KR20210100564A - Method, apparatus and program for providing study group matching service - Google Patents

Abstract

A method for providing a study matching service is disclosed. A computer-implemented method comprises the steps of: providing online learning content to a user; monitoring the learning process of a user for the provided online learning content; determining the learning characteristics of the user based on the monitoring result; and recommending a study group matching the learning characteristics of the user.

Description

Study matching service method, device and program {METHOD, APPARATUS AND PROGRAM FOR PROVIDING STUDY GROUP MATCHING SERVICE}

The present invention relates to a method of providing a study matching service.

Since the establishment of the modern education system, until now, it has been common for all students to learn what they are learning from a teacher who is a 'sage in the front'.

However, in recent years, rather than gathering in the same physical space and learning from the 'wise man in front', education to learn from the 'guide standing next to' has been activated. Currently, the largest education-related companies in Korea are all online lecture sites.

Nevertheless, hagwons do not disappear because they are not managed, and online lectures will not be able to completely replace hagwons. As a result, study rooms related to self-directed learning were created.

In the case of civil service exams, there are many people who want to study together even though adults who take a very important exam within a year are candidates. It may be difficult to achieve the goal due to lack of willpower alone, so even with good materials, we are trying to supplement it by studying the areas that are difficult to meet alone.

Publication No. 10-2018-0037079, published on April 11, 2018

The problem to be solved by the present invention is to provide a study matching service providing method.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

A study matching service providing method according to an aspect of the present invention for solving the above-described problems includes the steps of providing online learning content to a user, monitoring the user's learning process for the provided online learning content, the monitoring Based on the results, determining the learning characteristics of the user and including the steps of recommending a study group matching the learning characteristics of the user, including a study matching service providing method.

In addition, the step of determining the learning characteristics comprises the steps of determining the learning amount of the user, determining the learning level of the user, and determining the learning pattern of the user, recommending the study group may include recommending one or more study groups matching the learning amount, learning level and learning pattern of the user.

In addition, the monitoring includes monitoring the concentration of the user for each time period, and the determining of the learning pattern includes determining a warm-up time required for the user to concentrate and the user continuously It may include the step of determining the continuous concentration time to concentrate.

In addition, the monitoring of the concentration includes: monitoring the user's problem-solving speed and the user's correct answer rate for each time period; and calculating the user's concentration based on the user's problem-solving speed and the user's correct answer rate. may include.

In addition, obtaining information about a study group including one or more users, obtaining a learning goal of the study group, providing online learning content corresponding to the learning goal to users included in the study group Step, obtaining the learning results of the users included in the study group, to each of the users included in the study group, may further include the step of sharing the learning results of other users included in the study group.

In addition, the step of providing online learning content to users included in the study group includes generating multiple-choice questions and providing the generated multiple-choice questions to users included in the study group, the The step of generating a multiple-choice item includes: obtaining a query for generating a multiple-choice item and a plurality of options for the query; obtaining difficulty information for each of the plurality of options; determining the difficulty of the multiple-choice item and determining two or more options to be included in the multiple-choice question among the plurality of options according to the step and the difficulty of the multiple-choice question.

In addition, the step of generating the multiple-choice question is to be provided to each of the users included in the study group according to the step of determining the learning level of the users included in the study group, the learning level of the users included in the study group Determining the difficulty of multiple-choice questions and generating multiple-choice questions to be provided to each of the users included in the study group according to the determined difficulty level, the multiple-choice questions provided to users included in the study group are all the same And, it may include a step, characterized in that the type of option corresponding to the query is different according to each difficulty level.

In addition, the step of obtaining the learning results of the users included in the first study group, storing the learning process for the first online learning content of the first user included in the first study group, the learning process is the The first online learning process by the first user using the stored learning process and a process in which the first user solves the problems included in the first online learning content, including information on the required time and correct answer Information for generating a ghost for the content, wherein the ghost is information for displaying a process by which the first user solves the problems included in the first online learning content and whether the answer is correct according to the time required for solving each problem of the first user Including, further comprising a step, and the step of sharing the learning results, when the second user included in the first study group starts learning using the first online learning content, the first 1 The method may further include displaying the ghost of the first user together with the online learning content.

