JP2019036042A - Method of determining aptitude test, update method, determination device, update device, program and recording medium - Google Patents

Abstract

To achieve a human resource matching system with high accuracy.SOLUTION: A determination/update apparatus 1 for determining whether test data T is suitable for selection of an applicant to a specific organization includes: an attention evaluation result generation part 12 for generating an attention evaluation result R0 which is an evaluation result of an attention person on the basis of the test data T; a comparative evaluation result generation part 13 for generating comparative evaluation results R1 to Rx which are evaluation results of a plurality of comparative persons on the basis of the test data T; a similarity degree calculation part 14 for calculating similarity degrees M1 to Mx between each of the comparative evaluation results R1 to Rx and the attention evaluation result R0; a ranking part 15 for ranking the comparative persons in descending order of the similarity degrees; a comparison part 16 for comparing high-ranking persons of the comparative persons whose ranks are higher than a predetermined level with the attention person for personnel evaluation in the organization; and a determination part 17 for determining whether the test data T conforms to the selection of the applicant to the organization on the basis of the comparison result.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a human resource matching system that matches job seekers and job seekers, and more particularly, to appropriate tests such as competency tests for evaluating the suitability of job seekers.

(General human resource matching issues)
Generally, human resource matching includes the following four steps (1) to (4). (1) A recruitment agency creates job seekers and job seeker profiles (Model).
(2) The job seeker finds a combination of job seekers that seems good, either explicitly or implicitly (Match).
(3) Encourage job seekers and job seekers to interact (Introduce).
(4) The job seeker and the job seeker interact (Interact). As a result, both profiles are also updated (Model).

Here, in human resource matching, ensuring the reliability of the job seeker (or job seeker) through the intervention of a third party who specializes in human resource introduction is an important factor. However, the existing human resource matching system has the following problems.
• How to create job seekers and job seeker profiles.
-How to perform matching calculation from these profiles.
• How to introduce job seekers to job seekers (or job seekers to job seekers).
• How to support job seekers and job seeker interactions.
• How to get feedback from job seekers and job seekers.

  If a third party such as a consultant tries to solve these problems, after all, it is necessary to rely on personal factors such as “experience” and “intuition”. In addition, current human resource matching often assumes introductions where job seekers (or job seekers) who are applicants are leading, and in such introductions, there is inevitably room for consultants to intervene. Because it is subject to restrictions, it cannot maintain “reliability”.

(Lack of attention to human ability)
Further, in the conventional human resource matching, for example, for bookkeeping ability, English ability, etc., the ability is grasped by objective information such as the examination and qualification, and appropriate human resources are selected. However, it is difficult to objectively evaluate the human abilities that are considered to be most important for business execution after employment, resulting in a mismatch between the job seeker and the actual job seeker. There were many things.

  In order to overcome these human resource matching problems, we use aptitude tests called “competency tests” to ascertain the individual characteristics of human resources as objectively as possible, and the human resources required by job seekers There are also attempts to accurately supply (for example, Patent Documents 1 and 2 below).

  However, the aptitude tests disclosed in these documents take the method of answering the test consisting of exactly the same items for the assumed test format and summing the scores. It does not exceed the category. For this reason, human arbitration is inevitably involved in how points are allocated to individual items. In other words, there is no logical basis that should be the most fundamental of "how to answer what item and how much ability is judged". As a result, these conventional techniques have not yet solved the problem of grasping individual characteristics of human resources as objectively as possible.

  In addition, since the image of the person who can demonstrate his / her ability after joining the company differs for each organization seeking human resources, there is no aptitude test that accurately corresponds to the selection of applicants in all organizations.

JP 2001-184392 A JP 2005-18274 A

  The present invention aims to overcome the drawbacks of the prior art while solving the above-described general human resource matching problem.

  In other words, a workplace that can eliminate the room for human allegiance in human resource introduction, supply the job seekers with the job they really want, and demonstrate their abilities to job seekers. The objective is to realize a highly accurate human resource matching system that can provide

  In order to solve the above problems, the determination method according to the present invention is a determination method for determining whether an aptitude test for evaluating the suitability of a job seeker is suitable for selection of applicants to a specific organization. Based on the aptitude test, an attention evaluation result creating step for creating an attention evaluation result that is an evaluation result of one notable person belonging to the group, and the attention belonging to the group based on the aptitude test A comparison evaluation result creating step for creating a comparison evaluation result, which is an evaluation result of a plurality of comparison persons other than a person, for each comparison person, and a similarity between each of the comparison evaluation results and the attention evaluation result A similarity calculating step for calculating a degree; a ranking step for ranking the comparison person in descending order of the similarity; A comparison step that compares the personnel evaluation in the group with the person of interest, and a determination step that determines whether the suitability test is suitable for selection of applicants to the group based on the comparison result. Features.

  In the determination method, it is preferable to repeat the comparison evaluation result creation step, the similarity calculation step, the ranking step, the comparison step, and the determination step while changing the comparison person belonging to the group.

  In order to solve the above problems, a determination method according to the present invention is a determination method for determining whether or not an aptitude test for evaluating the suitability of a job seeker is suitable for selection of applicants to a specific organization. An evaluation result creating step for creating an evaluation result of the applicant to the group based on the aptitude test, and a personnel evaluation in the group of the applicant after employment when the applicant finds a job in the group And a determination step of determining whether the suitability test is suitable for selection of applicants to the group based on the comparison result.

  An update method according to the present invention is an update method for updating an aptitude test for evaluating the aptitude of a job seeker, wherein the aptitude test is applied to the specific organization using the determination method according to the present invention. And a renewal step for updating the suitability test based on a determination result in the relevance determination step.

  An update method according to the present invention is an update method for updating an aptitude test for evaluating the aptitude of a job seeker, obtaining an answer to the aptitude test of an applicant to a specific group, and the applicant A teacher data storage step for storing a personnel evaluation in the organization of the applicant after employment at the organization in association with the answer and storing it as a teacher data in a database, and for a plurality of applicants, the teacher data storage step. And updating the aptitude test by performing machine learning based on the accumulated teacher data.

