CN112766792A - Capacity tree creating method - Google Patents
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Abstract
The invention relates to a capacity tree creating method, which comprises the following steps: acquiring a creation request, and searching an initialization capability tree according to the creation request; the initialization capability tree comprises an initialization name, an object set, an object range set, initialization description data, an index set, a unit set, an algorithm set, an initialization comment and a scale set; acquiring data; generating a first capability tree according to the first name, the first object range, the first description data, the index set, the unit set, the algorithm set and the scale set, and sending the audit; and when the first capability tree passes the examination, acquiring data, generating a second capability tree according to the first name, the first object range, the first description data, the first index, the first unit, the first algorithm and the first table, and storing the second capability tree.
Description
Technical Field
The invention relates to the technical field of education evaluation, in particular to a capacity tree creating method.
Background
At present, China makes great progress in education assessment, education assessment theories are gradually deepened, and education assessment practice activities are widely developed, but the problems of single mode, low assessment technical level and the like exist. In the education assessment, assessment results are usually qualitative descriptions, such as workload of workers, achievement of students, teaching level of teachers and the like, and are often classified as qualified, unqualified and the like. If the teaching levels of both teachers were qualified, then try to ask both teachers whose teaching levels were higher?
Thus, if only the qualitative description is left, two or more individuals with substantially the same qualitative description cannot be compared. Then, how to quantify the qualitative description and achieve science, objectivity, reasonability, fairness and justice as much as possible is a problem worthy of deep research.
Disclosure of Invention
The invention aims to provide a capacity tree creating method aiming at the defects of the prior art, which quantifies qualitative description, realizes specific quantitative evaluation on a single individual in multiple aspects and unidirectional comparison evaluation among a plurality of individuals, and further realizes more scientific, objective, reasonable, fair and fair evaluation results.
In order to achieve the above object, the present invention provides a capability tree creating method, including:
acquiring a creating request, and searching an initialization capability tree according to the creating request; the initialization capability tree comprises an initialization name, an object set, an object range set, initialization description data, an index set, a unit set, an algorithm set, an initialization comment and a scale set;
acquiring first data, and updating the initialization name according to the first data to obtain a first name;
acquiring second data, determining a first object in the object set according to the second data, searching the object range set according to the first object, and determining a first object range;
acquiring third data, and updating the initialization description data according to the third data to obtain first description data;
generating a first capability tree according to the first name, the first object range, the first description data, the index set, the unit set, the algorithm set, the initialization comment and the scale set, and sending an audit;
when the first capability tree passes the examination, fourth data is obtained, a first index in the index set is determined according to the fourth data, the corresponding unit set is determined and searched according to the first index, and a first unit set is obtained;
acquiring fifth data, determining a first unit in the first unit set according to the fifth data, and searching a corresponding algorithm set according to the first unit to obtain a first algorithm set;
acquiring sixth data, and determining a first algorithm in the first algorithm set according to the sixth data;
acquiring seventh data, and updating the initialization comment according to the seventh data to obtain a first comment;
acquiring eighth data, and determining a first scale in the scale set according to the eighth data;
and generating and storing a second capability tree according to the first name, the first object range, the first description data, the first index, the first unit, the first algorithm, the first comment and the first scale.
Preferably, when the first capability tree is not passed through the audit, the first capability tree is stored.
Preferably, after the fourth data is obtained and the first indicator in the indicator set is determined according to the fourth data, the capability tree creating method further includes:
acquiring a sub-index creating instruction, determining a first sub-index in the index set according to the sub-index creating instruction, and establishing an incidence relation between the first index and the first sub-index.
Preferably, the set of units comprises a plurality of units; each of the units comprises a plurality of test questions; after the sixth data is obtained and the first algorithm in the first algorithm set is determined according to the sixth data, the capability tree creating method further includes:
and establishing a relation between the first algorithm and the first unit, wherein the first algorithm is used for calculating the scores of the plurality of test questions in the first unit.
Further preferably, the first comment is a plurality of comments; each first comment corresponds to a different score range; the capability tree creation method further comprises:
and when the score is within the score range, outputting a corresponding first comment.
The capacity tree creating method provided by the embodiment of the invention quantifies qualitative description, realizes specific quantitative evaluation on multiple aspects of a single individual and unidirectional comparison evaluation among multiple individuals, and further realizes more scientific, objective, reasonable, fair and fair evaluation results.
