JP2004212895A - System, method and program to support education - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an education support system in which learning efficiency is improved. <P>SOLUTION: A computer 1 for distributing educational lecture contents to students located at remote areas, is provided with a lecture obtaining means for obtaining the contents of the lecture conducted by a lecturer as electronic data and storing the data and a distribution means for distributing the lecture contents obtained by the lecture obtaining means to student's terminals connected to a network to realize the education support system. In the system, the computer is provided with a database constructing means in which lecture contents data obtained by the lecture obtaining means are analyzed and a database related to the lecture contents is constructed and the distribution means has a function of transmitting the contents of the lecture contents database to the student's terminals in accordance with the requests made from the terminals. <P>COPYRIGHT: (C)2004,JPO&NCIPI

Description

[0001]
TECHNICAL FIELD OF THE INVENTION
The present invention relates to an education support system, and more particularly to a system that supports distance education for delivering lecture content data to a user through a network. It also relates to educational support methods and educational support programs.
[0002]
[Prior art]
Traditionally, the content of lectures for various examinations and company training, etc. is captured as data such as video and audio, and it is taken using a network or by delivering a medium storing the contents of the lectures. There is a system that makes it possible for students to attend. When a network is used, real-time broadcasting and recorded broadcasting can be performed. In such lectures and trainings, not all students can fully understand the course at a time, and in order to compensate for the lack of understanding, the students have to listen again to the recorded contents.
[0003]
However, even if the student wants to listen only to the explanation of a certain chapter or section when reviewing the course again for review, he / she must listen to the lecture from the beginning, or I was forced to search, was very labor intensive, and in some cases spent more than several times the actual training.
[0004]
Patent Literature 1 discloses an example of a technique for solving such a problem. This is to add a time stamp to the recorded lecture content when the student clicks at a place where the student has wondered or wants to ask a question during the lecture. This makes it possible to easily specify the question location, and to search for it again when listening again.
[0005]
The invention of Patent Document 1 discloses that when question data is transmitted from a student, a lecturer (a teacher or the like) answers the question.
[0006]
[Patent Document 1]
JP-A-2002-139983
[0007]
[Problems to be solved by the invention]
However, the following problems occur in the above-described related art, particularly, in the invention described in Patent Document 1.
[0008]
First, even if a time stamp is added, the lecturer is only informed of the part of the question that was felt at that time, or only to assist the user in referring to it. The technology has not solved it at all. In other words, for questions that arise during the course in real time, write down the relevant chapters and sections, etc., and you can easily find the reference points at the time of review, but later The problem is that a lot of time is spent reviewing the question because it is necessary to find the relevant lecture.
[0009]
In addition, when the lecturer answers the question, the actual lecture is interrupted during the real-time lecture, which causes a problem that the progress of the lecture itself and the progress of the training of other students are hindered.
[0010]
[Object of the invention]
The present invention improves the inconvenience of the above-described conventional example, and in particular, can provide an answer to a question easily at the time of attending a lecture, thereby facilitating a review and improving educational efficiency. Its purpose is to provide a system.
[0011]
[Means for Solving the Problems]
Therefore, in the present invention, a computer that distributes educational lecture contents acquires lecture contents obtained by a lecturer as electronic data and stores the contents, and connects the lecture contents data acquired by the lecture acquisition means to a network. And a distribution means for distributing to the student terminal that has been provided, and in a system for supporting education for a student at a remote place, the computer analyzes the lecture content data acquired by the lecture acquisition means, and stores a database relating to the lecture content. In addition to the database construction means for constructing, the distribution means has a function of transmitting the contents of the lecture content database to the student terminal in response to a request from the student terminal.
[0012]
Further, the present invention provides a computer system for distributing educational lecture contents, wherein the computer acquires image data and audio data of the lecture by the instructor as image data and audio data. A distribution means for distributing the lecture content data obtained by the means to the student terminals connected to the network, and supporting the education of the student at a remote place. Language analysis means for analyzing a language in the data; and database construction means for extracting a term that meets a predetermined criterion from the analyzed language and constructing a database relating to the term. It has a function of transmitting the contents of the content database to the student terminal in response to a request from the student terminal. That, it may be a configuration that.
[0013]
With such a configuration, first, the contents of the lecture are taken in as electronic data by the lecture acquisition means provided in the computer, which is the system itself of the present invention, and the lecture content data (content) is transmitted to the network. Is delivered to the student terminal through Then, while the content is distributed to the student terminal (at the time of real-time distribution), the computer analyzes the captured lecture content data, and a database concerning the content of the lecture is constructed and stored. For example, speech data of a lecturer in a lecture is subjected to language analysis by a computer, and terms that match predetermined criteria are extracted from the analysis language. Then, commentary data on the term is stored in association with the keyword, and a database is constructed. Thereafter, the data in the database is transmitted from the computer to the student terminal at the request of the student.
[0014]
Therefore, when a student attends a lecture, especially when reviewing (re-learning), he / she requests a questioned part or a part that he / she wants to take the lecture again on the basis of the terms that are characteristic of the part. Lecture content data of a part can be easily searched from the database and can be referred to. As a result, the review can be easily performed in a short time, so that the learning efficiency can be improved.
[0015]
Further, the database construction unit includes a keyword extraction unit that extracts, as a keyword, a term that matches a predetermined criterion among the languages analyzed by the language analysis unit, and a time at which the keyword is extracted by the keyword extraction unit. It is preferable to include an association database forming unit that associates and stores the voice data acquired in the preceding and following time zones with the keyword.
[0016]
As a result, when a keyword such as an important word explained during the lecture by the lecturer is extracted, before and after the time at which the keyword is issued, audio data including the language described by the lecturer is added to the keyword as commentary data. Associated and stored in the computer. Then, a student who requests a commentary on the keyword at the time of attending or reviewing the course can easily obtain commentary data by searching with the keyword. Therefore, the commentary data that the student refers at the time of attending and reviewing the course is a word explained by the instructor before and after the keyword, so the relevance with the keyword is high, and it is useful as commentary data, The students can further improve their understanding and improve the learning efficiency.
[0017]
Further, the database forming unit may have a function of storing image data obtained in a time zone before and after the time when the keyword is extracted by the keyword extracting unit in association with the keyword. Similarly, the language analysis unit has a text conversion function of converting the analyzed voice data into text data, and the database forming unit outputs the voice acquired in a time period around the time when the keyword is extracted by the keyword extraction unit. A function of storing text data converted from data by a text conversion function in association with the keyword may be provided. Further, the computer is provided with presentation information acquisition means for acquiring presentation information, which is the content displayed on the information presentation means used in the lecture, as electronic data. And a function of storing presentation information acquired in a time zone before and after the given time in association with a keyword.
[0018]
In this way, the instructor's explanation data of keywords explained during the lecture is not only based on the instructor's voice data, but also image data of the lecture at that time and text data of the audio data Further, the information presented at that time by the board writing or the presentation by the computer is stored in the database in association with the keyword. Therefore, the student can refer to all the commentary in the lecture on the keyword, and the learning efficiency can be further improved.
[0019]
Further, the keyword extracting unit has a function of extracting a keyword based on term data registered in advance, and extracting a term detected a plurality of times by the language analysis unit as a keyword. Further, the language analysis means has a function of analyzing the volume of the sound data to be language-analyzed, and the keyword extraction unit is adapted to detect a portion of the sound data having a relatively high volume based on the analysis by the language analysis means. It has a function to extract terms as keywords.
[0020]
As a result, terms registered in advance, repeated terms, and terms explained by the instructor in a loud voice are extracted as keywords, and the information explained before and after that is registered in the database as commentary data. Therefore, a database of more important terms can be created. At the same time, not every term is registered as a keyword, but it is possible to suppress enlargement of the database, improve search performance, and reduce the processing load on the system itself.
[0021]
The language analyzing means has a function of analyzing the language of the voice data obtained by the voice obtaining means and extracting terms relating to the operation, and the database construction means has a predetermined term in which the extracted operating terms are registered in advance. When it is determined that the term is used, it is desirable to operate on the term in the audio data acquired in a time zone before and after the time when the operation term is extracted.
[0022]
By adopting such a configuration, the computer performs processing for constructing a database for terms described before and after the operation terms registered in advance when the operation terms are registered in advance. That is, since the operation term becomes a key for database construction, the process of extracting the term as a keyword is facilitated, the efficiency of database construction can be improved, and the processing load on the system itself can be reduced.
[0023]
Further, when the computer determines that the operation term extracted by the language analysis means is a predetermined term registered in advance, the computer executes the processing in the audio data acquired in a time period around the time when the operation term is extracted. It is assumed that the system includes a table of contents creating means for creating table of contents data of the lecture content based on the term, and the distribution means has a function of transmitting the contents of the table of contents data to the student terminal in response to a request from the student terminal. desirable. Then, when the table of contents creating means determines that the operation term extracted by the language analysis means is a predetermined term registered in advance, the voice data acquired in a time period around the time when the operation term was extracted is obtained. It is more desirable to have a function to add the additional data based on the terms in the table of contents to the created table of contents data.
[0024]
With such a configuration, the table of contents is created in response to the instructor issuing a term indicating that a table of contents is to be created during a lecture, and the table of contents data is generated in the order in which the lecture was performed. Sent to students. Therefore, even when lectures are given in an actual lecture in a different order from the schedule, the learner can easily review by referring to the table of contents. In particular, when additional data is added to the table of contents data, for example, additional data indicating that the item is an important item or an insignificant item is added, thereby increasing the efficiency of the review by the student. Can be.
