KR102596567B1 - Method, server and program of real-time peer assessment and collaborative learning based on game theory to improve collective intelligence - Google Patents

Abstract

The present invention relates to a peer evaluation and cooperative learning method for developing collective intelligence. The instructor-centered unilateral knowledge transfer education method, which is difficult to solve in the current education system, is transformed into a team-centered, decentralized education method that allows mutual cooperative learning without a separate instructor. Switching to an educational method has the effect of improving the collective intelligence of learners and complementing the shortcomings of cooperative learning.

Description

Game theory-based peer evaluation and collaborative learning method, server and program for developing collective intelligence {Method, server and program of real-time peer assessment and collaborative learning based on game theory to improve collective intelligence}

The present invention relates to a game theory-based peer evaluation and cooperative learning method for developing collective intelligence.

In order to increase the collective intelligence required in the future society based on convergence knowledge, a platform that allows for team project-based cooperative learning that can break away from the current teaching and learning method that delivers knowledge centered on the instructor and express collective intelligence is needed.

As in the past, when collaborative learning is performed based on team projects and the instructor evaluates the team, individual team members find themselves in a situation where their evaluation depends on the actions or abilities of other team members, and they are responsible for the team project. Because they are not properly compensated for the time or effort they put into it, they feel dissatisfied with the team project or the effectiveness of cooperative learning is significantly reduced.

Therefore, there is a need for a platform that can solve these problems, but the technology to provide this has not yet been disclosed.

Registered Patent Publication No. 10-2217783, (2021.02.15)

The present invention, in order to solve the problems described above, is intended to assign teachers and learners within a plurality of evaluation channels to a plurality of teams and perform projects for each team on educational content.

In addition, the present invention seeks to calculate the collective intelligence contribution for each teacher and learner in the evaluation channel based on the project performance results of each team and the peer evaluation results received from each teacher and learner, and provide coin rewards based on this.

In addition, the present invention seeks to request a screening for collective intelligence certification and issue a certificate if the number of coins provided to a specific teacher-learner exceeds the number set as qualification for collective intelligence certification.

In addition, the present invention seeks to calculate the collective intelligence contribution of each teacher and learner based on multiple peer evaluations and provide this to the teachers and learners within the evaluation channel.

In addition, the present invention uses a blockchain-based content sharing platform that includes smart contract functions and important characteristics of shareability, transparency, integrity, and reliability for the purpose of increasing the collective intelligence of learners. We introduce a cooperative learning method in which various learners are randomly divided into several teams to carry out projects for each team, and coin rewards and certificates are issued according to the results of the competition. The disadvantages of cooperative learning are that it is difficult to internally motivate learning and promote learning activities. We plan to use a real-time peer evaluation method based on game theory to complement and consider the strategic situation of the peer evaluator.

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

The peer evaluation and collaborative learning method for developing collective intelligence according to an embodiment of the present invention to solve the above-mentioned problems is a method performed by a server, and is a method that randomly selects a plurality of professors and learners within a blockchain-based content sharing platform. Selecting and allocating each to a plurality of evaluation channels; Allocating each teacher and learner belonging to the plurality of evaluation channels to a plurality of teams; -The plurality of teams are each assigned within the plurality of evaluation channels and include a content production team, a content evaluation team, and a problem-solving team. -Produced by the first teacher and learner in the content production team of a specific evaluation channel among the plurality of evaluation channels. receiving educational content; Receiving project performance results for each team from a plurality of teams in the specific evaluation channel with respect to the received educational content; Requesting a peer evaluation of team contribution from each professor and learner belonging to a plurality of teams within the specific evaluation channel, and receiving a result of the peer evaluation from the terminal of each professor and learner in response to the request; Calculating a collective intelligence contribution for each of a plurality of professors and learners belonging to the specific evaluation channel based on the received project performance results for each team and the received peer evaluation results; and providing a coin reward to each of the plurality of teachers and learners belonging to the specific evaluation channel based on the calculated results.

In addition, the server provides a predetermined coin reward to the evaluation channel based on the number of uses of the educational content at each preset time period, and provides the provided coin reward based on the collective intelligence contribution for each of the plurality of professors and learners. can be distributed.

Additionally, requesting screening for collective intelligence certification for professors and learners whose coins exceed the number set for collective intelligence certification qualifications; And if the screening result passes, it may further include issuing a collective intelligence certificate to the teacher-learner.

In addition, the collective intelligence contribution calculation step calculates the collective intelligence contribution for each of a plurality of teachers and learners belonging to the evaluation channel based on the results of two or more peer evaluations received at different times, and the calculated collective intelligence Contributions can be disclosed to multiple teachers and learners belonging to the above evaluation channel.

In addition, the server accumulates and stores first evaluation feedback information for the educational content received from the terminal of at least one second teaching learner in the content evaluation team of the specific evaluation channel, and stores the accumulated first evaluation feedback. If the information satisfies the preset first disclosure condition, the educational content can be fully disclosed on the content sharing platform.

In addition, when an upgraded version of the disclosed educational content is received from the terminal of at least one third teacher-learner subscribed to the platform, a second version of the upgraded version received from the terminal of the at least one second teacher-learner is received. Evaluation feedback information is accumulated and stored, and when the accumulated second evaluation feedback information satisfies a preset second disclosure condition, the upgraded version is fully disclosed on the content sharing platform.

Additionally, the server may calculate collective intelligence points for the educational content based on a preset collective intelligence evaluation algorithm whenever an upgraded version of the educational content is received.

In addition, the server may provide a predetermined coin according to the collective intelligence contribution to the teacher and learner who contributed to the collective intelligence of the educational content based on the number of uses of the educational content at each preset time period.

In addition, the server may calculate the collective intelligence points based on at least one of the number of upgrades to the educational content, the number of teachers and learners participating in the specific channel, and the number of solutions to problems that occurred with the educational content. there is.

