Training evaluation apparatus and training evaluation method

Abstract

To properly evaluate training for a worker who has been trained in the past, while considering practical work.SOLUTION: A training evaluation apparatus 1 includes: a training recognition unit 13 which acquires leaning tendency of a trainee from a history of training that the trainee received; a work recognition unit 14 which acquires work achievement of the trainee from a history of work that the trainee performed; an effect prediction unit 16 which predicts training effect to be obtained by re-training the trainee, by proceeding with a training level of re-training in accordance with the learning tendency of the trainee, the training level according to the work achievement of the trainee being defined as a training level at the beginning of the retraining of the trainee; and a display control unit 18 which displays a result predicted by the effect prediction unit 16 on a screen.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an education evaluation device and an education evaluation method. [Background technology]

[0002] CAI (computer-assisted instruction) is an educational system that uses a computer. The following has been proposed as a system for collectively evaluating the results of training multiple workers. Patent Document 1 describes an education support system having the following features. "The first invention is a system for supporting education for a group of education recipients, which is made up of a plurality of education recipients, and is characterized by comprising: storage means for storing attribute data of each education recipient; input means for inputting basic proficiency data of each education recipient for each of a plurality of education items; education item evaluation means for evaluating the proficiency of each education recipient for each education item for each education recipient; education item overall evaluation means for calculating an overall evaluation of the education items for the group of education recipients based on the evaluation results obtained by the education item evaluation means; education item selection means for comparing the overall evaluations of the education items obtained by the education item overall evaluation means and selecting education items with low proficiency levels; and education recipient selection means for selecting which education recipients should receive the education items selected by the education item selection means."

[0003] Patent Document 2 describes a quality evaluation and training system for elevator maintenance work, which has the following features. "It is configured to include an information processing device with a server function that has a display unit capable of displaying processing information, and processes and manages information related to elevator maintenance work as the processing information by operation input, operation setting, or operation instruction on the operation unit, and a database that is controlled by the information processing device and allocates, accumulates, and stores the information related to the maintenance work in various tables, The database includes a maintenance work table storing maintenance work-related information for each maintenance work of the elevator, a maintenance work-specific actual work time calculation table storing information related to actual work time characteristics at the site for each maintenance work of the elevator, a faulty part table storing faulty part-related information for each part of the elevator, and a maintenance work training plan formulation table storing maintenance work training-related information that is decided based on a result of referring to the maintenance work-related information in the maintenance work table, the actual work time characteristics-related information in the maintenance work-specific actual work time calculation table, and the faulty part-related information in the faulty part table for each maintenance work. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2001-228792 [Patent Document 2] Japanese Patent Application Laid-Open No. 2017-111481 Summary of the Invention [Problem to be solved by the invention]

[0005] Even if a worker receives a high evaluation when he or she receives training at an educational institution, he or she may receive a low evaluation when he or she subsequently performs on-site work. For example, a worker may have acquired specialized knowledge when he or she receives training and obtains a license, but if that specialized knowledge is not used for a long period of time afterwards, the worker may forget the specialized knowledge. Therefore, if site supervisors or other managers decide which workers to assign to a work site only by looking at the licenses held by each worker, they may end up assigning workers who are not immediately effective at the work site. In such cases, it is necessary to retrain the workers.

[0006] In this way, if there is no continuity between the evaluation at the time of work training and the evaluation at the time of work on site, it is difficult to objectively and specifically measure the effectiveness of the training, which may hinder efficient human resource development. Note that conventional training evaluation systems such as those in Patent Documents 1 and 2 only evaluate workers at the time of receiving training, and do not evaluate them at the time of actually performing on-site work.

