JP2912699B2 - Teaching material generator - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION [Overview] Regarding a teaching material generation device used in the field of computer-assisted learning / education, a teaching material generation device capable of generating a more sophisticated and complicated teaching material than a teaching material handled by a conventional generation type CAL A problem sentence generation processing unit that generates a problem by combining type data that defines the framework of the problem and parameter data that defines the attribute of the problem and the content of the problem corresponding to the attribute; Define the procedure for calculating the correct answer for each attribute,
A problem solving processing unit that generates correct answer data by a procedure corresponding to the parameter data and the attribute of the problem; a learner's answer data for the problem generated by the problem sentence generating processing unit; And a strategy processing unit that rewrites the next question attribute and parameter data corresponding to the attribute in accordance with the determination content of the answer determination processing unit.

[Industrial application field] The present invention relates to a teaching material generation device used in the field of computer-aided learning / education.

In the field of computer aided learning / education (CAL / CAI: CAL), various types of CAL teaching materials are required. For example, a learning material creation support system called an authoring tool is often used as a means for creating a drill-and-practice (repetitive practice) type CAL teaching material that requires a learner to provide some kind of answer. Simulation CA
There are many examples of L teaching materials created in an object-oriented programming language.

In the field of such CALs, there has been a demand for the development of a generation method for generating more sophisticated and complicated teaching materials than before.

[Prior Art] In the field of conventional CAL, there are not a few teaching materials in which questions and correct answers are fixed.

On the other hand, there has been considered a method in which a system automatically generates a teaching material called a generation type CAL. This is to generate a problem by inserting parameters (data) selected using random numbers into a problem type (prototype) prepared in advance in a teaching material such as a calculation drill.

FIG. 16 shows a system flow based on the concept of the conventional generation type CAL.

First, a problem is determined by a strategy, and a problem is generated and presented. Then, the answer is received from the learner, the correct answer is obtained, and finally the answer is determined. By repeatedly performing these processes, a teaching material is generated.

FIG. 17 shows an example of such a teaching material data generation process.
This example shows the teaching material data of a problem that is generated when a learner answers incorrectly from the teaching material data of the original question. The contents of the strategy in this example are to be used for problem solving in the generated problem. We try to keep the level of calculation constant to some extent. For example, <speed> is between 50 and 180, <time>
Is determined by a random number between 1 and 5.

When the learner answers, the level of calculation used in solving the problem is reduced within the above range. That is, both <speed> and <time> are set to small values.

FIG. 18 shows an example of a process of generating data generated at the time of learning and an example of a screen display of a question at the time of presentation.

FIG. 19 shows another example of the process of generating the teaching material data.

The content of the strategy in this example is to keep the level of calculation used for solving the problem in the generated problem to a certain degree. For example, <a> is between 1 and 8,
<X> is determined by a random number between -10 and 10.

If the learner answers incorrectly, the level of calculation used in problem solving is reduced within the above range. That is, <a>,
<X> is also set to a small value.

FIG. 20 shows another example of the generation process of the data generated at the time of learning and another example of the screen display at the time of presentation.

[Problems to be Solved by the Invention] However, in such a conventional method of generating teaching materials, the content of a question sentence is changed from a content of a problem determined by an arbitrary strategy, and a complicated display on a graphic is performed. However, there is a problem in that it is not possible to change the text and automatically obtain a correct answer from the content of the question sentence, and thus it is not possible to generate a sophisticated and complicated teaching material.

The present invention has been made in view of such a conventional problem, and provides a learning material generating apparatus capable of generating a learning material that is more advanced and complicated than the learning material handled by the conventional generating CAL. It is an object.

[Means for Solving the Problems] FIG. 1 is an explanatory view of the principle of the present invention.

