JP2016184434A - Student support program, information processing apparatus, and student support method - Google Patents

Student support program, information processing apparatus, and student support method Download PDF

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Publication number
JP2016184434A
JP2016184434A JP2016124436A JP2016124436A JP2016184434A JP 2016184434 A JP2016184434 A JP 2016184434A JP 2016124436 A JP2016124436 A JP 2016124436A JP 2016124436 A JP2016124436 A JP 2016124436A JP 2016184434 A JP2016184434 A JP 2016184434A
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student
friend
example
information
service
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JP2016124436A
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Inventor
佐伯 敦
Atsushi Saeki
敦 佐伯
慎太郎 木田
Shintaro Kida
慎太郎 木田
郁子 簾内
Ikuko Sunouchi
郁子 簾内
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富士通株式会社
Fujitsu Ltd
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Abstract

[PROBLEMS] To realize appropriate communication support. A student who may drop out is extracted, friend information of the extracted student is acquired, and service provision information usable in a student cafeteria is transmitted to the student and a friend corresponding to the friend information. Providing a service corresponding to the service provision information to at least one of the student and the friend when the student's settlement time and the friend's settlement time in the student cafeteria are within a predetermined time To cause the computer to execute the process. [Selection] Figure 4

Description

  The present invention relates to a communication support program, a communication support method, and an information processing apparatus.

  Conventionally, there is a mechanism for supporting communication by providing a meeting place for others. In addition, using a database in which a plurality of user information is stored, a user is selected that matches the input information, or a mechanism that encourages the user who has the portable terminal to go out using coupon information or the like is provided. There are methods (see, for example, Patent Documents 1 and 2).

JP 2003-263453 A JP 2001-145785 A

  However, a mechanism that encourages communication support or going out may have an adverse effect on the user depending on the position or situation of the support side.

  For example, in recent years, dropouts of students have become a problem in school management, and it is important to continue student motivation before improving learning effects. Therefore, if a sign of dropout can be detected, it is possible to respond such as following up early. However, even if the student's attendance at lectures, etc. is managed and a student with a low attendance rate is identified, it is unclear whether or not the student will drop out, and the school may drop out due to an interview etc. Communication with a student can be counterproductive.

  In one aspect, the present invention aims to realize appropriate communication support.

  The communication support program in one aspect extracts a student who may drop out, obtains friend information of the extracted student, and provides a service that can be used in the student cafeteria for the student and a friend corresponding to the friend information Information is transmitted, and when the settlement time of the student in the student cafeteria and the settlement time of the friend are within a predetermined time, at least one of the student and the friend responds to the service provision information Providing a service to a computer to execute a process.

  Appropriate communication support can be realized.

It is a figure which shows the example of schematic structure of a communication assistance system. It is a figure which shows an example of a function structure of a communication assistance apparatus. It is a figure which shows an example of the hardware constitutions which can implement | achieve a communication assistance process. It is a flowchart which shows an example of the communication assistance process in this embodiment. It is a flowchart which shows 1st Example of a communication assistance process. It is a flowchart which shows 2nd Example of a communication assistance process. It is a figure for demonstrating the example of a definition of learning log data and learning action. It is a figure which shows the example of calculation of learning action. It is a figure for demonstrating the example of extraction of a dropout predictor. It is a figure for demonstrating the extraction example of a dropout predictor based on the index value of learning action. It is a figure (the 1) which shows the acquisition example of friend information. It is a figure (the 2) which shows the acquisition example of friend information. It is a figure (the 3) which shows the acquisition example of friend information. It is a figure which shows an example of coupon issue. It is a figure which shows the example of a screen at the time of coupon issue.

  Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. In the following explanation, as an aspect of the communication support system, for example, a student who may drop out of a school (for example, a university, a technical school, a prep school, etc.) An example of support will be described.

<Example of schematic configuration of communication support system>
FIG. 1 is a diagram illustrating a schematic configuration example of a communication support system. A communication support system 10 illustrated in FIG. 1 includes a communication support apparatus 11 as an example of an information processing apparatus, and client terminals 12-1 to 12-n (hereinafter collectively referred to as “client terminal 12” as necessary). Have. In addition, the communication support apparatus 11 and the client terminal 12 are connected in a state in which various information can be transmitted and received by a communication network 13 typified by the Internet or a local area network (LAN), for example.

  The communication support apparatus 11 grasps the status and behavior of each user using log data of the client terminal 12 used by each user (student etc.). In addition, the communication support apparatus 11 extracts a target person (for example, a sign of dropout) based on a predetermined condition, and performs communication support for the extracted target person.

  Specifically, the communication support apparatus 11 analyzes, for example, log data (hereinafter referred to as “learning log data”) including learning behavior, which is an example of behavior information, using the client terminal 12 used by a user or the like. For example, the learning effect of the student can be enhanced based on the correlation between the learning behavior and the grade. Therefore, the added value of a school or the like as a place for learning can be increased.

  Here, the learning log data refers to, for example, attendance status, test results, report submission status and evaluation results, electronic bulletin boards, etc. for each user acquired from the client terminal 12 via the communication network 13 or the like. Participation in media (posting, browsing, etc.). A specific example of learning log data will be described later.

  That is, for example, the communication support apparatus 11 uses the learning log data described above to detect a sign of a student dropout, and performs appropriate follow-up that prompts the detected student to go to school. Examples of follow-up include, but are not limited to, communication support for meeting a friend and attending a lecture.

  The communication support apparatus 11 may be, for example, a personal computer (PC) or a server, but is not limited to this.

  The client terminal 12 is a device used by a target person (student or the like) that supports communication. The client terminal 12 transmits / receives various information to / from the communication support apparatus 11 via the communication network 13. For example, the client terminal 12 transmits an input of attendance status of a student lecture, a lecture report, and the like to the communication support apparatus 11 via the communication network 13.

  Further, for example, the client terminal 12 corresponding to the dropout predictor or the friend of the dropout predictor can receive provision of a predetermined service from the communication support apparatus 11.

  The client terminal 12 may be a mobile terminal such as a smartphone, a mobile phone, or a tablet terminal, and may be a notebook PC, a PC, or the like, but is not limited thereto. The client terminal 12 may be a dedicated terminal installed in each lecture room of a school, for example.

  Here, in the communication support system 10 described above, even if the learning log data described above is analyzed and a sign of a student dropout is detected, it is not always known whether the detection is correct. As an index of detection, for example, attendance rate of compulsory lectures, correspondence from the educational affairs section, etc. can be considered. However, from these indicators, you can know the level of students' awareness, whether they are just serious, whether they are only interested in a certain class, whether they really want to leave school, or want to go to school. I can't.

  In addition, for example, a student who has a low attendance rate for compulsory lectures may be identified as a sign of dropout, and an email may be sent out, such as “There seems to be a lack of attendance. Do you have any problems?” If the student is simply unscrupulous, the student may have a bad impression, such as “Looks at the school side,” “Excessive care”, and may be counterproductive.

