JP2010145915A - Training support device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To clarify the suitability between training and trainees by clarifying the characteristics of the training. <P>SOLUTION: A training support device includes: a training information storage section 111 in which base concept information indicating a phase of training and relations between phases is correlated with element information indicating the characteristics of the training at each phase and stored; a trainee information storage section 112 in which evaluation information of participant's evaluation on the elements of the training is correlated with participant's individual property information and stored; a model value calculation section 107 which calculates correlation values between all elements by carrying out correlation analysis on the basis of the evaluation information, and calculates the correlation values between elements and individual properties by carrying out correlation analysis on the basis of the evaluation information and the individual property information; and a training model display section 108 which generates and displays a training model in which the whole image representing the relations between the phases and elements on the basis of the base concept information, the relations between elements having correlations in the whole image and relations between the elements and individual properties are shown. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a training support apparatus, and more particularly to adaptation between a training and a trainee.

  In companies, various trainings are conducted for the purpose of new employee training and skill improvement. In order to conduct appropriate and effective human resource development, it is desirable to select the best lectures for the trainees. Conventionally, various techniques have been proposed for efficient education and training. For example, Patent Document 1 proposes a method of extracting the needs of already attended students as a factor by performing factor analysis on the information collection result by a questionnaire and examining separation / integration of the contents of education and training. Patent Document 2 proposes an apparatus for diagnosing human behavioral characteristics and skills, formulating and executing a development plan based on the diagnosis results, and storing and sharing the results and results in a database.

JP 2002-49303 A JP 2004-246507 A JP 2002-258731 A Japanese Patent Laid-Open No. 8-110752 JP 2003-308000 A JP 2002-351295 A JP 2003-150039 A

  However, by analyzing the training experience of the trainees, it was possible to improve the content of the training and provide appropriate training to those who are going to receive training in the future. The analysis was not performed. For this reason, they have not been able to present the training that suits the trainees, the characteristics of the trainees that are suitable for receiving the training, and the points to improve the training.

  An object of the present invention is to clarify the compatibility between a training and a trainee by clarifying the characteristics of the training.

  In the training support device according to the present invention, when there are a plurality of phases and phases from the time when the trainee takes the training until the result is obtained, the base concept information indicating the relationship between the phases, and each phase Training information storage means for storing training information generated by associating element information including elements expressed by words indicating the characteristics of the training in

  Trainee information for storing trainee information generated by associating evaluation information indicating evaluation of the training element by the trainee of the training with personal characteristic information indicating personal characteristics of the trainee for each student Taking out the trainee information of all participants from the storage means and the trainee information storage means, and performing correlation analysis based on the evaluation information included in the trainee information, and calculating the correlation value between all elements, training Training model value calculation means for calculating a correlation value between each element and each individual characteristic by performing correlation analysis based on evaluation information and personal characteristic information included in the raw information, and stored in the training information storage means By referring to the training information, the overall image of the training showing the relationship between phases and phases-elements and the correlation value calculated by the training model value calculation means are preset. A training model generating means for generating a training model indicating a relation between elements exceeding the threshold value and a relation between the elements and personal characteristics, and a training model output means for outputting the training model. .

  Also, an element receiving means for receiving the element as the attendance requirement for the training, and the trainee information of all participants are extracted from the trainee information storage means, the element is used as a target variable, and the personal characteristics are correlated as an explanatory variable. Correlation value calculating means for calculating a correlation value by performing analysis, an element received by the element receiving means, and the element specified by the correlation value calculated by the correlation value calculating means and personal characteristics It is characterized by having attendance requirement generating means for generating the relationship as attendance requirement information, and attendance requirement output means for outputting the attendance requirement information.

  In addition, personal characteristic receiving means for receiving personal characteristic information on the personal characteristics of new trainees who are going to take training, and by performing correlation analysis, the elements whose information is stored in the training information storage means, A correlation function between the individual characteristics whose information is stored in the trainee information storage means is calculated, and a preference function indicating a relationship between the training and a student's preference for the training is calculated based on the calculated correlation value. A preference function generating means for generating for each individual characteristic, and an attendance for determining the suitability of the new trainee for the training based on the result obtained by substituting the individual characteristic information of the new trainee for each preference function. The new trainee determines whether the new trainee has received the training according to a result of collating the personal characteristics information of the new trainee received by the personality accepting means and the attendance suitability. And measures generating means for generating information relating to measures when attending, and having a a measure output means for outputting information about the measures.

  The training support apparatus according to the present invention is an element expressed by a word indicating the characteristics of the training in each phase indicating a process from when the trainee takes the training until it produces results, Generated by associating element receiving means for receiving elements, evaluation information indicating evaluation of the elements of the training by the students of the training, and personal characteristic information indicating the personal characteristics of the students for each student Trainee information storing means for storing trainee information, and extracting trainee information of all participants from the trainee information storing means, and performing correlation analysis using the elements as objective variables and the individual characteristics as explanatory variables Correlation value calculation means for calculating a correlation value at the element, the element received by the element reception means, the element specified by the correlation value calculated by the correlation value calculation means and the individual And having a student requirement generating means for generating the relationship between sex, as attendance requirement information, and a course requirement output means for outputting the course requirement information.