In addition, the server reflects the first evaluation feedback information for the educational content in calculating the collective intelligence contribution of the first teaching learner, the number of upgrade version uploads for the educational content, and the second evaluation for each upgraded version. The evaluation feedback information is reflected in calculating the collective intelligence contribution of the third teacher and learner, and the number of uploads for at least one of the first evaluation feedback information for the educational content and the second evaluation feedback information for the upgraded version is calculated. 2 The collective intelligence contribution of teachers and learners can be reflected in the calculation.

In addition, the peer evaluation and collaborative learning program for developing collective intelligence according to an embodiment of the present invention to solve the above-mentioned problems randomly selects a plurality of professors and learners within a blockchain-based content sharing platform to conduct a plurality of evaluations. The first process to assign each to a channel; a second process for allocating each teacher and learner belonging to the plurality of evaluation channels to a plurality of teams; -The plurality of teams are each assigned within the plurality of evaluation channels and include a content production team, a content evaluation team, and a problem-solving team. -Produced by the first teacher and learner in the content production team of a specific evaluation channel among the plurality of evaluation channels. a third process for receiving educational content; a fourth process for receiving project performance results for each team from a plurality of teams in the specific evaluation channel with respect to the received educational content; A fifth process for requesting a peer evaluation of team contribution from each teacher and learner belonging to a plurality of teams within the specific evaluation channel and receiving the results of the peer evaluation from the terminal of each teacher and learner in response to the request; A sixth process for calculating a collective intelligence contribution for each of a plurality of teachers and learners belonging to the specific evaluation channel, based on the received results; And a seventh process of providing coin rewards to each of a plurality of teachers and learners belonging to the specific evaluation channel is performed based on the calculated results.

In addition, a peer evaluation and cooperative learning server for developing collective intelligence according to an embodiment of the present invention for solving the above-described problems includes at least one processor including one or more cores; A communication unit that communicates with the teacher-learner's terminal; and a database, wherein the processor randomly selects a plurality of professors and learners in a blockchain-based content sharing platform and assigns each to a plurality of evaluation channels, and assigns each professor and learner belonging to the plurality of evaluation channels to a plurality of evaluation channels. Each is assigned to a team, and - the plurality of teams are each allocated within the plurality of evaluation channels and include a content production team, a content evaluation team, and a problem solving team - within the content production team of a specific evaluation channel among the plurality of evaluation channels. 1 Receive educational content produced by a teacher-learner, receive project performance results for each team from multiple teams within the specific evaluation channel for the received educational content, and each professor belonging to multiple teams within the specific evaluation channel Requesting a peer evaluation of team contribution from a learner, receiving the results of the peer evaluation from the terminal of each teaching learner in response to the request, and based on the received results, multiple members belonging to the specific evaluation channel It is characterized by calculating the collective intelligence contribution for each of the professors and learners, and providing coin rewards to each of the plurality of professors and learners belonging to the specific evaluation channel based on the calculated results.

In addition, the processor provides a predetermined coin reward to the evaluation channel based on the number of uses of the educational content at each preset time period, and provides the provided coin reward based on the collective intelligence contribution for each of the plurality of professors and learners. It is characterized by distributing.

In addition, the processor requests a screening for collective intelligence certification for professors and learners whose coins exceed the number set as the collective intelligence certification qualification, and if the screening result passes, provides a collective intelligence certificate to the professors and learners. Issued.

In addition, the processor calculates the collective intelligence contribution for each of a plurality of professors and learners belonging to the evaluation channel based on two or more peer evaluation results received at different times, and evaluates the calculated collective intelligence contribution. It is characterized by being open to multiple teachers and learners belonging to the channel.

In addition, the processor accumulates and stores first evaluation feedback information for the educational content received from the terminal of at least one second teaching learner in the content evaluation team of the specific evaluation channel, and stores the accumulated first evaluation feedback. If the information satisfies a preset first disclosure condition, the educational content is fully disclosed on the content sharing platform.

Additionally, when an upgraded version of the disclosed educational content is received from the terminal of at least one third teacher-learner subscribed to the platform, the processor is configured to update the upgraded version received from the terminal of the at least one second teacher-learner. The second evaluation feedback information for is accumulated and stored, and when the accumulated second evaluation feedback information satisfies a preset second disclosure condition, the upgraded version is fully disclosed on the content sharing platform.

Additionally, the processor is characterized in that it calculates collective intelligence points for the educational content based on a preset collective intelligence evaluation algorithm whenever an upgraded version of the educational content is received.

In addition, the processor is characterized in that it provides a predetermined coin according to the collective intelligence contribution to the teacher and learner who contributed to the collective intelligence of the educational content based on the number of uses of the educational content at each preset time period.

In addition, the processor calculates the collective intelligence points based on at least one of the number of upgrades to the educational content, the number of teachers and learners participating in the specific channel, and the number of solutions to problems that occurred with the educational content. It is characterized by

In addition, the processor reflects the first evaluation feedback information for the educational content in calculating the collective intelligence contribution of the first teaching learner, the number of upgrade version uploads for the educational content, and a second evaluation for each upgraded version. The evaluation feedback information is reflected in calculating the collective intelligence contribution of the third teacher and learner, and the number of uploads for at least one of the first evaluation feedback information for the educational content and the second evaluation feedback information for the upgraded version is calculated. 2 It is characterized by reflecting the collective intelligence contribution of teachers and learners in the calculation.

In addition to this, another method for implementing the present invention, another system, and a computer-readable recording medium recording a computer program for executing the method may be further provided.

According to the present invention as described above, professors and learners within a plurality of evaluation channels can be assigned to a plurality of teams, and projects for each team can be performed on educational content.

In addition, according to the present invention, based on the project performance results of each team and the peer evaluation results received from each teacher and learner, the collective intelligence contribution for each teacher and learner in the evaluation channel is calculated, and a coin reward is provided based on this. There is.

