KR20230145761A - Learners management system and method - Google Patents

Abstract

The present invention relates to a learner management system and method, and to a technology that can provide customized motivation-enhancing content to learners in order to strengthen the learner's motivation to learn and support the learner's self-directed learning.

Description

Learners management system and method {Learners management system and method}

The present invention relates to a learner management system and method, and more specifically, to a system that can provide customized motivational content to support learners' self-directed learning in a situation where the need for online education is increasing and its use is increasing. It relates to learner management systems and methods.

Recently, as the number of online classes increases, the importance of motivation-boosting measures to support learners' self-directed learning is increasing.

In order to strengthen the learning motivation of such learners, the learner's learning progress, degree of achievement compared to other learners who are currently learning the same online content, expected performance based on data of other learners who have studied the same online content in the past, etc. It is necessary to support current learners by combining the above.

However, conventional methods have been proposed to support learners' online learning by simply analyzing the learner's attitude toward listening to online content, or by identifying learning history through online content and analyzing vulnerabilities through question and answer accordingly.

In other words, in order to support learners' self-directed learning, conventional technologies block temptations that hinder the learner's learning through comparison with other learners, or provide compensation based on learning results.

However, these conventional technologies attempt to achieve self-directed learning by simply providing information or providing simple rewards without considering the learner's emotions, but depending on the data provided, they may have a negative impact on self-directed learning. There is a problem.

In this regard, Korean Patent Publication No. 10-2021-0034281 (“Learning management and reward system for increasing learner participation rate in social media platforms supporting MOOC and flipped learning” refers to social media supporting MOOC and flipped learning. By providing a learning management server on the platform, the learning information of learners who watched the learning content is stored, and the viewing completion rate and learning completion rate are analyzed by subject/subject and provided to the learner so that the learner can recognize the areas in which learning is needed. We are launching a technology to increase academic enthusiasm by providing differentiated compensation.

Domestic public patent No. 10-2021-0034281 (publication date 2021.03.30.)

The present invention was created to solve the problems of the prior art as described above. The purpose of the present invention is to quantitatively calculate the learner's academic achievement for online learning provided to the learner, and based on this, the learner's self-directed learning. To provide a learner management system and method that can provide customized motivational content to support.

In the learner management system according to an embodiment of the present invention, a learner information collection unit that receives online learning-related information for each online class and each learner from a linked online learning management server, and transmits the online learning-related information A state calculation unit that calculates a learning state score for each learner using a preset weight for each item included in the online learning-related information, and a pre-stored motivational content database, the online learning-related information and the learning state. It is preferable to include a content extraction unit that extracts content to strengthen motivation for each learner using the score, and a content transmission unit that transmits the extracted content to a terminal owned by the corresponding learner.

Furthermore, it is desirable for the state calculation unit to set different weights for each item depending on the online class.

Furthermore, the learner management system is created for each online class from a linked SNS server and further includes a group conversation collection unit that receives conversation information occurring in a group chat room in which a large number of learners of the corresponding online class participate. It is desirable to do so.

Furthermore, it is preferable that the learner information collection unit extracts information related to conversation participation in the group chat room for each learner from the conversation information provided by the group conversation collection unit and includes it as the online learning related information.

Furthermore, the learner management system further includes an individual conversation collection unit that receives conversation information occurring in individual chat rooms created for each online class and each learner from a linked SNS server, and the learner management system is linked to The individual chat room is created through an SNS server, and the individual chat room responds to inquiry information input from the learner based on the online class-related data entered and the online class-related data updated from the external administrator who manages the online class. It is desirable for the conversation information to be generated through a chatbot that provides information.

Furthermore, it is preferable that the learner information collection unit extracts conversation participation information in an individual chat room for each learner from the conversation information provided by the individual conversation collection unit and includes it as the online learning-related information.

Furthermore, the learner management system transmits preset survey data to the terminal device possessed by the learner before extracting content for strengthening motivation for each learner through the content extraction unit, and transmits the data input through the terminal device. It is desirable to further include a learner disposition management unit that analyzes response information and analyzes disposition information for each learner.

Furthermore, the content extraction unit preferably extracts content for strengthening motivation for each learner using the online learning-related information, the learning status score, and the analyzed tendency information from a pre-stored motivation-enhancing content database.

Furthermore, the learner tendency management unit transmits the content extracted for each learner through the content transmission unit, recalculates the learning state score for each learner through the state calculation unit, and uses the recalculated learning state score. , it is desirable to update the tendency information for each learner.