  Therefore, in the present embodiment, in a situation where the detection of the predictor is not necessarily correct, the student does not directly follow the student who is concerned about dropout. It is preferable to follow up with students who come to school meals and circle facilities) and communicate with their close friends.

  That is, as one aspect of the communication support system 10 in the present embodiment, for example, school attendance support is provided to give a chance to come to school for a student who has a possibility of dropout (a sign of dropout).

<Example of Functional Configuration of Communication Support Device 11>
Next, a functional configuration example of the communication support apparatus 11 in the communication support system 10 described above will be described with reference to the drawings. FIG. 2 is a diagram illustrating an example of a functional configuration of the communication support apparatus. The communication support apparatus 11 illustrated in FIG. 2 includes an input unit 21, an output unit 22, a storage unit 23, a learning behavior acquisition unit 24 as an example of a behavior information acquisition unit, a target person extraction unit 25, and friend information acquisition. Means 26, service providing means 27, settlement means 28, screen generation means 29, transmission / reception means 30, and control means 31.

  The input unit 21 accepts various inputs such as start and end of various instructions, input of settings, and the like from a user (here, an administrator) who uses the communication support apparatus 11. Specifically, the input unit 21 receives each instruction such as a learning action acquisition instruction, a target person extraction instruction, a friend information acquisition instruction, a service provision instruction, a payment instruction, a screen generation instruction, and a transmission / reception instruction in the present embodiment.

  The input of information acquired by the input means 21 may be input by an input interface such as a keyboard or a mouse, or may be a touch panel type input using a screen. Furthermore, the input unit 21 may include a voice input unit that inputs voice using, for example, a microphone.

  The output unit 22 outputs the content input by the input unit 21 and the content executed based on the input content. The output means 22 may have a display means such as a display or a monitor when outputting by screen display, for example, and may have an audio output means such as a speaker when outputting by sound. . The input unit 21 and the output unit 22 may be integrated with input / output, such as a touch panel.

  The storage unit 23 stores various information necessary for the present embodiment. Specifically, the storage unit 23 stores the above-described learning log data, learning behavior, subject extraction conditions, subject extraction results, friend information for each student, service information (for example, coupon information), settlement results, The contents displayed on the screen are stored.

  In addition, the storage unit 23 stores setting information and the like for executing each process capable of realizing the communication support process in the present embodiment, and stores the execution progress and results of various processes. The storage unit 23 can read and write various stored information at a predetermined timing as necessary. The storage means 23 is a collection of various types of information as described above, and is a database structured systematically so that such information can be searched and extracted using, for example, keywords. You may have the function of. The storage means 23 is composed of, for example, a hard disk or a memory.

  The learning behavior acquisition unit 24 acquires behavior information (for example, learning behavior) of a predetermined person (for example, a student) based on, for example, information included in the learning log data described above. The student learning behavior is set in advance based on, for example, attendance status of each lecture (class), test results, report submission status and report evaluation, and changes in status over time. It is an indicator. In addition, the definition of the learning behavior set in advance will be described later.

  Further, the learning behavior acquisition unit 24 acquires the learning behavior of the student as a numerical value (point) based on, for example, one or a plurality of index values from the definition of the learning behavior set in advance. As the index value, for example, at least one of “aggressiveness”, “planning”, “continuity” and the like for each lecture can be used, but the index value is not limited thereto. In the present embodiment, it is possible to appropriately extract target persons such as dropout predictors by digitizing learning behavior.

  Note that the content acquired by the learning action acquisition unit 24 is not limited to the above-described index value, and for example, the attendance status (for example, attendance rate) of a lecture for each student may be extracted.

  The target person extraction means 25 extracts the target persons (students and the like) that satisfy a predetermined condition based on values (for example, index values and attendance rates) obtained from the learning behavior obtained by the learning behavior acquisition means 24. Means. For example, the target person extraction means 25 extracts a target person who satisfies a predetermined condition by comparing a value obtained from the learning behavior with a preset threshold value. The predetermined condition is, for example, a condition for detecting a sign of dropout in a school or the like, but is not limited thereto.

  Further, the target person extraction means 25 extracts, for example, target persons by statistical evaluation for all lectures taken by students, absolute evaluation based on individual grades, relative evaluation with other students, etc. The target person can be extracted based on the above. Furthermore, the subject extraction means 25 can also extract the subject by combining the above-described extraction methods.

  The friend information acquisition unit 26 acquires the friend information in the school of the target person (for example, the dropout predictor) extracted by the target person extraction unit 25. Specifically, the friend information acquisition unit 26 refers to the learning log data and the like, and extracts one or a plurality of friends who may soon meet the target person (dropout predictor). Here, the friend information of the target person can be acquired based on, for example, the communication status with another person by social media or mail used by the target person. Here, the social media includes, for example, electronic bulletin boards, blogs, social bookmarks, social networking services, image and video sharing sites, customer reviews on mail order sites, and the like, but is not limited thereto.

  The friend information acquisition unit 26 acquires, for example, user information (personal information) of a person (for example, a student) who is exchanging with a target person (pronounced dropout) through an electronic bulletin board or a person who is discussing on social media. . Further, the friend information acquisition unit 26 refers to a student information table (personal table) included in the learning log data stored in the storage unit 23 based on the acquired user information, and the same user exists. Judge that they are friends in school.

  In addition, whether or not you are likely to meet in the near future, for example, refer to the learning information for each student from the learning log data, take the same course as the subject, the lecture is in the morning or afternoon in the near future Students can be judged as friends who may meet soon. Note that “coming soon” is a predetermined period such as within 3 days or within a week including today. The determination of whether or not a friend is likely to meet soon is not limited to the above-described determination.

  Moreover, the friend information acquisition means 26 may select and acquire a friend with excellent results from learning behavior or the like among a plurality of friends obtained by the above-described processing. It can be judged that a friend who has excellent grades has a high attendance rate for lectures. Therefore, by communicating with a friend who has excellent grades, it is possible to make it easy for those who have dropped out to attend lectures.

  In addition, although information, such as social media mentioned above and an email, may be acquired from the external apparatus connected via the communication network 13, for example, it is not limited to this. The external device described above is, for example, a providing database that provides social media providing services or a mail server that manages mail transmission / reception, but is not limited thereto. In the present embodiment, friend information can be easily acquired by managing the above-described social media, e-mail, and the like in a restricted environment so that only students, teachers, and the like who are enrolled in the school can use.

  Note that the friend information acquisition means 26 may acquire not only student friends but also close teacher information by the same method as described above. Appropriate school attendance support can also be provided by supporting communication with close teachers.

  The service providing unit 27 provides a common service for meeting the dropout predictor obtained by the target person extracting unit 25 and one or a plurality of friends obtained by the friend information obtaining unit 26. There should be more than one friend than one. The presence of multiple people improves the willingness of dropouts to attend school and induces multiple friends to attend the lecture, thereby making it easier for dropout predictors to attend the lecture.