In addition, according to the present invention, if the number of coins provided to a specific teacher-learner exceeds the number set as a qualification for collective intelligence certification, a certificate for contribution to collective intelligence can be issued by requesting a screening for collective intelligence certification.

In addition, according to the present invention, the collective intelligence contribution of each teacher and learner is calculated based on multiple peer evaluations and provided to the teachers and learners within the evaluation channel, thereby increasing reliability.

In addition, according to the present invention, the instructor-centered unilateral knowledge transfer education method, which is difficult to solve in the current education system, is converted into a team-centered decentralized education method that allows mutual cooperative learning without a separate instructor, thereby increasing the collective intelligence of learners. intelligence) and complement the shortcomings of cooperative learning.

In addition, according to the present invention, the real-time peer evaluation service can be widely used for corporate talent recruitment, personnel evaluation of institutions and companies, etc. in addition to performance evaluations at schools and lifelong education centers, so that it can be used as a global tool with the collective intelligence required in the era of the Fourth Industrial Revolution. It can contribute to nurturing creative talent, developing convergence knowledge, and establishing a lifelong education system in preparation for an aging society.

In addition, according to the present invention, a blockchain-based content sharing platform including smart contract functions and important characteristics of shareability, transparency, integrity, and reliability is used for the purpose of increasing the collective intelligence of learners. We introduce a cooperative learning method in which various learners are randomly divided into several teams to carry out projects for each team, and coin rewards and certificates are issued according to the results of the competition. Cooperative learning is a method of learning that is difficult to internally motivate learning and promote learning activities. By using a real-time peer evaluation method based on game theory to complement the shortcomings and consider the strategic situation of the peer evaluator, it is effective in solving the problems of the prior art.

The 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 description below.

Figure 1 is a schematic diagram of a peer evaluation and cooperative learning system for developing collective intelligence according to an embodiment of the present invention.
Figure 2 is a block diagram of a peer evaluation and cooperative learning system for developing collective intelligence according to an embodiment of the present invention.
Figure 3 is a flowchart of a peer evaluation and cooperative learning method for developing collective intelligence according to an embodiment of the present invention.
Figure 4 is a diagram illustrating calculating the collective intelligence contribution of each teacher and learner participating in the content sharing platform.
Figure 5 is a diagram illustrating receiving educational content produced by a first teacher-learner.
FIG. 6 is a diagram illustrating receiving evaluation feedback information from a content evaluation team regarding the educational content of FIG. 3.
Figure 7 is a diagram illustrating receiving an upgraded version of the educational content of Figure 3 from a third teacher/learner.
FIG. 8 is a diagram illustrating receiving evaluation feedback information from a content evaluation team for the upgraded version of FIG. 5.

The advantages and features of the present invention and methods for achieving them will become clear by referring to the embodiments described in detail below along with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and may be implemented in various different forms. The present embodiments are merely provided to ensure that the disclosure of the present invention is complete and to provide a general understanding of the technical field to which the present invention pertains. It is provided to fully inform the skilled person of the scope of the present invention, and the present invention is only defined by the scope of the claims.

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

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which the present invention pertains. Additionally, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless clearly specifically defined.

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

Figure 1 is a schematic diagram of a peer evaluation and cooperative learning system 10 for developing collective intelligence according to an embodiment of the present invention.

Referring to Figure 1, peer evaluation and cooperative learning for developing collective intelligence according to an embodiment of the present invention are briefly illustrated.

The peer evaluation and collaborative learning system 10 for developing collective intelligence according to an embodiment of the present invention is a system where a specific teacher and learner participating in a content sharing platform creates and uploads educational content, and a plurality of teams within the evaluation channel share their own information. Each team carries out a project, and real-time peer evaluation is conducted in the process.

By going through this series of processes, many teachers and learners discuss and cooperate with each other, which not only improves the quality of educational content, but also helps teachers and learners grow and improve collective intelligence.

In addition, these collective intelligence contributions are rewarded with coins, and teachers and learners who have contributed above a certain level are reviewed and provided with collective intelligence certificates.

In an embodiment of the present invention, collective intelligence may mean collective intelligence. Therefore, in embodiments of the present invention, collective intelligence may be replaced with the word collective intelligence.

In an embodiment of the present invention, content may include a combination of broadcast, video, image, text, and web document, and content may refer to a set of contents.

The collective intelligence formation and compensation method using a blockchain-based educational content sharing platform according to an embodiment of the present invention introduces the concept of mutual direct learning, allowing learners to become instructors and instructors to become learners.

Therefore, in the embodiment of the present invention, lectlearners (lecturer=learner) refers to learners and instructors, and instructors and learners.

The peer evaluation and collaborative learning method for developing collective intelligence according to an embodiment of the present invention breaks away from the current teaching and learning method that transfers knowledge centered on the instructor in order to cultivate talent for a future society based on convergence knowledge, and shares each other's ideas without the instructor. We propose a method of forming collective intelligence in the process of sharing and sharing knowledge, and provide a certain coin based on a smart contract to professors and learners who provide or use content through a blockchain-based educational content sharing platform and to peer evaluators who evaluate the value of the content. We seek to create an educational ecosystem capable of sustainable growth by proposing a method of compensation.

Below, this embodiment will be described in more detail with reference to other drawings.

Figure 2 is a block diagram of a peer evaluation and cooperative learning system 10 for developing collective intelligence according to an embodiment of the present invention.

Referring to FIG. 2, the peer evaluation and collaborative learning system 10 for developing collective intelligence according to an embodiment of the present invention includes a server 100, and the server 100 communicates with the teacher-learner's terminal.

Specifically, in the embodiment of the present invention, the teaching learner includes at least one first teaching learner, at least one second teaching learner, and at least one third teaching learner, but is not limited to this and this is to classify each. It was named for this.

The server 100 includes a processor 110, a communication unit 120, a database 130, and a subscription module 140.

