JP7005219B2 - Aptitude test judgment device, update device, program and recording medium - Google Patents

Description

The present invention relates to a human resources matching system for matching job seekers with job seekers, and more particularly to an aptitude test such as a competency test for evaluating the aptitude of job seekers.

(General human resources matching issues)
Generally, human resources matching is composed of the following four steps (1) to (4). (1) The staffing agency creates profiles of job seekers and recruiters (Model).
(2) The job seeker finds a combination of job seekers that seems to be good, either explicitly or implicitly (Match).
(3) Meet job seekers and job seekers so that they can interact with each other (Introduce).
(4) Interact between job seekers and recruiters. As a result, both profiles are also updated (Model).

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

When a third party such as a consultant tries to solve these problems, in the end, they have to rely on personal factors such as "experience" and "intuition". In addition, many of the current human resources matching assume that the job seeker (or recruiter) who wants to be referred is the leading referral, and in such referrals, there is inevitably room for consultants to intervene. "Guarantee of reliability" cannot be maintained because restrictions are applied.

(Lack of attention to human ability)
Further, in the conventional human resources matching, for example, regarding bookkeeping ability, English ability, etc., the ability is grasped by objective information such as certification test and qualification, and appropriate human resources are selected. However, after employment, it is difficult to objectively evaluate the human abilities that are considered to be the most important for business performance, and as a result, there is a mismatch between the human resources that the recruiter is looking for and the actual employment. There were many things.

In order to overcome such problems of human resources matching, we use aptitude tests called "competency tests" to grasp the individual characteristics of human resources as objectively as possible, and human resources required by recruiters. There are also attempts to accurately supply the above (for example, Patent Documents 1 and 2 below).

However, the aptitude tests disclosed in these documents take the method of answering a test consisting of exactly the same items for the assumed test format and summing the scores, which is a classical test theory. It does not go beyond the scope of. For this reason, it is inevitable that a person's arbitrariness is involved in how to allocate points to each item. In other words, there is no logical basis that should be the most fundamental: "Which item and how to answer and which ability is judged to be how much". As a result, these conventional techniques have not solved the problem of grasping the individual characteristics of human resources as objectively as possible.

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

Japanese Unexamined Patent Publication No. 2001-184392 Japanese Unexamined Patent Publication No. 2005-18274

An object of the present invention is to overcome the shortcomings of the prior art while solving the above-mentioned general problems of human resource matching.

In other words, a workplace where it is possible to eliminate the room for arbitrary intervention of human resources in the introduction of human resources, supply the human resources that are truly sought after to the job seekers, and demonstrate their abilities to the job seekers. The challenge is to realize a highly accurate human resources matching system that can provide the above.

In order to solve the above problems, the determination method according to the present invention is a determination method for determining whether the aptitude test for evaluating the aptitude of a job seeker is suitable for the selection of applicants to a specific organization. Then, based on the aptitude test, the attention evaluation result creation step for creating the attention evaluation result which is the evaluation result of one attention person belonging to the group, and the attention belonging to the group based on the aptitude test. The comparison evaluation result creation step of creating the comparison evaluation result which is the evaluation result of a plurality of comparison persons other than the person for each comparison person, and the similarity between each of the comparison evaluation results and the attention evaluation result. A similarity calculation step for calculating the degree, a ranking step for ranking the comparison persons in descending order of similarity, and personnel affairs of the comparison persons having a higher rank than a predetermined value in the organization. It is characterized by having a comparison step for comparing the evaluation with the person of interest, and a determination step for determining whether the aptitude test is suitable for the selection of applicants to the organization based on the comparison result. ..

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, the determination method according to the present invention is a determination method for determining whether the aptitude test for evaluating the aptitude of a job seeker is suitable for selecting an applicant to a specific organization. , An evaluation result creation step that creates an evaluation result of an applicant to the organization based on the aptitude test, and a personnel evaluation of the applicant after employment in the organization when the applicant finds a job in the organization. It is characterized by having a comparison step for comparing the evaluation result with the evaluation result, and a determination step for determining whether the aptitude test is suitable for the selection of applicants to the organization based on the comparison result.