[0007] The present invention has been made in consideration of the above circumstances, and its main objective is to perform an appropriate education evaluation of workers who have received previous education, taking into account their actual work experience. [Means for solving the problem]

[0008] In order to solve the above problems, the education evaluation device of the present invention has the following features. The present invention includes: an education recognition unit that acquires a student's learning tendency from a history of educational content that the student has received; a task recognition unit that acquires a task achievement level of the student from a history of tasks performed by the student; an effect prediction unit that predicts an educational effect of the re-education of the student by setting an education level corresponding to the task achievement level of the student as the education level at the start of the re-education of the student and progressing the education level of the re-education in accordance with the learning tendency of the student; The present invention is characterized by comprising a display control unit that displays the prediction results by the effect prediction unit on a screen. Other features will be described later. [Effects of the Invention]

[0009] According to the present invention, it is possible to perform an appropriate training evaluation of workers who have received training in the past, taking into account their actual work experience. [Brief explanation of the drawings]

[0010] [Figure 1] 1 is a configuration diagram of an education evaluation device according to an embodiment of the present invention. [Figure 2] 3 is a flowchart showing the operation of the education evaluation device according to the present embodiment. [Figure 3] 10 is a time series graph showing the learning curve of a typical student A according to the present embodiment. [Figure 4] 10 is a time series graph showing the predicted curve of a typical student A according to the present embodiment. [Figure 5] 10 is a time series graph showing the learning curve of late bloomer student B according to this embodiment. [Figure 6] 10 is a time series graph showing the predicted curve of late bloomer student B according to this embodiment. [Figure 7] 10 is a time series graph showing the learning curve of precocious student C according to the present embodiment. [Figure 8] 10 is a time series graph showing a predicted curve for precocious student C according to this embodiment. [Figure 9] 4 is a time series graph showing a learning curve different from that of FIG. 3 for a standard student A according to the present embodiment. [Figure 10] 10 is a time series graph showing a predicted curve from the learning curve of FIG. 9 for a standard student A according to the present embodiment. [Figure 11] 7 is a time series graph showing the effect of re-education, different from that shown in FIG. 6, for late bloomer participant B according to this embodiment. [Figure 12] FIG. 1 is a hardware configuration diagram of an education evaluation device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0012] FIG. 1 is a configuration diagram of an education evaluation device 1. The education evaluation device 1 has, as processing units, an education recognition unit 13, an activity recognition unit 14, an effect prediction unit 16, an education planning unit 17, and a display control unit 18. An education history 11, a work history 12, and an education item DB 15 are stored in an internal storage unit of the education evaluation device 1 or in an externally accessible storage unit.

[0013] The training items that the trainees take are registered in the training item DB 15. For ease of understanding, the following will be described as an assembly process for assembling i (i=0 to 12) parts, and for example, i=3 indicates a state in which a fourth part is additionally assembled to a structure in which the first to third parts have been assembled. Alternatively, the training items may be items that shorten the assembly time as much as possible.

[0014] The education history 11 is data showing the history of the education content received by the trainee. For example, if there is an education history 11 showing that the trainee has received education up to the assembly process i=8, at the time of receiving that education, the trainee has acquired the specialized knowledge and assembly skills to be able to construct a structure in which the first to eighth parts are assembled. The work history 12 is data showing the history of work performed by the student. For example, if there is work history 12 showing that the student performed up to assembly step k=6, at the time of performing the work, the student actually constructed a structure in which the first to sixth parts were assembled.

[0015] The education history 11 and work history 12 may be input into the education evaluation device 1 by a person such as a student, or may be input mechanically using input means such as scanning and reading the degree of completion of a structure. Ideally, the degree of learning retention (value of i) acquired as the education history 11 matches the degree of work achievement (value of k) in the actual work history 12. However, there are cases where the value of i and the value of k do not match, for example, because the student forgets specialized knowledge acquired a year ago.

[0016] The education recognition unit 13 acquires the learning tendency of the student from the history of the educational content that the student has received. Therefore, the education recognition unit 13 recognizes the educational process (educational items in the educational item DB 15) that the student has received from the education history 11 as the degree of learning retention, and acquires the learning tendency as summary data such as a learning curve. The task recognition unit 14 acquires the task achievement level of the student from the history of the tasks performed by the student. Therefore, the task recognition unit 14 recognizes the on-site task process performed by the student from the task history 12 and acquires the task achievement level.

[0017] The effect prediction unit 16 makes an evaluation to predict the effect of re-education for the student based on the task achievement level and the learning tendency of the task recognition unit 14. The education planning unit 17 selects from the education item DB 15 the education items necessary for the re-education of the trainee based on the evaluation by the effect prediction unit 16, and plans the results as a curriculum. The display control unit 18 displays the evaluation (prediction result) output by the effect prediction unit 16 and the curriculum output by the education planning unit 17 on the screen.

[0018] FIG. 2 is a flowchart showing the operation of the education evaluation device 1. An administrator (such as an instructor) of the education evaluation device 1 prepares history data (education history 11, work history 12) (S11). The education recognition unit 13 recognizes the education process from the education history 11 of S11 and acquires the learning tendency (S12). The work recognition unit 14 recognizes the on-site work process from the work history 12 of S11 and acquires the work achievement level (S13). The effect prediction unit 16 predicts the re-training effect from the task achievement level in S13 and the learning tendency in S12 (S14). The effect prediction unit 16 determines whether re-training is necessary or not from the prediction result in S14 (S15). For example, if the degree of learning retention (value of i) and the task achievement level (value of k) are nearly identical (90% or more), the learning effect has not declined, and so re-training is unnecessary (No in S15). On the other hand, if re-education is necessary (Yes in S15), the education planning unit 17 plans a curriculum from the necessary education items in the education item DB 15 (S16).

[0019] The re-education effects predicted by the effect prediction unit 16 in S14 for three students with different learning tendencies will be described below. (Student A) A student with a standard learning speed (described in Figures 3 and 4). (Participant B) A late bloomer who learns slowly in the first half of the study schedule but whose learning speed increases in the second half of the study schedule (explained in Figures 5 and 6). (Participant C) A precocious participant who learns quickly from the first half of the study schedule (explained in Figures 7 and 8).

[0020] Figure 3 is a time series graph showing the learning curve of a typical student A. This learning curve indicates the learning tendency of student A that is determined by the education recognition unit 13 from the education history 11 of student A. The horizontal axis of the time series graph shows the number of days of education the participant received. The vertical axis of the time series graph shows the assembly process (value i in Figure 1) as the level of education that the participant has mastered from day 0 to day 3. In Figure 3, because Trainee A's learning speed is standard, the learning curve shows that the retention of what he learned increases proportionally as the number of days of training increases. By the end of the third day, Trainee A had completed learning all of the assembly processes (i=12).

[0021] Figure 4 is a time series graph showing the predicted curve for a typical student A. The horizontal and vertical axes of the time series graph in Figure 4 have the same meaning as those in Figure 3. However, while the number of days on the horizontal axis was the number of days of training in Figure 3, in Figure 4, day 0 is the work day in the work history 12 (for example, the day one year after the training completion date), and days 1 and onwards represent the number of days of re-training to be carried out after that work day.

[0022] The predicted curve shown by the solid line is a learning curve showing the re-training effect predicted by the effect prediction unit 16 from the work history 12. The learning curve shown by the dashed line is a reproduction of the learning curve in Figure 3. Note that the learning curve and the forecast curve are not limited to curves but also include straight lines, and for convenience (because the name learning curve is well known), we refer to the "curve" including examples of straight lines. In other words, the learning curve and the learning line have the same meaning, and the forecast curve and the forecast line have the same meaning.

[0023] On day 0 (work day), the task recognition unit 14 acquires the task achievement level (k=8 steps) from the task history 12. In other words, it is assumed that 4 steps have been forgotten due to the passage of time, compared to the learning retention level (i=12 steps) from one year ago. The effect prediction unit 16 sets (day 0, process 8) as the origin and creates a prediction curve extending from the origin along the learning curve in Figure 3. Therefore, the effect prediction unit 16 creates a prediction curve by, for example, shifting the learning curve passing through the origin (0,0) in parallel vertically (by +8) so that the learning curve passes through the origin (0,8). An upward arrow in the graph indicates parallel shift.

[0024] That is, the effect prediction unit 16 sets the education level at the start of the re-education of the student to (the same) education level according to the student's task achievement level (k=8 steps), and predicts the educational effect of the re-education of the student by progressing the education level of the re-education according to the student's learning tendency (extending the prediction curve in FIG. 4). As a result, it is predicted that re-education participant A will be able to return to the level of retention of what he learned one year ago (i=12 steps) based on the task achievement level of the work day through one day of re-education. By displaying the graph in Fig. 4 on the screen, the display control unit 18 makes it easier for the work manager to make work plans, such as re-introducing re-education participant A to the work site from the second day.

[0025] Then, the display control unit 18 displays on the screen, as the prediction result by the effect prediction unit 16, a time series graph of a predicted line extended from the start point of the re-education of the student (the 8th step on the 0th day) along the learning line (the solid line in Figure 4) indicating the learning tendency of the student. Furthermore, the education planning unit 17 plans a curriculum indicating the education content from the education level at the start of the re-education of the trainee (i=8th process on day 0) to the education level at the end of the re-education (i=12th process on day 1). In other words, the education planning unit 17 excludes levels lower than the trainee's work achievement level (up to the 7th process) from the re-education curriculum as education levels unnecessary for the trainee.

[0026] Figure 5 is a time series graph showing the learning curve of late bloomer participant B. The meanings of the horizontal and vertical axes of the time series graph in Figure 5 are the same as those of the learning curve graph for Student A in Figure 3. In Figure 5, because participant B's learning speed (slope of the learning curve) was slow in the first half of the training, he only achieved task achievement level i=1 on the first day and task achievement level i=2 on the second day. However, participant B's learning speed increased in the second half of the training, so on the third day he achieved task achievement level i=12, completing the training.

[0027] Figure 6 is a time series graph showing the predicted curve for student B, who is a late bloomer. The horizontal and vertical axes of the time series graph in Figure 6 have the same meanings as those of the predicted curve graph for student A in Figure 4. On day 0 (work day), the task recognition unit 14 acquires the task achievement level (k=8 steps) from the task history 12. Then, the effect prediction unit 16 creates a predicted curve by shifting the broken-line learning curve passing through the origin (0,0) vertically (by +8 minutes) so that it passes through the origin (0,8), just like in Figure 4. Here, because the learning curve for Trainee B rises gradually in the first half, the predicted curve also rises gradually. Therefore, the number of days in which Trainee B can return to the level of learning retention from one year ago (i=12 steps) is 2.2 days, and it is expected that Trainee B will require longer re-education than Trainee A. By referring to the graph in Fig. 6 displayed on the screen by the display control unit 18, the work manager can easily make work plans, such as re-entering Trainee B to the work site from the third day, or abandoning the re-entering of Trainee B if the work period is only two days.

[0028] Figure 7 is a time series graph showing the learning curve of precocious student C. The meanings of the horizontal and vertical axes of the time series graph in Figure 7 are the same as those of the learning curve graph for Student A in Figure 3. In Figure 7, student C's learning speed was fast from the first half of the training, so on the first day he achieved a task achievement level of i=10, and on the second day he achieved a task achievement level of i=11. Then, on the third day he achieved a task achievement level of i=12, completing the training.

[0029] FIG. 8 is a time series graph showing the predicted curve for precocious student C. The horizontal and vertical axes of the time series graph in Figure 8 have the same meanings as those of the predicted curve graph for student A in Figure 4. On day 0 (work day), the task recognition unit 14 acquires the task completion level (k=8 steps) of the task from the task history 12. Then, the effect prediction unit 16 creates a predicted curve by shifting the broken-line learning curve passing through the origin (0,0) vertically (by +8 minutes) so that it passes through the origin (0,8), just like in Figure 4. Here, because the learning curve for Trainee C rises sharply in the first half, the predicted curve also rises sharply. Therefore, the number of days in which Trainee C can return to the level of retention of the learning from one year ago (i=12 steps) is 0.3 days, and it is expected that Trainee C will require less re-education than Trainee A. By referring to the graph in Fig. 8 displayed on the screen by the display control unit 18, the work manager can easily make work plans, such as re-entering Trainee C into the work site from day 1 (the day after the start of re-education).

