JP7330647B2 - MEMORY ERROR PREVENTION EFFECT LEARNING DEVICE AND METHOD BY PRE-RECALLING - Google Patents

Description

The present disclosure relates to a memory error prevention effect learning device and method using preemptive calling.

Conventionally, in the transportation field such as railways, the medical field such as hospitals, and other fields such as factories and plants, forgetting (forgetting) sometimes leads to serious accidents, and prevention is important. be. Forgetfulness is caused by memory errors that prevent us from remembering appointments at the right time. Preventing this memory error is important to prevent forgetting. Therefore, as a method of preventing memory errors, preemptive calling has been proposed (see, for example, Non-Patent Document 1).

FIG. 1 is a diagram for explaining an example of conventional imaging-type anticipatory calling, FIG. 2 is a diagram for explaining an example of conventional repetitive anticipatory calling, and FIG. FIG. 4 is a graph explaining an example of overspeeding due to forgetfulness when calling ahead in a conventional experiment, FIG. 5 shows an example of overspeeding due to forgetting when calling ahead in a conventional experiment It is an explanatory graph.

Preemptive recall is a method of recalling important information in advance to strengthen memory and prevent memory errors. There are two types of preemptive calls: imaging type preemptive calls and repetitive preemptive calls.

Imaging preemptive recall is a method to enhance memory and prevent memory errors by preliminarily imagining an actor performing a schedule, as shown in FIG.

On the other hand, repetitive preemptive recall is a method of intermittently recalling information that should not be forgotten, as shown in Fig. 2, to keep the information that must be consciously maintained and prevent memory errors. is.

It has been confirmed experimentally that these two types of preemptive calls are effective in preventing memory errors. In addition, as shown in Figures 3 to 5, train operation simulators have shown that temporarily forgetting (memory error) the existence of temporary slow-moving sections on railways has the effect of preventing overspeeding. Experimentally confirmed.

Fuminori Sato, Noriko Onoma, Takayuki Masuda, ``Prevention of excessive speed using preemptive summons,'' RTRI Report, Vol.34, No. 1, pp. 15-20, 2020

However, in workplaces where accidents and incidents occur due to forgetting to do so, even if the manager explains to the workers that the use of preemptive calls is effective in preventing such accidents, the workers are not aware of the effect. It is difficult to permeate preemptive calls in the workplace unless the motivation to do so is felt. It is important how to make the worker feel the effect of the preemptive call in a short time and how to increase the motivation for the preemptive call.

Here, by solving the problems of the conventional technology, the learner can appropriately learn the vulnerability of memory and the effect of preventing memory errors by preemptive recall, thereby reducing memory errors. It is an object of the present invention to provide a memory error prevention effect learning device and method by means of a preemptive call that can effectively prevent memory errors.

For this reason, in the memory error prevention effect learning device by preemptive calling, the memory error prevention effect learning device by preemptive calling is provided with a client operated by the learner and a server communicably connected to the client. wherein the server stores execution software that causes the learner to perform a memory task, and transmits a control signal of the execution software to the client, the client including a display unit and an input unit. , when the control signal is received from the server, displaying on the display unit that the memory task will be performed under the condition of no call-out, and from the input unit after the learner performs the memory task under the condition of no call-out When the answer is entered, the fact that the memorized task will be performed under the condition of being called out is displayed on the display unit, and after the learner performs the memorized task under the condition of being called out, when the answer is input from the input unit, The results of each executed memory task are displayed on the display unit.

In another memory error prevention effect learning device by preemptive recall, the memory task further includes a memorization task that the learner should memorize and a work task that the learner should do. I'm in.

Further, in the memory error prevention effect learning device by prefetch recall, the memorization task is a character string, and the work task is calculation.

In yet another preemptive recall memory error prevention effect learning device, the learner continues to repeat calculations during a given calculation time.

Furthermore, in another memory error prevention effect learning device by preemptive recall, when the learner performs the memory task under the condition of no recall, the learner utters the result of the calculation each time the calculation is performed, and the recall is performed. When the memorization task is performed under the condition of , the result of the calculation and the character string are uttered each time the calculation is performed.