However, in some embodiments, the server 100 may include fewer or more components than those shown in FIG. 2 .

The communication unit 120 communicates with the terminal of the teacher and learner connected to the server 100, and specifically, the terminal of the first teacher and learner (30), the terminal of the second teacher and learner (50), and the terminal of the third teacher and learner ( 70) and communicate with.

The database 130 stores various commands, algorithms, etc. for executing peer evaluation and collaborative learning methods for developing collective intelligence according to an embodiment of the present invention. In addition, information on teachers and learners, educational content, evaluation feedback information, Peer evaluation results, calculated collective intelligence contribution, coin reward details, etc. are stored.

The registration module 140 performs a membership registration process for teachers and learners in response to membership registration requests from teachers and learners participating in a blockchain-based content sharing platform, and encrypts the membership registration results and stores them in the database 130. .

The processor 110 is responsible for controlling the components within the server 100, and performs each step by executing commands and algorithms within the database 130 to perform peer evaluation and collaborative learning to develop collective intelligence according to an embodiment of the present invention. Execute the method.

Below, with reference to the flowchart of FIG. 3, the peer evaluation and cooperative learning method and server 100 for developing collective intelligence according to an embodiment of the present invention will be described in more detail.

Figure 3 is a flowchart of a peer evaluation and cooperative learning method for developing collective intelligence according to an embodiment of the present invention.

Figure 4 is a diagram illustrating calculating the collective intelligence contribution of each teacher and learner participating in the content sharing platform.

Figure 5 is a diagram illustrating receiving educational content produced by a first teacher-learner.

FIG. 6 is a diagram illustrating receiving evaluation feedback information from a content evaluation team regarding the educational content of FIG. 3.

Figure 7 is a diagram illustrating receiving an upgraded version of the educational content of Figure 3 from a third teacher/learner.

FIG. 8 is a diagram illustrating receiving evaluation feedback information from a content evaluation team for the upgraded version of FIG. 5.

The processor 110 randomly selects a plurality of teachers and learners in the blockchain-based content sharing platform and assigns them to a plurality of evaluation channels. (S110)

In an embodiment of the present invention, it is exemplified that a plurality of professors and learners in a content sharing platform are randomly assigned to a plurality of evaluation channels, but the present invention is not limited to this.

For example, a plurality of evaluation channels may each be specialized in expertise, major, etc., and the server 100 may request the teacher-learner to input his/her major, experience, certification, etc. when joining the content sharing platform.

In addition, the processor 110 analyzes data related to expertise, such as major, experience, and certification, input from the joining professor and learner, and selects at least one evaluation channel optimized for the joining professor and learner to allocate the evaluation channel. .

At this time, one teacher-learner does not belong to one evaluation channel, but may be assigned to and belong to multiple different evaluation channels.

The processor 110 assigns each teacher and learner belonging to a plurality of evaluation channels to a plurality of teams. (S120)

At this time, a plurality of teams are assigned to each of the plurality of evaluation channels and include a content production team, a content evaluation team, and a problem-solving team.

The main task of professors and learners in the content production team is to produce educational content, the main task of professors and learners in the content evaluation team is to evaluate the produced educational content, and the problem solving team solves various problems related to educational content. Our main task is to solve problems.

However, just because the team is divided, it does not mean that each teacher and learner is limited to performing only the role of the team.

The server 100 receives educational content produced by the first teacher-learner in the content production team of a specific evaluation channel among the plurality of evaluation channels through the communication unit 120. (S130)

The server 100 receives project performance results for each team from a plurality of teams within a specific evaluation channel with respect to the educational content received in S130. (S140)

The processor 110 requests a peer evaluation of the team contribution from each teacher and learner belonging to a plurality of teams within a specific evaluation channel, and receives the results of the peer evaluation from each teacher and learner's terminal in response to the request. (S150)

When educational content produced by the content production team or by the first teacher-learner within the content production team is received, each team within the evaluation channel carries out a project for each team targeting the educational content.

The processor 110 receives the project performance results for each team through the communication unit 120, and requests peer evaluation of the team contribution from each teacher and learner in the evaluation channel at preset time periods.

Each teacher and learner inputs peer evaluations of other teachers and learners in his or her team through the terminal and transmits them to the server 100.

At this time, it is not limited to peer evaluation of other teachers and learners belonging to one's own team, and even other teams can evaluate other teachers and learners belonging to the same evaluation channel.

In an embodiment of the present invention, the server 100 can conduct peer evaluation in real-time or dynamically.

The processor 110 calculates the collective intelligence contribution for each of a plurality of teachers and learners belonging to a specific evaluation channel based on at least one of the project performance results and peer evaluation results for each team in S150. (S160)

The processor 110 provides coin rewards to each of a plurality of teachers and learners belonging to a specific evaluation channel based on the results calculated in S160. (S170)

In the present invention, in the process of evaluating, reviewing, and updating educational content produced by teachers and learners, each team and each teacher and learner performs their assigned role while simultaneously learning and conducting projects for each team to form collective intelligence. .

And, in order to make these activities more active, a certain amount of compensation is needed, and the server 100 compensates this with coins and provides coin rewards according to the degree of contribution to collective intelligence.

To this end, the server 100 calculates the collective intelligence contribution for each of a plurality of professors and learners, as shown in FIG. 4, and provides coin compensation to each person based on this.

In one embodiment, the processor 110 may provide a predetermined coin reward to the evaluation channel based on the number of times the educational content is used at each preset time period.

The processor 110 provides a predetermined coin reward to the evaluation channel and distributes the coin reward based on the collective intelligence contribution to each of the plurality of professors and learners.

Therefore, since teachers and learners who use the content sharing platform receive coin rewards according to their contribution to collective intelligence, they have the effect of actively participating in collective intelligence.

In addition, the processor 110 may request screening for collective intelligence certification for professors and learners whose coins provided exceed the preset number of collective intelligence certification qualifications.