  In the present embodiment, one or a plurality of friends obtained by the friend information acquisition unit 26 are used as friend candidates, and the service providing unit 27 allows a dropout predictor to select a friend who provides a service from the acquired friend candidates. It may be transmitted to the client terminal 12 of the dropout predictor. In that case, the service providing unit 27 may provide a predetermined service to a friend selected from the friend candidates obtained from the client terminal 12 of the dropout predictor. Thereby, the dropout predictor selects a friend, so that the dropout predictor can easily come to a place where the service is used (for example, a school cafeteria).

  Here, the predetermined service includes, for example, coupon information, but is not limited thereto. The coupon information is transmitted to the client terminal 12 of the dropout predictor and his friend by e-mail, for example. The client terminal 12 in this case is a portable terminal possessed by, for example, a dropout predictor or his friend. Thereby, coupon information can be memorize | stored in a portable terminal and can be carried around easily. In addition, it is not limited to this, For example, coupon information may be printed on printing media, such as paper, and may be utilized.

  The coupon information in the present embodiment is preferably information for supporting communication by allowing the dropout predictor and his friend to meet. For this reason, the coupon information includes, for example, a school meal coupon that discounts the fee for a set meal in a student cafeteria (hereinafter referred to as “school meal”), a product coupon that discounts the price of a store product, etc. It is not limited to this. Moreover, in this embodiment, the form which transmits coupon information to the client terminal 12 of a dropout first person and issues a coupon to the friend from the transmitted client 12 may be sufficient.

  Further, after providing the above-described service (first service), the service providing unit 27 provides the second service to the friend or the like when, for example, the school attendance support for the dropout predictor is successful. You can also. The second service content may be the same as or different from the first service content. Thereby, it is possible to provide services to students (friends) who cooperate in school attendance support for dropout predictors regardless of the result.

  The service providing unit 27 provides the service so that the student is not aware that he / she is a dropout predictor or a friend of the dropout predictor. For example, in issuing a coupon or the like, it is preferable to issue a coupon with an effect such as one of some campaigns on the school side, but the present invention is not limited to this.

  The settlement means 28 performs a settlement process based on the service contents when a predetermined condition is satisfied for the service provided by the service providing means 27 described above, for example. In addition, as a case where the predetermined condition is satisfied, for example, there is a case where the school attendance support to the dropout signer is successful, but the present invention is not limited to this, for example, even if the school attendance support is not successful You may perform a checkout process for cooperating with school support.

  In addition, the case where school attendance support for dropouts has been successful means, for example, that coupons issued to dropouts and their friends (one or more) as an example of service within a predetermined time (for example, within 5 minutes, etc.) It can be judged that the school attendance support has been successful when it is used. In addition, the present invention is not limited to this. For example, when each coupon is used within a predetermined time, and a dropout predictor attends a predetermined lecture (for example, the morning or afternoon lecture of the day). In addition, a predetermined condition may be satisfied.

  The settlement can be performed using a settlement function (for example, Mobile Suica (registered trademark)) or the like possessed by a mobile terminal (client terminal 12) of the dropout predictor or his friend. In this case, whether or not a service such as a coupon has been used within a predetermined time, for example, whether or not coupon information is recorded at the same time that the meal fee for school meals is settled using the settlement function described above. to decide.

  In addition, the above-mentioned meal adjustment in the school meals, etc. is carried out at a normal fee, and if the support for attending school for dropouts is successful, an extra payment corresponding to the coupon discount will be paid by the settlement means 28 at a later date. Cash backed.

  In other words, the settlement means 28 detects a specific coupon ID at the time of meal settlement, acquires the same coupon ID from a predetermined number of student mobile terminals (client terminals 12) including dropout predictors within a certain period of time, and settles it. If it is done, it will be recognized as a discounted person. For all or some of the students, the discount will be returned afterwards. In the present embodiment, either the settlement process described above or the second service provision by the service provision unit 27 described above may be performed, or both may be performed.

  Thereby, in this embodiment, for example, when the settlement time of the dropout person and the payment time of the friend are within a predetermined time, the service provision information is provided to at least one of the dropout person and the friend. It is possible to provide a service according to the requirements.

  The screen generation unit 29 displays various setting screens, processing results, and the like on the output unit 22 for executing various processes such as acquisition of learning behavior, extraction of target persons, acquisition of friend information, provision of services, and settlement processing. Generate a screen. For example, the screen generation unit 29 generates a screen example when a coupon is issued.

  The transmission / reception means 30 is a communication means for transmitting / receiving various types of information to / from an external device such as the client terminal 12 via the communication network 13, for example. The transmission / reception means 30 can receive various information already stored in the external device or the like, and can transmit the result processed by the communication support device 11 to the external device or the like via the communication network 13. it can.

  The control unit 31 controls the entire components of the communication support apparatus 11. Specifically, the control means 31 performs each control regarding communication support based on the instruction | indication from the input means 21, etc. by users, such as an administrator, for example. Here, each control refers to, for example, acquisition of learning behavior in the learning behavior acquisition means 24 described above, extraction of a sign of dropout in the target person extraction means 25, acquisition of friend information for each student in the friend information acquisition means 26, service There is provision of a predetermined service in the providing means 27. Examples of each control include, but are not limited to, the above-described settlement process in the settlement unit 28 and screen generation in the screen generation unit 29.

<Example of Hardware Configuration of Communication Support Device 11>
Here, in the communication support apparatus 11 described above, an execution program (communication support program) that can cause a computer to execute each function is generated, and the execution program is installed in, for example, a general-purpose PC or server. Communication support processing in this embodiment can be realized. Here, an example of a hardware configuration of a computer capable of realizing the communication support process in the present embodiment will be described with reference to the drawings.

  FIG. 3 is a diagram illustrating an example of a hardware configuration capable of realizing communication support processing. 3 includes an input device 41, an output device 42, a drive device 43, an auxiliary storage device 44, a main storage device 45, a central processing unit (CPU) 46 that performs various controls, and a network connection. And a device 47, which are connected to each other by a system bus B.

  The input device 41 has a pointing device such as a keyboard and a mouse operated by a user such as an administrator, and a voice input device such as a microphone. The execution instruction of the program from the administrator or the like, various operation information, software, etc. Enter the information to start

  The output device 42 has a display for displaying various windows and data necessary for operating the computer main body for performing the processing in the present embodiment. Can be displayed. In addition, the output device 42 can print the above processing result or the like on a print medium such as paper and present it to an administrator or the like.

  Here, the execution program installed in the computer main body in this embodiment is provided by a portable recording medium 48 such as a Universal Serial Bus (USB) memory, a CD-ROM, or a DVD. The recording medium 48 on which the program is recorded can be set in the drive device 43, and an execution program included in the recording medium 48 is transferred from the recording medium 48 to the auxiliary storage device 44 via the drive device 43 based on a control signal from the CPU 46. To be installed.