  The determination apparatus according to the present invention is a determination apparatus for determining whether or not an aptitude test for evaluating the aptitude of a job seeker is suitable for selection of applicants to a specific group, based on the aptitude test. , An attention evaluation result creating unit that creates an attention evaluation result that is an evaluation result of one notable person belonging to the group, and a plurality of comparison persons other than the notable person belonging to the group based on the aptitude test A comparison evaluation result creation unit that creates a comparison evaluation result for each comparison person, and a similarity calculation unit that calculates the similarity between each of the comparison evaluation results and the attention evaluation result A ranking unit that ranks the comparison person in descending order of similarity, and a higher rank of the comparison person having a higher rank than the predetermined level is compared with the attention person for personnel evaluation in the group. A comparing unit, based on the comparison result, the aptitude test is characterized by having a a determination section for determining are adapted to selection of applicants to the organization.

  The determination apparatus according to the present invention is a determination apparatus that determines whether an aptitude test for evaluating the aptitude of a job seeker is suitable for selection of applicants to a specific group, and based on the aptitude test, An evaluation result creation unit that creates an evaluation result of an applicant to the group, and a comparison that compares the personnel evaluation in the group of the applicant after employment with the evaluation result when the applicant finds a job in the group And a determination unit for determining whether the suitability test is suitable for selection of applicants to the group based on the comparison result.

  An update device according to the present invention is an update device that updates an aptitude test for evaluating the suitability of a job seeker, and is based on each part of the determination device according to the present invention and a determination result by the determination unit. And an update unit for updating the test.

  The update apparatus according to the present invention is an update apparatus that updates an aptitude test for evaluating the aptitude of a job seeker, and obtains an answer to the aptitude test of an applicant to a specific group; and When an applicant finds employment in the group, a personnel evaluation acquisition unit that acquires a personnel evaluation in the group of the applicant after employment, and teacher data creation that creates teacher data by associating the personnel evaluation with the answer And a machine learning unit that updates the aptitude test by performing machine learning based on the teacher data created for a plurality of applicants.

  The program according to the present invention is a program that causes a computer to function as each unit of the determination device according to the present invention or each unit of the update device according to the present invention.

  The recording medium according to the present invention is a computer-readable recording medium on which the program according to the present invention is recorded.

  According to the present invention, it is possible to objectively determine whether an aptitude test is suitable for selection of applicants for a specific group. Therefore, by performing human resource matching using an aptitude test with a good judgment result or an aptitude test updated based on the judgment result, there is no room for human allegiance to intervene in the recruitment and recruiters It is possible to provide a matching system that can provide human resources who are truly looking for, and provide job opportunities to job seekers who can demonstrate their abilities.

It is a block diagram which shows schematic structure of the determination / update system which concerns on 1st Embodiment. It is a flowchart which shows the whole flow of the determination method and the update method by the determination / update apparatus which concerns on 1st Embodiment. It is a flowchart of the fitness determination which concerns on 1st Embodiment. It is an example of the screen of a competency test. It is a table | surface which shows a part of specific example of the content of the question of a competency test, and a weighting coefficient. It is a flowchart which shows the modification of the process sequence by the determination / update apparatus which concerns on 1st Embodiment. It is a block diagram which shows schematic structure of the determination / update system which concerns on 2nd Embodiment. It is a flowchart which shows the whole flow of the determination method and the update method by the determination / update apparatus which concerns on 2nd Embodiment. It is a flowchart of the fitness determination which concerns on 2nd Embodiment. It is a block diagram which shows schematic structure of the update system which concerns on 3rd Embodiment. It is a flowchart which shows the whole flow of the update method by the update apparatus which concerns on 3rd Embodiment. It is a flowchart of teacher data acquisition concerning a 3rd embodiment.

  Embodiments of the present invention will be described below with reference to the accompanying drawings.

[First Embodiment]
(Overall system configuration)
FIG. 1 is a block diagram showing a schematic configuration of a determination / update system according to the first embodiment of the present invention. This determination / update system includes a determination / update device 1 and a plurality of client terminals C0 to Cx. The determination / update device 1 and the client terminals C0 to Cx are devices owned by a specific organization, and each client terminal C0 to Cx is connected to the determination / update device 1 via a local area network LN or the like. . In the present embodiment, the “organization” is mainly assumed to be a corporation for the purpose of for-profit activities such as a company, but is not limited thereto, and may include a public institution or a non-profit organization. Hereinafter, an organization assuming that the aptitude test determined / updated by the determination / update device 1 is used for selection of the applicant may be referred to as a “target company”.

  The determination / update apparatus 1 has both functions of the determination apparatus and the update apparatus described in the claims, and can be configured by, for example, a general-purpose personal computer or a server apparatus. The determination / update device 1 includes a CPU (not shown), a memory (not shown), an auxiliary storage device 10 and the like as a hardware configuration, and the CPU reads various programs into the memory and executes them. Various arithmetic processes are executed. When the determination / update device 1 is configured by a general-purpose personal computer, the determination / update device 1 includes a display device (not shown) such as a liquid crystal display and an input device (not shown) such as a keyboard or a touch panel. May be connected. Further, the auxiliary storage device 10 can be configured by, for example, a hard disk drive (HDD) or a solid state drive (SSD). The auxiliary storage device 10 may be built in the determination / update device 1 or may be provided as an external storage device separate from the determination / update device 1.

  Each of the client terminals C0 to Cx can be configured by a portable computer such as a general-purpose personal computer or a tablet. Among the client terminals C0 to Cx, the client terminal C0 is a terminal used by one notable person belonging to the target company. The person of interest is not particularly limited as long as it is a person belonging to the target company, but is preferably a high performance person (high performer) in the target company. On the other hand, the client terminals C1 to Cx are terminals used by a plurality of comparison persons other than the target person belonging to the target company. The comparative person is randomly extracted from persons belonging to the same target company as the target person, and the number of persons is not particularly limited.

