JP2007188314A - Video data extraction system and computer program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To fetch useful reference data which can rationalize and objectify extraction of successful case data helpful to a student. <P>SOLUTION: The system is provided with: a personal DB accumulating personal data including age, grade, and character of a student; a video DB accumulating a plurality of video data related to a learning method strung to the personal data; an input means for receiving an input of a user ID of a student who wants the video data; an extraction means for extracting appropriate video data from the video DB by comparing desired personal data to be personal data corresponding to the user ID inputted by the input means with the personal data strung to the video DB; and an output means for outputting indices of the extracted video data to a screen. The extraction means performs comparison operation to the desired personal data related to the inputted user ID with the personal data strung to all the video data and extracts video data of the closest correspondence. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to an information providing technology for extracting moving image data to be browsed using communication technology and information processing technology.

To improve student learning, reforming the learning method is the fastest way.
For that purpose, data collection and successful case data accumulation are progressing. Successful video data is being prepared as a library against the backdrop of video shooting, capturing of the data after shooting into a computer, and the enhancement of the capacity of a personal computer capable of processing a large amount of data.

JP-A-2005-182272 was extracted as a prior art related to the present application (see Patent Document 1).
This technology is used to manage the teachers and students of the cram school, and to create efficient learning instructions and instruction reports. More specifically, the management computer stores a management database in which data relating to students and instructors is registered, and a curriculum database in which curriculums are registered, and operates the management program to create a management database and a curriculum database. Use and create learning instructions. Before the lesson, the instruction manual is printed and distributed to the instructor. After the lesson, the mark sheet is read with a scanner, and an instruction report including advice on the learning result is prepared and given to the student. The server at the headquarters communicates with the classroom management computer and can download the progress status data of all students and lecturers so that the status of the entire study school can be grasped instantaneously.

JP 2005-182272 A JP 2003-242307 A JP 2005-242018 A

(Acquisition technology of customs and personality data); In-house patent application for patent application In addition, the inventor of the present application has already applied for Japanese Patent Application Laid-Open No. 2003-242307 as a technology for acquiring customs and personality data (see Patent Document 2). .
This technology diagnoses the subjects' motivation, factors of motivation, ability, and proper occupation, and by looking at the diagnostic sheet for motivation improvement that describes the diagnostic results, It is a technology that improves the motivation of the subject by knowing, knowing how to stretch, knowing the brakes of the mind that are hindering the exertion of the advantage, knowing how to resolve it, and knowing the occupation that takes advantage of the advantage.
In other words, the motivation improvement diagnostic sheet is based on the answers to the questions prepared in advance for diagnosing one's own environment, such as the subject's “motivation”, “motivation factor”, “ability”, “appropriate occupation”, etc. A score is calculated for each diagnosis item, and the score is converted into a graph, a table, a sentence (comment) or the like and displayed. Graphs and tables make it easier to understand the results of diagnosis visually. In addition, text (comments) can convey content that cannot be expressed by numerical values, graphs, tables, and the like.

(Acquisition of data for improving results); Patent of prior application of the company Further, the inventor of the present application has already applied for Japanese Patent Application Laid-Open No. 2005-242018 as a data acquisition technique for improving results (see Patent Document 3). ).
This technology calculates priorities as review points to overcome weaknesses and creates different curriculums for each student.
That is, a plurality of learning unit data determined as units of the learning process is stored in association with the system diagram showing the correlation of each learning unit data. Create academic ability tests using a systematic diagram and test question database. Grade the achievement test that the student has taken, and receive the results of the achievement test. For each stored learning unit data, extract the learning units related to the academic ability test problem, arrange them in chronological order from the oldest, give priority to the learning unit with the poor results of the academic ability test, and the priority that students should learn Is calculated. Students can understand priorities, improving learning efficiency.

The so-called genius-type student as a success story is not helpful for the majority of students. This is because the learning method is completely different from the effort-type students. However, although this is empirically grasped, the objectivity is poor.
In other words, even though data for browsing can be collected, there is no objective evaluation axis for the data.

On the other hand, as described above, the video library of successful cases is beginning to be enriched. However, the video data requires time corresponding to the recording time. For this reason, students who want to refer to successful cases will only take time if they do not provide carefully selected reference data.
Therefore, it is necessary to extract successful cases that could be helpful for the students, but there was no rational method for the extraction.

  In addition, when operating a cram school, it is natural to consider the entrance rate and profit rate. For example, when developing a franchise, it is important to construct a system to persuade the franchisee (owner). For this reason, it was excellent in objectivity in addition to the rule of thumb.

  Therefore, the problem to be solved by the present invention is to provide a technology that contributes to extracting useful reference data by rationalizing and objectively extracting successful case data that is helpful for information providers and users. There is to do.

The object of the invention described in claims 1 to 9 is to extract useful reference data by rationalizing and objectively extracting successful case data that is useful for the information provider and the user. It is to provide a system that contributes to
Further, an object of the invention described in claims 10 to 18 is to extract useful reference data by rationalizing and objectively extracting successful case data that is helpful to the information provider and the user. It is to provide a computer program that contributes.

(Claim 1)
According to the first aspect of the present invention, there is provided a personal database storing personal data including the age, grade and personality of a student, a moving image database storing a large number of moving image data related to a learning method, and moving image data. By comparing the input means for accepting the input of the user ID of the desired student, the desired personal data corresponding to the user ID input from the input means, and the personal data associated with the video database The present invention relates to a moving image data extraction system comprising extraction means for extracting appropriate moving image data from a moving image database, and output means for outputting an index of moving image data extracted by the extraction means on a screen.
The extraction means compares the desired personal data associated with the user ID input by the input means with the personal data associated with all the moving picture data, and obtains at least one moving picture data having a high degree of coincidence. A moving image data extraction system is provided.