  The training support apparatus according to the present invention is a training element storage means for storing information on elements expressed by words indicating the characteristics of the training in each phase indicating a process from when the trainee takes the training to when it produces results. Training that stores trainee information generated by associating the evaluation information indicating the evaluation of the elements of the training by the students of the training with the personal characteristics information indicating the personal characteristics of the students for each student Information was stored in the training element storage means by performing correlation analysis with raw information storage means, personal characteristic reception means for receiving personal characteristic information relating to the personal characteristics of new trainees who are going to take training. The correlation value between the element and the personal characteristics whose information is stored in the trainee information storage means is calculated, and the training and the participants for the training are calculated based on the calculated correlation value Preference function generating means for generating a preference function indicating a relationship with the preference level for each individual characteristic, and new training based on the result obtained by substituting the individual characteristic information of the new trainee for each preference function The new training based on the result of matching the attendance suitability with the attendance suitability calculating means for obtaining the attendance suitability for the training, the personal characteristics information of the new trainee accepted by the personal characteristics accepting means It has a policy generation means for generating information on a policy when a student attends the training, and a policy output unit for outputting information on the policy.

  According to the present invention, the relationship between the phases indicating the process from the time when the trainee takes the training until the achievement of results, the relationship between the elements indicating the characteristics of the training in each phase, Can display a correlated relationship among the relationships between the elements and the individual characteristics of the students. Thereby, it is possible to easily grasp the entire image of the training. In addition, it is possible to select a trainee to take this training, and to allow a certain trainee to consider improvement measures for selecting an appropriate training.

  In addition, since personal characteristics correlated with the elements required for attending the training are displayed, a trainee image suitable for attending the training can be presented.

  In addition, when a new trainee attends the training, it is possible to present measures to be taken by the new trainee so that the effect of attending the training is maximized before attending the training.

  Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings. In companies, lectures such as OffJT and hands-on courses are provided for human resource development, or OJT is held. Various expressions such as training, lectures, lectures, lectures, etc. are used as education for human resource development. In this embodiment, such education for human resource development is collectively referred to as “training”. Used for explanation. The person who has undergone the training and the person who intends to receive the training are collectively referred to as “trainee”. In addition, among trainees, trainees who have already received training are limited to “participants” of the training, and trainees who are going to receive training are limited to “new trainees”. In the present embodiment, since the training is mainly assumed as the contents of the training, receiving (receiving) the training like a “student” may be referred to as “taken”. Furthermore, in this embodiment, it is necessary to input information necessary for carrying out the processing to the computer. As this input person, a person who plans or manages the training, or a trainee can be considered. However, in the following description, such information input persons are collectively referred to as “managers”.

  FIG. 1 is a hardware configuration diagram of a computer forming a training support apparatus according to the present invention. The server computer that forms the training support apparatus in the present embodiment can be realized with a general-purpose hardware configuration that has existed in the past. That is, as shown in FIG. 1, the computer is provided with a CPU 1, a ROM 2, a RAM 3, an HDD controller 5 connected to a hard disk drive (HDD) 4, a mouse 6 and a keyboard 7 provided as input means, and a display device. An input / output controller 9 for connecting each display 8 and a network controller 10 provided as a communication means are connected to an internal bus 11. Although not shown, the training support device can receive information input from the information terminal device used by the trainee via a network connected to the network controller 10. Further, the training support apparatus can be realized by a single computer as shown in FIG. 1, but may be configured to realize processing functions to be described later distributed to a plurality of computers.

  FIG. 2 is a block configuration diagram of the training support apparatus in the present embodiment. The training support apparatus according to the present embodiment can be broadly divided into three groups each having a reference number of the 100s, 200s and 300s. The first group 100 includes a base concept information reception unit 101, an element information reception unit 102, a training information generation unit 103, a attendance result reception unit 104, an individual characteristic reception unit 105, a trainee information generation unit 106, and a training model calculation unit. Model value calculation unit 107 provided, training model display unit 108 provided as a training model generation unit and output unit, training information storage unit 111, trainee information storage unit 112, threshold storage unit 113, training model information storage unit 114 and a training element storage unit 115. In the first group 100, an overall image of the training (a flowchart showing the cause and effect) is obtained from the input information relating to the training, the evaluation information on the training of a plurality of students, and the personal characteristic information. Associate elements that affect and elements or personal characteristics.

  The second group 200 includes a selection element receiving unit 201, an attendance requirement calculation unit 202 provided as a correlation value calculation unit, an optimum attendance requirement display unit 203 provided as an attendance requirement generation unit and an output unit, and a threshold storage unit 211. And the optimum requirement storage unit 212, and further, the trainee information storage unit 112 and the training element storage unit 115 generated in the first group 100. In the second group 200, by obtaining the trainee's attendance conditions (individual characteristics) such that the values (evaluation points) of the elements that affect the achievement level of the phase specified in the first group 100 are maximized. Clarify the trainee image suitable for the training. In other words, by selecting the elements to be analyzed from the elements included in one of the phases, the effects of the training are clarified and the characteristics of the training have to be improved. Indicate whether the trainee is taking or desirable.

  The third group 300 includes an individual characteristic accepting unit 301, a attendance suitability calculation unit 302 provided as a preference function generation unit and a attendance suitability calculation unit, a attendance suitability display unit 303, a measure generation unit 304, and a measure display unit. 305, a fitness / measure storage unit 311, and a trainee information storage unit 112 and a training element storage unit 115 generated in the first group 100. In the third group 300, when a new trainee attends, the degree of conformity to the training is requested, and measures to be taken by the trainee are presented so that the effect of the training is maximized before attending the training. .