  The judging / updating device 1 judges whether the suitability test for evaluating the suitability of the job seeker is suitable for selection of applicants to a specific group, and further updates the suitability test based on the judgment result. It has a function to do. In order to realize such a function, as shown in FIG. 1, the determination / update apparatus 1 includes, as function blocks, a test execution unit 11, an attention evaluation result creation unit 12, a comparison evaluation result creation unit 13, and a similarity. The calculation unit 14, the ranking unit 15, the comparison unit 16, the determination unit 17, and the update correction unit 18 are included. These functional blocks are realized by the CPU of the determination / update apparatus 1 executing the program according to the present embodiment. The program according to the present embodiment may be recorded on a non-transitory computer-readable recording medium such as a CD-ROM, and the program is determined / updated by causing the determination / update device 1 to read the recording medium. You may install in the apparatus 1. Alternatively, the program code of the program may be downloaded to the determination / update device 1 via a communication network.

  The auxiliary storage device 10 stores test data T and personnel evaluation data B. The test data T is electronic data of an aptitude test for evaluating the aptitude of the job seeker. In the present embodiment, the aptitude test is a competency test. In the initial state, the competency test stored in the auxiliary storage device 10 is an existing competency test that is generally used. Hereinafter, the competency test data may be simply referred to as “test data”.

  Personnel evaluation data B is data relating to personnel evaluation of employees belonging to the target company. The personnel evaluation data B includes each employee's department, age, title, annual income, years of service, and the like.

  Here, existing competency tests are not specific to a particular organization. On the other hand, the competence and human nature that are regarded as important in business execution differ from group to group, so existing competency tests do not always match the selection of applicants in a specific group.

  Therefore, the determination / update device 1 updates the competency test so that it matches a specific organization. Specifically, the determination / update apparatus 1 determines whether the competency test is suitable for selection of applicants to the target company according to the flowcharts shown in FIGS. 2 and 3, and based on the determination result. Update competency test.

(Overall flow of judgment / update method)
FIG. 2 is a flowchart showing the overall flow of the determination method and the update method by the determination / update apparatus 1. First, in step S <b> 1, existing test data is stored in the auxiliary storage device 10. Subsequently, in step S2, the determination / update apparatus 1 performs a fitness determination to determine whether the competency test is suitable for selection of applicants to the target company (a fitness determination step). This step S2 is performed by the determination method according to the present embodiment, and the specific procedure is shown in the flowchart of FIG. The flowchart of FIG. 3 will be described later.

  Referring to FIG. 2 again, when the determination result of the suitability determination is not good (NO in step S3), the process proceeds to step S4, and the determination / update device 1 updates the aptitude test based on the determination result. (Update step). If the determination result of the suitability determination is good (YES in step S3), the determination / update device 1 ends the process without updating the aptitude test based on the determination result. As described above, the determination / update device 1 determines whether the existing competency test is suitable for selection of applicants to a specific group as an overall processing procedure, and the determination result is not good. Update competency test. This makes it possible to improve the accuracy of matching between a specific group and a job seeker by modifying the competency test so that it matches the selection of applicants to a specific group.

(Judgment method of conformity)
Next, the flowchart of the fitness determination shown in FIG. 3 will be described with reference to FIG.

  First, in step S20, the test execution unit 11 shown in FIG. 1 performs a competency test on employees who use the client terminals C0 to Cx. Specifically, the test execution unit 11 reads the test data T from the auxiliary storage device 10, extracts the test screen D from the test data T, and transmits it to the client terminals C0 to Cx. Thereby, in each client terminal C0-Cx, the screen of the competency test is displayed, and the user of each client terminal C0-Cx receives the competency test according to the displayed screen.

  FIG. 4 is an example of a competency test screen displayed on each of the client terminals C0 to Cx. The examinee selects an answer from the six options set for each question. The layout of the screen, the contents of questions, the number of questions, etc. are not particularly limited. Further, the “return” and “next” buttons in FIG. 4 can be omitted, and the screen may be switched to the next question screen when the examinee answers the question at the bottom of the screen.

  Data of responses to the competency tests of the users of the client terminals C0 to Cx are transmitted to the determination / update device 1 shown in FIG. 1 as responses A0 to Ax, respectively. The answer A0 from the client terminal C0 (that is, the answer by the person of interest) is input to the attention evaluation result creation unit 12 of the determination / update apparatus 1. On the other hand, the responses A1 to Ax from the client terminals C1 to Cx (that is, responses by the comparison person) are input to the comparison evaluation result creation unit 13 of the determination / update device 1.

  Subsequently, in step S21 shown in FIG. 3, the attention evaluation result creation unit 12 creates a attention evaluation result R0 that is an evaluation result (latent scale value) of the person of interest from the answer A0 (attention evaluation result creation step). Specifically, the attention evaluation result creation unit 12 analyzes the answer A0, and sets a scale score obtained by summing a value obtained by multiplying the elementary score corresponding to the answer number of each question by a predetermined weighting coefficient as the attention evaluation result R0. calculate.

FIG. 5 is a table showing a part of specific examples of contents of the competency test questions and weighting factors. The competency test questions in this embodiment are classified into 9-dimensional major classifications such as “instruction / determination” and “support / collaboration”, and each large sentence is further classified into three-dimensional minor classifications. . For example, the major classification of “instruction / determination” is divided into three-dimensional sub-classes of “leadership skills”, “vision setting”, and “judgement”, and four questions are assigned to each sub-class. . The competency test includes a total of 113 questions including 5 items of IM (impression management) in addition to the above 9 × 3 × 4 = 108 items.
In each question, the prime score for the answered number is set as shown in Table 1, for example.

  Furthermore, a weighting factor is set for each question, and in an initial state (that is, before update), the weighting factor of all questions is 1.0. The attention evaluation result creation unit 12 calculates the attention evaluation result R0 by summing the values obtained by multiplying the prime score corresponding to the answer number in each question by the weighting coefficient.

  Further, in step S22 shown in FIG. 3, the comparative evaluation result creation unit 13 creates comparative evaluation results R1 to Rx that are evaluation results of the comparative person from each of the answers A1 to Ax (attention evaluation result creation step). ). The method by which the comparison evaluation result creation unit 13 calculates the comparison evaluation results R1 to Rx is the same as the method by which the attention evaluation result creation unit 12 calculates the attention evaluation result R0. The attention evaluation result R0 created by the attention evaluation result creation unit 12 and the comparison evaluation results R1 to Rx created by the comparison evaluation result creation unit 13 are input to the similarity calculation unit 14.