  The auxiliary storage device 44 is a storage means such as a hard disk, and stores the execution program in the present embodiment, a control program provided in the computer, etc. based on a control signal from the CPU 46, and performs input / output as necessary. be able to. The auxiliary storage device 44 stores the above-described learning log data, learning behavior, target person extraction results, friend information, service information (for example, coupon information), settlement results, screen generation results, and the like. The auxiliary storage device 44 can read and write necessary information from each stored information based on a control signal from the CPU 46 and the like.

  The main storage device 45 stores an execution program read from the auxiliary storage device 44 by the CPU 46. The main storage device 45 includes a read only memory (ROM), a random access memory (RAM), and the like. The auxiliary storage device 44 and the main storage device 45 correspond to the storage unit 23 described above, for example.

  The CPU 46 controls processing of the entire computer such as various operations and data input / output with each hardware component based on a control program such as an operating system and an execution program stored in the main storage device 45. Each process can be realized. Various information necessary during the execution of the program can be acquired from the auxiliary storage device 44, and an execution result or the like can be stored.

  Specifically, the CPU 46 corresponds to the program on the main storage device 45 by causing the communication support program installed in the auxiliary storage device 44 to be executed based on, for example, a program execution instruction obtained from the input device 41. Process. For example, by executing the communication support program, the CPU 46 acquires the learning behavior by the learning behavior acquisition unit 24 described above, the extraction of the dropout predictor by the target person extraction unit 25, and the friend of the dropout predictor by the friend information acquisition unit 26. Extraction processing is performed. Further, the CPU 46 performs processing such as provision of a service by the service providing unit 27, a settlement process by the settlement unit 28, and a screen generation by the screen generation unit 29. In addition, the processing content in CPU46 is not limited to this. The contents executed by the CPU 46 can be stored in the auxiliary storage device 44 as necessary.

  The network connection device 47 acquires an execution program, software, setting information, and the like from an external device or the like connected to the communication network 14 by connecting to the communication network 14 or the like based on a control signal from the CPU 46. Further, the network connection device 47 can provide the execution result obtained by executing the program or the execution program itself in the present embodiment to an external device or the like.

  With the hardware configuration described above, the communication support process in the present embodiment can be executed. Further, by installing the program, the communication support processing in the present embodiment can be easily realized by a general-purpose PC or server.

<Example of communication support processing>
Here, an example of the communication support process in the present embodiment will be described using a flowchart. FIG. 4 is a flowchart illustrating an example of the communication support process in the present embodiment.

  The communication support process shown in the example of FIG. 4 acquires action information of each person in order to extract a predetermined target person such as a school dropout predictor described above (S01). For example, in the process of S01, for example, a learning behavior or the like in the school life of each student who goes to school is acquired. The learning behavior may be, but is not limited to, the attendance status of each lecture, the test result, whether or not a report has been submitted, the result of the report or test, and the like. Moreover, you may acquire learning action as historical information accompanying progress of time.

  Next, a communication assistance process extracts the target person who satisfy | fills a predetermined condition based on the action information acquired by S01 (S02). The target person is, for example, a communication support target person such as a dropout predictor of school or the like, but is not limited thereto. Further, the communication support process extracts one or a plurality of friend information corresponding to the extracted target person (S03).

  The communication support process provides a predetermined service to the dropout predictor and his friend (S04). The provision of the predetermined service is, for example, issuing a predetermined coupon or the like to the client terminal 12 or the like of the dropout predictor or his friend, but is not limited thereto. The predetermined coupon is used, for example, for communication support for meeting a sign of dropout and a friend. For example, the coupon may be a common school meal coupon, and a certain service is provided by gathering multiple people. It is preferable that the coupon is obtained (for example, the discount rate increases as the number of people who use the coupon increases simultaneously).

  The coupon issuance may be sent by e-mail or the like to a portable terminal (client terminal 12) possessed by a dropout predictor or his friend, for example, by a dedicated application installed in the client terminal 12 in advance. Information may be downloaded. In the case of downloading, for example, the address information of the download destination is transmitted in advance to the dropout predictor or his friend by e-mail or the like, and actual coupon information is acquired based on the address information.

  Further, the communication support process performs a settlement process based on the usage status of the provided service (S05). Specifically, for example, when school attendance support for dropout predictors is successful, a discount based on coupon information is validated.

  Here, in the communication support process, it is determined whether or not the communication support is continued (S06). When the communication support process is continued (YES in S06), the process returns to S03. Further, the communication support process ends when communication support is not continued (NO in S06). That is, in the present embodiment, it is possible to prevent a student from dropping out by continuing the communication support described above. Further, the execution progress, execution result, etc. in the communication support processing described above can be stored in the storage means 23 or the like by being included in the learning log data described above, or can be stored in the storage means 23 as another file. .

<First Example of Communication Support Processing>
Next, a specific example of the communication support process described above will be described using a flowchart and the like. FIG. 5 is a flowchart showing a first embodiment of the communication support process.

  In the first embodiment shown in FIG. 5, lectures that are open to one or a plurality of students are extracted (S11), and it is determined whether or not learning behaviors for all lectures are calculated for each student (S12). The target students may be class units, undergraduate units, grades, etc., or all students.

  Here, when the learning behavior for all the lectures has not been calculated (NO in S12), the learning behavior in the lecture for which the learning behavior has not been calculated is calculated (S13). A specific example of calculation of learning behavior will be described later. Further, from the learning behavior result (for example, the number of points) obtained from the processing of S13, students having a threshold value (for example, 3 points) or less are extracted as dropout predictors (S14). In the first embodiment, the process moves to the next lecture for which the learning behavior is not calculated (S15), and the process returns to S12. In other words, in the processes of S12 to S15, for example, students who have dropped out are extracted for all the lectures extracted in S11.

  In the first example, when learning behavior is calculated for all lectures in the processing of S12 (YES in S12), the learning behavior described above is a threshold value for each dropout predictor extracted in the processing of S14. The following number of lectures is acquired (S16). In the first embodiment, it is determined whether or not the number of lectures with a sign of dropout extracted by the process of S16 is equal to or greater than a predetermined number (for example, 5 lectures) (S17). In S17, YES), a friend for the dropout predictor is acquired.

  For example, in the first embodiment, a friend who is a sign of dropout is acquired based on a communication status such as social media or email. Specifically, for example, respondents who have responded to discussions on social media of dropout predictors are extracted (S18), or respondents who also reply (view) to write on bulletin boards of dropout predictors. Although it extracts (S19), it is not limited to this. Further, the person extracted by the processing of S18, S19, etc. is extracted as a friend candidate (S20).

  The friend candidates are extracted including other social media until a predetermined number (for example, five people) is reached. The extraction of friend candidates can preferentially select a recently replied person or a person who has a large number of replies among a plurality of replyers, but is not limited thereto. Further, by limiting access to the above-described social media such as discussions and bulletin boards to students who attend the target school (university, vocational school, prep school) or the like, it is possible to easily extract friend candidates. Even if access is not limited, friend candidates can be extracted by referring to a student table (individual table) set in advance by a user name, for example.

