JP2001228786A - Method and device for network type learning - Google Patents

Abstract

PROBLEM TO BE SOLVED: To continue learning and the reflect the desire of a learning person for the learning even though the network is not connected. SOLUTION: The device is provided with a server 1 and a learning terminal 2 both of which are connected by a network 3, a learning content group accumulating means 4 which accumulates learning content groups, a learning path predicting means 7 which predicts the learning path for a specific learning person from common learning targets for all learning persons and learning person information of a specific learning person, a learing content selecting means 5 which selects learning contents from the means 4 based on the predicted learning path and a learning mans 6 which conducts the learning based on the selected learning content.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

[0001] The present invention relates to a learning method and apparatus using terminals and networks.

[0002]

2. Description of the Related Art In a conventional learning method and apparatus using a terminal, a learner learns in accordance with the instruction of the teaching material using the teaching material recorded on a recording medium such as a CD-ROM. When updating or changing the content,
The recording medium itself had to be exchanged, and even if there was a change during learning, the only way to reflect the change was to purchase a new recording medium or stop learning. For this reason, a network-type learning system has been proposed so that teaching materials can be downloaded and updated at any time from a server via a network.

[0003] In the conventional network-type learning system, a learner performs learning using a terminal device and transfers the learning result to a server using a network. At the same time, the evaluation of the student, the preparation of the teaching material, the instruction of the next teaching material, and the like are transmitted to the terminal device.

FIG. 12 shows an example of such a conventional network type learning system. In FIG. 12, the terminal device 1
Reference numeral “00” indicates that the learner actually performs the learning, and the learning material content storage unit 101 and the learning history storage unit 102
, Teaching material presenting means 103, history updating means 104, and network transmitting / receiving means 105. The teaching material content storage unit 101 includes texts, figures, tables, and the like serving as teaching materials. The learning history storage unit 102 stores each learner's personal learning history. The teaching material presentation means 103 presents the learning material to the learner. History update means 1
04 updates the learning history of the learner. The network transmission / reception unit 105 performs data communication with the server 200.

[0005] The server 200 receives the learning history of the learner, evaluates the learner, instructs the terminal device of the next teaching material,
It is for creating new teaching materials, and learner evaluation means 2
01, the next teaching material instruction means 202, and the teaching material creating means 203
And the network transmission / reception means 105. The learner evaluation means 201 receives the learner's learning history sent from the terminal device 100, and evaluates the learner by comparing it with other learners. The next teaching material instruction means 202 receives the learning history of the learner and the evaluation of the learner from the learner evaluation means 201 and calculates the next teaching material for the learner. The teaching material creating unit 203 creates a new teaching material. The network transmitting / receiving means 105 performs data communication with the terminal device as described above.

When the learner starts learning, the terminal device 100
The teaching material presentation means 103 in the teaching material content storage means 1
01, the teaching material is presented to the learner. At the same time, the history updating means 104 sequentially updates the learning history of the learner. When the learning has progressed to a certain point, the terminal device transmits the learning history of the learner to the server 200 using the network transmission / reception unit 105. The server 200 having received the learning history performs the evaluation of the learner by the learner evaluation means 2.
01, the evaluation is performed, and after receiving the evaluation, the next teaching material instruction unit 202 calculates the content to be learned next by the learner. Finally, the calculated learning content is transmitted to the terminal device 100 using the network transmitting / receiving means 105. The learning is performed by the learner while repeating the above processing.

[0007]

However, such a conventional network-type learning system is based on the premise that a network is connected, and it is impossible to continue learning when the network is not connected. .
Also, when downloading educational materials from the network, there is no mechanism to provide feedback corresponding to individual learners, so basically downloading "all" information takes time, and the amount of downloads is reduced. If it is large, there will be a deviation from the timing at which the learner learns, such as at night. In addition, since the teaching material is always provided only unilaterally to the learner, there is a problem that the request of the learner is not reflected at all.

The present invention solves such a problem in a conventional network-type learning system. Even if a network is not connected, learning can be continued and a network that can reflect the needs of the learner can be reflected. It is intended to provide a type learning method and apparatus.

[0009]

In order to solve the above-mentioned problems, a network-type learning method according to the present invention uses a server and a terminal device connected by a network to select a specific learning content from a learning content group. When learning by selecting, the learning path that determines the learning order for the specific learner is predicted from the common learning target for all the learners and the learner information of the specific learner, and the predicted learning path is The learning content is provided to the specific learner based on the learning path, and a learning path is predicted for each learner from a given common learning target model, and the learning path is determined based on the predicted learning path. By selecting the learning content from the learning content group, the learning can be advanced with contents that are easy for the learner to learn.