Study matching service providing apparatus according to an aspect of the present invention for solving the above-described problems, a memory for storing one or more instructions, and a processor for executing the one or more instructions stored in the memory, the processor is the one By executing the above instructions, the study matching providing method according to the disclosed embodiment is performed.

The study matching service providing program according to an aspect of the present invention for solving the above-described problems is combined with a computer that is hardware, and stored in a computer-readable recording medium to perform the study matching providing method according to the disclosed embodiment do.

Other specific details of the invention are included in the detailed description and drawings.

According to the disclosed embodiment, by matching the study group based on the users' usual learning patterns, it is possible to conveniently receive study matching without a separate action, as well as to form a study group in which the learning patterns are well suited to each other. There is this.

In addition, even after the study group is formed, it is managed online, and by performing learning through the online platform, it is easy to check the amount of learning with each other, which has the effect of enabling efficient study learning.

Effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a diagram illustrating a system according to a disclosed embodiment.
2 is a flowchart illustrating a method of providing a study matching service according to an embodiment.
3 is a flowchart illustrating a method of recommending a study group by determining a user's learning characteristics according to an embodiment.
4 is a flowchart illustrating a method of determining a user's learning pattern according to an embodiment.
5 is a flowchart illustrating a concentration monitoring method according to an embodiment.
6 is a flowchart illustrating a study group management method according to an embodiment.
7 is a flowchart illustrating a method for generating and providing online learning content according to an embodiment.
8 is a flowchart illustrating a method of generating multiple-choice questions for learning of a study group according to an embodiment.
9 is a flowchart illustrating a method of providing study group learning through online according to an embodiment.
10 is a diagram illustrating an example of generating multiple-choice questions.
11 is a diagram illustrating an example of a method of assisting study group learning by using a ghost.
12 is a block diagram of an apparatus according to an embodiment.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only these embodiments allow the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully understand the scope of the present invention to those skilled in the art, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. As used herein, the singular also includes the plural unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, and "and/or" includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various elements, these elements are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first component mentioned below may be the second component within the spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless specifically defined explicitly.

As used herein, the term “unit” or “module” refers to a hardware component such as software, FPGA, or ASIC, and “unit” or “module” performs certain roles. However, “part” or “module” is not meant to be limited to software or hardware. A “unit” or “module” may be configured to reside on an addressable storage medium or to reproduce one or more processors. Thus, by way of example, “part” or “module” refers to components such as software components, object-oriented software components, class components and task components, processes, functions, properties, Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays and variables. Components and functionality provided within “parts” or “modules” may be combined into a smaller number of components and “parts” or “modules” or as additional components and “parts” or “modules”. can be further separated.

In this specification, a computer means all types of hardware devices including at least one processor, and may be understood as encompassing software configurations operating in the corresponding hardware device according to embodiments. For example, a computer may be understood to include, but is not limited to, smart phones, tablet PCs, desktops, notebooks, and user clients and applications running on each device.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a diagram illustrating a system according to a disclosed embodiment.

Referring to FIG. 1 , the system includes a server 100 , a user terminal 200 , and a plurality of other user terminals 300 .

The server 100, the user terminal 200, and the plurality of other user terminals 300 according to the disclosed embodiment may be a type of the above-described computer, but is not limited thereto. For example, the server 100 is a cloud server. may mean, but is not limited thereto.

In the disclosed embodiment, the user terminal 200 receives the online learning content provided from the server 100 , acquires a learning performance result based on the interaction with the user, and transmits it to the server 100 .

In the disclosed embodiment, the type of online learning content is not limited, and for example, the online learning content may include, but is not limited to, Internet lectures, slides, multiple-choice questions, and multiple-choice questions.

Accordingly, the user terminal 200 may view, read, or solve problems on the online learning content provided from the server 100 , and in the process of the user performing learning using the online learning content, the user terminal 200 ), the collected information may be transmitted to the server 100 .