The renewal method according to the present invention is an renewal method for updating the aptitude test for evaluating the aptitude of a job seeker, and the aptitude test is an applicant to the specific organization by using the determination method according to the present invention. It is characterized by having a goodness-of-fit determination step for determining whether or not the product conforms to the selection, and an update step for updating the aptitude test based on the determination result in the goodness-of-fit determination step.

The renewal method according to the present invention is an renewal method for renewing an aptitude test for evaluating the aptitude of a job seeker. A teacher data storage step for storing the personnel evaluation of the applicant in the group after employment in a database as teacher data in association with the answer, and a teacher data storage step for a plurality of applicants when employment is carried out in a group. It is characterized by having an update step for updating the aptitude test by repeating the above steps and performing machine learning based on the accumulated teacher data.

The determination device according to the present invention is a determination device for determining whether the aptitude test for evaluating the aptitude of a job seeker is suitable for the selection of applicants to a specific organization, and is based on the aptitude test. , The attention evaluation result creation unit that creates the attention evaluation result which is the evaluation result of one attention person belonging to the group, and a plurality of comparison persons other than the attention person belonging to the group based on the aptitude test. A comparison evaluation result creation unit that creates comparison evaluation results for each comparison person, and a similarity calculation unit that calculates the degree of 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 similarity, and a superior person having a higher rank than a predetermined value among the comparison persons are compared with the attention person for personnel evaluation in the organization. It is characterized by having a comparison unit and a determination unit for determining whether the aptitude test conforms to the selection of applicants to the organization based on the comparison result.

The determination device according to the present invention is a determination device for determining whether the aptitude test for evaluating the aptitude of a job seeker is suitable for selecting an applicant to a specific organization, and is based on the aptitude test. Comparison between the evaluation result creation department that creates the evaluation results of applicants to the group and the personnel evaluation of the applicant after employment in the group when the applicant gets a job in the group. It is characterized by having a unit and a determination unit for determining whether the aptitude test conforms to the selection of applicants to the organization based on the comparison result.

The renewal device according to the present invention is an renewal device for updating an aptitude test for evaluating the aptitude of a job seeker, and the aptitude is based on each part of the determination device according to the present invention and the determination result by the determination unit. It is characterized by having an update unit for updating the test.

The renewal device according to the present invention is an renewal device for renewing an aptitude test for evaluating the aptitude of a job seeker, and has an answer acquisition unit for acquiring an answer to the aptitude test of an applicant to a specific organization, and the above-mentioned. When an applicant gets a job in the organization, the personnel evaluation acquisition department that acquires the personnel evaluation of the applicant in the organization after employment and the teacher data creation that creates teacher data by associating the personnel evaluation with the answer. It is characterized by having a unit 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 part of the determination device according to the present invention or each part 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 the aptitude test is suitable for the selection of applicants to a specific organization. Therefore, by performing human resources matching using an aptitude test with good judgment results or an aptitude test updated based on the judgment results, there is no room for human arbitrariness to intervene in temporary staffing, and recruiters. It is possible to realize a matching system that can supply the human resources that are truly sought after and provide a workplace where job seekers can demonstrate their abilities.

It is a block diagram which shows the schematic structure of the determination / update system which concerns on 1st Embodiment. It is a flowchart which shows the overall flow of the determination method and the update method by the determination / update apparatus which concerns on 1st Embodiment. It is a flowchart of conformity determination which concerns on 1st Embodiment. This is an example of the competency test screen. It is a table which shows the content of a question of a competency test and a part of a concrete example of a weighting coefficient. It is a flowchart which shows the modification of the processing procedure by the determination / update apparatus which concerns on 1st Embodiment. It is a block diagram which shows the schematic structure of the determination / update system which concerns on 2nd Embodiment. It is a flowchart which shows the overall flow of the determination method and the update method by the determination / update apparatus which concerns on 2nd Embodiment. It is a flowchart of conformity determination which concerns on 2nd Embodiment. It is a block diagram which shows the 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 device which concerns on 3rd Embodiment. It is a flowchart of teacher data acquisition which concerns on 3rd Embodiment.

Hereinafter, embodiments of the present invention will be described 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 assumed to be a corporation mainly for the purpose of profit-making activities such as a company, but is not limited to this, and may include a public institution or a non-profit organization. In the following, an organization that assumes that the aptitude test determined / updated by the determination / update device 1 will be used for the selection of applicants may be referred to as a “target company”.