  Next, in the first embodiment, a friend list indicating friend candidates obtained by the process of S20 is sent by mail to the client terminal 12 of the dropout predictor (S21). The dropout predictor acquires at least one friend selected from the friend list (S22), and sends a common school meal coupon mail as an example of the first service to the client terminal 12 of the acquired friend (S23). ). Here, the selection of the friend in the dropout predictor selects at least one person from the friend list displayed on the screen of the client terminal 12. Further, the information on the selected friend is transmitted to the communication support apparatus 11 via the communication network 13.

  Further, in the first example, it is determined whether or not the school meal coupon transmitted in the process of S23 has been used by the dropout predictor and his friend within a predetermined time (S24). In the first embodiment, a dropout predictor attends a predetermined lecture (S25), and it is determined whether the dropout attendee attends the lecture (S26).

  In the first embodiment, when a sign of dropout attends a lecture (YES in S26), it is determined that the support for the dropout sign by the friend is successful, and further, as an example of the second service, a school meal A coupon mail is sent (S27). In the process of S27, a school meal coupon mail may also be sent to the dropout predictor. Moreover, in the process of S27, the adjustment process (for example, cashback) by the adjustment means 28 mentioned above may be performed.

  Also, in the first example, when the dropout predictor is not attending the lecture (NO in S26), the process is ended as it is. If the number of lectures with a sign of dropout is not equal to or greater than the predetermined number in the process of S17 described above (NO in S17), the process ends.

  Here, in the first embodiment described above, the learning behavior is calculated for all the lectures that are open, but the present invention is not limited to this, and only the required lectures that are open may be targeted. In that case, in the process of S11 described above, only the required compulsory lectures being opened are extracted, and in the processes after S12, the same processes as those described above are performed for all the extracted compulsory lectures.

<Second embodiment of communication support processing>
Next, a second embodiment of the communication support process described above will be described using a flowchart and the like. FIG. 6 is a flowchart showing a second embodiment of the communication support process. In the first embodiment, the dropout predictor is determined based on whether or not the calculation result of the learning behavior is equal to or less than the threshold value. Make a predictor's judgment. In the following processing, portions different from those of the first embodiment will be mainly described, and other descriptions will be omitted.

  In the second embodiment shown in FIG. 6, first, lectures being opened for one or a plurality of students are extracted (S31), and it is determined whether or not attendance status for all lectures has been acquired (S32). The attendance status is information included in the learning log data described above, for example. The target students may be class units, undergraduate units, grades, etc., or all students.

  If the attendance status for all lectures has not been acquired (NO in S32), the attendance status is extracted from the learning behavior of the lecture for which learning behavior has not been acquired (S33), and the attendance rate is a threshold (for example, 20%, etc.) ) The following students are extracted as students who have dropped out (S34). In the second embodiment, the process moves to the next lecture for which the attendance status has not been acquired (S35), and the process returns to S32. In other words, in the processes of S32 to S35, for example, students who have dropped out are extracted for all the lectures extracted in S31.

  In the second embodiment, when the attendance status for all lectures is acquired in the process of S32 (YES in S32), the attendance rate described above is equal to or less than the threshold for each dropout predictor extracted in the process of S34. The number of lectures is acquired (S36). In the first embodiment, it is determined whether or not the number of lectures with a sign of dropout extracted by the processing of S36 is equal to or greater than a predetermined number (for example, 5 lectures) (S37). In S37, YES), a friend for the dropout predictor is acquired.

  In addition, about the process of S38-S47, since the process similar to the process of S18-S27 of 1st Example mentioned above is performed, concrete description here is abbreviate | omitted.

  As described above, in this embodiment, it is possible to appropriately extract dropout predictors from learning behavior, attendance status, and the like. In addition, appropriate communication support can be provided to the extracted dropout predictor. Therefore, it is possible to support dropout predictors and to reduce dropouts.

<About the definition example of learning log data and learning behavior>
Next, specific examples of the definition of learning log data and learning behavior used in the communication support system 10 described above will be described. FIG. 7 is a diagram for explaining a definition example of learning log data and learning behavior. 7A shows a specific example of learning log data, and FIG. 7B shows a definition example of learning behavior.

  Learning log data, for example, personal information and lectures for each student enrolled in school, attendance status of classes, whether or not posted on the campus bulletin board, various information such as report submission and test results as log data in a table, etc. Managed.

  Examples of tables included in the learning log data include, for example, a personal table that manages the name, grade, and contact information (email address, mobile number, etc.) of each student, and materials and materials related to lectures that students are taking There are a material table and a material details table. In addition, a lecture table for managing the number and content of lectures taken by each student, a class frame table for each lecture, a class attendance table for managing the attendance status of lectures and classes for each student, and a school bulletin board There is a bulletin board table to manage. Necessary when creating a bulletin board posting table for managing posted content for each student, a speech table for managing the content of comments for each student, a table for managing themes and content discussed by students, and reports There is a report teaching material table for managing teaching materials and report deadlines, submission status, evaluation information of submitted reports, and the like. The contents of the table included in the learning log data are not limited to this.

  Further, in the present embodiment, data of items corresponding to the definition of learning behavior is extracted from FIG. The items to be extracted include, for example, “document presentation date”, “document publication date”, “document reference date”, “attendance status”, “postboard information, browsing information, follow-up information”, “discussion” Post information, browsing information, rating information, etc. In addition, for example, “Q & A posting information, reply information, browsing information”, “FAQ browsing information”, “first reference date and time of report teaching material”, “report evaluation information, deadline”, “first test teaching material” Reference date / time ”,“ test evaluation information, time limit ”,“ questionnaire answer information, time limit ”, and the like, but are not limited thereto.

  Further, the learning behavior acquisition unit 24 acquires the learning behavior based on the extraction items described above and the learning behavior definition that is preset and stored in the storage unit 23 or the like as shown in FIG. Here, as the definition of learning behavior, for example, “the reference rate of the teaching materials published before the class is high” or “referring to the teaching materials published before the class is referred to sequentially” It is not limited to this. In the present embodiment, the learning behavior is digitized (pointed) based on a predetermined index value from the above-described definition of the learning behavior.

  Specifically, the learning behavior is quantified by absolute evaluation or relative evaluation using at least one index value among “aggressiveness”, “planning”, “continuity”, etc. of the student with respect to the lecture. For example, in the example of FIG. 7B, “aggressiveness” means, for example, “a high reference rate of material teaching materials released before a class (lecture)” or “a question or unknown point in class” ”,“ Viewing content posted by other students ”,“ high posting rate for each bulletin board ”, etc. In addition, “planning” refers to, for example, “referencing material teaching materials published before class in standard time”, “referring material teaching materials published after class date in level time” There is. “Continuity” includes, for example, “high class attendance rate”, “a large amount of postings”, “a large amount of browsing”, and the like.

  In the present embodiment, quantification is performed based on the index for learning behavior as described above. In the present embodiment, the types and number of indicators and the content of the definition of learning behavior are not limited to this.