  Each component will be described together in the description of the processing described later. The components 101 to 108, 201 to 203, and 301 to 305 in the training support apparatus are realized by a cooperative operation of a computer that forms the training support apparatus and a program that operates on the CPU 1 mounted on the computer. Moreover, each storage part 111-114, 211-212, 311 is implement | achieved by HDD4 mounted in the training assistance apparatus.

  Further, the program used in this embodiment can be provided not only by communication means but also by storing it in a computer-readable recording medium such as a CD-ROM or DVD-ROM. The program provided from the communication means or the recording medium is installed in the computer, and various processes are realized by the CPU of the computer sequentially executing the installation program.

  First, the process of obtaining a training model in the first group 100 will be described using the flowchart shown in FIG. Since this process is carried out with the same content for each training, only one training will be described. That is, a training model obtained as a result of this processing is generated for each training.

  The training manager inputs base concept information relating to the training to be evaluated using an input means such as the keyboard 7. Specifically, an arbitrary number of phases and relation information between phases indicating a process from when a trainee takes a course to producing results are input. The base concept information receiving unit 101 receives information input by the administrator (step 101). FIG. 4 is a diagram conceptually illustrating an example of a base concept information input screen and base concept information input from the input screen. This input screen is generated and displayed by an application that manages the input / output sheet. When the administrator designates a phase and a plurality of phases in a predetermined cell on the sheet, the relationship between the phases is input. Base concept information is specified. FIG. 4 shows an example in which three phases of “take a course”, “internal change obtained by attendance”, and “external result obtained by attendance” are input to the phase input area 31 by the administrator. Yes. Note that the phase name input to the input area 31 is automatically output to the corresponding area 32. Further, FIG. 4 shows an example of input of relation information indicated by arrows in the relation information input area 32 in the table. From this relationship information, it can be understood that the phases are arranged in the order of “take course”, “internal change obtained by attendance”, and “external outcome obtained by attendance”.

  When the base concept information is set, the element information receiving unit 102 displays an element information input screen, and allows the administrator to input elements expressed by words (keywords) indicating the characteristics of the training in each phase. FIG. 5 is a diagram conceptually showing an input screen example of element information and input element information. This input screen is generated and displayed by an application that manages input / output sheets. When the administrator inputs an arbitrary number of elements from the element input area 34 corresponding to each phase in the input screen displayed by the element information receiving unit 102, the element information receiving unit 102 is input by the administrator. Information is received (step 102). In the example shown in FIG. 5, it can be understood that the administrator has designated and input words that are elements such as “English lecture” and “Internet lecture” as “elements related to“ take ””. In other words, this training is characterized by being taught in English using English texts, utilizing the Internet, and working in groups. The keyword indicating the characteristic content of this training is set as an element related to the phase for “element to perform”. For other phases, keywords indicating the characteristics of the number of cases are defined in that phase.

  When elements are set for each phase by the administrator as described above, the training information generation unit 103 stores the training information by associating the base concept information with the element information in which the phase and the element are associated with each other. Register in the unit 111 (step 103). Further, by registering information relating the elements to each phase, that is, the element information shown in FIG. 5, in the training element storage unit 115, the elements input by the administrator are accumulated. The training element storage unit 115 is used in the second and third groups in the subsequent stage.

  The above processing can be performed at any time if the content of the training is planned, whereas the processing described below is information input by trainees of the training, and is performed after the training is completed. .

  After the training is completed, an evaluation result about the training is obtained by conducting a questionnaire to each trainee. FIG. 6 is a table showing the evaluation points referred to when inputting the evaluation results in a table format. The trainee evaluates the training by referring to this table and inputting an evaluation score for each element. In this embodiment, all elements are unified so that a positive evaluation point is high and a negative evaluation point is low. FIG. 7 is a diagram illustrating an example of an evaluation input screen and an example of setting an input evaluation. The attendance result reception unit 104 takes out the elements set for the training from the training information storage unit 111, and displays them on the evaluation input screen as they are as elements to be evaluated or edited into words for inquiring the trainee. When each trainee inputs an evaluation (evaluation score) for the training from the input screen, the attendance result receiving unit 104 receives the input evaluation (step 104). Each evaluation point may be input by the trainee, or the operator may input the contents entered in the questionnaire form. The same applies to the personal characteristics described below.

  Subsequently, when the individual characteristics of each individual trainee are input from a predetermined personal characteristics input screen (not shown), the personal characteristics receiving unit 105 receives the input personal characteristics (step 105). FIG. 8 is a diagram showing an example of setting individual characteristics of a trainee. The personal characteristics may be specified using a commercially available psychological test, or may be input by self-evaluation. Although no specific example is shown, evaluation points are similarly set in the personal characteristics as shown in FIG. 6 for each element. In FIG. 8, four indicators are illustrated as personal characteristics, but the contents and number are not limited to this example. The same applies to the following description. 7 and 8, the input values for each item are represented by character strings so that the contents of the input can be easily grasped. Actually, however, the computer uses these input values for calculation, so that it is handled as numerical data. It is.

  When information about each trainee is set as described above, the trainee information generation unit 106 generates training information by associating the evaluation information and the personal characteristic information of the trainee, and the trainee information storage unit 112. (Step 106). An example of the trainee information registered in the trainee information storage unit 112 is shown in FIG.