The information collected and transmitted may include, but is not limited to, the user's learning time, whether or not to take a break during learning, and the break time, the time required for solving the problem and the result of solving the problem.

The server 100 may obtain information about each user by extracting not only subjects and contents that each user learns, but also patterns and characteristics in which each user performs learning based on information collected from user terminals. .

The server 100 creates a study group including one or more users based on the obtained information, and may recommend the study group to other users matching the study group.

In addition, when a study group is formed through this, the server 100 provides learning content for group learning of the study group, and records information that study members perform learning according to the study group to manage the study. .

In addition, the server 100 may generate and provide learning content of different difficulty according to the learning progress of the study group and the level of each member, and in this process, different difficulty levels to enable common learning among study group members It is characterized by matching the contents of the learning content with

Hereinafter, a study matching service providing method according to the disclosed embodiment will be described in detail with reference to the drawings.

Each step described in this specification is described as being performed by a computer, but the subject of each step is not limited thereto, and at least a portion of each step may be performed in different devices according to embodiments.

Hereinafter, the term “computer” may refer to the server 100 illustrated in FIG. 1 , but is not limited thereto. At least some or all of the steps described below may be performed not only by the server 100 but also by other subjects, but is not limited thereto.

2 is a flowchart illustrating a method of providing a study matching service according to an embodiment.

In step S110, the computer provides the online learning content to the user.

In the disclosed embodiment, the type of online learning content is not limited, and for example, the online learning content may include, but is not limited to, Internet lectures, slides, multiple-choice questions, and multiple-choice questions.

In addition, the learning target of the online learning content is not limited, and online learning content for all kinds of learning such as entrance exams, language studies, public examinations, and civil service exams can be created and provided.

In step S120, the computer monitors the learning process of the user for the provided online learning content.

For example, the user's learning process for online learning content may include information on the user's viewing time, speed, stop, and stop time of the provided Internet lecture.

In addition, the user's learning process for the online learning content may include the user's residence time per page for the provided slides, the number of learned pages, and a response to user interaction included in the page.

In addition, the user's learning process for the learning content may include information about the user's solution time for the provided problems, the number of solved problems, and the answers entered by the user and the noon of the answers, but is limited thereto no.

In step S130, the computer determines the learning characteristics of the user based on the monitoring result.

For example, the computer may not only acquire information on a subject, subject, progress, etc. that the user learns, but may also collect information corresponding to various learning patterns of the user.

In step S140, the computer recommends a study group matching the learning characteristics of the user.

In one embodiment, the computer can recommend a study group including other users with the same subject, subject and progress as the user, as well as searching for a study group including users matching the user's various learning patterns and By recommending, the user can recommend a study group suitable for performing learning together.

3 is a flowchart illustrating a method of recommending a study group by determining a user's learning characteristics according to an embodiment.

In the step of determining the above-described learning characteristics, the computer may perform the step (S210) of determining the learning amount of the user.

In the disclosed embodiment, the amount of learning may include, but is not limited to, a user's learning time and an amount of online learning content learned by the user.

In addition, the computer may perform the step (S220) of determining the learning level of the user.

In the disclosed embodiment, the learning level may mean the user's ability in a specific learning field, but is not limited thereto. For example, the learning level may be determined based on the amount of learning of the user, the learning stage in which the user is learning, the user's test result, and the like, but is not limited thereto.

In addition, the computer may perform the step of determining the user's learning pattern (S230).

In the disclosed embodiment, the user's learning pattern may include various factors such as a time period for the user to learn, a time for continuing learning, a timing for increasing concentration during learning, and the like, but is not limited thereto.

In the step of recommending the study group described above, the computer may perform the step (S240) of recommending one or more study groups matching the learning amount, learning level and learning pattern of the user.

That is, the computer may recommend to the user a study group having a characteristic matching the user, or a study group including a user having a characteristic matching the user.

According to an embodiment, the computer may provide information about one or more study groups to the user, and the user may select one of the recommended study groups and apply for a subscription.