(Glossary)
"Personal data" refers to various types of data consisting of student learning methods such as textbook learning, notebook summarization, memorization, and problem exercises, in addition to the student's name, gender, age, grade, personality diagnosis results, and academic ability diagnosis results. And stored in a personal database. Personal data is databased by interviewing students and graduates who are already enrolled in a cram school.
The “moving image data” is data generated by using the learning method as moving image data, and is associated with personal data.
“Preferred personal data” includes the age, grade and personality of the student, as well as the gender. The personality is a personality diagnosis result output using the personality diagnosis system. In addition, academic achievement results may be used. In this claim, if the student inputs this desired personal data in advance, it is stored as the user ID, and then the appropriate moving image data stored in the moving image database is quickly extracted by inputting the user ID. . If the desired personal data is not input, such as a new student, a new entry is set.
The “user ID” is a unique ID individually set by a student or a teacher, such as a name, alphabet, or number.
The “index” is obtained by indexing personal data related to a student starring in moving image data for each learning method. Usually, it is displayed like student B, student C, student D, and the like. There are also cases where the index is further subdivided, such as textbook learning, note summarization, memorization, and problem exercises.
The “output means” may be output to a PC monitor or the like, for example, or may be output by printout.

(Function)
The moving image database stores moving image data in which learning methods for a plurality of students are recorded in advance.
In the personal database, the age, grade and personality of students are stored in a database, and user IDs are individually set.
When the input means accepts input of the user ID of a student who desires video data, the personal data is accessed from the received user ID to extract the corresponding personal data, and all the video data recorded in the video database is extracted. Compare with the associated personal data.
Then, the extracting means extracts at least one moving image data associated with the personal data having a high degree of coincidence with the desired personal data.
The output means outputs the index of the extracted moving image data on the screen.
That is, since the extracted moving image data extracts the students whose personal data is the same or similar, it is possible to view the learning method of the students with the same personal data as moving image data. Therefore, it is possible to rationalize and objectively extract the success case data that is useful for the student himself, and contribute to extracting useful reference data.

(Claim 2)
The invention described in claim 2 is a personal database storing personal data including the age, grade and personality of students, a moving image database storing a large number of moving image data related to the learning method, and moving image data. An input means for inputting desired personal data including the age, grade and personality of the desired student, an extraction means for extracting appropriate video data from the video database, and an index of the video data extracted by the extraction means on the screen output And a moving image data extraction system.
The extraction means compares the desired personal data input by the input means with the personal data associated with all the moving picture data, and extracts at least one moving picture data having a high degree of coincidence. I will provide a.

(Function)
The moving image database stores moving image data in which learning methods for a plurality of students are recorded in advance.
In the personal database, the age, grade and personality of students are stored in a database.
When the input means inputs desired personal data including the age, grade, and personality of the student who wants video data, the personal data is accessed based on the desired personal data, and the corresponding personal data is extracted and recorded in the video database. Compared with personal data associated with all moving image data.
Then, the extracting means extracts at least one moving image data associated with the personal data having a high degree of coincidence with the desired personal data.
The output means outputs the index of the extracted moving image data on the screen.
Since the extracted moving image data extracts the students whose personal data is the same or similar, the learning method of the students with the same personal data can be viewed as moving image data. In addition, in the invention of Claim 2, a new entrance school student demonstrates a high effect for browsing the learning method. That is, since the effect of improving the enrollment rate of new students can be expected, the system can be expected to improve the efficiency of franchise development, for example.

(Claim 3)
The invention according to claim 3 limits the moving image data extraction system according to claim 1 or 2, and includes radar graph creating means for converting the personal data into a radar graph, and the radar graph creating means The graphed radar graph is recorded in the moving image database.

(Glossary)
A “radar graph” is a graph in which value axes are set for personal data (confidence level, seriousness level, social level, etc.) and data is displayed radially from the central point by personal data. In some cases, it is called a radar chart.
(Function)
Since visibility is improved by making personal data into a radar graph, it is easy to judge personal data formed only of numerical values. The type of graph is not limited to the radar graph, but may be other graphs. However, it is preferable to use a radar graph that allows the data to be viewed at a glance because the visibility is improved.

(Claim 4)
Invention of Claim 4 limited the moving image data extraction system in any one of Claims 1-3, The said extraction means is a graph area of the radar graph which the said radar graph preparation means produces And a graph area of a radar graph stored in advance in the moving image database, and a plurality of moving image data having a high area matching ratio in the radar graph are extracted in order.

(Function)
By making personal data into a radar graph using image processing software or the like and constructing the graph area for comparison calculation, the processing speed of the comparison calculation can be increased.

(Claim 5)
The moving image data extraction system according to any one of claims 1 to 4, wherein the moving image data extraction system is limited, and includes an index creation unit that indexes the radar graph extracted by the extraction unit, and the output unit Is characterized in that a predetermined number of indexes created by the index creation means are displayed on the screen.

(Function)
The index creation means indexes the radar graph. The index is output and displayed on the screen. A user (student or the like) who uses the system browses a plurality of indexes displayed on the screen, and can select the desired index from each of the indexes to view moving image data. That is, it is easy to view the moving image data of other students who meet their own conditions.

(Claim 6)
The invention according to claim 6 limits the moving image data extraction system according to any one of claims 1 to 5, and the personal data includes a social degree, a delicate degree, an independence degree, a seriousness degree, and a confidence. It is a personality item value composed of degree and emotional stability.

(Function)
The personal data is configured as a personality item value including sociality, sensitivity, independence, seriousness, confidence, and emotional stability. That is, an item indicating a personality is defined in advance, and functions as a standard for knowing a student's learning method suitable for the personality. Usually, students often pay attention to the learning methods of students with high academic ability, but they place importance on learning methods suitable for individual personality in addition to academic ability.

(Claim 7)
The invention according to claim 7 limits the moving image data extraction system according to any one of claims 1 to 6, and the personal data includes the achievement level calculated from the achievement test and the achievement data value. It is generated as follows.