  When the information necessary for generating the training model is input as described above, the model value calculation unit 107 subsequently obtains data necessary for generating and displaying the training model by performing the following calculation. First, the model value calculation unit 107 includes two elements, each of which is an arbitrary element from the related phases among the phases set for the training from the training information stored in the training information storage unit 111. And the evaluation information of all the trainees for the two extracted elements is extracted from the trainee information storage unit 112 (step 107). For example, it is assumed that “Internet conference” is extracted from the phase “taken”, and “information collecting ability” is extracted from the phase “internal change obtained by taking” related to “taken”. The phase relationship can be recognized by referring to the relationship information included in the base concept information of the training information. Then, each evaluation point of all trainees for the “Internet conference” and “information gathering ability” extracted in this way is extracted.

  When the evaluation information of all the trainees corresponding to the element is extracted in this way, the model value calculation unit 107 calculates the correlation value between the extracted evaluation information by performing the correlation analysis. Then, this calculation result is set as a correlation value between the elements (step 108). Here, the model value calculation unit 107 compares the threshold value stored in the threshold value storage unit 113 with the calculated correlation value between the elements, and if the correlation value between the elements is equal to or greater than the threshold value, the element value A correlation value is set as a drawing target (Y in step 109, 110). This set content can be expressed by flag information or the like, and is referred to when the training model is displayed. On the other hand, if the correlation value between the elements does not reach the threshold, it is set that the correlation value between the elements is not to be rendered (N in step 109, 111). The threshold value used here is a threshold value used for determining whether or not to draw a relationship between elements in the training model, and is set in advance by an administrator or the like, and is changed as necessary. Or you may make it input each time using the keyboard 7 grade | etc.,.

  The above processing in steps 107 to 111 is repeated until it is executed for all the two element sets (N in step 112).

  When correlation values are calculated for all two element sets and the right or wrong of the drawing target is set (Y in step 112), the model value calculation unit 107 then selects an arbitrary element from the extracted elements. One element is selected, and the evaluation information of all the trainees corresponding to the selected element is extracted from the trainee information storage unit 112. Further, the individual specific values of all trainees are taken out (step 113). Then, the model value calculation unit 107 performs a correlation analysis to calculate a correlation value between the extracted evaluation information (evaluation point) and the personal characteristic (value), and uses the calculated result as a correlation between the element and the personal characteristic. A value is set (step 114). Here, the model value calculation unit 107 compares the threshold value stored in the threshold value storage unit 113 with the calculated correlation value between the element-personal characteristics, and whether the correlation value between the element-personal characteristics is higher than the threshold value. If the values are the same, the fact that the correlation value between the element and the personal characteristic is to be rendered is set (Y in step 115, 116). This set content can be expressed by flag information or the like, and is referred to when the training model is displayed. On the other hand, if the correlation value between the element-personal characteristics is lower than the threshold, it is set that the correlation value between the element-personal characteristics is not to be rendered (N in step 115, 117). The threshold value used here is a threshold value used for determining whether or not to draw the relationship between the element-individual characteristics in the training model, and is set in advance by an administrator or the like, and is changed as necessary. .

  The processes in steps 113 to 117 described above are repeated until the processes of all the two elements are performed (N in step 118).

  When the correlation values are calculated for all the elements and the information necessary for displaying the training model is obtained by setting the appropriateness of the drawing target (Y in step 118), the model value calculation unit 107 performs the above processing. The correlation value calculated by the above is stored by storing it in the training model information storage unit 114 (step 119).

  Subsequently, the training model display unit 108 generates a training model and displays it on the display 8 (step 120). A display example of this training model is shown in FIG. As illustrated in FIG. 10, the phases 35 a, 35 b, and 35 c of “take a course”, “internal change obtained by attendance”, and “external result obtained by attendance” are displayed in the order shown in the relation information. Is done. Each element 36 included in each phase is displayed with its element name surrounded by a solid line in a dotted line indicating each corresponding phase. In this way, the training model display unit 108 displays the overall image (skeleton) of the training by visualizing the relationship between phases and phases-elements with reference to the training information stored in the training information storage unit 111. To do. Further, the training model display unit 108 refers to the flag information set in step 110 and connects the elements set as the drawing target with arrows, and the correlation value between the elements in the vicinity of the line. Is displayed. For example, the element “Learning English” in the phase “Take the class” and the element “Logical thinking ability” in the phase “Internal change obtained by taking the class” have a correlation value (0.55) that is equal to or higher than the threshold. An arrow and a correlation value are displayed as in FIG. Furthermore, when the training model display unit 108 refers to the flag information set in step 116 and there is an element-personal characteristic element that is set as a drawing target, the personal characteristic 37 indicates the corresponding element. In the vicinity of 36, the personal characteristic name is displayed surrounded by a broken line. Furthermore, the personal characteristics and the elements are connected by arrows. In this way, the training model display unit 108 adds the relationship between the elements whose correlation values calculated by the model value calculation unit 107 are equal to or greater than a preset threshold and the relationship between the elements and personal characteristics to the overall image. This creates a training model and completes the training model. Thus, the training model of training is generated and displayed by visualizing the relationship between elements and the relationship between elements and personal characteristics.

  A training model can be obtained for each training by performing the above processing for the training to be analyzed.