Drawings
Fig. 1 is a flowchart of a capability tree creating method according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
The capacity tree creating method provided by the invention quantifies qualitative description, realizes specific quantitative evaluation on multiple aspects of a single individual and unidirectional comparison evaluation among multiple individuals, thereby realizing more scientific, objective, reasonable, fair and fair evaluation results.
Fig. 1 is a flowchart of a capability tree creating method according to an embodiment of the present invention, and the following describes a technical solution of the present invention in detail with reference to fig. 1.
specifically, the initialized capability tree includes an initialized name, an object set, an object range set, initialized description data, an index set, a unit set, an algorithm set, an initialized comment and a scale set. The initialization capability tree can be understood as a template of which all information is initial values or information to be configured, and the creation process of the invention is a process of updating the initial values in the initialization capability tree and configuring the information to be configured according to the acquired data, and finally creating an evaluation template meeting the requirements.
102, acquiring first data, updating an initialization name according to the first data, and obtaining a first name;
specifically, the first name is the name of the created capability tree. After the creation is successful, the capability tree can be found through a precise search or a fuzzy search.
103, acquiring second data, determining a first object in the object set according to the second data, searching an object range set according to the first object, and determining a first object range;
specifically, the object may be understood as a group corresponding to the evaluation, and the object range may be understood as a classification under the group. Generally, the selected object is one, and the selected object range can be one or more.
specifically, the third data may be understood as a text description for describing functions, effects, and the like of the capability tree. Preferably, the number of words of the third data may be limited.
Specifically, the capability tree may be searched according to the keyword in the first description data.
105, generating a first capability tree according to the first name, the first object range, the first description data, the index set, the unit set, the algorithm set, the initialization comment and the scale set, and sending an audit;
specifically, the first capability tree may be understood as a basic version of the capability tree created according to the acquired data, and may be further configured subsequently.
When the first capability tree is not approved, executing step 120, and storing the first capability tree; when the first capability tree audit passes, step 106 is performed.
Step 106, acquiring fourth data, determining a first index in the index set according to the fourth data, and determining and searching a corresponding unit set according to the first index to obtain a first unit set;
specifically, each index corresponds to a plurality of units, each unit comprises a plurality of test questions, and corresponding scores can be obtained through the solution of the examiners to the test questions and are used for the subsequent calculation evaluation of the indexes.
In a preferred scheme, after the fourth data is acquired, the first index in the index set is determined according to the fourth data, the sub-index creating instruction is acquired, the first sub-index in the index set is determined according to the sub-index creating instruction, and the association relationship between the first index and the first sub-index is established. The sub-indicators are configured in the same way as the indicators. When the index and the sub-index have an association relationship, the sub-index and the index have a level attribute and the level of the sub-index is lower than that of the index.
Step 107, acquiring fifth data, determining a first unit in the first unit set according to the fifth data, and searching a corresponding algorithm set according to the first unit to obtain a first algorithm set;
specifically, the unit set may be understood as a set including a plurality of topic groups, and the algorithm set corresponding to each topic group is also different.
Step 108, acquiring sixth data, and determining a first algorithm in the first algorithm set according to the sixth data;
specifically, since the test questions differ in each cell, the method of calculating the score value also differs.
In a preferred embodiment, the unit set includes a plurality of units, and each unit includes a plurality of test questions. After obtaining the sixth data and determining the first algorithm in the first algorithm set according to the sixth data, the capacity tree creating method further comprises establishing a relationship between the first algorithm and the first unit, wherein the first algorithm is used for calculating the scores of the plurality of test questions in the first unit.
Step 109, acquiring seventh data, and updating the initialization comment according to the seventh data to obtain a first comment;
specifically, the first comment may be a specific text language or picture or a numerical value or grade.
In a preferred embodiment, the first score is a plurality of first scores, each of the first scores corresponding to a different score range. And when the score is within the score range, outputting the corresponding first comment.
Step 110, acquiring eighth data, and determining a first scale table in the scale table set according to the eighth data;
in particular, a scale may be understood as a criterion for specifying a test question or indicator within a unit or the capability tree.
And step 111, generating and storing a second capability tree according to the first name, the first object range, the first description data, the first index, the first unit, the first algorithm, the first comment and the first scale.
Specifically, the second capability tree is a configured capability tree that can be used for evaluation.
The invention is further described below by taking the cognitive test of students as an example.