For example, the server 100 may request authentication from a place such as an educational institution certification committee, as shown in FIG. 1 .

If the screening result passes, the processor 110 issues a collective intelligence certificate to the relevant teacher-learner.

In one embodiment, the educational content produced by the content production team is reviewed to see if it can be fully disclosed on the server 100, and when its qualifications are verified, it is disclosed on the server 100. Previously, it was disclosed within the evaluation channel as shown in FIG. 5. In this state, evaluation/screening is conducted by the content evaluation team.

Specifically, the processor 110 stores and accumulates first evaluation feedback information about the educational content received from the terminal 50 of at least one second teaching learner in the content evaluation team of the corresponding evaluation channel in the database 130. .

Then, when the accumulated second evaluation feedback information satisfies the preset first disclosure condition, the processor 110 discloses the entire corresponding educational content to the content sharing platform as shown in FIG. 6.

The second teacher-learner may be a member of the content evaluation team within the evaluation channel, but is not limited to this.

The first disclosure condition refers to the minimum qualification for whether the educational content may be disclosed in its entirety on the server 100.

In one embodiment, when educational content is uploaded from the first teaching learner's terminal 30, the processor 110 analyzes at least one of the topic, content, and keyword of the educational content to provide a first evaluation including a plurality of evaluation items. You can create a form.

Then, the second teacher-learner reviews the educational content, evaluates scores for a plurality of evaluation items in the first evaluation form, and uploads the first evaluation feedback information to the server 100.

At this time, the first disclosure condition includes the number of second teaching learners who uploaded the first evaluation feedback information and the average score of each evaluation item, and the processor 110 stores the corresponding educational content when the first disclosure condition is satisfied, as shown in FIG. 4 Convert it to public as shown so that professors and learners who have signed up for the platform can view it.

In one embodiment, when the educational content is converted to public view, the processor 110 may provide coin compensation according to the production of the educational content to the first teacher-learner based on the accumulated first evaluation feedback information.

At this time, the processor 110 may provide a certain coin reward to the first teacher/learner based on a smart contract.

The coin used in the embodiment of the present invention may be a coin that has already been issued and used on the market (e.g., Bitcoin, Ethereum, Ada, etc.), or at least one coin produced for use in a content sharing platform may be used. It may also be used.

In this way, when self-produced coins are used, when the coins are listed on the exchange, professors and learners can exchange their coins for cash, which enables the continued growth of the educational ecosystem based on the compensation structure. .

Through this configuration, in the embodiment of the present invention, the server 100 performs screening/evaluation of the educational content produced and uploaded by the content production team or the first teacher and learner, and then discloses the educational content to the platform and server 100. I do it.

Below, we will explain how the educational content published on the platform and server 100 is updated.

Although the educational content produced by the first teacher-learner has been released to the server 100, the educational content produced and released in this way may have modifications or may need to be updated due to the development of knowledge and technology in the relevant field.

After the server 100 according to an embodiment of the present invention discloses educational content to teachers and learners, it allows teachers and learners to edit and modify it and upload an upgraded version.

At this time, the processor 110 converts the upgraded version of the educational content to full disclosure only when it satisfies a certain standard, just as when converting the educational content to full disclosure on the server 100.

In one embodiment, when the processor 110 receives an upgraded version of the educational content published on the platform from the terminal 70 of at least one third teacher-learner subscribed to the platform, the processor 110 The second evaluation feedback information about the upgraded version received from the terminal 50 is stored and accumulated in the database 130.

And, if the accumulated second evaluation feedback information satisfies the preset second disclosure condition, the processor 110 fully discloses the upgraded version to the content sharing platform as shown in FIG. 8.

7, until the accumulated second evaluation feedback information satisfies the preset second disclosure condition, the educational content of the previous version of the upgraded version is fully disclosed on the server 100, and the upgraded version is disclosed within the evaluation channel for screening. /Evaluation is in progress.

In one embodiment, the processor 110 may compare the upgraded version and the educational content to generate a second evaluation form including a plurality of evaluation items.

Then, the second teacher-learner reviews the upgraded version of the educational content, evaluates scores for a plurality of evaluation items in the second evaluation form, and uploads the second evaluation feedback information to the server 100.

At this time, the second disclosure condition includes the number of second teaching learners who uploaded the second evaluation feedback information and the average score of each evaluation item, and the processor 110 creates an upgraded version of the corresponding educational content when the second disclosure condition is satisfied. Convert to full disclosure as shown in Figure 6 so that professors and learners who have signed up for the platform can view it.

In one embodiment, the processor 110 provides a certain coin reward to at least one of the first teacher-learner, the second teacher-learner, and the third teacher-learner based on a smart contract when the upgraded version is fully disclosed on the content sharing platform. You can.

In one embodiment, the processor 110 may calculate collective intelligence points for the educational content based on a preset collective intelligence evaluation algorithm whenever an upgraded version of the educational content is received.

Specifically, the processor 110 selects among the accumulated second evaluation feedback information for the educational content, the number of upgrades to the educational content, the number of teachers and learners participating in the specific channel, and the number of solutions to problems that occurred with the educational content. Collective intelligence points can be calculated based on at least one.

In an embodiment of the present invention, the collective intelligence points calculated for educational content represent a quantification of how much the educational content has been updated through the efforts of teachers and learners and how actively various problems have been solved.

Therefore, educational content with a higher collective intelligence point can mean that more effort from teachers and learners has been invested.

In one embodiment, the processor 110 provides a predetermined coin to a teacher and learner who contributed to collective intelligence about the educational content based on the number of times the educational content is used every preset time period.

Specifically, the teachers and learners who contributed to the collective intelligence of the educational content are the first teacher and learner who created the educational content, the second teacher and learner who evaluated the educational content and its upgraded version, and the third teacher and learner who uploaded the upgraded version of the educational content. Includes teachers and learners.