The determination / update device 1 has both the functions of 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 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. Execute various arithmetic processing. When the determination / update device 1 is composed of a general-purpose personal computer, the determination / update device 1 includes a display device such as a liquid crystal display (not shown) and an input device such as a keyboard or a touch panel (not shown). 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 client terminal C0 to Cx can be configured by a portable computer such as a general-purpose personal computer or a tablet. Of the client terminals C0 to Cx, the client terminal C0 is a terminal used by one person of interest belonging to the target company. The person of interest is not particularly limited as long as it belongs to the target company, but it is preferable that the person is a 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 attention person belonging to the target company. The comparison person is randomly selected from people who belong to the same target company as the person of interest, and the number of people is not particularly limited.

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

Test data T and personnel evaluation data B are stored in the auxiliary storage device 10. The test data T is electronic data of an aptitude test for evaluating the aptitude of a job seeker, and 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. In the following, the competency test data may be simply referred to as "test data".

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

Here, the existing competency tests are not specialized for each specific organization. On the other hand, the abilities and humanity that are emphasized in business performance differ from group to group, so existing competency tests are not always suitable for the selection of applicants in a specific group.

Therefore, the determination / update device 1 updates the competency test so as to be suitable for a specific organization. Specifically, the determination / update device 1 determines whether the competency test conforms to the 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 tests.

(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 device 1. First, in step S1, the existing test data is stored in the auxiliary storage device 10. Subsequently, in step S2, the determination / update device 1 performs a goodness-of-fit determination for determining whether the competency test conforms to the selection of applicants to the target company (goodness-of-fit determination step). This step S2 is carried out by the determination method according to the present embodiment, and the specific procedure thereof is shown in the flowchart of FIG. The flowchart of FIG. 3 will be described later.

Referring to FIG. 2 again, if the determination result of the goodness of fit 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 goodness of fit 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. In this way, the determination / update device 1 determines whether the existing competency test is suitable for the selection of applicants to a specific organization as an overall processing procedure, and when the determination result is not good. Update competency tests. As a result, the competency test can be modified to match the selection of applicants to a specific organization, and the accuracy of matching between the specific organization and the job seeker can be improved.

(How to judge the goodness of fit)
Subsequently, the flowchart of the goodness-of-fit determination shown in FIG. 3 will be described with reference to FIG.

First, in step S20, the test implementation unit 11 shown in FIG. 1 conducts a competency test for 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 the test screens D to the client terminals C0 to Cx. As a result, the competency test screen is displayed on each of the client terminals C0 to Cx, and the user of each client terminal C0 to Cx receives the competency test according to the displayed screen.

FIG. 4 is an example of a competency test screen displayed on each client terminal C0 to Cx. The examinee selects an answer from the six options set for each question. The layout of the screen, the content of the questions, the number of questions, etc. are not particularly limited. Further, the "Back" 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.

The data of the 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 the 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 device 1. On the other hand, the responses A1 to Ax (that is, the responses by the comparison person) from the client terminals C1 to Cx 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 the attention evaluation result R0, which is the evaluation result (latent scale value) of the attention person from the answer A0 (attention evaluation result creation step). Specifically, the attention evaluation result creation unit 12 analyzes the answer A0 and sets the scale score obtained by multiplying the raw 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 some of the contents of the competency test questions and specific examples of the weighting factors. The questions of the competency test in this embodiment are classified into 9-dimensional major categories such as "guidance / decision ability" and "support / collaboration ability", and each large literary class is further classified into 3D minor categories. .. For example, the major classification of "guidance / decision ability" is divided into three-dimensional subclasses of "leadership skill", "vision setting", and "judgment ability", and four questions are assigned to each subclass. .. The competency test includes a total of 113 questions, which are the above-mentioned 9 × 3 × 4 = 108 questions plus 5 IM (impression management) questions.
In each question, the raw score for the answered number is set as shown in Table 1, for example.

Further, a weighting coefficient is set for each question, and in the initial state (that is, before updating), the weighting coefficient of all the 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 raw score corresponding to the answer number in each question by the weighting coefficient.