[0025]
In addition, the computer receives question data regarding the lecture from the student terminal via the network, and includes question answering means for analyzing the content of the question data and searching for a term in the question data from the lecture content database. Preferably, the means has a function of transmitting the contents of the lecture content database searched by the question answering means to the student terminal.
[0026]
With such a configuration, when the computer receives a question from the student terminal, the computer searches data related to the question, that is, commentary data, from the database stored using the data in the question data as a search key. Then, by transmitting the search data to the student terminal, the student can obtain an explanation about the content input to the question data, and can easily learn. Further, since the answer to the question is automatically generated and distributed by the computer, it is possible to prevent the lecture from being hindered.
[0027]
The distribution means has a function of transmitting the keyword list data in the lecture content database to the student terminal, and the question answering means is transmitted by selecting a keyword in the keyword list data at the student terminal. The received question data may be received, and a search in the lecture content database may be performed for the selected keyword. Then, the question answering means receives the text data input at the student terminal as question data, further receives the voice data input at the student terminal as question data, and converts the question data based on the voice data. Linguistic analysis is also performed on the keywords within the language.
[0028]
In this way, even if question data is sent from the student terminal in various question formats, it is analyzed and searched for commentary data that can be answered. This makes it easy for the student to ask a question, and the answer is also made by referring to the database on the server, so that the response is quick and the learning efficiency can be further improved.
[0029]
Further, the distribution means temporarily stops distribution of the lecture content data to the student terminal when the question answering means acquires the question data from the student terminal, and thereafter temporarily stores the stored lecture content data. It is desirable to have a function of restarting the distribution to the student terminal from near the place where the distribution was stopped. Similarly, in response to a request from the student terminal, the distribution means temporarily suspends distribution of the lecture content data, and transmits the lecture content data acquired within a predetermined time before the distribution stop. It is further desirable to have a function of re-distributing the lecture content data to the student terminal from the vicinity of the point where the distribution of the stored lecture content data is temporarily stopped. Further, the computer is provided with presentation information acquisition means for acquiring the presentation information, which is the content displayed on the information presentation means used in the lecture, as electronic data, and the distribution means, in response to a request from the student terminal, The distribution of lecture content data is temporarily stopped, and the presentation information acquired within a certain period of time before the distribution is stopped is distributed to the student terminals. Thereafter, the distribution of the stored lecture content data is temporarily stopped. It may have a function of restarting distribution to the student terminal from the vicinity of the place where it has been performed. The distribution means restarts the distribution to the student terminal when the data for requesting the restart of the distribution of the lecture content data is received from the student terminal while the distribution of the lecture content data is temporarily stopped. May be configured.
[0030]
As a result, during the question of the student, the delivery of the lecture, that is, the reproduction on the terminal is temporarily stopped, and the answer can be waited. Alternatively, if the user wants to listen to the content of the lecture just before, the distribution of the subsequent lecture is stopped by making a request to that effect, and the content of the previous lecture is repeated. While the playback is stopped, the contents of the lecture are stored. Therefore, the student can deepen the understanding of the question items, and the degree of understanding of the contents of the previous lecture is suppressed from being insufficient. In particular, when the student gives an instruction to start the reproduction, the student can take a sufficient time to understand, and the learning efficiency can be improved.
[0031]
In addition, it is desirable that the computer be configured to include a student management unit that identifies a student terminal and stores data related to a lecture attended together with identification data of the student terminal. In particular, the student management means has a function of recognizing the progress of the lecture from the lecture content data acquired by the lecture acquisition means and storing data on the lecture attended for each student according to the progress of the lecture. It is even more desirable. As a result, the content of the course is stored and managed for each student, so that the student can easily grasp the content of the next course to be taken and the content of the course to be reviewed.
[0032]
Further, in the present invention, a method of distributing educational lecture contents by computer and supporting education for a student in a remote place, wherein the contents of the lecture by the lecturer are acquired and stored as image data and audio data. An acquisition step, and a distribution step of distributing the acquired lecture content data to the student terminals connected to the network, wherein the lecture acquisition step includes a language analysis step of analyzing a language in the acquired audio data, and a A database construction step of extracting a term that matches a predetermined standard from within the language and constructing a database related to the term, and the distribution step includes, in response to a request from a student terminal, a content of the lecture content database. There is also provided an education support method that includes a database distribution step for transmitting to a student terminal.
[0033]
A program for controlling the operation of a computer for distributing educational lecture contents to support education for a remote student, and acquires and stores the contents of a lecture by a lecturer as image data and audio data. And a distribution process for distributing the acquired lecture content data to student terminals connected to the network. The lecture acquisition process includes a language analysis process for analyzing a language in the acquired audio data, and an analysis process. A database construction process for extracting a term that matches a predetermined criterion from a given language and constructing a database related to the term, and delivering the content of the lecture content database in response to a request from a student terminal. And a program for education support that has a process of transmitting the program to the student terminal. Also it provides a 憶媒 body.
[0034]
With such a configuration, the same operation and effect as described above can be obtained, and the above object can be achieved.
[0035]
BEST MODE FOR CARRYING OUT THE INVENTION
<First embodiment>
Hereinafter, a first embodiment of the present invention will be described with reference to FIGS. FIG. 1 is a block diagram showing a configuration according to the first embodiment of the present invention. FIGS. 2 and 3 are functional block diagrams showing the configuration of some servers in the system. FIG. 4 is a diagram showing the structure of a database constructed in the system. 5 to 7 are diagrams illustrating an example of a user interface displayed on the student terminal. 8 and 9 are flowcharts showing the operation of the present system.
[0036]
(Constitution)
The educational support system according to the present invention includes a computer 1 that distributes educational lecture content connected to a network N, and stores lecture content data of a lecture in a student terminal 2 of a remote student connected to the network N. It is to deliver. In particular, correspondence education is provided in which lectures are delivered to students in real time using image (video) data and audio data. Hereinafter, this will be described in detail.
[0037]
The computer 1, which is the present system, is an aggregate of a plurality of server computers that perform various processes as described below in the present embodiment. As described above, by causing each server computer to execute each process, the processing speed can be improved by decentralization. However, the present system may be realized by one server computer, and in such a case, the processing performed by each server described below is performed by one computer.
[0038]
As shown in FIG. 1, the system includes a lecture acquisition means (11 or the like) for acquiring and storing lecture contents by a lecturer as electronic data, and connecting the lecture contents data acquired by the lecture acquisition means to a network N. Distribution means (14 and the like) for delivering to the student terminal 2 and database construction means (12 and the like) for analyzing the lecture content data acquired by the lecture acquisition means and constructing a database relating to the lecture content. Although not shown in FIG. 1, there is also provided a presentation information acquisition unit for acquiring presentation information, which is the content displayed on the information presentation unit used in the lecture, as electronic data.
[0039]
Specifically, the lecture acquisition means (11 and the like) includes an image / audio acquisition server 11 which is a server computer and a camera 11a which is installed in a classroom or the like where the lecture A is performed and captures the state of the lecture A as digital image data. Similarly, a microphone 11b installed in a classroom or the like to capture the audio of the lecture A as digital audio data, and an image and / or storage means (for example, a hard disk) for storing lecture content data including the captured image data and audio data. And a voice database 16. That is, the lecture acquisition unit 11 mainly includes an image acquisition unit, a voice acquisition unit, and a storage unit that stores acquired data.
[0040]
Further, the presentation information acquisition means acquires information presented by a lecturer or the like during a lecture, and thus acquires information on the contents of the lecture in the same manner as the lecture acquisition means. For example, the information presenting means is a computer that reproduces and displays presentation data that has been digitized in advance, a projector that actually projects the presentation data in a lecture classroom, and further, such as a whiteboard or a blackboard. Such as a board for board writing. When the information presenting means is a computer, a projector, or the like, the server 11 functions as the presentation information acquiring means because the computer is connected to the image / audio acquiring server 11.
[0041]
In such a case, the presentation data itself acquired by the image / audio acquisition server 11 becomes the presentation information. Further, when the information presenting means is a whiteboard, an apparatus having a function of recognizing written contents mounted on the board and obtaining such characters and graphics as electronic data, and a device connected to the apparatus. The image / voice acquisition server 11 that actually acquires presentation information serves as presentation information acquisition means. For example, a device that recognizes characters or the like written on a board for board writing and obtains the data as electronic data has a configuration capable of optically scanning the written surface of the board. In this case, the acquired electronic data, such as characters and graphic data, becomes the presentation information, but may be converted into text data by recognizing the written character data as characters.
[0042]
Further, when the board for board writing as the information presenting means is a blackboard, a camera that shoots from all angles to suppress that the blackboard is hidden behind the instructor and cannot be shot by the camera 11a. The image / sound acquisition server 11 for acquiring image data by a camera may constitute a presentation information acquisition unit. In this case, image data obtained by photographing the blackboard with the camera becomes presentation information. The presentation information acquired by the image / audio acquisition server 11 is stored in the image / audio database 16 by the server 11 as described above. Here, the case where the information presenting means is a board for whiteboard (such as a whiteboard) is mainly exemplified below, and the obtained presentation information is referred to as whiteboard data.
[0043]
The distribution unit 14 is a communication management server 14 that distributes the captured lecture content data in real time, that is, the images and audio data captured from the cameras 11a and 11b to the student terminal 2 directly. The communication management server 14 manages the connection status of the student terminals 2 to be delivered, and also attends the lectures so far for each of the student terminals 2, and furthermore, the current lecture locations (the lectures being distributed). Relative time from the start time of the event). This will be described later in detail. Here, the lecture content data distributed to the student terminal 2 by the distribution means is not limited to being distributed in real time. The lecture content data that has been captured and stored in the image / audio database 16 may be distributed in an on-demand format in response to a request from a student.