Subsequently, in step S23, the similarity calculation unit 14 calculates the similarity M1 to Mx between each of the comparison evaluation results R1 to Rx and the attention evaluation result R0 (similarity calculation step). The calculation method of the similarities M1 to Mx is not particularly limited. For example, the similarity M1 to Mx is calculated by the same method as the matching value calculation method described in paragraphs [0025] to [0030] of JP-A-2005-44280. Mx can be calculated.
Alternatively, the similarity may be calculated based on the difference between the attention evaluation result R0 and each of the comparative evaluation results R1 to Rx. In this case, for example, the similarity M1 between the comparative evaluation result R1 and the attention evaluation result R0 can be an absolute value of R0 / (R0-R1), and the smaller the absolute value of R0-R1, the more similar the degree of similarity. Get higher. The similarities M1 to Mx calculated by the similarity calculating unit 14 are input to the ranking unit 15.

  Subsequently, in step S24, the ranking unit 15 ranks comparison persons (that is, users of the client terminals C1 to Cx) in descending order of similarity based on the similarities M1 to Mx (ranking step). . Then, the ranking unit 15 inputs, to the comparison unit 16, data indicating higher ranks (for example, the top 10 people, the top 10%, etc.) having a higher rank than the comparison person and the attention person. Data indicating the superior and the person of interest includes an employee number.

Subsequently, in step S25, the comparison unit 16 compares the superior with the person of interest for personnel evaluation in the target company (comparison step). Specifically, the comparison unit 16 reads out data related to the personnel evaluation of the superior and the person of interest from the personnel evaluation data B stored in the auxiliary storage device 10, and performs the personnel evaluation of each superior and the personnel evaluation of the person of interest. And compare. The personnel evaluation used for the comparison target is preferably an objective representation of the person evaluation in the company with a numerical value, for example, an average salary increase. Average salary increase is
It can be calculated by (current annual income-annual income at the time of joining the company) / year of service. The higher the performer, the higher the average salary increase. In this case, the comparison unit 16 calculates the ratio of the average salary increase to the value of the person of interest for each of the superiors, and outputs the calculated ratio to the determination unit 17 as a comparison result. Another index of personnel evaluation is the rate of change in salary increase. The rate of change in salary increases
It can be expressed as the rate of change in [annual income (basic wage only) / year of service]. The higher the performer, the greater the rate of change in salary increases.

  Subsequently, in step S26, the determination unit 17 determines whether the competency test is suitable for selection of applicants to the target company based on the comparison result (determination step). Specifically, the determination unit 17 determines that the suitability of the applicant's selection to the target company of the competency test is higher as the tendency that the personnel evaluation of each superior is similar to the person of interest is larger. More specifically, it is determined that the degree of fitness is high when the ratio of the higher-ranking persons within the predetermined range (for example, 90% to 110%) is 80% or more (determination result) Is good).

  If the determination result by determination unit 17 is not good (NO in step S27), the series of processes shown in FIG. 3 ends. In this case, the determination result in step S2 (fitness determination step) shown in FIG.

  If the determination result by determination unit 17 is good (YES in step S27), the process proceeds to step S28. In step S28, the test execution unit 11 determines whether there is another person for comparison for obtaining the comparison evaluation result. If another comparison person exists in the company (YES in step S28), the process proceeds to step S29, and the test execution unit 11 performs the competency test by changing the comparison person. Specifically, a plurality of client terminals other than the client terminals C0 to Cx are randomly selected, and the test screen D is transmitted to the selected client terminal. Thereafter, steps S22 to S26 are repeated until NO is determined in step S27 or step S28. As a result, if the process is terminated even once even in step S27, the determination result in step S2 shown in FIG. On the other hand, when the process is ended by being NO in step S28 (that is, when the process is completed without being NO in step S27), the determination result of step S2 shown in FIG. 2 is good. .

  With the above flow, the suitability determination (step S2 in FIG. 2) for determining whether the competency test is suitable for selection of applicants to the target company is performed. In the fitness determination, steps S28 and S29 may be omitted and steps S20 to S26 may be performed only once in the flowchart of FIG. However, in order to accurately determine the suitability of the competency test, it is desirable not to omit steps S28 and S29, but to repeat steps S22 to S26 while changing the comparison person belonging to the target company.

  Reference is again made to FIG. If the result of the fitness determination in step S2 is good (YES in step S3), the process by the determination / update device 1 is terminated. By using competency tests that have been judged to have good conformity determination results to select applicants for the actual target company, applicants who are similar in ability and humanity to the person of interest with high accuracy You can select from.

  In particular, if the person of interest is a high performer, apply with high accuracy a person who can expect to have a high track record in the target company by using a competency test that has been judged to have good conformity judgment results. Can be selected from Specifically, among applicants, applicants whose competency test evaluation results (latent scale values) are close to the high performer's attention evaluation results are judged to be expected to be active after joining the company.

  On the other hand, when the result of the fitness determination in step S2 is bad (NO in step S3), the process proceeds to step S4. In step S4, the update correction unit 18 shown in FIG. 1 updates the test data T (update step). Specifically, in the comparison result calculated in step S25 shown in FIG. 3, the similarity between the evaluation result of the competency test of the superior person whose personnel evaluation is not similar to the attention person and the attention evaluation result R0 is lowered. In addition, the updating unit 18 weights each question of the test data T so that the similarity of the evaluation result of the competency test of the superior person whose personnel evaluation is similar to the attention person does not decrease with the attention evaluation result R0. To correct. As a result, the possibility that a person whose personnel evaluation is not similar to the person of interest will not be similar to the person of interest in the evaluation result of the competency test is reduced, and as a result, the suitability of the competency test is improved.

  Further, the process proceeds to step S2, and the relevancy determination is performed again for the updated competency test, and the test data is updated until YES in step S3. This makes it possible to develop aptitude tests suitable for selecting applicants for actual target companies.

  And, by using the developed aptitude test to select applicants for the target company and match the human resources of those who want to work for the target company, it is possible to efficiently recruit human resources who can fully demonstrate their abilities after finding employment in the target company. Matching can be achieved, and mismatches between the job seeker and the actual job seeker can be reduced. Therefore, while eliminating the room for human allegiance in human resource introduction, it is possible to supply the human resources that are truly required to job seekers and to demonstrate their abilities to job seekers. A matching system that can provide

(Modification)
In the present embodiment, it is not essential to update the aptitude test. In this modification, a mode in which the aptitude test is not updated will be described.

