Method for analyzing learning characteristics and adjusting learning environment using artificial intelligence and service providing server used therefor

Abstract

A method for analyzing learning characteristics and adjusting a learning environment using artificial intelligence and a service providing server used therein are disclosed. The present invention is realized through a process of transmitting learning content stored in a first storage area among a plurality of storage areas to a learner's terminal, receiving learner's response information regarding the learning content from the learner's terminal, determining whether to move and store the learning content from the first storage area to a second storage area based on the learner's response information, and analyzing the learner's learning characteristics based on the learner's response information. This invention not only enables precise analysis of a learner's individual learning characteristics, such as learning ability and learning achievement level during repeated learning, using artificial intelligence, but also enables active configuration of the learner's learning environment based on the learner's individual learning characteristics analyzed through artificial intelligence.

Description

[Technical Field]

[0001] The present invention relates to a learning characteristics analysis and learning environment adjustment method using artificial intelligence, and a service providing server used therein. More specifically, the present invention relates to a learning characteristics analysis and learning environment adjustment method using artificial intelligence, which not only enables precise analysis of a learner's individual learning characteristics, such as learning ability and learning achievement level during repetitive learning, but also enables active configuration of a learner's learning environment by reflecting the learner's individual learning characteristics analyzed through artificial intelligence, and a service providing server used therein. [Background technology]

[0002] Ebbinghaus's forgetting curve is empirical evidence that shows how important it is to review what has been learned, and it is widely known that periodic repetitive learning is the most effective way to retain learned content for the long term.

[0003] However, due to various problems such as lack of will and ability to learn, it is difficult to successfully implement cyclical repetition of learning content.

[0004] Furthermore, in order to successfully retain the learning content for a long time, the amount of repetition learning content and the repetition learning cycle must be differentiated according to the individual ability of the learner, but there is a practical problem in that it is very difficult for learners to decide for themselves the amount of repetition learning content and the repetition learning cycle that suits them.

[0005] Meanwhile, a method for supporting long-term memory of learning content to solve these problems has been proposed in Korean Patent Registration No. 10-2405520.

[0006] However, even with this method, technical limitations have been pointed out, such as the fact that learners cannot directly participate in setting the learning environment, such as the repetitive learning cycle and amount of repetitive learning, that it is not possible to analyze the learners' individual learning characteristics, such as their learning ability and level of achievement, as they progress through repetitive learning, and that it is not possible to actively set the learning environment to reflect the learners' individual learning characteristics. Summary of the Invention [Problem to be solved by the invention]

[0007] Therefore, the object of the present invention is to provide a method for analyzing learning characteristics and adjusting learning environment using AI, which not only enables precise analysis of a learner's individual learning characteristics such as learning ability and learning achievement level during repetitive learning through AI, but also enables active setting of the learner's learning environment by reflecting the learner's individual learning characteristics analyzed through AI, and a service providing server used therefor.

[0008] The problems to be solved by the present invention are not limited to the problems mentioned above, but include other technical problems that can be clearly understood by a person having ordinary skill in the art to which the present invention pertains from the following description. [Means for solving the problem]

[0009] To achieve the above object, the method for analyzing learning characteristics and adjusting learning environment using artificial intelligence according to the present invention includes the steps of: (a) a service providing server transmitting learning content stored in a first storage area among a plurality of storage areas to a learner's terminal device; (b) the service providing server receiving learner's response information regarding the learning content from the learner's terminal device; (c) the service providing server determining whether to move and store the learning content from the first storage area to a second storage area based on the learner's response information; and (d) the service providing server analyzing the learner's learning characteristics based on the learner's response information.

[0010] Preferably, the method further includes (e) the service providing server adjusting at least one of the repetitive learning period and the amount of repetitive learning per session of the learner based on the analysis result of the learning characteristics of the learner.

[0011] Meanwhile, the service providing server according to the present invention includes a transmitting unit that transmits learning content stored in a first storage area among a plurality of storage areas to a learner's terminal, a receiving unit that receives learner's response information to the learning content from the learner's terminal, and a computing unit that allows the service providing server to analyze the learner's learning characteristics based on the learner's response information.

[0012] Preferably, the calculation unit adjusts at least one of the repetitive learning period of the learner and the amount of repetitive learning per session based on the analysis result of the learning characteristics of the learner. [Effects of the Invention]

[0013] According to the present invention, not only can the individual learning characteristics of a learner, such as learning ability and learning achievement level, during repetitive learning be precisely analyzed through artificial intelligence, but also the learning environment of the learner can be actively set up to reflect the individual learning characteristics of the learner analyzed through artificial intelligence.