[0044]
The database construction means 12 includes an image / sound processing server 12 for analyzing and processing the image / sound data acquired by the image / sound acquisition server 11, and a keyword serving as a storage means for storing a database constructed as a result of the processing. And a database 17. The image / audio processing server 12 has a language analysis function 12a for analyzing the language in the acquired audio data, as shown in a functional block diagram of FIG. Database construction functions 12b and 12c for extracting terms that match the criteria and constructing a database relating to the terms.
[0045]
The language analysis function 12a recognizes voice data captured by the microphone 11b or the like in real time, analyzes nouns, verbs, particles, and the like in the voice and converts it into text. Further, when it detects that the recognized voice is in a predetermined language such as English, for example, it has a function of translating all sentences into Japanese. It should be noted that such a speech recognition technology is a known technology, and a detailed description thereof will be omitted.
[0046]
The database construction function further includes the following functions. That is, this function includes a keyword extraction function 12b for extracting, as a keyword, a term that matches a predetermined criterion among the languages analyzed by the language analysis function, and a lecture in which the keyword is extracted by the keyword extraction function. A database forming function 12c for storing keyword explanation data such as voice data acquired in a time zone before and after the time in association with the keyword. As a result, keywords such as important words explained during the lecture by the lecturer are extracted, and the keywords are stored in the server as a database together with the commentary data. Then, as described later, a student who requests a commentary on the keyword at the time of review can easily obtain commentary data by performing a search using the keyword, thereby improving review efficiency.
[0047]
The keyword extraction method by the keyword extraction function 12b includes the following. That is, the keyword extracting function 12b has a function of performing the following processing.
[0048]
First, a keyword is extracted based on term data 12d registered in advance. In this method, a lecturer who gives a lecture submits a lecture plan in advance, and keywords that are considered important in the lecture are registered (stored) in a database, and the same keywords as the registered keywords are extracted.
[0049]
As another method, a term analyzed by the language analysis function is monitored, the term itself and the time at which the term is detected are stored in a memory, and when the same term is detected a plurality of times, the term is used. Extract terms as keywords. Then, when a certain term is extracted as a keyword, commentary data of the term is stored in a keyword database as described later. Based on the detection time of the term detected so far, an already recorded image / audio database is stored. And voice data before and after the time at which the term was previously detected is registered as commentary data in the keyword database. For example, when a certain term is detected three or more times, it is registered as a keyword, and the contents spoken by the lecturer before and after all three times are registered as commentary data. Thus, the terms spoken multiple times by the instructor are likely to be important, and such terms are automatically extracted and a database is constructed.
[0050]
Further, as another method, the language analysis function described above has a function of analyzing the volume of sound data to be subjected to language analysis. Extract as keywords. This is because the part spoken by the instructor in a loud voice is likely to be important, and the term uttered in such a case is automatically extracted.
[0051]
Then, with respect to the keywords extracted as described above, the database forming function 12c associates the audio data / image data 12e and the like acquired in the time period before and after the lecture time with which the keywords were extracted with the keywords such as a hard disk. It is stored in a keyword database 17 as a storage means. At this time, for example, by storing voice data and image data for one minute before and after the time when the keyword was issued as commentary data, commentary data related to the keyword is included in the voice data, or keyword-related data is included in the image data. Since the image on the board is included in the image data and the gestures of the lecturer and the materials brought in are included in the image data, a database effective for review is formed as described later. At this time, when acquiring the audio data before and after the time when the keyword is issued, the image / audio data stored in the image / audio database 16 at the time of acquisition by the image / audio acquisition server 11 is used.
[0052]
In addition, the database forming function 12c includes not only voice data and image data as keywords but also text data of voice data, and is presented to the information presenting means and acquired by the presentation information acquiring means as described above. It also has a function of associating and storing presentation data, and also board data 12e composed of figures and characters, which are presentation information described on the board board. That is, the voice data acquired in the time zone (for example, one minute before and after) before and after the time when the keyword is extracted by the keyword extraction function is converted into text data and stored in association with the keyword. Presentation information such as board writing data acquired in a time zone before and after the extracted time is stored in association with a keyword. By the way, when converting voice data into text, the language analysis function 12a is provided with a text conversion function of converting the analyzed voice data into text data.
[0053]
Here, an example of the database constructed as described above is shown in FIG. As shown in this figure, the keyword "network" is registered as an important term in advance, and if such a term is explained by a lecturer during a lecture, the sound data and moving image data before and after that, and further, If there is data, text data written on a blackboard is given a predetermined file name (for example, file1) and registered in the database in association with the term "network". At this time, there are two (plural) databases relating to the “network”. However, this is the case where the explained time is different, and the explanation data is associated with each explanation. As a result, the commentary data related to the keyword is abundant, and a student who cannot understand even by listening to a certain commentary data can also hear another commentary data of the same keyword, which is effective auxiliary data for understanding the lecture. .
[0054]
Further, in the present system, the student terminal 2 has a function of transmitting question data relating to the lecture via the network N. In response to this, the system side receives the transmitted question data and simultaneously transmits the question data. A question answering server 15 (question answering means) for analyzing and responding to the contents is provided. Specifically, the question answering server 15 searches for a term in the question data received via the communication management server 14 from the keyword database 17 which is a lecture content database, and searches for the voice data and the like corresponding to the keyword. The data of the content that is the commentary such as the image data is extracted. The extracted data (data in the lecture content database) is transmitted to the terminal 2 of the student who has asked the question via the question answering server 15 and the communication management server 14 which are also distribution means.
[0055]
With such a configuration, when the computer 1, that is, the system itself receives a question from the student terminal 2, data related to the question, ie, commentary, from a database that stores data in the question data as a search key. Search for data. Then, by transmitting the searched commentary data (answer data) to the student terminal 2, the student can obtain a commentary on the content input in the question data, and can easily review. In addition, since an answer to the question is automatically generated and distributed by the server, it is possible to prevent a normal lecture from being hindered.
[0056]
Here, the present system responds to a question from the student terminal 2 by the following method. First, the question answering server 15, the communication management server 14, and the like, which are distribution means, transmit keyword list data in the keyword database 17 (lecture content database) constructed by the image / voice processing server 12 to the student terminal 2. . Then, the transmitted keyword list is displayed on the student terminal 2, and is transmitted to the system 1 itself when the student clicks a keyword in doubt or a keyword to be reviewed, and transmits the keyword to the communication management server. The question answering server 15 receives the information via the server 14. Thereafter, the question answering server 15 searches the keyword database 17 for the keyword in the data received as the question data as described above, and sends the corresponding commentary data such as voice data to the student terminal 2. Return to.
[0057]
Further, the student terminal 2 can transmit the question data to the system 1 by text data input freely, and also by voice data input by the student talking. The question answering server 15 receives this, and if it is text data, extracts a keyword from it by a language analysis function, and if it is voice data, similarly extracts a keyword by a language analysis function by voice recognition. Then, the database is searched for the keyword.
[0058]
In this way, by diversifying the question format of the student, it becomes easy for the student to ask a question, and since the answer is made by referring to the database on the server, it is quick, and the review efficiency is further improved. Improvement can be achieved.
[0059]
In order to realize the above-described question answering process, the question answering server 15 has, for example, a configuration shown in FIG. FIG. 3 is a functional block diagram showing the configuration of the question answering server 15. The server 15 first has a question data analysis function 15a for analyzing the format of the received question data 15f (eg, voice, freely input text, etc.), and analyzes the question data in sentences. A language analysis function 15b and a keyword extraction function 15c for extracting a keyword, a database search function 15d for searching whether or not a database of the queried keyword exists, and the student terminal 2 via the communication management server 14 And a reply transmitting function 15e for transmitting the reply to the user. More specific processing operations will be described in detail in the operation description.
[0060]
Here, the present system 1 is further provided with a reproduction management server 13 as a distribution means for managing the distribution status of the lecture content data to the student terminals 2. The server 13 temporarily suspends the distribution of the lecture content data to the student terminal 2 when the question response server acquires the question data from the student terminal 2. That is, when the data acquired from the image / audio acquisition server 11 is directly distributed to the student terminal 2, a stop command is issued to the image / audio acquisition server 11, and the data is temporarily stored in the image / audio database. If the playback management server 13 itself distributes the lecture content data, the distribution management server 13 suspends the distribution. At this time, the playback management server 13 stores information on the lecture stop time of the student terminal 2 (for example, relative time from the start time of the lecture being distributed). Then, while receiving the data for requesting the restart of the delivery of the lecture content data from the student terminal 2 while temporarily stopping the delivery of the lecture content data, based on the stored information about the lecture suspension time. And restart the distribution to the student terminal 2 from the time of the suspension. In particular, when the distribution is restarted from the lecture content data several seconds before the stop, the learner can remember how much the lecture has already been taken and can easily take the lecture continuously. At this time, the distribution restart request from the student terminal 2 is performed by an action such as clicking a predetermined button on the user interface displayed on the student terminal 2.
[0061]
By doing so, the progress of the lecture while the student is asking a question is suppressed, so that the student can be prevented from missing the lecture. Then, as soon as the student is ready, the distribution is started again from the paused point, so that the content can be continuously taken without skipping a part of the lecture.
[0062]
Here, when the distribution of the lecture content data is temporarily stopped due to the student's question, the reproduction management server 13 may resume the distribution after a certain period of time without a request from the student. As a result, long-term attendance by the student can be suppressed, concentration can be maintained, and even if the student forgets to make a resumption request, that fact is not considered. Can be recognized.