In this case, the group members of the study group are provided with information including the learning amount, learning level, and learning pattern of the user who applied for subscription, based on this, it is possible to determine whether to approve the user's study group subscription.

The computer can obtain information about the study group to which the user is subscribed, and monitor and manage the learning of the study group.

4 is a flowchart illustrating a method of determining a user's learning pattern according to an embodiment.

In the disclosed embodiment, the computer collects information on different learning habits for each user, but collects information on the user's concentration level in order to create a study group among users whose concentration patterns are matched between different users. there is.

In one embodiment, in the step of the above-described computer monitoring the user's learning, the computer may perform the step (S310) of monitoring the concentration of the user for each time period.

The user's concentration may be determined based on various methods, and according to an embodiment, the computer determines whether the user engages in an action other than learning (for example, Internet surfing or messenger chatting, etc.) through a device using online learning. It is possible to determine the user's concentration level based on the time it takes to obtain information about the online learning content or to perform learning according to each online learning content, but is not limited thereto. A specific example of determining the user's concentration level will be described later. do.

In addition, in the step of determining the above-described learning pattern, the computer may perform the step (S320) of determining the warm-up time required for the user to concentrate.

For example, the warm-up time may mean a time required for the user to achieve a concentration level greater than or equal to a preset reference value after starting learning.

In addition, the computer may perform the step (S330) of determining the continuous concentration time during which the user can continuously concentrate.

For example, the continuous concentration time may mean a time during which the user maintains the concentration level greater than or equal to a preset reference value.

Furthermore, when the concentration of the user falls below the preset reference value, the computer may determine the concentration recovery time, which means the time it takes to recover the concentration of the preset reference value or more again.

5 is a flowchart illustrating a concentration monitoring method according to an embodiment.

In the step of monitoring the concentration level described above, the computer may perform a step (S410) of monitoring the user's problem-solving speed for each time period.

In one embodiment, the computer may monitor the user's problem solving, but monitor the speed the user takes to solve the problem by time period. For example, the computer measures the amount of time a user takes to solve a problem for each problem, and can calculate an average problem-solving speed within the corresponding time unit in a preset time unit (eg, 10 minutes). .

In addition, the computer may perform a step (S420) of monitoring the user's correct rate for each time period.

For example, the computer may determine whether the questions solved by the user are correct or not, and may calculate the percentage of correct answers to the questions within the corresponding time unit in a preset time unit (eg, 10 minutes).

In addition, the computer may perform the step of calculating the concentration of the user based on the user's problem solving speed and the user's correct rate (S430).

For example, the computer may calculate the user's concentration for each time period based on the user's problem-solving speed and correct answer rate, which may be performed as an absolute evaluation based on a preset reference value, and upper and lower limits of the user's concentration. It can also be performed as a relative evaluation in the range between.

For example, it can be determined that the user's concentration level is high as the user's problem solving speed is faster and the correct answer rate is higher. However, since the difficulty level may be different for each problem, the computer may calculate the user's concentration level by giving a predetermined weight to the problem-solving speed and the correct answer rate measured based on the difficulty for each problem.

Accordingly, the computer can acquire the user's concentration pattern, and the user and the concentration pattern match or search for similar users within a predetermined range to form a study group with the user.

6 is a flowchart illustrating a study group management method according to an embodiment.

In step S510, the computer may obtain information about the study group including one or more users.

For example, the computer may obtain information about the study group when the user applies for a subscription according to the above-described process, and there is a study group for which the subscription is approved.

The information on the study group may include information on the members of the study group, and may include information on the learning performed by the study members individually or at the study group level.

The computer provides online learning content according to the information on the study group members, and monitors the learning process accordingly.

In step S520, the computer may obtain the learning goal of the study group.

For example, the computer may acquire a long-term goal of a study group, as well as a learning goal to be completed within a predetermined period (eg, one week). For example, the computer may acquire information to solve 100 questions of a specific subject within a week in a study group.