In FIG. 1, reference numeral 10 denotes problem sentence type data 12a, graphic type data 12b composed of a model 12c and a control program 12d, and problem solving data 13 having demons 13a.
Input device for inputting teaching material data 11 consisting of parameter data 14, strategy data 15, incorrect answer data 16, and learner's answer data 17, and passing parameter data 14 through question sentence type data 12 a A question sentence generation processing unit 24 that generates a question sentence 31 according to, a graphic generation processing unit 25 that generates a graphic 32 from the graphic type data 12b,
A problem solving processing unit 26 that generates query data 34 from the parameter data 14 and extracts correct data 33 from the model 12c and the control program 12d corresponding to the demon 13a, and an answer that compares the answer data 17 and the correct data 33 of the learner. Judgment processing unit 27
A processing unit 22 including a strategy processing unit 23 that generates teaching material data 35 by rewriting the parameter data 14 from the strategy data 15 and the incorrect answer data 16 to the parameter data 37 and updating the model 12c to the model 36, This is a processing device including a memory 30 having a function as a work area for generating each data.

[Operation] In the present invention, as shown in FIGS. 1 (A) and 1 (B), the input device 10 sends teaching material data 11 [question type data 12
(Problem sentence type data 12a, graphic type data (program) 12b, model 12c, control program 12d), problem solving data 13 (demon 13a), parameter data 14,
Strategy data 15, wrong answer data 16], learner's answer data
17 is input and stored in the memory 30.

The processing unit 22 sequentially executes a strategy process, a problem sentence generation process, a graphic generation process, a problem solution process, and an answer determination process. Then, the process returns to the strategy processing again.

The question sentence generation processing unit 24 generates and outputs a question sentence 31 by passing the parameter data 14 through the question sentence type data 12a.

The graphic generation processing unit 25 stores graphic type data.
A graphic 32 is generated and output from the model 12c and the control program 12d that constitute 12b.

The problem solving processing unit 26 generates the inquiry data 34 from the parameter data 14,
In the demon 13a, the corresponding model 12c, and
The correct answer data 33 is extracted from the control program 12d.

The answer determination processing unit 27 compares the answer data 17 of the learner with the correct answer data 33 and determines whether the answer of the learner is correct.

The strategy processing unit 23, based on the strategy data 15 and the incorrect answer data 16,
Rewrite parameter data 14 to parameter data 37,
In addition, thereby, the teaching material data 35 is generated by updating the model 12c to the model 36.

Such processing is repeated until the learner finishes halfway.

In this way, it is possible to change the content of a question in a question sentence or to change a complicated display such as a scientific experiment on a graphic from the content of a question determined by an arbitrary (professor) strategy, and With a structure that enables a correct answer to be automatically obtained from the content of the question, a more advanced and complicated teaching material than the teaching material handled by the conventional generation type CAL can be generated.

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

 2 to 15 are views showing an embodiment of the present invention.

In FIG. 2, reference numeral 10 denotes an input device.
Is the learning material data 11 [question type data 12 (question type data 12a, graphic type data (program) 12b, model 12c, control program 12d), problem solving data 13 (demon 13a), parameter data 14, strategy Data 15, wrong answer data 16], and learner's answer data 17 are input.

The teaching material data 11 includes the following 12 to 16 data as constituent elements.

The question type data 12 is the framework of the question,
It has the following problem sentence type data 12a and graphic type data (program) 12b. The question sentence type data 12a is a framework of the question sentence. The graphic type data (program) 12b is a graphic framework. The model 12c is a framework of the content of the problem such as a physical model. The control program 12d is the model 12c
Is performed in general.

Problem solving data 13 solves a problem (correct answer data 33
The next demon 13a
have. The demon 13a is a formula calculation program corresponding to each object constituting the model 12c.

The parameter data 14 determines the content of the question.

The strategy data 15 is used when determining the content of the question of the teaching material 35 to be generated next.

The wrong answer data 16 contains the following teaching materials when the wrong answer is made.
It is used to determine the content of 35 questions.

Reference numeral 20 denotes a processing device. The processing device 20 includes an input unit 21 and a processing unit.
The processing unit 22 includes a strategy processing unit 23, a question sentence generation processing unit 24, a graphic generation processing unit 25, a problem solving processing unit 26, and an answer determination processing unit 27.
Consists of

The input unit 21 receives input of the learning material data 11 [question type data 12 (question type data 12a, graphic type data (program) 12b, model 12c, control program 12
d), problem solving data 13 (demon 13a), parameter data 14, strategy data 15, incorrect answer data 16], and learner answer data 17 are input and stored in the memory 30.