In yet another memory error prevention effect learning device using preemptive recall, the client transmits the results of each of the memory tasks to a server, and the server aggregates the results of each of the memory tasks and and the client displays the aggregated results of each memory task on the display.

In the memory error prevention effect learning method by preemptive calling, a memory error prevention effect learning method by preemptive calling using a client operated by a learner and a server communicably connected to the client, comprising: The client receives a control signal for the execution software from the server that stores execution software for causing the learner to perform the memory task, and the client displays to the learner that the memory task will be performed on the condition that no call is made . a step of inputting an answer from the input unit of the client after the learner performs the memory task under the condition of no call-out; and a step of the client performing the memory task under the condition of the call-out . A step of displaying on the display unit to the learner that the instruction will be executed; a step of inputting an answer from the input unit of the client after executing the memory task under the condition that the learner is called ; a client displaying the results of each memory task performed to the learner on a display .

According to the present disclosure, the learner can appropriately learn the vulnerability of memory and the effect of memory error prevention by prefetch recall, so that memory error can be effectively prevented.

FIG. 10 is a diagram illustrating an example of conventional imaging-type preemptive calling; FIG. 10 is a diagram illustrating an example of a conventional repetitive preemptive call; It is a figure explaining the example of the excessive speed by forgetting in the conventional investigation. It is a graph explaining an example of overspeeding due to forgetfulness when making a preemptive call in a conventional experiment. It is a graph explaining an example of overspeeding due to forgetfulness when no preemptive call was made in a conventional experiment. 1 is a schematic diagram showing a configuration of a learning device according to an embodiment; FIG. 2 is a flowchart for explaining the overall flow of memorization tasks in the present embodiment. 2 is a flow chart explaining the flow of one trial of a memory task in the present embodiment. FIG. 10 is a diagram showing an example of performing a memory task without calling out according to the present embodiment; FIG. 10 is a diagram showing an example of performing a memory task while calling out according to the present embodiment; 10 is a graph showing an example of memory task performance in the present embodiment. 10 is a graph showing an example of memorized task results for each calculation time in the present embodiment. 2 is a graph showing an example of memory task performance in an experiment conducted with university students as subjects. FIG. 10 is a graph showing an example of memory task performance for each calculation time in an experiment conducted with university students as subjects. FIG.

Hereinafter, this embodiment will be described in detail with reference to the drawings.

FIG. 6 is a schematic diagram showing the configuration of the learning device according to this embodiment.

In the figure, 10 is a learning device as a memory error prevention effect learning device by preemptive calling in the present embodiment, and is a type of learning device used by a learner to learn the memory error prevention effect by preemptive calling. computer system. The learning device 10 includes a server 11 as a main device and a client 12 as at least one learner terminal communicably connected to the server 11 .

The server 11 is a computer equipped with arithmetic units such as CPU and MPU, storage devices such as magnetic disks and semiconductor memories, input devices such as keyboards, mice and touch panels, display devices such as CRTs and liquid crystal displays, and communication interfaces. . The computer is, for example, a personal computer, a workstation, a server, etc., but it may be of any kind as long as it operates according to a program such as application software installed in a storage device. It may be a computer, or a group of computers in which a plurality of computers are communicably connected via a network. Furthermore, the server 11 may be communicably connected to an external database or the like.

In addition, it is desirable that one client 12 is assigned to each learner, and the number is five in the example shown in FIG. varies depending on the number of Each client 12 includes an arithmetic unit such as a CPU and MPU, a storage device such as a magnetic disk and a semiconductor memory, an input device such as a microphone, a keyboard, a mouse and a touch panel, a display device such as a CRT and a liquid crystal display, and a communication interface. It's a computer. The computer is, for example, a personal computer, a tablet computer, or the like, but may be any type of computer that operates according to programs such as application software installed in a storage device.

The server 11 and the client 12 are connected via a wired or wireless communication network so as to be able to communicate bidirectionally. The communication network is, for example, LAN, WAN, intranet, Internet, etc., but may be of any type as long as it is a network capable of two-way data communication.