The processor 110 calculates the collective intelligence contribution according to the first teacher-learner's production of the educational content based on the accumulated first evaluation feedback information for the educational content.

Accordingly, the processor 110 determines the status of the first teacher-learner based on at least one of the project performance results of the team to which the first teacher-learner belongs, the received peer evaluation results of multiple meetings, and the first evaluation feedback information accumulated for the educational content. Collective intelligence contribution can be calculated.

The processor 110 calculates the collective intelligence contribution of the third teacher-learner based on the number of uploads of the upgraded version of the educational content and the accumulated second evaluation feedback information for each upgraded version.

Accordingly, the processor 110 selects among the project performance results of the team to which the third teacher-learner belongs, the received multiple peer evaluation results, the number of uploads of upgraded versions of the educational content, and the accumulated second evaluation feedback information for each upgraded version. Based on at least one, the collective intelligence contribution of the third teacher learner can be calculated.

The processor 110 calculates the collective intelligence contribution of the second teaching learner based on the number of uploads for at least one of the first evaluation feedback information accumulated for the educational content and the second evaluation feedback information accumulated for the upgraded version.

Accordingly, the processor 110 provides the project performance results of the team to which the second teaching learner belongs, the received multiple peer evaluation results, the accumulated first evaluation feedback information for the educational content, and the accumulated second evaluation feedback information for the upgraded version. Based on the number of uploads for at least one of the above, the collective intelligence contribution of the second teacher-learner can be calculated.

In one embodiment, the processor 110 calculates a coin reward based on the number of times the educational content is used at each preset time period, and the collective intelligence calculated for each of the first teaching learner, the second teaching learner, and the third teaching learner Coin rewards can be calculated based on contribution.

In addition, the present invention provides coins as a reward to teachers and learners who upload upgraded versions, thereby encouraging active participation so that educational content is constantly updated.

The method according to an embodiment of the present invention described above may be implemented as a program (or application) and stored in a medium in order to be executed in conjunction with a server, which is hardware.

The above-mentioned program is C, C++, JAVA, machine language, etc. that can be read by the processor (CPU) of the computer through the device interface of the computer in order for the computer to read the program and execute the methods implemented in the program. It may include code coded in a computer language. These codes may include functional codes related to functions that define the necessary functions for executing the methods, and include control codes related to execution procedures necessary for the computer's processor to execute the functions according to predetermined procedures. can do. In addition, these codes may further include memory reference-related codes that indicate at which location (address address) in the computer's internal or external memory additional information or media required for the computer's processor to execute the above functions should be referenced. there is. In addition, if the computer's processor needs to communicate with any other remote computer or server in order to execute the above functions, the code uses the computer's communication module to determine how to communicate with any other remote computer or server. It may further include communication-related codes regarding whether communication should be performed and what information or media should be transmitted and received during communication.

The storage medium refers to a medium that stores data semi-permanently and can be read by a device, rather than a medium that stores data for a short period of time, such as a register, cache, or memory. Specifically, examples of the storage medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc., but are not limited thereto. That is, the program may be stored in various recording media on various servers that the computer can access or on various recording media on the user's computer. Additionally, the medium may be distributed to computer systems connected to a network, and computer-readable code may be stored in a distributed manner.

The steps of the method or algorithm described in connection with embodiments of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or a combination thereof. The software module may be RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), Flash Memory, hard disk, removable disk, CD-ROM, or It may reside on any type of computer-readable recording medium well known in the art to which the present invention pertains.

Above, embodiments of the present invention have been described with reference to the attached drawings, but those skilled in the art will understand that the present invention can be implemented in other specific forms without changing its technical idea or essential features. You will be able to understand it. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive.

10: Peer evaluation and cooperative learning system for developing collective intelligence
30: First teaching-learner terminal
50: Second teaching-learner terminal
70: Third teaching-learner terminal
100: server
110: processor
120: Department of Communications
130: database
140: Subscription module

Claims (21)