  FIG. 6 is a flowchart showing a modification of the processing procedure performed by the determination / update apparatus 1. This flowchart is obtained by replacing step S4 with step S4 'in the flowchart shown in FIG.

  In FIG. 6, when the result of the fitness determination in step S2 is poor (NO in step S3), the process proceeds to step S4 '. In step S4 ', other test data is stored in the auxiliary storage device 10 shown in FIG. The other test data is aptitude test data other than the competency test that has been used for the determination of the degree of conformity. Then, the process proceeds to step S <b> 2, and the fitness level is determined again. As described above, the suitability test suitable for the selection of the applicant to the actual target company can be found by performing the fitness determination while changing the test data until YES in Step S3.

  In this way, by selecting applicants to the target company using the aptitude test determined to be good in the fitness determination, and matching the human resources of those who wish to find employment in the target company, It is possible to efficiently match human resources who can fully demonstrate their abilities.

[Second Embodiment]
Subsequently, a second embodiment of the present invention will be described. In the first embodiment described above, a person of interest and a comparative person are extracted from the same group, and compared with the evaluation result of the aptitude test and the personnel evaluation, the aptitude test is performed by the applicant of the specific group. It was judged whether it was suitable for selection. However, when one recruitment agency develops aptitude tests by this method, it is necessary to provide personal information such as cooperation of many employees or personnel evaluation. Therefore, it is not realistic to apply the determination / update method according to the first embodiment to many organizations.

  Therefore, in the second embodiment, it is determined whether or not the aptitude test is suitable for selection of applicants to the group by repeating the follow-up survey of those who have found employment in a specific group. In the second embodiment, components having the same functions as those in the first embodiment are denoted by the same reference numerals, and detailed description thereof is omitted.

(Overall system configuration)
FIG. 7 is a block diagram illustrating a schematic configuration of a determination / update system according to the second embodiment. This determination / update system includes a determination / update device 2, an applicant terminal E, and a recruiter terminal F. The determination / update device 2 is a device owned by a recruitment agency, the applicant terminal E is a terminal owned by the applicant, and the job offerer terminal F is a terminal owned by the job seeker.

  The recruiter is a specific organization that is assumed to use the aptitude test determined / updated by the determination method and the update method according to the present invention for selection of applicants. In this embodiment, the “target company” Sometimes called. The applicant is a person who wishes to find a job at the target company.

  The determination / update device 2 has the functions of both the determination device and the update device described in the claims, and can be configured by, for example, a general-purpose personal computer or a server device. The hardware configuration of the determination / update device 2 is the same as that of the determination / update device 1 described in the first embodiment. The applicant terminal E and the recruiter terminal F can be configured by a portable computer such as a general-purpose personal computer or a tablet, for example. The determination / update device 2, the applicant terminal E, and the recruiter terminal F are connected to each other by a communication network such as the Internet.

  The judgment / update device 2 judges whether the suitability test for evaluating the suitability of the job seeker is suitable for selection of applicants to a specific group, and further updates the suitability test based on the judgment result. It has a function to do. In order to realize such a function, as shown in FIG. 7, the determination / update device 2 includes a test execution unit 11, an evaluation result creation unit 20, a personnel evaluation acquisition unit 21, a comparison unit 22, a determination unit as functional blocks. The unit 17 and the update unit 18 are included. These functional blocks are realized by the CPU of the determination / update device 2 executing the program according to the present embodiment. The program according to the present embodiment may be recorded on a non-transitory computer-readable recording medium such as a CD-ROM, and the program is determined / updated by causing the determination / update device 2 to read the recording medium. You may install in the apparatus 2. Alternatively, the program code of the program may be downloaded to the determination / update device 2 via a communication network.

  The auxiliary storage device 10 stores test data T. The test data T is electronic data of an aptitude test for evaluating the aptitude of the job seeker. In the present embodiment, the aptitude test is a competency test. In the initial state, the competency test stored in the auxiliary storage device 10 may be an existing competency test that is generally used, or a competency updated by the determination / updating method in the first embodiment. It may be a test. Hereinafter, the competency test data may be simply referred to as “test data”.

  Similar to the determination / update device 1 according to the first embodiment, the determination / update device 2 updates the competency test so that it matches a specific organization. Specifically, the determination / update device 2 determines whether the competency test is suitable for selection of applicants to a specific organization according to the flowcharts shown in FIGS. 8 and 9, and based on the determination result. Update the competency test.

(Overall flow of judgment / update method)
FIG. 8 is a flowchart showing the overall flow of the determination method and the update method by the determination / update device 2. First, in step S5, when there is an applicant for the target company (YES), in step S6, the determination / update device 2 determines whether the competency test is compatible with the selection of the applicant for the target company. Degree determination is performed (fitness determination step). Step S6 is performed by the determination method according to the present embodiment, and the specific procedure is shown in the flowchart of FIG. The flowchart of FIG. 9 will be described later.

  Referring to FIG. 8 again, when the determination result of the suitability determination is not good (NO in step S7), the process proceeds to step S8, and the determination / update device 2 updates the aptitude test based on the determination result. (Update step). If the determination result of the fitness determination is good (YES in step S7), the process returns to step S5, and if an applicant to the target company appears (YES), steps S6 to S8 are repeated again. As described above, the determination / update device 2 determines whether the existing competency test is suitable for selection of applicants to a specific group as a whole processing procedure, and the determination result is not good. Update competency test. Accordingly, the competency test can be modified to match the selection of applicants to a specific group, and the accuracy of matching between the specific group and the job seeker can be continuously improved.

(Judgment method of conformity)
Next, the flowchart of the fitness determination shown in FIG. 9 will be described with reference to FIG.

  First, in step S60, the test execution unit 11 shown in FIG. 7 performs a competency test on the applicant. Specifically, the test execution unit 11 reads the test data T from the auxiliary storage device 10, extracts the test screen D, and transmits it to the applicant terminal E. As a result, the competency test screen is displayed on the applicant terminal E, and the applicant receives the competency test according to the displayed screen.