The learning device 10 according to the present embodiment can be applied not only in the transportation field such as railways, but also in the medical field such as hospitals, various other technical fields such as various factories and plants, and various industrial fields. Although it is effective, here, for convenience of explanation, the case of application to train operation on a railroad will be explained. In addition, as explained in the section "Background Art", there are two types of preemptive calling, the imaging type and the repetitive type. .

In order to increase the motivation for repetitive preemptive calling, it is necessary to ``(1) make people realize that human memory is more fragile than expected'', It is effective to let the learners experience the three realities of "(3) Realize that most people are effective in preventing memory errors with this repetitive preemptive recall". Conceivable. Therefore, the learning device 10 according to the present embodiment is a device that allows the learner to experience these three actual feelings. When the learning device 10 is used in group training, it is desirable that each trainee as a learner uses one client 12 . Then, the server 11 controls actual feeling software as execution software for executing the memory task in order to experience the actual feeling (1) to (3) on the client 12 . Further, it is possible to collect the information obtained by the client 12 in the server 11 and display the result on each client 12 . In addition, the client 12 has a voice recognition function to determine how well the client 12 is able to call out during the memory task. Moreover, it is desirable that the training instructor or the like can operate the server 11 to control the progress of the training.

From a functional point of view, the server 11 includes a storage unit for storing programs such as the actual software, a timer for measuring elapsed time, responses input to each client 12, and speech recognition results of each client 12. It is desirable to include a counter that tallies Note that the client 12 instead of the server 11 may include timers and counters. In addition, each client 12 has a display unit that displays memory tasks, etc., an input unit that is operated by the learner who uses the client 12 to input answers, etc., and a learner who uses the client 12. Including a voice recognition unit that recognizes voices such as calls, and a judgment unit that judges the correctness of the calls made by the learner using the client 12 and the correctness of the answers input to each client 12 It is desirable to be It should be noted that the server 11 may include the speech recognition section and the judgment section instead of each client 12 .

Then, the server 11 storing the actual software, which is execution software for causing the learner to perform the memory task, transmits a control signal of the actual software to each client 12, and each client 12 transmits the control signal to the server 11. When the learner receives a call from the user, it displays on the display section that the memory task will be performed with the condition of no call-out. Display on the display section that the memory task will be performed under the condition, and when the learner inputs the answer from the input section after performing the memory task on the condition that there is a call, the result of each executed memory task is displayed on the display section. It is designed to In addition, each client 12 transmits the result of each memory task to the server 11, the server 11 aggregates the result of each memory task and transmits it to each client 12, and each client 12 receives the aggregated result. The results of each memorized task may be displayed on the display unit.

Next, the memory problem will be described. First, the overall flow of the memorization task to be executed will be described.

FIG. 7 is a flow chart for explaining the overall flow of memorization tasks in this embodiment.

In the actual feeling software of the present embodiment, the learner is made to perform the memory task twice (steps S1 and S2 in FIG. 7). In step S1, the learner performs the memory task under the condition of no calling, i.e., performs the memory task without calling out, and in step S2, the learner performs the memory task under the condition of calling out. Perform, i.e. perform a memory task while calling. The procedure for the memory task is the same, except that the call is made or not. In steps S1 and S2, one trial, which is a series of memorization tasks, is performed about five times, that is, about five trials each. Finally, in step S3, the results (scores) of each memorized task are fed back.

The graph in FIG. 7 shows an example of feedback results for each learner displayed on the display section of each client 12 . In the graph, the vertical axis indicates the number of correct answers in the learner's answers, and the horizontal axis indicates the distinction between step S1 (without callout) and step S2 (with callout).

Next, the details of the memory task will be described.

FIG. 8 is a flowchart explaining the flow of one trial of the memory task in this embodiment, FIG. 9 is a diagram showing an example of performing a memory task without calling out in this embodiment, and FIG. 10 is this embodiment. FIG. 12 is a diagram showing an example of performing a memory task while calling out to the child. In FIGS. 9 and 10, (a) is a diagram showing an example of the work task displayed on the client's display unit, and (b) is a diagram showing an example of answers to the task issued by the learner.