Further, in step S22 shown in FIG. 3, the comparison evaluation result creation unit 13 creates comparison evaluation results R1 to Rx, which are evaluation results of the comparison person, from each of the responses A1 to Ax (attention evaluation result creation step). ). The method in which the comparative evaluation result creating unit 13 calculates the comparative evaluation results R1 to Rx is the same as the method in which the attention evaluation result creating 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 comparative evaluation results R1 to Rx and the attention evaluation result R0 (similarity calculation step). The method for calculating the similarity M1 to Mx is not particularly limited, but for example, the similarity M1 to M1 can be calculated by the same method as the method for calculating the matching value 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 higher the similarity. It gets higher. The similarity degrees M1 to Mx calculated by the similarity degree calculation unit 14 are input to the ranking unit 15.

Subsequently, in step S24, the ranking unit 15 ranks the comparison persons (that is, the users of the client terminals C1 to Cx) in descending order of the similarity based on the similarity M1 to Mx (ranking step). .. Then, the ranking unit 15 inputs data indicating a higher rank person (for example, a top 10 person, a top 10%, etc.) and a noteworthy person having a higher rank than a predetermined value among the comparison persons into the comparison unit 16. Examples of data indicating the superior and the person of interest include employee numbers.

Subsequently, in step S25, the comparison unit 16 compares the superior person with the person of interest for personnel evaluation in the target company (comparison step). Specifically, the comparison unit 16 reads data related to the personnel evaluation of the superior and the attention person from the personnel evaluation data B stored in the auxiliary storage device 10, and the personnel evaluation of each superior and the personnel evaluation of the attention person. And compare. The personnel evaluation used for comparison preferably objectively expresses the person evaluation in the company numerically or the like, and examples thereof include an average salary increase. The average salary increase is
It can be calculated by (current annual income-annual income at the time of joining the company) / years of service, and the higher the performer, the higher the average salary increase tends to be. In this case, the comparison unit 16 calculates the ratio of the average salary increase to the numerical value of the person of interest for each of the higher-ranking persons, and outputs the calculated ratio to the determination unit 17 as a comparison result. Another index for personnel evaluation is the rate of change in salary increases. The rate of change in salary increases is
It can be expressed as the rate of change in [annual income (basic wage only) / years of service], and 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 conforms to the selection of the applicant to the target company based on the comparison result (determination step). Specifically, the determination unit 17 determines that the greater the tendency for the personnel evaluation of each superior person to be similar to that of the person of interest, the higher the degree of suitability of the applicant's selection for the company subject to the competency test. More specifically, when the ratio of the higher-ranked persons within the predetermined range (for example, 90% to 110%) is 80% or more, it is determined that the goodness of fit is high (judgment result). Is good).

If the determination result by the 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 of step S2 (goodness of fit determination step) shown in FIG. 2 is defective.

If the determination result by the 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 comparison person for obtaining the comparison evaluation result. When another comparison person exists in the company (YES in step S28), the process proceeds to step S29, and the test execution unit 11 changes the comparison person to perform the competency test. 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 terminals. After that, steps S22 to S26 are repeated until the result becomes NO in step S27 or step S28. As a result, if the process is completed by becoming NO in step S27 even once, the determination result in step S2 shown in FIG. 2 becomes defective. On the other hand, when the process is completed by becoming NO in step S28 (that is, when the process is completed without becoming NO in step S27 even once), the determination result in step S2 shown in FIG. 2 is good. ..

According to the above flow, the goodness-of-fit determination (step S2 in FIG. 2) for determining whether the competency test conforms to the selection of applicants to the target company is performed. In the goodness-of-fit determination, in the flowchart of FIG. 3, steps S28 and S29 may be omitted, and steps S20 to S26 may be performed only once. However, in order to accurately determine the goodness of fit of the competency test, it is desirable not to omit steps S28 and S29 and to repeat steps S22 to S26 while changing the comparison person belonging to the target company.

Refer to FIG. 2 again. If the result of the goodness-of-fit determination in step S2 is good (YES in step S3), the process by the determination / update device 1 is terminated. By using the competency test, which is judged to have a good goodness-of-fit judgment result, to select applicants for the actual target company, applicants can be selected with high accuracy and similar abilities and humanity to the person of interest. You can select from.