In the learner management method according to an embodiment of the present invention, in the learner management method using a learner management system performed on a computer, in the learner information collection unit, for each online class and each learner In the learner information collection step (S100) of receiving online learning-related information, the state calculation unit uses the online learning-related information from the learner information collection step (S100) to preset differently for each online class and each item. A learning state calculation step (S200) of calculating a learning state score for each learner by applying weights, and online learning-related information by the learner information collection step (S100) using a pre-stored motivational content database in the content extractor. and a content extraction step (S300) of extracting content for strengthening motivation according to the learning state score calculated by the learning state calculation step (S200), and a content transmission unit extracting content by the content extraction step (S300). It includes a content transmission step (S400) of transmitting content to a terminal possessed by the corresponding learner, and it is desirable to repeat the above steps according to the learning progress of each online class.

Furthermore, the learner management method generates information for each online class from a connected SNS server in the group conversation collection unit, and information about conversations taking place in a group chat room in which a large number of learners of the corresponding online class participate. It further includes a group conversation transmission step (S110), wherein the learner information collection step (S100) uses the conversation information from the group conversation transmission step (S110) to provide information related to conversation participation in the group chat room for each learner. It is desirable to extract and include it as the online learning-related information.

Furthermore, the learner management method includes an individual conversation transmission step (S120) in which conversation information taking place in an individual chat room created for each online class and each learner is transmitted from the linked SNS server in the individual conversation collection unit. It further includes that the learner information collection step (S100) uses the conversation information from the individual conversation transmission step (S120) to extract conversation participation-related information for each learner and include it as the online learning-related information. desirable.

Furthermore, the individual chat room is provided through a chatbot that provides response information based on inquiry information input from learners based on online class-related data entered and online class-related data updated by an external administrator who manages the online class. It is desirable for conversation information to be generated.

Furthermore, in the content extraction step (S300), the learner propensity management unit transmits preset survey data to a terminal possessed by the learner, analyzes response information input through the terminal, and determines the propensity of each learner. Information related to online learning in the learner tendency analysis step (S310) of analyzing information and the learner information collection step (S100) using the pre-stored motivation-enhancing content database in the content extraction unit. and a content selection step (S320) of extracting content for strengthening motivation for each learner using the learning state score calculated by the learning state calculation step (S200) and the tendency information by the learner tendency analysis step (S310). ) is preferably included.

Furthermore, the learner management method transmits the content extracted by the content selection step (S320) through the content transmission step (S400), and then performs the learner information collection step (S100) and the learning state calculation step (S200). It is preferable to perform the content selection step (S320) by re-performing and updating the tendency information by the learner tendency analysis step (S310) using the newly calculated learning state score.

The learner management system and method of the present invention configured as described above promotes the learning motivation of learners who receive online content and participates in online classes, especially for self-directed learning, and stops the learning process halfway. In order to help students complete the course without giving up, there is an advantage in providing customized motivational content to learners by utilizing learner participation attitude data in the chat room generated while the online class is in progress.

In particular, unlike existing self-directed learning methods that block temptations that interfere with learning, we provide motivation-enhancing content that includes appropriate discourse, images, and videos according to the learner's current learning status, thereby motivating the learner. It has the advantage of allowing learners to proceed with self-directed learning to the end without giving up midway.

Figure 1 is an exemplary configuration diagram showing a learner management system according to an embodiment of the present invention.
Figure 2 is a timing diagram showing a learner management system and method according to an embodiment of the present invention.
Figure 3 is a flow diagram showing a learner management method according to an embodiment of the present invention.

Hereinafter, the learner management system and method of the present invention will be described in detail with reference to the attached drawings. The drawings introduced below are provided as examples so that the idea of the present invention can be sufficiently conveyed to those skilled in the art. Accordingly, the present invention is not limited to the drawings presented below and may be embodied in other forms. Additionally, like reference numerals refer to like elements throughout the specification.

At this time, if there is no other definition in the technical and scientific terms used, they have the meaning commonly understood by those skilled in the art to which this invention pertains, and the gist of the present invention is summarized in the following description and attached drawings. Descriptions of known functions and configurations that may be unnecessarily obscure are omitted.

In addition, a system refers to a set of components including devices, mechanisms, and means that are organized and interact regularly to perform necessary functions.

The learner management system and method according to an embodiment of the present invention encourage the learning motivation of learners who receive online content and participate in online classes, especially for self-directed learning, and give up the learning process midway. This is about a technology that can provide customized motivational content to learners by utilizing learner participation attitude data in chat rooms generated while online classes are in progress, in order to complete the course without having to do so.