According to an embodiment, the computer may present short-term learning goals to study group members based on the study group's long-term learning goals and learning progress. That is, the computer may generate a learning schedule for achieving a long-term goal, and thus may generate and provide short-term learning goals according to the learning schedule to users.

In step S530, the computer may provide the online learning content corresponding to the learning goal to the users included in the study group.

In step S540, the computer may obtain the learning results of the users included in the study group.

In step S550, the computer may share the learning results of other users included in the study group to each of the users included in the study group.

That is, the computer can be understood as a subject that operates the online learning platform, and users perform learning using the learning contents provided through the platform, and the computer responds to the user's learning pattern in the process of performing the learning. It collects information about and recommends suitable study groups for users accordingly.

After that, the learning conducted in the study group also proceeds through the online learning content provided through the computer, so there is no need for separate management, authentication, or testing between users, and by allowing the learning records to be automatically accumulated and shared, study group members It can prevent the liver from deceiving the amount of learning.

In addition, it is possible to provide competition according to the learning results within a study group or between study groups, and according to an embodiment, it is possible to motivate users to learn by paying compensation according to the competition or posting the competition results. .

7 is a flowchart illustrating a method for generating and providing online learning content according to an embodiment.

In the step of providing online learning content to users included in the study group described above, the computer may generate multiple-choice questions and provide the generated multiple-choice questions to users included in the study group.

Specifically, in the step of generating the multiple-choice item described above, the computer may perform the step of obtaining a query for generating the multiple-choice item and a plurality of options for the query (S610).

For example, the computer may obtain information on multiple-choice questions from the question bank database, and the database includes a query corresponding to each multiple-choice question, and at least one correct answer and a plurality of incorrect answers to the question. It may include options.

The computer may generate multiple-choice questions by selecting a question for each multiple-choice question and options including one or more correct and incorrect answers therefrom.

In addition, the computer may perform a step ( S620 ) of obtaining difficulty information for each of the plurality of options.

In one embodiment, the difficulty information for each option includes the difficulty information labeled by the author who wrote each option, and the user's incorrect answer rate for each option (for example, the more frequently selected as an incorrect answer, the more difficult the option can be determined based on).

In addition, the computer may perform the step of determining the difficulty of the multiple-choice question ( S630 ).

For example, the computer may determine the difficulty of the multiple-choice item based on the learning state of the user who will provide the generated multiple-choice item.

Also, the computer may perform the step of determining two or more options to be included in the multiple-choice question among the plurality of options according to the difficulty of the multiple-choice question ( S640 ).

For example, the computer may include more high-difficulty options as the difficulty of multiple-choice questions increases, and may include more low-difficulty choices as the difficulty level decreases.

According to an embodiment, the difficulty level of the option may be simply divided into “difficult” and “normal”. An option classified as “difficult” may mean an option selected by users with an incorrect answer more than a preset number of times, but is not limited thereto.

In one embodiment, when there are five multiple-choice questions, if there is only one correct answer, the difficulty of the multiple-choice question is divided into five levels depending on how many options classified as “difficult” are included in the remaining four incorrect answers. may be distinguished, but is not limited thereto.

Referring to FIG. 10 , an example of generating multiple-choice questions is illustrated.

For the multiple-choice problem 400 shown in FIG. 10, the computer selects and places the query 410, and selects and places a plurality of options 420 to 460 corresponding thereto, thereby generating the multiple-choice problem 400. there is.

Also, the computer may generate a multiple-choice question including two or more determined options (S650), and provide the generated multiple-choice question to the user (S660).

The difficulty information of each option may be updated based on the user's feedback on the provided multiple-choice question (eg, whether the answer is correct and information on the option selected as an incorrect answer in case of an incorrect answer, etc.).

* FIG. 8 is a flowchart illustrating a method of generating multiple-choice questions for study of a study group according to an embodiment.

In the step of generating the multiple choice questions described above, the computer may perform a step (S710) of determining the learning level of the users included in the study group.

In addition, the computer may perform a step (S720) of determining the difficulty of multiple-choice questions to be provided to each of the users included in the study group according to the learning level of the users included in the study group.