The processing unit 22 sequentially executes a strategy process, a problem sentence generation process, a graphic generation process, a problem solution process, and an answer determination process. Then, the process returns to the strategy processing again.

The question sentence generation processing unit 24 generates a question sentence 31 by passing the parameter data 14 through the question sentence type data 12a,
Output to the display area 41 of the display device 40 (via the output unit 28).

The graphic generation processing unit 25 generates the graphic 32 from the model 12c and the control program 12d that constitute the graphic type data (program) 12b, and outputs the graphic 32 (via the output unit 28) to the display area 41 of the display device 40. I do.

The problem solving processing unit 26 generates the inquiry data 34 from the parameter data 14,
In the demon 13a, the corresponding model 12c, and
The correct answer data 33 is extracted from the control program 12d.

The answer determination processing unit 27 compares the answer data 17 of the learner with the correct answer data 33 and determines whether the answer of the learner is correct.

The strategy processing unit 23, based on the strategy data 15 and the incorrect answer data 16,
Rewrite parameter data 14 to parameter data 37,
In addition, thereby, the teaching material data 35 is generated by updating the model 12c to the model 36.

Such processing is repeated until the learner finishes halfway.

The output unit 28 outputs data and the like to a display area 41 of the display device 40.

The memory 30 is accessed by the strategy processing unit 23, the question sentence generation processing unit 24, the graphic generation processing unit 25, the problem solving processing unit 26, and the answer determination processing unit 27, and has a function as a work area.

 The display device 40 has a display area 41.

 Next, the operation will be described.

 First, the concept of generating teaching materials is shown in FIG.

In FIG. 3, is a display screen of the original question, and when the learner has answered correctly, the display screen shown in FIG. At this time, the problem changes from the one for finding the power to the one for the frictional force. When the learner answers incorrectly, the display screen becomes, and the problem becomes a request for work.

The display screen of the original question is generated from the teaching material data of the original question as shown in FIG. By passing parameter data through the question sentence type data, a question sentence is generated,
A graphic is generated based on the graphic type data.

Materials 35 of questions generated when the student answers correctly
The display screen of the question generated at that time is shown in FIG. 5, and the learning material data 35 of the question generated when the learner answers incorrectly, and the display screen of the question generated at that time are shown in FIG. Shown in the figure.

 Hereinafter, the processing will be described more specifically.

The question sentence type data 12 is shown in FIG. 7, where clause 1 includes a premise and clause 2 includes a target. The premise includes an attribute name and an attribute value, and the target includes a target name and a target attribute name. . By passing the parameter data 14 through the type data 12 of this problem sentence, as shown in FIG. 8, “time = 2, friction force = 5
When 0 and the moving distance = 10, find the power related to the person. Is generated.

The graphic type data 12b is composed of a model 12c and a control program 12d as shown in FIG.
A graphic 32 is generated by the model 12c and the control program 12d as shown in FIG.

Next, the inquiry data 34 is generated from the parameter data 14, the demonstration 13a in the problem solving data 13 is activated, and the correct answer data 33 is extracted (see FIG. 11).

The demons 13a are set for a person and an object, respectively, as shown in FIG. Demons set for people
13a is a set of programs consisting of the work, work rate, and time formulas. The demon 13a set for the object is a set of programs composed of formulas of work, frictional force, and moving distance. Here, refer to, quote, and transcribe the Demon 13a formula of work rate = work ÷ time and work = frictional force x travel distance, and search, refer to, quote,
The transcription is made and the correct answer data 33 is extracted.

Here, the power 250 is extracted as the correct answer data 33 (see FIG. 11).

 Next, the strategy processing shown in FIG. 13 is performed.

If the learner is the correct answer, first refer to Demon 13a,
An attribute name related to the parameter data 14 is determined by a random number from the official relationship of each object.

Next, the consistency of the attribute names related to the parameter data 14 determined in the above processing is checked from the official relationship between the objects and the like. If the difficulty of the problem decreases,
Redefine the attribute name.

