TWI704516B - Method of evaluating industrial competencies - Google Patents

Abstract

A method of evaluating industrial competencies, performed by a computer host. The method includes the following steps. Step S1-1 is to define a preliminary test indicator layer, which includes a plurality of blocks. Step S1-2 is to perform a series of preliminary tests for a professional to obtain a series of preliminary test responses of the professional. Step S1-3 is to classify the professional into one of the blocks according to the series of the preliminary test responses, and assign a preliminary test indicator element to the professional. Step S1-4 is to perform the steps S1-2 and S1-3 respectively for a plurality of professionals to assign a plurality of preliminary indicator elements to the professionals.

Description

Industrial function evaluation method

The invention relates to computer software, a system installed with computer software, in particular to a method and system for evaluating industrial functions.

Education includes physical education and online education. Physical education refers to, for example, in real environments such as schools and cram schools, the educator implements teaching activities for the learners. In a broader sense, it can cover school education, family education, and even social education.

As for online education, there are broad and narrow senses. In a broad sense, online education refers to online learning. Specifically, it refers to non-face-to-face education activities realized through the delivery of digital content under the Internet environment. It can make full use of the massive database provided by modern information technology. , Or multiple audio-visual media to provide rich learning methods. In a narrow sense, online education refers to the training of online knowledge.

Compared with the two, physical education can promote the development of practical and communicative skills in the real environment, and develop more soundly under the guidance of educators. The words and deeds of educators may become the object of learning and imitating by the educated, and have a subtle influence on the educated. In this regard, online education is incomparable. Therefore, so far, physical education is still the main channel of educational activities.

In order to provide students with good teaching quality, the management of teachers is very important. In addition to teachers, all kinds of professionals should pass a variety of assessments and trainings to provide high-quality services. but, At present, various online education platforms only provide general ordering functions or contact functions, which are not enough to give full play to the core meaning of professional evaluation and training.

Therefore, it is indeed necessary to provide an innovative method and system to strengthen the quality of professionals and facilitate the expansion of industrial functions.

In view of this, in view of this, the present invention urgently intends to provide a comprehensive resource integration covering the aspects of school management, teaching management, teacher training, and industrial function evaluation. The present invention is based on various industrial function indicators and knowledge nodes, constructs an integration method and system, establishes certification standards, and supplies professional courses required by various industries. In addition, it can be combined with online evaluation, video and audio evaluation, or actual evaluation to implement the industrial function evaluation method and system of the present invention in a remote manner.

Therefore, the industrial function evaluation method and system of the present invention can provide functions such as training, evaluation, certification, workplace supply and demand, and customer feedback for various industrial functions, and integrate the industrial side, training side, and customer side. Link, establish a reliable and complete system to link the market. Professionals are distinguished according to their positions, and the teaching steps are specifically evolved into teaching concept nodes, teaching technology nodes, and industrial technology nodes, thus forming a systematic content, which becomes the main axis of the integration method and system of teachers or technicians.

In view of this, the present invention provides an industry function evaluation method, which includes a plurality of steps executed by a host computer. The steps at least include: Step S1-1 is to define a preliminary test index layer, which includes a plurality of blocks. Step S1-2 is to implement a series of preliminary test assessments for a professional to obtain a series of preliminary test responses of the professional. Step S1-3 is to classify the professional into one of the blocks according to the answer responses of the series of preliminary tests, and assign a preliminary test indicator element to the professional. Step S1-4 is right For multiple professionals, perform the steps S1-2 and S1-3 respectively to assign multiple preliminary indicator elements to the professionals.

Through the industrial function evaluation method and system of the present invention, various professionals can organize alliances according to the electronic business model platform established by the present invention to restructure their business. Accordingly, the central goal of the present invention to create a virtuous circle of the education system instead of vicious competition is achieved.

1: Industrial function integration system

11: Initial test indicator layer

11-1, 11-2, 11-3: column

11-W, 11-X, 11-Y, 11-Z: OK

111: Initial test indicator element

112: Arrow

12: Essential functional layer

121: Essential Functional Elements

13: Training course layer

131: Training Course Elements

14: Retest index layer

141: Retest indicator element

50: computer host

51: Motherboard

52: Hard Disk

53: Memory

54: processor

60: terminal device

71: Code

72: interpreter

73: Compiler

Ta1 to Ta3: Professionals (teachers) represented by the initial test indicator elements

Tb1 to Tb2: Professionals (teachers) represented by the element of the initial test indicators

Tc1 to Tc3: Professionals (teachers) represented by the element of the initial test indicators