  The content of the competency test is the same as that in the first embodiment. Response data for the applicant's competency test is transmitted as the response A to the determination / update device 2 and input to the evaluation result creation unit 20.

  Subsequently, in step S61, the evaluation result creating unit 20 creates the applicant's evaluation result R from the answer A (evaluation result creating step). The specific calculation method of the evaluation result R is the same as that in the first embodiment, and the evaluation result creating unit 20 analyzes the answer A and assigns a predetermined weight to the score corresponding to the answer number of each question. The scale score obtained by summing the values multiplied by the coefficients is calculated as the evaluation result R.

  Thereafter, when the applicant finds a job at the target company (YES in step S62), the process proceeds to step S63. In step S63, the personnel evaluation acquisition unit 21 acquires the applicant's personnel evaluation from the target company. Specifically, the personnel evaluation acquisition unit 21 automatically transmits the questionnaire data Q to the job offerer terminal F after a predetermined period (for example, one year later) after the applicant finds employment. The contents of the questionnaire data Q are not particularly limited as long as they relate to personnel evaluations of applicants who have found employment, and may be those that allow a predetermined option to be selected or may be in a free description format. On the other hand, when the person in charge of personnel of the target company answers the questionnaire data Q, the response L is transmitted from the job offerer terminal F to the personnel evaluation acquisition unit 21 of the determination / update device 2, and the personnel evaluation acquisition unit 21 Create a personnel evaluation P from the answer L.

As will be described later, the personnel evaluation P is data to be compared with the evaluation result R, and is desirably an objective index such as a numerical value. For example, personnel evaluation P is
(Applicant's first year salary increase) / (Average salary increase for all employees of the target company in the first year)
It can be. In this case, if the personnel evaluation P is equal to 1, the personnel evaluation for the applicant is average, and it can be determined that the personnel evaluation for the applicant is better as the personnel evaluation P increases from 1.

  Subsequently, in step S64, the comparison unit 22 compares the personnel evaluation P with the evaluation result R (comparison step). Specifically, the comparison unit 22 determines whether the personnel evaluation P and the evaluation result R are approximate.

  Subsequently, in step S65, the determination unit 17 determines whether the competency test is suitable for selection of applicants to the target company based on the comparison result (determination step). Specifically, when the personnel evaluation P is good and the evaluation result R is good, or when the personnel evaluation P is bad and the evaluation result R is bad, the determination unit 17 has a good conformity determination. Is determined. Conversely, when the personnel evaluation P is good and the evaluation result R is bad, or when the personnel evaluation P is bad and the evaluation result R is good, the determination unit 17 determines that the fitness determination is bad. To do. In addition, the number of steps of the pass / fail levels of the personnel evaluation P and the evaluation result R, and the relationship between the degree of matching and the result of the conformity determination are not particularly limited.

  With the above flow, the fitness determination (step S6 in FIG. 8) is performed to determine whether the competency test is compatible with the selection of applicants to the target company. If the result of the fitness determination in step S6 is good (YES in step S7), the process returns to step S5. On the other hand, when the result of the fitness determination in step S6 is poor (NO in step S7), the process proceeds to step S8. In step S8, the update unit 18 shown in FIG. 7 updates the test data T (update step). Specifically, in step S61 shown in FIG. 9, the updating unit 18 corrects the weighting factor of each question of the test data T so that the evaluation result R created from the answer A approximates the personnel evaluation P. As a result, the possibility that the evaluation result of the applicant's competency test and the actual personnel evaluation after the employment do not match is reduced, and as a result, the suitability (accuracy) of the competency test is improved.

  Note that step S5 shown in FIG. 8 is performed as long as the target company exists. That is, the competency test determination and update process by the determination / update device 2 is performed every time an applicant to the target company appears and continues as long as the target company exists. Therefore, by repeating the determination and update processing, the fitness test conformity can be continuously improved. In addition, even if the required human resources change due to changes in the business format and corporate culture of the target company, the competency test can be continuously updated to respond to the change.

  Note that step S6 may be performed for all applicants to the target company, or only for those who have obtained an offer from the target company. Thereby, the efficiency of the determination / update process can be increased.

  As described above, in this embodiment, the accuracy of the aptitude test is improved by comparing the evaluation result of the aptitude test with the actual personnel evaluation of the job hunter. In other words, the content of the aptitude test can be developed in accordance with the environment of each organization without any human intervention. By using the aptitude test developed in this way to select applicants for the target company and to match the human resources of those who want to work for the target company, it is possible to improve the efficiency of human resources who can fully demonstrate their abilities after finding employment in the target company. It can be matched well, and the mismatch between the job seeker and the actual job seeker can be reduced.

[Third Embodiment]
Subsequently, a third embodiment of the present invention will be described. In the third embodiment, as in the second embodiment, it is determined whether the aptitude test is suitable for the selection of applicants to the group by repeating the follow-up survey of the job seeker. In the third embodiment, components having the same functions as those in the first and second embodiments are denoted by the same reference numerals, and detailed description thereof is omitted.

(Overall system configuration)
FIG. 10 is a block diagram illustrating a schematic configuration of an update system according to the third embodiment. This update system includes an update device 3, a data server 4, an applicant terminal E, and recruiter terminals F1 to Fy. The update device 3 and the data server 4 are devices owned by a recruitment agency. Each of the applicant terminals E is a terminal owned by an unspecified number of different applicants, and the number of applicant terminals E is not particularly limited. The recruiter terminals F1 to Fy are terminals owned by different recruiters. The job seeker is an organization affiliated with a recruitment agency, and the number thereof is y. Hereinafter, an organization that owns the recruiter terminal Fm (hereinafter, m is an integer of 1 to y) among the recruiter terminals F1 to Fy is referred to as a target company Gm.

  The update device 3 can be configured by, for example, a general-purpose personal computer or a server device, and the hardware configuration is the same as the determination / update devices 1 and 2 described in the first and second embodiments. In FIG. 10, the auxiliary storage device 10 is not shown.