<Example of Calculation of Learning Behavior in Learning Behavior Acquisition Unit 24>
Next, a calculation example of learning behavior in the learning behavior acquisition unit 24 will be described with reference to the drawings. FIG. 8 is a diagram illustrating a calculation example of learning behavior. The example of FIG. 8 shows an example in which points are calculated for each student with respect to learning behavior for a lecture “XX lecture” as an example.

  In FIG. 8, the indicators “high reference rate of material teaching materials released before class”, which is an example of aggressiveness, and “high class attendance rate” and “a large amount of submissions” are examples of continuity. The total points for students (A to G) are shown.

  In the example of FIG. 8, the point total is calculated statistically based on the results of the past four classes or themes. For example, in the example of “the reference rate of material teaching materials released before class is high” shown in FIG. 8, how much time has passed since the material teaching materials released before the first to fourth classes are released. The reference rate is set based on whether the reference is made after elapse of time. Also, when the reference rate is high (when referring immediately after being published), a high point is set.

  In the example of “high class attendance” shown in FIG. 8, “○” indicates attendance, “×” indicates absence, and the attendance rate in the first to fourth times is 80% or more. If it is less than 80%, it is set as 0P. Further, in the example of “large post amount” shown in FIG. 8, a high point is given when the total post amount in the themes 1 to 4 is large.

  Furthermore, in this embodiment, the learning action points (P) for each student are totaled from the point results of each student statistically calculated for each index. In addition, in the aggregation result by the example of FIG. 8, “A-kun 2.5P”, “B-kun 1.5P”, “C-kun 2.0P”, “D-kun 1.0P”, “E-kun 0.0P” , “F-kun 2.5P”, “G-kun 3.0P”.

  In the example of FIG. 8, an evaluation function for performing a relative evaluation with other students is set in advance, and points are set in a range of 0 to 1 based on the evaluation function. The calculation is not limited to this. For example, calculation of points may be performed not only by relative evaluation but also by absolute evaluation according to learning behavior. Regarding the digitization of learning behavior based on the index value, for example, a correlation coefficient between the score data and the index value data may be calculated, and the numerical conversion may be performed based on the calculated correlation coefficient.

<Extraction example of dropout predictor in subject extraction means 25>
Next, an example of extracting a dropout predictor by the target person extracting means 25 will be described with reference to the drawings. FIG. 9 is a diagram for explaining an example of extraction of dropout predictors. In the present embodiment, the target person extraction means 25 extracts, for example, a dropout predictor who is an example of the target person from the learning behavior of all lectures currently open.

  In the example of FIG. 9, the transition of the learning action point for each class number of each student (A to G) is shown in a graph with respect to “civil law I” and “commercial law I”. The target person extraction means 25 extracts, for example, students whose learning action points are equal to or less than a preset threshold value in a single lecture from the above-described results. Furthermore, the target person extraction means 25 extracts students whose learning action points are equal to or less than a threshold value in other lectures, for example. For example, in the example of FIG. 9, Mr. E can be determined to be a dropout predictor because it is determined to be a threshold value (eg, 3.0 P) or less in each lecture of “Civil Code I” and “Commercial Code I”.

  Note that the criteria are not limited to this. For example, if the required lecture is only “Civil Code I”, only the required lecture may be targeted. May be. When all lectures are targeted, the judgment may be made when the lectures of several percent or more of the whole are below the threshold, or by comparing the average point with the reference value.

  In addition, in the example of FIG. 9, Mr. F's learning action point is below a threshold value (for example, 3.0P) in “Civil Code I”, but is above the threshold value in “Commercial Code I”. In such a case, it can be determined that the person is not a sign of dropout.

  In addition, as another pattern for extracting dropout signs, for example, the target person extracting means 25 may narrow down the lectures to “mandatory lectures” instead of all lectures, or group related lectures in groups. The target person may be extracted. Moreover, you may extract the subject extraction means 25 by an attendance rate. When extracting by the attendance rate, the target person extraction means 25, for example, when the attendance rate of all lectures is 20% or less, etc. Can be extracted.

  Furthermore, in this embodiment, a dropout predictor may be extracted for each index value of the learning behavior. FIG. 10 is a diagram for explaining an example of extraction of dropout predictors based on an index value of learning behavior. In the example of FIG. 10, the point of each index value (for example, aggressiveness, planability, continuity) for the learning behavior described above is shown on a radar chart for each student (for example, Mr. A to G). Thereby, the magnitude of each index value can be compared at a glance, and the balance and characteristics of each index can be confirmed for each student.

  In the example of FIG. 10, a radar chart of the average values of all students is displayed. Thereby, it can be easily compared with the average value for each student, and it is possible to grasp the tendency of the learning behavior of each student compared with the average value. Moreover, FIG. 10 can display the tendency by a class unit, for example by displaying by class class, for example, and can aim at improvement of the content of a class, how to advance.

  Here, in the example of FIG. 10, when extracting the dropout predictor, it is compared with the average index value for each index value, and is equal to or less than the average value in a predetermined number (for example, two) or more index values. Further, when the difference from the average value is equal to or greater than the threshold (2.0P), it is extracted as a dropout predictor. Note that the extraction condition is not limited to this. For example, when two of the three index values are 0 points, they may be extracted as dropout predictors.

  8 to 10 described above can be generated by, for example, the screen generating unit 29 described above and displayed on the output unit 22 such as a display or stored in the storage unit 23. As shown in FIG. 8 and FIG. 9, by visualizing learning behavior, it is possible to grasp the characteristics and trends of classes and individual students that cannot be seen with scores and evaluations, and further confirm the behavior for each student. Therefore, the sign of dropout can be extracted early, and appropriate communication support can be provided to the extracted students.

<Acquisition example of friend information>
Next, an example of friend information acquisition in the above-described friend information acquisition means 26 will be described with reference to the drawings. FIGS. 11 to 13 are diagrams (parts 1 to 3) illustrating examples of acquiring friend information. 11 shows an example of information related to discussion in social media, etc. FIG. 12 shows an example of information related to electronic bulletin board in social media, etc. FIG. 13 shows priority friend points for selecting friend candidates. An example is shown. In addition, about FIGS. 11-13, it is assumed that Mr. A is the dropout predictor described above, and A's friend information is acquired.

  In FIG. 11, items include, for example, “lecture”, “title” of discussion, “poster”, “content of speech”, “replyer”, and the like. In the example of FIG. 11, a student returning a reply to Mr. A's post on the discussion material is shown. Specifically, for example, about the title “About ○○” in the “Civil Code” lecture, Mr. B and Mr. C responded to what A posted “I think it ’s ○○”. Show.

  In FIG. 12, items include, for example, “lecture”, “theme” of electronic bulletin board, “poster”, “content of speech”, “replyer”, and the like. In the example of FIG. 12, a student returning a reply to A's post on the electronic bulletin board is shown. Specifically, it shows that Mr. B responded to what Mr. A posted “I think it is XX” on the theme “About XX” in the lecture of “Civil Law”, for example. .