In a manner performed by the server,
Randomly selecting a plurality of professors and learners within a blockchain-based content sharing platform and assigning them to a plurality of evaluation channels;
Allocating each teacher and learner belonging to the plurality of evaluation channels to a plurality of teams; -The plurality of teams are each assigned within the plurality of evaluation channels and include a content production team, a content evaluation team, and a problem-solving team-
Receiving educational content produced by a first teacher-learner in the content production team of a specific evaluation channel among the plurality of evaluation channels;
Receiving project performance results for each team from a plurality of teams in the specific evaluation channel with respect to the received educational content;
Requesting a peer evaluation of team contribution from each professor and learner belonging to a plurality of teams within the specific evaluation channel, and receiving a result of the peer evaluation from the terminal of each professor and learner in response to the request;
Calculating a contribution to collective intelligence (hereinafter referred to as 'collective intelligence contribution') of each of a plurality of teachers and learners belonging to the specific evaluation channel based on the received project performance results for each team and the received peer evaluation results;
Comprising the step of providing coin compensation to each of a plurality of professors and learners belonging to the specific evaluation channel based on the calculated results,
The server is,
Accumulating and storing first evaluation feedback information for the educational content received from the terminal of at least one second teaching learner in the content evaluation team of the specific evaluation channel,
If the first evaluation feedback information satisfies the preset first disclosure condition, the educational content is fully disclosed on the content sharing platform,
An upgraded version of the fully disclosed educational content is received from the terminal of at least one third teacher-learner subscribed to the content sharing platform, and a second evaluation of the upgraded version is performed from the terminal of the at least one second teacher-learner. When feedback information is received, accumulate and store second evaluation feedback information for the upgraded version,
If the second evaluation feedback information satisfies the preset second disclosure condition, the upgraded version is fully disclosed on the content sharing platform,
Calculating points for the collective intelligence of the educational content based on the number of upgrades to the educational content, the number of teachers and learners participating in a specific evaluation channel, and the number of solutions to problems occurring in the educational content,
Calculating the collective intelligence contribution for the first teaching learner based on the first evaluation feedback information for the educational content,
Calculate the collective intelligence contribution of the third teacher-learner based on the number of uploads of upgraded versions of the educational content and the second evaluation feedback information for each upgraded version,
Characterized in calculating the collective intelligence contribution of the second teaching learner based on the number of uploads for the first evaluation feedback information for the educational content and the second evaluation feedback information for the upgraded version,
Peer evaluation and cooperative learning methods for developing collective intelligence. According to paragraph 1,
The server is,
Provide a predetermined coin reward to the evaluation channel based on the number of uses of the educational content at each preset time period,
Characterized in distributing the provided coin rewards based on the collective intelligence contribution to each of the plurality of professors and learners.
Peer evaluation and cooperative learning methods for developing collective intelligence. According to paragraph 1,
Requesting screening for collective intelligence certification for professors and learners whose coins exceed the number set as collective intelligence certification qualifications; and
If the screening result passes, further comprising issuing a collective intelligence certificate to the teacher-learner,
Peer evaluation and cooperative learning methods for developing collective intelligence. According to paragraph 1,
The collective intelligence contribution calculation step is,
Based on the results of two or more peer evaluations received at different times, the collective intelligence contribution for each of the plurality of teachers and learners belonging to the evaluation channel is calculated,
Characterized in that the calculated collective intelligence contribution is disclosed to a plurality of teachers and learners belonging to the evaluation channel,
Peer evaluation and cooperative learning methods for developing collective intelligence. According to paragraph 1,
The server is,
When the educational content is uploaded from the terminal of the first teaching learner, a first evaluation form containing a plurality of evaluation items is generated based on the topic, content, and keywords of the educational content, and the first evaluation feedback information is After the second teacher-learner reviewed the educational content, he or she evaluated the scores for a plurality of evaluation items in the first evaluation form,
If the number of second professors and learners who uploaded the first evaluation feedback information and the average score of the evaluated scores satisfy the first disclosure condition, the educational content is disclosed so that professors and learners who have signed up for the sharing platform can view it. do,
When the educational content is released, coin compensation for production of the educational content is provided to the first teacher and learner based on the first evaluation feedback information.
Peer evaluation and cooperative learning methods for developing collective intelligence. According to clause 5,
The server is,
Compare the educational content and the upgraded version, and generate a second evaluation form containing a plurality of evaluation items for the upgraded version as a result of the comparison, wherein the second evaluation feedback information is provided to the second teacher and learner for the upgraded version. After reviewing, the scores for a plurality of evaluation items in the second evaluation form were evaluated,
If the number of second professors and learners who uploaded the second evaluation feedback information and the average score of the evaluated scores satisfy the second disclosure condition, the upgraded version of the educational content can be viewed by the professor and learner who joined the sharing platform. Open to the public so that
Calculating points for collective intelligence based on the information of the second evaluation feedback, the number of upgrades to the educational content, the number of teachers and learners participating in the specific evaluation channel, and the number of problems that occurred with the educational content. Characterized by,
Peer evaluation and cooperative learning methods for developing collective intelligence. delete According to paragraph 1,
The server is,
Characterized by providing a predetermined coin according to the collective intelligence contribution to the professor and learner who contributed to the collective intelligence of the educational content based on the number of uses of the educational content at each preset time period.
Peer evaluation and cooperative learning methods for developing collective intelligence. delete delete In a program combined with a computer and stored on a computer-readable recording medium to implement peer evaluation and cooperative learning methods for developing collective intelligence,
The above program is,
A first process of randomly selecting a plurality of teachers and learners within a blockchain-based content sharing platform and assigning them to a plurality of evaluation channels;
a second process for allocating each teacher and learner belonging to the plurality of evaluation channels to a plurality of teams; -The plurality of teams are each assigned within the plurality of evaluation channels and include a content production team, a content evaluation team, and a problem-solving team-
A third process for receiving educational content produced by a first teacher-learner in the content production team of a specific evaluation channel among the plurality of evaluation channels;
a fourth process for receiving project performance results for each team from a plurality of teams in the specific evaluation channel with respect to the received educational content;
A fifth process for requesting a peer evaluation of team contribution from each teacher and learner belonging to a plurality of teams within the specific evaluation channel and receiving the results of the peer evaluation from the terminal of each teacher and learner in response to the request;
A sixth process for calculating a contribution to collective intelligence (hereinafter referred to as 'collective intelligence contribution') of each of a plurality of teachers and learners belonging to the specific evaluation channel, based on the received results; and
Performing a seventh process of providing coin rewards to each of a plurality of professors and learners belonging to the specific evaluation channel based on the calculated results,
Accumulating and storing first evaluation feedback information for the educational content received from the terminal of at least one second teaching learner in the content evaluation team of the specific evaluation channel,