(Function)
In addition to the personality item value specified in claim 5, the personal data is also given an academic ability data value calculated from an academic ability diagnostic test. That is, if personal data is composed of personality item values and achievement data values, it contributes to extracting a more accurate learning method.

(Claim 8)
The invention according to claim 8 limits the moving image data extraction system according to any one of claims 1 to 7, and the moving image data includes a textbook learning category, a note summary category, a memorization category, and a problem exercise. It is characterized by being classified by each category.

(Function)
In the video data, there are textbook learning categories in which learning methods for textbook learning are recorded, note summary categories in which notes are organized, memorization categories in which effective memorization methods are recorded, and how to solve problems It is recorded in the recorded problem exercise category. These can be individually selected and browsed. For example, it is also possible to select a note summary of student B and a problem exercise of student C.

(Claim 9)
The invention according to claim 9 limits the moving image data extraction system according to claim 1 or 2, wherein the student selects moving image data from a plurality of indexes output on the screen by the output means, and the selection And a printout output means for printing out the moving image data.

(Function)
In the present invention, the moving image data selected by the student can be printed out in addition to the screen display. Since students can freely browse the learning method as a printed paper medium, it is useful as a format and assistance for the learning method.

(Claim 10)
The invention according to claim 10 is a host comprising a personal database storing personal data including the age, grade and character of the student, and a moving image database storing a large number of moving image data relating to the learning method in association with the personal data. The present invention relates to a program for a terminal device connected to a computer via a network.
The program includes an input procedure for accepting input of a user ID of a student who wants moving image data, desired personal data corresponding to the user ID input from the input procedure, and a personal associated with the moving image database. An extraction procedure for extracting appropriate video data from the video database by comparing the data, and an output procedure for outputting an index of the video data extracted by the extraction procedure on the screen. A terminal that compares the desired personal data associated with the user ID input by the input procedure with the personal data associated with all the moving image data, and extracts at least one moving image data having a high degree of coincidence. Computer program to be executed by the computer of the device It is.

Here, the “host computer” is a computer having a personal database and a moving image database. The terminal device is a network such as the Internet. The “terminal device” is a computer that is installed with the program of the claimed mine and can be connected to the host computer via a network. Typical networks include the Internet and an intranet.
The object of the present invention is to extract moving image data from each database by inputting necessary data to a terminal device physically located away from the host computer. In addition, as a form of using the terminal device, there is a franchisee that is a member of this system.

(Claim 11)
The invention described in claim 11 comprises a personal database storing personal data including the age, grade and personality of the student, and a moving image database storing a large number of moving image data relating to the learning method in association with the personal data. Input procedure to input desired personal data including age, grade and personality of students who want video data to computer, extraction procedure to extract appropriate video data from the video database, and extraction procedure extracted An output procedure for outputting an index of moving image data on a screen.
The extraction procedure compares the desired personal data input by the input procedure with personal data associated with all moving image data, and extracts at least one moving image data having a high degree of coincidence. A computer program to be executed.

(Claim 12)
The invention according to claim 12 is a computer program according to claim 10 or claim 11, and includes a radar graph creation procedure for converting the personal data into a radar graph, and the radar graph creation procedure is graphed. The radar graph is recorded in the moving image database.

(Claim 13)
The invention according to claim 13 limits the computer program according to any one of claims 10 to 12, and the extraction procedure includes a graph area of a radar graph created by the radar graph creation procedure, and A graph area of a radar graph accumulated in advance in the moving image database is compared, and a plurality of moving image data having a high area matching ratio in the radar graph are extracted in order.

(Claim 14)
The invention described in claim 14 limits the computer program according to any one of claims 10 to 13, and includes an index creation procedure for indexing a radar graph extracted by the extraction procedure, and the output The procedure is characterized in that a predetermined number of indexes created by the index creation procedure are displayed on the screen.

(Claim 15)
The invention according to claim 15 limits the computer program according to any one of claims 10 to 14, and the personal data includes a social degree, a delicate degree, an independence degree, a seriousness degree, a confidence degree, It is a personality item value that includes emotional stability.

(Claim 16)
The invention according to claim 16 limits the computer program according to any one of claims 10 to 15, and the personal data is generated as achievement data calculated from achievement ability test. It is characterized by that.

(Claim 17)
The invention according to claim 17 limits the computer program according to any one of claims 10 to 16, and the moving image data includes a textbook learning category, a notebook summary category, a memorization category, and a problem exercise category. It is characterized by being classified by each category.

(Claim 18)
The invention according to claim 18 limits the computer program according to claim 10 or claim 11, and the student selects moving image data from a plurality of indexes output on the screen by the output procedure, and the selection is made. A printout output procedure for printing out moving image data is provided.

The computer program according to any one of claims 10 to 18 may be provided by being stored in a recording medium. Here, the “recording medium” is a medium that can carry a program that cannot occupy space by itself, such as a flexible disk, a hard disk, a CD-ROM, an MO (magneto-optical disk), a DVD- ROM, PD, etc.
It is also possible to transmit from a computer storing a program according to these inventions to another computer through a communication line.
Moreover, it is also possible to form a system (server) according to another claim by preinstalling or downloading the program according to the invention according to claim 1 to a general-purpose terminal device.

According to the invention described in claims 1 to 9, it contributes to extracting useful reference data by rationalizing and objectively extracting successful case data that is helpful to the information provider and the user. A video data extraction system could be provided.
Further, according to the invention described in claims 10 to 18, it is possible to extract useful reference data by rationalizing and objectively extracting successful case data that is useful for the information provider and the user. We were able to provide a contributing computer program.