[0063]
In addition, in response to a request from the student terminal 2, the playback management server 13, which is a distribution unit, temporarily suspends the distribution of the lecture content data, and the lecture management server 13 acquires the lecture acquired within a predetermined time before the distribution stop. It also has a function of redistributing the content data to the student terminal 2. For example, a re-distribution request from the student terminal 2 is performed by clicking the instructor image displayed on the student terminal 2, whereby the contents explained by the instructor in the immediately preceding minute are as follows. It is redistributed and reproduced on the student terminal 2. The detection that the lecturer image has been clicked is performed by fixing a camera for photographing the lecture classroom, setting the position of the lecturer during the lecture in advance, and setting a predetermined area of the image to be distributed to the student terminal 2. Is set as the redelivery request area. Then, when such an area is clicked or the like, information to that effect is transmitted to the system side. Or, on the contrary, without determining the position of the lecturer, the difference between the classroom screen when the lecturer is not in the lecture classroom and the latest lecture screen when the lecturer is lecture is taken, and the area around the changing area is requested to be re-distributed. May be set.
[0064]
Even in the above case, the distribution of the lecture content data is temporarily stopped at the time of the re-distribution request, so that after the reproduction of the re-delivery data is completed, the distribution from the continuation may be automatically continued. Alternatively, when the student who has heard only the necessary portion of the re-delivery data makes a request for resumption at the terminal, the distribution of the lecture may be resumed at the time of the request.
[0065]
Further, the reproduction management server 13 has a function of redistributing the presentation information displayed on the information presenting means to the student terminal 2 in response to a request from the student terminal 2. For example, redistribute board data composed of characters and figures described on a board board. Specifically, the request for the contents of the board writing from the student terminal 2 is performed by clicking on the location of the board writing board displayed on the student terminal 2, and thereby, the content written on the board board currently is described. The data of the character or graphic being sent is transmitted to the student terminal 2. At this time, to detect that the board for board writing is clicked, the position of the board for board writing in the classroom is specified in advance, and such an area is set as a board content request area. Then, when such an area is clicked or the like, information to that effect is transmitted to the system side. The board writing data is an enlarged view of a board writing board portion of the image in the classroom captured by the camera 11a, or a camera attached at a position different from the camera 11a that mainly captures the image of the lecture classroom. The image of the board board taken from another angle, or the board data obtained by recognizing the contents written on the board board and acquiring such characters and figures as electronic data may be used. The board writing data transmitted to the student terminal 2 is displayed on the lecture screen (see reference numeral D1 in FIG. 5 described later) or the lecturer's operation personal computer screen (see reference numeral D2 in FIG. 5). ), Or when another screen is opened on the terminal 2.
[0066]
In this way, the learner can quickly obtain the explanations that he or she missed or could not understand, and furthermore, the contents of the hard-to-see cardboard can be quickly acquired, thereby suppressing the delay in understanding due to the inability to acquire them. And the learning efficiency can be improved.
[0067]
(motion)
Next, the operation of the system according to the present invention will be described with reference to FIGS. 5 to 7 show user interfaces displayed on the display of the student terminal 2. 8 and 9 are flowcharts showing the operation of the system. FIG. 8 shows an operation particularly when acquiring the lecture content data and constructing a keyword database, and FIG. 9 shows an operation particularly when responding to a question from the student terminal.
[0068]
First, in the system, image data and audio data from the camera 11a and microphone 11b installed in a lecture classroom or the like are captured by the image / voice acquisition server 11, respectively (step S1, lecture acquisition step), and these data are Are stored in the image / sound database 16 (step S2). At this time, the moving image and audio data, which are image data, are converted into, for example, MPEG format data. As described above, since the data is compressed and stored, when the data is subsequently distributed to the student terminal 2 by the reproduction management server 13 via the network N (step S4), the transmission / reception amount of the data is suppressed. are doing. The process of taking in and storing the lecture content data is always performed.
[0069]
Here, if there is a language in the audio data of the captured data (Yes in step S3), the analysis of the language is performed in the image / audio processing server 12 (step S6, language analysis step). . That is, the captured voice data is always subjected to voice recognition processing, it is determined whether or not a predetermined language is included, and if the language is included, the language is analyzed in detail. Specifically, nouns, verbs, and particles are detected from sentences contained in the voice, and specific nouns are extracted. At this time, the specific noun is, for example, a term registered in advance in a storage unit (not shown) in the system, and is extracted (step S7). Then, audio data and image data before and after the extracted keyword is issued, as well as cardboard data are stored in association with each other as commentary data of the keyword (see FIG. 4). In this way, a keyword database is constructed (step S8, database construction step). Then, after that (proceeding from step S8), or even if no keyword is extracted (negative determination in step S7), the lecture content data up to that point is distributed to the student terminal 4 (step S4, distribution step). .
[0070]
FIGS. 5 to 7 show the state of the delivered lecture content data at this time. First, FIG. 5 is an example of a user interface displayed on the student terminal 2. As shown in the figure, on the display of the student terminal 2, an image (moving image) D1 acquired by the camera 11a for photographing the lecture classroom is displayed on the right side, and the operation of the personal computer used for the lecture is displayed on the left side. The screen (moving image) D2 is displayed. At the lower part of the display, voice recognition processing is performed on the voice spoken by the instructor, and text data is displayed (see D3). Further, in the lower right corner, an operation panel D4 operated by the student is displayed. From a speaker (not shown) of the student terminal 2, audio data spoken by the instructor acquired by the microphone 11 b is output. However, the user interface displayed on the student terminal is not limited to the format shown in FIG. For example, only the lecture screen D1 and the operation panel D4 may be displayed. As shown in FIG. 7, a personal computer screen D2 operated by the lecturer, a text screen D3 in which audio data is converted into text, and an operation panel D4 are displayed. You may.
[0071]
Here, the user interface provided from the system side and displayed on the student terminal 2 has a function for the student to transmit question data to the system. For example, as shown in FIG. 5, a text input box D4a is formed on the operation panel D4. By inputting a certain keyword and clicking a help button D4b, the input keyword is used as question data. The information can be transmitted from the terminal 2 to the question answering server 15 of the system 1. The means for asking a question is not limited to this. As shown in FIG. 6, a term displayed on the text screen D3 is dragged and selected with a mouse as shown by a symbol D3a. By clicking the button D4b, the selected keyword is transmitted to the question answering server 15. Further, by inputting a voice through a microphone (not shown) provided in the student terminal 2, the voice data is transmitted to the question answering server 15 as question data. However, the method of transmitting the question data is not limited to the above.
[0072]
As another question data transmission method, for example, as shown in FIG. 7, a list of keywords stored in the keyword database 17 is distributed to the student terminal 2 in advance by distribution means, and the keywords are displayed in a pop-up display. (See symbol D2a), a keyword selected from the above may be transmitted as question data.
[0073]
Then, if a question or the like arises while taking the lecture online, the student transmits question data to the system as described above. Such question data is received by the question answering server 15 (step S5), and the server 15 responds to this (reference A). Such processing will be specifically described with reference to FIG.
[0074]
Upon receiving the question data, the system 1 temporarily stops the delivery of the lecture content data by the reproduction management server 13 (step S6). Then, the question response server 15 recognizes what kind of data the received question data is. If this is the keyword itself selected from the keyword list shown in FIG. A database search is performed using keywords (step S12).
[0075]
On the other hand, if the question data is voice data (negative determination in step S7 and affirmative determination in step S8), voice recognition processing is performed to convert the data into text (step S9). Then, the question data that is text data from the beginning or the question data that has been converted into text by speech recognition as described above (negative determination in step S8) is subjected to language analysis processing (step S10), and the keywords in the text are extracted. (Step S11). In the keyword extraction process, for example, terms analyzed as nouns are extracted as keywords. Then, by searching the keyword database 17 at the same time (step S12), it is checked whether the keyword exists in the database (step S13).
[0076]
If the keyword included in the question data is registered as a database (Yes in step S13), the commentary data registered in association with it is transmitted to the student terminal 2 as a response to the question ( Step S14, database distribution step). For example, as shown in FIG. 4, instructor's voice data and cardboard data associated with the keyword are transmitted to the student terminal 2 as commentary data and displayed on the terminal. On the other hand, if it is not in the database (No in Step S13), the fact that there is no commentary data is transmitted to the student terminal 2 (Step S15). Note that the presentation information including the audio data and the cardboard data may be transmitted when the student terminal 2 requests redelivery as described above.
[0077]
After that, the student who has received the answer to the question transmits a reproduction request to the system 1 by clicking a reproduction button (see reference numeral D4c in FIG. 5) in the operation panel D4 displayed on the terminal (step S4). S16). Then, the stored lecture content data is distributed from the place where the reproduction management server 13 is temporarily stopped (step S17), and the student can continue the lecture. Then, the above-described processing is repeatedly executed, and the lecture proceeds. Here, even if there is no reproduction request in step S16, distribution may be resumed after a certain period of time has elapsed.
[0078]
In this way, a database of keywords considered to be important in the lecture content is automatically constructed, and such data is automatically distributed in response to a request from the student. Therefore, when taking a lecture for the first time or when reviewing (re-attending), the student can easily refer to the commentary data on a questioned part or a part where he / she wants to take the lecture again. Since the review can be easily performed, the learning efficiency can be improved.
[0079]
In addition, questions can be easily asked by diversifying the question format of the students, and efficient learning can be performed. In particular, when a question is selected from a keyword list, the question method is easy.
[0080]
Incidentally, the processing by each server is executed by incorporating a program for controlling the operation of each server to realize each processing into the CPU of each server. Then, the program is provided by being stored in advance in each server, or being read out in each server from a portable medium such as a CD-ROM.
[0081]
<Second embodiment>
Next, a second embodiment of the present invention will be described with reference to FIGS. FIG. 10 to FIG. 15 are diagrams showing a data configuration used in the present embodiment. FIG. 16 is a diagram showing a user interface displayed on the student terminal, and shows table of contents data. 17 to 25 are flowcharts showing the operation.