[0014] The effects of the present invention are not limited to the effects mentioned above, but include other effects that can be clearly understood by those skilled in the art from the following description. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a block diagram of a learning characteristic analysis and learning environment adjustment system using artificial intelligence according to an embodiment of the present invention. [Figure 2] 1 is a functional block diagram showing the structure of a service providing server that executes a learning characteristic analysis and learning environment adjustment method using artificial intelligence according to one embodiment of the present invention. [Figure 3]4 is a signal flowchart illustrating a process of analyzing learning characteristics and adjusting a learning environment using artificial intelligence according to an embodiment of the present invention. [Figure 4] 1 is a flowchart illustrating a detailed process of a learning characteristic analysis method using artificial intelligence according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0016] The present invention will be described in more detail below with reference to the drawings. It should be noted that the same components are denoted by the same reference numerals throughout the drawings. In addition, detailed descriptions of known functions and configurations that may unnecessarily obscure the gist of the present invention will be omitted.

[0017] 1 is a configuration diagram of a system for analyzing learning characteristics and adjusting a learning environment using AI according to an embodiment of the present invention. Referring to FIG. 1, the system for analyzing learning characteristics and adjusting a learning environment using AI according to an embodiment of the present invention includes a learner terminal 100 and a service providing server 200, and may further include an administrator terminal 300.

[0018] The learner terminal 100 is a wireless communication terminal such as a PC or smartphone owned by a learner who uses the learning characteristic analysis and learning environment adjustment service using artificial intelligence according to the present invention, and such learner terminal 100 can be installed with a predetermined learner application program required to use the learning characteristic analysis and learning environment adjustment service using artificial intelligence according to the present invention.

[0019] The service providing server 200 is a server installed and operated by a business that provides the learning characteristic analysis and learning environment adjustment service using artificial intelligence according to the present invention. The service providing server 200 sequentially transmits learning contents stored in a first storage area among a plurality of storage areas to the learner terminal 100, receives learner response information regarding the learning contents from the learner terminal 100, determines whether to move and store the learning contents from the first storage area to the second storage area based on the learner response information, analyzes the learner's learning characteristics based on the learner's response information, and adjusts at least one of the learner's repetitive learning cycle and the amount of repetitive learning per session based on the analysis results of the learner's learning characteristics.

[0020] The administrator terminal 300 is a wireless communication terminal such as a PC or smartphone carried by an administrator such as a teacher or lecturer who manages the learning of learners. Such an administrator terminal 300 can be installed with an administrator application program required to use the learning characteristic analysis and learning environment adjustment service using artificial intelligence according to the present invention.

[0021] Meanwhile, the service providing server 200 is configured to be able to communicate with the learner terminal 100 and the administrator terminal 300 via a communication network. The communication network provides a connection path so that the service providing server 200, the learner terminal 100, and the administrator terminal 300 can transmit and receive packet data after connecting to each other.

[0022] The communication network may include, for example, wired networks such as LANs (Local Area Networks), WANs (Wide Area Networks), MANs (Metropolitan Area Networks), and ISDNs (Integrated Service Digital Networks), as well as wireless networks such as wireless LANs, CDMA, Bluetooth (registered trademark), and satellite communications, but the scope of the present invention is not limited thereto.

[0023] In addition, the service providing server 200 can provide an application or web environment in which the service according to the present invention is implemented.

[0024] The services provided by the service providing server 200 may be provided through an application installed on the learner's terminal 100, and the application may refer to an application program such as an app executed on a mobile terminal (smartphone).

[0025] 2 is a functional block diagram showing the structure of a service providing server 200 that executes a method for analyzing learning characteristics and adjusting a learning environment using artificial intelligence according to an embodiment of the present invention. Referring to FIG. 2, the service providing server 200 that executes a method for analyzing learning characteristics and adjusting a learning environment using artificial intelligence according to an embodiment of the present invention includes a receiving unit 210, a storage unit 230, a calculation unit 250, and a transmitting unit 270.

[0026] The receiving unit 210 of the service providing server 200 can receive study category selection information and response information to the study content from the learner terminal 100, and can receive study content by category from the administrator terminal 300.