Hereinafter, the present invention will be described in more detail based on embodiments and drawings. The drawings used here are FIGS. 1 to 20.
FIG. 1 is a conceptual diagram conceptually showing a moving image data extraction system, FIG. 2 is a configuration diagram showing a configuration example of a diagnostic apparatus according to ETS, and FIG. 3 is a function realized by the diagnostic apparatus. 4 is a diagram illustrating an example of a question sentence file, FIG. 5 is a diagram illustrating an example of an answer file, and FIG. 6 illustrates an example of a motivation score file. FIG. 7 is an explanatory diagram showing an example of a motivation score file, and FIG. 8 is a flowchart showing processing of the moving image data extraction system.

  9 is a block diagram showing the hardware configuration of the system server, FIG. 10 is a block diagram showing the data storage area of the moving image database, and FIG. 11 is a block showing the data storage area of the personal database. FIG. 12 is a block diagram showing the hardware configuration of the student PC, FIG. 13 is a diagram explaining the matching process, and FIGS. 14 and 15 are diagrams explaining the index screen. 16 is a diagram showing a state in which the moving image data is reproduced from the index screen, FIG. 17 is a diagram showing a state in which the category in the moving image data is printed out, and FIGS. It is a figure explaining 2nd embodiment, FIG. 22 is a conceptual diagram which shows the moving image data extraction system in 3rd embodiment.

  As shown in FIG. 1, the moving picture data extraction system performs a personality diagnosis test related to the student A using the personality diagnosis program (ETS) installed in the system server 10 of the cram school, and the diagnosed student A's A learning method of another student who is most likely to have a personality is extracted from the moving image database 20 (hereinafter referred to as a moving image DB), and the extracted learning method is output as moving image data. Is a system that enables browsing.

(ETS)
Here, ETS (see Japanese Patent Application Laid-Open No. 2003-242307) used as a personality diagnosis for students will be described with reference to FIGS.

  FIG. 2 is a configuration diagram illustrating a configuration example of a diagnostic apparatus according to ETS. The diagnostic apparatus includes, for example, a central processing unit 101, an input device 102, a file device 103, and a display device 104. These devices may be configured by a single computer or a plurality of computers connected to a network. In some cases, each function is realized in cooperation. It is also conceivable to diagnose a client via a network using this diagnostic apparatus as a server.

  The file device 103 includes a question sentence file, answer file, answer classification file, motivation score file, motivation factor score file, ability score file, occupation score file, motivation score file, motivation factor score file, ability Files such as score files, occupation score files, and template files are stored.

In order to make this diagnosis, a question sentence necessary for the diagnosis is prepared in advance. This question sentence is stored in the file device 103 as a question sentence file. In the example of the question sentence file shown in FIG. 4, the question sentences corresponding to the question numbers 1 to 6 are described.
In question number 1, “a parent is angry for some reason every day” is entered as a question sentence. Similarly, Question 2 is “I am a bad person”, Question 3 is “devised to improve my performance in a shorter time”, and Question 4 is “Speaking and talking to the other party” If you think the idea is wrong, you will want to change it. ”Question 5 says,“ If you do your best, you can do most things. ” "I don't get the idea."

  These questions are displayed on the display device 104 by the central processing unit 101. For example, a printing device or a display corresponds to the display device. The subject reads and answers the displayed question.

  FIG. 3 is a functional diagram showing an example of functions implemented in this diagnostic apparatus. The answer input unit 201 has a function for inputting a test subject's answer to the question sentence according to an instruction from the central processing unit 101. According to the answer input field displayed on the display by the central processing unit 101, an answer is input by operating the input device 102, for example, a keyboard or a mouse, characters entered on an answer sheet are read by OCR, or entered on a mark sheet. It is input by reading the mark. The input method is not limited to these.

  The input answer is stored in the file device 103 by the central processing unit 101. The answer in this example is “YES” or “NO” corresponding to the question number. FIG. 5 shows an answer file, which is an example of an answer file stored in the file device 103 by the answer storage unit 202. It can be seen that “YES” is entered for question numbers 3 and 5 and “NO” is entered for question numbers 1, 2, 4, and 6. In addition to this example, various answer examples such as “always so”, “sometimes so”, “not so” can be considered for the question, and the file format changes according to the answer example.

  The score calculation means 203 has a function of calculating each score of “motivation”, “factor of motivation”, “ability”, and “appropriate occupation”. The central processing unit 101 calculates a score based on the answer file and the score file stored in the file device 103.

  6 and 7 illustrate the score calculation of “motivation”. The motivation score file shown in FIG. 6 stores the score by associating the question number with the diagnostic item related to “motivation”. In the case of this file, “Motivation” is the major item, and “Self-determination”, “Self-possibility”, “Ambient protection”, “Target clarity” are the medium items for further analysis of “Motivation”. ”And“ Goal achievement image ”.

  Here, “motivation” means that the subject has “self-determination power”, that is, a feeling that “it decides and makes progress on its own”, and “self-possibility”, that is, a feeling that “I can do various things” , “Ambient Protectiveness”, that is, how much you feel that you are warmly watched from the surroundings, “Goal Clarity”, that is, “How clear are your dreams and goals”, “Achieving Goals” “Image quality”, that is, “Do you have an image until you achieve your dreams and goals?”

  It can be seen from the motivation score file that the question number regarding “self-determination” is 6. Question number 6 contains “NO” as a scoring standard. This represents the score when the test subject answers “NO”. That is, if “NO” is answered with respect to question number 6, one point is assigned.

  From the answer file shown in FIG. 5, the subject of this embodiment answers “NO” with respect to question number 6, so one point is assigned. This score is stored in the motivation score file of FIG. 6 as “+1” next to question number 6 as a score relating to self-determination. By repeating this for each question, a score is given to the question regarding self-determination. As a result, the score for each stored question number is summed to calculate the total score. In this case, the total score is 5 points. A score of “10” in the full score represents a score when all the questions about self-determination are answered by a person who has self-determination. For example, the correction method includes a correction method such as a case where it is desired to express it with 100 points or a case where it is desired to express it with%. In this case, in order to correct to 100 points, a correction method of “× 10 (10 times)” is included, and as a result, the correction points are 50 points.