Further, in the network-type learning method according to the present invention, the learning path is predicted after temporarily changing a common learning goal for all the learners by using a learning strategy that determines a learning procedure. And using a common learning goal model for all learners,
When learning is performed simultaneously in a plurality of terminal devices, by giving a common change instruction to the entire learner through content selection, it is possible to implicitly give an instruction to all learners.

Further, in the network-type learning method according to the present invention, a common learning target for all the learners is changed for each learner using a learning strategy that determines a learning procedure, and then the learning path is changed. It is characterized by predicting, using a common learning goal model for all learners,
When learning is performed simultaneously on a plurality of terminal devices, by giving a specific change instruction to some learners through content selection, some learners can be notified without being known to other learners. You can give instructions.

Further, in the network-type learning method according to the present invention, the learning content group includes material data for generating learning content, and a learning content generation rule suitable for the predicted learning path is defined as learner information and the learner information. Learning content is generated by applying the learning content to the material data, and the learning content is provided to the specific learner. By performing the prediction including the condition (1), the learning path prediction can be simplified.

Further, the network type learning method of the present invention is characterized in that, when predicting the learning path, learner information including the learning history of the specific learner is used, and based on the learning history. By predicting the learning path, it is possible to delete unnecessary options that have already been learned, or to judge the level of the learner from the learning history and predict the learning path according to the level of the learner, By providing the learning content to the specific learner based on this prediction, it is possible to advance the learning optimal for the learner. In addition, when a learning strategy is edited, the learning history is used as the learner information of the learner, so that the learning strategy can be updated at any time while referring to the learning progress of the learner. How to proceed.

Further, the network-type learning method of the present invention is characterized in that, when predicting the learning path, learner information including a request for the learning content of the specific learner is used. By estimating the learning path of the learner and accepting a positive request for the learning content by the learner at the time of the prediction, the learner can continue learning without getting tired. Furthermore, by using the learner's request for the learning content as the learner information of the learner when editing the learning strategy, the learner's learning strategy can be updated at any time while referring to the learner's request. It is easy to learn and can advance learning.

Further, the network type learning apparatus of the present invention comprises a server and a terminal device connected by a network, a learning content group storage means for storing a learning content group, and a learning target common to all learners and specified. Learning path prediction means for predicting a learning path for the specific learner from learner information of the learner; and learning content selection for selecting learning content from the learning content group storage means based on the predicted learning path. Means, and learning means for performing learning based on the selected learning content, wherein a learning path is predicted for each learner from a given common learning target model, and the predicted learning path is By selecting the learning content from the learning content group based on It is possible.

Further, the network-type learning apparatus according to the present invention, wherein the learning means includes a learning history transmitting means for outputting a learning history of the specific learner and transmitting the learning history to the learning path predicting means. By predicting the learning path based on the learning history, unnecessary options that have already been learned are deleted, or the level of the learner is determined from the learning history, and the learning path according to the level of the learner is determined. Can be predicted, and by providing the learning content to the specific learner based on the prediction, it is possible to advance the learning optimal for the learner.

[0017] The network-type learning apparatus of the present invention further comprises a learning strategy editing means for inputting the learner information and editing a learning strategy that determines a learning procedure, and a method for transmitting the edited learning strategy to the learning path. A learning strategy transmitting means for transmitting to the prediction means, and using a common learning target model for all the learners, when learning is performed simultaneously on a plurality of terminal devices, the entire learner By giving a common change instruction through content selection, it is possible to implicitly give an instruction to all learners. In addition, by giving a specific change instruction to some learners through content selection, an instruction can be given to some learners without being known to other learners.

Further, the network type learning apparatus of the present invention comprises: a request inputting means for inputting a request for the learning content of the specific learner in an input format related to the learning content;
A request input format editing unit that edits the input format; and a request input format transmission unit that transmits the input format to the request input unit. The learner information including the request is input to the learning path prediction unit. In the course of the learning path prediction, by accepting the learner's positive request for the learning content and improving the accuracy of the learning path prediction, the learner can continuously You can proceed with learning.

The network type learning apparatus of the present invention includes a server connected to a network and a terminal device, wherein the server includes a learning content database storing all learning content groups and learning targets, and the terminal device includes: Learning content group storage means for storing the learning content group and the learning target received from the server,
Learning path prediction means for predicting a learning path for the specific learner from the learning target input from the learning content group storage means and the learner information of the specific learner; and the learning based on the predicted learning path. Learning content selection means for selecting learning content from the content group storage means; learning means for learning based on the selected learning content; and a learning target extracted from a learning content database in the server to the terminal device. And a learning content transmitting unit that receives a learning path predicted by the terminal device, extracts necessary learning content from a learning content database in the server, and transmits the learning content to the terminal device. Since the terminal device is provided with a learning path predicting means, information necessary for learning is After storing the cement group storage means,
Even in an environment where a network cannot be used, learning can be advanced while re-estimating the learning path, and learning can be advanced more adaptively to the learner.

In the network-type learning apparatus according to the present invention, the learning path predicting means may include learner information including a learning history of the specific learner and / or learner information including a request for learning content of the specific learner. The method is characterized by predicting a learning path by using the learning method, so that the learning method can be optimized for the learner, and the learner can continuously learn without getting tired.

Further, in the network-type learning apparatus according to the present invention, the learning content database in the server stores a learning strategy that defines a learning procedure, and the learning path predicting means uses the learning strategy to execute the learning path. Is predicted, and learning can be advanced more appropriately for the learner.

Further, in the network-type learning apparatus according to the present invention, the learning content group stored in the learning content group storage medium includes material data for generating learning content, and the learning content database stores rules for generating learning content. Storing the learning content, and selecting the learning content by applying the rule to the learner information and the material data, and determining the learning content itself when predicting a learning path. By performing the prediction including the conditions for generating the learning content without performing the above, the learning path prediction can be simplified.

Further, according to the present invention, a simple network-type learning system can be realized by using a program in which the above-described network-type learning method or apparatus is realized by software and recorded on a recording medium.

[0024]

Embodiments of the present invention will be described below with reference to the drawings. (Embodiment 1) Regarding the first embodiment of the present invention,
This will be described with reference to FIGS. 1, 2, 3, and 9. FIG. 1 is a block diagram showing an example of a network-type learning device according to the first embodiment, wherein 1 is a learning server, 2 is a learning terminal such as a personal computer, and 3 is a network connecting the learning server 1 and the learning terminal 2. It is. Reference numeral 4 denotes a learning content group storage medium as learning content group storage means existing in the learning terminal 2, which uses a portable recording medium such as a DVD-ROM or a fixed recording medium such as a hard disk. It also includes a read function. As the learning content group storage medium 4, a storage content in which the learning content group is stored in advance on these recording media, or a storage content downloaded from the learning server 1 and used is used. Reference numeral 5 denotes a learning content selection unit existing in the learning terminal 2, and reference numeral 6 denotes a learning unit that exists in the learning terminal 2 and includes a monitor, a keyboard, a mouse, and the like on which a learner performs learning. 7 is a learning path predicting means for predicting a learning path existing in the learning server 1, 8 is a learning content database which exists in the learning server 1 and manages all learning contents, 9 is a learning content database which exists in the learning server 1, It is a learner information database that manages all learner information. Reference numeral 10 denotes a learning path transmitting unit that extends over the learning terminal 2, the learning server 1, and the network 3, and reference numeral 11 denotes a learning history transmitting unit that extends over the learning terminal 2, the learning server 1, and the network 3. Learning path transmitting means 10 and learning history transmitting means 11
Is communication means provided in both the learning terminal 2 and the server 1 for transmitting and receiving data through the network 3. 12 is a learning target managed by the learning content database, 13 is learner information managed by the learner information database, 14 is a learning path predicted by the learning path predicting means 7, and 15 is a learning content group storage medium 4. The learning content group 16 stored in the learning content group 16 is the learning content selected by the content selection means 5 from the learning content group 15. 2 shows an example of a learning target, FIG. 3 shows an example of learner information, and FIG. 9 shows an example of a learning route.

Hereinafter, how the learning content given to the learner is selected will be described with reference to FIGS. First, the learning route prediction means 7 of the learning server 1
The operation of will be described. The learning path prediction means 7 predicts a learning path 14 to be learned by the learner using the learning target 12 and the learner information 13. In the learning goal shown in FIG. 2, ten or more clicks are made in the example 1 of the unit 1, but as shown in FIG. 3, as learner information, 13 clicks are already made in the example 1 of the unit 1. Therefore, it is determined that Example 1 has been completed, but since the exercise has not been started, it is predicted that the exercise will be performed. In learning objectives, exercises 1/2 /
Since it is sufficient to clear one of the three, it is predicted that one will be learned. Here, the prediction is performed using the learner's preference written in the learner information and the learning history. Increase the accuracy of For example, this learner has a preference that he likes flowers and dislikes dogs, so the subject of the exercise 1/2/3 written in the learning objective is animals / shopping / walking. In addition, the probability that the exercise 1 using an animal as a subject is selected becomes low. In the learning history, in the example 1 of the unit 1, the animal is clicked four times, the shopping is clicked twice, and the walk is clicked seven times, and it may be determined that the user is interested in the subject of the walk. As a result of such a judgment, the learning path predicting means 7 performs the unit 1
Predict that Exercise 3 will be used. Figure 9 is exercise 3
13 is an example of a learning path in a case where it is predicted to use the learning path. Although the subsequent paths are not mentioned in the drawing, the flow of learning after the practice exercise of the unit 1 can be similarly predicted.

Next, the overall flow will be described with reference to FIG. The learning path predicting means 7 of the learning server 1 uses the learning target 12 managed in the learning content database 8 and the learner information 13 managed in the learner information database 9 as described above to perform learning. Predict the route. The predicted learning path 14 is
The information is transmitted from the learning server 1 to the learning terminal 2 by the learning path transmitting means 10 and is input to the content selecting means 5. The content selection means 5 of the learning terminal 2 determines the learning content 16 to be presented next from the learning content group 15 stored in the learning content group storage medium 4 based on the learning path 14 and performs learning. Output to the means 6, the learner performs learning using the learning content 16. Here, exercise 3 corresponds to this. The learning result is transmitted from the learning terminal 2 to the learning server 1 by the learning history transmitting unit 11 and is added to the learner information 13 of the learner information database 9. When the learning path prediction means 7 predicts the learning path again,
A learning path is predicted using the updated learner information 13.

As described above, according to the first embodiment, a learning path is predicted from a given common learning target model based on a learner's preference, and a learning content group is predicted based on the predicted learning path. By causing the learner to select the learning content, there is an effect that the learner can proceed with learning in a content that is easy to learn.

In the first embodiment, the learner information includes not only the learning history of the learner but also information relating to the level of the learner, or the level of the learner is determined from the learning history. If it can be determined, the learning path itself can be optimized according to the level, and the learner can proceed with the optimal learning.

When the learner information includes a request for the learning content of the learner, the learning path may be predicted by using the request, and the learner may be bored by following the learner's request. There is an effect that learning can be continuously advanced without using.

Further, in the first embodiment, as shown in FIG. 1, the respective units are allocated to the learning server 1 and the learning terminal 2 for the sake of convenience. However, as long as logically similar operations are possible, the respective units are allocated. May be present in any device.

(Embodiment 2) Next, a second embodiment of the present invention will be described with reference to FIGS. FIG.
FIG. 3 is a block diagram illustrating an example of a network-type learning device according to a second embodiment. 4, means and data given the same numbers as those in FIG. 1 are the same as the means and data in FIG. 1, and a description thereof will be omitted. In FIG. 4, the learning content database 8, the learner information database 9, and the learning history transmitting means 11 on the learning server 1 are abbreviated for simplifying the drawing.

In FIG. 4, reference numeral 17 denotes a learning strategy editing means for the learning instructor existing in the learning server 1 to edit the learning strategy, and 18 denotes a learning strategy transmitting means for transmitting the learning strategy. 19 is a learning strategy created and edited by the learning strategy editing means 17. FIG. 5 is an example of a learning strategy edited by the learning strategy editing unit 17. Here, a goal that is lower than the goal described in the common learning goal is set for the learner A, and a higher goal is set for the learner B, and further, for the other learners, Goals have been modified.

Now, the overall flow is described in Embodiment 1 of the present invention.
The description is omitted because it is almost the same as that described above, and only the operation of the learning path prediction means 7 and the operation of editing the learning strategy will be described with reference to FIGS. Since the learning strategy editing unit 17 receives the learner information 13 as an input, the learning instructor can edit the learning strategy while referring to the learning status of each learner. For example, when performing group learning, it may be necessary to end the learning within a limited time, and in such a case, progress may be delayed from the learning situation of each learner. If the learning instructor determines that there is, the goal is adjusted temporarily or for each learner according to the degree of delay, as shown in FIG. Can edit the learning strategy. The learning strategy 19 created as a result is transmitted to the learning route predicting means 7 by the learning strategy transmitting means 18. The learning path prediction means 7 predicts a learning path with the given learning strategy 19 as the highest priority. For example, for the learner A, the click target number of the example 1 of the unit 1 is reduced from the original target number 10 to 5; for the learner B, the click target number is increased from 10 to 15, and for the other learners, the click target number is reduced from 10 to 7. And so on.

As described above, according to the second embodiment, a learning path is predicted by temporarily changing a common learning target model for all learners, and learning is simultaneously performed in a plurality of terminal devices. In this case, by giving a common instruction to the entire learner through the content selection means 5, there is an effect that it is possible to implicitly give an instruction to the learner.

Further, a learning path is predicted by changing a learning target model common to all the learners for each learner, and when learning is simultaneously performed in a plurality of terminal devices, some learners are required to learn. On the other hand, by giving a specific instruction through content selection, there is an effect that it is possible to give an instruction to some learners without being known to other learners.

In addition, when a learning strategy is edited, a learning history is used as learner information of a learner, especially when performing collective learning or learning in which physical locations are separated and simultaneously proceeding. Thus, the learning strategy can be updated at any time while referring to the learning progress of the learner, so that there is an effect that the learner can proceed with an appropriate learning.

In the second embodiment, when a learning strategy is edited, a request for a learning content of the learner is used as the learner information of the learner, so that the learning strategy is referred to by the learner. Can be updated at any time, and there is an effect that the learner can easily learn and can proceed with the learning.

In the second embodiment, as shown in FIG. 4, the respective units are allocated to the learning server 1 and the learning terminal 2 for the sake of convenience. However, as long as logically the same operation is possible, the respective units are allocated. May be present in any device.

(Embodiment 3) Next, a third embodiment of the present invention will be described with reference to FIGS. FIG.
FIG. 9 is a block diagram illustrating an example of a network-type learning device according to Embodiment 3. 6, means and data given the same numbers as those in FIG. 1 or FIG. 4 are the same as the means and data in FIG. 1 or FIG. In FIG. 6,
To simplify the drawing, a learning content database 8, a learner information database 9, a learning strategy editing unit 17, a learning strategy transmitting unit 18, and a learning history transmitting unit 11 on the learning server 1 are abbreviated.

In FIG. 6, reference numeral 20 denotes a request input format editing means which exists in the learning server 1 and edits an input format for a learning instructor to input a request for learning content;
Reference numeral 21 denotes a request input form transmitting unit existing between the learning server 1 and the learning terminal 2; 22, a request input unit existing on the learning terminal 2; and 23, a request transmitting unit existing across the learning server 1 and the learning terminal 2. , 24 are request input formats. Request input format transmitting means 21 and request transmitting means 23
Is communication means provided in both the learning terminal 2 and the server 1 for transmitting and receiving data through the network 3. FIG. 7 is an example of a user interface when a learner inputs a request. 25 is a screen of the learning means 6, 26
Denotes an area in which the learning content 16 is displayed, and 27 denotes a screen of the request input means 22. In this example, the user interface of the request input unit 22 is included in the user interface of the learning unit 6.

Now, the overall flow is described in Embodiment 1 of the present invention.
Since the operation is almost the same as that described above, the description is omitted, and the operation in which the learner inputs a request for learning will be described. First, the learning instructor creates an input format related to the learning content, for example, a request input format in a format as shown in FIG. Next, the request input format transmitting means 21
Transmits the created request input form 24 to the request input means 22, and the learner inputs the request. As a result, the request for the learning content of the learner is added to the learner information 13 by the request transmitting unit 23, and the learning route prediction unit 7 predicts the learning route using the updated learner information 13.

As described above, according to the third embodiment, the learner inputs a request for the learning content in an input format related to the learning content, so that the learner can easily input a request for the learning content as needed. In addition, the wishes of the learner are reflected in the education, and the learning efficiency can be improved.

In the third embodiment, as shown in FIG. 6, the respective units are allocated to the learning server 1 and the learning terminal 2 for the sake of convenience. However, as long as logically the same operation is possible, the respective units are allocated. May be present in any device.

(Embodiment 4) Next, a fourth embodiment of the present invention will be described with reference to FIGS. 2, 3, 8, and 9. FIG. FIGS. 2, 3, and 9 have already been described in the first embodiment of the present invention, and a description thereof will be omitted. FIG. 8 is a block diagram illustrating an example of a network-type learning device according to the fourth embodiment, in which components denoted by the same reference numerals as those in the above embodiments have the same functions. 1 is a learning server, 2 is a learning terminal, 3 is a learning server 1 and a learning terminal 2.
It is a network that connects between Reference numeral 4 denotes a learning content group storage medium existing in the learning terminal 2. Reference numeral 5 denotes a learning content selection unit existing in the learning terminal 2, and reference numeral 6 denotes a learning unit existing in the learning terminal 2 for a learner to perform learning.
7 is a learning path predicting unit that is present in the learning terminal 2 and predicts a learning path. 8 is a learning content database that is present in the learning server 1 and manages all learning contents.
11 is a learning history transmitting means existing in the learning terminal 2, 12 is a learning target managed by the learning content database 8, 13 is learner information, 14 is a learning path predicted by the learning path predicting means 7, and 15 is a learning path. The learning content group stored in the content group storage medium 4, 16 is the learning content selected by the content selection means 5 from the learning content group 15, 28 is provided in both the learning terminal 2 and the server 1, and 3 is a learning content group transmitting means, which is a communication means for transmitting and receiving data through the learning terminal 3. The learning target is extracted from the learning content database 8 in the learning server 1 and transmitted to the learning terminal 2, and is predicted by the learning terminal 2. Upon receiving the learning route, the learning content database 8 in the learning server 1
And a function of extracting necessary learning content from the learning terminal 2 and transmitting the extracted learning content to the learning terminal 2. Reference numeral 29 denotes a group of all learning contents managed by the learning content database 8.

Hereinafter, the entire flow will be described with reference to FIGS. 8, 2, 3, and 9. FIG. First, the learning content group storage unit 28 transmits the learning target 12 managed in the learning content database 8 of the learning server 1 to the learning terminal 2 and stores the learning target 12 in the learning content group storage medium 4. Next, the learning route prediction means 7 of the learning terminal 2 predicts a learning route using the learning target 2 and the learner information 13. The predicted learning path 14 is the content selection means 5
Is given to the learning content group transmitting means 28.
The learning content group transmitting unit 28 determines which of the learning content groups 29 managed in the learning content database 8 of the learning server 1 may be required as the learner progresses. Is determined based on the contents written in the learning path 14, a learning content group that may be needed is transmitted from the learning server 1 to the learning terminal 2, and the learning content group storage medium 4 accumulate. For example, when learning targets and learner information as shown in FIGS. 2 and 3 are given, if a learning path as shown in FIG. One learning content group relates to exercise 3. As a result, the learning content group transmitting unit 28 stores only the learning content group for the exercise 3 from the all learning content groups 29 managed by the learning server 1 in the learning content group storage medium 4, and at the same time, The learning path 14 is also stored in the learning content group storage medium 4.

Next, the content selection means 5 executes the learning path 14 stored in the learning content group storage medium 4.
, The appropriate learning content 16 is selected from the learning content group 15 similarly stored in the learning content group storage medium 4, and the learning means 6
Is presented to the learner. Further, the history of learning by the learner is input from the learning means 6 to the learning path predicting means 7 by the learning history transmitting means 11, and the learning path is re-predicted if necessary.

As described above, according to the fourth embodiment, all the information necessary for the operation of the content selection means 5 is stored in the learning content group storage medium 4, and there is no need to fix the learning terminal. There is an effect that learning can be performed in the same manner with the terminal device. Also, content selection means 5
The network 3 is unnecessary for its own operation, and there is an effect that learning can be continued even in an environment where network connection is not possible.

Further, since the learning route predicting means 7 is provided in the learning terminal 2, even after the information necessary for learning is stored in the learning content group storage medium 4, even if the network is unusable, the learning route The learning can be advanced while re-predicting the route, and there is an effect that the learning can be advanced more adaptively to the learner.

In the fourth embodiment, the learning strategy described in the second embodiment can be applied to the fourth embodiment. Since the learning strategy is provided from the learning content database 8 in the learning server 1, the learning strategy is stored in the learning content group storage medium 4 by the learning content group transmitting unit 28, so that even in a situation where the network 3 cannot be used. A learning path prediction using a learning strategy can be performed. Similarly, the learning route can be predicted using the learner information including the request for the learning content of the specific learner described in the third embodiment.