Example 1
Initializing the capability tree includes:
{ "initialization name: 000 ";
"object set: students, parents, teachers";
"set of object ranges: a student: grade 1, grade 2, grade 3, grade 4, grade 5, grade 6; a parent: grade 1, grade 2, grade 3, grade 4, grade 5, grade 6; a teacher: grade 1, grade 2, grade 3, grade 4, grade 5, grade 6 ";
"initialization description data: 000 ";
"index set: cognition, mood control, sociality ";
"Unit set: unit 1, unit 2 for evaluating cognition ability; unit 1, unit 2 for evaluating emotion control ability; unit 1, unit 2 "for evaluating sociality;
"set of algorithms: an algorithm 11 corresponding to the unit 1 for evaluating cognition, and an algorithm 12 corresponding to the unit 2 for evaluating cognition; an algorithm 21 corresponding to the unit 1 for evaluating the emotion control ability, and an algorithm 22 corresponding to the unit 2 for evaluating the emotion control ability; algorithm 31 corresponding to unit 3 for evaluating sociality, algorithm 32 corresponding to unit 2 for evaluating sociality;
"initialization comment: 000 ";
"set of scales: time-limited 30-second answer for all test questions' }
A first name of the created first ability tree is determined as 'ability evaluation', the first object is 'student', the first object range is 'grade 1', and the first description data is 'ability evaluation of grade 1 student' according to the acquired first data, second data and third data.
Determining a first name of the created second ability tree, that is, "ability evaluation", the first object is a "student", the first object range is "grade 1", the first description data is "ability evaluation of grade 1 student", the first index is "cognition", the first unit is "unit for evaluating cognition 1", the first algorithm is "algorithm 11 corresponding to unit for evaluating cognition 1", the first comment is "score corresponding to the first index is greater than or equal to 80", outputting "strong cognition", the score corresponding to the first index is greater than or equal to 30 and less than 80 ", outputting" normal cognition ", the score corresponding to the first index is less than 30", outputting "poor cognition", and the first scale is "answer for 30 seconds when all the test questions are limited", according to the obtained fourth data, fifth data, sixth data, seventh data and eighth data.
The above examples are only for the deep understanding of the present invention and are not intended to limit the scope of the present invention. Any of the above information may be changed and replaced as needed, or an initialization information item of the initialization capability tree may be added as needed.
The capacity tree creating method quantifies qualitative description, realizes specific quantitative evaluation on multiple aspects of a single individual and unidirectional comparison evaluation among multiple individuals, and accordingly realizes more scientific, objective, reasonable, fair and fair evaluation results.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (5)
1. A capability tree creation method, the capability tree creation method comprising:
acquiring a creating request, and searching an initialization capability tree according to the creating request; the initialization capability tree comprises an initialization name, an object set, an object range set, initialization description data, an index set, a unit set, an algorithm set, an initialization comment and a scale set;
acquiring first data, and updating the initialization name according to the first data to obtain a first name;
acquiring second data, determining a first object in the object set according to the second data, searching the object range set according to the first object, and determining a first object range;
acquiring third data, and updating the initialization description data according to the third data to obtain first description data;
generating a first capability tree according to the first name, the first object range, the first description data, the index set, the unit set, the algorithm set, the initialization comment and the scale set, and sending an audit;
when the first capability tree passes the examination, fourth data is obtained, a first index in the index set is determined according to the fourth data, the corresponding unit set is determined and searched according to the first index, and a first unit set is obtained;
acquiring fifth data, determining a first unit in the first unit set according to the fifth data, and searching a corresponding algorithm set according to the first unit to obtain a first algorithm set;
acquiring sixth data, and determining a first algorithm in the first algorithm set according to the sixth data;
acquiring seventh data, and updating the initialization comment according to the seventh data to obtain a first comment;
acquiring eighth data, and determining a first scale in the scale set according to the eighth data;
and generating and storing a second capability tree according to the first name, the first object range, the first description data, the first index, the first unit, the first algorithm, the first comment and the first scale.
2. The capability tree creation method of claim 1, further comprising:
storing the first capability tree when the first capability tree audit does not pass.
3. The method of claim 1, wherein after obtaining fourth data from which the first metric in the set of metrics is determined, the method further comprises:
acquiring a sub-index creating instruction, determining a first sub-index in the index set according to the sub-index creating instruction, and establishing an incidence relation between the first index and the first sub-index.
4. The capability tree creation method of claim 1, wherein the set of cells comprises a plurality of cells; each of the units comprises a plurality of test questions; after the sixth data is obtained and the first algorithm in the first algorithm set is determined according to the sixth data, the capability tree creating method further includes:
and establishing a relation between the first algorithm and the first unit, wherein the first algorithm is used for calculating the scores of the plurality of test questions in the first unit.