  According to the present embodiment, since the training model can be visualized in this way, the overall image of the training such as the relationship between phases can be easily grasped by referring to this training model. Furthermore, since the relationship between the factors that affect the degree of achievement of the training and the relationship between the factors and the individual characteristics are also displayed, the selection of the trainees who will take this training and the appropriate training for certain trainees Can be considered for improvement.

  Note that the degree of achievement of the training is, for example, in the “take” phase, in which the principal component analysis is performed on all elements included in this phase and the first principal component obtained as a result is “taken”. The overall achievement index is obtained by calculating the correlation value between each element and the overall achievement index for the overall achievement index. Then, a highly correlated element is obtained as an element that affects the achievement level.

  Next, processing in the second group 200 will be described using the flowchart shown in FIG. This process is performed for each training, and an optimum attendance requirement is generated for each training.

  First, the selected element reception unit 201 extracts all elements included in the training from the training element storage unit 115. FIG. 12 is a diagram showing the elements registered in the training element storage unit 115 according to the present embodiment classified for each phase, but according to the example shown in FIG. I will take it out. Subsequently, the selected element reception unit 201 displays a predetermined selection screen including all the extracted elements on the display 8 (step 201). When the manager selects an element that should be the target of the training from the display, in other words, an element that should be the attendance requirement, the selected element receiving unit 201 receives the selected one or more elements (step 202). . This accepted element will be referred to as a “selected element”. In this embodiment, the selection element is selected from the elements registered in the training element storage unit 115. However, the administrator may input the selection element in characters, or the training element storage unit 115. All the elements registered in the above may be extracted as selection elements.

  Subsequently, the attendance requirement calculation unit 202 extracts from the trainee information storage unit 112 the personal characteristic information of all the trainees and the evaluation information corresponding to the selected element selected in Step 202 (Step 203). FIG. 13 shows a set of the extracted evaluation information (selection evaluation information) and personal characteristic information. FIG. 13 shows a set of selection evaluation information and personal characteristic information of the trainee 1 who has finished the training. According to this example, it is possible to grasp that the four elements of “logical thinking ability”, “exemplify proposal”, “organize information”, and “commit intention” are selection elements. Although FIG. 13 illustrates an example of only one trainee's information, the attendance requirement calculation unit 202 takes out this set of information for all trainees.

  The attendance requirement calculation unit 202 calculates a correlation value by performing a correlation analysis with the selected element as an objective variable and the extracted personal characteristic as an explanatory variable. More specifically, one objective variable is selected from the selection evaluation information and one explanatory variable is selected from the individual characteristic information (step 204), and correlation analysis is performed for each of the selected sets to obtain a correlation value (step 205). ). Here, the attendance requirement calculation unit 202 compares the threshold value stored in the threshold value storage unit 211 with the calculated correlation value, and if the correlation value is higher than or equal to the threshold value, the element selected as the objective variable And the personal characteristic selected as the explanatory variable are set to be high (Y in step 206, step 207). The fact that this has been set is temporarily held by flag information or the like. If the correlation value is lower than the threshold value, it is determined that the correlation between the element of the set and the personal characteristics is low, and the process proceeds to the next step 208. Note that the threshold value used here is a threshold value used for determining whether or not there is a correlation between an element and a personal characteristic, and is set in advance by an administrator or the like, and is changed as necessary. Or you may make it input each time using the keyboard 7 grade | etc.,.

  The processing in steps 203 to 207 is repeated until it is executed for all the combinations of elements and personal characteristics (N in step 208). FIG. 14 shows a correlation table including correlation values calculated between each selected element and each individual characteristic. In FIG. 14, when the threshold value is set to 0.3, for example, the position where the absolute value of the correlation value is equal to or larger than the threshold value is hatched. It should be noted that the position where hatching is performed corresponds to a combination of a selection element and a personal characteristic that are determined to have a high correlation by referring to the flag information set in step 207, that is, to have a correlation.

  When analyzed with reference to the example shown in FIG. 14, “confidence to oneself” among personal characteristics has a low correlation, so it is considered that it does not affect the results. In other words, trainees with high “self-confidence” will not affect the outcome even if they take this training. Therefore, it is not appropriate that trainees with such characteristics are suitable for taking this training. Not to say. In addition, “stress tolerance” has a positive correlation with “logical thinking ability” and “organize information”, and “position” has a positive correlation with “logical thinking ability”. It is done. In other words, it can be said that people with high stress tolerance and people with high positions are suitable for taking this training. On the other hand, “positiveness” has a positive correlation with “make proposal” and “convey intention”, but has a negative correlation with “organize information”. In such a case, the optimum attendance requirement can be determined by describing the priority order in advance between the objective variables. FIG. 15 shows an example when priority is added to the selected element. According to this priority setting example, in this case, the priority of “organize information” is higher than that of “specify proposal” and “commit intention”. It can be said that

  If the processing results illustrated in FIG. 14 are obtained by performing the processing of steps 203 to 206 for all combinations of selected elements and personal characteristics (Y in step 208), then the optimum attendance requirement display is performed. The unit 203 performs the above-described analysis processing and displays the result on the display 8 as attendance requirement information (step 209). An example of this display is shown in FIG. FIG. 16 shows the selection element selected in step 202 and the relationship between the selection element specified by the correlation value calculated by the attendance requirement calculation unit 202 and the personal characteristics. According to FIG. 16, for example, it is possible to grasp that trainees who have high positiveness, low stress tolerance, and high job titles with respect to the selected elements are attendance requirements for this training, that is, human resources that are suitable for this training. . “Self-confidence” is set to “no requirement” because there is no correlation with any selection factor. This means that self-confidence is not a good or bad judgment for this training. In the case of “no requirement”, it may not be included in the attendance requirement information. The optimum attendance requirement display unit 203 stores the attendance requirement information generated for display in the optimum requirement storage unit 212.