[0082]
(Constitution)
The education support system according to the second embodiment includes substantially the same components as those of the system according to the above-described first embodiment, but further includes the following components.
[0083]
First, in the system of the present embodiment, keywords prepared in advance by a lecturer who gives a lecture are stored (keyword database 17). The stored keywords are, for example, technical terms considered by the lecturer that are important in the lecture, and terms that are difficult for the learner to understand based on the experience of the lecturer. In some cases, data (text data and image data) serving as a commentary on the keyword is prepared in advance by the lecturer and stored in association with the keyword.
[0084]
The system also stores keywords related to operations (illustration of storage means is omitted), which are also terms prepared in advance by the lecturer or system operator. One example is shown in FIG. These operation keywords K1 are “see”, “open the page” and the like, and an auxiliary keyword K2 corresponding to each operation keyword K1 is also predetermined. The operation flag K3 is assigned. For example, as will be described later, after recognizing the operation keyword K1 "Please see", the language before and after the voice keyword is analyzed from the stored voice data, and the auxiliary keyword K2 "Next chapter" is recognized. At times, the “reference” operation K3 corresponds. In another example, after recognizing the action keyword "draw a line", if there is a term "important" in the text before and after the action keyword, the "important" action corresponds. The subsequent processing differs depending on the operation set as described above.
[0085]
Here, there is an operation keyword “explain” corresponding to the “explanation” operation, and an operation keyword “return” corresponding to the “return” operation. As described above, when the above-described operation keyword is detected, the process proceeds to each operation process.
[0086]
Therefore, in order to realize the above-described processing, the image / sound processing server 12, which is a language analyzing unit, always analyzes the language of the sound data acquired by the microphone 11b and the image / sound acquisition server 11, and operates the operation. It has a function of extracting terms related to the above (the above operation keywords). This function extracts auxiliary keywords before and after the action keyword after extracting the action keyword, but the order may be reversed. That is, an auxiliary keyword as shown in the first column K2 of FIG. 10 is stored in the system in advance, and if such a keyword is detected from the audio data during the lecture, it is determined whether there is an operation keyword K1 before and after the keyword. That may be detected. In any case, the operation shown in FIG. 11 is set by the keyword and the operation keyword included in the audio data during the lecture. The operation to be set is not limited to the operation shown in FIG.
[0087]
Here, an example of the processing by the system 1 will be described. When it is determined that the operation keyword extracted as described above is a predetermined term registered in advance, for example, the term “description”, the operation keyword is extracted. Database construction processing for the terms in the audio data acquired in the time zone before and after the time. Specifically, when the lecturer speaks “Chapter 1 describes the network”, the sentence is subjected to language analysis, and an operation keyword “explains” is extracted. Then, a keyword search is further performed on the sentence, and a “network” is extracted. Database construction is realized for the word "network". First, the set value K5 of the operation flag K4 shown in FIG. 11 is assigned to the word "network". Then, audio data explained by the instructor, image data during the lecture, board writing data, and the like are stored as a database in association with the words (see FIG. 12). Note that a pointer is given to each of the constructed keyword data.
[0088]
At this time, the operation set value K5 is also stored together with the keyword as described above. However, when a plurality of operations correspond to one keyword, the logical sum of the set values is applied. For example, as the keyword at the top of FIG. 12, a set value obtained by calculating the logical sum of the operation flag of “description” (set value 000000001000) and “reference” (set value of 000000000001) is applied. Thus, by referring to the flag, it is possible to know what kind of operation the keyword was extracted at, and it is possible to particularly identify whether the keyword is a question or an answer. The effect will be described later.
[0089]
In this way, the process of constructing a database for the terms described before and after the sentence using the term "description" as a key facilitates the process of extracting the terms as keywords, thereby improving the efficiency of database construction. And the processing load on the computer can be reduced.
[0090]
Incidentally, the above-described language analysis function also operates on question data based on voice data from the student terminal 2. For example, when the voice data “There is a question” is transmitted from the student terminal 2, a language analysis is performed on the voice data, and a transition to a question operation is performed by matching with the operation keyword (FIG. 10). reference).
[0091]
Further, when the system 1 determines that the operation keyword extracted by the language analysis means is a predetermined term registered in advance, the audio data acquired in a time period around the time when the operation keyword is extracted is included. Table of contents which prepares table of contents data of the contents of the lecture based on the terms in the table. For example, the table of contents creating means is realized by the image / sound processing server 12. Specifically, as described above, when the operation keyword is “move” shown in FIGS. 10 and 11, the “chapter” number and “section” number before and after that, and further, the page number are extracted. Based on this, the order in which the lectures were performed is recognized, and the order is arranged in a list as table of contents data. The table of contents data is transmitted to the student terminal 2 via the communication management server 14, so that the student can refer to the information for review.
[0092]
At this time, although not shown in FIGS. 10 and 11, a function of registering a term “table of contents” as an operation keyword and using a text spoken after the term as a title of the table of contents is provided by an image / audio processing server. 12 may be provided. In other words, this is a function of creating table of contents data by a method different from the method described above. For example, when the instructor utters “Creating a table of contents, Chapter 1 Work and Computer”, an item “Chapter 1...” Is generated as shown in FIG. Similarly, a section is formed in the first chapter. The detailed processing contents will be described together with the description of the operation.
[0093]
Also, as a function added to the table of contents creation function, when it is determined that the operation keyword extracted by the language analysis means is a predetermined term registered in advance such as "important", the time when the operation keyword is extracted is determined. A function may be provided for adding additional data based on the terms in the audio data acquired in the preceding and following time zones to the table of contents data being created. For example, as described above, if "This chapter is important" is issued immediately after "Table of contents creation ...", the table of contents generated at that time is "Important" as shown in FIG. A mark is added. The student who sees this can easily recognize important points at the time of review, and can improve review efficiency.
[0094]
Further, in the above description, the case where the table of contents data is created from scratch has been described, but data relating to the table of contents prepared by the instructor in advance may be stored in the system. Then, based on this, the creation of the table of contents is confirmed, and if different, the order of the table of contents itself is changed. In other words, even if lectures are given in a different order from the original schedule described in the text in the actual lecture, that will be reflected in the table of contents, so students will refer to the changed table of contents By doing so, you can easily review.
[0095]
In the database forming function, the keyword database constructed as described above is also associated with the table of contents data. Data relating to this association is called an association database, and is stored in storage means (not shown) in the system. Specifically, for each chapter / section, the described keyword is associated with a pointer. For example, as shown in FIG. 13, pointers (main) of keywords described at the time of explanation of chapters and sections set in advance by the instructor are associated with each other. Pointers associated with lecture content data such as chapters and sections, and keywords are associated (keyword pointer (reference)). At this time, the keyword pointers described in other chapters and sections are associated. Furthermore, since the order actually described by the instructor may be changed, the actual change order is assigned as a numerical value. For example, when a certain keyword is stored in the item “Chapter 1,“ Network ”, Section 1,“ LAN ”, the pointer value (for example, a1) is stored in association with it. At this time, if it is described that another chapter should be referred to, the pointer (for example, c1) given to that chapter is also stored in association with it. Then, from the initial lecture order, the "WAN" section of the network chapter should be explained, but if the instructor explained the "Internet" section, the value of the order following the "LAN" section will be assigned. . Therefore, the value relating to the actual order may be used at the time of creating a table of contents or reviewing. This will also be described in detail when describing the operation.
[0096]
In addition, a question database for associating questions and answers is constructed. This is shown in FIG. At this time, the keyword data is included in the question data from the student, and a pointer corresponding to the keyword is given. Then, the pointer of the commentary data (answer) such as the voice data when the instructor answers the keyword is associated with the pointer of the question data. Further, pointers of a plurality of commentary data detected for the keyword may be associated with the keyword. At this time, it is desirable to associate the keyword data itself. Then, when another student asks the same question next time, by referring to such a database, the answer process can be promptly performed, or the system itself later frequently answers the question (so-called “so-called”). When creating data relating to Q & A), the data can be easily created with reference to the data.
[0097]
Further, the communication management server 14 in the system identifies the student terminal 2 and stores the content of the lecture attended together with the identification data of the student terminal 2 to manage the content of the student. For example, the student database shown in FIG. 15 includes an ID, which is identification data for each student, code data for each course, a start / completion date and time, a value indicating to which chapter / section the student has currently taken, each chapter. -End flag data indicating that the attendance of the section has been completed is stored. As a result, the content of the course is stored and managed for each student, so that the system itself recognizes the content to be delivered next to the student terminal, the course that the student will take next, and the content of the course to be reviewed. Can be easily grasped.
[0098]
(motion)
Next, an operation in the second embodiment will be described with reference to FIG. First, as described in the first embodiment, lecture content data by the lecturer is captured by the image / audio acquisition server 11, and the data is stored and distributed to the student terminal 2. In addition, among such data, particularly, voice data is subjected to voice recognition and language analysis (step S101).
[0099]
At this time, the image / sound processing server 12 extracts the verb in the sound data, compares the words before and after the verb with the operation keywords registered in advance, and extracts the same (step S102). If there is no operation keyword, the above process is always executed (if the determination is negative in step S102, the process returns to step S101).
