is realized, so that the major optimization work is simple and rapid, and time and labor are saved.
Drawings
FIG. 1 is a flow chart of the steps of discipline expertise optimization.
Detailed Description
The first embodiment is as follows:
a discipline professional optimization method comprises the following steps:
s1, analyzing the overall process data of similar professional evaluation by a correlation comparison algorithm based on the evaluation data domain of the same profession of the data system according to the professional name and the professional index data, finding out valuable information, and organizing the information based on a certain form;
s2, comparing and associating the professional name and the key dimension with the domain data of the same profession in the big data system, analyzing and outputting the professional optimization process of the same kind, and prompting the key point;
s3, outputting data support and suggestion strategies developed by professional optimization work, and generating professional optimization work guidance and evaluation reports, wherein the report contents comprise a basic data report and an optimization work analysis report, the basic data report contents comprise the current national or regional overview of a professional, the ranking condition of each index data of the professional and the gap condition of each index of the professional and a first-class professional, and the optimization work analysis report contents comprise course display and key point reminding of the optimization work of the same professional, the work suggestion and promotion guidance for the professional.
As a preferable scheme: the specific steps analyzed in the step S2 are:
(1) based on the professional name, positioning the associated professional data and associating based on the index dimension;
(2) traversing the associated data, and extracting the attention index data for summary and carding;
(3) and extracting data of corresponding fields for calculation based on the optimization algorithm, and outputting a calculation result.
As a preferable scheme: professional index data include teacher's education course credit, education practice time, education practice base number, student than, have high-class job title teacher and account for the proportion of professional teacher, have master doctor's degree teacher and account for the proportion of professional teacher, student's average education practice expenditure, employment rate, number of recruits, newly-born report rate, professional teacher's age structure, course structure etc. above-mentioned index data general divide into five dimensions: course and teaching, students, support conditions, teachers and teams, cooperation and practice. The data of the five dimensions are structured and then stored in a certain data space, then collision analysis is carried out on the data and global data of the same profession in a big data system, the data of the whole process of evaluating the optimization of the same profession is analyzed through a clustering association comparison algorithm, the regional layout data of the profession is analyzed, data support of the evaluating work of the profession is output, different analysis support data are output according to different dimensions, and the data support data are output based on a data venation form.
As a preferable scheme: the method also comprises a step of displaying the evaluation results in a report and map mode.
In order to clearly and intuitively present the evaluation result, the evaluation result is presented in the form of a table, a chart, or the like in the present embodiment.
As a preferable scheme: the method also comprises a step of displaying professional evaluation work courses of all ranking levels in a graph-context manner.
As a preferable scheme: the key dimensions comprise key index dimensions of areas where schools are located and people want to know.
Example two:
a system for discipline expertise review, comprising:
the data clustering module is used for acquiring subject professional data from a data system and classifying and gathering the data;
the data analysis module is used for carrying out comparison and analysis according to the professional names and the key dimensions, outputting professional optimization courses of the same type and prompting key points;
and the result evaluation module is used for outputting key points and strategies developed by professional evaluation work.
The system also comprises a visualization module which is used for visually outputting the evaluation result.
EXAMPLE III
A computer storage medium having stored thereon an executable program which, when executed by a processor, performs the steps of the method for discipline assessment.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.
Claims (9)
1. A disciplinary major evaluation method is characterized by comprising the following steps:
s1, analyzing the overall process data of similar professional evaluation by a correlation comparison algorithm based on the evaluation data domain of the same profession of the data system according to the professional name and the professional index data, finding out valuable information, and organizing the information based on a certain form;
s2, comparing and associating the professional name and the key dimension with the domain data of the same profession in the big data system, analyzing and outputting the professional optimization process of the same kind, and prompting the key point;
s3, outputting data support and suggestion strategies developed by professional optimization work, and generating professional optimization work guidance and evaluation reports, wherein the report contents comprise a basic data report and an optimization work analysis report, the basic data report contents comprise the current national or regional overview of a professional, the ranking condition of each index data of the professional and the gap condition of each index of the professional and a first-class professional, and the optimization work analysis report contents comprise course display and key point reminding of the optimization work of the same professional, the work suggestion and promotion guidance for the professional.
2. The discipline major review method as claimed in claim 1, wherein: the specific steps analyzed in the step S2 are:
(1) based on the professional name, positioning the associated professional data and associating based on the index dimension;
(2) traversing the associated data, and extracting the attention index data for summary and carding;
(3) and extracting data of corresponding fields for calculation based on the optimization algorithm, and outputting a calculation result.
3. The discipline major review method as claimed in claim 1, wherein: the professional index data comprises the education course scores of teachers, the education practice time, the number of education practice bases, the student ratio, the proportion of teachers with high-class titles to teachers with special purposes, the proportion of teachers with large doctor degrees to teachers with special purposes, the average education practice expenses, the employment rate, the number of recruits, the new-born report rate, the age structure of teachers with special purposes and the course structure.
4. The discipline major review method as claimed in claim 1, wherein: the method also comprises a step of displaying the evaluation results in a report and map mode.
5. The discipline major review method as claimed in claim 1, wherein: the method also comprises a step of displaying professional evaluation work courses of all ranking levels in a graph-context manner.
6. The discipline major review method as claimed in claim 1, wherein: the key dimensions comprise key index dimensions of areas where schools are located and people want to know.
7. A discipline major evaluating system is characterized by comprising:
the data clustering module is used for acquiring subject professional data from a data system and classifying and gathering the data;
the data analysis module is used for carrying out comparison and analysis according to the professional names and the key dimensions, outputting professional optimization courses of the same type and prompting key points;
and the result evaluation module is used for outputting key points and strategies developed by professional evaluation work.
8. The discipline expertise optimizing system of claim 7, wherein: the system also comprises a visualization module which is used for visually outputting the evaluation result.
9. A computer storage medium having stored thereon an executable program which, when executed by a processor, performs the steps of the discipline assessment method as claimed in any one of claims 1-6.
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| CN202011062681.6A CN112200699A (en) | 2020-09-30 | 2020-09-30 | Subject professional optimization evaluation method and system |
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| CN202011062681.6A CN112200699A (en) | 2020-09-30 | 2020-09-30 | Subject professional optimization evaluation method and system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112711601A (en) * | 2021-03-29 | 2021-04-27 | 广州欧赛斯信息科技有限公司 | Information processing method and system for higher education professional data indexes |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103198379A (en) * | 2013-03-14 | 2013-07-10 | 安阳师范学院 | Digitized professional building assessment method |
| CN104850921A (en) * | 2014-02-13 | 2015-08-19 | 同济大学 | Scientific and technological achievement transformation information system |
| CN106960273A (en) * | 2017-03-01 | 2017-07-18 | 河南科技学院 | A kind of appraisement system and assessment method of Graduates Employment competitiveness index |
-
2020
- 2020-09-30 CN CN202011062681.6A patent/CN112200699A/en active Pending
Patent Citations (3)
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| CN103198379A (en) * | 2013-03-14 | 2013-07-10 | 安阳师范学院 | Digitized professional building assessment method |
| CN104850921A (en) * | 2014-02-13 | 2015-08-19 | 同济大学 | Scientific and technological achievement transformation information system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112711601A (en) * | 2021-03-29 | 2021-04-27 | 广州欧赛斯信息科技有限公司 | Information processing method and system for higher education professional data indexes |
| CN112711601B (en) * | 2021-03-29 | 2021-07-13 | 广州欧赛斯信息科技有限公司 | Information processing method and system for higher education professional data indexes |
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Application publication date: 20210108 |