Td1 to Td3: Professionals (teachers) represented by the element of the initial test indicators

E ₁ to E ₃ : Institution

J: Total number of institutions

K: Total number of periods

k: time period ordinal

N: Total number of professionals

n: professional number

M: Total number of assessments

m: evaluation number

P ₁ to P ₅ : time period

Q ₁ to Q ₅ : Evaluation

T ₁ to T ₃ : Teacher

T : Matrix of opening hours

Y : demand period matrix

U : matching matrix

X : response matrix

Γ12: Essential functional morphism

Γ23: Training Course Morphism

Γ34: Retesting morphism

FIG. 1 shows a structural diagram of an industrial function integration system according to an embodiment of the present invention.

Figure 2 shows that the initial test indicator layer of the present invention is divided into twelve blocks.

FIG. 3 shows a classification diagram of the industrial function integration system according to an embodiment of the present invention.

Fig. 4 shows a schematic diagram of a training course morphology based on the matching between the teaching time and the required time according to an embodiment of the present invention.

FIG. 5A shows a flowchart of an industry function evaluation method according to an embodiment of the present invention.

FIG. 5B shows a flowchart of an industry function evaluation method according to an embodiment of the present invention.

FIG. 6A shows a block diagram of a form of industrial function integration system of the present invention.

FIG. 6B shows a block diagram of another form of industrial function integration system of the present invention.

Figure 7A shows the structure of the "EKO STEM Education Institution" created by this article.

Figure 7B shows the detailed structure diagram of the "EKO Business Unit (System Introduction)" of Figure 7B.

Figure 8 shows the architecture diagram of the "registration system" created by this author.

Figure 9 shows the system architecture diagram of the "Membership Classification" created by this article.

Several embodiments of the present invention will be provided below. It can be understood that these embodiments are not intended to limit. The multiple technical features of one embodiment of the present invention can be modified, substituted, combined, and separated to be applied to other embodiments.

In addition, the various methods or systems of the present invention can be implemented in any desired and suitable manner. For example, they can be implemented in hardware or software. Unless otherwise specified, the various functional elements, blocks, and means of the present invention may include a suitable processor, a controller, a functional unit, a circuit, a program logic, or a microprocessor. Settings, etc., can be operated to perform these functions. There may be a dedicated hardware component and/or programmable hardware component that can be configured to operate in a required and suitable manner.

FIG. 1 shows a structural diagram of an industrial function integration system 1 according to an embodiment of the present invention.

The industrial function integration system 1 of the present invention includes a preliminary test index layer 11, a necessary function layer 12, a training course layer 13, and a retest index layer 14. The preliminary test index layer 11 includes a plurality of preliminary test index elements 111 (in order to keep the diagram concise, they are only marked in Td3). The mandatory functional layer 12 includes multiple mandatory functional elements 121 (in order to keep the diagram concise, it is only marked in J5). The training course layer 13 includes a plurality of training course elements 131 (in order to keep the diagram concise, only marked in L6). The retest index layer 14 includes a plurality of retest index elements 141 (in order to keep the diagram concise, they are only marked in F4). The names of the above-mentioned layers and the names of the elements are only exemplary. The above layers and elements can be organized and stored by any suitable data structure.

A mandatory functional morphology Γ12 is defined between the preliminary test indicator layer 11 and the mandatory functional layer 12. A training course morphism Γ23 is defined between the necessary functional layer 12 and the training course layer 13. A retest index morphism Γ34 is defined between the training course layer 13 and the retest index layer 14. Morphism It is the corresponding process of maintaining structure between two data structures. Morphisms include but are not limited to functions or mappings.

FIG. 2 shows that the initial test indicator layer 11 of the present invention is divided into twelve blocks.

Each layer can be divided into multiple blocks. Each block can represent each level and/or each field. Take the initial test index layer 11 as an example. In Figure 2, the initial test index layer 11 consists of three columns 11-1, 11-2, 11-3 and four rows 11-W, 11-X, 11-Y, and 11- Z is divided into twelve blocks, and the number of these blocks can be increased or decreased. The preliminary indicator elements 111 are classified into the blocks through a classification algorithm of the present invention (to be introduced below).

Each arrow 112 indicates an advance relationship between each element, and is used to indicate the order of learning, upgrading, grading, or agency. The industrial function integration system 1 of the present invention extracts one or more elements based on the advanced relationship, for example, to the workplace for supply and demand. As a mode of operation, when a professional represented by a high element at the tip of an arrow 112 is vacant, another professional represented by a low element at the end of the arrow 112 can fill it up. As another mode of operation, a professional represented by a low element at the end of an arrow 112 can go to another professional represented by a high element at the tip of the arrow 112 through training. For example, in Figure 1, when the professional Ta1 in row 11-W is full, the professional Ta2 or professional Ta3 in the same row 11-W can do it for him.