First, the flow of one memory task trial as shown in FIG. 8 will be described. In the present embodiment, the memorization task includes a memorization task that the learner should memorize and a work task that the learner should perform. In one trial, which is a series of memorization tasks, the learner first memorizes the memorization task displayed on the display unit of his client 12 for a predetermined time, and then displays it on the display unit of the client 12. After completing the task for the set task, the user operates the input unit of the client 12 to input the memorized task by heart as an answer. Whether the answer is correct or not is judged by the judging section of the server 11 or the client 12 . In addition, since the learner performs the work task aloud, the client 12 recognizes the contents of the work task using the voice recognition function, and the judging unit determines whether or not the execution result of the work task is appropriate. can also judge.

The above-mentioned memorization task is neither particularly difficult nor particularly easy to memorize, but is a level task that can be memorized in a short time by a normal learner with normal effort. . Desirably, it is a character string consisting of about 3 to 5 characters, and the character string is displayed on the display section of the client 12 for a predetermined time (for example, about 1 second) (step S11 in FIG. 8). . Then, the learner is required to memorize the displayed character string during the predetermined time. The character string may consist of any type of characters, may have any number of characters, may have meaning, or may be meaningless. good. Here, as an optimum example, the case where the character string is nonsense consisting of three alphabetic characters such as "xtk" and "jsh" will be described.

The work task is specifically a task of intellectual work, but it is neither particularly difficult nor particularly easy, and if an ordinary learner makes ordinary efforts, it can be completed in a short time. It is a task of a level that can be worked on in time. The work task is desirably a simple calculation that allows mental calculation, the same calculation is repeated multiple times, and the content is displayed on the display unit of the client 12. and Further, the calculation may be addition, subtraction, multiplication, division, or the like. Here, as a perfect example of a calculation as a working problem, given a three-digit number as the initial minuend and a one-digit number as the subtrahend, the difference resulting from subtracting the subtrahend from the minuend is given by A case will be described in which the minuend in the subtraction is used and the subtraction is repeated (the subtrahend is constant). It is assumed that the learner utters the difference, which is the result of the subtraction, each time the subtraction is performed. Also, the learner continues to repeat the subtraction during the calculation time given in each trial (step S12 in FIG. 8).

The character string, initial minuend, subtrahend, and calculation time are randomly set by the server 11 or the client 12 and displayed.

Note that the duration of each trial is different. However, the average calculation time in each step (steps S1 and S2 in FIG. 7) is about 10 seconds. Trials with computation times of 1 second and 3 seconds are always included in each step. The calculation time for other trials is randomly set by the server 11 or the client 12, and is about 10 seconds on average.

Further, in steps S1 and S2 in FIG. 7, the calculation time of each trial is made to match. For example, in step S1 (without calling), if the calculation time of 5 trials is 1 second, 3 seconds, 10 seconds, 17 seconds and 19 seconds, respectively, in step S2 (with calling), 5 trials are 1 second, 3 seconds, 10 seconds, 17 seconds and 19 seconds, respectively. However, the order of calculation time is not necessarily the same between step S1 and step S2.

Then, after the predetermined calculation time has passed, the learner remembers the character string displayed as the memorization task and operates the client 12 to input it from the input unit (step S13 in FIG. 8).

As described above, the sequence of memorizing a character string as a memorization task, executing a calculation task, and inputting a memorization task is regarded as one trial, and about five trials are performed in steps S1 and S2 in FIG. required to do so.

Here, the character string, which is a memorization task, will be explained.

As mentioned above, the character string for the memorization task does not necessarily have to be a nonsensical alphabetical character string such as "xtk", "jsh", or the like. Even for meaningful katakana-written strings (eg, "stove"), performance on the memory task without yelling is lower than with yelling. However, the tendency for performance on the memory task without arousal to be lower than that with arousal was greater with the use of nonsensical alphabetical strings, and with repetitive preemptive arousal. It is predicted that the memory error prevention effect will be more strongly felt.

When using meaningless alphabetic character strings or meaningful katakana character strings, predetermine and pool 10 to 20 meaningless character strings or meaningful character strings. It is desirable that the server 11 or the client 12 randomly selects a character string to be used from each of them. However, the same character string is not used more than once.