In particular, if the person of interest is a high performer, apply with high accuracy to a person who can be expected to leave a high track record in the target company by using the competency test in which the result of the goodness of fit judgment is judged to be good. Can be selected from the person. Specifically, among the applicants, the applicant whose competency test evaluation result (latent scale value) is close to the attention evaluation result of the high performer is judged to be expected to play an active role after joining the company.

On the other hand, if the result of the goodness-of-fit determination in step S2 is defective (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 degree of similarity with the attention evaluation result R0 of the evaluation result of the competency test of the superior whose personnel evaluation is not similar to that of the person of interest is reduced. In addition, the update unit 18 has a weighting coefficient for each question in the test data T so that the degree of similarity with the attention evaluation result R0 of the evaluation result of the competency test of the superior whose personnel evaluation is similar to that of the person of interest does not decrease. To fix. As a result, a person whose personnel evaluation is not similar to the person of interest is less likely to be dissimilar to the person of interest in the evaluation result of the competency test, and as a result, the goodness of fitness of the competency test is improved.

Further, the process proceeds to step S2, the goodness of fit is determined again for the updated competency test, and the test data is updated until YES in step S3. This makes it possible to develop an aptitude test suitable for selecting applicants for actual target companies.

Then, by using the developed aptitude test to select applicants for the target company and match the human resources of those who want to get a job at the target company, we can efficiently utilize the human resources who can fully demonstrate their abilities after getting a job at the target company. It is possible to match, and it is possible to reduce the mismatch between the human resources required by the job seeker and the actual job seeker. Therefore, a workplace where it is possible to eliminate the room for arbitrary intervention of human resources in temporary staffing, supply the human resources that are truly sought after to the job seekers, and demonstrate their abilities to the job seekers. It is possible to realize a matching system that can provide.

(Modification example)
It should be noted that updating the aptitude test is not essential in this embodiment. In this modification, a form in which the aptitude test is not updated will be described.

FIG. 6 is a flowchart showing a modified example of the processing procedure by the determination / update device 1. In this flowchart, in the flowchart shown in FIG. 2, step S4 is replaced with step S4'.

In FIG. 6, when the result of the goodness-of-fit determination in step S2 is defective (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 the data of the aptitude test other than the competency test used for the goodness-of-fit determination up to that point. Then, the process proceeds to step S2, and the goodness of fit is determined again. In this way, by performing the goodness-of-fit determination while changing the test data until YES in step S3, it is possible to find an aptitude test suitable for selecting an applicant to an actual target company.

In this way, by using the aptitude test judged to be good in the goodness-of-fit judgment to select applicants for the target company and match the human resources of those who want to get a job at the target company, after employment at 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, the aptitude test is performed by an applicant to a specific group by extracting a person of interest and a person for comparison from within the same group and comparing them with the evaluation result of the aptitude test and the personnel evaluation. It was judged whether it was suitable for the selection. However, when one staffing agency develops an aptitude test by this method, it is necessary to cooperate with many employees or provide personal information such as personnel evaluation. Therefore, it is not realistic to apply the determination / update method according to the first embodiment to a large number of organizations.

Therefore, in the second embodiment, by repeating the follow-up survey of the person who has found employment in a specific group, it is determined whether the aptitude test is suitable for the selection of the applicant to the group. In the second embodiment, the components having the same functions as those in the first embodiment are designated by the same reference numerals, and detailed description thereof will be omitted.

(Overall system configuration)
FIG. 7 is a block diagram showing a schematic configuration of the 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 temporary staffing agency, the applicant terminal E is a terminal owned by the applicant, and the recruiter terminal F is a terminal owned by the recruiter.

The recruiter is a specific organization that assumes that the aptitude test determined / updated by the determination method and the update method according to the present invention will be used for the selection of applicants, and in the present embodiment, the “target company”. It may also be called. Applicants are those who wish to find employment in the target company.

The determination / update device 2 has both the functions of 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. 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 determination / update device 2 determines whether the aptitude test for evaluating the aptitude of the job seeker is suitable for the selection of applicants to a specific organization, and further updates the aptitude test based on the determination result. Has the function of In order to realize such a function, as shown in FIG. 7, the determination / update device 2 has a test execution unit 11, an evaluation result creation unit 20, a personnel evaluation acquisition unit 21, a comparison unit 22, and a determination unit as functional blocks. It has a unit 17 and an update unit 18. These functional blocks are realized by the CPU of the determination / updating device 2 executing the program according to the present embodiment. The program according to the present embodiment may be recorded on a non-temporary computer-readable recording medium such as a CD-ROM, and the program is determined / updated by having the determination / update device 2 read the recording medium. It may be installed in the device 2. Alternatively, the program code of the program may be downloaded to the determination / update device 2 via the communication network.