  According to the present embodiment, the purpose of the training is clarified by allowing the administrator to designate an element (selection element) corresponding to the effect targeted by the training from among the elements set in the phase, and selecting By finding the correlation between the elements and the individual characteristics of the participants of this training, it is possible to present a trainee image suitable for taking this training.

  Next, processing in the third group 300 will be described using the flowchart shown in FIG. This process is performed for each new trainee, and measures to be taken when the new trainee attends a certain training are generated.

  When a personal characteristic value of a new trainee to be processed is input from a predetermined personal characteristic input screen (not shown), the personal characteristic receiving unit 301 receives the input personal characteristic value (step 301). FIG. 18 is a diagram showing an example of setting each characteristic value of the individual characteristics of the trainee. Input and reception of personal characteristic values may be performed in the same manner as step 105 in the first group.

  Subsequently, when a training that a new trainee intends to attend is designated, the attendance suitability calculation unit 302 extracts all elements included in the training from the training element storage unit 115 (step 302). Also, the attendance suitability calculation unit 302 takes out the personal characteristic information of all the trainees from the trainee information storage unit 112 (step 303). The trainee information is shown in FIG. The attendance suitability calculation unit 302 calculates a correlation value between each element extracted in step 302 and the personal characteristic information extracted in step 303 (step 304). In this process as well, correlation values are obtained for all combinations of elements and personal characteristics as in the above-described process for calculating correlation values. FIG. 19 shows the correlation between elements obtained by calculating correlation values for all combinations and personal characteristics. FIG. 19 shows correlation values calculated from each combination of elements and personal characteristics. In FIG. 19, only the above-described selection elements are shown for convenience of explanation. And when the threshold value was set to 0.3, the position where the absolute value is equal to or greater than the threshold value was hatched.

  Subsequently, the attendance suitability calculation unit 302 compares the threshold value stored in the threshold value storage unit 211 with all the calculated correlation values, and determines the number of occurrences of correlation values that are higher than or equal to the threshold value for each individual characteristic. (Step 305). This process will be described with reference to FIG.

  First, in the case of the personal characteristic “forward-looking”, the correlation value with the element “logical thinking ability” is 0.23, so there is no correlation. It is not added as the number of appearances above the threshold when there is no correlation. That is, the number of appearances is naturally 0. In addition, the elements “make proposal” and “transmit intention” have positive correlations because the correlation values are 0.55 and 0.39, respectively. 1 is added as the number of appearances equal to or greater than the threshold when there is a positive correlation. On the other hand, the element “organize information” has a negative correlation because the correlation value is −0.34. -1 is added as the number of appearances equal to or greater than the threshold value when there is a negative correlation. Then, the number of appearances equal to or greater than the threshold in the personal characteristic “forward-looking” can be obtained as 0 + 1 + (− 1) + 1 = 1. Similarly, in the case of the personal characteristic “confidence to self”, since there is no correlation with all elements, the number of appearances exceeding the threshold can be obtained as 0 + 0 + 0 + 0 = 0. In the case of the personal characteristic “stress tolerance”, since there is a positive correlation between “logical thinking ability” and “organize information”, the number of appearances exceeding the threshold can be obtained as 1 + 0 + 1 + 0 = 2. Similarly, the number of appearances exceeding the threshold in the personal characteristic “position” can be obtained as 1 + 0 + 0 + 0 = 1.

When the number of appearances equal to or greater than the threshold value of the correlation value is obtained as described above, the attendance suitability calculation unit 302
Subsequently, a preference function indicating the relationship between the training and the preference of the student with respect to the training is generated for each individual characteristic based on the obtained number of appearances (step 306). This will be described with reference to FIG. For example, when three variables “high”, “normal”, and “low” can be set as characteristic values of “forward”, that is, when “forward” is a three-level evaluation, FIG. As shown in a), the number of appearances = 1 is set as the maximum value, and this is divided so that the number of variables can be set. That is, in the case of “forward-facing”, a preference function is defined in which “high” has a maximum value of 1, “low” has a minimum value of 0, and “normal” has a preference of 0.5 in between. The In addition, when five variables “high”, “high”, “normal”, “low” and “low” can be set as the characteristic value of “stress tolerance”, that is, “stress tolerance” is evaluated in five levels. In this case, as shown in FIG. 20B, the number of appearances = 2 is set as the maximum value, and this is divided so that the number of variables can be set. That is, in the case of “stress tolerance”, “high” is the maximum value of 2, “low” is the minimum value of 0, and the interval between them is equally divided by the number of variables “high”, “normal” and “low” A preference function that defines the preferences of 1.5, 1.0, and 0.5 is defined. The preference function is similarly defined for other personal characteristics. In FIG. 20, since the number of appearances is a positive value, the preference function is defined by a positive function, that is, a straight line going up to the right. The preference function is defined by a straight line going up to the left with a slope of.

