JPH07140892A - System for education - Google Patents

Abstract

PURPOSE:To provide a system for education capable of dealing with learners' personal characteristics while the system is simple. CONSTITUTION:The learner inputs the answer data for outputted question data to a simulation means 12 according to the question data. The simulation means 12 executes simulation in accordance with the selected question data and the answer data and outputs the result of the simulation. An evaluating means 10 evaluates the result of the simulation as an input variable by fuzzy inference and outputs the result of the evaluation. A class changing means 8 receives the result of the evaluation and makes judgment as to whether the change of a class is necessary or not. The class changing means outputs class change data when the change is judged to be necessary. A class determining means 4 changes the class according to the class change data upon receipt of the data and applies the data to a question data selecting means 6. The question data selecting means 6 selects the question data corresponding to the new class and outputs the data as a next question to the learner.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an educational system, and more particularly to improving the degree of adaptability to each learner.

[0002]

2. Description of the Related Art In recent years, an educational system using a computer has been proposed and put into practical use. These computer-aided learning systems display problems on a CRT or the like and obtain answers from learners. Generally, the question is designed for the typical learner.

There is also an educational system constructed by using the technology of artificial intelligence.

[0004]

However, the above-mentioned conventional techniques have the following problems.

In a general computer-aided learning system, problems are created assuming a typical learner.
For this reason, it was not possible to effectively learn according to each learner, without taking into consideration differences in learners' knowledge and duties.

Further, in the case of the system using the artificial intelligence technology, it is possible to learn according to the ability of each learner, but it takes a lot of effort to construct the system and a complicated and expensive computer is required. Is.

It is an object of the present invention to solve the above problems and to provide an educational system which is a simple system and can cope with the characteristics of individual learners.

[0008]

According to a first aspect of the present invention, there is provided an educational system comprising a problem data storage means for storing a plurality of problem data according to a learner's class, a class decision data inputted by a learner, or a class changing means. Class determination means for determining the learner's class based on the class change data of
Based on the class determined by the class determination means,
Based on the answer data, the question data selecting means for selecting and outputting the corresponding question data from the question data storage means, receiving the selected question data, and receiving the answer data input by the learner for the question data A simulation means for performing a simulation and outputting a simulation result, an evaluation means for evaluating the simulation result and outputting the evaluation result, and receiving the evaluation result and outputting the class change data when it is determined that the class needs to be changed Equipped with class changing means.

The educational system according to a second aspect of the invention further comprises learning history storage means for storing learning history data for each learner.

The education system according to a third aspect is characterized in that the evaluation means obtains the evaluation result by fuzzy inference using the simulation result as an input variable.

[0011]

The teaching system according to claim 1 comprises class determining means for determining the class of the learner based on the class determining data input by the learner, and the problem data based on the determined class is learned. Given to the person. Furthermore, the result of simulating the response data is evaluated, the class is changed, and the next question data is given. Therefore, depending on the learner's characteristics and degree of learning,
Appropriate education can be provided.

The education system according to a second aspect of the present invention comprises a learning history storage means for storing learning history data for each learner. Therefore, when the learning is restarted, it is possible to start from the class at the time of the previous interruption.

The educational system according to claim 3 is characterized in that the evaluation result is obtained by fuzzy inference using the simulation result as an input variable. Therefore, a more accurate evaluation result can be obtained.

[0014]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS FIG. 1 shows the overall configuration of an educational system according to an embodiment of the present invention. The problem data storage means 2 is
A plurality of question data corresponding to the learner's class are stored in advance. The class determining means 4 determines the learner's class based on the class determining data input by the learner.
The problem data selection means 6 selects and outputs the corresponding problem data from the problem data storage means based on the class determined by the class determination means.

The learner inputs the answer data to the simulation means 12 according to the output question data. The simulation means 12 performs a simulation based on the selected question data and answer data, and outputs the simulation result. Evaluation means 10
Uses the simulation result as an input variable, evaluates it by fuzzy inference, and outputs the evaluation result. The class changing means 8 receives the evaluation result and determines whether or not the class needs to be changed. When it is judged that the change is necessary, the class change data is output.

Upon receiving the class change data, the class determining means 4 changes the class in accordance with the data and gives it to the problem data selecting means 6. The problem data selection means 6 selects the problem data corresponding to the new class and outputs it to the learner as the next problem.