[0027] The calculation unit 250 of the service providing server 200 determines whether to move and save the learning content from the first storage area to the second storage area based on the learner's response information to the learning content, analyzes the learner's learning characteristics based on the learner's response information, and adjusts at least one of the learner's repetitive learning cycle and the amount of repetitive learning per session based on the analysis results of the learner's learning characteristics.

[0028] The common storage space in the storage unit 230 of the service providing server 200 stores learning contents by learning subject category, and the individual storage space allocated to each learner's account has multiple storage areas. The transmission unit 270 of the service providing server 200 sequentially transmits the learning contents stored in the multiple storage areas provided in the learner's individual storage space to the learner's terminal device 100.

[0029] 3 is a signal flow chart illustrating a process of analyzing learning characteristics and adjusting a learning environment using artificial intelligence according to an embodiment of the present invention. Hereinafter, a process of analyzing learning characteristics and adjusting a learning environment using artificial intelligence according to an embodiment of the present invention will be described with reference to FIGS.

[0030] The storage unit 230 of the service providing server 200 stores learning contents for each learning category, and in implementing the present invention, the service providing server 200 can also receive and store such learning contents for each category from the administrator terminal device 300 (S405).

[0031] Meanwhile, in implementing the present invention, the learning contents stored in the storage unit 230 can be configured in the form of memo-type learning cards on which contents to be familiarized according to the learning progress are recorded, or in the form of question-type learning cards on which questions related to the learning contents are recorded.

[0032] In addition, when the learning content is configured in the form of a question-type learning card, it is preferable that the storage unit 230 of the service providing server 200 stores the correct answer information corresponding to the questions recorded on the corresponding question-type learning card.

[0033] Meanwhile, when a learner selects a learning category (e.g., intermediate English) through an application program installed on the learner terminal 100, the service providing server 200 can receive category selection information from the learner terminal 100.

[0034] In addition, the learner terminal 100 can also transmit learning goal setting information, including the target learning period and target learning amount input by the learner, together with the category selection information to the service providing server 200 (S410).

[0035] Here, the service providing server 200 can set the number of storage areas in the individual storage space allocated to the learner's account, the storage capacity of each storage area, and the repetitive learning cycle based on the category selection information and target learning period information received from the learner terminal 100 (S415).

[0036] For this purpose, the storage unit 230 of the service providing server 200 may store setting information for the number N of storage areas (i.e., the number of repeated learning times) according to the learning category and target learning period P, each storage capacity (e.g., the storage limit of learning cards), and the repeated learning period T, as shown in Table 1 below.

[0037] [Table 1]

[0038] In addition, in implementing the present invention, the service providing server 200 can set the number of storage areas, the storage capacity of each storage area, and the repetitive learning cycle in the individual storage space allocated to the learner's account based on the category selection information and target learning amount information (e.g., 100 English words) received from the learner's terminal device 100 (S415).

[0039] For this purpose, the storage unit 230 of the service providing server 200 may store setting information for the number N of storage areas according to the learning category and target learning amount S, each storage capacity (e.g., learning card storage limit), and repetitive learning period T, as shown in Table 2 below.

[0040] [Table 2]

[0041] Thus, according to the present invention, the learner can directly participate in setting the learning environment, such as the repetitive learning period and the repetitive learning amount, by inputting the target learning period and the target learning amount through the learner terminal 100.

[0042] In addition, the service providing server 200 can generate learning content for the learner by storing the learning cards corresponding to the category selected by the learner (or the learning cards corresponding to the learning amount selected by the learner from the total learning cards corresponding to the category selected by the learner) among the learning cards stored in the common storage space of the storage unit 230 up to the storage limit of the first storage area among multiple storage areas provided in the individual storage space of the corresponding learner (S420).

[0043] Meanwhile, when the learning start time (e.g., 5:00 p.m.) that the service providing server 200 received and stored from the learner's terminal 100 as a result of the learner's selection along with the learning category in the aforementioned step S410 arrives, the service providing server 200 sequentially transmits the learning cards stored in the first storage area to the learner's terminal 100 S425.

[0044] Then, the learner, who has checked the contents of the learning card through the learner terminal device 100, inputs response information to the contents of the learning card into the application program installed on the learner terminal device 100, and the service providing server 200 sequentially receives the learner's response information from the learner terminal device 100 (S430).

[0045] Specifically, if the learning card is a memo-type learning card, the learner can input response information into the learner terminal device 100 by selecting the ``Mastery Completed'' button if he / she determines that he / she has fully mastered the contents of the learning card within a specified time limit (e.g., 1 minute), or by selecting the ``Mastery Incomplete'' button if he / she determines that he / she has not fully mastered the contents of the learning card within the time limit.