[0100]
When the operation keyword is extracted, the operation flag variable in the system is cleared (step S103), and whether the operation keyword is related to the operation “description” or the operation “return” is determined. Is checked (step S104). If it is not related to the operations "description" and "return" (No in step S104), it is checked whether there is an auxiliary keyword before and after the sentence from which the operation keyword is extracted (step S105). At this time, the auxiliary keyword is a keyword related to the operation keyword disclosed in FIG. The extraction of the auxiliary keyword is a keyword other than the operation keyword registered in advance, or a term repeatedly issued by the instructor as described in the first embodiment, and further, the tone is strong. The term that was generated. If the auxiliary keyword is not extracted (negative determination in step S105), the above processing is repeated (return to step S101). If the auxiliary keyword is extracted, refer to the relation table between the operation keyword and the operation flag in FIG. 10 and FIG. , An operation flag variable is set (step S106). However, if the operation keyword is related to the operation “description” or “return” in step S104 (Yes in step S104), the operation flag corresponding to each operation regardless of the presence or absence of the auxiliary keyword Is set (step S106).
[0101]
Thereafter, the value of the operation flag variable is checked (step S107), and each process is performed according to the value (steps S108 to S115). After executing each process, the process returns to the normal language analysis process (return to step S101).
[0102]
Next, each processing will be described, but for convenience of description, the description will be made from “explanation processing” (see FIG. 21). First, when the operation keyword “I explain” is extracted in step S102 of FIG. 17, the process proceeds from step S104 to step S106, and the operation flag becomes the value of “explanation”. Then, the process proceeds to the explanation process of step S112 (reference B5).
[0103]
Then, first, it is detected whether or not there is a keyword as an important phrase in sentences before and after the word is spoken (step SB51). The keyword is a keyword which is closely related to the lecture and registered in advance by the lecturer, or a term repeatedly issued by the lecturer (see the description in the first embodiment).
[0104]
When the keyword is extracted, the keyword is registered in the database (step SB52), and the value of the operation flag is also stored (see FIG. 12). Then, the voice data and the image data spoken before and after the time at which the keyword is issued are registered in the keyword database in association with the keyword (step SB53, see FIG. 12). At this time, it is also detected whether or not the board writing is being performed (step SB54). If the board writing is being performed, the board writing data is converted to text or captured as still image moving data (step SB55). It is added (step SB56). Here, the detection of the board writing in step SB54 can be realized by providing a writing pressure detecting means or the like on the board writing board.
[0105]
Thereafter, a pointer is given to the keyword added to the database, and the keyword is registered with the pointer (step SB57, see FIG. 12).
[0106]
Also, when the set operation flag is an “important” operation (step S111), the operation is substantially the same as the above-described operation (reference B4). However, the processing until the operation is determined to be “important” is different. In other words, in the "explanation" operation, keyword extraction was performed in any case when the operation keyword was the term "explanation", but to be recognized as "important" operation, " This is a case where after an operation keyword such as "please" is detected (Yes in step S102), an auxiliary keyword such as "important" or "point" is extracted (see FIG. 10).
[0107]
Note that the “important process” (step S111) is also executed in a table of contents process described later, but the process at that time is different from the above process.
[0108]
Next, the table of contents processing (step S108 in FIG. 17) will be described with reference to FIG. 18 (reference B1). First, as described above, the language "table of contents creation" is extracted from the audio data, and when the table of contents is set, the words of the chapter / section and the number of the number are extracted from the audio data before and after the table operation ( Step SB11). The extracted chapters and sections become the next chapters / sections.
[0109]
When these are extracted, it is checked whether or not there is table of contents data created by the lecturer and stored in the system in advance (step SB12). If the table of contents data is stored (Yes in step SB12), the section or section for which the lecture was given before may be replaced with, for example, “to which chapter or section, (The current position) or "end flag data indicating that the attendance of each chapter / section has been completed" is detected (step SB13). Then, matching is performed with the previously stored table of contents data, and it is determined whether or not the order is correct (step SB14). Specifically, it is assumed that the next chapter / section from the instructor's remark is “Chapter 1 Section 7”. If the number "1.5" is detected with reference to the "current position" shown in FIG. 15, it can be understood that the student is currently attending "Chapter 1, Section 5". Then, referring to the description portion of the chapter / section of the table of contents data prepared in advance as shown in FIG. 16, it can be seen that the chapter 1 / section 5 is followed by the chapter 1 / section 6. Therefore, in such a case, it is determined that the order is not correct.
[0110]
If the order is correct, the table of contents data is set as it is without changing the table of contents data (step SB18). On the other hand, if the order is not correct as in the above specific example (No in step SB14), the order of the table of contents is changed (step SB15) and the table of contents is reset (step SB18). At this time, in the table of contents newly created by changing the order, the chapter and section numbers are kept as they were, and only the display order is changed. As a result, the consistency with the content of the instructor's remark is maintained. Note that the initial value of the order of the table of contents is “Chapter 1, Section 1”, and such data is stored in the processing server in advance. Similarly, the lecture start position of each student, that is, the initial value of the current position shown in FIG. 15 is also “Chapter 1 Section 1”.
[0111]
If there is no table of contents data in the above step SB12 (negative determination), a chapter / section title term is extracted from before and after the audio data from which the chapter / section number has been extracted (step SB16). In the title term extraction process, for example, a sentence that appears next to the term “chapter” is extracted as a title. Then, the extracted table of contents is added to the table of contents data together with the chapter and section numbers (step SB17), and the table of contents is set (step SB18).
[0112]
Then, the above processing is executed each time a chapter / section changes, thereby creating a table of contents as shown in FIG. However, the table of contents may be created at the time of end processing described later using the above-described association database.
[0113]
When the table of contents is created, for example, during the chapter / section number extraction processing in step SB11, the instructor remarks that "this chapter is important", and if the term "important" is detected by this, step SB18 At the time of the table of contents setting operation, the word "important" is added to the section (chapter or section) of the table of contents (see FIG. 16). By doing so, the learner can know important points only by looking at the table of contents, and can improve review efficiency.
[0114]
Next, the chapter / section switching process shown in step S109 of FIG. 17 will be described with reference to FIG. 19 (reference B2). First, as described above, when the movement operation is set by the operation keyword and the auxiliary keyword, a chapter, a section, or a page number is extracted from the audio data before and after the movement operation (step SB21). If it is a chapter or section number, set it as the next chapter or section from the previous chapter or section, and if it is a page number, the chapter or section corresponding to the page from the pre-registered database It is set (step SB22). At the same time, a keyword database accumulated during a lecture in a moving chapter or section and data referred to in the chapter or section are set so as to be associated with the chapter or section. That is, as shown in FIG. 13, a preparation is made so that a pointer (main, reference) of the keyword database is registered in a certain chapter or section.
[0115]
Subsequently, data on the current position of the attended lecture (current position data shown in FIG. 15) is read out of the data of the attended students from the student database (step SB23).
[0116]
Then, the actual order (change) is set to the data shown in FIG. 13 which is the association database (step SB24), and the current position data of the student in the student database shown in FIG. 15 is updated (step SB25). . As a result, it is possible to grasp the course progress of each student. Incidentally, such processing may be performed at the same time when the above-mentioned table of contents creation processing is executed.
[0117]
Further, at the time of this chapter / section switching processing, processing for setting a pointer to the lecture content data itself corresponding to the chapter / section is performed. That is, a pointer is set in the lecture content data described after the switching process, and by referring to the pointer, the lecture content data of each chapter / section can be easily accessed. If the value of this pointer is determined in advance according to the chapter / section, it is easy to refer to it later.
[0118]
Next, the reference processing shown in step S110 of FIG. 17 will be described with reference to FIG. 20 (reference B3). First, a chapter / section number, a page number, and a keyword are extracted from the sentence from which the action keyword is extracted (step SB31), and the contents are used as reference data. Then, a pointer corresponding to each reference data is set (step SB32). Specifically, in response to the instructor's remark “See Chapter 1, Section 5”, the lecture content data stored in the image / audio database (see reference numeral 16 in FIG. 1) A pointer for referring to the lecture content data relating to Chapter 1, Section 5 is set, and such a pointer is separately recorded in the storage means as an “important” item. Thereafter, the pointer of the reference data is registered in the chapter or section in the current lecture in the association database (step SB33). By referring to this pointer, it is possible to access the lecture content data of the chapter / section to be referenced. At this time, if the lecture content data relating to the chapter / section to be referred to has not yet been stored, a pointer is set to refer to data that should be stored later.
[0119]
Subsequently, a keyword is extracted from before and after the sentence (an affirmative determination in step SB34). If the keyword is registered in the keyword database (an affirmative determination in step SB35), association is performed in the same manner as described above. The pointer of the reference data is registered in association with the corresponding section of the currently attending chapter or section of the database (step SB36). However, if there is no keyword or the keyword is not registered in the database, the process ends. As a result, the data referred to in each chapter, section, and the like is registered in the association database. Based on the data, the learner can review and improve the learning efficiency. For example, the reference data is transmitted from the server when a reference button is displayed on the screen of the student terminal during the lecture of each chapter or section, and the student presses this button.
[0120]
Next, the question processing shown in step S113 in FIG. 17 will be described with reference to FIG. 22 (reference B6). Here, in the present embodiment, when the question data from the student terminal 2 is voice data, that is, when a question is made by microphone input at the student terminal 2, such voice data is replaced with voice data by the instructor. Similarly, processing is performed by the processing shown in FIG. Therefore, first, the processing up to the step S113 when there is a question from the student will be described.
[0121]
When there is a question by voice data from the student terminal during the lecture, the question is taken into the system itself via the network (step S101), and an operation keyword and an auxiliary keyword are extracted (steps S102 to S104). When a question action is set by the extracted action keyword or the like (see FIG. 10), a question process is executed.
[0122]
Then, words before and after the voice are extracted as keywords (step SB61), and a pointer value is set to the question keyword (step SB62). At this time, it is checked whether or not the lecture is distributed in real time (step SB63). If it is real time, it is checked whether or not there is a comment from the lecturer within a certain time (step SB611).