Or, according to an education and training theory, in any layer, the advanced relationship represented by an arrow is positively related to the depth of the element. For example, the knowledge at the tip of the arrow is shallower, while the knowledge at the end of the arrow is The knowledge involved is deeper; or, it is positively related to the breadth of the element. For example, the knowledge at the tip of the arrow is broader, while the knowledge at the end of the arrow is narrow.

(Professional preliminary assessment)

In the case that the professional is a teacher, a teacher will go through a series of assessments (in this stage, the preliminary test assessment), for example, you can analyze the series of assessments using the student-problem (SP) table of Sato Ryohiro Multiple answer responses, and calculate the teacher’s correct answer rate and attention coefficient (caution index, CT) to determine the teacher’s initial test indicator element 111, and then classify it into these blocks of the initial test indicator layer 11 (from three columns) 11-1, 11-2, 11-3, and one of the four lines 11-W, 11-X, 11-Y, 11-Z), according to which the classification algorithm is implemented.

The specific content of each assessment in the series of assessments depends on the subject or degree. Optionally or preferably, it is a binary (binary) scoring assessment, or its response is based on a threshold and two For example, if it is a topic for application, if the answer is more than 50 points, it will be given a binary value of '1', and if the answer is less than 50 points, it will be given a binary value of '0', but it is not limited to this.

Note that the calculation method of the coefficient is as follows:

Assuming there are N teachers and M binary scoring measures, then X = ( x _nm ) _{N × M} is a response matrix of the teachers in these assessments. If the nth teacher answers the mth assessment correctly, then an element x _nm =1 of the response matrix X , otherwise the element is 0. Therefore, the attention coefficient CT _{n of the nth} teacher can be calculated as follows:

是第m個評量的答對人數，而

是第n個教師的答對題數，且

是該M個評量的平均答對人數。(請注意式子的方框「□」是符號，類似於n、m。) among them,

Is the number of correct answers in the mth assessment, and

Is the number of correct answers of the nth teacher, and

It is the average number of correct answers for the M evaluations. (Please note that the box "□" in the formula is a symbol, similar to n, m.)

Finally, based on the correct answer rate of the teachers, the teachers can be classified into the columns 11-1, 11-2, and 11-3 from good to bad; and based on the attention coefficients of the teachers, the teachers can be classified It is classified into these rows 11-W, 11-X, 11-Y, and 11-Z from good to bad, so as to belong to these blocks. As mentioned above, the number of these blocks can be increased or decreased.

Table 1 is an answer response matrix X of an embodiment of the present invention, which shows that there are eleven teachers Ta1 to Ta3, Tb1 to Tb2, Tc1 to Tc3, Td1 to Td3, and they face a series of assessments Q ₁ to Q _{5. There} are different numbers of correct answers, and the teacher information in Table 1 is arranged from top to bottom according to the number of correct answers. In addition, Table 1 also lists the attention coefficient CT and blocks of each teacher.

FIG. 3 shows a classification diagram of the industrial function integration system 1 according to an embodiment of the present invention.

In this embodiment, the answer rate is set to have an upper interval of 75% to 100%, which corresponds to column 11-1, a middle interval of 50% to 75%, which corresponds to column 11-2, and a lower interval of 0 to 50%, which corresponds to column 11-3. Next, note that the coefficient is set to have a leftmost interval from 0 to 0.5, which corresponds to row 11-W; a slightly left interval from 0.5 to 1, which corresponds to row 11-X; a slightly right interval from 1 to 1.5, which Corresponds to row 11-Y; and a rightmost interval is above 1.5, which corresponds to row 11-Z. Therefore, there are twelve intervals 11-1W, 11-1X, 11-1Y, 11-1Z, 11-1W, 11-2X, 11-2Y, 11-2Z, 11-3W, 11-3X, 11-3Y, 11-3Z, these The number of intervals can be increased or decreased according to the number of blocks, and their ranges can also be set according to actual needs.

It is understandable that the higher the correct answer rate, the better the teacher, and the higher the attention coefficient, the more unstable the teacher, but it may also indicate that the teacher is good at one subject but not good at another.