In particular, the learner management system and method according to an embodiment of the present invention, unlike existing self-directed learning methods that block temptations that interfere with learning, provide appropriate discourse, images, and videos tailored to the learner's current learning state. By providing motivation-enhancing content that includes, you can strengthen the learner's learning motivation, allowing the learner to proceed with self-directed learning to the end without giving up midway.

Figure 1 is an example configuration diagram showing a learner management system according to an embodiment of the present invention, and Figure 2 is a timing diagram of the learner management system according to an embodiment of the present invention. A learner management system according to an embodiment of the present invention will be described in detail with reference to FIGS. 1 and 2.

The learner management system according to an embodiment of the present invention is provided through an online learning management server (online learning management system) that provides learning content to learners, or from an instructor (lecturer, teacher, etc.) that creates learning content provided to learners. , when it is desired to receive a learner management service to strengthen the learner's motivation for self-directed learning, the operation is performed in connection with the online learning management server 10 that provides the corresponding learning content.

As shown in FIG. 1, the learner management system according to an embodiment of the present invention includes a learner information collection unit 100, a status calculation unit 200, a content extraction unit 300, and a content transmission unit 400. As described above, it is preferable that the above configurations are operated by a server (integrated server, etc.) that provides a learner management service. To this end, it is preferable that each of the above components be individually or integratedly included in at least one operation processing means including a computer to perform the operation.

To learn more about each configuration,

As shown in FIG. 2, the learner information collection unit 100 preferably receives online learning-related information for each online class and each learner from a connected online learning management server.

At this time, the online learning management server preferably provides learning content upon request to each learner, analyzes the provided learning content, and generates learning content completion information for each learner.

The learner information collection unit 100 requests transmission of the online learning-related information to the connected online learning management server, but since it includes personal information, etc., after performing the login process, etc., for each online class, Each learner will receive information related to online learning.

The learner information collection unit 100 receives learner information including at least one of ID, name, age, and gender that can identify the learner, and learning content completion information for each learner from the online learning management server. It is desirable, and of course, you will also receive the online class content title and instructor name so that you can specify which online class it is.

The learner information collection unit 100 analyzes the above-described information and derives learning status information including attendance information and assignment submission information for each online class and each learner.

At this time, deriving the learning status information for each online class for the same learner may be interesting for all classes, but in reality, this is often not the case, so taking this into consideration, even for the same learner It is desirable to derive learning status information for each online class.

As shown in FIG. 2, the status calculation unit 200 receives the learning status information derived from the online learning-related information for each online class and each learner from the learner information collection unit 100, and performs the learning It is desirable to calculate the learning status score for each online class and each learner based on the preset weight for each item constituting the status information.

As an example of the process of calculating the learning state score based on the weight in the state calculation unit 200, attendance information (number of days of attendance/number of class days), assignment submission deadline information based on the assignment deadline (before the deadline/on the day of the deadline/ After the deadline/not submitted), assignment grading scores, etc. are weighted according to importance, and the learning status score is calculated by adding up the weights for each item.

The weights can be set differently for each item depending on the online class, and it is most desirable for the instructor creating the learning content to set the weights by considering the difficulty of the online class and the difficulty of the assignment.

As shown in FIG. 2, the content extraction unit 300 extracts the information related to online learning for each online class and each learner from the learner information collection unit 100 in a pre-stored motivational content database. It is desirable to extract content for strengthening motivation for each learner using learning state information and the learning state score calculated by the state calculation unit 200.

At this time, the pre-stored motivation-enhancing content database may include text, images, and videos that can enhance learning motivation. For example, "A (specific learner's name) completes online class B. "I am an early bird who submitted the assignment C among the learners." It is desirable to include motivation-enhancing content, including text such as 'early bird' badge image, graphs emphasizing the order of assignment submission, photos, videos, hint data for the next assignment, etc. These motivation-enhancing contents receive text, images, and videos containing the message they want to provide to learners from the instructor who creates the learning content, or they select the motivation-enhancing content that was most popular/responsive to learners who have taken online classes in the past. It can be managed separately, but this is not limited.

As shown in FIG. 2, the content transmission unit 400 preferably transmits the content extracted from the content extraction unit 300 to a terminal possessed by the corresponding learner.

At this time, it is preferable that the content transmission unit 400 receives information on a terminal capable of providing synchronization content from the user in advance, and based on this, the content extracted by the content extraction unit 300 (synchronization content ) can be provided to learners to strengthen the learning motivation of learners participating in online classes, especially for self-directed learning.