In addition, the computer generates multiple-choice questions to be provided to each of the users included in the study group according to the determined difficulty level, but the multiple-choice questions provided to the users included in the study group are all the same, and each difficulty level According to the method, step S730 may be performed, which is characterized in that the type of option corresponding to the query is changed.

In other words, for study group learning, it is necessary to solve, compare, and discuss the same problem with each other. According to the disclosed embodiment, a study group is formed among users whose learning level and learning pattern are matched with each other.

However, there may be a gap in learning level within each other, and in this case, learning efficiency may decrease when learning is performed on the same problem.

Accordingly, the computer generates and provides multiple-choice questions including the same query to all users, but by making the choices different, the difficulty of the multiple-choice questions can be adjusted according to the difficulty of the options.

In this case, all study group members solve the same problem and derive the same correct answer, so comparison and discussion are possible. This has the effect of enabling common learning and learning by level at the same time.

9 is a flowchart illustrating a method of providing study group learning through online according to an embodiment.

In the above-described step of obtaining the learning results of the users included in the first study group, the computer stores the learning process for the first online learning content of the first user included in the first study group, the learning process may perform step S810, including information on a process, required time, and correct answer for the first user to solve the problems included in the first online learning content.

That is, the computer may collect information according to the actual required time as if the first user records a process of learning by using the first online learning content.

In addition, the computer generates a ghost for the first online learning content of the first user by using the stored learning process, wherein the ghost is a method in which the first user solves problems included in the first online learning content. Step S820 may be performed, which includes information for displaying the process and the correct answer according to the time required for solving each problem of the first user.

That is, the computer may generate a ghost capable of displaying a state in which the first user performs learning by using the first online learning content. Here, the ghost may mean displaying the learning state of the first user based on information on which the first user has previously performed learning, even in a situation in which the first user is not actually learning.

In addition, in the step of sharing the above-described learning results, when the second user included in the first study group starts learning by using the first online learning content, the computer uses the first online learning content together with the A step of displaying the ghost of the first user ( S830 ) may be performed.

That is, by displaying the ghost from which the first user has been learning on the learning screen of the second user, the second user can perform learning according to the learning speed of the first user, and also allows the second user to perform the learning according to the learning speed of the first user. It is possible to motivate people to continuously focus on learning so as not to fall behind the learning speed of

Referring to FIG. 11 , an example of a method of assisting study group learning by using a ghost is illustrated.

Referring to FIG. 11 , a multiple-choice question 500 and a query 510 and a plurality of options 520 to 560 included in the multiple-choice question 500 are illustrated.

Also, at the bottom of FIG. 11, ghosts 570 and 580 of other users A and B are shown. The ghost illustrated in FIG. 11 is provided as an example, and the display form and method of the ghost according to the disclosed embodiment is not limited thereto.

That is, referring to FIG. 11 , a user is currently solving problem 13, and at the same time, another user A is also solving problem 13, and another user B is solving a ghost problem 15 is displayed on the screen. .

User A and User B have already performed learning including solving the problem in the past, and the process is recorded by the computer.

The recorded information is generated as a ghost reflecting the actual learning time and displayed on the learning screen of another user as shown in FIG. 11, and the user can adjust his or her learning pace by referring to the displayed ghost.

Referring to FIG. 11 , the ghost may also display whether other users have correct answers to each question, but is not limited thereto.

According to an embodiment, when there is no learning record of another user, the computer may generate a virtual ghost based on the existing learning pattern of the other user.

For example, based on the learning level and problem-solving speed of other users, the computer virtually generates another user's problem-solving time and correct answer for each of the problems the user wants to solve, and based on this, a ghost is displayed to the user. By doing so, it is possible to display a virtual ghost for the user even when there is no other user who has previously solved the problem.

According to an embodiment, the virtual ghost may be generated to solve a problem a little faster than the actual ability of other users and to show a slightly higher correct answer rate. In this case, the user can be expected to focus more on learning to catch up with the generated virtual ghost.

In addition to the above-described ghost, the ghost may be generated, provided, and utilized in various ways.