Regarding the calculation of the attention coefficient, refer to Table 1. Taking teacher Tb1 as an example, the correct answer rate is 4/5=0.8, which belongs to the upper range, which corresponds to column 11-1, and the average number of correct answers u = (7+6 +6+5+4)/5=5.6. Based on this, the attention coefficient CT _{Tb1 of the} teacher Tb1 is calculated as follows:

It belongs to the slightly right interval, which corresponds to row 11-X. Therefore, the teacher Tb1 is classified into the block 11-1X.

In addition to the attention coefficient, the field fit coefficient can also be used to participate in the classification algorithm. The field fit coefficient refers to the measure of the subject's suitability in a specific field. For example, the domain fit coefficient can indicate that the subjects are more suitable for the teaching of Chinese language than the teaching of mathematics; or, in English teaching, it is more suitable for conversational teaching than writing teaching. Subjects may get a comprehensive good evaluation due to the high correct answer rate, but in actual practice, they must still choose the most suitable field to work in, and the field fit coefficient can play a role in this situation. Note that the coefficient, domain fit coefficient, or other similar coefficients can be called auxiliary factors.

As an example, based on the theory of education and training, the initial test indicator elements 111 in Figure 1 can be given the following connotations: Ta1 is the ability and cultivation of education expertise; Ta2 is the understanding of students' physical and mental characteristics and learning development; Ta3 is to understand the education stage and the development trend of education, and to grasp important educational issues; Tb1 is to have the teaching field or subject expertise; Tb2 is to have the teaching field or subject teaching knowledge; Tc1 is to set the teaching goals with reference to the curriculum outline and student characteristics Carry out courses and teaching plans; Tc2 is to flexibly adjust teaching design and teaching materials according to students' learning progress and needs; Tc3 is to integrate knowledge concepts and life experiences to activate teaching content; Td1 is to use appropriate teaching strategies and communication and interactive skills to help students Learning; Td2 is the use of multiple teaching media, information technology and resources to assist teaching; and Td3 is based on student learning performance, taking remedial measures or providing deepening and broadening of learning.

(The morphism program from the initial test indicators to the necessary functions)

An industry may require a professional who enters the industry to have certain vocational skills, and the requirements are the necessary functional elements 121 of the necessary functional layer 12. Moreover, different industries require different necessary skills. For this, in the industrial function integration system 1 of the present invention, the necessary functional elements 121 of the necessary functional layer 12 can be redefined according to an industrial condition. The industry condition is one or more quantitative parameters (or coefficients).

As an example, based on the theory of education and training, the necessary functional elements 121 in Figure 1 can be given the following connotations: J1 means possessing educational expertise and mastering important educational issues; J2 means possessing domain or subject knowledge and related teaching skills; J3 To have curriculum and teaching design ability; J4 is to make good use of teaching strategies for effective teaching; and J5 is the use of appropriate methods for learning assessment.

In addition, a mandatory functional morphology Γ12 can be defined according to the industry conditions, which is between the preliminary test indicator layer 11 and the mandatory functional layer 12, and is used to map the preliminary indicator elements 111 to the mandatory functional elements 121 . Morphism is the corresponding process of maintaining structure between two data structures. The necessary functional morphism Γ12 can be constructed by the correct answer rate, the auxiliary factor, and even the multiple boundaries of the intervals obtained from the preliminary evaluation.

After each preliminary assessment element 111 corresponds to a mandatory functional element 121 through the mandatory functional morphology Γ12, the professional represented by the preliminary assessment element 111 can be determined by the industrial function integration system 1 as having necessary functions, and Doesn't have any necessary skills.

In order to promote the convenience of dispatching professionals to different industries, multiple types of industrial conditions can be established for common industry categories, and multiple types of necessary functional morphologies Γ12 are defined respectively. Once the industry function integration system 1 receives a dispatch instruction from an industry category (for example, an organization requires a teacher to teach), it can automatically and quickly extract the type of necessary functional morphology Γ12 for the industry category in order to select a suitable Of professionals.

In addition, a professional may follow an advanced relationship (as shown by arrow 112) through a number of training courses (to be introduced below), from a low element to a high element, and flow occurs. That is to say, the element 111 of the initial trial evaluation representing the professional is changed due to these training courses, and there is no need to perform the initial trial evaluation again to recalculate. Then, a mandatory functional morphology Γ12 can correspond a changed initial evaluation element 111' to a newly determined mandatory functional element 121'. This means that the industrial function integration system 1 can update and determine the quality of these professionals at any time, especially when multiple professionals are frequently studying and flowing.