  In FIG. 13, items include, for example, “friend”, “number of replies”, “average learning action point”, “priority friend point”, and the like. In the example of FIG. 13, for example, Mr. A responds to a discussion on social media or an electronic bulletin board, and the top several students who have higher average learning action points as candidate friends for issuing school meal coupons. Extract. In this case, a value obtained by multiplying the number of replies by the learning action point is calculated as a priority friend point, and the top several names (for example, five) are extracted from the priority friend points as friend candidates. This is because it can be determined that the number of replies is a close friend, and a student with a large average learning point can be determined to be willing to lecture (honor student).

  Thereby, in the example of FIG. 13, B-kun, E-kun, F-kun, G-kun, and I-kun are extracted. Note that the acquisition example of the friend candidate is not limited to this. For example, a student having a point equal to or higher than a predetermined threshold (for example, 50 points) may be acquired as the friend candidate.

<Coupon issue example in service providing means 27>
Next, a coupon issue example that is an example of service provision in the service provision unit 27 will be described. The service providing means 27 issues coupons to dropout predictors (target persons) and their friends. The coupon to be issued is, for example, a school meal coupon or a coupon at a school shop, but is not limited thereto.

  Further, it is preferable that the coupon is not effective unless a plurality of people are available. Thereby, the dropout predictor needs to meet with a plurality of friends in order to use the coupon, so that it becomes easy to communicate with the friends. In addition, the service providing means 27 determines that the dropout predictor and his friend have met each other when the coupon is used within a predetermined period of time, and further predicts that the dropout is given in the lecture (class) before or after using the coupon. When a person is present, a coupon discount can be validated or a coupon can be issued again.

  Here, FIG. 14 is a diagram illustrating an example of coupon issue. In the example of FIG. 14, setting conditions for reissuing a coupon corresponding to the provision of the second service described above are shown. In the example of FIG. 14, Mr. A is a dropout predictor, and Mr. B and Mr. C are friends whose coupons have been delivered by providing the first service.

  In such a case, conditions for distributing coupons to Mr. A to Mr. C again can be set as shown in FIG. 14, for example. For example, when all three people are attending a predetermined lecture (for example, an afternoon lecture) targeted by Mr. A to Mr. C, it can be set so that all three can use the coupon.

  Moreover, as shown in FIG. 14, it may be set so that only Mr. A to Mr. C can be used on condition that Mr. A is present, or only Mr. A who has attended can use it. , You can set it so that only you and A can use it. In addition, if only you and A are attending, you may be able to use all three, and if you and A who use a coupon attend, you can use only A and the other person. May be set.

  By setting a plurality of conditions for issuing coupons in advance, different coupons can be issued for each student or for each lecture, so that coupons can be issued appropriately. In addition, the settlement means 28 can appropriately settle the coupon. In addition, about the issue example of a coupon, it is not limited to this.

<Screen example in screen generation means 29>
Next, a screen example in the screen generation unit 29 will be described with reference to the drawings. FIG. 15 is a diagram illustrating a screen example when a coupon is issued.

  The example of FIG. 15A shows an example of a mail reception screen sent to the client terminal 12 (for example, a portable terminal) of the student A who is the dropout predictor described above. In the example of FIG. 15A, a coupon is displayed as “campaign notice”, and a screen that allows a friend who shares the coupon to be selected is generated. That is, in this embodiment, in order to make student A who is a dropout predictor attend school, a school meal coupon is issued and the student A and a friend share the coupon.

  When a friend is selected, for example, as shown in FIG. 15B, the student A who is a sign of dropout generates a screen for selecting a friend to share a coupon and displays it on the client terminal 12. The students displayed on the screen are friend candidates acquired by the above-described friend information acquisition means 26. Specifically, for example, a student A who is a sign of dropout is a friend who is taking the same class on the day of a morning class (it seems to use school meals). The screen displays a friend's face image, name information, and the like, but is not limited thereto. Moreover, in this embodiment, as shown to FIG. 15 (B), the friend who shares a coupon with respect to a some date can be selected.

  The dropout predictor selects a friend from the screen shown in FIG. A mark indicating the selection is displayed on the screen for the selected friend. In the example of FIG. 15B, “◯” is displayed for the selected friend, but the display format is not limited to this.

  When the selection is completed, it is transmitted to the client terminal 12 (for example, a mobile terminal) of the friend B of the student A who is a dropout signer, and the mail reception screen shown in FIG. Is displayed.

  Thereby, in order to use a coupon, the opportunity to meet the friend B and the student A can be given and communication can be supported. In addition, communication increases the possibility of taking subsequent actions together, so the attendance rate for lectures can be improved at the same time.

  When the dropout predictor attends the lecture, a second school meal coupon is issued to the friend who cooperated as shown in FIG.

  In providing services in this embodiment, when issuing coupons to dropout predictors and their friends, it is assumed that they are dropout predictors based on the contents of the email, etc., or supporting communication of dropout predictors It is preferable to use a text that will not be noticed. Thereby, it is possible to provide support for school dropouts without giving a bad impression to the school.

  As described above, according to the present embodiment, appropriate communication support can be realized. For example, in this embodiment, when a student who is a dropout is extracted from the learning behavior, friend information of the student is read out, and a service that can be used on campus is addressed to the student and to the friend. Further, in the present embodiment, when it is detected that the use time of the dropout predictor and the friend service is within a predetermined time, provision of the service can be made effective for at least one of the dropout signer and the friend. Thereby, in this embodiment, the motivation for a student to go to school can be given and communication with a friend can be promoted.

  In the above-described embodiment, an example of communication support for detecting and improving a student dropout sign has been described. However, the present invention is not limited to this and can be widely applied to other communication support.

  Each embodiment has been described in detail above. However, the present invention is not limited to the specific embodiment, and various modifications and changes other than the above-described modification are possible within the scope described in the claims. .