Similarly, the total score for the “self-possibility” is 4 points, the full score is 10 points, and the correction point is 40 points. The total score for “surrounding protection” is 3, the perfect score is 10, and the correction score is 30 points. The total score for “Target Clarity” is 2, the perfect score is 5, and the correction score is 40 points. The total score for “Target Achievement Image” is 3 points, the full score is 20 points, and the correction points are 15 points.
The student's habits and personality data are generally acquired as described above, and the student's “motivation”, “motivation factor”, “ability”, “appropriate occupation”, and the like are diagnosed and secured as personality data.

Next, the moving image data extraction system according to the present invention will be described.
The video data extraction system includes a student PC 50 used by students to browse videos, a system server 10 for controlling the entire system, a video DB 20 connected to the system server 10 via a network, a personal database 30 (hereinafter referred to as a personal DB). ).

As shown in FIG. 8, the student A executes ETS using the student PC 50 installed in the learning cram school. The result is transmitted to the system server 10 as personality data (S101).
The system server 10 receives the personality data transmitted from the student PC 50 via a network or the like (S102). The received personality data is digitized and recorded as personality item values for each personality item (sociality, sensitivity, independence, seriousness, confidence, emotional stability) (S103).
The recorded personality item value is subjected to image processing as a two-dimensional radar graph and recorded in the moving image DB 20 (S104).
Further, personality data of a plurality of students that have already been subjected to radar graph processing is recorded in the moving image DB 20, and it is searched whether there is data matching the personality item value of the student A from the moving image DB 20 (S 105). .

  If there is data that matches the personality item value of student A, a radar graph of the data is extracted (S106). On the other hand, if there is no data matching the personality item value of student A, a radar graph approximating the personality item value of student A is extracted (S107). Then, an index that can be selected for reproducing moving image data is created from the extracted radar graphs (S108), and the created index is displayed on the screen (S109).

  The student A selects a desired index from the indexes displayed via the display unit 55 of the student PC 50 (S110). The system server 10 retrieves the moving image data related to the selected index from the moving image DB 20 and distributes it to the student PC 50 via the system server 10 (S111). By receiving the moving image data (S112), the student PC 50 can browse other students' learning methods that are the same as or similar to their personalities based on the moving image data.

(System server)
Next, the hardware configuration of the system server 10, the moving image DB 20, the personal DB 30, and the student PC 50 will be described.
As shown in FIG. 9, the system server 10 has a function of controlling the entire system as a server that plays a central role in the system. The CPU 11 performs various arithmetic processes, and temporarily writes these data when writing various data. RAM (memory) 12 to be expanded, a storage unit 13 for storing an operating system, application software, and various data, an input unit 14 such as a mouse and a keyboard, a display unit 15 such as a display, and an electric communication line such as the Internet and an intranet A communication unit 16 that can be connected to the printer, an output unit 17 that can output various data to a printer, and an input / output port 18 that is an input / output unit for various data.

(Movie DB)
FIG. 10 is a conceptual diagram showing a data storage area of the moving image DB 20.
The moving image DB 20 is connected to the system server 10 via a LAN and includes a moving image data recording area and a printing data recording area.
In the moving image data recording area, moving image data is recorded so that the student browses the learning method of another student that best suits his / her personality and refers to the learning method. Each video data consists of categories such as “textbook learning category”, “note summary category”, “memorization category”, “question exercise category”, etc. In this embodiment, the length is about 3 to 5 minutes per category. It shall consist of

  Examples of “textbook learning categories” include “mark important points”, “mark by color”, “use the red highlighter as the most important point”, and “notebook category” "Make it easy to see", "Write down the points that the teacher emphasized separately", "Problem exercise category", "Repeat the same question three or more times", "Mark important points", "Memorization category" ”Is recorded so that students can visually grasp these, such as“ use notes for memorization ”,“ paste paper of important words on toilet wall ”,“ learn using association game ”, etc. .

  In the print data recording area, print data capable of printing out the moving image data divided for each category is recorded. For this print data, for example, the other three students desired by the student A are selected, and the categories (“textbook learning”, “notebook summary”, “memorization”, “problem exercise”) of the three students are selected. ) Can be output arbitrarily.

  The moving image DB 20 in the present embodiment is installed on the LAN, but is not particularly limited. For example, it may be incorporated as a storage device of the system server 10 or connected as an external storage device via a USB cable or the like. However, it is reasonable to install it on the LAN because it enables sharing of moving image data among a plurality of learning cram schools.

(Personal DB)
FIG. 11 is a conceptual diagram showing a storage area of the personal DB 30. The personal DB 30 is connected to the system server 10 and the moving image DB 20 via a LAN and includes a personal data recording area and a user ID recording area.
In the personal data recording area, in addition to the student's name, gender, age, grade, personality diagnosis result, achievement diagnosis result, various data consisting of each student's learning methods such as textbook learning, notebook summarization, memorization, problem exercises, etc. As recorded.

  The user ID recording area is an identifier associated with personal data recorded in the personal data recording area. That is, when the student inputs the user ID, the student's personal data recorded in advance in the personal DB 30 is called up. Note that the user ID includes names, alphabets, and numbers arbitrarily set by students and teachers.

(Student PC)
As shown in FIG. 12, the student PC 50 has the same basic configuration as the system server 10, and includes a CPU 51, a RAM 52, a storage unit 53, an input unit 54, a display unit 55, a communication unit 56, and an output unit that perform various arithmetic processes. 57 and an input / output port 58. The student PC 50 is connected to the system server 10 via a LAN, and can reply to the ETS provided by the system server 10 or browse the moving image data recorded in the moving image DB 20. It is also possible for the system server 10 to convert the moving image data desired by the student into print data and print out the print data from the output unit 57.