(Morphism procedures from essential functions to training courses)

Alternatively or preferably, the industrial function integration system 1 of the present invention recommends a professional who lacks a necessary function to take a training course to improve his quality. Similarly, a training course morphology Γ23 can be defined according to the industry conditions, which is between the necessary functional layer 12 and the training course layer 13, and is used to map the necessary functional elements 121 to the training course elements 131, can refer to the necessary functional morphism Γ12 to make adjustments. Among them, the main factor of adjustment is the matching between the start period and the demand period.

Typically, organizations are providers of training courses, and teachers are demanders of training courses. The following will take institutions and teachers as examples to illustrate the morphology procedures from necessary functions to training courses.

When conducting the training course morphism Γ23, first of all, an organization provides a feasible start time period. Then, a teacher conforming to the opening period of the institution is projected to the institution. Then, the industrial function integration system 1 of the present invention can calculate the matching of the teaching/demand time of each institution and each teacher to perform morphism.

Table 2 is an institution opening timetable according to an embodiment of the present invention, and Table 3 is a teacher demand timetable according to an embodiment of the present invention. Table 2 and Table 3 exemplarily list five time periods P ₁ to P ₅ , three institutions E ₁ to E ₃ and three teachers T ₁ to T _{3 respectively} . (In particular, this morphism program uses only a few elements of the aforementioned evaluation program as an example, and has no direct relationship with elements such as Ta1, but elements such as Ta1 can be applied in a similar manner.) In each field of each table, A value of 1 means that the institution has opened classes during this time period, or the teacher has a demand during this time period, and a value of 0 means that there is no class opening or no demand.

The calculation method of the training course morphism Γ23 of the present invention in terms of the matching of the teaching/demand time is as follows:

First, let a class start time matrix T = ( t _jk ) _{J × K} be the two of J institutions (such as the above-mentioned institutions E ₁ to E ₃ ) in K time periods (such as the above-mentioned time periods P ₁ to P ₅ ) Element matrix, where t _jk =1 means that the j-th institution has opened courses in the k-th period.

And let a demand period matrix Y = ( y _ik ) _{I × K} be the binary matrix of Y teachers (for example, the above-mentioned teachers T ₁ to T ₃ ) in the K periods, where y _ik =1 represents the i-th Teachers have demand in the k-th period.

Next, establish a morphism matching matrix U :

Among them, if a matching u _ij =1 of the matching matrix U , it means that an institution’s opening period is consistent with a teacher’s demand period; if the matching is not 1, it means the institution’s opening period There is a difference between the demand period of the teacher and the teacher, and must be adjusted in order to morphism again.

Table 4 is the matching matrix U calculated based on the data in Table 2 and Table 3.

The following is an example of calculating a match:

The matching u ₁₂ between teacher T ₁ and institution E ₂ is: matching u ₁₂ =1-[1×(1-1)+1×(1-1)+0×(1-0)+0×(1) -1)+×(1-1)]/5=1

Then the matching u ₁₂ between the teacher T ₁ and the institution E ₂ is 1, which means that the two can complete the morphism.

The matching u ₃₃ between teacher T ₃ and institution E ₃ is: matching u ₃₃ =1-[1×(1-0)+0×(1-1)+0×(1-1)+0×(1) -1)+0×(1-1)]/5=0.8

Then the matching u ₃₃ between teacher T ₃ and institution E ₃ is 0.8, which means that the demand period of teacher T ₃ is slightly different from that of institution E ₃ and must be adjusted in order to morph again.

Fig. 4 shows a schematic diagram of a training course morphism Γ23' based on the matching of teaching/demand time according to an embodiment of the present invention.

In addition to the example in FIG. 4, in a broader sense, the training course morphism Γ23 can be carried out based on the matching between the teaching content and the required content to form the element 121 and the element 131 shown in Figure 1. Correspondence between.

As an example, based on the theory of education and training, the training curriculum elements 131 in Figure 1 can be given the following connotations: L1 is the teacher’s career position; L2 is the principle and system of education; L3 is the student development and guidance; L4 is the curriculum and Teaching practice; L5 is subject content knowledge; and L6 is subject content teaching.

(Professional retest evaluation)

Similar to the initial test evaluation, when the professional is a teacher, a teacher can go through a series of evaluations to determine the teacher's retest index element 141 and then classify it. However, it is advantageous to evaluate the morphism Γ34 through a retest, which may provide a "test-free" judging function. Specifically, the retest evaluation morphism Γ34 is defined between the training course layer 13 and the retest evaluation layer 14 and is used to correspond the training course elements 131 to the retest evaluation elements 141.