Then, an attribute value is set by a random number in the content of the strategy data 15.

When the learner answers incorrectly, first, the learner refers to the incorrect answer time data 16 and changes the inquiry content.

Then, an attribute value is set with a random number based on the contents of the strategy data 15.

If the learner answers correctly, teaching material data 35 as shown in FIG. 14 is generated. The parameter data 14 is changed to parameter data 37. The display screen is as shown in FIG.

If the learner answers incorrectly, teaching material data 35 as shown in FIG. 15 is generated. The parameter data 14 is changed to parameter data 37. The display screen is as shown in FIG.

[Effects of the Invention] As described above, according to the present invention, it is possible to change the content of a question sentence from a content of a problem determined by an arbitrary (professor) strategy or to perform a complicated operation such as a scientific experiment on a graphic. By making it possible to change the display and to automatically obtain the correct answer from the content of the question in the question sentence, it is more sophisticated and complicated than the teaching materials handled by the conventional generated CAL. Can provide teaching materials.

[Brief description of the drawings]

1 (A) and 1 (B) are diagrams for explaining the principle of the present invention, FIG. 2 is a block diagram showing an embodiment of the present invention, FIG. 3 is a diagram showing a concept of generating teaching materials, and FIG. Fig. 5 shows the correspondence between the teaching material data of the question and the screen display. Fig. 5 shows the correspondence between the teaching material data when the answer is correct and the screen display. Fig. 6 shows the correspondence between the teaching material data and the screen display when the answer is incorrect. 7 is a diagram showing question sentence type data, FIG. 8 is a diagram showing question sentence generation, FIG. 9 is a diagram showing graphic type data, FIG. 10 is a diagram showing graphic generation, FIG. 11 is a diagram showing a problem solving (extraction of correct answer data) process, FIG. 12 is a diagram showing a demon set for an object, FIG. 13 is a flowchart of a strategy process, and FIG. Fig. 15 shows the process of generating the teaching material data, Fig. 15 shows the process of generating the learning material data when the learner answers incorrectly, Fig. 16 Fig. 17 shows an example of the process of generating teaching material data, Fig. 18 shows an example of the process of generating data generated during learning, and a screen display of a problem during presentation Fig. 19 shows another example of the process of generating the teaching material data. Fig. 20 shows another example of the process of generating the data generated during learning and the screen display of the question at the time of presentation. It is a figure showing the example of. In the figure, 10 ... input device, 11 ... teaching material data, 12 ... question type data,
12a: question sentence type data, 12b: graphic type data (program), 12c: model, 12d: control program,
13 ... problem solving data, 13a ... demon, 14 ... parameter data, 15 ... strategy data, 16 ... wrong answer data, 17 ... learner's answer data, 20 ... processing unit, 21 ... input unit, 22 ... processing unit, 23 ... Strategy processing unit, 24 ... Question sentence generation processing unit, 25 ... Graphic generation processing unit, 26 ... Question solution processing unit, 27 ... Answer judgment processing unit, 28 ... Output unit, 30 ... Memory, 31 ... Question sentence, 32 …
Graphic, 33… Correct data, 34… Inquiry data, 35
… Teaching material data, 36… Model, 37… Parameter data, 40
... display device, 41 ... display area.

Claims (2)

(57) [Claims] 1. A problem sentence generation processing unit for generating a problem by combining type data defining a framework of a problem and parameter data defining a problem attribute and a problem content corresponding to the attribute; A problem solving processing unit that defines a procedure for calculating a correct answer and generates correct data by a procedure corresponding to the parameter data and the attribute of the problem; and a learner's answer to the problem generated by the problem sentence generating processing unit. An answer determination processing unit that compares the data and the correct answer data generated by the problem solving processing unit; and a strategy for rewriting the next question attribute and parameter data corresponding to the attribute according to the determination content of the answer determination processing unit. A teaching material generating device comprising a processing unit. 2. The method according to claim 1, wherein the strategy processing section selects an arbitrary question attribute when the answer is correct in the answer determination processing section, and selects a predefined question attribute when the answer is incorrect. 2. The teaching material generating device according to claim 1, wherein: