JPH06250582A - Knowledge fractionating method for supporting knowledge acquisition - Google Patents

Abstract

(57) [Summary] [Objective] The purpose of the present invention is to support knowledge acquisition support for classifying knowledge according to attributes such as ability, occupation and interest for each user. It is to provide a knowledge separation method for. According to the present invention, by comparing the user information and the value of the attribute attached to the acquisition target knowledge of each knowledge database among predetermined items, the importance of at least two levels is divided. Of the ranks, the knowledge is classified by adding an appropriate rank to the acquisition target candidate knowledge as the value of the importance, and the knowledge presentation method is presented by collating the added importance value with a predetermined criterion. decide.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a knowledge classification system for supporting knowledge acquisition, and a knowledge classification system for supporting knowledge acquisition for classifying knowledge in order to support knowledge acquisition of individual users. Regarding

[0002]

2. Description of the Related Art Here, a knowledge acquisition activity such as "memorizing" knowledge of English words, kanji, historical years, etc. will be described as an example.

Printed publications such as reference books and word books have been conventionally used for knowledge acquisition activities such as memorizing English words, Chinese characters, and historical years. The printed publication provides all users with the same knowledge through the same structure and presentation method.

However, there are individual differences among users in the attributes such as occupation, knowledge acquisition ability, learning calendar, and interest of the user.
Furthermore, even for the same user, the value of each attribute changes due to factors such as time. These individual differences and fluctuations affect the efficiency of knowledge acquisition activities.

For example, even if a user who is in the first year of junior high school is presented with knowledge to be learned in the first year of high school, that knowledge is too difficult for the user to understand or acquire. . However, when the user was in the first year of high school, this knowledge was appropriate for acquisition, and when the user was in the first year of college, this knowledge was now too easy. Not suitable for acquisition.

[0006]

As described above, since there is no conventional method of presenting knowledge according to each user's attribute, the knowledge acquisition activity efficiency of the user is maintained at a high level and a constant level. There is a problem that you cannot do it.

Further, in order to present the knowledge tailored to the attributes of each user, it is necessary to classify the knowledge to be presented according to the attributes of each user.

The present invention has been made in view of the above points,
In the knowledge acquisition activity of memorization of knowledge, in order to support individual users, knowledge classification method for knowledge acquisition support that separates knowledge according to attributes such as ability, occupation and interest for each user The purpose is to provide.

[0009]

The present invention provides at least the following:
User information obtaining means for obtaining user information about one or more predetermined users, a user information database for storing the user information, knowledge of acquisition target, and at least one or more predetermined information By dividing the knowledge database that stores the attributes of the acquisition target knowledge and the value of the attribute added to the user information and the acquisition target knowledge of each knowledge database between the predetermined items, divide into at least two stages or more By adding an appropriate rank among the obtained importance ranks to the acquisition target candidate knowledge as the value of the importance, it is used as a knowledge importance level classification means for classifying the knowledge and when presenting the knowledge to the user. Component and at least one
The component database that stores the attributes of one or more predetermined elements and the component database are searched, and the value of the importance added by the knowledge importance classification means is collated with a predetermined standard to determine the knowledge. And a means for determining a presentation method.

[0010]

According to the present invention, by using the characteristics of the individual user input by the user, the individual knowledge referred to as the acquisition target candidate from the knowledge database storing the knowledge candidates to be acquired by the user is particularly acquired. For example, by classifying knowledge based on individual characteristics by adding an appropriate rank among multiple ranks of importance, such as knowledge to be acquired and knowledge expected to be acquired, as the importance of the knowledge, , When 100 pieces of knowledge for a certain user are prepared, each 100 pieces of knowledge is given a degree of importance to be classified, and the method and order of presenting the knowledge are devised based on the value of the given degree of importance. By increasing the number, the knowledge acquisition efficiency of the user is improved.

[0011]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment of the present invention for supporting a knowledge acquisition activity of memorizing English words will be described below with reference to the drawings.

FIG. 1 is a block diagram showing the configuration of a knowledge classification system for knowledge classification according to an embodiment of the present invention. In the same figure, the knowledge classification system includes a user information input unit 1
0, user information database 11, knowledge importance degree determination unit 1
2, knowledge database 13, component database 14
And a screen presentation unit 15.

In the figure, the user information input section 10 is
Users enter information about themselves. further,
It refers to the reference table that is held based on the input data and automatically sets the data as user information. The user database 11 receives and stores the user information by the user information input unit 10.

The knowledge importance degree judgment unit 12 judges the importance degree of each piece of knowledge. FIG. 2 shows a detailed configuration of the knowledge importance level judgment unit according to the embodiment of the present invention. In the figure, the knowledge importance degree judgment unit 12 includes a data reference unit 121 for referring to the contents of the usage data register 122 and the knowledge data register 123, data of the user data register 122 from the data reference unit 121, and the knowledge data register 123. Data collation / registration unit 124 for collating data and registering the result in data collation result register 125, data collation result register 125
Knowledge importance calculator 1 that calculates the importance of knowledge of data
26, an importance calculation register 127 for accumulating the calculation result (value) of the knowledge importance calculation unit 126, and a knowledge number for each knowledge based on the content (value) of the importance calculation register 127, and the knowledge number The importance number corresponding to
It is composed of a knowledge number / importance degree registration unit 128 to be registered in the importance degree information register 129 and an information transmission unit 130 for transmitting the data to the screen presenting unit 15 when the content of the registration register 129 is importance degree ≠ 0. .

The knowledge database 13 stores knowledge to be acquired by the user and information about each piece of knowledge.

The component database 14 stores the components of the knowledge presentation screen and information about the components.

The screen presenting unit 15 determines the components used for presenting the knowledge and the arrangement method thereof, and outputs the knowledge presenting screen. It is composed of a device that presents the constituent elements, a display that outputs the configured screen, a printer, and the like.
The screen presenting unit 15 also has a role of connecting the user and the device, such as displaying a message to obtain information from the user.

Next, a procedure for transmitting data between the respective devices shown in FIG. 1 will be described.

A user who uses for the first time inputs user information 101 in the user information input section 10. FIG. 3 shows a user information input screen according to an embodiment of the present invention. In the figure, for example, the user information 101 includes “name”, “age”, “occupation”, “specialty”, “English history”, “substantial academic ability”, “interest”, “desired level” of the user. , "Learned knowledge", "presentation usage element", "presentation usage rule",
There is an item called "user number". Here, since each item of “substantial academic ability”, “learned knowledge”, “presentation use element”, “presentation use rule”, and “user number” is set by the system, it is not an input target item. is there.

The user information input unit 10 also converts the data of the "occupation" and "English history" items input by the user using a conversion table possessed by the user. In the case of "occupation", the occupation data conversion table is used, and in the case of "English history", the English history correspondence table is used to convert to desired data. The “substantial academic ability” is also set in the user information input unit 10.

From the second use, when the user inputs a name, the data stored in the user information database 11 is referred to and the user information is automatically registered.
It is also possible for the user to simply confirm and correct.

The user information 101 input and set in the user information input unit 10 is sent to the knowledge importance degree judgment unit 12.

The knowledge importance degree judgment unit 12 has a knowledge importance degree judgment rule, and presents among the knowledge stored in the knowledge database 13 based on the user information 101 received from the user information input unit 10. The importance of each of the target knowledges is judged using the knowledge importance judgment rule, and the importance information 102 is created. Then, the knowledge number and the importance of the knowledge data whose importance is not 0 are sent to the screen presenting unit 15 together with the user information 101 as knowledge number information 103 and importance 102.

The screen presenting unit 15 has a presenting screen creating rule, retrieves knowledge from the knowledge database 13 based on the user information 101, the importance information 102 and the knowledge number information 103 received from the knowledge importance judging unit 12, From the constituent element database 14, constituent element data such as sentences, photographs and video images used for the screen for presenting knowledge is selected. In addition, the method of arranging the components is determined by selecting a rule used for presentation from the presentation screen creation rules. Then, a knowledge presentation screen is created based on these knowledge, component data, and rules, and output to a display screen or paper. Further, the user information 101 possessed by the image presenter 15 is sent to the user information database 11. Further, the user information 1 included in the image presentation unit 15
01 is also sent to the user information database 11.

As a result, the user information database 11
Saves the sent user information 101 and prepares for future use. The user information database 11 stores the information input from the user information input unit 10 and the user information 101 from the screen presenting unit 15.

The above is the data transmission mode between the respective devices according to the embodiment of the present invention.

4 and 5 are flow charts showing the processing operation of an embodiment of the present invention. As a condition of the processing of this example, the user is "a business employee who graduated from high school and started working as a trading company that imports furniture and has Eiken level 3 as a qualification".

Step 1) The user information input section 10 presents the user information input screen as shown in FIG.
Ask to enter information about yourself. FIG. 6 is an example of a screen when a user's occupation is input according to an embodiment of the present invention.
Is an example of a screen for inputting a specialty item when the occupation item of the embodiment of the present invention is “company employee”, and FIG. 8 is an example of a screen for inputting a user desired level according to the embodiment of the present invention. is there. Here, as illustrated in FIG. 6 to FIG. 8, selection candidates are presented in the data input of the “occupation”, “specialty”, “English history”, and “desired level” items, and the user is selected from them. Select and enter.

The above is the first time input, and from the second time and thereafter, by inputting the user's name or the like, it is possible to refer to the user information database 11 and display the already registered contents.

The target field for selecting the "specialty" item changes in accordance with the "profession" input by the user. For example, "profession"
If you enter "3rd year, 4th year in university", the field for selecting the "specialization" item will be "literature, Noh, engineering, physics, education ..." using the occupation data exchange table, and "occupation" will be changed to " If you say "employee", the selected fields are "steel / sales, general affairs,
Technology ... Bank / Sales / General Affairs ... "

Further, the item of "interest" is an item for the user to designate a specialized field or the like that he or she wants to study with particular emphasis at that time.

The "desired level" item is an item for setting the level of the presented knowledge to the level at which the user wants to acquire, and like the "interesting" item, is set only when the user has a desire. It On the screen shown in FIG. 8, a desired learning level number is selected and input.

Step 2) According to the input from the user in Step 1, name = user A, age 19 years old, occupation = company employee, specialty = trade / sales, English history = Eiken Level 3, interest = none in particular, User information was obtained with the desired level = 4 (university 1st, 2nd year, junior college).

The user information input section 10 converts the "occupation" data from "employee" to "6" using the occupation data exchange table of Table 1 which is possessed internally in order to simplify future data operations. , Re-register.

[Table 1]

In the same step, "English history" data is also converted. It is replaced with the data value of the "English history symbol" item in the English history correspondence table of Table 2 and re-registered. In this case, it changes from "Eiken 3rd grade" to "3".

[Table 2]

The "English history symbol" item in the English history correspondence table is "1".
(Rank) ”to“ 6 (Rank). ”These ranks have the same settings as those of the“ occupation ”item.

That is, if the value of "occupation" data is "3", it indicates a high school student, and if it is "5", it indicates a college student or a graduate student. If it is "5", it indicates that the student has academic ability equivalent to a college student or a graduate student.

As can be seen from the comparison between FIG. 8 and Table 1, the “desired level” item rank is also set to be the same as the “occupation” item rank.

Step 3) After conversion of the "occupation" and "English history" data, the "substantial academic ability" is set by the system.

The "substantial academic ability" item does not appear on the input request screen because the system determines and sets it from the values of "occupation", "desired level" and "English history".

If there is no "desired level" data,
The “substantial academic ability” data is registered in the user information 101 with the larger value of the “occupation” data and “English history” data after conversion. However, if the "desired level" data is set, the "desired level" data and the "English history" data are compared, and the larger value of the two is registered as the "substantial academic achievement" item data. To be done. This time, since the "desired level" is "4" and the "English experience" is "3", the value of "real academic ability" data is "4".

Step 4) The user information set as described above is sent to the knowledge importance level judgment unit 12.

Step 5) The knowledge importance degree judgment unit 12 takes out the presentation target knowledge and its attached data from the knowledge database 13 and puts them in the prepared arguments.

Step 6) The data related to the knowledge to be presented is collated with the user information 101 through the above argument in accordance with the knowledge importance level judgment rule. FIG. 9 shows an example of the knowledge importance level judgment rule according to the embodiment of the present invention. FIG. 10 is a diagram showing an example of knowledge data in the knowledge database according to the embodiment of the present invention. Knowledge data is knowledge "word knowledge"
In addition to "semantic knowledge", "knowledge number", "field", and "adequacy"
There is an item of attached data called.

The "field" is information indicating a profession / academic field in which "semantic knowledge" is often used. The knowledge importance degree determination unit 16 uses the field / specialty correspondence table illustrated in Table 3 to associate the “field” data with the data input in the “specialty” item of the user information.

[Table 3]

The collation of the "field" data and the "specialty" data, which is performed by referring to this correspondence table, is one of the procedures for determining the importance of knowledge.

The "adequacy" is information indicating an appropriate time when the "knowledge" is taught. FIG. 11 shows an example of criteria of suitability according to an embodiment of the present invention. Matching the "appropriateness" data with the "occupation" data of the user is also one of the procedures for determining the importance of knowledge.

The knowledge importance level judgment unit 12 inputs an argument of M-result for inputting the result of examination on the knowledge "field" data, an argument of A-Result for inserting the result of examination on the knowledge "appropriateness" data, and It has a place to calculate the importance. The initial value of importance is 0. The knowledge importance determination unit 12 also uses the k-box to capture the “field” data of knowledge, the U-BOX to capture the user's “specialty” data, and the J-box for the “profession” data. BOX, "interest"
It has arguments I-BOX for data, H-BOX for "desired level" data, and C-BOX for "English history" data.

The knowledge data to be subjected to the importance degree judgment processing are respectively fetched into these arguments, and the user data similarly fetched is compared with the knowledge importance rule shown in FIG. 9 to obtain the result. Set to M-Result or A-Result.

Here, a method of obtaining M-Result and A-Result will be described. FIG. 12 shows the M-Result of one embodiment of the present invention.
It is a flowchart which shows the procedure which calculates | requires A-Result. In this example, the importance levels of the knowledge numbers 121, 122, and 123 shown in FIG. 10 are calculated from the arguments M-Result and A-Result. First, the process of calculating the knowledge of the knowledge number 121 will be described.

The knowledge importance degree judgment unit 12 determines that U-BOX =
Trade / sales, J-BOX = 6, I-BOX = nil (no data), H-BOX = 4, C-BOX = 3, kb
ox = general, d-box = 2, and data is set in each argument. Processing is performed using these values and the knowledge importance level determination rule of FIG.

Since there is no data in the I-BOX this time, first, a check is made to see if the value of k-box is "general". In this case, since it is YES, the value of S1 is put into M-Result, and the process of obtaining the value of M-Result ends.

Next, processing for obtaining the value of A-Result is performed. Since there is a value in H-BOX, H-BOX and d
-The contents of the boxes are compared. Since the H-BOX data is larger than the d-box data this time, the d-box data value, that is, 2, is written in the A-Result.

Next, the importance is calculated based on the values of M-Result and A-Result. FIG. 13 shows a flowchart of a process for calculating the importance according to the embodiment of the present invention.

Since the M-Result data is S1, 1 is added to the importance. Next, since there is data in A-Result and the symbol "Interested" is not attached to that data,
Further, 1 is added, and the importance becomes “2”.

Then, C-BOX data and A-Result
The data in C-BOX is larger than the data in C. Judging from the English history of the user, this is knowledge number 1
It is highly likely that 21 has already been learned.

Therefore, the importance level or 0.5 is subtracted, and the knowledge level of the knowledge number 121 is determined to be 1.5.

The knowledge importance of knowledge number 122 is calculated in the same flow as the above processing. K-in the case of knowledge number 122
box = check, d-box = 4: 6.

This time, the field data in the k-box is converted into user specialized data with reference to the field / specialization correspondence table in Table 3. Therefore, the k-box data is "humanities, faculty of commerce, faculty of economics, ..., Sales, ...". In this, U-BOX
There is a field called "sales" which is the same as the data of. Then, S2 is registered in M-Result. Next, the process proceeds to the process of obtaining the value of A-Result.

This time, there are two data in the d-box. In this way, when a plurality of data are present in the d-box, the data with the smaller value is processed first.
Therefore, the “4” data in the H-BOX and the “4” data in the d-box are compared, and since both values are equal, A-Re
“4” is set as the value of sult.

Then, 2 is added to the degree of importance from the value of M-Result, and 1 is added to the degree of importance from the value of A-Result.

After this, the C-BOX and the A-Result data are compared, but the A-Result value is larger, so in the end,
The importance number of the knowledge number 122 is 3.

The data of knowledge number 123 is k-box = chemistry and d-box = 2. "Chemistry" in k-box
Even if is converted, the same data as the U-BOX data cannot be found. Therefore, M-Result remains nil.

Next, the d-box data and the H-BOX
When comparing the data of No. 2, the value of H-BOX is larger in 2 and 4, so 2 is entered as the data of A-Result.

Although M-Result is nil, since there is a value in A-Result, 1 is added to the importance first. Then, in comparing the data of C-BOX and A-Result, C
-Because it was judged that BOX had a larger value, 0.5
Is subtracted, and the importance of knowledge number 123 becomes 0.5.

Step 8) The importance thus determined is registered in the importance information register 129 as importance information.

Step 9) The knowledge number is also registered in the knowledge number register 129 as knowledge number information.

Step 10) The knowledge importance degree judgment unit 12
The user information, the importance degree information of knowledge whose importance degree is not 0, and the knowledge number information are sent together to the screen presenting unit 15. The screen presenting unit 15 receives the information of knowledge number 121-importance level 1.5, knowledge number 122-importance level 3, knowledge number 123-importance level 0.5 and user information.

Step 11) The screen presenting section 15 retrieves knowledge from the knowledge database 13 by the knowledge number.

Step 12) The screen presenting section 15 selects a constituent element to be used for presenting knowledge from the constituent element database 14 on the basis of the presentation screen creation rule possessed therein. FIG. 14 shows an example of the presentation screen creation rule according to the embodiment of the present invention. The presentation screen creation rules are "present knowledge only according to importance, present knowledge and example sentence, present illustration, photograph or video in addition to knowledge and example sentence", "rule regarding screen configuration", "multi-knowledge There are "rules for arranging on the same screen", "basic rules for arranging components", etc.

Step 13) A method of arranging knowledge and components is determined based on the above-mentioned presentation screen creation rule.

Step 14) The screen presenting section 15 presents knowledge. A display screen, a paper surface, or the like is prepared as a medium for displaying knowledge.

Here, the knowledge numbers 121, 122, 123
An example of a knowledge presentation screen in the case of presenting the above knowledge on the same screen is shown. FIG. 15 is a diagram showing a screen presented by a knowledge number according to an embodiment of the present invention.

Step 15) The screen presentation unit 15 registers the numbers of the constituent elements and presentation rules used for the knowledge presentation in the user information for each knowledge.

Step 16) The screen presenting section 15 sends the user information to the user information database 11.

Step 17) User information database 1
1 stores the user information received from the screen presenting unit 15 and prepares for the case where this user starts the apparatus next time.

FIG. 16 shows an example of a user information storage mode according to an embodiment of the present invention. In the figure, the user number is automatically added by the device itself, and the "substantial academic ability" is determined and set by the device. Furthermore, the “learned knowledge”, the “presentation utilization element”, and the “presentation utilization rule” are registered by the post-use device. The remaining items such as “name”, “age”, “occupation”, “specialty”, “desired level”, “English history”, “interest”, etc. are registered by the user's input.

With respect to the above operation, an experiment was conducted in order to investigate the influence of each of the semantic / physical quantity elements of the laid out information on the retention of the memory regarding the information. Here, we picked up the newspaper as information that was read out.

As a result, physical quantity factors such as the size of a photograph or headline are easily recognized, such as “if the content of the article is interesting, it is easy to memorize” or “if there is a photograph or headline that matches the content of the article, it is easy to remember”. We concluded that the impact of the content of the article was greater than the impact. That is, when a person records information, if the content of the information is interesting, the memory and impression of the information will remain strong without much effort. However, such natural memory becomes difficult when the acquisition target information is not particularly interested.

In addition, in a situation where a plurality of pieces of information must be stored, if there is information that does not need to be stored in the information to be acquired, there is a waste of storing unnecessary information. Presence hinders the acquisition of information that is truly needed.