[0030] As explained above with reference to Fig. 3 to Fig. 8, even if trainees A, B, and C have the same task achievement level (k = 8 steps), the effect prediction unit 16 predicts the outlook for future re-training by referring to the trends in past learning (learning curves). This makes it easier to create a task plan by accurately predicting re-training that reflects the individual learning trends. Furthermore, the education planning unit 17 creates a necessary curriculum for the re-learning, omitting the learning content of the process up to i<8 as it has already been achieved, and including the learning content of the process from i=8 to i=12. Here, the education planning unit 17 sets a re-education period in accordance with the learning pace of each student in the necessary curriculum as follows, so that each student can take a curriculum that is comfortable for their learning speed and has no wasted learning content. For standard participant A, the retraining period is set to 1 day (the number of days when the predicted curve in Figure 4 is i=12). For late bloomer participant B, the retraining period is set to 2.2 days (the number of days when the predicted curve in Figure 6 becomes i=12). For precocious participant C, the retraining period is set to 0.3 days (the number of days when the predicted curve in Figure 8 becomes i=12).

[0031] FIG. 9 is a time series graph showing a learning curve for a typical student A, different from that shown in FIG. In Figure 3, the vertical axis of the time series graph shows an upward-sloping learning curve indicating the assembly process (value of i), where the higher the value (the higher the graph), the greater the learning effect. On the other hand, the vertical axis of Figure 9 shows the work time (minutes) required to create the same assembly (work product) as an indicator of learning effect. In this case, the downward-sloping learning curve indicates that the lower the value (the lower the graph), the greater the learning effect. Before the start of the training (day 0), participant A took 30 minutes to complete the assembly work, but by getting the hang of the work through the training, he was able to reduce the time to 10 minutes by the end of the training (day 3). In this way, a workplace where shortening the assembly work time is necessary is, for example, a workplace where a conveyor belt is used for assembly line work, and workers assigned to a certain process are subject to a work time limit (the conveyor belt cannot be stopped due to delays in work).

[0032] FIG. 10 is a time series graph showing the predicted curve from the learning curve of FIG. 9 for a typical student A. The horizontal and vertical axes of the time series graph in Fig. 10 have the same meanings as those of the time series graph in Fig. 9. However, as with Fig. 4, the number of days on the horizontal axis is the number of days of re-training conducted after the work day, with day 0 being the work day in the work history 12, and days 1 and onwards being the number of days of re-training conducted after that work day. The forecast curve predicted by the effect prediction unit 16 is shown by a solid line, and the learning curve in Fig. 9 is shown again by a dashed line.

[0033] The effect prediction unit 16 creates the predicted curve shown by the solid line in Fig. 10 from the learning curve shown by the dashed line in Fig. 4, just as it creates the predicted curve shown by the solid line in Fig. 4 from the learning curve shown by the dashed line in Fig. 4. In other words, the effect prediction unit 16 creates a predicted curve by vertically shifting (by -6 minutes) the learning curve that passes through the origin (0th day, 30 minutes) acquired by the task recognition unit 14 from the task history 12 so that the task achievement level of the task (0th day, 24 minutes) passes through it. The downward arrow in the graph indicates parallel movement. This indicates that retraining participant A will be able to return to the level of previous learning (10 minutes) after two days of retraining, based on the level of achievement of the work on the work day. In this way, the effect prediction unit 16 can create a prediction curve even for a learning curve in which the learning effect is downward.

[0034] Figure 11 is a time series graph showing the effect of re-education on late bloomer participant B, different from that shown in Figure 6. In FIG. 11, similarly to FIG. 6, the effect prediction unit 16 creates a predicted curve by translating the broken-line learning curve passing through the origin (0,0) so that the task achievement level (0,8) of the task passes through it. On the other hand, Figure 11 differs from Figure 6 in that the learning curve is shifted in parallel to the left. This allows the learning characteristics of the latter half (i≧8) of the learning curve shown by the dashed line, which shows a gradual rise in the first half (e.g., i≦2) but a rapid rise in the second half (e.g., i≧2), to be reflected in the prediction curve.