Hereinafter, the processing until the moving image data suitable for the student A is extracted by the system server 10 will be described in more detail.
(S103: Record personality item value)
The student A transmits an ETS answer using the student PC 50, and the system server 10 that has received the student personality data calculated by the ETS answer records the personality data in the storage unit 13 as the personality item value. . This personality item value may be arbitrary, such as a five-step evaluation from 1 to 5, or a ten-step evaluation from 1 to 10. In this embodiment, in order to identify the personality of the student and extract an appropriate learning method, six personality items of sociality, delicateness, independence, seriousness, confidence, and emotional stability are adopted. However, it is not particularly limited.

(S104: Creation of radar graph)
The personality item value recorded in the storage unit 13 is a function in which the radar graph creation means performs image processing on the personality item value of the student A (for example, social degree 3) into a two-dimensional graph. The radar graph thus converted is shown in FIG.
That is, the personality item values of the six items of social degree, delicate degree, independence degree, seriousness degree, confidence degree, and emotional stability are made into a radar graph. The radar graph image is recorded in the moving image DB 20 in association with the moving image data in which the student's learning method captured in advance is recorded.

(S105-107: matching process)
Next, the matching processing unit searches whether there is data matching the radar graph of student A from the radar graphs of other students recorded in advance in the moving image DB 20. As shown in FIG. 13, it can be seen that the radar graph of student A has a very matched graph area of the radar graph of student B recorded in the moving image DB 20. Then, a plurality of moving image data having a high area matching ratio in the radar graph are extracted in order.

In this way, if there is a radar graph having the same graph area, the radar graph is extracted. Although only student B is shown in FIG. 13, a radar graph having the same graph area as student B is also retrieved and extracted.
If there is no matching radar graph in the moving image DB 20, the radar graph having the most approximate shape is searched and extracted.

(S108 to 111: Index creation to video distribution)
A plurality of radar graphs extracted by the matching processing means are created as indexing that can be selected by the index creating means for reproducing moving image data. The index is output and displayed on the display unit 55 of the student PC 50. A user (student or the like) who uses the system browses a plurality of indexes displayed on the screen, and can select the desired index from each of the indexes to view moving image data. That is, it is easy to view the moving image data of other students who meet their own conditions.
FIG. 14 is a diagram showing an index screen, showing a form in which an index on which a biaxial map in which “character” is arranged on the vertical axis and “scholastic ability” is arranged on the horizontal axis is displayed on the display unit 55. . Also, the broken line portion in the figure is a selectable area, and other students suitable for the student A extracted by the matching processing means are displayed.

Here, students B, C, and D are extracted as students that match student A, but the number of students is not limited to three, and can be increased or decreased as necessary. However, if about three people are extracted, the accuracy of the matching process can be improved.
As a selection method from the index, the cursor is moved using the input unit 54 to select a desired student index. As shown in FIG. 15, when student B is selected, the screen is switched and “textbook learning”, “note summary”, “memorization”, and “problem exercise” are displayed as the study know-how of student B. Here, it is assumed that the student A moves the cursor and selects “note summary”.

  Then, the system server 10 distributes the moving image data of the “note summary category” selected from the moving image DB 20 to the student PC 50. Then, on the display unit 55 of the student PC 50, moving image data as a learning method for the “note summary” of the student B is reproduced. As shown in FIG. 16, this is explained by displaying an image of a note actually used by student B.

First, notes are classified into three categories: [notebooks used at school], [notebooks for practicing problems at home], and [notebooks that summarize grammar items such as reference books].
Also, in the most common English notebooks, [Copy the text of the textbook and write a translation], [Write the word separately from the text, write an example], [Grammar items and what the teacher said There is a learning method of “collecting at the bottom of the notebook”.
[Copy the text of the textbook and write a translation] summarizes the textbook text while using a color pen for easy viewing. Use "red" for new words and "blue underline" for new grammar.

[Write the word separately from the text and fill in an example] makes a certain space in the corner of the note and always adds an example sentence.
[Summarize the grammar and teacher's comments at the bottom of the notebook] Leave a free space at the bottom of the notebook and write down the new grammar and the teacher ’s “important” More specific examples are described.

  In this way, the student A can browse the learning method in the “note summary category” of the student B with the moving image data with the audio explanation, and can thus browse the successful case data that is used as a reference.

(Printout function)
In the present system, photo data such as “note summary category” displayed on the display unit 55 can be printed out. Although the student A can arbitrarily set the printout, for example, the student A's subjectivity, such as being helpful or interested, will ultimately determine which student's learning method to print out. You can do it.

  FIG. 17 is a diagram of a print medium that is output when the “note summary category” displayed on the display unit 55 is selected. This is realized by a printing unit. If student A imitates or refers to the data output as a paper medium in this way, it will be useful as a format and assistance for the learning method, and it will be easy to actually reflect it in his own notes. .

(User ID registration)
The above shows an example corresponding to a new registrant or a new enrollment student whose personal data is not recorded in the personal DB 30, and in the case of an enrollment student already enrolled in a school, it can be handled by registering a user ID. ing.
That is, the user ID is issued by registering personal data including age, school year, gender, and personality (test) at the time of registration. By inputting this user ID, registered personal data is called up, so that it is possible to save the trouble of inputting desired personal data every time it is used. As an example used by a student at a school, it is mainly a case where a student wants to view moving image data of a student different from the extracted moving image data. That is, it is highly effective for newly enrolled students to browse the learning method. For this reason, it contributes to the improvement of the enrollment rate in franchise development.

(Second embodiment)
Next, another embodiment of the moving image data extraction system will be described with reference to FIGS.
The moving image data extraction system according to the second embodiment performs a personality diagnosis test using the personality diagnosis program (ETS) installed in the system server 10 and an academic ability diagnosis test using the achievement diagnosis program (PCS) on the student A. The other student's learning method that is considered to be closest to the diagnosed student A's personality and scholastic ability is extracted from the video DB 20, and the extracted learning method is output as video data, so that the learning of other students can be performed. It is a system that enables browsing of methods.