[0123]
When the lecture is not a real-time lecture, for example, at the time of review, or when there is no instructor's remark even in the case of a real-time lecture, the system automatically executes as described in the first embodiment. An answer generation process is performed. Specifically, it is checked whether or not the question keyword is registered in the keyword database (step SB64). If the question keyword is registered (yes in step SB65), the voice data or Commentary data such as image data and cardboard data is transmitted to the student terminal as a response to the question (step SB67). On the other hand, if there is no data (No at Step SB65), the fact that there is no answer data is transmitted to the student terminal (Step SB69). Thereafter, as shown in FIG. 14, the pointer of the question keyword and the pointer of the data in the keyword database used as the answer are registered as a question database in association with each other, and are used for the later question and Q & A data creation. Sometimes used.
[0124]
If there is a comment from the instructor in step SB611 (affirmative determination), language analysis of the comment is performed. That is, the processing shown in FIG. 17 is performed again on the utterance, and the action keyword in the utterance is detected. At this time, if the instructor makes a statement such as "return to text" and the operation keyword is a return operation (Yes in step SB612), no answer is given by the instructor's statement, and the process proceeds to step SB64. Since the answer is given from the database, the keyword database is searched. Otherwise (No at Step SB612), a reply process is performed (Step SB613). The operation of this answer process is shown in FIG. 23 (reference B9). The "return" operation at the time of this question processing is different from the return operation in FIG.
[0125]
In the answer process, first, it is searched whether or not there is a keyword in the language spoken by the instructor (step SB91). If there is a keyword, the keyword is recorded in the keyword database (step SB92), and at the same time, the set value of the answer operation flag shown in FIG. 11 is stored. At this time, if the instructor's remark does not include a keyword, it is stored in the keyword database using the keyword in the question data together with the answer operation flag setting value. Then, the audio data, the image data, and the board data described before and after that are stored together with the keyword as shown in FIG. 12 (step SB93). Thereafter, a pointer is set to the answer data (step SB94).
[0126]
Subsequently, the answer data is transmitted to the student terminal 2 (step SB95). Then, as shown in FIG. 14, the pointer set for the question keyword and the pointer set for the keyword data stored as the answer data are stored as a question database in association with each other (step SB96), and as shown in FIG. In this way, the pointer is set in the reference column in the association database so as to correspond to the chapter or section currently being attended (step SB97).
[0127]
Next, the aside processing shown in step S114 of FIG. 17 will be described with reference to FIG. 24 (reference B7). When the aside processing is started, the keyword here is automatically set to, for example, "9999" (step SB71). Subsequently, the keyword (9999) and the set value of the operation flag are stored in the keyword database (step SB72), and the voice data and the like described by the instructor at this time are also stored in association (step SB73).
[0128]
Thereafter, the language spoken by the instructor is analyzed, and when it is recognized that the operation is the return operation by the operation keyword, the process returns to the normal process (step SB75). If the returning operation is not performed, the explanation of the instructor will be stored as an apostrole (negative determination in step SB74).
[0129]
Finally, the end processing shown in step S115 in FIG. 17 will be described with reference to FIG. 25 (reference B8). First, table of contents data is created from the association database shown in FIG. 13 in the order in which the lectures were actually given, as shown in FIG. 16 (step SB81). At this time, if the table of contents data has been created in the order of actual lectures in the above-described table of contents processing, this processing is processing for forming a table of contents screen.
[0130]
Subsequently, with reference to the keyword database shown in FIG. 12 and the association database shown in FIG. 13, important points are extracted from the lecture contents (step SB82). For example, the operation flag in the keyword database is checked, and a pointer of the keyword having the set value of the important operation flag is extracted. Then, the association database is used to check which chapter / section the pointer is in, and the chapter / section is extracted as an important part. Further, the operation flag of the keyword database is checked, and a chapter / section number in which pointers with many questions are concentrated is searched in the association database. Then, the chapter / section is extracted as an important part. Alternatively, a chapter / section having a large number of keyword pointers (main, reference) in the association database is simply searched and extracted as an important part.
[0131]
Subsequently, the important points extracted as described above are reflected in the table of contents (step SB83). For example, as shown in FIG. 16, an important mark is added to a display position of a chapter / section determined to be important. By transmitting the table of contents data to a student who takes a course for the first time or a student who takes a review, the order of lectures and important points can be known, and learning efficiency can be improved. Further, at this time, each chapter or section in the table of contents may display another chapter or section number referred to in the chapter or section based on the reference pointer of the association database.
[0132]
Subsequently, referring to the question database shown in FIG. 14, "Q & A data" which is an answer to the frequently asked question is created (step S84). That is, an answer to the keyword of the question is searched using the pointer, and data associated with the keyword of the question is created. In this way, by creating the Q & A data and transmitting the Q & A data to the students who take the course for the first time before the lecture or to the students at the time of the review, the students can refer to the data, The learning efficiency can be improved.
[0133]
Subsequently, diversion data is extracted from the operation flag setting value of the keyword database and the association database. Then, a setting corresponding to the part corresponding to the after-talk data is deleted from the lecture content data to be delivered to the student terminal 2 and set to be delivered (step SB85). At this time, an attendant button is displayed on the student terminal during the course (see FIG. 6 described in the first embodiment). Is done. Therefore, normally, only the data related to the lecture is distributed to the student terminal, so that it is possible to reduce the attendance time and suppress the communication amount.
[0134]
Thereafter, the student database indicating the attending status of the student is updated (step SB86). For example, the current position and the end flag of each chapter / section shown in FIG. 15 are set.
[0135]
【The invention's effect】
Since the present invention is configured and functions as described above, according to this, a database relating to the lecture is constructed from the lecture content data acquired for education through the network, and the database is connected to the student terminal according to the student's request. Students are required to request a part that they have doubts or want to take the lecture again on when taking a lecture, especially when reviewing (re-learning), based on the terms that are characteristic of the part. As a result, the lecture content data of the explanation part is automatically retrieved from the database and distributed to the student terminal, so that the student can save the trouble of searching the lecture content data for the place he wants to attend. There is an unprecedented superior effect that the time can be shortened and the learning efficiency can be improved.
[0136]
In particular, by linguistically analyzing speech data explained by the instructor and constructing a database for keywords extracted from the data based on predetermined criteria, only a database for a predetermined keyword is created, and the database is enlarged. And the processing load on the system can be reduced.
[0137]
The commentary data stored in association with the keyword is voice data or cardboard data acquired around the time when the keyword is extracted, so that the commentary data highly relevant to the keyword is stored. Are stored in the database, the trainees who can refer to this can further improve their understanding and improve learning efficiency.
[0138]
Also, when extracting keywords from audio data explained by the lecturer based on pre-registered term data, or when using terms explained more than once as keywords, the keyword database is prevented from becoming too large. At the same time, it is possible to create a database of more important terms, thereby improving search performance and reducing the processing load on the system itself while maintaining the quality of the database.
[0139]
In addition, by setting an operation keyword that is a trigger when extracting a keyword, the process of extracting a term as a keyword is facilitated, the efficiency of database construction can be improved, and the processing load on the system itself can be reduced. Can be planned.
[0140]
Furthermore, in the case of a configuration having a function of creating table of contents data, even when lectures are given in a different order from the schedule in an actual lecture, students can easily refer to the table of contents to make the lecture easier. You can take a course (especially a review).
[0141]
Further, the distribution of the accumulated keyword database to the student terminal is configured such that a term in the question data received from the student terminal is searched from the lecture content database, and the searched data is transmitted. In this case, the student can obtain appropriate explanation data for his / her question as a response, and can easily learn. At this time, by responding to all types of questions, it becomes easy for students to ask questions, and the answers are made by referring to the database on the server, so that the response is quick and the learning efficiency is further improved. Can be achieved.
[0142]
In addition, when question data is received from the student terminal, the distribution of the lecture content data is temporarily stopped, and then the stored lecture content data is distributed to the student terminal from the vicinity where the distribution was temporarily stopped. In the case of resuming, the students can wait for the answer, and during that time, the lecture is not in progress, so they can deepen their understanding of the questions and the level of understanding of the contents of the previous lecture is poor. It is suppressed that it becomes sufficient.
[Brief description of the drawings]
FIG. 1 is a block diagram illustrating a configuration according to a first embodiment of the present invention.
FIG. 2 is a functional block diagram illustrating a configuration of an image / sound processing server disclosed in FIG. 1;
FIG. 3 is a functional block diagram illustrating a configuration of a question answering server disclosed in FIG. 1;
FIG. 4 is a diagram showing an example of data stored in a keyword database disclosed in FIG.
FIG. 5 is a diagram illustrating an example of a user interface displayed on a student terminal.
FIG. 6 is a diagram illustrating an example of a user interface displayed on a student terminal.
FIG. 7 is a diagram illustrating an example of a user interface displayed on a student terminal.
FIG. 8 is a flowchart showing the operation of the system according to the first embodiment, particularly showing the operation when acquiring lecture content data and constructing a keyword database.
FIG. 9 is a flowchart illustrating an operation of the system according to the first embodiment, and particularly illustrates an operation when responding to a question from a student terminal.
FIG. 10 is a diagram showing an example of data relating to an operation stored in the system according to the second embodiment of the present invention.
11 is a diagram illustrating an example of a value corresponding to each operation illustrated in FIG. 10;
FIG. 12 is a diagram illustrating an example of a keyword database constructed in the second embodiment.
FIG. 13 is a diagram illustrating an example of an association database constructed in the second embodiment.
FIG. 14 is a diagram showing an example of data associated with question data from a student terminal.
