JP4187682B2 - Learning item proposal method and computer - Google Patents

Description

The present invention relates to a learning item proposing method and a computer , and more particularly, to a learning item proposing method and a computer for proposing learning items according to a learning target to a user.

  In order to achieve results such as obtaining qualifications and changing jobs, it is essential to conduct appropriate learning in advance. At that time, the learner selects a learning content according to his / her learning target from books, learning using the Internet (e-learning), a workshop, and the like, and proceeds with the learning.

However, there are generally various learning contents that can be selected by the learner, and it is difficult for the learner to determine whether the learning content selected by the learner is useful.
As a technique for coping with such a problem and supporting a learner, a technique for presenting evaluation information of another user similar to the user's preference has been conventionally disclosed (see, for example, Patent Document 1).

Furthermore, conventionally, a technique has been disclosed in which a user evaluates knowledge content, shares the information among users, and can search for knowledge content required by the user (see, for example, Patent Document 2). .
JP 2000-187666 (paragraph numbers [0023] to [0025], FIG. 1) JP 2004-62124 A (paragraph numbers [0038] to [0039], FIG. 4)

  By the way, in order to achieve the learning goal, it is important to plan learning items for efficient learning, for example, after subscribing to a certain book and taking an education course of a certain workshop.

  However, the techniques described in Patent Literature 1 and Patent Literature 2 described above only introduce pieces of information evaluated by other users. Therefore, there is a problem that even if the user refers to such information, it does not lead to the planning of learning items according to his / her learning goal.

This invention is made in view of such a point, and provides the learning item proposal method and computer which can propose an appropriate learning item with respect to the user who has the learning goal which should be achieved. Objective.

  In the present invention, in order to solve the above problem, as shown in FIG. 1, in the learning item proposal method for proposing learning items according to the learning target to the user, the learning target accepting unit 1 b The learning result detection unit 1c receives an input of the learning target 3, and detects a learning result 4a close to the learning target 3 from the learning history database 1a in which the learning history and the learning result of each user are stored in association with each other. There is provided a learning item suggestion method characterized in that the history notification means 1d outputs the learning history 4b corresponding to the detected learning result 4a as the recommended learning item 5.

  According to the above method, the learning target accepting unit 1b accepts the input of the learning target 3 from the learning planned user 2. Then, the learning result detection means 1c detects the learning result 4a close to the learning target 3 from the learning history database 1a, and the learning history notification means 1d sets the learning history 4b corresponding to the detected learning result 4a as the recommended learning item. Is output as 5.

  Further, in the present invention, in order to solve the above-mentioned problem, as shown in FIG. 1, in the learning item suggestion apparatus 1 that proposes learning items according to the learning target to the user, the learning target user 2 can change the learning target 3. A learning target receiving unit 1b for receiving an input; a learning result detecting unit 1c for detecting a learning result 4a close to the learning target 3 from a learning history database 1a in which learning history and learning results of each user are stored in association; There is provided a learning item proposing device 1 having learning history notifying means 1d for outputting a learning history 4b corresponding to the detected learning result 4a as a recommended learning item 5.

  According to said structure, the learning target reception means 1b receives the learning target 3 from the learning planned user 2. Then, the learning result detection means 1c detects the learning result 4a close to the learning target 3 from the learning history database 1a, and the learning history notification means 1d sets the learning history 4b corresponding to the detected learning result 4a as the recommended learning item. Is output as 5.

  According to the present invention, the learning result 4a close to the learning target 3 input from the learning planned user 2 is detected, and the learning history 4b corresponding to the detected learning result 4a is output as the recommended learning item 5. As a result, it becomes possible to notify the learning planned user 2 of the learning history 4b of the user who has left the learning result 4a close to the learning target 3 as an actual result, and an appropriate learning item corresponding to the learning target 3 is given to the user. Can be proposed.

Hereinafter, embodiments of the present invention will be described with reference to the drawings.
FIG. 1 is a principle configuration diagram showing the principle of the present invention. The outline of the present embodiment will be described below with reference to FIG.

  The learning item proposal device 1 shown in FIG. 1 has a function of proposing learning items according to the learning target 3 to the user. The learning item proposing device 1 includes a learning history database 1a, a learning target receiving unit 1b, a learning result detecting unit 1c, and a learning history notifying unit 1d.

  In the learning history database 1a, the learning history and learning results of each user are stored in association with each other. In the learning history, contents that the user has used for learning are recorded. The learning results include, for example, learning results shown in the learning history, such as acquired qualifications and points, projects achieved, and scales.

  The learning target receiving means 1b receives input of the learning target 3 from a large number of learning planned users 2, for example, how many points are acquired with a certain qualification. In this reception, the learning target 3 is input on the terminal used by the user connected to the learning item proposing device 1 via a network, for example. Then, the learning target receiving unit 1b acquires the input learning target 3.

  The learning result detecting means 1c detects a learning result 4a close to the learning target 3 received by the learning target receiving means 1b from the learning history database 1a. For example, when a learning target 3 is acquired to acquire how many points with a certain qualification, a learning result 4a that retains a score close to the above score with the qualification is detected.

  The learning history notifying unit 1d acquires the learning history 4b corresponding to the learning result 4a detected by the learning result detecting unit 1c from the learning history database 1a, and outputs the learning history 4b as the recommended learning item 5.

  Next, operation | movement of the learning item proposal apparatus 1 comprised as mentioned above is demonstrated. Here, description will be made on the assumption that the planned learning user 2 who plans his / her learning target 3 uses the learning item proposal device 1.

  First, the learning target accepting unit 1b accepts an input of the learning target 3 designed by the learning planned user 2 from, for example, a terminal used by the learning planned user 2. The accepted learning target 3 is transferred to the learning result detection means 1c.

Next, the learning result detection means 1c detects the learning result 4a close to the learning target 3 from the learning history database 1a. The detected learning result 4a is transferred to the learning history notifying unit 1d.
Further, the learning history notification unit 1d acquires the learning history 4b corresponding to the detected learning result 4a from the learning history database 1a. Then, the learning history notification unit 1 d outputs the acquired learning history 4 b as the recommended learning item 5. In addition, this recommended learning item 5 is output, for example with respect to the terminal which the learning plan user 2 uses, and the content is displayed on the screen of the terminal.

  In this way, the learning history and learning results for each user are stored in the learning history database 1a in association with each other, and the learning result 4a close to the learning target 3 input from the learning planned user 2 is detected and detected. The learning history 4b corresponding to the learning result 4a is output as the recommended learning item 5. As a result, it becomes possible to notify the learning planned user 2 of the learning history 4b of the user who has left the learning result 4a close to the learning target 3 as an actual result, and an appropriate learning item corresponding to the learning target 3 is given to the user. Can be proposed.

  Next, this embodiment will be specifically described. In addition, the function of the learning item suggestion apparatus 1 as described above can be realized by a server. Below, it demonstrates using the learning assistance server to which this function was applied.

FIG. 2 is a diagram showing a system configuration of a learning support service using the learning support server of the present embodiment.
In the learning support service shown in FIG. 2, a learning support server 100 that supports learning of members who use this service, a member terminal 200 that is used by members, and a content vendor 300 that provides content to members are provided via, for example, the Internet 900. Connected configuration. In practice, member terminals corresponding to the number of members are connected. In addition, many content vendors such as publishers that sell books and training institutions that hold workshops are connected.

  The member terminal 200 is a terminal used by members of services provided by the learning support server 100. This terminal has a function of communicating with the learning support server 100 via the Internet 900 and a function of displaying the screen information received from the learning support server 100 on the screen. The screen information transmitted by the learning support server 100 is described in a structured language such as HTML (Hyper Text Markup Language). The member terminal 200 displays the screen information received from the learning support server 100 on the screen by a browser installed in advance in an internal storage device or the like.

  The content vendor 300 is a supplier that provides content to members. A server 310 connected to the Internet 900 is installed in the content vendor 300. The server 310 functions as a content providing server that provides e-learning via the Internet 900 according to the content provision form. In addition, the server 310 can also perform acceptance processing for attending a seminar.

  The learning support server 100 receives member learning objectives from a large number of member terminals via the Internet 900. Then, according to the learning objectives of the member, the learning course map showing the recommended contents of learning, such as what learning content should be used and in what order, is output so that the member can find the optimal learning plan To help you build

  The learning support server 100 includes a career DB (database) 112 in which careers achieved as a result of learning, such as results of qualification tests, are registered for each member who has registered for use of the service, learning goals, and the like. A registered target DB 114, a learning history DB 115 in which contents used for learning, a learning period thereof, and the like are recorded are held.

  Next, main operations of the system configured as described above will be described. In the following description, it is assumed that the member 20 who uses the member terminal 200 has already registered in the learning support server 100 and has formulated a learning target.

When the learning support server 100 receives from the member terminal 200 the current career and learning target of the member 20, the learning support server 100 registers them in the career DB 112 and the goal DB 114.
Thereafter, the learning support server 100 receives from the member 20 a search request for a model member that becomes a model when achieving its learning goal. In response to this search request, the learning support server 100 searches for members who have achieved a career close to the learning target. When such a member is detected, the list is presented to the member 20 as a model member candidate list.

  When the member 20 selects one member from the presented list, the learning support server 100 acquires the history and learning history of the selected member from each DB, and outputs them to the member terminal 200 as a learning course map. In the learning course map, the history of the selected member, the contents used to achieve the history, the order of the contents, and the like are displayed.