In one example, a series of retest assessments Q1' to Q5' can be established in a manner similar to Table 1. If a teacher has taken one or more specific training courses, the series of retest assessments Q1' to Q5' One or more of the answer responses are directly set as correct (given a binary value of '1', or called a positive binary value).

In another example, conversely, one or more specific training courses can be taken as the qualifications for the series of retest assessments Q1' to Q5', that is, if a teacher does not take the specific training courses, then One or more answer responses of the series of retest assessments Q1' to Q5' are directly set as wrong answers (given a binary value of '0', or called a reversed binary value).

As an example, based on the theory of education and training, the re-examination indicator elements 141 in Figure 1 can be given the following connotations: E1 is the principle and system of education; E2 is the development and guidance of children or young people; E3 is the curriculum and teaching of primary and secondary schools; and E4 is the subject content.

Teachers who have passed the preliminary examination may only serve as part-time teachers, while those who have passed the re-examination evaluation may serve as full-time teachers, and their assignments are based on actual needs.

Regarding the advanced relationship between the re-examination indicator elements 141 of the re-examination indicator layer 14, according to the theory of education and training, the subject content represented by E4 is relatively narrow knowledge, while the educational principles and systems represented by E1 are broader knowledge. Therefore, E4 follows the arrow to E1. Also refer to the relevant paragraphs in Figure 1.

However, for example, broader knowledge may be shallow at the same time, and deeper knowledge may be narrow at the same time. This incommensurability between elements may make it difficult to determine the advanced relationship between elements. In this case, it is possible to extract elements of the lower layer (for example, the training course layer 13, the necessary functional layer 12, or the preliminary test index layer 11) as auxiliary factors.

Specifically, in FIG. 1, for example, the path E4 leads to E1 is divided into two according to the arrow. One path is E4→E2→E1, and the other path is E4→E3→E1. The reason is that it is difficult to determine the advanced relationship between the development of children or adolescents represented by E2 and the curriculum and teaching of primary and secondary schools represented by E3, so that there is no common path between E2 and E3. When a professional vacancy with an indicator of E4 is vacant, should it be filled by a professional with an indicator of E2, or a professional with an indicator of E3? Faced with this problem, the industrial function integration system 1 of the present invention can be configured to extract a training course element L3 of the training course layer 13 as an auxiliary factor, because the training course element L3 of the training course layer 13 has a matching connection. Towards E2 and E3 are the common factors of the two. The other elements of the training course layer 13 are not common factors of the two, and cannot directly solve the problem of incommensurability between E2 and E3.

5A and 5B show a flowchart of an industry function evaluation method according to an embodiment of the present invention.

The above-mentioned principle and structure can be integrated into the invented industrial function evaluation method, which includes multiple steps and is executed by a computer host. The computer host can access the data structure of the initial test indicator layer 11, the necessary functional layer 12, the training course set 13, and the retest indicator layer 14 in FIG.

As shown in FIG. 2 and FIG. 5A, the industrial function evaluation method of the present invention includes steps S1-1 to S1-4.

Step S1-1 is to define a preliminary test indicator layer 11, which includes multiple blocks.

Step S1-2 is to implement a series of preliminary test assessments for a professional to obtain a series of preliminary test responses of the professional.

Step S1-3 is to classify the professional into one of the blocks according to the answer responses of the series of preliminary tests, and assign a preliminary test indicator element 111 to the professional.

Optionally or preferably, the series of preliminary evaluations can be displayed through a display connected to the host computer, and through a receiver connected to the host computer, such as a keyboard, a mouse, or a touch Board to receive the response to the initial test of the series. The series of preliminary test assessments and the series of preliminary test responses are transmitted and processed in the form of data in the host computer. Of course, it is not necessary to print the series of preliminary test assessments through a printer and read the response of the series of preliminary tests through a card reader. The industrial function integration system 1 of the present invention only requires the series of preliminary assessments and the series of preliminary tests. Correspondence between response responses.

In particular, each of the series of preliminary test assessments is a binary (binary) scoring assessment, or the response to the first test is binary based on a threshold value. In particular, for N professionals and M assessments, a response matrix X = ( x _nm ) _{N × M} can be established for an initial trial answer; where, if the nth professional answers the mth assessment correctly, the initial trial answer One element of the reaction matrix X , x _nm =1, otherwise the element is zero.

Step S1-3 may further include multiple sub-steps S1-3a to S1-3c.

Step S1-3a is to calculate the correct answer rate of the first trial of the series of first trial responses, and the correct answer rate of the first trial is proportional to the number of correct answers in the first trial. In particular, the correct answer rate of the initial test is the number of correct answers in the initial test divided by the total number of questions in the initial test.