If the first evaluation feedback information satisfies the preset first disclosure condition, the educational content is fully disclosed on the content sharing platform,
An upgraded version of the fully disclosed educational content is received from the terminal of at least one third teacher-learner subscribed to the content sharing platform, and a second evaluation of the upgraded version is performed from the terminal of the at least one second teacher-learner. When feedback information is received, accumulate and store second evaluation feedback information for the upgraded version,
If the second evaluation feedback information satisfies the preset second disclosure condition, the upgraded version is fully disclosed on the content sharing platform,
Calculating points for the collective intelligence of the educational content based on the number of upgrades to the educational content, the number of teachers and learners participating in a specific evaluation channel, and the number of solutions to problems occurring in the educational content,
Calculating the collective intelligence contribution for the first teaching learner based on the first evaluation feedback information for the educational content,
Calculate the collective intelligence contribution of the third teacher-learner based on the number of uploads of upgraded versions of the educational content and the second evaluation feedback information for each upgraded version,
Characterized in calculating the collective intelligence contribution of the second teaching learner based on the number of uploads for the first evaluation feedback information for the educational content and the second evaluation feedback information for the upgraded version,
Peer evaluation and cooperative learning program to develop collective intelligence. At least one processor including one or more cores;
A communication unit that communicates with the teacher-learner's terminal; and
Contains a database,
The processor,
Multiple instructors and learners within the blockchain-based content sharing platform are randomly selected and assigned to multiple evaluation channels, respectively.
Each teacher and learner belonging to the plurality of evaluation channels is assigned to a plurality of teams, and the plurality of teams are respectively assigned to the plurality of evaluation channels and include a content production team, a content evaluation team, and a problem-solving team.
Receiving educational content produced by the first teacher-learner in the content production team of a specific evaluation channel among the plurality of evaluation channels,
Receiving project performance results for each team from a plurality of teams within the specific evaluation channel for the received educational content,
Requesting a peer evaluation of team contribution from each teacher and learner belonging to a plurality of teams within the specific evaluation channel, and receiving the results of the peer evaluation from the terminal of each teacher and learner in response to the request,
Based on the received project performance results for each team and the received peer evaluation results, the contribution to collective intelligence (hereinafter referred to as 'collective intelligence contribution') of each of the plurality of teachers and learners belonging to the specific evaluation channel is calculated,
Based on the calculated results, coin rewards are provided to each of the plurality of professors and learners belonging to the specific evaluation channel,
Accumulating and storing first evaluation feedback information for the educational content received from the terminal of at least one second teaching learner in the content evaluation team of the specific evaluation channel,
If the first evaluation feedback information satisfies the preset first disclosure condition, the educational content is fully disclosed on the content sharing platform,
An upgraded version of the fully disclosed educational content is received from the terminal of at least one third teacher-learner subscribed to the content sharing platform, and a second evaluation of the upgraded version is performed from the terminal of the at least one second teacher-learner. When feedback information is received, accumulate and store second evaluation feedback information for the upgraded version,
If the second evaluation feedback information satisfies the preset second disclosure condition, the upgraded version is fully disclosed on the content sharing platform,
Calculating points for the collective intelligence of the educational content based on the number of upgrades to the educational content, the number of teachers and learners participating in a specific evaluation channel, and the number of solutions to problems occurring in the educational content,
Calculating the collective intelligence contribution for the first teaching learner based on the first evaluation feedback information for the educational content,
Calculate the collective intelligence contribution of the third teacher-learner based on the number of uploads of upgraded versions of the educational content and the second evaluation feedback information for each upgraded version,
Characterized in calculating the collective intelligence contribution of the second teaching learner based on the number of uploads for the first evaluation feedback information for the educational content and the second evaluation feedback information for the upgraded version,
Peer evaluation and cooperative learning server for developing collective intelligence. According to clause 12,
The processor,
Provide a predetermined coin reward to the evaluation channel based on the number of uses of the educational content at each preset time period,
Characterized in distributing the provided coin rewards based on the collective intelligence contribution to each of the plurality of professors and learners.
Peer evaluation and cooperative learning server for developing collective intelligence. According to clause 12,
The processor,
Request screening for collective intelligence certification for professors and learners whose coins provided above exceed the number set for collective intelligence certification qualifications,
If the screening result passes, a collective intelligence certificate is issued to the teacher-learner.
Peer evaluation and cooperative learning server for developing collective intelligence. According to clause 12,
The processor,
Based on the results of two or more peer evaluations received at different times, the collective intelligence contribution for each of the plurality of teachers and learners belonging to the evaluation channel is calculated,
Characterized in that the calculated collective intelligence contribution is disclosed to a plurality of teachers and learners belonging to the evaluation channel,
Peer evaluation and cooperative learning server for developing collective intelligence. According to clause 12,
The processor,
When the educational content is uploaded from the terminal of the first teaching learner, a first evaluation form containing a plurality of evaluation items is generated based on the topic, content, and keywords of the educational content, and the first evaluation feedback information is After the second teacher-learner reviewed the educational content, he or she evaluated the scores for a plurality of evaluation items in the first evaluation form,
If the number of second professors and learners who uploaded the first evaluation feedback information and the average score of the evaluated scores satisfy the first disclosure condition, the educational content is disclosed so that professors and learners who have signed up for the sharing platform can view it. do,
When the educational content is released, coin compensation for production of the educational content is provided to the first teacher and learner based on the first evaluation feedback information.
Peer evaluation and cooperative learning server for developing collective intelligence. According to clause 16,
The processor,
Compare the educational content and the upgraded version, and generate a second evaluation form containing a plurality of evaluation items for the upgraded version as a result of the comparison, wherein the second evaluation feedback information is provided to the second teacher and learner for the upgraded version. After reviewing, the scores for a plurality of evaluation items in the second evaluation form were evaluated,
If the number of second professors and learners who uploaded the second evaluation feedback information and the average score of the evaluated scores satisfy the second disclosure condition, the upgraded version of the educational content can be viewed by the professor and learner who joined the sharing platform. Open to the public so that
Calculating points for collective intelligence based on the information of the second evaluation feedback, the number of upgrades to the educational content, the number of teachers and learners participating in the specific evaluation channel, and the number of problems that occurred with the educational content. Characterized by,
Peer evaluation and cooperative learning server for developing collective intelligence. According to clause 12,
The processor,
Characterized in calculating points for the collective intelligence of the educational content based on a preset collective intelligence evaluation algorithm each time an upgraded version of the educational content is received.
Peer evaluation and cooperative learning server for developing collective intelligence. According to clause 18,
The processor,
Characterized by providing a predetermined coin according to the collective intelligence contribution to the professor and learner who contributed to the collective intelligence of the educational content based on the number of uses of the educational content at each preset time period.
Peer evaluation and cooperative learning server for developing collective intelligence. delete delete