(Embodiment 5) Next, a fifth embodiment of the present invention will be described with reference to FIGS. 3, 9, 10, and 11. FIG. FIG. 3 shows learner information according to the first embodiment, and a detailed description thereof will be omitted. FIG. 9 is a diagram illustrating a learning path according to the first embodiment, which is used for comparison with the operation according to the fifth embodiment. FIG. 10 is an example of a learning rule in the case where the learning path includes a learning rule in the first to fourth embodiments of the present invention. FIG. 11 is a block diagram showing an example of a network-type learning device according to the fifth embodiment of the present invention. In contrast to the configuration of the fourth embodiment shown in FIG. The information 13 is added, and the learning content group 15 stored in the learning content group storage medium 4 is modified so as to also include the learning material 15A. Other details are the same as those in FIG. Omitted.

The operation of the content selection means 5 when the learning path includes a learning rule will be described below. FIG.
As shown in (1), the learning path here is described in a rule-like manner in which the order of questions is determined with reference to the learner information of the learner in Example 1. Content selection means 5
The rule-based description in the learning path is compared with the learner's preference and learning history described in the learner information to determine what content should be generated. For example,
In FIG. 10, “thing that has been clicked a lot in example 1” is “bus stop” in the learner information in FIG. 3. Further, “thing that suits the learner's taste” is “flower”. As described above, it is possible to determine what kind of content is appropriate by using the rule-like description in the learning path and the learner information. Next, based on the determination result, the content for “bus stop” and the content for “flower” are generated from the learning material 15A stored in the learning content group storage medium 4. In the rule-based description of FIG. 10, since it is described that a practice method 1/2/3 is to be performed using a combination of audio material, video material, and commentary material, the learning content group storage medium 4 The learning content is generated using an appropriate learning material 15A. That is, as shown in FIG.
There is no need to specify the final form of the learning content in the learning path, and the learning material
If only 5A is stored, the learning content can be generated.

As described above, according to the fifth embodiment, the learning path prediction can be simplified by performing the prediction including the conditions for generating the learning content without determining the learning content itself when predicting the learning path. This has the effect. It is to be noted that the learning path is determined when the learning path can be determined with high probability, and when the learning path cannot be determined. Can make the learner proceed with learning adaptively.

Also, when an environment where a network cannot be used as described in the fourth embodiment can occur,
By performing the prediction including the condition and storing it in the content storage medium 4 as the learning path, the learner can appropriately proceed with the learning even if the network cannot be used.

[0054]

As described above, the network-based learning method and apparatus of the present invention, when selecting and learning a specific learning content from a learning content group, have a common learning goal for all learners. A learning path that determines a learning order for the specific learner is predicted from the learner information of the specific learner, and the specific learner is allowed to select a learning content based on the predicted learning path. Yes, the learning path is predicted for each learner from the given common learning target model, and the learning content is selected from the learning content group based on the predicted learning path, so that the learner can easily learn. An excellent network that can continue learning even when the network is not connected and can reflect learners' requests It is possible to provide a click-type learning method and apparatus.

[Brief description of the drawings]

FIG. 1 is a schematic block diagram of a network-type learning device according to a first embodiment of the present invention.

FIG. 2 is a schematic diagram showing an example of a learning target used in Embodiments 1 and 4 of the present invention.

FIG. 3 is a schematic diagram showing an example of learner information used in the first, fourth, and fifth embodiments of the present invention;

FIG. 4 is a schematic block diagram of a network-type learning device according to a second embodiment of the present invention.

FIG. 5 is a schematic diagram showing an example of a learning strategy used in Embodiment 2 of the present invention.

FIG. 6 is a schematic block diagram of a network-type learning device according to a third embodiment of the present invention.

FIG. 7 is a schematic diagram showing an example of a request input unit used in Embodiment 3 of the present invention.

FIG. 8 is a schematic block diagram of a network-type learning device according to a fourth embodiment of the present invention.

FIG. 9 is a schematic diagram illustrating an example of a learning path used in Embodiments 1 and 4 of the present invention.

FIG. 10 is a schematic diagram showing an example of a rule-like description of a learning path used in Embodiment 5 of the present invention.

FIG. 11 is a schematic block diagram of a network-type learning device according to a fifth embodiment of the present invention.

FIG. 12 is a schematic block diagram of a network-type learning device in a conventional example.