Test data T is stored in the auxiliary storage device 10. The test data T is electronic data of an aptitude test for evaluating the aptitude of a job seeker, and 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. In the following, the competency test data may be simply referred to as "test data".

The determination / update device 2 is updated so that the competency test is suitable for a specific organization, as in the determination / update device 1 according to the first embodiment. Specifically, the determination / update device 2 determines whether the competency test is suitable for the selection of applicants to a specific organization according to the flowcharts shown in FIGS. 8 and 9, and is 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 to the target company (YES), in step S6, the determination / update device 2 determines whether the competency test conforms to the selection of the applicant to the target company. The degree is judged (goodness of fit judgment step). This step S6 is carried out by the determination method according to the present embodiment, and the specific procedure thereof is shown in the flowchart of FIG. The flowchart of FIG. 9 will be described later.

Referring to FIG. 8 again, if the determination result of the goodness of fit 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 goodness of fit determination is good (YES in step S7), the process returns to step S5, and if an applicant for the target company appears (YES), steps S6 to S8 are repeated again. In this way, the determination / update device 2 determines whether the existing competency test is suitable for the selection of applicants to a specific organization as an overall processing procedure, and when the determination result is not good. Update competency tests. This allows the competency test to be modified to match the selection of applicants for a particular organization and continuously improve the accuracy of matching between a particular organization and job seekers.

(How to judge the goodness of fit)
Subsequently, the flowchart of the goodness-of-fit determination shown in FIG. 9 will be described with reference to FIG. 7.

First, in step S60, the test implementation unit 11 shown in FIG. 7 conducts a competency test on the applicant. Specifically, the test implementation 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. The data of the response to the competency test of the applicant is transmitted to the determination / update device 2 as the response A and input to the evaluation result creation unit 20.

Subsequently, in step S61, the evaluation result creation unit 20 creates the applicant's evaluation result R from the response A (evaluation result creation step). The specific calculation method of the evaluation result R is the same as that in the first embodiment, and the evaluation result creation unit 20 analyzes the answer A and gives a predetermined weight to the raw 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.

After that, when the applicant finds a job in the target company (YES in step S62), the process proceeds to step S63. In step S63, the personnel evaluation acquisition unit 21 acquires the personnel evaluation of the applicant from the target company. Specifically, the personnel evaluation acquisition unit 21 automatically sends the questionnaire data Q to the recruiter terminal F after a predetermined period (for example, one year later) after the applicant gets a job. The content of the questionnaire data Q is not particularly limited as long as it relates to the personnel evaluation of the applicant who has found a job, and may be one that allows a predetermined option to be selected, or may be 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 answer L is transmitted from the recruiter 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 it is desirable that it is an objective index such as a numerical value. For example, the personnel evaluation P is
(Applicant's first year salary increase) / (Average salary increase for all employees of the target company in the first year)
Can be. In this case, if the personnel evaluation P is equal to 1, it can be judged that the personnel evaluation for the applicant is average, and the larger the personnel evaluation P is from 1, the better the personnel evaluation for the applicant.

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 close to each other.

Subsequently, in step S65, the determination unit 17 determines whether the competency test conforms to the selection of the applicant 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 goodness-of-fit judgment. Is determined. On the contrary, 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 goodness of fit judgment is bad. do. The number of stages of the quality of the personnel evaluation P and the evaluation result R, and the relationship between the degree of consistency between the two and the result of the goodness of fit determination are not particularly limited.

According to the above flow, the goodness-of-fit determination (step S6 in FIG. 8) for determining whether the competency test conforms to the selection of applicants to the target company is performed. If the result of the goodness-of-fit determination in step S6 is good (YES in step S7), the process returns to step S5. On the other hand, if the result of the goodness-of-fit determination in step S6 is defective (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 update unit 18 corrects the weighting coefficient of each question in 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 employment do not match is reduced, and as a result, the goodness of fit (accuracy) of the competency test is improved.

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

It should be noted that step S6 may be carried out for all applicants to the target company, or may be carried out only for those who have obtained a job offer from the target company. This makes it possible to improve the efficiency of the determination / update process.

As described above, in the present embodiment, the accuracy of the aptitude test is improved by comparing the evaluation result of the aptitude test with the personnel evaluation of the actual employment. In other words, the content of the aptitude test can be developed according to the environment of each organization without the intervention of human arbitrariness. By using the aptitude test developed in this way to select applicants for the target company and match human resources for those who wish to find employment in the target company, it is possible to efficiently utilize human resources who can fully demonstrate their abilities after employment in the target company. It can be matched well and the mismatch between the human resources that the job seeker is looking for 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, the follow-up survey of the employment is repeated to determine whether the aptitude test is suitable for the selection of applicants to the organization. In the third embodiment, the components having the same functions as those in the first and second embodiments are designated by the same reference numerals, and detailed description thereof will be omitted.

(Overall system configuration)
FIG. 10 is a block diagram showing a schematic configuration of the 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 staffing agency. Each of the applicant terminals E is a terminal owned by an unspecified number of applicants different from each other, and the number of applicant terminals E is not particularly limited. Recruiter terminals F1 to Fy are terminals owned by different recruiters. Recruiters are organizations affiliated with temporary staffing agencies, and the number is y. In the following, among the recruiter terminals F1 to Fy, the organization that owns the recruiter terminals Fm (hereinafter, m is an integer of 1 to y) is referred to as the target company Gm.

The update device 3 can be configured by, for example, a general-purpose personal computer or a server device, and its hardware configuration is the same as the determination / update devices 1 and 2 described in the first and second embodiments. Note that FIG. 10 omits the illustration of the auxiliary storage device 10.

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

The update device 3 has a function of updating the aptitude test for evaluating the aptitude of the job seeker by machine learning. In order to realize such a function, the update device 3 has 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-temporary computer-readable recording medium such as a CD-ROM, and the program is installed in the update device 3 by causing the update device 3 to read the recording medium. You may. Alternatively, the program code of the program may be downloaded to the update device 3 via the communication network.

The data server 4 is a server device capable of storing 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 are electronic data of the aptitude test for evaluating the aptitude of the job seeker, and in the present embodiment, the aptitude test is a competency test. The 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 such that the test data T1 to Ty are suitable for the selection of applicants to the target companies G1 to Gy, respectively. And / or it is desirable that the data is updated by the determination / updating method according to the second embodiment.

The databases DB1 to DBy are storage devices for storing teacher data for machine learning, which will be 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. In the following, the competency test data may be simply referred to as "test data".

The update device 3 updates the test data Tm so as to match the specific target company Gm. The processing procedure by the update device 3 is shown in FIGS. 11 and 12.

(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, which will be described later, is acquired (teacher data acquisition step). Specifically, when the response acquisition unit 30 acquires the response Am to the test data Tm of the applicant to the target company Gm and the applicant gets a job at the target company Gm, the target of the applicant after employment. The personnel evaluation (referred to as personnel evaluation Pm) in the company Gm is associated with the answer Am and stored in the database DBm as teacher data PAm. By continuously repeating this for other applicants, the 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 the organizations other than the target company Gm. Then, when the 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 a large number of accumulated teacher data and updates the test data (update step).

(How to get teacher data)
Subsequently, the flowchart of the teacher data acquisition shown in FIG. 12 will be described with reference to FIG. 10.

First, in step S90, the test implementation unit 11 shown in FIG. 10 conducts a competency test on the applicant. Specifically, when an applicant for the target company Gm appears, the test implementation unit 11 reads the test data Tm corresponding to the target company Gm from the data server 4, extracts the test screen Dm, and the applicant makes a request. Send 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 content of the competency test is the same as that in the first and second embodiments. The data of the response to the competency test of the applicant is transmitted to the update device 3 as the response Am, and in step S91, the response acquisition unit 30 acquires the response Am.