  Then, when the member 20 refers to the learning course map or the like and determines that the learning is likely to continue with such contents, the selected member is determined as his / her model. The learning support server 100 registers the determined member as a model member in the target DB 114 or the like.

  The learning support server 100 also has a function of automatically detecting model member candidates and notifying members. As a result, even when a member who becomes his model is not registered when the search request is made, if such a member is registered in the future, the learning support server 100 notifies the member 20 to that effect. Will be notified. Based on this notification, the member 20 can also register as a model member by referring to the information of the model member candidate.

  Thereafter, when the member 20 learns according to the registered model member's learning course map, the learning support server 100 accepts purchase of content from the member 20. The learning support server 100 acquires a model member list indicating a list of model members from the target DB 114 and presents it to the member 20.

  When the member 20 selects a desired model member from the presented list, the learning support server 100 acquires the learning history and history of the selected model member from each DB, and outputs the learning course map of the model member. To do. When the content displayed on the learning course map is referred to by the member 20, the evaluation of the model member with respect to the content is displayed.

  When the member 20 refers to the evaluation of the model member and purchases a desired content from the learning course map, the learning support server 100 notifies the server 310 in the content vendor 300 that provides the content of the purchase of the content. To do. As a result, the content is provided to the member 20. When the content is purchased, points that can be used to purchase the content are issued as rewards for the model member who provided the learning course map to which the content is referenced.

  When a member registered in the model member list adds new content to the learning history, the learning support server 100 notifies the member 20 that the learning history has been updated. The member 20 can purchase recommended content based on the notification.

  In this way, it is possible to propose an appropriate learning course map for members who plan to study, so for members, such as the development of an optimal learning plan and the provision of content according to the progress of learning, etc. Various support can be provided.

Hereinafter, a method for realizing the processing illustrated in FIG. 2 will be specifically described.
FIG. 3 is a diagram illustrating a hardware configuration example of the learning support server according to the present embodiment. As shown in FIG. 3, the entire learning support server 100 is controlled by a CPU (Central Processing Unit) 101. The CPU 101 is connected to a ROM (Read Only Memory) 102, a RAM (Random Access Memory) 103, a hard disk drive (HDD) 104, a graphic processing device 105, an input interface 106, and a communication interface 107 via a bus 108. Is connected.

  The ROM 102 stores basic programs and data executed by the CPU 101. The RAM 103 temporarily stores at least part of an OS (Operating System) program and application programs to be executed by the CPU 101. The RAM 103 stores various data necessary for processing by the CPU 101. The HDD 104 stores an OS and application programs.

  A display device 11 is connected to the graphic processing device 105. The graphic processing device 105 displays an image on the screen of the display device 11 in accordance with a command from the CPU 101. The input interface 106 transmits a signal sent from the mouse 12 or the keyboard 13 to the CPU 101 via the bus 108. The communication interface 107 transmits and receives data to and from other devices via the Internet 900.

  With the hardware configuration as described above, the processing functions of the present embodiment can be realized. The member terminal 200 and the server 310 in the content vendor 300 can also implement the processing functions of the present embodiment with the same hardware configuration as described above.

Next, functions provided in the learning support server 100 in order to implement the present embodiment will be described.
FIG. 4 is a functional block diagram illustrating functions of the learning support server according to the present embodiment. The learning support server 100 according to the present embodiment includes a control unit 120 that performs predetermined processing in response to a request from a member terminal and the like, and a storage unit 110 that is referred to by each unit constituting the control unit 120. .

  The storage unit 110 includes a member DB 111, a career DB 112, a goal DB 114, a learning history DB 115, a content list DB 116, an evaluation DB 117, a purchase DB 118, a point DB 119, and a model member candidate DB 113. The control unit 120 includes a member registration receiving unit 121, a history management unit 122, a learning target management unit 123, a learning history management unit 124, a model member management unit 125, and a purchase management unit 126.

In the member DB 111, personal attributes such as name and job content are registered for each member who uses the learning support service of the present embodiment.
The career DB 112 registers, for each member, the career achieved by the member, such as the acquired qualification, the result of the qualification examination, the job change result, or the project performed, and the time required for the achievement. In addition, a list of members who refer to the member as a model member is registered in the DB, and is used to pay attention to future trends of the model member.

  In the target DB 114, as a learning target of each member, in addition to the target of the target such as the qualification, project or skill to be achieved, the target or target such as the class or the score for the qualification, the scale or the number of the project for the target, etc. Specific target values and the time required to achieve the target are registered. In addition, an allowable range indicating how much error is allowed in a target value, a required period, and the like when searching for a model member of the member or a model member is also registered in the DB.

  In the learning history DB 115, a list of contents used for learning by each member and a required period required for learning of each content are registered. In this DB, only the content evaluated by the member is registered. As a result, there is an advantage that the content considered to have been learned by the member can be reflected in the learning history.

  In the content list DB 116, content purchased by members, such as content purchased at the time of learning and content purchased at a best seller, for example, is registered as a list.

In the evaluation DB 117, five-level evaluations and impressions are registered in this embodiment as evaluations of contents used by each member for learning.
In the purchase DB 118, model members associated with the purchase of the content are registered, for example, a learning course map in which the content is referred to is provided together with the purchased content.

In the point DB 119, points held by members are registered.
In the model member candidate DB 113, a list of members who are candidates for model members is registered.
The member registration receiving unit 121 receives member registration of the learning support service from the member terminal, and registers the individual attribute of the member in the member DB 111. Also, a password for using the service is issued together with a member ID that is unique to the member.

  The career management unit 122 receives a career input from a member terminal and registers it in the career DB 112. Furthermore, when there is a learning history related to the input career, the learning history is acquired and registered in the learning history DB 115. Thereby, the learning history corresponding to the career already achieved at the time of registration is registered in the learning history DB 115.

  The learning target management unit 123 receives an input of a learning target from the member terminal and registers it in the target DB 114. As the learning target, a target object, a specific target value, a required period until the target is achieved, an allowable range for model member search, and the like are registered.

  The learning history management unit 124 receives an input of learning history from the member terminal. At that time, when a member selects a content from the content list DB 116 or inputs a new content, the member asks whether or not to evaluate the content. When the member evaluates the content, the evaluation is registered in the evaluation DB 117 and the content is registered in the learning history DB 115.

  Further, when the content is added and the learning history of the member is updated, the member referring to the member as a model is acquired from the history DB 112. Then, in this embodiment, the acquired member is notified that the learning history has been updated using electronic mail.

  When the model member management unit 125 receives a model member search request from a member, the model member management unit 125 searches the target DB 114 for members who have achieved a career close to the member's learning target. At the time of this search, a history that satisfies an allowable range specified in advance with respect to the target value and required period of the member is detected. In the present embodiment, the age of the model member candidate and its allowable range are also specified during the search. A model member candidate is acquired by such a search and presented to the member as a model member candidate list. Then, a learning course map of the selected member is output, and registration processing in the history DB 112 and the target DB 114 is performed for the member determined as the model member. For the career DB 112, the member is registered as a reference member in association with the member determined as the model member. In addition, a member determined as a model member is registered in the target DB 114 in association with the member.

  In addition, for example, once every week, a model member candidate is searched for every predetermined period, registered in the model member candidate DB 113, and the member is notified by e-mail that the model member candidate is newly registered. Then, a list of members registered in the model member candidate DB 113 is presented to the notified member as a model member candidate list. Further, in the same manner as described above, a learning course map of the selected member is output, and registration processing is performed for the member determined as the model member.

  Upon receiving a content purchase request from a member, the purchase management unit 126 presents a list of model members registered in the target DB 114 as a model member list. Then, a learning course map of the selected member is output. When content is selected from the learning course map, the evaluation DB 117 is referred to and the evaluation of the content is output. Further, when the member decides to purchase the content, the content purchase processing is performed. In this purchase process, the model member who provides the learning course map in which the content is referenced is registered in the purchase DB 118 in association with the purchased content. In addition, a purchase notification is made to a content vendor that provides the content.

  Further, in response to the member reference request notified by the learning history management unit 124, the content added by the model member to the learning history is presented as new recommended content. If the member purchases the content, the same purchase processing as described above is performed on the content.

  Further, the purchase management unit 126 refers to the purchase DB 118 and adds a predetermined point to the model member associated with the newly purchased content according to the purchased content, and registers it in the point DB 119.

Next, an example of the data structure of each DB held in the learning support server 100 will be described.
FIG. 5 is a diagram illustrating an example of the data structure of the member DB. As shown in FIG. 5, the member DB 111 stores a “member ID” in which a number for uniquely identifying a member is registered, and “name”, “gender”, “age”, “ “Residence”, “E-mail address”, “Industry”, “Occupation”, “Specific work”, “Work experience years”, “Overseas work experience”, and “Education”. Actually, the password issued to the member is also registered.

  FIG. 6 is a diagram illustrating a data structure example of the history DB. As shown in FIG. 6, the history DB 112 includes items “member ID”, “history ID”, “numerical value”, “start date / time”, “end date / time”, “achievement required period”, and “reference member”. Composed.

  In this embodiment, qualifications, skills, and other items that are the subject of career and learning objectives are classified as tree categories, and each category is associated with a systematically assigned category ID. Yes. For example, in the case of qualifications, language qualifications are assigned in the 1000s range, accounting qualifications are assigned in the 2000s range, and among the language qualifications, “1001” is assigned to TOEIC, “1002” is assigned to TOEFL, etc. .