  The applicant terminal E and the recruiter terminals F1 to Fy can be configured by, for example, a general-purpose personal computer or a portable computer such as a tablet. The update device 3, the applicant terminal E, and the recruiter terminals F1 to Fy are connected to each other via a communication network such as the Internet.

  The update device 3 has a function of updating an aptitude test for evaluating the aptitude of the job seeker by machine learning. In order to realize such a function, the update device 3 includes a test execution unit 11, a personnel evaluation acquisition unit 21, an answer acquisition unit 30, a teacher data creation unit 31, and a machine learning unit 32 as functional blocks. . These functional blocks are realized by the CPU of the update device 3 executing the program according to the present embodiment. The program according to the present embodiment may be recorded on a non-transitory computer-readable recording medium such as a CD-ROM, and the program is installed in the updating apparatus 3 by causing the updating apparatus 3 to read the recording medium. May be. Or you may download the program code of a program to the update apparatus 3 via a communication network.

  The data server 4 is a server device that can store big data, and is connected to the update device 3. The data server 4 includes a test data storage unit 40 that stores test data T1 to Ty, and databases DB1 to DBy. In FIG. 10, the data server 4 is connected to one update device 3, but may be connected to a plurality of devices similar to the update device 3.

  The test data T1 to Ty is electronic data of an aptitude test for evaluating the aptitude of the job seeker. In the present embodiment, the aptitude test is a competency test. Test data T1 to Ty are prepared corresponding to the target companies G1 to Gy, respectively. The test data T1 to Ty may be data having the same weighting coefficient, but the first embodiment is adapted so that the test data T1 to Ty are suitable for selection of applicants to the target companies G1 to Gy, respectively. It is desirable that the information is updated by the determination / update method according to the second embodiment.

  The databases DB1 to DBy are storage devices for storing teacher data for machine learning described later. With the operation of the determination / update system according to the present embodiment, teacher data is accumulated in the databases DB1 to DBy. Hereinafter, the competency test data may be simply referred to as “test data”.

  The update device 3 updates the test data Tm so that it matches the specific target company Gm. The processing procedure by the update device 3 is shown in FIGS.

(Overall flow of update method)
FIG. 11 is a flowchart showing the overall flow of the update method by the update device 3. First, in step S9, teacher data for machine learning described later is acquired (teacher data acquisition step). Specifically, when the answer acquisition unit 30 acquires an answer Am for the test data Tm of the applicant to the target company Gm, and the applicant finds a job at the target company Gm, the target of the applicant after employment The personnel evaluation (referred to as personnel evaluation Pm) at the company Gm is associated with the answer Am and stored in the database DBm as teacher data PAm. By repeating this continuously for other applicants, teacher data PA1 to PAy corresponding to the target companies G1 to Gy are accumulated in the databases DB1 to DBy, respectively. A more specific procedure of step S9 is shown in the flowchart of FIG. The flowchart of FIG. 12 will be described later.

  Referring to FIG. 11 again, by repeating step S9, teacher data is accumulated for the target company Gm and groups other than the target company Gm. If teacher data is sufficiently accumulated (YES in step S10), the process proceeds to step S11. In step S11, the machine learning unit 32 shown in FIG. 10 performs machine learning based on the accumulated many teacher data and updates the test data (update step).

(Teacher data acquisition method)
Next, the teacher data acquisition flowchart shown in FIG. 12 will be described with reference to FIG.

  First, in step S90, the test execution unit 11 shown in FIG. 10 performs a competency test on the applicant. Specifically, when an applicant for the target company Gm appears, the test execution unit 11 reads the test data Tm corresponding to the target company Gm from the data server 4 and extracts the test screen Dm. It transmits to the applicant terminal E to be used. As a result, the competency test screen is displayed on the applicant terminal E, and the applicant receives the competency test according to the displayed screen.

  The contents of the competency test are the same as those in the first and second embodiments. Response data for the applicant's competency test is transmitted to the update device 3 as a response Am, and the response acquisition unit 30 acquires the response Am in step S91.

  Thereafter, when the applicant finds a job at the target company (YES in step S92), the process proceeds to step S93. In step S93, the personnel evaluation acquisition unit 21 acquires the applicant's personnel evaluation Pm from the target company Gm. The acquisition method and contents of the personnel evaluation Pm are the same as those in the second embodiment. Specifically, the personnel evaluation acquisition unit 21 transmits the questionnaire data Q to the recruiter terminal Fm after a predetermined period (for example, one year later) after the applicant finds employment. On the other hand, when the personnel manager of the target company Gm answers, the answer Lm is transmitted from the job offerer terminal Fm to the personnel evaluation acquisition unit 21 of the update device 3, and the personnel evaluation acquisition unit 21 performs the personnel evaluation from the answer Lm. Create Pm.

  Subsequently, in step S94, the teacher data creation unit 31 creates the teacher data PAm by associating the personnel evaluation Pm with the answer Am acquired by the answer acquisition unit 30. Subsequently, in step S95, the teacher data creation unit 31 stores the teacher data PAm in the database DBm corresponding to the target company Gm.

  With the above flow, a teacher data acquisition step (step S9 in FIG. 11) for acquiring teacher data is performed. By repeating this step S9, teacher data is accumulated for the target company Gm and groups other than the target company Gm. If teacher data corresponding to at least one organization is sufficiently accumulated (YES in step S10), the process proceeds to step S11. The number of teacher data serving as a criterion for determining whether or not to move to step S11 is not particularly limited as long as the number is sufficient for machine learning. In the following, it is assumed that teacher data PAn corresponding to the target company Gn (hereinafter, n is an integer of 1 to y) is sufficiently accumulated.

  In step S11, the machine learning unit 32 shown in FIG. 10 performs machine learning based on a large number of teacher data PAn accumulated in the database DBn corresponding to the target company Gn, and the evaluation result calculated from the answer An and the answer The weighting coefficient of each question of the test data Tn is corrected so that the personnel evaluation Pn associated with An matches. That is, when the applicant's personnel evaluation Pn is good, the weighting coefficient is corrected so that the evaluation result calculated from the answer An of the applicant is also good with high probability. Thereby, the machine learning unit 32 updates the test data Tn corresponding to the target company Gn (update step). Although the machine learning method in this embodiment is not specifically limited, For example, a neural network can be used.