Step S1-3b is to calculate an auxiliary factor for the answer response of the initial test series. The auxiliary factor may be an attention coefficient, and the attention coefficient is a function with an average number of correct answers as a parameter. Alternatively, the auxiliary factor may be a domain fit coefficient, and the domain fit coefficient is obtained from a personality test.

其中，

是第m個初試評量的答對人數，而

是第n個專業人員的答對題數，且

是該M個初試評量的平均答對人數。 In particular, the attention coefficient (CT _n ) is:

among them,

Is the number of correct answers in the m-th initial test, and

Is the number of correct answers for the nth professional, and

It is the average number of correct answers for the M initial assessments.

Step S1-4 is to perform the steps S1-2 and S1-3 respectively for multiple professionals to assign a plurality of preliminary test indicator elements 111 to the professionals to form the classification as shown in FIG. 2.

Optionally or preferably, the industrial function evaluation method of the present invention may further include steps S1-5: Determine a plurality of advanced relationships among the professionals according to the plurality of blocks where the professionals are located .

Next, as shown in FIG. 2 and FIG. 5B, optionally or preferably, the industrial function evaluation method of the present invention may further include steps S2-1 to S2-3. Step S2-1 is to define a mandatory functional layer 12, which includes multiple mandatory functional elements 121. Step S2-2 is to define a necessary functional morphism Γ12 according to an industry condition. Step S2-3 is to map the preliminary indicator elements 111 to the necessary functional elements 121 with the necessary functional morphology Γ12. Wherein, the necessary functional morphism Γ12 is constructed based on the correct answer rate, the auxiliary factor, and/or multiple boundaries of the intervals obtained from the series of preliminary test evaluations.

Alternatively or preferably, the industrial function evaluation method of the present invention may further include steps S3-1 and S3-2. Step S3-1 is to define a training course layer 13 which includes a plurality of training course elements 131. Step S3-2 is to define a training course morphism Γ23, which is used to map the necessary functional elements 121 to the training course elements 131, where the training course morphism Γ23 is a match between the start period and the demand period And/or the matching between the teaching content and the required content.

；其中，若該匹配性矩陣U的一匹配性u _ij =1，則表示一供給方的一供給時段與一需求方的一需求時段一致；若該匹配性不為1，則表示該供給方的該供給時段與該需求方的該需求時段有差異，必須加以調整，以便再次配對。 In particular, in terms of the matching between the starting period and the demand period, for the supply of J suppliers in K periods, a starting period matrix T = ( t _jk ) _{J × K} can be established, where t _jk =1 Indicates that the j-th supplier has supply in the k-th period. For the demand of Y demanders in the K periods, a demand period matrix Y = ( y _ik ) _{I × K} can be established, where y _ik =1 means that the i-th demander has demand in the k-th period. A matching matrix

; Wherein, if a matching u _ij =1 of the matching matrix U , it means that a supply period of a supplier is consistent with a demand period of a demander; if the matching is not 1, it means that the supplier The supply period of is different from the demand period of the demander, and must be adjusted to match again.

Alternatively or preferably, the industrial function evaluation method of the present invention may further include steps S4-1 and S4-2. Step S4-1 is to define a retest index layer 14 which includes multiple retest index elements 141. Step S4-2 is to define a retest index morphism Γ34 for mapping the training course elements 131 to the retest evaluation elements 141, wherein the retest index morphism Γ34 is configured by a series of retest evaluations .

In particular, the retest index morphism Γ34 can be configured to: if a professional has taken one or more specific training courses, one or more response responses of the series of retest assessments will be directly given a positive binary value. 1'; or, the retest index morphology Γ34 can be configured as: if a professional has not taken the specific training courses, then one or more response responses measured in the series of retests are directly set to give an inverse binary The value is '0'.

In addition, for several retest indicator elements 141 that are incommensurable, a common factor of the training course layer 13, the necessary functional layer 12, or the initial test indicator layer 11 is extracted as an advanced relationship between these elements An auxiliary factor of.

FIG. 6A shows a block diagram of a form of industrial function integration system 1 of the present invention.

The industrial function integration system 1 of the present invention includes a computer host 50, and the computer host 50 is suitable for connecting to a terminal device 60. The computer host 50 is used as a server, and it can be a cloud host or a high-end host. The host computer 50 includes a motherboard 51, a hard disk 52, a memory 53, and a processor 54 which are arranged on the motherboard 51 and electrically connected to the motherboard 51.