The learner management system according to an embodiment of the present invention can sufficiently provide learner-tailored learning motivation-reinforcing content through the above-described configurations, but by analyzing the learner's status more in-depth, it not only strengthens the learner's self-directed learning, but also provides the learner's self-directed learning. In order to reduce the burden on instructors to support self-directed learning, learner-tailored motivational content can be provided by utilizing learner participation attitude data in chat rooms generated during online classes.

To this end, the learner management system according to an embodiment of the present invention is preferably configured to further include a group conversation collection unit 500, as shown in FIG. 1.

The group conversation collection unit 500 is generated for each online class from the linked SNS server 20 and transmits conversation information occurring in a group chat room in which a number of learners learning through the corresponding online class participate. It is desirable to receive it.

That is, while an online class is in progress in connection with the online learning management server 10, a server (integrated server, etc.) providing a learner management service creates a related group chat room in connection with the SNS server 20.

The group chat room is a chat room created in place of an offline meeting while an online class is in progress, and information is generally exchanged through conversations between learners.

For example, during the 8 weeks that online class A was in progress, 10 students participating in online class A participated in the so-called ‘open chat room’ through KakaoTalk to share information about the online class (assignment submission deadline, current content progress, etc.) , expected progress of the next content, etc.).

In consideration of this, the group conversation collection unit 500 collects the conversation information occurring in a group chat room in which a number of learners learning through the corresponding online class participate, and the learner information collection unit 100 will be transmitted to.

The learner information collection unit 100 derives conversation participation-related information for each learner participating in the group conversation in the group chat room from the conversation information provided by the group conversation collection unit 500. The learner information collection unit 100 includes the derived conversation participation-related information as the online learning-related information, analyzes this, and provides information on the proportion of participation conversations among the total conversation information and learning relevance information of participation conversations for each learner. It is desirable to derive it and include it as the learning state information.

For example, when the group chat room is created as a group task is assigned in an online class, the difference in conversation content according to the group task is taken into consideration. In other words, immediately after a group task is assigned, the conversation is mainly about what the group task is, how to approach it, researching related data, etc., and at a certain point, the conversation is about problem solving methods, experiment methods, etc. Immediately before group assignment submission (announcement), conversations about writing assignments, sharing results, etc. appear.

In general, assignments have specific problems and are repeatedly applied to each class, so based on the analysis of conversation information, it is possible to determine whether conversations are related to the learning content (group assignments, etc.), or in other words, whether meaningful content related to the learning content is being discussed. You can determine whether it is a conversation content (derive learning relevance information) and analyze the proportion of participating conversations among the total conversation information. At this time, by analyzing the conversation information, the content of the group conversation is analyzed through natural language processing or morphological analysis, and the words that make up the group conversation are extracted and utilized.

To take another example, if the group chat room is a simple open chat room for an online class, rather than asking questions about group assignments with a special purpose, many learners learning through the online class ask various opinions about the learning content. Giving and receiving is common. At this time, in analyzing the content of the ongoing conversation and deriving information on the proportion of participatory conversation among the total conversation information and learning relevance information of the participatory conversation for each learner, some response information to the question information is not related to the learning content itself. However, there is a possibility that the information is incorrect, and in this case, the proportion information of participating conversations and the learning relevance information can be increased, but there may be problems with reliability if such derived information leads to the correct evaluation score of the corresponding learner.

However, as a result of various experiments with the learner management system according to an embodiment of the present invention, the learning content itself, that is, in the case of unmotivated learners who are not willing to participate actively in online classes, contains incorrect information. It was confirmed that they did not even participate in the conversation provided as response information. Accordingly, it is possible to sufficiently analyze learning status information for the corresponding learner by analyzing the degree of participation in the group conversation.

In addition, the learner management system according to an embodiment of the present invention is preferably configured to further include an individual conversation collection unit 600, as shown in FIG. 1.

It is preferable that the individual conversation collection unit 600 receives conversation information occurring in individual chat rooms created for each online class and each learner from the linked SNS server 30.

At this time, the linked SNS server 20 and the linked SNS server 30 may be the same server, and this is not limited.

The individual conversation collection unit 600 is connected to the online learning management server 10 on a server (integrated server, etc.) that provides learner management services and is connected to the SNS server 30 while an online class is in progress. An individual chat room is created.

The individual chat room is a chat room created in place of an offline meeting while an online class is in progress, and is preferably a chat room for Q&A.