  As shown in FIG. 18 and FIG. 19, PCS is a method for calculating priorities as review points for overcoming weaknesses and creating a different curriculum for each student. A plurality of learning unit data is stored in correspondence with a system diagram showing the correlation of each learning unit data, and an academic ability test is created using the system diagram and the test question database.

  Then, the student takes the academic ability test taken and receives the result of the academic ability test. For each stored learning unit data, extract the learning units related to the academic ability test problem, arrange them in chronological order from the oldest, give priority to the learning unit with the poor results of the academic ability test, and the priority that students should learn Is calculated. (For details, refer to JP-A-2005-242018). In this way, each student's achievement data calculated by the PCS and each data such as the textbook type used by the student are digitized and recorded in the storage unit 13 as achievement data values.

FIG. 20 shows a conceptual diagram of this system, and FIG. 21 is a flowchart showing a series of processing of this system.
As shown in FIG. 21, the student A executes ETS using the student PC 50 installed in the learning cram school, and transmits the result to the system server 10 as personality data (S201). The system server 10 receives the personality data transmitted from the student PC 50 via a network or the like (S202).
Subsequently, the student A executes PCS, and transmits the result as academic achievement data to the system server 10 (S203). The system server 10 receives the achievement data transmitted from the student PC 50 (S204).

The received personality data is used as personality item values, and academic achievement data is digitized and recorded as academic achievement data values (S205).
The recorded personality item values and academic achievement data values are image-processed as a two-dimensional radar graph and recorded in the moving image DB 20 (S206).
In addition, personality data and achievement data of a plurality of students that have already been subjected to radar graph processing are recorded in the moving image DB 20, and whether there is data matching the personality item values and achievement data values of the student A from the moving image DB 20. (S207).

  If there is data that matches the personality item value and achievement data value of student A, a radar graph of the data is extracted (S208). On the other hand, if there is no data that matches the personality item value and achievement data value of student A, a radar graph that approximates the personality item value and achievement data value of student A is extracted (S209). Then, an index that can be selected for reproducing moving image data is created from the extracted radar graphs (S210), and the created index is displayed (S211).

  When the student A selects a desired index from the indexes displayed on the display unit 55 of the student PC 50 (S212), the moving image data related to the selected index is taken out from the moving image DB 20 and via the system server 10. It is distributed to the student PC 50 (S213). By receiving the moving image data (S214), the student PC 50 can browse other students' learning methods that are the same as or similar to their personality and academic ability by the moving image.

  In the video data extraction system according to the second embodiment, in addition to ETS for grasping the personality, a PCS for grasping the student's academic ability is employed to extract students having a closer personality and academic ability from the video DB 20. be able to. For this reason, it is possible to rationalize and objectively extract success case data that is useful for the student himself / herself, and to extract useful reference data.

(Third embodiment: Franchise system)
In addition, the moving image data extraction system in the first embodiment and the second embodiment described above can be franchised (hereinafter referred to as FC).
FIG. 22 shows a conceptual diagram when the present system is franchised. That is, an FC owner terminal device is connected to the system server 10 in which a program such as ETS or PCS is installed, and the moving image DB 20 and personal DB 30 connected to the system server 10 via a network. As the network, the Internet may be used, but it is assumed that an intranet with a dedicated line is constructed for security reasons.

Although the terminal device in this embodiment is described as being in the Osaka branch and the Hokkaido branch, of course, there may be branches in exceptional places.
The actual operation is as follows.
For example, when a student inputs a user ID (or desired personal data) to a terminal device in the Hokkaido branch, the input data is transmitted to the system server 10. The system server 10 compares the desired personal data associated with the user ID input to the student with the personal data associated with all the moving image data, and extracts moving image data with a high degree of matching from the moving image DB 20. . Then, the index of the extracted moving image data is output to the screen of the terminal device, and if the student selects a desired index, the moving image data is reproduced.

  On the system server 10 side, filtering processing can be performed by an authentication server having an access restriction function for the moving image DB 20 to eliminate access by users other than the FC owner. When the FC owner accesses, the system logs in to the system using authentication means such as an ID and password dedicated to the FC owner. If the login is successful and authentication is performed, various data in the moving image DB 20 can be used.

(Remote home learning)
Further, in the above-described embodiment, processing from personality diagnosis and scholastic ability diagnosis to viewing of video data is performed at a learning cram school, but in addition to this, the system server 10 is accessed from a PC at the student's home, and the video data It is also possible to browse. In this case, a Web browser installed on the student's home PC is used to access the Web server connected to the system server 10 or the video DB 20 and the student is logged in to the Web server using an authentication means such as a student-specific ID and password. Then, the personality diagnosis test and the scholastic ability diagnosis test are executed, and the moving image data is browsed from the results.

It is the conceptual diagram which showed notionally the moving image data extraction system. It is a block diagram which shows the structural example of the diagnostic apparatus which concerns on ETS. It is a functional diagram which shows the example of the function implement | achieved by the diagnostic apparatus. It is a figure which shows an example of a question sentence file. It is a figure which shows an example of an answer file. It is explanatory drawing which shows an example of a motivation score file. It is explanatory drawing which shows an example of a motivation score file. It is a flowchart which shows the process of a moving image data extraction system. It is the block diagram which showed the hardware constitutions of the system server. It is the block diagram which showed the data storage area of the moving image database. It is the block diagram which showed the data storage area of the personal database. It is the block diagram which showed the hardware constitutions of PC for students. It is a figure explaining the matching process. It is explanatory drawing of an index screen. It is explanatory drawing of an index screen. It is the figure which showed the state by which moving image data was reproduced | regenerated from the index screen. It is the figure which showed the state which printed out the category in moving image data. It is the flowchart which showed the process about PCS. It is a systematic diagram calculated by PCS. It is a conceptual diagram of the moving image data extraction system in 2nd embodiment. It is a flowchart which shows the process of the moving image data extraction system in 2nd embodiment. It is a conceptual diagram which shows the moving image data extraction system in 3rd embodiment.