5. The capability tree creation method of claim 4, wherein the first comment is plural; each first comment corresponds to a different score range; the capability tree creation method further comprises:
and when the score is within the score range, outputting a corresponding first comment.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030078900A1 (en) * | 2001-06-29 | 2003-04-24 | Dool Jacques Van Den | Distributed decision processing system with advanced comparison engine |
| WO2003090132A2 (en) * | 2002-04-18 | 2003-10-30 | Qinetiq Limited | Decision aiding tool |
| CN102364513A (en) * | 2011-10-24 | 2012-02-29 | 彭苏勉 | Undergraduate comprehensive quality and capability evaluation system |
| CN103971555A (en) * | 2013-01-29 | 2014-08-06 | 北京竞业达数码科技有限公司 | Multi-level automated assessing and training integrated service method and system |
| CN104008143A (en) * | 2014-05-09 | 2014-08-27 | 启秀科技（北京）有限公司 | Vocational ability index system establishment method based on data mining |
| KR20140134346A (en) * | 2013-05-13 | 2014-11-24 | 이경환 | Diagnostic System of Strategy Management Ability Self-Actualization and Diagnostic Method |
| CN104463385A (en) * | 2013-09-12 | 2015-03-25 | 郑州学生宝电子科技有限公司 | Data statistics analysis platform for teaching tests |
| CN105550955A (en) * | 2015-12-09 | 2016-05-04 | 汤锐华 | Data collection system |
| CN107730083A (en) * | 2017-09-18 | 2018-02-23 | 上海量明科技发展有限公司 | The ability quantization method and device of object |
| CN108446848A (en) * | 2018-03-21 | 2018-08-24 | 北京理工大学 | Individual networks awareness of safety scalar quantization evaluation method |
| JP6405441B1 (en) * | 2017-12-21 | 2018-10-17 | 株式会社リンクアンドモチベーション | Information processing apparatus, information processing method, and program |
| CN112053267A (en) * | 2020-08-30 | 2020-12-08 | 高岩峰 | Subject ability classification clustering evaluation system |
-
2021
- 2021-01-29 CN CN202110128311.6A patent/CN112766792A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030078900A1 (en) * | 2001-06-29 | 2003-04-24 | Dool Jacques Van Den | Distributed decision processing system with advanced comparison engine |
| WO2003090132A2 (en) * | 2002-04-18 | 2003-10-30 | Qinetiq Limited | Decision aiding tool |
| CN102364513A (en) * | 2011-10-24 | 2012-02-29 | 彭苏勉 | Undergraduate comprehensive quality and capability evaluation system |
| CN103971555A (en) * | 2013-01-29 | 2014-08-06 | 北京竞业达数码科技有限公司 | Multi-level automated assessing and training integrated service method and system |
| KR20140134346A (en) * | 2013-05-13 | 2014-11-24 | 이경환 | Diagnostic System of Strategy Management Ability Self-Actualization and Diagnostic Method |
| CN104463385A (en) * | 2013-09-12 | 2015-03-25 | 郑州学生宝电子科技有限公司 | Data statistics analysis platform for teaching tests |
| CN104008143A (en) * | 2014-05-09 | 2014-08-27 | 启秀科技（北京）有限公司 | Vocational ability index system establishment method based on data mining |
| CN105550955A (en) * | 2015-12-09 | 2016-05-04 | 汤锐华 | Data collection system |
| CN107730083A (en) * | 2017-09-18 | 2018-02-23 | 上海量明科技发展有限公司 | The ability quantization method and device of object |
| JP6405441B1 (en) * | 2017-12-21 | 2018-10-17 | 株式会社リンクアンドモチベーション | Information processing apparatus, information processing method, and program |
| CN108446848A (en) * | 2018-03-21 | 2018-08-24 | 北京理工大学 | Individual networks awareness of safety scalar quantization evaluation method |
| CN112053267A (en) * | 2020-08-30 | 2020-12-08 | 高岩峰 | Subject ability classification clustering evaluation system |
Non-Patent Citations (2)
| Title |
|---|
| 卓建南;尹少丰;: "普通高校体育教师教学能力量化评价研究", 安徽师范大学学报(自然科学版), vol. 31, no. 06, pages 609 - 612 * |
| 钟嘉鸣;祝庚;叶丽娟;: "信息化进程中高校信息素养指标评价体系的分析与设计", 中国教育信息化, no. 07, pages 21 - 23 * |
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