  The attendance suitability calculation unit 302 subsequently obtains each characteristic value of the individual characteristics of the new trainee accepted in step 301, and uses the obtained property values to determine the attendance suitability of the new trainee according to the following procedure. (Step 307). The degree of attendance is an index indicating how well a new trainee fits in the training. The larger the index value is, the more suitable it is, that is, it indicates that it is a suitable person to take the training.

  First, the attendance suitability calculation unit 302 calculates the preference for each individual characteristic by substituting the acquired characteristic value into each corresponding preference function. According to the setting example shown in FIG. 18, since the “forward-looking” of this new trainee is “high”, the preference is obtained as 1 with reference to FIG. Similarly, since the “stress tolerance” of this new trainee is “low”, the preference is obtained as 0 with reference to FIG. Preference can be obtained in the same manner for other personal characteristics. When the preference is calculated for each individual characteristic, the sum is obtained. Furthermore, the attendance suitability calculation unit 302 obtains the total number of appearances of each individual characteristic. That is, since the number of appearances of each individual characteristic obtained in step 305 is 1, 0, 2, 1, the sum of these is 4. Here, in this embodiment, the degree of attendance is

Attendance suitability = sum of preferences / sum of appearances

It is calculated by the following formula. For example, if the total sum of preference levels is 3, the degree of fitness for attendance of this new trainee can be calculated as 3/4 = 0.75. The attendance suitability calculation unit 302 stores the attendance suitability obtained as described above in the suitability / measure storage unit 311.

  The attendance suitability display unit 303 displays the attendance suitability obtained by the attendance suitability calculation unit 302 on the display 8 (step 308). In addition, if the above steps 301 to 307 are performed for all new trainee candidates, which new trainee is suitable for taking this training by comparing the suitability of attendance of each new trainee. You can consider that.

  Finally, the measure generation unit 304 generates measures for the new trainees that are not optimal by collating the individual characteristics of the new trainees with the correlation values set in the correlation table shown in FIG. Step 309). The generated measure is stored in the fitness / measure storage unit 311. The stored measure may be stored as a code for specifying the measure, or may be stored as visually recognizable character information. Here, “non-optimal” means a personal characteristic in which the characteristic value of the personal characteristic correlated with the element is not a preferable characteristic value. For example, the element “logical thinking ability” and the individual characteristic “stress tolerance” are determined to have a correlation because the correlation value 0.54 is equal to or greater than a threshold value. In other words, this is because people with higher “stress tolerance” have more logical thinking ability (it is easier to achieve results of strengthening logical thinking ability). The indicator that is good. In other words, this training is not suitable for new trainees with low “stress tolerance”. Therefore, if a new trainee with low “stress tolerance” is to take this training, a measure is presented that it is desirable to take some training to improve “stress tolerance” before attendance. The measure display unit 305 displays the generated measure on the display 8 as character information as a result of the analysis by the measure generation unit 304 (step 310).

  The trainee or the manager who plans the training can examine specific plans to be taken for the new trainee by referring to the displayed measures. For example, if a measure that increases stress tolerance is displayed to improve logical thinking ability, implement a measure that increases stress tolerance before attending this training. .

  According to the present embodiment, when a new trainee takes a training, the degree of conformity to the training is requested and a new trainee who maximizes the attendance effect of the training should be taken before attending the training. Measures can be presented.

It is a hardware block diagram of the computer which forms the training assistance apparatus which concerns on this invention. It is a block block diagram of the training assistance apparatus in this Embodiment. It is the flowchart which showed the process in the 1st group in this Embodiment. It is the figure which showed notionally the example of an input screen of the base concept information in this Embodiment, and the base concept information input from this input screen. It is the figure which showed notionally the example of the input screen of the element information in this Embodiment, and the input element information. It is the figure which showed the evaluation score referred when inputting the result of evaluation by a trainee in this Embodiment in a table format. It is the figure which showed the example of an input screen of the evaluation result in this Embodiment, and the example of the setting of the input evaluation. It is the figure which showed the example of a setting of the trainee's individual characteristic in this Embodiment. It is the figure which showed the example of the trainee information registered into the trainee information storage part in this Embodiment. It is the figure which showed the example of a display of the training model in this Embodiment. It is the flowchart which showed the process in the 2nd group in this Embodiment. It is the figure which classified and showed the element registered into the training element storage part in this Embodiment for every phase. It is the figure which showed the group of evaluation information and personal characteristic information extracted from the trainee information storage part in this Embodiment. It is the figure which showed the correlation table containing the correlation value calculated between each selection element and each individual characteristic in the process in a 2nd group. It is the figure which showed the example at the time of adding a priority with respect to the selection element in this Embodiment. It is the figure which showed the example of a display of the result of having implemented the analysis process in the process in a 2nd group. It is the flowchart which showed the process in the 3rd group in this Embodiment. It is the figure which showed the example of a setting of each characteristic value of a personal characteristic of a new trainee in this Embodiment. It is the figure which showed the correlation table containing the correlation value calculated between each element and each individual characteristic in the process in a 3rd group. It is the conceptual diagram which showed the example of the preference function calculated | required in this Embodiment.