FIG. 6B shows a block diagram of another form of industrial function integration system 1 of the present invention.

The host computer 50 is configured to implement the industrial function evaluation method of the present invention. It is composed of a set of program codes 71, which can be translated by the interpreter 72 or the compiler 73 and executed by the host computer 50. That is, the set of program codes 71, interpreter 72, or compiler 73 of FIG. 5B can be implemented by the operation of the hard disk 52, memory 53, and processor 54 of FIG. 5A.

The terminal device 60 is connected to the host computer 50 through a wired network or a wireless network to access data of the host computer 50. The terminal device 60 is a personal computer, a notebook computer, a tablet computer, or a smart phone.

7A, FIG. 7B, FIG. 8, and FIG. 9 exemplarily show that the omni-directional resource cutting-edge integration system of the present invention is presented in the form of a portal website for users to access and use. The content of each webpage of the portal site is shown in the figures, and will not be repeated here.

Although the present invention has been described through multiple embodiments, it is understandable that many other modifications and changes can be made to realize other embodiments as long as they do not deviate from the spirit of the present invention and the claimed scope of patent application.

1: Industrial function integration system

11: Initial test indicator layer

111: Initial test indicator element

112: Arrow

12: Essential functional layer

121: Essential Functional Elements

13: Training course layer

131: Training Course Elements

Γ12: Essential functional morphism

Γ23: Training Course Morphism

Γ34: Retesting morphism

Claims (4)

An industrial function evaluation method. The industrial function evaluation method is composed of a set of program codes, which is translated by a translator or a compiler and executed by a computer host. The method includes: Step S1-1: Define a preliminary test indicator layer, which includes multiple blocks; Step S1-2: For a professional, implement a series of preliminary test assessments to obtain a series of preliminary test responses for the professional; Step S1-3: According to the series In response to the initial test, classify the professional into one of the blocks, and assign an initial test indicator element to the professional; Step S1-4: For multiple professionals, perform steps S1-2 and S1 respectively -3, to assign a plurality of preliminary test indicator elements to the professionals, and each preliminary test indicator element has a first data structure; step S1-5: determine the plurality of areas where the professionals are located There are multiple advanced relationships among professionals, and each advanced relationship is a sequence of learning, upgrading, refilling, or agency; Step S2-1: Define a necessary functional level, which includes multiple necessary functional elements and each necessary function The element has a second data structure; Step S3-1: Define a training course layer, which includes multiple training course elements, and each retest indicator element has a third data structure; Step S3-2: Define a training course morphology, It is used to correspond the necessary functional elements to the training course elements, where the training course morphology is the match between the start period and the demand period and/or the match between the teaching content and the required content; S4-1: Define a retest index layer, which includes multiple retest index elements, and each retest index element has a fourth data structure; and Step S4-2: Define a retest index morphology, which is used to correspond the training course elements to the retest evaluation elements, where the retest index morphology is configured by a series of retest evaluations; where The retest index morphology is configured to: if a professional has taken one or more specific training courses, one or more response responses of the series of retest assessments will be directly given a positive binary value; or, the retest index Morphism is configured such that if a professional does not take the specific training courses, one or more response responses measured by the series of retests are directly set to give an inverse binary value; and among them, for the retests For several retest indicator elements of the indicator elements, a common factor of the training course layer, the necessary functional layer, or the initial test indicator layer is extracted as an auxiliary factor of an advanced relationship between the elements. The industrial function evaluation method according to claim 1, wherein, step S1-3 further includes: sub-step S1-3a: calculating the first trial answer correct rate of the first trial answer response, the first trial answer correct rate is proportional to the number of first answer correct questions ; Sub-step S1-3b: Calculate another auxiliary factor for the answer response of the initial test series, the other auxiliary factor is an attention coefficient or a field fit coefficient, wherein the attention coefficient is a parameter of an average number of correct answers The field fit coefficient is obtained from a personality test, and the field fit coefficient is a measure of the professional’s suitability in a teaching field; and sub-step S1-3c: According to the correct answer rate and the other auxiliary Factor to classify the professional into one of these blocks. The industrial function evaluation method described in claim 1 further includes: Step S2-2: Define a mandatory functional morphology according to an industrial condition; and Step S2-3: Use the mandatory functional morphology for the initial test indicator elements Shoot to correspond to these necessary functional elements. The industrial function evaluation method described in claim 2, wherein the necessary functional morphology is set based on the correct answer rate, the auxiliary factor, and/or based on different correct answer rates and different auxiliary factors obtained from the series of preliminary test assessments To construct multiple boundaries of multiple intervals.

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