[0035] FIG. 12 is a diagram showing the hardware configuration of the education evaluation device 1. As shown in FIG. The education evaluation device 1 is configured as a computer 900 having a CPU 901 , a RAM 902 , a ROM 903 , a HDD 904 , a communication I / F 905 , an input / output I / F 906 , and a media I / F 907 . The communication I / F 905 is connected to an external communication device 915. The input / output I / F 906 is connected to an input / output device 916. The media I / F 907 reads and writes data from a recording medium 917. Furthermore, the CPU 901 executes a program (also called an application or an app for short) loaded into the RAM 902 to improve and control each processing unit. This program can be distributed via a communication line or recorded on a recording medium 917 such as a CD-ROM and distributed.

[0036] In the education evaluation device 1 of this embodiment described above, the effect prediction unit 16 predicts the effect of subsequent re-education as a prediction curve based on the learning curve calculated by the education recognition unit 13 from the education history 11 and the task achievement level calculated by the task recognition unit 14 from the task history 12. Furthermore, the education planning unit 17 plans the learning content and number of learning days of the re-education curriculum in accordance with the prediction curve. As a result, the education evaluation device 1 can support efficient human resource development by individually optimizing the learning content that should be taught and the learning content that does not need to be taught. Also, companies that provide work sites, such as electric power companies that manage nuclear power plants, and companies that provide workers, such as human resource management companies, can reduce the effort required to individually exchange information on worker skills and can deploy workers who have learned the appropriate skills at the appropriate time to the work site.

[0037] Furthermore, the present invention is not limited to the above-described embodiments, and various other applications and modifications are possible without departing from the spirit of the present invention as defined in the claims. For example, the above-described embodiments provide a detailed and specific description of the configuration of the educational evaluation device 1 in order to clearly explain the present invention, and the device is not necessarily limited to having all of the components described. Furthermore, it is possible to replace part of the configuration of one embodiment with the components of another embodiment. It is also possible to add the components of another embodiment to the configuration of one embodiment. It is also possible to add, replace, or delete other components from part of the configuration of each embodiment.

[0038] Furthermore, the above-described configurations, functions, processing units, etc. may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. As the hardware, a broad processor device such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit) may be used. Furthermore, each component of the education evaluation device 1 according to the above-described embodiment may be implemented in any hardware as long as the respective hardware can transmit and receive information to and from each other via a network. Furthermore, the processing executed by a certain processing unit may be realized by a single piece of hardware, or may be realized by distributed processing using multiple pieces of hardware. [Explanation of symbols]

[0039] 1 Educational evaluation equipment 11 Educational History 12 Work History 13 Educational Awareness Department 14 Task Recognition Unit 15 Educational item DB 16. Effect Prediction Department 17 Educational Planning Department 18 Display control unit

Claims

1. an education recognition unit that acquires the learning tendencies of a student from the history of the educational content that the student has received; a task recognition unit that acquires a task achievement level of the student from a history of tasks performed by the student; an effect prediction unit that predicts an educational effect of the re-education of the student by setting an education level corresponding to the task achievement level of the student as the education level at the start of the re-education of the student and progressing the education level of the re-education in accordance with the learning tendency of the student; a display control unit that displays the prediction result by the effect prediction unit on a screen. Educational evaluation equipment.

2. The display control unit displays on the screen, as a prediction result by the effect prediction unit, a time series graph of a prediction line extending from a start point of the re-education of the student along a learning line indicating the learning tendency of the student. The educational evaluation device according to claim 1 .

3. The education evaluation device further includes an education planning unit, The education planning unit plans a curriculum indicating education content from an education level at the start of the re-education to an education level at the end of the re-education. The educational evaluation device according to claim 1 .

4. The education evaluation device includes an education recognition unit, an activity recognition unit, an effect prediction unit, and a display control unit, the education recognition unit acquires the learning tendency of the student from a history of the educational content received by the student; the task recognition unit acquires the task achievement level of the student from a history of the task content performed by the student; the effect prediction unit sets an education level corresponding to the task achievement level of the student at the start of the re-education of the student, and progresses the education level of the re-education in accordance with the learning tendency of the student, thereby predicting an education effect of the re-education of the student; The display control unit displays the prediction result by the effect prediction unit on a screen. Educational evaluation methods.

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