In other words, the individual chat room contains pre-entered online class-related data (for example, lesson plan information) and online class-related data (for example, class progress) updated from an external administrator (instructor, etc.) who manages the online class. It is desirable that chatbot learning is carried out based on actual class progress information, information related to tasks that have occurred, etc., and that the conversation information is generated by the chatbot providing response information in response to inquiry information input from the learner.

For example, through the individual chat room, learners enter inquiry information related to the class (assignment submission deadline, current attendance status, learning progress, level of achievement within the learning group, etc.), and the chatbot provides response information. The conversation information is created by providing .

Considering this, the individual conversation collection unit 600 collects the conversation information occurring in the corresponding individual chat room and transmits it to the learner information collection unit 100.

It is preferable that the learner information collection unit 100 extracts conversation participation information in individual chat rooms for each learner from the conversation information provided by the individual conversation collection unit 600 and includes it as the online learning-related information.

Afterwards, it is preferable that the learner information collection unit 100 analyzes this, derives information on the activity level of the online class corresponding to each learner, and includes it as the learning status information.

Of course, the state calculation unit 200 calculates the learning state score for each learner using preset weights for the above-described weight information of participation conversations, learning relevance information of participation conversations, and activeness information.

In addition, the learner management system according to an embodiment of the present invention extracts personalized motivation-enhancing content for each learner prior to extracting content for strengthening motivation for each learner through the content extraction unit 300. information is analyzed. To this end, as shown in FIG. 1, it is configured to further include a learner tendency management unit 700.

Depending on the learner's tendency, even if the motivation-enhancing content is the same, the motivation-enhancing content may be different when provided as a video and as a text, so it is desirable to perform an analysis on this.

It is preferable that the learner propensity management unit 700 transmits preset survey data to a terminal possessed by the learner, analyzes response information input through the terminal, and analyzes propensity information for each learner.

The survey data is a type of psychological analysis, and it is desirable to analyze the learner's preferred content type among text, video, and image, and preferred content length.

As an example of the survey data, basic information about the learner's self-direction can be analyzed by deriving a response to 'I have never been late in submitting an assignment', and 'learning information through the subject bulletin board'. By eliciting response questions such as 'I tend to acquire learning information using SNS' and 'I am accustomed to acquiring learning information using SNS', the basic tendency of the relevant learner's learning pattern is identified/analyzed.

In addition, you can analyze the learner's interest in learning achievement by deriving response questions such as 'I am interested in what percentile I belong to' and 'I am accustomed to receiving information through text.' ', 'I tend to prefer infographics' are derived to analyze the type of motivational message (content type, etc.) preferred by the corresponding learner.

Of course, the question information in the above-mentioned survey data is only an example, and response questions can be derived through various question information to analyze the learner's tendency information, but this is not limited.

It is preferable that the content extraction unit 300 extracts content for strengthening motivation for each learner by using the tendency information analyzed by the learner tendency management unit 700.

It is desirable that the learner management system according to an embodiment of the present invention not only provides motivational content, but also determines changes in the learner's status with respect to the provided motivational content.

In detail, the learner management system preferably transmits the content extracted for each learner through the content transmission unit 400 and then recalculates the learning status score for each learner through the status calculation unit 200. do. The learner propensity management unit 700 updates propensity information for each learner using the recalculated learning status score. In other words, if the learning state score is higher than a predetermined standard and the previous learning state score is maintained or increases, it can be determined that the motivation for learning has been strengthened by the provided motivation-enhancing content, and the learning state score is greater than or equal to the predetermined standard. However, if the score is lower than the previous learning status score, it can be determined that the reinforcement of learning motivation by the provided motivation reinforcement content has had an adverse effect, and through this, the disposition information for each learner is updated.

Figure 3 is an exemplary sequence diagram showing a learner management method according to an embodiment of the present invention. As shown in Figure 3, the learner management method according to an embodiment of the present invention includes a learner information collection step (S100), learning It is comprised of a state calculation step (S200), a content extraction step (S300), and a content transmission step (S400). Additionally, in the learner management method according to an embodiment of the present invention, it is preferable that each step is performed by the learner management system.

To learn more about each step,

In the learner information collection step (S100), the learner information collection unit 100 receives online learning-related information for each online class and each learner from a connected online learning management server.

At this time, the online learning management server provides learning content upon request to each learner, analyzes the provided learning content, and generates learning content completion information for each learner.

The learner information collection step (S100) requests transmission of the online learning-related information to the connected online learning management server, but since it includes personal information, etc., after performing the login process, etc., for each online class, Each learner will receive information related to online learning.