In addition, the following additional remarks are disclosed regarding the above Example.
(Appendix 1)
Extract students who may drop out, obtain friend information of the extracted students,
Send service provision information available in the student cafeteria to the student and the friend corresponding to the friend information,
Provide a service according to the service provision information to at least one of the student and the friend when the student's checkout time and the friend's checkout time are within a predetermined time in the student cafeteria A communication support program for causing a computer to execute processing.
(Appendix 2)
2. The communication support program according to appendix 1, wherein a student who takes the same course as the student in the near future is obtained as the friend information by referring to the course information of the student and the friend.
(Appendix 3)
The learning action point for the student's lecture is calculated, and when the learning action point is equal to or less than a preset threshold, the student is extracted as a student who may drop out. Communication support program.
(Appendix 4)
The communication support program according to appendix 3, wherein the learning action point is calculated based on at least one index value set in advance.
(Appendix 5)
The communication support program according to appendix 3 or 4, wherein the student who is likely to drop out is extracted by statistically judging the learning action points based on all lectures that each student takes.
(Appendix 6)
Any one of Supplementary notes 1 to 5, wherein when the student and the friend use the service within a predetermined time and attend a predetermined lecture, the student and the friend pay for the provided service. Communication support program described in the section.
(Appendix 7)
When the student and the friend use the service within a predetermined time and attend a predetermined lecture, the student and the friend provide a second service to at least one of the student and the friend. The communication support program according to any one of appendices 1 to 6.
(Appendix 8)
8. The communication support program according to any one of appendices 1 to 7, wherein the student selects a friend who provides the service from among friends included in the friend information.
(Appendix 9)
9. The communication support program according to any one of appendices 1 to 8, wherein a screen for displaying the service on a client terminal possessed by the student and the friend is generated.
(Appendix 10)
An extraction step to extract students who are likely to drop out;
A friend information acquisition step of acquiring student friend information extracted by the extraction step;
Service provision information that can be used in a student cafeteria is transmitted to the student and the friend corresponding to the friend information, and the settlement time of the student and the settlement time of the friend in the student cafeteria is within a predetermined time. And a service providing step of providing a service according to the service providing information to at least one of the student and the friend.
(Appendix 11)
An extraction means to extract students who may drop out,
Friend information acquisition means for acquiring student friend information extracted by the extraction means;
Service provision information that can be used in a student cafeteria is transmitted to the student and the friend corresponding to the friend information, and the settlement time of the student and the settlement time of the friend in the student cafeteria is within a predetermined time. In this case, the information processing apparatus includes service providing means for providing a service corresponding to the service providing information to at least one of the student and the friend.

10 Communication Support System 11 Communication Support Device (Information Processing Device)
DESCRIPTION OF SYMBOLS 12 Client terminal 13 Communication network 21 Input means 22 Output means 23 Storage means 24 Learning action acquisition means (action information acquisition means)
25 Target person extraction means 26 Friend information acquisition means 27 Service provision means 28 Payment means 29 Screen generation means 30 Transmission / reception means 31 Control means 41 Input device 42 Output device 43 Drive device 44 Auxiliary storage device 45 Main storage device 46 CPU
47 Network connection device 48 Storage medium

The present invention relates to a student support program, an information processing apparatus, and a student support method .

The student support program in one aspect extracts students who may drop out based on attendance status of compulsory lectures from the attendance status of each acquired student, and 1 based on the extracted friend information of the student Alternatively, a computer is caused to execute a process of providing a common service to a plurality of friend students and the extracted students.

Claims (9)

  1. Extract students who may drop out, obtain friend information of the extracted students,
    Send service provision information available in the student cafeteria to the student and the friend corresponding to the friend information,
    Provide a service according to the service provision information to at least one of the student and the friend when the student's checkout time and the friend's checkout time are within a predetermined time in the student cafeteria A communication support program for causing a computer to execute processing.
  2.   2. The communication support program according to claim 1, wherein a student who takes the same course as the student in the near future is acquired as the friend information by referring to the course information of the student and the friend.
  3.   The learning action point with respect to the student's lecture is calculated, and when the learning action point is equal to or less than a preset threshold, the student is extracted as a student who is likely to drop out. Communication support program.
  4.   4. The student according to claim 1, wherein when the student and the friend use the service within a predetermined time and attend a predetermined lecture, the student and the friend pay for the provided service. The communication support program according to item 1.
  5.   When the student and the friend use the service within a predetermined time and attend a predetermined lecture, the student and the friend provide a second service to at least one of the student and the friend. The communication support program according to any one of claims 1 to 4.
  6.   The communication support program according to any one of claims 1 to 5, wherein the student selects a friend who provides the service from among friends included in the friend information.
  7.   The communication support program according to claim 1, wherein a screen for displaying the service on a client terminal possessed by the student and the friend is generated.
  8. An extraction step to extract students who are likely to drop out;
    A friend information acquisition step of acquiring student friend information extracted by the extraction step;
    Service provision information that can be used in a student cafeteria is transmitted to the student and the friend corresponding to the friend information, and the settlement time of the student and the settlement time of the friend in the student cafeteria is within a predetermined time. And a service providing step of providing a service according to the service providing information to at least one of the student and the friend.
  9. An extraction means to extract students who may drop out,
    Friend information acquisition means for acquiring student friend information extracted by the extraction means;
    Service provision information that can be used in a student cafeteria is transmitted to the student and the friend corresponding to the friend information, and the settlement time of the student and the settlement time of the friend in the student cafeteria is within a predetermined time. In this case, the information processing apparatus includes service providing means for providing a service corresponding to the service providing information to at least one of the student and the friend.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6485764B1 (en) * 2018-05-02 2019-03-20 株式会社Poper Classroom management server, classroom management system, classroom management method and program

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002334200A (en) * 2001-05-11 2002-11-22 Ntt Data Corp Method and system for supporting cancellation prediction
JP2005141492A (en) * 2003-11-06 2005-06-02 Sumitomo Mitsui Card Co Ltd Information processing system, information processing method, program, and recording medium
JP2007058355A (en) * 2005-08-23 2007-03-08 Alion System:Kk Business model for supporting social withdrawal/truancy using it (information technology)
JP2008262430A (en) * 2007-04-13 2008-10-30 Aomoriken Kogyo Gijutsu Kyoiku Shinkokai Attendance/absence data display method for supporting inference for attendance/absence action dominant psychology of student
JP2009100789A (en) * 2007-10-19 2009-05-14 Konami Digital Entertainment Co Ltd User management system, user management method and user management program
JP2013198541A (en) * 2012-03-23 2013-10-03 Namco Bandai Games Inc Server system, program, information storage medium, and electronic instrument
US20140047051A1 (en) * 2011-09-30 2014-02-13 Tencent Technology (Shenzhen) Company Limited Method and system for sending prompt information to social networking services community users

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2002334200A (en) * 2001-05-11 2002-11-22 Ntt Data Corp Method and system for supporting cancellation prediction
JP2005141492A (en) * 2003-11-06 2005-06-02 Sumitomo Mitsui Card Co Ltd Information processing system, information processing method, program, and recording medium
JP2007058355A (en) * 2005-08-23 2007-03-08 Alion System:Kk Business model for supporting social withdrawal/truancy using it (information technology)
JP2008262430A (en) * 2007-04-13 2008-10-30 Aomoriken Kogyo Gijutsu Kyoiku Shinkokai Attendance/absence data display method for supporting inference for attendance/absence action dominant psychology of student
JP2009100789A (en) * 2007-10-19 2009-05-14 Konami Digital Entertainment Co Ltd User management system, user management method and user management program
US20140047051A1 (en) * 2011-09-30 2014-02-13 Tencent Technology (Shenzhen) Company Limited Method and system for sending prompt information to social networking services community users
JP2013198541A (en) * 2012-03-23 2013-10-03 Namco Bandai Games Inc Server system, program, information storage medium, and electronic instrument

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6485764B1 (en) * 2018-05-02 2019-03-20 株式会社Poper Classroom management server, classroom management system, classroom management method and program

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