JPH096222A - Electronic learning apparatus - Google Patents

Abstract

PURPOSE: To provide an electronic learning apparatus comprising a means to propose a learning plan suitable for a learner, a means to manage the learning in progress along the plan, and a means to evaluate while considering the learning hours. CONSTITUTION: The electronic learning apparatus is composed of a CPU1 control and process a main apparatus, a display apparatus 2 to display the information for learning corresponding to a learning plan and learning situation, a key board to carry out various kinds of input, and a memory apparatus 4. The memory apparatus 4 is provided with standard learning time information 4a in which targets and standards of respective unites, subjects, and learning hours are saved, a question and answer region 4b to memory questions and answers to the questions, a learning plan region 4c to store a learning plan obtained by calculation from the learning results, an input by a learner, etc., and a learning situation and evaluation region 4d to store actual learning hours, progress, and evaluation. This learning apparatus can manage a learning plan management suitable for a learner while considering the learning hours and history of the learner.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an electronic learning machine, and more particularly to a learning machine which shows a learning plan and enables learning to be managed by the plan.

[0002]

2. Description of the Related Art In one type of conventional electronic learning machine, the point at which learning is finished after the start of learning is determined only by the learner's intention. Moreover, the evaluation of learning was made by the degree of comprehension expressed by the correct answer rate by the learner who solved the problem. As described above, the conventional electronic learning machine of this type does not have a function of making and storing a learning plan and managing the learning progress according to the plan. on the other hand,
Japanese Patent Laid-Open No. 6-95580 discloses a device having a function that can be used for making a plan, in which a learning amount for one time is an amount obtained by averaging the learning amounts up to that time. Some have been set. With this function, it is possible to determine the learning amount for one time in advance. Some other conventional examples output the problem solved on the day at the end of learning. With this function, it is possible to confirm the learning amount for one time later. Further, conventionally, what the learner evaluates is the degree of comprehension of the learned content, and does not consider the time taken for understanding and answering.

[0003]

As described above, the above-described conventional electronic learning machine does not have a function of making a sufficient learning plan, so that in the case of individual learning, there is no planning and the learning time or amount is small. Too much more than necessary, and conversely too little,
Or, there was a risk that the difference would occur depending on the day.
In such learning, it is difficult for the learning to become a habit and it is difficult to achieve the goal. Such problems can be solved by making a plan, but even if the learner makes a plan himself, there are the following problems. First, as learning progresses, it is difficult and difficult for the learner to check the learning history himself and make a plan that suits him. Also,
Even if you make a plan, if you do not manage the learning situation, it will be difficult to achieve your goals, the effect of making a plan will be diminished, and the learner will manage the speed and progress of learning himself. Is difficult Thus, in order to make a learning plan and to know the achievement level of the plan, the learner had to manage the learning by making a plan table himself and taking more time. Further, even if the function of the conventional electronic learning machine is used, there is only an insufficient function such that the learning amount for one learning is limited to the same amount as before, or the learning amount is confirmed later.

Furthermore, when the content learned and the degree of understanding of one learner and another learner are exactly the same, the same evaluation is performed by the evaluation methods so far. However, even if the contents learned and the degree of comprehension are the same, it is considered that there is a difference in the ability of the learner if there is a difference in the time taken for learning and answering. For this reason, the conventional evaluation method that considers only the learning content and the degree of comprehension cannot perform a completely personalized evaluation, so that there is a problem in that a slight difference in ability cannot be expressed. The present invention is for solving the above-mentioned problems, proposes a learning plan suitable for the learner, manages the learning so that the learning progresses according to the plan, and further evaluates the learning time. It is an object of the present invention to provide an electronic learning machine having a means for

[0005]

In order to achieve the above object, the present invention provides (1) means for displaying information for input or learning to a learner, and input of information instructed by the learner Input means, learning amount data for each learning item or question and standard learning time data thereof, content data of each learning item or question and answer content data, learning machine control data, and data based on input by the input means Or a processing means for controlling the operation of the learning machine and processing the data, or
(2) In (1) above, as learning plan data in the learning machine control data, learning amount data, learning time data, and learning history data based on input by the input means are used, or (3) above (2). ), The learning plan data can be changed by the learner by the input means, or (4) above (1)
In the above, the electronic learning machine is characterized in that the learning result data based on the input by the input means is evaluated by the learning amount data and the standard learning time data.

[0006]

[Operation] Each learning item is stored as data in the storage means,
Alternatively, there is a learning amount for each question and its standard learning time data, learning content data, and learning machine control data, and the learning plan made by these data is input by the learner through the input means. According to the learning plan, the learning result is obtained as a learning amount and a learning time. Then, the result of this learning can be reflected in the evaluation of the ability of the learner and the learning plan. In addition to the above points, by using the learning history data based on the learner's input operation as the learning plan data in the learning machine control data, the learning management can be performed according to the learning plan suited to the individual learner. In addition to the above, since the learner can change the learning plan by the input means, it is possible to deal with a large change in the learning time and the learning amount depending on the condition of the learner on that day and the difficulty of the learning content. it can. Further, the data of the learning result is evaluated by the learning amount data and the standard learning time data. Since this evaluation takes time into consideration, it is possible to make a more appropriate evaluation and to reflect it in the subsequent learning plan.

[0007]

DESCRIPTION OF THE PREFERRED EMBODIMENTS In the embodiments of the present invention, learning consists of a part for obtaining new knowledge by reading an explanation, a part for solving a problem related to it and confirming an understanding level, or each part thereof. The learning amount here means the number of pages,
Although it can be represented by various units such as the number of units and the number of problems, in the embodiment, both the explanation part and the problem part are represented by the number of pages. As a result, the learning amount becomes one unit, and the learning plan can be easily represented. 1 to express the amount of problem in pages
Keep the number of issues per page constant. The learning time is the time for reading and understanding the explanation and the time for answering, and the learning machine continues to measure from the start of learning to the end of learning. The learning plan is that the learning machine or the learner decides the learning amount and time with reference to the learning history before starting the learning. Further, since the progress of learning is also managed by time, the state of the middle and end of learning is detected by measuring the time, and is grasped as the learning state together with the learning amount.
Further, the learning result is evaluated (for example, the degree of comprehension represented by the score of the test).
You can read the achievement status of the plan from the learning amount and time. These two pieces of information will also be used in the next learning plan. The plan that the learning machine proposes to the learner is the average of the learning amount and the time per learning so far. It is also possible to gradually increase the speed and amount of learning when proposing a learning plan. It is possible to change the plan if the learner wishes. By displaying the elapsed time and the remaining time on the screen based on this learning plan, the achievement of the plan is supported. The evaluation for learning is expressed by the correct answer rate by solving the problem corresponding to the learned content. What is evaluated here is the level of understanding of the learning content.
In the embodiment, more detailed evaluation is made in consideration of the time taken for the answer in addition to those. Specifically, the standard learning time information is compared with the learning time, and the ratio is used when the learning machine evaluates the learner.

Next, a more specific embodiment of the present invention will be described. FIG. 1 is a block diagram showing the configuration of an embodiment of an electronic learning machine of the present invention. The electronic learning machine in the present embodiment comprises a CPU 1 for controlling and processing the device, a display device 2 for displaying a learning plan and a learning situation, a keyboard 3 for a learner to make various inputs described below, and a storage device 4. ing. The storage device 4 stores standard learning time information 4a that records goals and standards of learning time of each unit and subject, and a question / answer area 4 that stores questions and answers to them.
b, a learning plan area 4c for storing a learning plan calculated from a learning result or input by a learner, and a learning situation / evaluation area 4d for storing an actual learning time, progress, and evaluation.

The operation procedure in the present embodiment configured as described above will be described with reference to the flow chart of FIG.
First, a learning plan is made in step S1. The decision of the learning plan is as follows. Before you start learning, decide how much you will learn at one time and how long it will take. This is because the data prepared in the learning planning area 4c is read out. FIG. 3 shows a display image of the display device 2 at that time. Since the learning machine presents the plan to the learner in this way, when the learner commands execution in the image, the learner has selected this learning plan prepared by the learning machine. Actually, in the first learning, since there is no learning history so far, the default number of pages (for example, 5) and the standard learning time information 4a are calculated and presented.
After the second time, as will be described later, the history of the learning situation / evaluation area 4d is referred to in step S8, the results up to that time are averaged, and the amount and time per one time are obtained and presented.

In this way, once a learning plan is created and presented by the learning machine, the learner then selects whether to adopt or change this plan on the screen of FIG. 3 (step S
2). If accepted, click Execute. Then, learning is started. If you want to change it, click Change. Then, a cursor appears at a position with a number indicating the learning amount, and a new number is keyed in there. When you press the Enter key, the cursor moves to the number indicating the time, so perform the same operation. Here, when the enter key is pressed, the cursor moves to the learning amount again. When execution is clicked at any point, the plan in the state input at that point is stored in the learning plan area 4c.

As the contents of the change of the learning plan, for example, the time may be changed without changing the learning amount. Specifically, it may be possible to predict that the learning time will change significantly depending on the condition of the learner and the difficulty of the learning content. However, even in this case, it is necessary not to allow completely free change. Because the proposed plan was derived from the achievements of the learners to date, extreme changes may not fit the actual situation, and it may be impossible or too easy to achieve the plan. This is because the effects that have been taken off are weakened. Therefore, it is necessary to set so that changes outside a certain range are not input. For example, only accept changes within 20%. The range of this numerical value can also be changed.
If a plan that exceeds the settings is entered, a screen that conveys a message to the learner should appear immediately after clicking Execute. The learning speed of the learner up to that point is shown, and since this plan does not match the actual situation, it is urged to set it within the range of 30 minutes ± 6 minutes, for example. Similarly, when the learning amount is changed, the time is automatically changed and displayed according to the amount input by the learner. It is also possible to change this time. In that case, the time is changed as described above.

Further, when proposing the learning plan in step S1, it is possible to gradually increase the learning plan. For example, slightly increase the learning amount obtained from the learner's history (for example, one page each time),
Alternatively, it is realized by making the time shorter than the required time (for example, 1 minute each time). However, no matter how much the learning ability improves, there is a limit to the learning speed. Therefore, it is necessary to set a limit so that the learning speed is not further increased when the learning speed reaches a certain level. This is set in advance as learning plan data for the learning machine. Since it is about the limits of human ability, we do not allow changes in settings.

When learning is actually started by clicking "Execute" with the keyboard on the plan screen (step S3),
The elapsed time is displayed on the screen. FIG. 4 shows the screen at that time. This example shows the scheduled time (here, 30
The point starts from (minute) and moves on the line chart showing time. For the scheduled time, refer to the learning plan area 4c, and for the lapse of time, click Execute to start timing. When the scheduled time has passed or the scheduled learning amount is finished, a termination instruction is issued (step S4). If the planned learning amount cannot be completed after the scheduled time, it is possible to extend the time and continue the learning, and encourage the student to end the planned range as much as possible (step 6). . If you want to extend the time and continue learning,
Extra time can be set. The screen for setting the extension time will open, so enter the time there or use the estimated time required from the learning speed this time to the end of the system. When the extension time is set in this way, the process is repeated from step S3.

Upon completion of learning, the actual learning time and learning amount are stored in the learning status / evaluation area 4d in step S7. Using the total learning time, the total learning amount, and the number of learnings stored in this learning situation / evaluation area 4d, the average of the learning time and amount per learning is calculated, and the next learning plan is prepared (step S8).

Further, in addition to the above-mentioned plan for one learning, an embodiment in which another form of plan is made is possible. For example, you can plan for a long time.
Specifically, the plan is to set the number of times of study required before the course is completed, and is supposed to be a review for exam study. In this case, the long-term plan setting screen opens only when there is a request from the learner in step S1. In this screen, the time portion of the screen of FIG. 3 is the number of times. When the target number and amount of learning are input on this screen, the screen of FIG. 3 is subsequently opened, and the amount and time required for one learning are displayed. The amount is obtained by dividing the planned learning amount by the planned number of times of learning, and the time is obtained based on the data of the learning situation / evaluation area 4c as the time corresponding to the amount. Before the next learning starts (step S1 in the next learning), the following items are always displayed. First, the learning speed up to now is 1
It is represented by the learning amount per day, and the number of times of learning required until the learning is continued when the learning is continued at that speed is displayed. This allows the learner to know the achievement level of the plan at that time. Next, the learning speed required to finish at the scheduled number of times is also displayed in the form of the learning amount. This gives the learner
You can compare your current learning rate to determine if the plan is achievable. With this information as a reference,
If the plan appears to be difficult to achieve at the current learning rate, the learner can choose to change the plan thereafter or change the learning rate to achieve the plan. The plan is changed by opening the long-term plan setting screen again, and the learning speed is changed by the screen for making a learning plan for the day shown in FIG.

At the end of one learning, the learning result is evaluated in step S9. In the evaluation here, the learning time is measured and the function of recording is used to evaluate the learning time. As an evaluation method considering the learning time, for example, the following methods can be considered. Standard learning time information 4
The comparison between a and the learning time of the learner stored in the learning situation area 4d is used. If a test is performed using the questions in the question / answer area 4b and the degree of comprehension is measured, if the answer is completed within the time, the scoring result is used as it is as the final evaluation. When the specified time is exceeded, the score of the test considering time in addition to the scoring result, for example, (score)-(score)
* (Excess time) / (Standard time) is also shown and this is the final evaluation.

[0017]

As is apparent from the above, the present invention brings the following effects. (1) By displaying the elapsed time during learning, the learner can be directed toward achieving the plan. In addition, by facilitating the end of the planned learning amount even after the scheduled time, it is easy to achieve the previous plan. (2) The means for presenting the learning plan makes it possible to automatically make a plan. In addition, there is a restraining effect that an unfeasible plan is not established. Also, if you have made a plan for the future, by always displaying the achievement status,
You can either go towards achieving the plan or change the plan that is infeasible. (3) Since it is possible to gradually make a plan with high goals, it is expected that the ability of the learner will be improved if the learning is advanced according to the plan. (4) By allowing the learner to change the learning plan proposed by the learning machine, it is possible to deal with a large change in the learning time and the learning amount depending on the condition of the learner on that day and the difficulty of the learning content. Even in that case, by not allowing extreme changes, it is possible to avoid making plans that do not match the actual situation. (5) By adding more time to the conventional evaluation method that considers only the learned content and its comprehension level, it is possible to perform evaluation that is more personalized. Also, in the case of the test, it seems effective to learn all the questions even after the time has passed. Even in that case, by considering the time, it is possible to perform an appropriate evaluation that can show the difference from the case where the answer is given within the time. Further, since the standard learning time is displayed, it is possible to know the target learning speed and reflect it in future learning plans. (6) By providing an electronic learning machine provided with the above-mentioned means, it is particularly effective for self-study type learning.

[Brief description of drawings]

FIG. 1 is a block diagram showing a configuration of an embodiment of the electronic learning machine.

FIG. 2 is a flowchart showing an operation procedure of the embodiment shown in FIG. 1 of the electronic learning machine.

FIG. 3 is a diagram of a screen of a display device for displaying a learning plan of the embodiment and inputting the learning plan.

FIG. 4 is a diagram showing that the passage of time is displayed on the screen of the display device during learning.

[Explanation of symbols]

1 ... CPU, 2 ... Display device, 3 ... Keyboard, 4 ... Storage device, 4a ... Standard learning time information, 4b ... Question / answer area, 4c ... Learning planning area, 4d ... Learning situation / evaluation area.

Claims (4)

[Claims] 1. A means for displaying information for input or learning to a learner, an input means for inputting information instructed by the learner, learning amount data for each learning item or question, and standard learning thereof. Storage means for storing time data, content data of each learning item or content data of questions and answers, learning machine control data and data based on the input by the input means, and control operation of the learning machine and data processing. An electronic learning machine comprising a processing means. 2. The electronic learning machine according to claim 1, wherein learning amount data, learning time data, and learning history data based on an input by the input unit are used as learning plan data in the learning machine control data. 3. The electronic learning machine according to claim 2, wherein the learning plan data can be changed by a learner by the input means. 4. The electronic learning machine according to claim 1, wherein the learning result data based on the input by the input means is evaluated by the learning amount data and the standard learning time data.