Even when both meaningless alphabetic character strings and meaningful katakana character strings are used, the server 11 or the client 12 randomly selects from the pooled character strings as described above. is desirable. However, when performing the memory task without calling out (step S1 in FIG. 7) and when performing the memory task with calling out (step S2 in FIG. 7), meaningful katakana character strings and meaningless strings It is more desirable to make the number (number of trials) the same as the alphabetical character string. The reason for this is to make the difficulty of the memory task without and with the evocation of the same level. If a meaningful katakana notation string is presented only to the memory task performed without arousal, the meaningful katakana notation string is easier to remember than the nonsensical alphabet notation string. , the difficulty level of the memory task performed without arousal is lowered, and the difference in performance between the memory task performed without arousal and the memory task performed with arousal is less likely to appear.

Calculations and calling will now be described.

As described above, the number of trials for memory task without calling and memory task with calling is about 5 trials, respectively. Therefore, about 10 trials are performed in total.

When performing without calling, in the calculation in step S12 in FIG. 8, the learner performs subtraction using a three-digit number as the initial minuend and a one-digit number as the subtrahend, and pronounces the result of the subtraction. while continuing to repeat. As described above, the calculation as the work task may be addition, subtraction, multiplication, division, or the like, but for the convenience of explanation, subtraction will be explained here. In the example shown in FIG. 9, the initial minuend is 130 and the subtrahend is 3, which is displayed on the display of the client 12 so that the learner can see the difference that is the result of subtracting the subtrahend from the minuend. Assume that the minuend in the next subtraction is repeated, and the result of the subtraction is pronounced as 130, 127, 124, 121, and so on each time the subtraction is performed. Note that the learner does not vocalize the memorization task.

On the other hand, when performing while calling out, in the calculation in step S12 in FIG. Continue repeating, alternately saying the character string In the example shown in FIG. 10, similar to the example shown in FIG. 9, the initial minuend is 130 and the subtrahend is 3 is displayed on the client 12 display. It is assumed that the memorized character string is "jsh". Therefore, the learner repeats the subtraction while using the difference obtained by subtracting the subtrahend from the minuend as the minuend in the next subtraction, and each time the subtraction is performed, the result of the subtraction and the memorized character string are 130, Assume that the voices are alternately uttered such as jsh, 127, jsh, 124, jsh, 121, jsh, and so on. That is, the work task is executed while recalling the memorized task.

At this time, the server 11 or the client 12 recognizes the result of the subtraction made by the learner by the speech recognition unit, and the voice of the memorization task called out, and the judgment unit judges and records the result of the subtraction. . If it is determined that the result of the subtraction is significantly inappropriate, for example, if the same number has been uttered a certain number of times (for example, three times) or more, the determination unit of the server 11 or the client 12 , it is desirable to determine that the learner did not perform the work task seriously (the validity of the calculation is low), and exclude the trial from the analysis target when calculating the score of the memory task.

In addition, when performing a memorization task while calling out, the judging unit of the server 11 or the client 12 determines the validity of the calculation and also determines whether the character string being called out is the given memorization task. It is desirable to determine if the columns match. If it is determined that the content of the call is extremely inappropriate, for example, the character string is not called at all, or the wrong character string is used in the first and second calls. , the determination unit of the server 11 or the client 12 determines that the repetitive preemptive call was not performed appropriately, and removes the trial from the analysis target when calculating the memory task score Exclusion is desirable.

Now let's talk about feedback.

FIG. 11 is a graph showing an example of memorized task results in this embodiment, and FIG. 12 is a graph showing an example of memorized task results for each calculation time in this embodiment.

In this embodiment, after all the memory tasks have been performed, in step S3 in FIG. 7, the results of each learner are displayed on the client 12 operated by each, like the graph shown in FIG. displayed in the In this graph, the vertical axis indicates the accuracy rate [%] of the character string that is the memorization task input by each learner in step S13 in FIG. A distinction is made between the case (no callout) and the case where the memory task was performed while calling out (with callout). As shown in FIG. 11, the correct answer rate is low when the memory task is performed without calling out. At the same time, the correct answer rate increases when the memory task is performed while calling out. be able to.