In detail, it is desirable to receive learner information including at least one of ID, name, age, and gender that can identify the learner, and learning content completion information for each learner from the online learning management server. Of course, You will also receive the online class content title and instructor name so that you can specify which online class it is.

By analyzing the above-mentioned information, learning status information including attendance information and assignment submission information is derived for each online class and each learner.

At this time, deriving the learning status information for each online class for the same learner may be interesting for all classes, but in reality, this is often not the case, so taking this into consideration, even for the same learner It is desirable to derive learning status information for each online class.

As shown in FIG. 3, the learner management method according to an embodiment of the present invention further performs a group conversation transmission step (S110) and uses conversation information in deriving the learning status information.

In detail, the group conversation transmission step (S110) is generated for each online class from the SNS server 20 linked to the group conversation collection unit 500, and the number of people learning through the corresponding online class You will receive conversation information that takes place in the group chat room in which the number of learners is participating.

That is, while an online class is in progress in connection with the online learning management server 10, a server (integrated server, etc.) providing a learner management service creates a related group chat room in connection with the SNS server 20.

The group chat room is a chat room created in place of an offline meeting while an online class is in progress, and information is generally exchanged through conversations between learners.

For example, during the 8 weeks that online class A was in progress, 10 students participating in online class A participated in the so-called ‘open chat room’ through KakaoTalk to share information about the online class (assignment submission deadline, current content progress, etc.) , expected progress of the next content, etc.).

Taking this into consideration, the group conversation transmission step (S110) collects the conversation information occurring in a group chat room in which a number of learners learning through the corresponding online class participate.

Using this, the learner information collection step (S100) derives conversation participation-related information for each learner participating in the group conversation in the group chat room from the conversation information obtained in the group conversation transmission step (S110).

In detail, the derived conversation participation-related information is included in the online learning-related information, and this is analyzed to derive information on the proportion of participation conversations among the total conversation information and learning relevance information of participation conversations for each learner, and thus the learning status. It is included as information.

In addition, the learner management method according to an embodiment of the present invention further performs an individual conversation transmission step (S120), as shown in FIG. 3, and uses conversation information in deriving the learning status information. .

In detail, in the individual conversation transmission step (S120), the individual conversation collection unit 600 collects conversation information occurring in individual chat rooms created for each online class and each learner from the linked SNS server 30. You will receive it. At this time, the linked SNS server 20 and the linked SNS server 30 may be the same server, and this is not limited.

The individual conversation transmission step (S120) is performed in connection with the SNS server 30 while an online class is in progress in connection with the online learning management server 10 on a server (integrated server, etc.) providing a learner management service. An individual chat room is created. The individual chat room is a chat room created in place of an offline meeting while an online class is in progress, and is preferably a chat room for Q&A.

In other words, the individual chat room contains pre-entered online class-related data (for example, lesson plan information) and online class-related data (for example, class progress) updated from an external administrator (instructor, etc.) who manages the online class. It is desirable that chatbot learning is carried out based on actual class progress information, information related to tasks that have occurred, etc., and that the conversation information is generated by the chatbot providing response information in response to inquiry information input from the learner.

For example, through the individual chat room, learners enter inquiry information related to the class (assignment submission deadline, current attendance status, learning progress, level of achievement within the learning group, etc.), and the chatbot provides response information. The conversation information is created by providing .

Using this, the individual conversation transmission step (S120) collects the conversation information occurring in the corresponding individual chat room.

Using this, the learner information collection step (S100) extracts conversation participation information in the individual chat room for each learner from the conversation information in the individual conversation transmission step (S120) and includes it as the online learning-related information. . Afterwards, in the learner information collection step (S100), this is analyzed to derive information on the activity level of the online class corresponding to each learner and included in the learning status information.

The learning state calculation step (S200) receives the learning state information derived from the online learning-related information for each online class and each learner in the learner information collection step (S100) from the state calculation unit 200. , the learning status score for each learner is calculated for each online class based on the preset weight for each item constituting the learning status information.

For example, attendance information (number of days of attendance/number of class days), assignment submission deadline information based on the assignment deadline (before the due date/on the day of the due date/after the due date/not submitted), assignment grading scores, etc. are weighted according to importance, and each item is weighted. The learning state score is calculated by adding up the star weights. Of course, the learning status score for each learner is calculated using preset weights for the above-mentioned proportion information of participation conversations, learning relevance information of participation conversations, and activeness information.