In an embodiment, a plurality of users may start solving problems together at the same time, and the real-time problem solving status of each user may be shared among different users as shown in FIG. 11 .

In this case, the service according to the disclosed embodiment can add fun of competition between users through gamification.

For example, each user may be provided with an item, and the item may include an item to disturb other users, an item to provide a hint of a problem to be solved by the user, or an item to lower the difficulty level.

For example, when using a disturbing item, the problem solving screen of the targeted user or other users is not displayed for a predetermined period of time, changing the type of options in the current problem being solved, changing the order of the options, etc. obstruction may be inflicted. The number of such items may be limited.

In addition, if you use an item, you can provide the effect of temporarily increasing the speed of problem solving by selecting two or three correct candidates from the problem you are solving, or by changing the option to a slightly easier one.

According to an embodiment, even while solving a problem with a Ghost, if a disturbing item is used, the Ghost's problem solving speed may be delayed by several seconds compared to the actual problem. You can solve problems while having the same pleasure.

In an embodiment, the ghost may be generated based on the user's existing problem-solving record. That is, the ghost is created based on one's average speed and ability to solve problems, and can be used to show one's usual learning patterns. Accordingly, the user can be stimulated in the same pattern as usual and perform consistent learning.

Also, the user may solve the same problem multiple times. When the number of readings increases in this way, the computer may display a ghost generated based on the user's immediately preceding solving record.

Accordingly, the user can check whether the problem solving speed and the correct answer rate are improving as the number of readings increases.

In an embodiment, the computer may increase the difficulty of the options included in each question as the number of readings by the user increases. In addition, the computer can transform the multiple-choice question of the form of “choosing one correct answer” into a problem of “choosing multiple correct answers” when the number of readings increases, or a problem of “choosing multiple incorrect answers” by varying the noon choice, By changing the number of correct answers according to the number of readings, the user cannot simply memorize the answer to solve the problem, and it is possible to learn in depth about each problem.

In one embodiment, the computer may generate incorrect answer notes by collecting only the questions that study members are wrong, and share it with each study member.

In addition, the computer may generate and share incorrect answer notes by collecting only the wrong questions of a plurality of study members, and the number of incorrect answers for generating incorrect answer notes may be determined according to the selection of study members.

12 is a block diagram of an apparatus according to an embodiment.

The processor 102 may include one or more cores (not shown) and a graphic processing unit (not shown) and/or a connection path (eg, a bus, etc.) for transmitting and receiving signals to and from other components. .

The processor 102 according to an embodiment performs the method described with reference to FIGS. 1 to 11 by executing one or more instructions stored in the memory 104 .

On the other hand, the processor 102 is a RAM (Random Access Memory, not shown) and ROM (Read-Only Memory: ROM) for temporarily and / or permanently storing a signal (or data) processed inside the processor 102. , not shown) may be further included. In addition, the processor 102 may be implemented in the form of a system on chip (SoC) including at least one of a graphic processing unit, a RAM, and a ROM.

The memory 104 may store programs (one or more instructions) for processing and controlling the processor 102 . Programs stored in the memory 104 may be divided into a plurality of modules according to functions.

The steps of a method or algorithm described in relation to an embodiment of the present invention may be implemented directly in hardware, as a software module executed by hardware, or by a combination thereof. A software module may contain random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any type of computer-readable recording medium well known in the art to which the present invention pertains.

The components of the present invention may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium. The components of the present invention may be implemented as software programming or software components, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, including C, C++ , Java, assembler, etc. may be implemented in a programming or scripting language. Functional aspects may be implemented in an algorithm running on one or more processors.