Also, the clients 12 operated by each can display the results of each learner on the display section as a graph shown in FIG. 12 . In this graph, the vertical axis indicates the accuracy rate [%] of the character string that is the memorization task input by each learner in step S13 in FIG. seconds). In the graph, the solid line shows the results when the memory task was performed while calling out (with calling), and the dashed line shows the results when the memory task was performed without calling out (without calling). showing. As shown in FIG. 12, when the memory task is performed without calling out, the accuracy rate decreases as the calculation time increases. In addition, if the memory task is performed while calling out, a high accuracy rate is maintained even if the calculation time is long. It is possible to realize that memory errors can be prevented by calling in advance.

Next, the results of experiments conducted by the inventors will be described.

FIG. 13 is a graph showing an example of memory task performance in an experiment conducted with university students as subjects, and FIG. 14 is a graph showing an example of memory task performance for each calculation time in an experiment conducted with university students as subjects.

The inventors conducted an experiment in which university students were asked to perform a memory task similar in content to the memory task described above, using the learning device 10 according to the present embodiment. The results of such experiments are shown in FIGS. 13 and 14. FIG. In this experiment, the number of trials for the memory task without calling and the memory task while calling was 20 trials, respectively, and the calculation time was 1 to 20 seconds. The range was changed every 1 second. FIG. 13 shows a graph similar to FIG. 11, but the accuracy rate on the vertical axis of the graph is shown in decimals. Also, FIG. 14 shows a graph similar to FIG.

As shown in Fig. 13, when comparing the accuracy rate when the memory task was performed without calling out and when the memory task was performed while calling out, the results of the memory task with calling out were found to be high. It was experimentally confirmed that the correct answer rate was higher, that is, the performance of the memory task was better than when the memory task was performed without calling out. It was also experimentally confirmed that the accuracy rate when the memory task was performed without calling out was much lower than when the memory task was performed with calling out.

Furthermore, as shown in FIG. 14, when the accuracy rate according to the length of the calculation time is compared, if the calculation time is 1 second, even if the memory task is performed without calling out, it is possible to memorize while calling out. When the task was performed, the accuracy rate was about the same, but when the calculation time was 2 seconds or more, the accuracy rate when the memory task was performed without calling out decreased, that is, the performance of the memory task was poor. It was confirmed experimentally that In addition, it was experimentally confirmed that when the calculation time was 5 seconds or longer, the correct answer rate decreased to 30% or less when the memory task was performed without calling out.

In this way, from the fact that it has been confirmed by experiments, it is assumed that when the memory task is performed using the learning device 10, the performance will be better when the call is made than when the call is not made. be done.

Finally, the motivational effect of repeated preemptive calling will be described.

Using the learning device 10 according to the present embodiment, the learner will experience "(1) human memory is more fragile than expected" through the experience of forgetting even after about three seconds without calling out. can be realized. In addition, through the experience of improving grades by calling out, the learner can "(2) realize that memory errors can be prevented by repeating preemptive calling". In addition, by presenting the aggregated results of the memory tasks of all the learners, the learners will be able to "(3) realize that this repetitive preemptive recall is effective in preventing memory errors for most people." "be able to. It is expected that such a realization will increase the motivation of the learner to make repeated preemptive calls.

As described above, in the present embodiment, the learning device 10 is provided with the client 12 operated by the learner and the server 11 communicably connected to the client 12. is. Then, the server 11 stores real feeling software, which is execution software for causing the learner to perform the memorized task, and transmits a control signal for the real feeling software to the client 12, and the client 12 has a display unit and an input unit. When a control signal is received from the server 11, the fact that the memorized task will be performed under the condition of no call-out is displayed on the display unit, and after the learner performs the memorized task under the condition of no call-out, an answer is sent from the input unit. When inputting, it is displayed on the display section that the memory task will be performed with the condition of being called out, and when the learner inputs the answer from the input section after performing the memory task with the condition of being called out, each executed memory task display the result of

Further, in the present embodiment, the learning method is a memory error prevention effect learning method by preemptive calling. Then, a step of displaying to the learner that the memory task will be performed without the call-out condition, a step of inputting the answer after the learner performs the memory task with the condition of the no-call-out, and a call-out condition. a step of displaying to the learner that the memory task will be performed under the condition that the learner performs the memory task under the condition that the learner inputs an answer after performing the memory task; and displaying to the learner.