After that, 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 sends the questionnaire data Q to the recruiter terminal Fm after a predetermined period (for example, one year later) after the applicant gets a job. In response to this, when the personnel manager of the target company Gm responds, the response Lm is transmitted from the recruiter terminal Fm to the personnel evaluation acquisition unit 21 of the update device 3, and the personnel evaluation acquisition unit 21 receives the personnel evaluation from the response Lm. Create Pm.

Subsequently, in step S94, the teacher data creation unit 31 creates the teacher data PAm in association with the personnel evaluation Pm and the response Am acquired by the response 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.

According to the above flow, the teacher data acquisition step (step S9 in FIG. 11) for acquiring the teacher data is performed. By repeating this step S9, teacher data is accumulated for the target company Gm and the organizations other than the target company Gm. Then, when the teacher data corresponding to at least one group is sufficiently accumulated (YES in step S10), the process proceeds to step S11. The number of teacher data as a criterion for moving to step S11 is not particularly limited as long as it is a sufficient number for machine learning. In the following, it is assumed that the 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 PAns accumulated in the database DBn corresponding to the target company Gn, and the evaluation result calculated from the answer An and the answer. The weight coefficient of each question of the test data Tn is modified so as to be consistent with the personnel evaluation Pn associated with An. That is, when the personnel evaluation Pn of the applicant 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. As a result, the machine learning unit 32 updates the test data Tn corresponding to the target company Gn (update step). The machine learning method in this embodiment is not particularly limited, but for example, a neural network can be used.

By continuously repeating steps S9 to S11, 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 to the target companies G1 to Gy, and the accuracy of matching between the target companies G1 to Gy and the job seeker can be continuously improved. can.

(Additional notes)
The present invention is not limited to the above embodiment, and various modifications can be made within the scope of the claims, and an embodiment obtained by appropriately combining the technical means disclosed in the embodiment is also the present invention. Included in the technical scope.

1 Judgment / update device 2 Judgment / update device 3 Update device 4 Data server 10 Auxiliary storage device 11 Test implementation unit 12 Attention evaluation result creation unit 13 Comparison evaluation result creation unit 14 Similarity calculation unit 15 Ranking unit 16 Comparison unit 17 Judgment Department 18 Update Department 20 Evaluation Result Creation Department 21 Personnel Evaluation Acquisition Department 22 Comparison Department 30 Answer Acquisition Department 31 Teacher Data Creation Department 32 Machine Learning Department 40 Test Data Storage Department A, A0 to Ax, Am, An Answer B Personnel Evaluation Data C0 -Cx client terminal D, Dm test screen DB1-DBy, DBm, DBn Database E Applicant terminal F, F1-Fy, Fm Recruiter terminal G1-Gy, Gm, Gn Target company (organization)
L, Lm Answers M1 to Mx Similarity P, Pm, Pn Personnel evaluation PA1 to PAy Teacher data PAm, PAn Teacher data Q Questionnaire data R Evaluation result R0 Attention evaluation result R1 to Rx Comparison evaluation results T, T1 to Ty, Tm , Tn test data

Claims (4)

It is a judgment device that determines whether the aptitude test for evaluating the aptitude of a job seeker is suitable for the selection of applicants to a specific organization.
Based on the aptitude test, the attention evaluation result creation unit that creates the attention evaluation result, which is the evaluation result of one person of interest belonging to the organization, and
Based on the aptitude test, a comparison evaluation result creation unit that creates a comparison evaluation result, which is an evaluation result of a plurality of comparison persons other than the attention person belonging to the group, for each comparison person, and a comparison evaluation result creation unit.
A similarity calculation unit that calculates the similarity between each of the comparative evaluation results and the attention evaluation result,
A ranking unit that ranks the comparison persons in descending order of similarity, and
Among the comparison persons, a comparison unit that compares a higher-ranked person with a higher rank than a predetermined value with the attention person for personnel evaluation in the organization.
Based on the comparison result, a determination unit for determining whether the aptitude test is suitable for the selection of applicants to the organization, and
A determination device characterized by having. An updater that updates aptitude tests to assess job seekers' aptitude.
Each part of the determination device according to claim 1 and
An update unit that updates the aptitude test based on the determination result by the determination unit, and an update unit.
A renewal device characterized by having. A program for operating a computer as each part of the determination device according to claim 1 or as each part of the update device according to claim 2 . A computer-readable recording medium on which the program according to claim 3 is recorded.

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