As mentioned above, although embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains can realize that the present invention can be embodied in other specific forms without changing its technical spirit or essential features. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

100 : server
200: user terminal
300: other user terminals

Claims (7)

A method performed by a computer comprising:
providing online learning content to users;
monitoring the user's learning process for the provided online learning content;
determining a learning characteristic of the user based on the monitoring result;
recommending a study group matching the user's learning characteristics;
obtaining information about a study group including a plurality of users;
obtaining a learning goal of the study group;
providing online learning content corresponding to the learning goal to a plurality of users included in the study group; and
obtaining a learning result from a plurality of users included in the study group, and sharing the learning results of other users included in the study group to each of the plurality of users included in the study group; includes,
The step of providing online learning content to a plurality of users included in the study group,
generating multiple-choice questions; and
providing the generated multiple choice questions to a plurality of users included in the study group; including,
The step of generating the multiple-choice question is,
determining the learning level of a plurality of users included in the study group;
Determining the difficulty of multiple-choice questions to be provided to each of the plurality of users included in the study group according to the learning level of each of the plurality of users included in the study group; and
Multiple-choice questions to be provided to each of the plurality of users included in the study group are generated according to the determined difficulty level, but the multiple-choice questions provided to the plurality of users included in the study group are all the same, according to each difficulty characterized in that the types of options corresponding to the query are changed; includes,
The step of generating the multiple-choice question is,
obtaining a query for generating a first multiple choice item and a plurality of options for the query;
acquiring difficulty information for each of the plurality of options, wherein the difficulty information for each of the plurality of options includes difficulty difficulty and normal difficulty;
According to the difficulty of the first multiple-choice question, two or more options to be included in the multiple-choice question among the plurality of options are determined, wherein the two or more options include one correct answer option and at least one incorrect answer option, The step of determining the number of difficult difficulty options included in the at least one incorrect answer option based on the difficulty level of the one-choice question; including,
The step of providing the generated multiple choice questions to a plurality of users included in the study group,
Providing the first multiple choice question to a plurality of users included in the study group, counting the number of readings of the first multiple choice question for each of a plurality of users included in the study group, proportional to the counted number of readings and adjusting the difficulty of the first multiple-choice question to be provided to each of the plurality of users included in the study group,
The step of sharing the learning results is,
In response to a plurality of users included in the study group being provided with the first multiple-choice question at the same time, the real-time problem solving status of a plurality of users included in the study group to other users included in the study group providing; and
In response to receiving an input of item use from any one of a plurality of users included in the study group, a hint of the first multiple-choice question is provided to any one of the users, or the first provided to any one of the users Including the step of adjusting the difficulty of multiple-choice questions or applying an effect that prevents other users from solving problems other than the one user,
How to provide study matching service. According to claim 1,
The step of determining the learning characteristics,
determining the learning amount of the user;
determining the learning level of the user; and
determining the user's learning pattern; including,
The step of recommending the study group,
Recommending one or more study groups matching the learning amount, learning level and learning pattern of the user; containing,
How to provide study matching service. 3. The method of claim 2,
The monitoring step is
monitoring the concentration of the user for each time period; including,
The step of determining the learning pattern,
determining a warm-up time required for the user to concentrate; and
determining a continuous concentration time during which the user can continuously concentrate; containing,
How to provide study matching service. 4. The method of claim 3,
The step of monitoring the concentration,
monitoring the user's problem solving speed and the user's correct rate for each time period; and
calculating the concentration of the user based on the user's problem solving speed and the user's correct answer rate; containing,
How to provide study matching service. According to claim 1,
The step of sharing the learning results is,
Storing a learning process for a first online learning content of a first user among a plurality of users included in the study group, wherein the learning process is a process in which the first user solves problems included in the first online learning content , including information on the required time and correct answer, a step;
A ghost for the first online learning content of the first user is generated using the stored learning process, wherein the ghost is a process in which the first user solves problems included in the first online learning content and whether the answer is correct. which includes information for displaying the first user's problem-specific solution according to the required time; and
displaying a ghost of the first user together with the first online learning content when a second user from among the plurality of users included in the study group starts learning using the first online learning content; further comprising,
How to provide study matching service. a memory storing one or more instructions; and
a processor executing the one or more instructions stored in the memory;
The processor by executing the one or more instructions,
An apparatus for performing the method of claim 1 . A computer program stored in a computer-readable recording medium in combination with a computer, which is hardware, to perform the method of claim 1.

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