As a result, the learner can appropriately learn the fragility of memory and the memory error prevention effect of prefetch recall, so memory errors are effectively prevented.

In addition, memory tasks include memorization tasks that the learner should memorize and work tasks that the learner should do. Therefore, the learner can experience a situation similar to an actual workplace environment in which it is necessary to maintain the memory of items to be memorized while performing tasks.

Furthermore, memorization tasks are character strings and task tasks are calculations. Therefore, the learner learns that memory errors are likely to occur even for relatively simple memorized contents if the learner does not call ahead, even if the work is relatively simple. be able to.

Moreover, the learner keeps repeating the calculations during the given calculation time. Therefore, the learner can experience a state similar to an actual workplace environment in which work continues without interruption.

Furthermore, when the learner performs the memory task without calling out, the learner utters the result of the calculation each time he/she performs the memory task. and a string. Therefore, the learner can realize the memory error prevention effect when the preemptive call is made while comparing with the case where the preemptive call is not made.

Furthermore, each client 12 sends the results of each memory task to the server 11, the server 11 aggregates the results of each memory task and sends them to each client 12, and each client 12 receives the aggregated The results of each memory task are displayed on the display. Therefore, by knowing the results of other learners, each learner can realize that the memory error prevention effect of repetitive preemptive calling is effective not only for himself but also for most other learners. can.

It should be noted that this disclosure describes features of preferred exemplary embodiments. Various other embodiments, modifications and variations within the scope and spirit of the claims appended hereto will naturally occur to those skilled in the art upon reviewing the disclosure herein. be.

INDUSTRIAL APPLICABILITY The present disclosure can be applied to a memory error prevention effect learning device and method using preemptive calling.

10 learning device 11 server 12 client

Claims (7)

A memory error prevention effect learning device by prefetch calling, comprising a client operated by a learner and a server communicably connected to the client,
the server stores execution software that causes the learner to perform a memory task, and transmits a control signal for the execution software to the client;
The client includes a display unit and an input unit, and upon receiving the control signal from the server, displays on the display unit that the memorized task is to be executed under the condition that the learner calls out. When an answer is entered from the input unit after executing the memory task under the condition of "none", the fact that the memory task will be executed under the condition of "calling" is displayed on the display unit, and the learner memorizes it under the condition of "calling". A memory error prevention effect learning device by pre-recalling, characterized in that, when an answer is input from the input unit after executing a task, the result of each executed memory task is displayed on the display unit. 2. The memory error prevention effect learning by prefetch recall according to claim 1, wherein the memory task includes a memorization task that is a matter that the learner should memorize and a work task that is a work that the learner should do. Device. 3. The memory error prevention effect learning device by prefetch recall according to claim 2, wherein the memorization task is a character string and the work task is calculation. 4. The memory error prevention effect learning device by prefetch call according to claim 3, wherein the learner continues to repeat the calculation during a given calculation time. When the learner performs the memory task under the condition of no call-out, the learner utters the result of the calculation each time it calculates, and when the learner performs the memory task under the condition of the call-out, 5. The memory error prevention effect learning device by prefetch calling according to claim 4, wherein said character string is uttered. The client transmits the results of each of the memory tasks to a server, the server aggregates the results of each of the memory tasks and transmits them to the client, and the client receives the aggregated results of each memory task. 6. The memory error prevention effect learning device by prefetch calling according to any one of claims 1 to 5, wherein is displayed on the display unit. A memory error prevention effect learning method by prefetch calling using a client operated by a learner and a server communicably connected to the client, comprising:
The client receives a control signal for the execution software from the server that stores execution software for causing the learner to perform the memory task, and the client displays to the learner that the memory task will be performed on the condition that no call is made . a step of displaying in the section ;
a step of inputting an answer from the client's input unit after the learner performs a memory task under the condition that no call is made;
a step of the client displaying to the learner on a display unit that the memory task will be performed on the condition that the client will be called;
a step of inputting an answer from the input unit of the client after the learner performs the memory task under the condition that the learner is called;
and a step of the client displaying the result of each executed memory task to the learner on a display unit .