The weights can be set differently for each item depending on the online class, and it is most desirable for the instructor creating the learning content to set the weights by considering the difficulty of the online class and the difficulty of the assignment.

The content extraction step (S300) uses a pre-stored motivational content database in the content extractor 300, online learning management information from the learner information collection step (S100), and the learning state calculation step (S200). ) Content for strengthening motivation is extracted according to the learning state score calculated by ).

As shown in FIG. 3, the content extraction step (S300) includes a learner tendency analysis step (S310) and a content selection step (S320).

Prior to extracting content to enhance motivation for each learner, the tendency information is analyzed to extract personalized motivation enhancement content for each learner. In other words, depending on the learner's tendency, even if the motivation-enhancing content has the same content, the motivation-enhancing content may be different when provided as a video and as a text, so it is desirable to perform an analysis on this.

In the learner propensity analysis step (S310), the learner propensity management unit 700 transmits preset survey data to a terminal possessed by the learner, analyzes response information input through the terminal, and analyzes each learner. Individual tendency information is analyzed. The survey data is a type of psychological analysis, and it is desirable to analyze the learner's preferred content type among text, video, and image, and preferred content length.

The content selection step (S320) includes online learning management information from the learner information collection step (S100), and the learning state calculation step (S200) in the content extraction unit 300 from a pre-stored motivational content database. The learning state score calculated by and the disposition information analyzed by the learner disposition analysis step (S310) are used to extract content for strengthening motivation accordingly.

At this time, the pre-stored motivation-enhancing content database may include text, images, and videos that can enhance learning motivation. For example, "A (specific learner's name) completes online class B. “I am an early bird who submitted the assignment C among the learners.” It is desirable to include motivation-enhancing content, including text such as 'early bird' badge image, graphs emphasizing the order of assignment submission, photos, videos, hint data for the next assignment, etc. These motivation-enhancing contents receive text, images, and videos containing the message they want to provide to learners from the instructor who creates the learning content, or they select the motivation-enhancing content that was most popular/responsive to learners who have taken online classes in the past. It can be managed separately, but this is not limited.

In the content transmission step (S400), the content transmission unit 400 transmits the content extracted through the content extraction step (S300) to a terminal possessed by the corresponding learner. Through this, the learning motivation of learners participating in online classes can be strengthened, especially for self-directed learning.

The learner management method according to an embodiment of the present invention does not stop at providing motivational content, but preferably determines changes in the learner's status with respect to the provided motivational content.

In detail, the learner management method repeatedly performs the above-described steps according to the learning progress of each online class, updating the tendency information for each learner and providing the most optimized motivational content.

That is, it is desirable to transmit the content extracted for each learner through the content transmission step (S400) and then recalculate the learning state score for each learner through the learning state calculation step (S200).

In the content extraction step (S300), tendency information for each learner is updated using the recalculated learning state score. In other words, if the learning state score is higher than a predetermined standard and the previous learning state score is maintained or increases, it can be determined that the motivation for learning has been strengthened by the provided motivation-enhancing content, and the learning state score is greater than or equal to the predetermined standard. However, if the score is lower than the previous learning status score, it can be determined that the reinforcement of learning motivation by the provided motivation reinforcement content has had an adverse effect, and through this, the disposition information for each learner is updated.

As described above, the present invention has been described with reference to specific details such as specific components and limited embodiment drawings, but this is only provided to facilitate a more general understanding of the present invention, and the present invention is not limited to the above-mentioned embodiment. No, those skilled in the art can make various modifications and variations from this description.

Accordingly, the spirit of the present invention should not be limited to the described embodiments, and all matters that are equivalent or equivalent to the claims of this patent as well as the claims described below shall fall within the scope of the spirit of the present invention. .

10: Online learning management server
20, 30: SNS server
100: Learner information collection department
200: Status operation unit
300: Content extraction unit
400: Content transmission unit
500: Group conversation collection unit
600: Individual conversation collection unit
700: Learner tendency management department

Claims (15)