Hardware for realizing the educational system of FIG. 1 by a personal computer is shown in FIG.
Shown in. The CPU 20 is connected to the bus line 32. The bus line 32 has a RAM 22, a hard disk 24, a CRT 26, a keyboard 28, and a mouse 3.
0 is also connected. The CPU 20 is the hard disk 2
Each part is controlled according to the program of 4. Also,
In this embodiment, an educational system for learning fuzzy techniques will be assumed and described.

3 and 4 show flowcharts of programs stored in the hard disk 24. First, in step S1, a question for determining the learner's class is displayed on the CRT 26 (see FIG. 5). Learners
The mouse pointer 40 is moved by the mouse 30 to select an answer. The question used in this example is shown in FIG. Answers to these are fetched as class determination data (step S2).

Next, the learner's class is determined based on the input answer (class determination data) (step S3). First, based on the question of FIG. 6A, the learner is divided into two knowledge levels according to the knowledge of fuzzy. Lower knowledge level: None, a little Higher knowledge level: Average knowledge, above-standard knowledge, specialized knowledge.

Next, based on the question of FIG. 6B, the learner is divided into the following four technical fields CE, IT, O and N. CE: Control IT: Information processing O: Electronic, electrical, mechanical, mathematics, physics N: Other.

Further, based on the question of FIG. 6C, the level of understanding of the learner is divided into two levels. Level 1: Manufacturing, service / maintenance, sales / sales, general manager, general affairs, planning, etc. Level 2: Research and development, manufacturing technology, technology, product development.

Next, by combining the above levels, FIG.
The class of the learner is determined based on the table (step S
3, class determination means). For example, "knowledge above the standard" for the question of FIG. 6A, "control" for the question of FIG. 6B,
The learner who answered “plan” to the question in FIG.
Classed into one class.

The hard disk 24, which is a problem data storage means, stores problem data according to the class of the learner in advance. For example, the problem for learners in the CE2 class is more difficult than the problem for learners in the CE1 class.

In this embodiment, the type of problem is changed according to the technical field of the learner. For example,
Fuzzy control for autopiloting an automobile is stored as a problem for learners in the technical field CE.
Further, for the learner in the technical field N, the fuzzy application problem regarding accounting processing is stored.

Further, the detail of the explanation of the problem is changed and stored depending on the level of comprehension. That is, more detailed explanations are stored for level 1 learners than for level 2 learners.

Hereinafter, the description will be made assuming that the learners are classified into the CE1 class. Based on the above result, the CPU 20 selects and reads the problem data from the hard disk and displays it on the CRT 26 (step S4,
Question data selection method). Here, it is assumed that the fuzzy control for auto-driving the automobile is selected and displayed as a problem. Since the level of comprehension is 1, the content of the problem is explained in detail on the CRT 26 (step S
5).

On the screen of the CRT 26, as shown in FIG. 8, sensor information (output of acceleration sensor, output of steering angle sensor, output of accelerator depression angle sensor, etc.) that can be used for control, control, etc. Actuators that can be used to perform steering (steering rotation device, accelerator stepping device, brake stepping device, etc.)
Is displayed. Further, the purpose of control is shown on another screen. In this example, the purpose is to prevent a collision, to keep the inter-vehicle distance larger than the ideal inter-vehicle distance (represented by the length of one vehicle body for each speed of 15 km), and to minimize the gasoline consumption. Be done. In addition,
As the class increases (CE2), the number of sensors and actuators that can be used increases and the purpose increases.

The learner looks at the problem displayed on the CRT 26 and determines the membership function and inference rule (answer data) necessary for the fuzzy control by the mouse 30 or the keyboard 2.
Input from 8 (step S6). The CPU 20 stores the input membership function and inference rule in the RAM 22. Further, the simulation data corresponding to the problem selected in step S4 is read from the hard disk 24. Here, the simulation data is data for performing a simulation for evaluating answer data to a question, and is stored in the hard disk 24 for each question. In this example, the road width,
It is data such as the traveling speed and position of another vehicle.

The CPU 20 simulates the running of the vehicle based on the read simulation data and the response data (membership function and inference rule) stored in the RAM 22 (step S7, simulation means). Then, the simulation result is output. Here, as simulation results, the number of collisions, safety factor, gasoline consumption rate (mileage per liter)
The data of is output. The safety coefficient is a coefficient expressed by the following formula. Safety factor = inter-vehicle distance in simulation / ideal inter-vehicle distance Here, the safety factor calculated and averaged every predetermined time is used.