Therefore, in order to support efficient knowledge acquisition activities, in order to appropriately determine the method of presenting knowledge,
It is necessary to rank the knowledge to be presented. Furthermore, since the importance and necessity of knowledge change depending on the knowledge acquirer and the situation at the time of knowledge acquisition, a ranking method that can flexibly deal with such changes is required.

In the present invention, by using the characteristics of the individual user input by the user, the individual knowledge referred to as the acquisition target candidate from the database storing the candidates for the knowledge to be acquired by the user is particularly acquired. It is possible to classify knowledge on the basis of individual characteristics by adding an appropriate rank among multiple ranks of importance, such as knowledge to be acquired and knowledge expected to be acquired, as the importance of the knowledge. To do.

[0083]

As described above, according to the present invention, by adding the degree of importance required as a result of comparison with the attribute of the user to each of the knowledge of the candidates for presentation, Knowledge can be separated according to attributes such as occupation and interest.

[Brief description of drawings]

FIG. 1 is a block diagram showing a configuration of a knowledge classification system according to an embodiment of the present invention.

FIG. 2 is a configuration diagram of a knowledge importance degree determination unit according to an embodiment of the present invention.

FIG. 3 is a diagram showing a user information input screen according to an embodiment of the present invention.

FIG. 4 is a flowchart (part 1) showing the processing operation of the embodiment of the present invention.

FIG. 5 is a flowchart (part 2) showing the processing operation of the embodiment of the present invention.

FIG. 6 is a diagram showing an example of a screen when a user occupation is input according to an embodiment of the present invention.

FIG. 7 is a diagram showing an example of a special item input screen when the occupation item of the embodiment of the present invention is “company employee”.

FIG. 8 is a diagram showing an example of a screen when a user desired level is input according to an embodiment of the present invention.

FIG. 9 is a diagram showing an example of a knowledge importance level judgment rule according to an embodiment of the present invention.

FIG. 10 is a diagram showing an example of knowledge data in a knowledge database according to an embodiment of the present invention.

FIG. 11 is a diagram showing an example of criteria of suitability according to an embodiment of the present invention.

FIG. 12 shows a flowchart of a procedure for obtaining an M-Result and an A-Result according to an embodiment of the present invention.

FIG. 13 is a flowchart of a process of calculating the importance according to the embodiment of this invention.

FIG. 14 is a diagram showing an example of a presentation screen creation rule according to an embodiment of the present invention.

FIG. 15 is a diagram showing a screen for presenting a knowledge number according to an embodiment of the present invention.

FIG. 16 is a diagram showing the contents of a user information database according to an embodiment of the present invention.

[Explanation of symbols]

 10 User Information Input Section 11 User Information Database 12 Knowledge Importance Determining Section 13 Knowledge Database 14 User Information Database 15 Screen Display Section 101 User Information 102 Importance Information 103 Knowledge Number Information 121 Data Reference Section 122 User Data Register 123 Knowledge Data Register 124 Data Collapse / Registration Unit 125 Data Matching Result Register 126 Knowledge Importance Calculation Unit 127 Importance Calculation Register 128 Knowledge Number / Importance Registration Unit 129 Knowledge Number / Importance Information Register 130 Information Transfer Unit

Continuation of front page (51) Int.Cl. ⁵ Identification code Office reference number FI Technical display location G09B 7/04 7517-2C

Claims (1)

[Claims] 1. At least a user information acquisition means for acquiring user information about at least one predetermined user, a user information database for storing the user information, and knowledge to be acquired. A knowledge database that stores one or more predetermined acquisition target knowledge attributes, and compares the user information and the value of the attribute attached to each acquisition target knowledge of the knowledge database between predetermined items. By doing so, a knowledge importance level classification means for classifying the knowledge by adding an appropriate rank to the acquisition target candidate knowledge among the ranks of the importance levels divided into at least two levels or more. A component database used for presenting knowledge to a user and a component database storing attributes of at least one or more predetermined components, and the component data. Knowledge fractionation scheme searches for the base, and having a means for determining the presentation of knowledge by collating the said knowledge severity classification unit predetermined reference values of the added the importance by.