[Explanation of symbols]

 1 learning server 2 learning terminal 3 network 4 learning content group storage medium 5 learning content selection means 6 learning means 7 learning path prediction means 8 learning content database 9 learner information database 10 learning path transmission means 11 learning history transmission means 12 learning target 13 Learner information 14 Learning path 15 Learning content group 16 Learning content 17 Learning strategy editing means 18 Learning strategy transmitting means 19 Learning strategy 20 Request input format editing means 21 Request input format transmitting means 22 Request input means 23 Request transmitting means 24 Request input format 25 Example screen of the learning means 6 26 Area where the learning content 16 is displayed 27 Example screen of the request input means 22 28 Learning content group transmission means 29 All learning content groups

 ──────────────────────────────────────────────────続 き Continued on the front page (72) Inventor Akira Taniguchi 1006 Kadoma Kadoma, Osaka Prefecture Matsushita Electric Industrial Co., Ltd. F term (reference) 2C028 BA01 BA02 BB04 BB05 BC02 BD02 CA06 CA11 CA13 CB02 DA04 5B049 AA02 BB22 DD01 EE00 FF03 GG04 GG07

Claims (15)

[Claims] 1. A learning target common to all learners and a specific learner when selecting and learning a specific learning content from a group of learning contents using a server and a terminal device connected by a network. A learning path in which a learning order for the specific learner is determined from the learner information in step (a), and providing learning content to the specific learner based on the predicted learning path. Type learning method. 2. The learning path is predicted after temporarily changing a common learning target for all the learners using a learning strategy that determines a learning procedure. The network-based learning method according to 1. 3. The learning path is predicted after changing a learning target common to all the learners for each learner using a learning strategy that determines a learning procedure. The network-type learning method according to claim 1 or 2. 4. The learning content group includes learning content generation material data, and a learning content generation rule adapted to the predicted learning path is applied to the learner information and the material data to generate the learning content. The network-based learning method according to any one of claims 1 to 3, wherein the learning content is generated and learning content is provided to the specific learner. 5. The network-based learning method according to claim 1, wherein learner information including a learning history of the specific learner is used when predicting the learning route. . 6. The network type according to claim 1, wherein, when predicting the learning path, learner information including a request for learning content of the specific learner is used. Learning method. 7. A learning content group storage unit that includes a server and a terminal device connected by a network and stores a learning content group, and a learning target common to all learners and learner information of a specific learner. Learning path predicting means for predicting a learning path for a specific learner; learning content selecting means for selecting learning content from the learning content group accumulating means based on the predicted learning path; and the selected learning content And a learning means for performing learning based on the network. 8. The learning means, wherein the learning means outputs a learning history of the specific learner from the learning history accumulating means for accumulating a learning history, and transmits the learning history to the learning path predicting means. 8. A history transmission means.
2. The network-type learning device according to item 1. 9. A learning strategy editing means for inputting the learner information and editing a learning strategy defining a learning procedure, and a learning strategy transmitting means for transmitting the edited learning strategy to the learning path predicting means. 9. The network-type learning device according to claim 7, comprising: 10. A request input unit for inputting a request for learning content of the specific learner in an input format related to learning content, a request input format editing unit for editing the input format, and the request input for the request input. 10. A network type learning according to claim 7, further comprising a request input format transmitting means for transmitting to the means, wherein learner information including the request is input to the learning path predicting means. apparatus. 11. A server, comprising: a server connected to a network; and a terminal device, wherein the server includes a learning content database storing all learning content groups and learning targets, and wherein the terminal device includes a learning content received from the server. Learning content group storage means for storing groups and learning targets; learning path prediction for predicting a learning path for the specific learner from a learning target input from the learning content group storage means and learner information for the specific learner Means, learning content selecting means for selecting learning content from the learning content group accumulating means based on the predicted learning path, learning means for performing learning based on the selected learning content, While extracting a learning target from the learning content database and transmitting it to the terminal device, A learning content transmitting unit that receives a learning path predicted by the terminal device, extracts necessary learning content from a learning content database in the server, and transmits the extracted learning content to the terminal device. 12. The learning route predicting unit predicts a learning route using learner information including a learning history of the specific learner and / or learner information including a request for learning content of the specific learner. The network-type learning device according to claim 11, wherein: 13. The learning content database in the server stores a learning strategy that determines how to proceed with learning, and the learning path prediction means predicts a learning path using the learning strategy. Claim 11 or Claim 1
3. The network learning device according to 2. 14. A learning content group stored in the learning content group storage medium includes material data for generating learning content, a learning content database stores rules for generating learning content, and the learning content selecting means includes: 14. The network-type learning device according to claim 11, wherein the learning content is selected by applying the rule to the learner information and the material data. 15. A recording in which a program that realizes the network-type learning method according to any one of claims 1 to 6 or the network-type learning device according to any one of claims 7 to 14 by software is recorded. Medium.