Next, the CPU 20 fuzzy infers the learner's score (1 to 100) using the number of collisions, the safety coefficient, and the gasoline consumption amount as input variables (step S8,
Evaluation means). 9 to 11 show the membership function of the input variable (the antecedent part). FIG. 12 shows the membership function of the consequent part. In addition, the fuzzy rules are shown in FIGS.
5 shows.

If the score (evaluation data) thus obtained is within a predetermined range (for example, 30 to 70), step S4 and subsequent steps are repeated again without changing the class (step S9). If the score is less than the lower limit, lower the class. If the score is larger than the upper limit, the class is raised (step S10). Then, step S4 and subsequent steps are repeated again. As described above, appropriate learning can be performed according to the level of the learner.

If learning is interrupted midway, C
The PU 20 stores the learning history of the learner up to that point (which class it belongs to, and which question it answers when a plurality of questions are given continuously) in the hard disk 24 (learning history storage). means). Therefore, when the learning is restarted next, the state at the time of interruption can be started.

In the above embodiment, fuzzy inference is used for evaluation, but other inference methods may be used.

Further, in the above embodiment, the educational system used for learning the fuzzy technique has been described, but the invention can be applied to learning of other techniques.

[0035]

The educational system according to the first aspect of the invention comprises the class determining means for determining the class of the learner based on the class determining data input by the learner, and the problem data based on the determined class. Is given to learners. Furthermore, the result of simulating the response data is evaluated, the class is changed, and the next question data is given. Therefore, appropriate education can be provided according to the characteristics and degree of learning of the learner.

An educational system according to a second aspect comprises a learning history storage means for storing learning history data for each learner. Therefore, when the learning is restarted, it is possible to start from the class at the time of the previous interruption.

The educational system according to claim 3 is characterized in that the evaluation result is obtained by fuzzy inference using the simulation result as an input variable. Therefore, a more accurate evaluation result can be obtained.

That is, according to the present invention, it is possible to provide an educational system which is a simple system and can cope with the characteristics of each individual learner.

[Brief description of drawings]

FIG. 1 is a diagram showing an overall configuration of an educational system according to an embodiment of the present invention.

FIG. 2 is a diagram showing a hardware configuration of the system of FIG.

FIG. 3 is a flowchart showing a program stored in a hard disk 24 of FIG.

FIG. 4 is a flowchart showing a program stored in a hard disk 24 of FIG.

FIG. 5 is a diagram showing a question displayed on the CRT 26.

FIG. 6 is a diagram showing an example of a question.

FIG. 7 is a diagram showing classification.

FIG. 8 is a diagram showing a display of a problem by the CRT 26.

FIG. 9 is a diagram showing a membership function relating to the number of collisions.

FIG. 10 is a diagram showing a membership function regarding a safety factor.

FIG. 11 is a diagram showing a membership function relating to a gasoline consumption rate.

FIG. 12 is a diagram showing a membership function regarding a score.

FIG. 13 is a diagram showing an evaluation rule.

FIG. 14 is a diagram showing an evaluation rule.

FIG. 15 is a diagram showing an evaluation rule.

[Explanation of symbols]

 2 ... Question data storage means 4 ... Class decision means 6 ... Question data selection means 8 ... Class change means 10 ... Evaluation means 12 ... Simulation means

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Claims (3)

[Claims] 1. A problem data storage means for storing a plurality of problem data according to a learner's class, and a learner's class based on class decision data input by the learner or class change data from the class changing means. Based on the class determining means to determine, the class determined by the class determining means,
Based on the answer data, the question data selecting means for selecting and outputting the corresponding question data from the question data storage means, receiving the selected question data, and receiving the answer data input by the learner for the question data Simulation means that performs simulation and outputs the simulation result, evaluation means that evaluates the simulation result and outputs the evaluation result, and outputs the class change data when it is determined that the class needs to be changed based on the evaluation result Educational system with class changing means. 2. The educational system according to claim 1, further comprising learning history storage means for storing learning history data for each learner. 3. The educational system according to claim 1 or 2, wherein the evaluation means obtains the evaluation result by fuzzy inference using the simulation result as an input variable.