In the “history ID”, a category ID corresponding to the category selected from among the above categories as the member matches his / her own career is registered.
In the “numerical value”, as a specific numerical value of the background, the score and class of the test are registered if it is a qualification, and the scale and number of people are registered if it is a project.

  In the “start date and time” and “end date and time”, the date and time when learning is started and the date and time when learning is completed are registered in order to achieve the career. In the “end date and time”, for example, the date of qualification acquisition is registered. In addition, in the “requirement required period”, the period required to achieve the career is calculated from the start date and time and the end date and time.

In the “reference member”, a member ID of a member who refers to the member as a model member is registered. A plurality of member IDs can be registered in the “reference member”.
FIG. 7 is a diagram illustrating a data structure example of the model member candidate DB. As shown in FIG. 7, the model member candidate DB 113 includes items of “member ID” and “model member candidate”.

  The “model member candidate” has items of “member ID”, “history ID”, and “target ID”. In the “member ID” and “history ID”, a member ID of a model member candidate detected for the member and a career ID corresponding to the career referred to by the member are registered. In the “target ID”, a target ID corresponding to a learning target that has not been achieved by the detected model member candidate is registered.

  In this way, by registering careers in association with each other, even if model member candidates have various careers, such as obtaining qualifications in multiple fields, refer to careers that match their learning goals. can do.

  In addition, by associating goals, for example, for a career that achieved 400 TOEIC scores, along with a career that has already been achieved, such as a learning goal that aims for 600 of the same qualification and a new learning goal that aims for a predetermined score in TOEFL It will be possible to present the learning objectives to be achieved. In this embodiment, the career that has been achieved in this way and the learning target that is scheduled to be achieved in the future are combined and output as a career map. Thus, by presenting together with the career and learning target, there is an advantage that the state of growth of the model member candidate can be understood at a glance.

  FIG. 8 is a diagram illustrating an example of a data structure of the target DB. As shown in FIG. 8, the target DB 114 includes “member ID”, “target ID”, “target value”, “target value allowable range”, “target required period”, “period allowable range”, “model candidate age”. , “Age tolerance”, and “model member”.

  In “Target ID”, the category ID of the category selected by the member as matching his / her learning target is registered. In the “target value”, a specific target value of the target such as a score or a grade of the qualification test is registered. In the “target required period”, a required period that the member desires to achieve the target is registered.

In the “model candidate age”, when searching for a model member candidate of the member, the age of the model member candidate designated by the member is registered.
In “Target value tolerance”, “Period tolerance” and “Age tolerance”, when searching for a model member candidate of the member concerned, it is allowed as an error with respect to the target value, target required period, and model candidate age. The range to be registered is registered. The “target value allowable range” indicates, for example, when the target is indicated by a score of the qualification test, the allowable range from the target score is indicated by a score. The “period allowable range” indicates the allowable range from the target required period in months. Furthermore, the “age tolerance” indicates the tolerance from the model candidate age by an age difference.

  In the “model member”, the career ID of the career referred to by the member and the target ID of the learning target that the model member has not achieved are registered in association with the member ID of the model member of the member. By associating the target ID in this way, the model member's career map can be output.

  FIG. 9 is a diagram illustrating a data structure example of the learning history DB. As illustrated in FIG. 9, the learning history DB 115 includes “member ID”, “content ID”, “title”, “provider”, “provided form”, “category”, “price”, “start date”, “ It consists of items of “end date and time”, “required period”, and “history ID”.

  In the “content ID”, “title”, “provider”, “provided form”, “category”, and “price”, information of contents learned by the member is registered as a list. In the “content ID”, an ID for uniquely identifying the content is registered. In the “provider”, the name of the content provider is registered. In the “provision form”, the form in which the content is provided is registered. In “Category”, the category ID of the category to which the user belongs is registered.

  In the “start date and time”, for example, the date and time when the content was purchased is registered as the date and time when the member started learning the content. In the “end date and time”, for example, the date and time when the member evaluated the content is registered as the date and time when the learning of the content is ended. In the “required period”, the learning period of the content calculated from the start date and time and the end date and time is registered.

In “Career ID”, a career ID corresponding to the career associated with the content is registered.
FIG. 10 is a diagram illustrating a data structure example of the content list DB. As illustrated in FIG. 10, the content list DB 116 includes a “member ID”, “content ID”, “title”, “provider”, “providing form”, in which information on the content purchased by the member is registered as a list. It consists of “category” and “price” items.

FIG. 11 is a diagram illustrating a data structure example of the evaluation DB.
As shown in FIG. 11, the evaluation DB 117 includes items of “member ID”, “content ID”, “5-level evaluation”, and “impression”.

  In the “content ID”, the content ID of the content evaluated by the member is registered. In the “five-step evaluation”, the member's evaluation for the content is registered in five steps, and in the “impression”, the member's comment for the content is registered.

  FIG. 12 is a diagram illustrating an example of a data structure of the purchase DB. As shown in FIG. 12, the purchase DB 118 is configured with items of “member ID”, “content ID”, “unit price”, “quantity”, “sales”, “purchase date”, and “model member”. .

  In the “content ID”, the content ID of the content purchased by the member is registered. In “Unit Price” and “Quantity”, the unit price and the number of purchased contents are registered, and in “Sales”, the sales amount calculated from the unit price and quantity is registered. The date of purchase of the content is registered in “Purchase Date”.

In the “model member”, the member ID of the model member associated with the purchase of the content, such as providing a learning course map in which the content is referenced, is registered.
FIG. 13 is a diagram illustrating a data structure example of the point DB. As illustrated in FIG. 13, the point DB 119 includes items of “member ID”, “sales date”, “content ID”, “unit price”, “quantity”, “sales”, and “point”.

  The member ID of the model member to whom the points are issued is registered in “member ID”, and the content ID of the content purchased based on the learning course map of the model member is registered in “content ID”. In the “sales date” and “unit price”, the sales date and unit price of the content are registered. In “quantity” and “sales”, the total number of contents purchased by the member and the total sales amount are registered together with the number and sales amount of the content purchased.

In “Points”, predetermined points for the content are registered, and the total of points issued to the model member is registered.
With the configuration as described above, the learning support service according to the present embodiment can provide appropriate support for the member to achieve the learning target.

Next, the processing operation executed by the learning support server 100 when realizing the learning support service of the present embodiment will be described.
FIG. 14 is a flowchart showing a processing procedure of member registration processing. In the following, the process illustrated in FIG. 14 will be described in order of step number.

  [Step S11] The member registration receiving unit 121 receives member registration of the learning support service from the member terminal, and displays a member registration page and the like on the member terminal. Then, personal attributes such as name and age input on the page are acquired and registered in corresponding items of the member DB 111. Thereafter, the process proceeds to step S12.

[Step S12] The member registration receiving unit 121 issues a member ID and a password to the registered member and registers them in each item of the member DB 111.
Next, processing when a member inputs a career will be described.

FIG. 15 is a flowchart showing the processing procedure of the history input process. In the following, the process illustrated in FIG. 15 will be described in order of step number.
[Step S21] The career management unit 122 acquires a member ID and password from the member terminal, and performs member authentication with reference to the member DB 111. Thereby, for example, a dedicated page prepared for each member is displayed. Then, the process proceeds to step S22.

  [Step S22] The career management unit 122 acquires the career inputted on the member's dedicated page from the member terminal. On the dedicated page, qualifications and skills are classified and displayed by category, and the member selects one that matches his / her career from among them. Then, the start date and time and end date and time of learning for the career are entered together with specific numerical values for the career.

The career management unit 122 receives a category selection result by the member, and creates a new record in the career DB 112 in which the category ID of the selected category is registered in “history”.
Then, each data corresponding to the history acquired above is registered in the “numerical value”, “start date / time”, and “end date / time” of the created record, and the process proceeds to step S23.

  [Step S23] The career management unit 122 asks the member whether or not there is a learning history associated with the inputted career. If there is an associated learning history, the process proceeds to step S24; otherwise, the process proceeds to step S26.

  [Step S24] The career management unit 122 creates a new record in the learning history DB 115 in which the career ID is registered in the “history”. After that, as a learning history related to the career, the content learned to achieve the career is acquired from the member terminal and registered in the record created above.

[Step S25] The career management unit 122 generates a learning course map for the member, and proceeds to step S26.
[Step S26] The career management unit 122 asks the member whether or not to input another career. If the member inputs another career, the process proceeds to step S22, and if not input, the process ends.

Hereinafter, screen transition on the member terminal side at the time of career input will be described.
FIG. 16 is a diagram showing a screen configuration example (part 1) for history input. As shown in FIG. 16, a screen 31 for selecting a type of career such as qualification acquisition or business career is first displayed for a member who inputs a career. The member selects an item corresponding to his / her history on the screen 31.

  FIG. 17 is a diagram showing a screen configuration example (part 2) for history input. For example, when qualification acquisition is selected on the above screen 31, a screen 32 for selecting the type of qualification such as language and accounting is displayed as shown in FIG. The member selects the type of acquired qualification on the screen 32.

  FIG. 18 is a diagram showing a screen configuration example (part 3) for history input. For example, when language is selected on the above screen 32, a screen 33 for selecting a language type such as English or Italian is displayed as shown in FIG. The member selects the learned language on the screen 33.

  FIG. 19 is a diagram showing a screen configuration example (part 4) for history input. For example, when English is selected on the screen 33, a screen 34 for selecting English-related qualifications such as TOEIC and TOEFL is displayed as shown in FIG. The member selects the acquired qualification on the screen 34.

  FIG. 20 is a diagram showing a screen configuration example (part 5) for history input. When the member selects one qualification on the screen 34 displayed as described above, a screen 35 for inputting the score and the acquisition date is displayed, and the member inputs his detailed data on the screen 35. . For example, data such as a score in a test and a date when the qualification is acquired is input. Actually, the input item of the year and month when learning is started is also displayed.

  For example, if TOEIC is selected on the above screen, the history management unit 122 creates a record in which the category ID “1001” corresponding to the qualification is registered in the “history” in the history DB 112. Then, the data input as described above is registered in the record created above.

  FIG. 21 is a diagram illustrating a configuration example (No. 6) of a history input screen. After registering the input history in the history DB 112, the history management unit 122 displays a screen 36 as shown in FIG. 21, and asks the member whether or not to register a learning history related to the input history.

In this way, it is possible to register the career achieved at the time of registration for this service.
Next, processing when a member inputs a learning target will be described.

FIG. 22 is a flowchart showing a processing procedure of learning target input processing. In the following, the process illustrated in FIG. 22 will be described in order of step number.
[Step S41] The learning target management unit 123 acquires a member ID and password from the member terminal, and performs member authentication with reference to the member DB 111. Then, after the member-dedicated page is displayed on the member terminal, the process proceeds to step S42.

  [Step S42] The learning target management unit 123 acquires the learning target input on the member's dedicated page from the member terminal. On the dedicated page, categories classified in the same manner as described above are displayed, and the member selects one of them that matches his / her learning goal. Then, together with specific target values for the learning target such as the number of qualification tests and the scale of the project, the time required to achieve the learning target is input.

  In addition, for the same page, as a specified item when searching for a model member candidate, the member accepts a target value and an allowable range of a target required period as an error with respect to his / her learning target, and the target age of the model member candidate and Enter the tolerance. The learning target management unit 123 also acquires these data from the member terminal.

The learning goal management unit 123 receives a category selection result by the member and creates a new record in the goal DB 114 in which the category ID of the selected category is registered as “goal”.
Then, the learning target management unit 123 registers each of the acquired data in the “target value”, “target required period”, and “model candidate age” of the created record, and sets the “period allowable range” and “target value”. In the “allowable range” and “age allowable range”, the allowable range designated by the member is registered. Thereafter, the process proceeds to step S43.

  [Step S43] The learning target management unit 123 asks the member whether or not to input another learning target. If the member inputs another learning target, the process proceeds to step S42, and if not, the process ends.

Next, processing when a member inputs a learning history will be described.
FIG. 23 is a flowchart showing a processing procedure of learning history input processing. In the following, the process illustrated in FIG. 23 will be described in order of step number.

  [Step S51] The learning history management unit 124 acquires the member ID and password from the member terminal, and performs member authentication with reference to the member DB 111. Then, after displaying the member-dedicated page on the member terminal, the process proceeds to step S52.

  [Step S52] The learning history management unit 124 asks the member whether or not the content is new content. If it is a new content, the process proceeds to step S53, and if not, the process proceeds to step S54.

  [Step S53] The learning history management unit 124 acquires content input on a dedicated page. Then, the content is registered in the content list DB 116, and the process proceeds to step S55.

  [Step S54] The learning history management unit 124 presents the content registered in the content list DB 116 to the member. Then, when the member selects the content being learned from the presented list, the process proceeds to step S55 in response to the selection result.

  [Step S55] The learning history management unit 124 asks the member whether or not to evaluate the content with respect to the content input at Step S53 or the content selected at Step S54. If evaluation is to be performed, the process proceeds to step S56; otherwise, the process proceeds to step S61.

  [Step S56] The learning history management unit 124 creates a record for the content in the evaluation DB 117. And the evaluation which the member input with respect to the said content is acquired. Here, the member's 5-level evaluation and comments are acquired and registered in the “5-level evaluation” and “impression” of the record created above. Thereafter, the process proceeds to step S57.

  [Step S57] The learning history management unit 124 asks the member whether or not to associate the evaluated content with the history. If so, the process proceeds to step S58; otherwise, the process proceeds to step S59.

  [Step S58] The learning history management unit 124 creates a record for the content in the learning history DB 115. Then, referring to the career DB 112, a list of careers achieved by the member is presented. When the member selects a career to be associated with the content, the learning history manager 124 registers a category ID corresponding to the selected career in the “history” of the record.

  Further, when the member inputs the date and time when the learning of the content is started and the date and time when the learning is finished, the learning history management unit 124 acquires them, and sets the acquired date and time in the “start date and time” and “end date and time” of the record. Register each. Thereafter, the process proceeds to step S59.

  [Step S59] The learning history management unit 124 refers to the history DB 112 and checks whether there is a member who has registered the member as a model member based on whether or not a “reference member” is registered. If the reference member is registered, the member is acquired and the process proceeds to step S60. If not registered, the process proceeds to step S61.

  [Step S60] The learning history management unit 124 refers to the member DB 111 and creates an e-mail addressed to the acquired e-mail address of the member. Then, the model member learning history is updated and distributed, and the process proceeds to step S61.

  [Step S61] The learning history management unit 124 asks the member whether or not to input another learning history. If a learning history is input, the process proceeds to step S52, and if not, the process ends.

Hereinafter, the above process will be described using a specific example.
FIG. 24 is a diagram illustrating a screen configuration example (part 1) for learning history input. As shown in FIG. 24, for a member who inputs a learning history, a screen 41 for selecting whether the content is new content or content that is currently being learned is displayed. The member selects content to be edited from the screen 41.

  FIG. 25 is a diagram illustrating a screen configuration example (part 2) for learning history input. For example, when the content currently being learned is selected on the screen 41, the content registered in the content list DB 116 is displayed as a list on the screen 42 as shown in FIG. This figure shows the content registered in association with the member with the member ID “515225” in the content list DB 116 shown in FIG. 10 described above. The member selects content to be edited from the screen 42.

  FIG. 26 is a diagram illustrating a configuration example (part 3) of learning history input. For example, when one content is selected on the screen 42, as shown in FIG. 26, a screen 43 for selecting an evaluation in five stages is displayed. The member inputs the evaluation on the screen 43.

  FIG. 27 is a diagram illustrating a screen configuration example (part 4) for learning history input. Further, after the evaluation is selected on the screen 43, a screen 44 for inputting the impression of the selected content is displayed as shown in FIG. The member inputs an arbitrary comment on the screen 44.

  FIG. 28 is a diagram illustrating a screen configuration example (part 5) for learning history input. After the impression is input on the screen 44, a screen 45 for inputting the learning start date and learning end date of the selected content is displayed as shown in FIG. On the screen 45, the member inputs, for example, the date of purchase of the content, the learning start date, and the date of evaluation of the content, the learning end date. In practice, the learning start time and end time can also be input.

  FIG. 29 is a diagram illustrating a configuration example (part 6) of learning history input. The learning history management unit 124 registers the evaluation of the content input on each of the screens 41 to 45 as described above in the evaluation DB 117, and then displays the screen 46 as shown in FIG. 29 and associates the content with the history. Ask members whether or not. The member selects “1. Associate this learning history with the history” on the screen 46 when associating with the history.

  FIG. 30 is a diagram illustrating a screen configuration example (part 7) for learning history input. When the selection related to the career is made on the screen 46, a screen 47 showing a list of the careers of the member is displayed as shown in FIG. The member selects a career to be associated on the screen 47.

  FIG. 31 is a diagram showing a screen configuration example (part 8) for learning history input. If, for example, TOEIC 600 points are selected from the history displayed on the screen 47, the learning history management unit 124 sets the category ID “1001” corresponding to the TOEIC to “history” of the record in the learning history DB 115. Register with.

Then, a screen 48 as shown in FIG. 31 is displayed to ask the member whether or not to input another learning history.
In this way, the content actually used for learning after the member has registered for this service can be registered as a learning history.

  In addition, by notifying that the learning history of the model member has been updated, it is possible to know the learning trend of the member that the user is paying attention to, such as the model member planning a new learning target and learning.

  Next, a process for registering a model member will be described. Model member registration includes new model member registration processing for detecting and presenting model member candidates in response to a search request from a member, and registering the selected model member candidate as a model member, and every predetermined period. There is a temporary new model member registration process in which a model member candidate is automatically detected and notified to the member, and the model member candidate selected by the notified member is registered as a model member. Hereinafter, each case will be described.

FIG. 32 is a flowchart showing a processing procedure of new model member registration processing. In the following, the process illustrated in FIG. 32 will be described in order of step number.
[Step S71] The model member management unit 125 acquires a member ID and password from the member terminal, and performs member authentication with reference to the member DB 111. Then, after the member-specific page is displayed on the member terminal, the process proceeds to step S72.

[Step S72] The model member management unit 125 displays a search page for searching for a model member in accordance with a member instruction. Then, the process proceeds to step S73.
[Step S73] The model member management unit 125 acquires a member who has achieved a career close to the learning goal of the member as a model member candidate, and proceeds to step S74. Details of this process will be described later.

  [Step S74] The model member management unit 125 refers to the model member candidate DB 113 and presents a list of acquired model member candidates as a model member candidate list. Then, the process proceeds to step S75.

[Step S75] When one model member candidate is selected from the list, the model member management unit 125 acquires the selection result and proceeds to step S76.
[Step S76] The model member management unit 125 refers to the member DB 111 and outputs the individual attributes of the model member candidate to the member terminal. Then, the process proceeds to step S77.

  [Step S77] The model member management unit 125 refers to the career DB 112 and acquires the career of the model member candidate. Further, the model member management unit 125 refers to the target DB 114 and acquires learning goals that the model member candidates have not achieved. For reference, the career ID and the target ID associated with the member ID of the model member candidate are used in the model member candidate DB 113. In the present embodiment, a career map is displayed on the member terminal using the obtained career and learning target. Thereafter, the process proceeds to step S78.

  [Step S78] When the member selects a career that matches him / her from the career map, the model member management unit 125 refers to the career DB 112 using the relevant career ID and outputs the selected career. Thereafter, the process proceeds to step S79. When a learning target is selected from the history map, the target DB 114 is referenced using the corresponding target ID. Then, after outputting the corresponding learning target, the history map is displayed again.

  [Step S79] The model member management unit 125 refers to the learning history DB 115 using the history ID corresponding to the selected history, and acquires the content associated with the history ID. Then, the learning course map of the model member candidate is displayed using the history acquired above and the content acquired from the learning history DB 115. Thereafter, the process proceeds to step S80.

  [Step S80] The model member management unit 125 asks the member whether or not to register the model member candidate as a model member. If registered as a model member, the process proceeds to step S81; otherwise, the process proceeds to step S82.

[Step S81] The model member management unit 125 sets the candidate model members as model members, and performs registration processing on the history DB 112 and the target DB 114.
For the career DB 112, the member ID of the member is registered in the “reference member” of the record corresponding to the model member. In addition, for the target DB 114, the member of the model member candidate determined as the model member in the “member ID”, “history ID”, and “target ID” constituting the “model member” of the record corresponding to the above member. The ID, and the history ID and target ID associated therewith are registered. Then, the process proceeds to step S82.

  [Step S82] The model member management unit 125 asks the member whether or not to reference other model member candidates. If so, the process proceeds to step S74; otherwise, the process ends.

Next, the model member candidate acquisition process (step S73 described above) will be described.
FIG. 33 is a flowchart illustrating a processing procedure of model member candidate acquisition processing. In the following, the process illustrated in FIG. 33 will be described in order of step number.

  [Step S83] The model member management unit 125 refers to the target DB 114 and acquires the learning target of the member. Then, the history DB 112 is referred to using the target ID corresponding to the acquired learning target, and a record in which the same ID as the target ID is registered in the history ID is extracted.

  Then, with reference to the “numerical value” of the extracted record, a record included in the “target value allowable range” with respect to the “target value” of the member is extracted. For example, if the target value is “600” and the target value allowable range is “± 20”, records in which numerical values corresponding to “580” to “620” are registered are extracted. Thereafter, the process proceeds to step S84.

[Step S84] The model member management unit 125 proceeds to step S85 if there are a plurality of members acquired above, and proceeds to step S88 if not.
[Step S85] The model member management unit 125 refers to the “required required period” of the record extracted above, and extracts records included in the “period allowable range” with respect to the “target required period” of the member. . Further, the required record achievement periods of the extracted records are rearranged in order from the member's target required period. Then, the process proceeds to step S86.

  [Step S86] The model member management unit 125 checks whether there are a plurality of members in the same required time period for achievement. If there are a plurality of members, the process proceeds to step S87; otherwise, the process proceeds to step S88.

  [Step S87] The model member management unit 125 refers to the member DB 111 and rearranges members having the same required time period in order of closeness to the personal attribute of the member. For example, for the age, after extracting records included in the “age tolerance” for the “model candidate age” of the member from the records extracted above, the records are rearranged in order of the age of the member. . In addition to the age, for example, the presence / absence of overseas work experience is examined, and the members are rearranged in the order of the personal attributes of the member. In this way, it is possible to preferentially notify members close to the member's environment. Then, the process proceeds to step S88.

  [Step S88] The model member management unit 125 refers to the target DB 114, and if a member detected as a model member candidate registers a learning target, the model member management unit 125 acquires a target ID corresponding to the learning target. Then, after registering the member ID of the detected member, the career ID, and the acquired target ID in the corresponding items of the model member candidate DB 113, the process returns to the main routine.

  Through the above processing, in response to a member search request, a member who has achieved a career close to the member's learning target can be notified as a model member candidate, and a learning course map of the selected model member candidate can be recommended to the member. .

  In addition, by preferentially detecting members whose achievement required period is close to the target required period, it is possible to notify model member candidates that can provide a more useful learning course map to the member's learning plan. Become. Furthermore, by extracting records included in a pre-designated period allowable range, it is possible to narrow down to model member candidates that can provide a learning course map that is nearly optimal for the member. For example, for members who are aiming to obtain a TOEIC score of 600 in half a year, the learning course map of a member who has obtained a similar score in nearly half a year is considered to be optimal. Members who have achieved a similar score in two years can be notified preferentially of members achieved in nearly six months.

Next, a process for automatically searching for model member candidates every predetermined period will be described.
FIG. 34 is a flowchart showing a processing procedure of model member candidate search processing. In the following, the process illustrated in FIG. 34 will be described in order of step number. This process is executed by the model member management unit 125 at a predetermined interval, for example, once a week.

[Step S91] The model member management unit 125 refers to the member DB 111 and sequentially selects registered members. Then, the process proceeds to step S92.
[Step S92] The model member management unit 125 refers to the target DB 114 and checks whether the model member of the selected member has been registered. If registered, the process proceeds to step S96; otherwise, the process proceeds to step S93.

[Step S93] The model member management unit 125 executes the model member candidate acquisition process in step S73 described above, and proceeds to step S94.
[Step S94] The model member management unit 125 refers to the model member candidate DB 113 and checks whether the model member candidate of the selected member is registered. If registered, the process proceeds to step S95; otherwise, the process proceeds to step S96.

  [Step S95] The model member management unit 125 refers to the member DB and acquires the e-mail address of the selected member. Then, an e-mail addressed to the e-mail address is created, and the model member candidate is newly registered and distributed. Thereafter, the process proceeds to step S96.

  [Step S96] The model member management unit 125 checks whether all members registered in the member DB 111 have been processed. If all members have been processed, the process ends. If not, the process proceeds to step S91.

Next, a temporary new model member registration process for registering a model member in response to a member registration request to which an e-mail is distributed in step S95 described above will be described.
FIG. 35 is a flowchart showing the processing procedure of the temporary new model member registration processing. In the following, the process illustrated in FIG. 35 will be described in order of step number.

  [Step S101] When the member to whom the e-mail is distributed in Step S95 described above makes a reference request for a model member candidate, the model member management unit 125 acquires the member ID and password of the member from the member terminal, and the member DB 111 Refer to to perform member authentication. Then, after the member-specific page is displayed on the member terminal, the process proceeds to step S102.

  [Step S102] The model member management unit 125 refers to the model member candidate DB 113 and presents a list of model member candidates of the members as a model member candidate list. Then, the process proceeds to step S103.

[Step S103] When one model member candidate is selected from the list, the model member management unit 125 acquires the selection result, and proceeds to step S104.
[Step S104] The model member management unit 125 refers to the member DB 111 and outputs the individual attributes of the model member candidate to the member terminal. Then, the process proceeds to step S105.

  [Step S105] The model member management unit 125 refers to the career DB 112 and acquires the career of the model member candidate. Further, the model member management unit 125 refers to the target DB 114 and acquires learning goals that the model member candidates have not achieved. At the time of reference, the history member ID and the target ID associated with the member ID of the model member candidate are used in the model member candidate DB 113. Then, a history map is displayed on the member terminal using the acquired history and learning target. Thereafter, the process proceeds to step S106.

  [Step S106] When the member selects a career that matches him / her from the career map, the model member management unit 125 refers to the career DB 112 using the relevant career ID and outputs the selected career. Thereafter, the process proceeds to step S107. When a learning target is selected from the history map, the target DB 114 is referenced using the corresponding target ID. Then, after outputting the corresponding learning target, the history map is displayed again.

  [Step S107] The model member management unit 125 refers to the learning history DB 115 using the history ID corresponding to the selected history, and acquires the content associated with the history ID. Then, the learning course map of the model member candidate is displayed using the history acquired above and the content acquired from the learning history DB 115. Thereafter, the process proceeds to step S108.

  [Step S108] The model member management unit 125 asks the member whether or not to register the model member candidate as a model member. If registered as a model member, the process proceeds to step S109; otherwise, the process proceeds to step S110.

  [Step S109] The model member management unit 125 sets the model member candidate as a model member, and performs registration processing similar to that in step S81 described above on the history DB 112 and the target DB 114. Thereafter, the process proceeds to step S110.

  [Step S110] The model member management unit 125 asks the member whether or not to reference other model member candidates. If so, the process proceeds to step S102; otherwise, the process ends.

  Hereinafter, the model member registration process described above will be described using a specific example. Since the new model member registration process and the temporary new model member registration process perform the same process for the selected model member candidate, the temporary new model member registration process will be described here. Further, here, the model member management unit 125 notifies the member having the member ID “515226” (hereinafter referred to as the member “515226”) in step S95 described above that the model member candidate is newly registered. Assume that an email has been delivered.

FIG. 36 is a diagram illustrating a text example of an e-mail notifying that a model member candidate is newly registered.
As shown in FIG. 36, the e-mail 50 delivered to the member “515226” in step S95 described above includes a content indicating that a model member candidate has been newly registered, and a URL (see the list). Uniform Resource Locator).

  FIG. 37 is a diagram showing a screen configuration example (No. 1) for new model member registration. When the URL described in the e-mail as described above is clicked, a screen 51 as shown in FIG. 37 is displayed, and the member “515226” is authenticated by the member ID and password.

  FIG. 38 is a diagram showing a screen configuration example (No. 2) for new model member registration. When the member authentication is correctly performed on the screen 51, a screen 52 as shown in FIG. 38 is displayed. Here, when “select from new model member candidate list” is selected, model member candidates registered in the model member candidate DB 113 are displayed, and when “select after searching” is selected, the above-described FIG. The new model member registration process shown in FIG.

  FIG. 39 is a diagram showing a screen configuration example (No. 3) for new model member registration. When “Select after searching” is selected on the screen 52, a model member candidate list screen 53 registered in the model member candidate DB 113 is displayed as shown in FIG. In addition, age and occupation of model member candidates are also displayed. Here, two model member candidates (member IDs “515225” and “221134”) associated with the member ID “515226” in the model member candidate DB 113 shown in FIG. 7 are displayed. The member selects one of the model member candidates.

  FIG. 40 is a diagram showing a screen configuration example (No. 4) for new model member registration. When a model member candidate is selected on the screen 53, a screen 54 showing the personal attributes of the selected model member candidate is displayed as shown in FIG. Here, it is assumed that the model member candidate with the member ID “515225” is selected.

  FIG. 41 is a diagram showing a screen configuration example (No. 5) for new model member registration. When “next” is clicked on the screen 54, a screen 55 showing the history map of the member selected above is displayed as shown in FIG. In this way, the process from the already achieved career to the current learning goal is displayed.

  FIG. 42 is a diagram illustrating a screen configuration example (No. 6) for new model member registration. When a history is selected on the screen 55, a screen 56 showing the contents of the selected history is displayed as shown in FIG. Here, it is assumed that a history of 600 TOEIC points is selected.

  FIG. 43 is a diagram showing a screen configuration example (part 7) for new model member registration. When “Next” is clicked on the screen 56, a screen 57 showing a learning course map for the history selected above is displayed as shown in FIG.

  FIG. 44 is a diagram showing a screen configuration example (No. 8) for new model member registration. When the content is selected on the screen 57, a screen 58 showing the evaluation of the selected content is displayed as shown in FIG.

  FIG. 45 is a diagram showing a screen configuration example (No. 9) for new model member registration. When “Next” is clicked on the above screen 58, a screen 59 asking whether or not to register the selected model member candidate as a model member is displayed as shown in FIG.

  In this way, a member who has a history that is close to the learning target of the member “515226” is presented as a model member candidate, and a learning course map of the model member candidate that the member “515226” is determined to be more suitable for himself is recommended. can do.

  Next, processing when a member purchases content during learning will be described. In this process, the member refers to the learning course map of his / her model member and the content purchase process showing the process when purchasing the content, and the member who is notified of the update of the learning history in step S60 described above. There is a new recommended content purchase process that indicates a process when a content is purchased. Hereinafter, each case will be described.

FIG. 46 is a flowchart showing a processing procedure of content purchase processing. In the following, the process illustrated in FIG. 46 will be described in order of step number.
[Step S111] The purchase management unit 126 acquires a member ID and password from the member terminal, and performs member authentication with reference to the member DB 111. Then, after the member-specific page is displayed on the member terminal, the process proceeds to step S112.

[Step S112] The purchase management unit 126 displays a content purchase page for purchasing content in accordance with a member instruction. Then, the process proceeds to step S113.
[Step S113] The purchase management unit 126 displays the learning course map of the model member, and acquires the content purchased by the member. Then, the process proceeds to step S114. The details of this process will be described later as a purchase content selection process.

  [Step S114] The purchase management unit 126 performs a purchase process for the content purchased by the member. First, a new record associated with the member is created in the purchase DB 118, and information on the purchased content is registered in “content ID”, “unit price”, “quantity”, and “sales” of the record. Furthermore, the date of purchase of the content is registered in “Purchase Date”. In the “model member”, the member ID of the model member who provided the learning course map to which the content is referred is registered. In the purchase process, the content purchase notification is output to the content vendor that provides the content. Thereafter, the process proceeds to step S115.

  [Step S115] The purchase management unit 126 creates a new record associated with the member in the content list DB 116, and registers the content information. Then, the process proceeds to step S116.

  [Step S116] The purchase management unit 126 asks the member whether or not to purchase other contents. If so, the process proceeds to step S112; otherwise, the process ends.

Next, the purchase content selection process (the above-described step S113) will be described.
FIG. 47 is a flowchart showing a processing procedure of purchase content selection processing. In the following, the process illustrated in FIG. 47 will be described in order of step number.

  [Step S121] The purchase management unit 126 refers to the target DB 114 and presents a list of model members of the member as a model member list. Then, the process proceeds to step S122.

[Step S122] When the member selects one model member from the list, the purchase management unit 126 acquires the selection result, and proceeds to step S123.
[Step S123] The purchase management unit 126 refers to the member DB 111 and outputs the personal attributes of the model member selected above to the member terminal. Then, the process proceeds to step S124.

  [Step S124] The purchase management unit 126 refers to the history DB 112 and acquires the history of the model member. Further, the purchase management unit 126 refers to the target DB 114 and acquires the learning target of the model member. For reference, the career ID and the target ID associated with the model member in the target DB 114 are used. Then, using the acquired career and learning target, the career map of the model member is displayed on the member terminal. Thereafter, the process proceeds to step S125.

  [Step S125] When the member selects a career that matches him / her from the career map, the purchase management unit 126 refers to the career DB 112 using the relevant career ID and outputs the selected career. Thereafter, the process proceeds to step S126. When a learning target is selected from the history map, the target DB 114 is referenced using the corresponding target ID. Then, after outputting the corresponding learning target, the history map is displayed again.

  [Step S126] The purchase management unit 126 refers to the learning history DB 115 using the history ID corresponding to the selected history, and acquires the content associated with the history ID. Then, the learning course map of the model member is displayed using the history acquired above and the content acquired from the learning history DB 115. Thereafter, the process proceeds to step S127.

[Step S127] When the member selects desired content from the learning course map, the purchase management unit 126 obtains the selection result, and proceeds to step S128.
[Step S128] The purchase management unit 126 refers to the evaluation DB 117, and outputs the five-level evaluation and impression of the content selected above. Then, the process proceeds to step S129.

  [Step S129] The purchase management unit 126 asks the member whether or not to purchase the content. If the content is to be purchased, the process returns to the main routine; otherwise, the process proceeds to step S130.

  [Step S130] The purchase management unit 126 asks the member whether or not to refer to another model member. If another model member is referred to, the process proceeds to step S121, and if not, the process ends.

  In this way, it is possible to recommend a model course map for model members to members who plan to study, and members can purchase desired content with reference to the course map that they can actually achieve. can do.

Next, new recommended content purchase processing will be described.
FIG. 48 is a flowchart showing a processing procedure of new recommended content purchase processing. In the following, the process illustrated in FIG. 48 will be described in order of step number.

  [Step S131] When the member to whom the e-mail is distributed in Step S60 mentioned above makes a request for reference to new recommended content, the purchase management unit 126 obtains the member ID and password of the member from the member terminal, and stores the member DB 111. Refer to member authentication. At this time, the corresponding model member and the added content described in the e-mail are also acquired. Then, after displaying the member-dedicated page on the member terminal, the process proceeds to step S132.

  [Step S132] The purchase management unit 126 refers to the learning history DB 115 and displays the content as a new recommended content on the member terminal. Then, the process proceeds to step S133.

  [Step S133] When the member selects the content, the purchase management unit 126 refers to the evaluation DB 117 and outputs a 5-level evaluation and impression of the content. Then, the process proceeds to step S134.

  [Step S134] The purchase management unit 126 asks the member whether or not to purchase the content. If the content is to be purchased, the process proceeds to step S135; otherwise, the process ends.

  [Step S135] The purchase management unit 126 performs a purchase process similar to that in step S114 described above on the content. Note that the member ID of the model member is registered in the “model member” of the purchase DB 118. Thereafter, the process proceeds to step S136.

  [Step S136] The purchase management unit 126 creates a new record associated with the member in the content list DB 116, registers the content information, and ends the process.

In this way, content newly added to the learning history by the model member can be recommended to the member.
Next, a process for adding points to members who are connected to sales of content will be described.

FIG. 49 is a flowchart illustrating a processing procedure of point addition processing. In the following, the process illustrated in FIG. 49 will be described in order of step number.
[Step S141] When sales of new content occur, the purchase management unit 126 refers to the purchase DB 118 and acquires a model member associated with the corresponding content. Then, the process proceeds to step S142.

  [Step S142] The purchase management unit 126 creates a new record associated with the model member in the point DB 119. Then, the content information is registered in each item of the created record. Note that the purchase date of the content is registered in the “sales date”. Furthermore, after registering predetermined points such as 3% of the sales amount of the content in “Points”, the total of points for the model member is calculated. Then, the process ends.

  In this way, points according to the sales of the content can be given to the model member, so that the purchase of new content is promoted by this point, and the use of the service can be promoted.

Hereinafter, the specific contents of the processing described above will be described using the system configuration shown in FIG. 2 described above using one specific case.
The member 20 shown in FIG. 2 is a 22-year-old woman who works for a manufacturing company and wants to acquire English skills that satisfy the requirements in order to be involved in the overseas business that he has desired. The member's 20 TOEIC score is currently 400 points, and the goal for the time being is to raise it to 600 points, which is the minimum line for engaging in overseas operations within the company.

  It is assumed that the member 20 inputs his / her age, sex, years of work experience, and the like when using the learning support service of the present embodiment, and a member ID “515226” is issued thereto. Further, when the member 20 inputs his / her learning target after inputting the personal attribute, the content is registered in association with the member ID “515226” of the target DB 114 as shown in FIG.

  After that, the member 20 first searches for his / her model member candidate, but it is assumed that no model member candidate that matches his / her learning target was found in the first search. Therefore, the member 20 decides to use a service that will be delivered by e-mail when a model member candidate suitable for himself / herself is newly registered.

  One week later, it is assumed that an e-mail notifying the member 20 that a model member candidate has been newly registered is delivered with the contents shown in FIG. When the member 20 clicks the link of this e-mail to log in to the service and opens the personal page, as shown in FIG. 39, two model member candidates who have obtained TOEIC 600 points are registered. It was.

  It is assumed that among the model member candidates presented as described above, the member 20 selects the 28-year-old office worker with the nickname “Annon” who is closer to his / her age. This model member candidate corresponds to the member with the member ID “515225”.

  The member 20 further refers to the learning course map as shown in FIG. 43 described above, and decides to register the model member candidate as a model member because he / she seems to be able to continue learning with this content. As a result, as shown in FIG. 6 described above, the member 20 is registered in the career DB 112 as a reference member with the member ID “515225”. Further, as shown in FIG. 8 described above, model member candidates selected by the member 20 as model members are registered in the target DB 114.

  After that, as a result of the member 20 collecting information from friends and colleagues, it is assumed that the learning course map of the model member registered above is sure to be optimal for him. Then, in order to start learning for obtaining TOEIC 600 points, the member 20 requests the learning support server 100 to purchase the content recommended in the learning course map.

  FIG. 50 is a diagram illustrating a screen configuration example (part 1) for learning content purchase. As shown in FIG. 50, when the learning support server 100 is requested to purchase content, a screen 61 for selecting whether to select new recommended learning content or the learning history of the model member is displayed. Here, it is assumed that the member 20 selects the one to select from the learning history of the model member.

  FIG. 51 is a diagram illustrating a screen configuration example (part 2) for learning content purchase. When a selection from the model member learning history is selected on the screen 61, a screen 62 showing a list of model members is displayed as shown in FIG. Here, the member 20 selects a model member with the nickname “Annyeong” (hereinafter, this member is referred to as a member “Annyon”).

  FIG. 52 is a diagram showing a screen configuration example (part 3) for learning content purchase. When a model member is selected on the screen 62, a screen 63 showing a history map of the selected model member is displayed as shown in FIG.

  FIG. 53 is a diagram showing a screen configuration example (part 4) for learning content purchase. When a history is selected on the screen 63, a screen 64 showing the contents of the selected history is displayed as shown in FIG. Here, it is assumed that the member 20 has selected 600 careers of TOEIC from the career map.

  FIG. 54 is a diagram showing an example (part 5) of a screen configuration for learning content purchase. When “next” is clicked on the screen 64, a screen 65 showing a learning course map of the selected history is displayed as shown in FIG. Here, it is assumed that the member 20 has selected the course “listening special training aiming at TOEIC 600 points” instructed at the beginning of the learning course map.

  FIG. 55 is a view showing a screen configuration example (part 6) for learning content purchase. When content is selected from the learning course map, a screen 66 showing the evaluation of the selected content is displayed as shown in FIG. It is assumed that the member 20 confirms this content and decides to purchase this content.

  FIG. 56 is a diagram showing a screen configuration example (part 7) for learning content purchase. When “Next” is clicked on the screen 66, a screen 67 asking whether or not to purchase the selected content is displayed as shown in FIG. The member 20 clicks “Next” and performs the purchase procedure of this content. Here, if the content vendor 300 shown in FIG. 2 is the Japan XX newspaper company, the learning support server 100 notifies the content vendor 300 of the purchase of the content. As a result, the member 20 can attend this course.

  In this way, the member 20 continued learning according to the learning course map of the member “Ann Young” who is his model member, and after 6 months, he was able to obtain a superb TOEIC score of 600 points or more. Since the content has been purchased for the member “Ann Young” with reference to his / her learning course map, points corresponding to the content purchased by the member 20 are added and registered in the point DB 119. The member “Ann Young” can use this point immediately in order to aim for the next learning target of TOEIC 800 points. The learning history until the member 20 obtains TOEIC 600 points is also registered in this service, and can be used as a reference for the member who aims at the same learning target next time.

  As described above, according to the present embodiment, the learning course map of the member having a history close to the learning target of the member who plans to learn is output. This makes it possible to recommend learning course maps for members who have a track record close to the above learning objectives, and to propose appropriate learning course maps according to their learning objectives to members who plan to study It becomes.

  In addition, by recommending the learning course map of the model member selected by the member, in addition to whether or not the member has a career that is close to his / her own learning objectives, the age and occupation are close to him / her. Therefore, it is possible to recommend a learning course map of a member judged to be suitable for achieving his / her learning goal.

  In the above, every time a model member adds content, the member who refers to the model member is notified of the update of the learning history. However, for example, the model member is in a predetermined period such as one week. For example, the member added referring to the model member may be notified every week. In this case, a list of added content is displayed as a new recommended content list, and when a member selects desired content, an evaluation of the content is displayed.

  Further, the above processing function can be realized by a server computer. In that case, a learning support program describing the processing contents of the functions that the learning support server should have is provided. The server computer executes the learning support program in response to the request from the client computer. As a result, the processing function is realized on the server computer, and the processing result is provided to the client computer.

  The learning support program describing the processing contents can be recorded on a recording medium readable by the server computer. Examples of the recording medium readable by the server computer include a magnetic recording device, an optical disk, a magneto-optical recording medium, and a semiconductor memory. Examples of the magnetic recording device include a hard disk device (HDD), a flexible disk (FD), and a magnetic tape. Optical disks include DVD (Digital Versatile Disc), DVD-RAM, CD-ROM (Compact Disc Read Only Memory), CD-R (Recordable) / RW (ReWritable), and the like. Magneto-optical recording media include MO (Magneto-Optical disk).

When the learning support program is distributed, for example, portable recording media such as DVDs and CD-ROMs on which the learning support program is recorded are sold.
For example, the server computer that executes the learning support program stores the learning support program recorded in the portable recording medium in its own storage device. Then, the server computer reads the learning support program from its own storage device and executes processing according to the learning support program. The server computer can also read the learning support program directly from the portable recording medium and execute processing according to the program.

(Supplementary note 1) In the learning item suggestion method for proposing learning items according to learning objectives to the user,
The learning target accepting means accepts an input of a learning target from a learning planned user,
The learning result detecting means detects the learning result close to the learning target from the learning history database in which the learning history and learning result of each user are stored in association with each other,
A learning history notification means outputs the learning history corresponding to the detected learning result as a recommended learning item.
A learning item proposal method characterized by that.

  (Supplementary Note 2) The learning target includes a target required period until the target is achieved, and the learning result includes a learning required period required for learning, and the learning result detecting means includes the learning target period. The learning item suggestion method according to supplementary note 1, wherein the learning result in which the learning required period is recorded is preferentially detected.

  (Additional remark 3) The said learning target reception means receives the input of the period allowable range which shows the error permitted with respect to the said target required period in the detection of the said learning result by the said learning result detection means with the said learning target, The learning item proposing method according to claim 2, wherein the learning result detecting means detects the learning result in which the learning required period included in the period allowable range is recorded with respect to the target required period.

(Additional remark 4) The said learning result detection means detects the said learning result of the user close | similar to the personal attribute of the said learning plan user preferentially, The learning item proposal method of Additional remark 1 characterized by the above-mentioned.
(Additional remark 5) Age is used for the said personal attribute, The learning item proposal method of Additional remark 4 characterized by the above-mentioned.

  (Additional remark 6) The said learning target reception means receives the input of the allowable range which shows the error accept | permitted with respect to the said learning target in the detection of the said learning result by the said learning result detection means with the said learning target, The said learning result The learning item suggestion method according to appendix 1, wherein the detection means detects the learning result included in the allowable range with respect to the learning target.

(Additional remark 7) The content evaluated by the user who left the said learning result is recorded on the said learning history, The learning item proposal method of Additional remark 1 characterized by the above-mentioned.
(Supplementary Note 8) The learning history notifying unit outputs the learning result detected by the learning result detecting unit, and outputs the learning history corresponding to the learning result selected by the learning planned user. The learning item proposal method according to Supplementary Note 1.