FIG. 15 is a diagram showing an example of the contents of a student database;
FIG. 16 is a diagram showing an example of table of contents data to be formed, particularly showing a state when displayed on a student terminal;
FIG. 17 is a flowchart showing the overall operation of the system itself in the second embodiment.
FIG. 18 is a flowchart showing an operation of a table of contents process disclosed in FIG. 17;
FIG. 19 is a flowchart showing an operation of a chapter / section switching process disclosed in FIG. 17;
FIG. 20 is a flowchart showing the operation of the reference process disclosed in FIG. 17;
FIG. 21 is a flowchart showing an operation of an explanation process or an important process disclosed in FIG. 17;
FIG. 22 is a flowchart showing the operation of the question processing disclosed in FIG. 17;
FIG. 23 is a flowchart showing an operation of an answer process that is a continuation of the question process disclosed in FIG. 17;
FIG. 24 is a flowchart showing the operation of the after-talk processing disclosed in FIG. 17;
FIG. 25 is a flowchart showing an operation of a termination process disclosed in FIG. 17;
[Explanation of symbols]
1 Education support system (server computer group)
2 Student terminals
11 Image / audio acquisition server (lecture acquisition means)
12. Image / sound processing server (database construction means, language analysis means)
13 Reproduction management server (distribution means)
14 Communication management server (distribution means, student management means)
15 Question answering server (question answering means, distribution means)
16 Image and audio database (lecture acquisition means)
17 Keyword database (database construction means)
18 Student database (Student management means)
11a Camera (Lecture acquisition means)
11b Microphone (learning means)
N network

Claims (25)

A computer that distributes the contents of the lecture for education acquires the contents of the lecture by the lecturer as electronic data and stores the contents of the lecture, and the lecture contents data acquired by the lecture acquisition means to the student terminal connected to the network. And a distribution means for distributing, and a system for supporting education for a student in a remote place,
The computer includes a database construction unit that analyzes the lecture content data acquired by the lecture acquisition unit, and constructs a database related to the lecture content.
An educational support system, wherein the distribution means has a function of transmitting the contents of the lecture content database to the student terminal in response to a request from the student terminal. A computer that distributes educational lecture contents acquires a lecturer's lecture contents as image data and audio data and stores the acquired lecture contents as image data and audio data, and a lecture acquired by the lecture acquisition device. A distribution means for distributing content data to a student terminal connected to a network, wherein the system supports education for a remote student.
The computer constructs a language analysis unit for analyzing a language in the voice data acquired by the speech acquisition unit, and extracts a term that matches a predetermined criterion from the analyzed language to construct a database relating to the term; Database construction means to perform,
An educational support system, wherein the distribution means has a function of transmitting the contents of the lecture content database to the student terminal in response to a request from the student terminal. The database construction unit includes a keyword extraction unit that extracts, as a keyword, a term that matches a predetermined criterion among the languages analyzed by the language analysis unit, and a time at which the keyword is extracted by the keyword extraction unit. The educational support system according to claim 2, further comprising: an association database forming unit that stores voice data acquired in a time zone before and after the keyword in association with the keyword. The said database formation part has the function which stores the said image data acquired in the time zone before and after the time when the said keyword was extracted by the said keyword extraction part in connection with the said keyword. 3. The educational support system described in 3. The language analysis unit has a text conversion function of converting the analyzed voice data to text data,
A function of storing the text data converted by the text conversion function from the voice data acquired in a time period around the time when the keyword is extracted by the keyword extraction unit in association with the keyword; The education support system according to claim 3, further comprising: The computer is provided with presentation information acquisition means for acquiring presentation information, which is the content displayed on the information presentation means used in the lecture, as electronic data,
The said database formation part has a function which stores the said presentation information acquired in the time zone before and after the time when the said keyword was extracted by the said keyword extraction part in connection with the said keyword, The Claims characterized by the above-mentioned. The educational support system according to 3, 4, or 5. 7. The education support system according to claim 3, wherein the keyword extracting unit has a function of extracting a keyword based on term data registered in advance. 8. The education support system according to claim 3, wherein the keyword extraction unit has a function of extracting, as a keyword, terms detected a plurality of times by the language analysis unit. The language analysis unit has a function of analyzing the volume of the sound data to be subjected to language analysis,
6. The keyword extracting unit according to claim 3, wherein the keyword extracting unit has a function of extracting, as a keyword, a term at a location having a relatively large volume in the voice data based on the analysis by the language analyzing unit. The education support system according to 6, 7, or 8. The language analysis unit has a function of analyzing the language of the audio data acquired by the audio acquisition unit and extracting terms related to the operation,
When the database construction unit determines that the extracted operation term is a predetermined term registered in advance, the database construction unit performs processing on the term in the audio data acquired in a time period around the time when the operation term is extracted. 10. The educational support system according to claim 2, wherein the educational support system is operated. When the computer determines that the operation term extracted by the linguistic analysis unit is a predetermined term registered in advance, audio data acquired in a time zone around the time when the operation term is extracted is included. Table of contents creation means to create table of contents data of lecture contents based on the terms of
11. The education support system according to claim 10, wherein the distribution unit has a function of transmitting the contents of the table of contents data to the student terminal in response to a request from the student terminal. The table of contents creating means, when judging that the operation term extracted by the language analysis means is a predetermined term registered in advance, obtains a speech acquired in a time zone around the time when the operation term was extracted. The education support system according to claim 11, further comprising a function of adding additional data based on a term in the data to the created table of contents data. The computer includes question answering means for receiving question data regarding a lecture from the student terminal via a network, analyzing the content of the question data, and searching for a term in the question data from the lecture content database. ,
The said distribution means has a function which transmits the content of the said lecture content database searched by the said question response means to the said student terminal, The claim 2,3,4,5,6,7 characterized by the above-mentioned. , 8,9,10,11 or 12 education support system. The distribution means has a function of transmitting keyword list data in the lecture content database to the student terminal,
The question answering unit accepts question data transmitted by selecting a keyword in the keyword list data at the student terminal, and performs a search in the lecture content database for the selected keyword. 14. The education support system according to claim 13, wherein: 14. The education support system according to claim 13, wherein the question answering unit accepts text data input at the student terminal as question data. The question answering unit accepts voice data input at the student terminal as question data, and linguistically analyzes the question data based on the voice data and performs processing on a keyword in the language. The education support system according to claim 13, 14, or 15. The distribution means temporarily stops distribution of the lecture content data to the student terminal when the question answering means acquires the question data from the student terminal, and thereafter, stores the stored lecture content data. 17. The education support system according to claim 13, further comprising a function of restarting the distribution to the student terminal from near the place where the distribution is temporarily stopped. In response to a request from the student terminal, the distribution unit temporarily suspends distribution of the lecture content data, and transmits the lecture content data acquired within a predetermined time before the distribution suspension. 4. A function of re-distributing to the terminal, and then restarting the distribution of the stored lecture content data to the student terminal from near the place where the distribution was temporarily stopped. The education support system according to 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, or 17. The computer is provided with presentation information acquisition means for acquiring presentation information, which is the content displayed on the information presentation means used in the lecture, as electronic data,
In response to a request from the student terminal, the distribution unit temporarily suspends distribution of the lecture content data, and transmits the presentation information acquired within a predetermined time before the distribution suspension to the student. 5. A function of distributing the lecture content data to the student terminal from the vicinity of the place where the distribution is temporarily stopped, and thereafter distributing the stored lecture content data to the student terminal. , 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, or 18. The distribution means distributes the lecture content data to the student terminal when receiving data requesting resumption of the distribution of the lecture content data from the student terminal while temporarily suspending the distribution of the lecture content data. 20. The education support system according to claim 17, 18 or 19, wherein: 6. The student computer according to claim 2, further comprising: student management means for identifying the student terminal and storing data on a lecture attended together with identification data of the student terminal. , 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20. The student managing means has a function of recognizing the progress of the lecture from the lecture content data acquired by the lecture acquiring means and storing data on the lecture attended for each student according to the progress of the lecture. 22. The education support system according to claim 21, wherein: A method of distributing educational lecture content by computer and supporting education for a student in a remote place, wherein a lecture acquisition step of acquiring and storing the contents of a lecture by an instructor as image data and audio data, A delivery process of delivering lecture content data to student terminals connected to the network,
The lecture acquisition step includes a language analysis step of analyzing a language in the acquired audio data, and a database construction step of extracting a term that matches a predetermined standard from the analyzed language and constructing a database related to the term. With
The education support method, wherein the distribution step includes a database distribution step of transmitting contents of the lecture content database to the student terminal in response to a request from the student terminal. A program for controlling the operation of a computer for distributing educational lecture content and supporting education for a student at a remote place. Comprising an acquisition process and a distribution process of distributing the acquired lecture content data to student terminals connected to the network,
The lecture acquisition process includes a language analysis process for analyzing a language in the acquired audio data, and a database construction process for extracting a term that matches a predetermined criterion from the analyzed language and constructing a database related to the term. With
An educational support program, wherein the distribution process includes a process of transmitting contents of the lecture content database to the student terminal in response to a request from the student terminal. A storage medium storing a program for controlling the operation of a computer for distributing educational lecture contents, the lecture acquisition processing for acquiring and storing the contents of a lecture by an instructor as image data and audio data, and the acquired lecture contents data And a distribution process for distributing to a student terminal connected to the network.
The lecture acquisition process includes a language analysis process for analyzing a language in the acquired audio data, and a database construction process for extracting a term that matches a predetermined criterion from the analyzed language and constructing a database related to the term. With
A storage medium storing an educational support program, characterized in that the distribution process includes a process of transmitting the contents of the lecture content database to the student terminal in response to a request from the student terminal.

Images