A learner information collection unit that receives online learning-related information for each online class and each learner from the linked online learning management server;
a state calculation unit that analyzes the online learning-related information based on a preset weight for each item included in the online learning-related information and calculates a learning status score for each learner;
A content extraction unit that extracts content for strengthening motivation for each learner from a pre-stored motivational content database using the online learning-related information and the learning status score; and
a content transmission unit that transmits the extracted content to a terminal possessed by the corresponding learner;
Including, learner management system.
According to clause 1,
The state operation unit is
A learner management system that sets different weights for each item depending on the online class.
According to clause 1,
The learner management system is
A group conversation collection unit that receives conversation information created for each online class and occurring in a group chat room in which a large number of learners of the corresponding online class participate from the linked SNS server;
Learner management system, further comprising:
In clause 3
The learner information collection department
A learner management system that extracts information related to conversation participation in a group chat room for each learner from the conversation information provided by the group conversation collection unit and includes it as the online learning-related information.
In clause 4
The learner management system is
An individual conversation collection unit that receives conversation information occurring in individual chat rooms created for each online class and each learner from the linked SNS server;
It further includes,
The learner management system is
The above individual chat room is created through a linked SNS server,
The individual chat room is
The conversation information is generated through a chatbot that provides response information based on the inquiry information input from the learner, based on the online class-related data entered and the online class-related data updated by the external administrator who manages the online class. Management system.
In clause 5
The learner information collection department
A learner management system that extracts conversation participation information in an individual chat room for each learner from the conversation information provided by the individual conversation collection unit and includes it as the online learning-related information.
In clause 6
The learner management system is
Before extracting content to strengthen motivation for each learner through the content extraction unit, preset survey data is transmitted to the terminal device possessed by the learner, response information input through the terminal device is analyzed, and each learner Learner tendency management department that analyzes individual tendency information;
Learner management system, further comprising:
In paragraph 7
The content extraction unit
A learner management system that extracts content for strengthening motivation for each learner from a pre-stored motivational content database using the online learning-related information, the learning status score, and the analyzed tendency information.
In paragraph 7
The learner tendency management department
After transmitting the content extracted for each learner through the content transmission unit,
A learner management system that recalculates the learning state score for each learner through the state calculation unit and updates the tendency information for each learner using the recalculated learning state score.
In the learner management method using a learner management system performed on a computer,
A learner information collection step (S100) in which online learning-related information for each online class and each learner is transmitted from the connected online management server in the learner information collection unit;
In the state calculation unit, a learning state score is calculated for each learner by applying different preset weights for each online class and each item to the online learning management information in the learner information collection step (S100). Operation step (S200);
In the content extraction unit, motivation is strengthened according to the online learning-related information by the learner information collection step (S100) and the learning state score calculated by the learning state calculation step (S200) using a pre-stored motivation enhancement content database. A content extraction step (S300) of extracting content for; and
A content transmission step (S400) of transmitting, in the content transmission unit, the content extracted by the content extraction step (S300) to a terminal possessed by the corresponding learner;
Includes,
A learner management method that repeatedly performs the above steps according to the learning progress of each online class.
According to clause 10,
The above learner management method is
A group conversation transmission step (S110) in which the group conversation collection unit receives conversation information created for each online class from a connected SNS server and is taking place in a group chat room in which a large number of learners of the corresponding online class participate (S110) ;
It further includes,
The learner information collection step (S100) is
A learner management method comprising extracting information related to conversation participation in a group chat room for each learner using the conversation information obtained in the group conversation transmission step (S110) and including it as the online learning related information.
According to clause 10,
The above learner management method is
An individual conversation transmission step (S120) in which the individual conversation collection unit receives conversation information occurring in individual chat rooms created for each online class and each learner from a connected SNS server;
It further includes,
The learner information collection step (S100) is
A learner management method that extracts conversation participation-related information for each learner using the conversation information obtained in the individual conversation transmission step (S120) and includes it as the online learning-related information.
According to clause 12,
The individual chat room is
The conversation information is generated through a chatbot that provides response information based on the inquiry information input from the learner, based on the online class-related data entered and the online class-related data updated by the external administrator who manages the online class. How to manage.
According to claim 11 or 12,
The content extraction step (S300) is
A learner tendency analysis step (S310) in which the learner tendency management unit transmits preset survey data to a terminal owned by the learner, analyzes the response information input through the terminal, and analyzes the tendency information for each learner (S310). ; and
In the content extraction unit, online learning-related information by the learner information collection step (S100) using the pre-stored motivation-enhancing content database, and the learning state calculation step (S200) A content selection step (S320) of extracting content for strengthening motivation for each learner using the calculated learning state score and the disposition information obtained from the learner disposition analysis step (S310);
Including, learner management method.
According to clause 14,
The above learner management method is
After transmitting the content extracted by the content selection step (S320) through the content transmission step (S400),
The learner information collection step (S100) and the learning state calculation step (S200) are re-performed, the newly calculated learning state score is used to update the tendency information in the learner tendency analysis step (S310), and the content selection step is performed. Learner management method for performing (S320).