  (Additional remark 9) The said learning history notification means acquires the learning target of the user who left the detected learning result from the said learning history database, and outputs the acquired said learning target and the said learning result together. The learning item suggestion method according to Supplementary Note 8, which is a feature.

  (Supplementary note 10) The supplementary granting means according to supplementary note 1, wherein the reward granting means gives a reward to a user who has left the recommended learning item as the learning history when the content recommended by the recommended learning item is purchased. Learning item proposal method.

  (Additional remark 11) When the said learning planned user selects the user who left the said learning result detected as a model user, the said learning history notification means will output the said learning history of the said model user. The learning item suggestion method according to 1.

  (Additional remark 12) The said learning result detection means searches the user who left the said learning result close | similar to the said learning target as said model user according to the search request | requirement of the said learning plan user, The additional remark 11 characterized by the above-mentioned. Learning item proposal method.

  (Additional remark 13) The said learning result detection means searches the user who left the said learning result close | similar to the said learning target for every predetermined period as the said model user, The learning item proposal method of Additional remark 11 characterized by the above-mentioned.

  (Additional remark 14) Let the user who left the said learning result detected be a model user of the said learning plan user, and a learning history update notification means will notify the said learning plan user when the said learning history of the said model user is updated. The learning item suggestion method according to supplementary note 1, wherein:

(Supplementary Note 15) In a learning item proposing device that proposes learning items according to learning objectives to a user,
A learning target receiving means for receiving an input of a learning target from a learning planned user;
Learning result detection means for detecting the learning result close to the learning target from the learning history database in which the learning history and learning result of each user are stored in association with each other;
Learning history notifying means for outputting the learning history corresponding to the detected learning result as a recommended learning item;
A learning item proposing device characterized by comprising:

(Supplementary Note 16) In a learning item proposal program for proposing learning items according to learning objectives to a user,
Computer
A learning target receiving means for receiving an input of a learning target from a learning planned user;
A learning result detecting means for detecting the learning result close to the learning target from a learning history database in which learning history and learning result of each user are accumulated and associated;
Learning history notifying means for outputting the learning history corresponding to the detected learning result as a recommended learning item;
Learning item suggestion program characterized by functioning as

(Supplementary Note 17) In a computer-readable recording medium storing a program for proposing learning items according to learning objectives to a user,
Computer
A learning target receiving means for receiving an input of a learning target from a learning planned user;
A learning result detecting means for detecting the learning result close to the learning target from a learning history database in which learning history and learning result of each user are accumulated and associated;
Learning history notifying means for outputting the learning history corresponding to the detected learning result as a recommended learning item;
A computer-readable recording medium storing a program that functions as a computer.

It is a principle block diagram which shows the principle of this invention. It is a figure which shows the system configuration | structure of the learning assistance service using the learning assistance server of this Embodiment. It is a figure which shows the hardware structural example of the learning assistance server of this Embodiment. It is a functional block diagram which shows the function of the learning assistance server of this Embodiment. It is a figure which shows the example of a data structure of member DB. It is a figure which shows the data structure example of career DB. It is a figure which shows the example of a data structure of model member candidate DB. It is a figure which shows the data structure example of target DB. It is a figure which shows the example of a data structure of learning log | history DB. It is a figure which shows the data structure example of content list DB. It is a figure which shows the data structure example of evaluation DB. It is a figure which shows the data structure example of purchase DB. It is a figure which shows the data structure example of point DB. It is a flowchart which shows the process sequence of a member registration process. It is a flowchart which shows the process sequence of a history input process. It is a figure which shows the example of a screen structure of the history input (the 1). It is a figure which shows the example of a screen structure of the history input (the 2). It is a figure which shows the example of a screen structure of the history input (the 3). It is a figure which shows the example of a screen structure of the history input (the 4). It is a figure which shows the example of a screen structure of the history input (the 5). It is a figure which shows the example of a screen structure of the history input (the 6). It is a flowchart which shows the process sequence of a learning target input process. It is a flowchart which shows the process sequence of a learning log | history input process. It is a figure which shows the example of a screen structure of the learning history input (the 1). It is a figure which shows the example of a screen structure of the learning history input (the 2). It is a figure which shows the example of a screen structure of the learning log | history input (the 3). It is a figure which shows the example of a screen structure of the learning history input (the 4). It is a figure which shows the example of a screen structure of the learning history input (the 5). It is a figure which shows the example of a screen structure of the learning history input (the 6). It is a figure which shows the example of a screen structure of the learning log | history input (the 7). It is a figure which shows the example of a screen structure of the learning history input (the 8). It is a flowchart which shows the process sequence of a new model member registration process. It is a flowchart which shows the process sequence of a model member candidate acquisition process. It is a flowchart which shows the process sequence of a model member candidate search process. It is a flowchart which shows the process sequence of a temporary new model member registration process. It is a figure which shows the example of a text of the email which notifies that a model member candidate is newly registered. It is a figure which shows the example of a screen structure of the new model member registration (the 1). It is a figure which shows the example of a screen structure of the new model member registration (the 2). It is a figure which shows the example of a screen structure of the new model member registration (the 3). It is a figure which shows the example of a screen structure of the new model member registration (the 4). It is a figure which shows the example of a screen structure of the new model member registration (the 5). It is a figure which shows the example of a screen structure of the new model member registration (the 6). It is a figure which shows the example of a screen structure of the new model member registration (the 7). It is a figure which shows the example of a screen structure of the new model member registration (the 8). It is a figure which shows the example of a screen structure of the new model member registration (the 9). It is a flowchart which shows the process sequence of a content purchase process. It is a flowchart which shows the process sequence of a purchase content selection process. It is a flowchart which shows the process sequence of a new recommendation content purchase process. It is a flowchart which shows the process sequence of a point addition process. It is a figure which shows the example of a screen structure of the learning content purchase (the 1). It is a figure which shows the example of a screen structure of the learning content purchase (the 2). It is a figure which shows the example of a screen structure of the learning content purchase (the 3). It is a figure which shows the example of a screen structure of the learning content purchase (the 4). It is a figure which shows the example of a screen structure of the learning content purchase (the 5). It is a figure which shows the example of a screen structure of the learning content purchase (the 6). It is a figure which shows the example of a screen structure of the learning content purchase (the 7).

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Learning item proposal apparatus 1a Learning history database 1b Learning target reception means 1c Learning result detection means 1d Learning history notification means 2 Learning planned user 3 Learning target 4a Learning result 4b Learning history 5 Recommended learning item 100 Learning support server 111 Member DB
112 Career DB
113 Model member candidate DB
114 Target DB
115 Learning history DB
116 Content list DB
117 Evaluation DB
118 Purchasing DB
119 point DB
121 Member Registration Reception Unit 122 Career Management Unit 123 Learning Target Management Unit 124 Learning History Management Unit 125 Model Member Management Unit 126 Purchasing Management Unit 200 Member Terminal

Claims (4)

Objective receiving means, the learning result detection means, and using a computer to function as learning record notification means, a learning item proposed to propose learning item in accordance with the objective relative to the learning schedule users,
The objective accepting means accepts the objective of learning schedule users, the target duration to achieve the objective, and the input period tolerance indicating an error allowed for the target duration,
The learning result detecting means, learning history for each user, the learning duration required for learning, and the learning history database learning results achieved is accumulated in association with the learning, the period allowed by the target duration Detecting the learning result that has achieved the learning target in the learning required period included in a range ;
The learning history notice means outputs a detected said learning result to be that learning history corresponding was as recommended study item acquired from the learning history database,
The learning item proposal method characterized by including . A learning item proposing method for proposing a learning item according to a learning target to a learning planned user using a computer functioning as a learning target accepting unit, a learning result detecting unit, and a learning history notifying unit,
The learning target receiving means receives an input of a learning target of the learning-scheduled user and an allowable range indicating an error allowed for the learning target;
The learning result detecting means detects the learning result included in the allowable range from the learning target from a learning history database in which learning history of each user and learning results achieved by learning are stored in association with each other. And
The learning history notifying means acquires a learning history corresponding to the detected learning result from the learning history database and outputs it as a recommended learning item.
The learning item proposal method characterized by including . A computer that has a function as a learning target accepting unit, a learning result detecting unit, and a learning history notifying unit, and proposes learning items according to the learning target to a learning planned user,
  The learning target receiving means receives an input of a learning target of the learning-scheduled user, a target required period until the learning target is achieved, and a period allowable range indicating an error allowed for the target required period,
  The learning result detection means includes a learning history of each user, a learning required period required for learning, and a learning history database in which learning results achieved by learning are stored in association with each other. Detecting the learning result that has achieved the learning target in the learning required period included in
  The learning history notification means acquires a learning history corresponding to the detected learning result from the learning history database and outputs it as a recommended learning item.
  A computer characterized by that. A computer that has a function as a learning target accepting unit, a learning result detecting unit, and a learning history notifying unit, and proposes learning items according to the learning target to a learning planned user,
  The learning target receiving means receives an input of a learning target of the learning-scheduled user and an allowable range indicating an error allowed for the learning target;
  The learning result detecting means detects the learning result included in the allowable range from the learning target from a learning history database in which learning history of each user and learning results achieved by learning are stored in association with each other. And
  The learning history notification means acquires a learning history corresponding to the detected learning result from the learning history database and outputs it as a recommended learning item.
  A computer characterized by that.

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