Explanation of symbols

10 System server 11, 51 CPU
12, 52 RAM
13, 53 Storage unit 14, 54 Input unit 15, 55 Display unit 16, 56 Communication unit 17, 57 Output unit 18, 58 Input / output port 20 Video database 30 Personal database 50 Student PC
DESCRIPTION OF SYMBOLS 101 Central processing unit 102 Input device 103 File device 104 Display device 201 Answer input means 202 Answer storage means 203 Score calculation means 204 Score conversion means 205 Diagnosis result output means

Claims (18)

A personal database that stores personal data including student age, grade and personality;
A video database that stores a lot of video data related to learning methods linked to personal data,
An input means for receiving an input of a user ID of a student who wants video data;
Appropriate video data is extracted from the video database by comparing the desired personal data, which is personal data corresponding to the user ID input from the input means, and the personal data associated with the video database. Extraction means;
An output means for outputting the index of the moving image data extracted by the extraction means;
A video data extraction system comprising:
The extraction means compares the desired personal data associated with the user ID input by the input means with the personal data associated with all the moving picture data, and obtains at least one moving picture data having a high degree of coincidence. A moving image data extraction system characterized by extraction. A personal database that stores personal data including student age, grade and personality;
A video database that stores a lot of video data related to learning methods linked to personal data,
Input means for inputting desired personal data including age, grade and personality of students who want video data;
Extraction means for extracting appropriate video data from the video database;
An output means for outputting the index of the moving image data extracted by the extraction means;
A video data extraction system comprising:
The extraction unit compares the desired personal data input by the input unit with personal data associated with all moving image data, and extracts at least one moving image data having a high degree of coincidence. To extract video data. A radar graph creating means for converting the personal data into a radar graph;
3. The moving image data extraction system according to claim 1, wherein the radar graph created by the radar graph creating means is recorded in the moving image database. The extraction unit compares the graph area of the radar graph created by the radar graph creation unit with the graph area of the radar graph stored in advance in the moving image database,
The moving image data extraction system according to any one of claims 1 to 3, wherein a plurality of moving image data having a high area matching ratio in the radar graph are extracted in order. Indexing means for indexing the radar graph extracted by the extracting means,
5. The moving image data extraction system according to claim 1, wherein the output unit displays a predetermined number of indexes created by the index creation unit on a screen.   The personal data is a personality item value including sociality, sensitivity, independence, seriousness, confidence, and emotional stability, according to any one of claims 1 to 5. The video data extraction system described.   The moving image data extraction system according to any one of claims 1 to 6, wherein the personal data is generated as academic achievement data values obtained from academic achievement test.   The moving image data extraction system according to any one of claims 1 to 7, wherein the moving image data is classified according to a textbook learning category, a note summary category, a memorization category, and a problem exercise category.   3. The printout output unit according to claim 1, further comprising: a printout output unit that allows a student to select moving image data from a plurality of indexes output on the screen by the output unit and print out the selected moving image data. Video data extraction system. Via a network to a host computer equipped with a personal database that stores personal data including the age, grade, and personality of students, and a video database that stores a large number of video data related to learning methods linked to personal data A program of a connected terminal device,
The program includes an input procedure for accepting input of a user ID of a student who wants video data;
Appropriate video data is extracted from the video database by comparing the desired personal data, which is personal data corresponding to the user ID input from the input procedure, with the personal data associated with the video database. Extraction procedure;
An output procedure for outputting an index of the video data extracted by the extraction procedure on the screen,
The extraction procedure compares the desired personal data associated with the user ID input by the input procedure with the personal data associated with all the video data, and at least one video data having a high degree of coincidence is calculated. A computer program that causes a computer of a terminal device to execute extraction. Via a network to a host computer equipped with a personal database that stores personal data including the age, grade, and personality of students, and a video database that stores a large number of video data related to learning methods linked to personal data A program of a connected terminal device,
The program includes an input procedure for entering desired personal data including age, grade and personality of students who want video data,
An extraction procedure for extracting appropriate video data from the video database;
An output procedure for outputting an index of the video data extracted by the extraction procedure on the screen,
The extraction procedure compares the desired personal data input by the input procedure with personal data associated with all moving image data, and extracts at least one moving image data having a high degree of coincidence. A computer program to be executed by a computer. A radar graph creating procedure for converting the personal data into a radar graph;
The computer program according to claim 10 or 11, wherein a radar graph obtained by graphing the radar graph creation procedure is recorded in the moving image database. The extraction procedure includes a graph area of a radar graph created by the radar graph creation procedure, and
Compare the graph area of the radar graph stored in advance in the video database,
The computer program according to any one of claims 10 to 12, wherein a plurality of moving image data having a high area coincidence ratio in the radar graph are extracted in order. An index creation procedure for indexing the radar graph extracted by the extraction procedure;
14. The computer program according to claim 10, wherein the output procedure displays a predetermined number of indexes created by the index creation procedure on a screen.   The personal data is a personality item value including sociality, sensitivity, independence, seriousness, confidence, and emotional stability, according to any one of claims 10 to 14. The computer program described.   The computer program according to any one of claims 10 to 15, wherein the personal data is generated as an achievement data value obtained from an achievement ability test.   The computer program according to any one of claims 10 to 16, wherein the moving image data is classified by a textbook learning category, a notebook summary category, a memorization category, and a problem exercise category. 12. The method according to claim 10, further comprising: a printout output procedure in which a student selects moving image data from a plurality of indexes output on the screen by the output procedure, and prints out the selected moving image data. Computer program.

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