  By repeating steps S9 to S11 continuously, the test data T1 to Ty corresponding to each target company G1 to Gy can be updated. As a result, the test data T1 to Ty can be modified to match the selection of applicants for the target companies G1 to Gy, and the matching accuracy between the target companies G1 to Gy and the job seeker can be continuously improved. it can.

(Additional notes)
The present invention is not limited to the above-described embodiment, and various modifications are possible within the scope of the claims, and a form obtained by appropriately combining technical means disclosed in the embodiment is also included in the present invention. Included in the technical scope.

DESCRIPTION OF SYMBOLS 1 Determination / update apparatus 2 Determination / update apparatus 3 Update apparatus 4 Data server 10 Auxiliary storage apparatus 11 Test execution part 12 Attention evaluation result creation part 13 Comparison evaluation result creation part 14 Similarity calculation part 15 Ranking part 16 Comparison part 17 Determination Unit 18 Update unit 20 Evaluation result creation unit 21 Personnel evaluation acquisition unit 22 Comparison unit 30 Answer acquisition unit 31 Teacher data creation unit 32 Machine learning unit 40 Test data storage unit A, A0 to Ax, Am, An Reply B Personnel evaluation data C0 ~ Cx Client terminal D, Dm Test screen DB1-DBy, DBm, DBn Database E Applicant terminal F, F1-Fy, Fm Job offerer terminal G1-Gy, Gm, Gn Target company (organization)
L, Lm Answer M1-Mx Similarity P, Pm, Pn Personnel evaluation PA1-PAy Teacher data PAm, PAn Teacher data Q Questionnaire data R Evaluation result R0 Attention evaluation result R1-Rx Comparative evaluation result T, T1-Ty, Tm , Tn test data

Claims (11)

A method for determining whether an aptitude test for assessing an applicant's aptitude is suitable for selection of applicants for a particular organization,
Attention evaluation result creation step for creating an attention evaluation result that is an evaluation result of one notable person belonging to the group based on the aptitude test;
Based on the aptitude test, a comparative evaluation result creation step for creating a comparative evaluation result for each comparative person, which is an evaluation result of a plurality of comparative persons other than the target person belonging to the group;
A similarity calculation step of calculating a similarity between each of the comparison evaluation results and the attention evaluation result;
A ranking step for ranking the comparison persons in descending order of the similarity;
A comparison step of comparing a higher rank among the comparison persons with a higher rank than the predetermined person with the attention person for personnel evaluation in the group;
A determination step of determining whether the suitability test is suitable for selection of applicants to the group based on the comparison result;
The determination method characterized by having.   2. The comparison evaluation result creation step, the similarity calculation step, the ranking step, the comparison step, and the determination step are repeated while changing the comparison person belonging to the group. The determination method described. A determination method that determines whether an aptitude test for assessing the aptitude of a job seeker is suitable for selecting applicants for a particular organization,
Based on the aptitude test, an evaluation result creating step for creating an evaluation result of applicants to the group;
When the applicant finds employment in the group, a comparison step of comparing a personnel evaluation in the group of the applicant after employment with the evaluation result;
A determination step of determining whether the suitability test is suitable for selection of applicants to the group based on the comparison result;
The determination method characterized by having. An update method for updating the aptitude test to assess the candidate's aptitude,
Using the determination method according to any one of claims 1 to 3, a suitability determination step for determining whether the suitability test is suitable for selection of applicants to the specific group,
An update step for updating the suitability test based on the determination result in the suitability determination step;
An update method characterized by comprising: An update method for updating the aptitude test to assess the candidate's aptitude,
When an answer to the aptitude test of an applicant to a specific group is obtained, and the applicant finds employment in the group, the personnel evaluation in the group of the applicant after employment is associated with the answer and teacher data A teacher data storage step to be stored in the database as
An update step for updating the aptitude test by repeating the teacher data storage step for a plurality of applicants, and performing machine learning based on the accumulated teacher data;
An update method characterized by comprising: A determination device for determining whether an aptitude test for evaluating the aptitude of a job applicant is suitable for selection of applicants for a specific group,
Based on the aptitude test, an attention evaluation result creating unit that creates an attention evaluation result that is an evaluation result of one notable person belonging to the group;
Based on the aptitude test, a comparative evaluation result creating unit that creates a comparative evaluation result that is an evaluation result of a plurality of comparative persons other than the target person belonging to the group for each comparative person;
A similarity calculation unit for calculating a similarity between each of the comparison evaluation results and the attention evaluation result;
A ranking unit that ranks the comparison persons in descending order of the similarity;
Among the comparison persons, a comparison unit that compares a higher rank higher than a predetermined order with the attention person for personnel evaluation in the group;
A determination unit that determines whether the suitability test is suitable for selection of applicants to the group based on the comparison result;
The determination apparatus characterized by having. A determination device that determines whether an aptitude test for evaluating the aptitude of a job seeker is suitable for selection of applicants for a specific group,
Based on the aptitude test, an evaluation result creation unit that creates an evaluation result of applicants to the group;
When the applicant finds employment in the organization, a comparison unit that compares the evaluation of the applicant in the organization after employment with the evaluation result;
A determination unit that determines whether the suitability test is suitable for selection of applicants to the group based on the comparison result;
The determination apparatus characterized by having. An update device for updating an aptitude test for evaluating a job applicant's aptitude,
Each part of the determination apparatus according to claim 6 or 7,
An update unit that updates the aptitude test based on a determination result by the determination unit;
An update device comprising: An update device for updating an aptitude test for evaluating a job applicant's aptitude,
An answer acquisition unit for acquiring an answer to the aptitude test of an applicant to a specific group;
When the applicant finds employment in the group, a personnel evaluation acquisition unit that acquires personnel evaluation in the group of the applicant after employment;
A teacher data creation unit that creates teacher data by associating the personnel evaluation with the answer;
A machine learning unit that updates the aptitude test by performing machine learning based on the teacher data created for a plurality of applicants;
An update device comprising:   A program that causes a computer to function as each unit of the determination device according to claim 6 or 7, or each unit of the update device according to claim 8 or 9.   A computer-readable recording medium on which the program according to claim 10 is recorded.

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