Explanation of symbols

  1 CPU, 2 ROM, 3 RAM, 4 hard disk drive (HDD), 5 HDD controller, 6 mouse, 7 keyboard, 8 display, 9 I / O controller, 10 network controller, 11 internal bus, 100 1st group, 101 base Concept information reception unit, 102 Element information reception unit, 103 Training information generation unit, 104 Attendance result reception unit, 105 Personal characteristic reception unit, 106 Trainee information generation unit, 107 Model value calculation unit, 108 Training model display unit, 111 Training Information storage unit, 112 Trainee information storage unit, 113 Threshold storage unit, 114 Training model information storage unit, 115 Training element storage unit, 200 Second group, 201 Selection element reception unit, 202 Attendance requirement calculation unit, 203 Optimal attendance Requirement display section, 211 threshold storage section, 12 optimum requirements storing unit, 300 a third group, 301 individuals characteristic acceptance unit, 302 participants adaptability calculation unit, 303 participants adaptability display unit, 304 measures generating unit, 305 measures the display unit, 311 fit and policies storing unit.

Claims (5)

If there are multiple phases and the phases from the time when the trainee takes the training until the achievement of the results, the base concept information indicating the relationship between the phases and the words indicating the characteristics of the training in each phase Training information storage means for storing training information generated by associating element information including elements to be expressed;
Trainee information for storing trainee information generated by associating evaluation information indicating evaluation of the training element by the trainee of the training with personal characteristic information indicating personal characteristics of the trainee for each student Storage means;
The trainee information of all participants is extracted from the trainee information storage means, and the correlation value between all elements is calculated by performing correlation analysis based on the evaluation information included in the trainee information, and is included in the trainee information. Training model value calculating means for calculating a correlation value between each element and each individual characteristic by performing a correlation analysis based on the evaluation information and the individual characteristic information to be obtained,
By referring to the training information stored in the training information storage means, an overall picture of the training showing the relationship between phases and phases-elements and a correlation value calculated by the training model value calculation means are preset. A training model generation means for generating a training model indicating a relationship between elements that are equal to or greater than a threshold value and a relationship between elements and personal characteristics;
Training model output means for outputting the training model;
A training support apparatus characterized by comprising: The training support apparatus according to claim 1,
Element receiving means for receiving the element as the attendance requirement for the training;
Correlation value calculation means for taking out the trainee information of all students from the trainee information storage means, calculating the correlation value by performing correlation analysis with the element as an objective variable and the personal characteristic as an explanatory variable,
Participation requirement generation means for generating the element received by the element reception unit and the relationship between the element specified by the correlation value calculated by the correlation value calculation unit and the personal characteristics as the learning requirement information;
Attendance requirement output means for outputting the attendance requirement information;
A training support apparatus characterized by comprising: In the training support device according to claim 2,
Personal characteristic reception means for receiving personal characteristic information on the personal characteristics of new trainees who are going to take training,
By performing correlation analysis, a correlation value between the element whose information is stored in the training information storage means and the personal characteristic whose information is stored in the trainee information storage means is calculated, and the calculated correlation A preference function generating means for generating a preference function for each individual characteristic indicating a relationship between the training and the preference of the student for the training based on the value;
Attendance suitability calculating means for obtaining attendance suitability of the new trainee for the training based on a result obtained by substituting the individual characteristic information of the new trainee for each preference function;
Measures for generating information on measures when the new trainee attends the training according to the result of collating the personal characteristics information of the new trainee received by the personal property receiving means and the degree of fitness for attendance Generating means;
Measure output means for outputting information on the measure;
A training support apparatus characterized by comprising: An element receiving means for receiving an element that is expressed by a word indicating the characteristics of the training in each phase indicating a process from when the trainee attends the training to obtaining results;
Trainee information for storing trainee information generated by associating evaluation information indicating evaluation of the training element by the trainee of the training with personal characteristic information indicating personal characteristics of the trainee for each student Storage means;
Correlation value calculation means for taking out the trainee information of all students from the trainee information storage means, calculating the correlation value by performing correlation analysis with the element as an objective variable and the personal characteristic as an explanatory variable,
Participation requirement generation means for generating the element received by the element reception unit and the relationship between the element specified by the correlation value calculated by the correlation value calculation unit and the personal characteristics as the learning requirement information;
Attendance requirement output means for outputting the attendance requirement information;
A training support apparatus characterized by comprising: Training element storage means for storing information on elements expressed by words indicating the characteristics of the training in each phase indicating the process from the time when the trainee takes the training until the achievement of results;
Trainee information for storing trainee information generated by associating evaluation information indicating evaluation of the training element by the trainee of the training with personal characteristic information indicating personal characteristics of the trainee for each student Storage means;
Personal characteristic reception means for receiving personal characteristic information on the personal characteristics of new trainees who are going to take training,
By performing correlation analysis, a correlation value between the element whose information is stored in the training element storage means and the personal characteristic whose information is stored in the trainee information storage means is calculated, and the calculated correlation A preference function generating means for generating a preference function for each individual characteristic indicating a relationship between the training and the preference of the student for the training based on the value;
Attendance suitability calculating means for obtaining attendance suitability of the new trainee for the training based on a result obtained by substituting the individual characteristic information of the new trainee for each preference function;
Measure generation that generates information on measures when the new trainee attends the training according to a result of collating the personal property information of the new trainee accepted by the personal property accepting means with the attendance suitability Means,
Measure output means for outputting information on the measure;
A training support apparatus characterized by comprising:

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