[0046] In addition, if the learning card is a question-type learning card, the learner can input response information to the learner terminal 100 by inputting an answer to the question on the corresponding learning card.

[0047] Meanwhile, the calculation unit 250 of the service providing server 200 determines whether to move and store the corresponding learning card from the first storage area to the second storage area based on the learner's response information to the learning card sent by the learner terminal 100 (S435).

[0048] Specifically, if the learning card is a memo-type learning card, the calculation unit 250 of the service providing server 200 moves and stores the corresponding learning card from the first storage area to the second storage area (i.e., deletes it from the first storage area and stores it in the second storage area) if the response information is "mastery completed," and if the response information is "mastery not completed" or no response information is received from the learner terminal 100 within a specified time limit, the calculation unit 250 can maintain the storage state of the corresponding learning card in the first storage area where it was already stored without moving and storing it.

[0049] In addition, if the study card is a question-type study card, the calculation unit 250 of the service providing server 200 compares the response information received from the learner terminal 100 with the correct answer information associated with the study card and stored, and if the two match, moves and stores the study card from the first storage area to the second storage area (i.e., deletes it from the first storage area and stores it in the second storage area), and if the two do not match, it does not move and stores the study card, but maintains its storage state in the first storage area where it was already stored.

[0050] In this way, the first learning is completed by sequentially transmitting the plurality of learning cards stored in the first storage area and receiving the response information for each learning card.

[0051] In this way, when implementing the present invention, it is preferable that the service providing server 200 completes the corresponding learning session when the sequential transmission of the learning cards stored in the storage area where the learning cards are being sequentially transmitted and the reception of response information for each learning card are completed.

[0052] Meanwhile, in implementing the present invention, the service providing server 200 may also transmit the learner's learning information, including the learning cards sent to the learner during the first learning process, the response information for each learning card, and the total execution ratio information of transfers and saves based on each response information, to the terminal device 300 of the administrator in charge of the learner's learning (S440).

[0053] After the first learning session is completed, when the repetitive learning period (e.g., 2 days) arrives, which the service providing server 200 received and stored from the learner's terminal 100 in accordance with the learner's selection along with the learning category in step S410, the service providing server 200 generates the second learning content by additionally storing the learning cards corresponding to the category selected by the learner among the learning cards stored in the common storage space of the storage unit 230 in the first storage area up to the remaining storage limit of the first storage area, and then repeats steps S425 to S440.

[0054] In addition, the service providing server 200 can calculate the remaining storage capacity in the second storage area while transferring and storing the data from the first storage area to the second storage area (S445), and can send a guidance message about the calculated remaining storage capacity information (for example, currently, five additional study cards can be stored in the second storage area, and when the storage capacity of the second storage area is exhausted, the study in the first storage area will be interrupted and the transfer of the study cards stored in the second storage area will begin) to the learner's terminal 100.

[0055] Meanwhile, upon seeing this guidance message, the learner can input a request for modifying the number of storage areas and storage capacity set in the above-mentioned step S415 through the learner terminal 100 (S450).

[0056] For example, a learner who wants to postpone the start of repetitive learning in the second storage area and continue learning in the first storage area can input a request to adjust the storage capacity of the second storage area (e.g., from 100 to 200), and a learner who does not want to engage in unnecessary repetitive learning due to too many storage areas can input a request to adjust the number of storage areas (e.g., from 7 to 5).

[0057] In addition, in implementing the present invention, the service providing server 200 may also transmit a request for approval of setting information modification including the request for modification of setting received from the learner terminal 100 in the above-mentioned step S445 to the administrator terminal 300 (S455).

[0058] Then, when the administrator inputs the result (Y / N) of the decision on whether to apply or not to modify the setting information based on the learning information of the relevant learner received from the service providing server 200 in the above-mentioned step S440 into the administrator terminal device 300, the service providing server 200 can grant the administrator the approval authority for the modified setting by executing the modified setting based on the information (Y / N) on whether to apply or not to modify the setting information received from the administrator terminal device 300 S460.

[0059] In addition, the administrator can also send customized learning content for the corresponding learner to the service providing server 200 via the administrator terminal 300 based on the learning information of the corresponding learner received from the service providing server 200 in the above-mentioned step S440 (S465).The service providing server 200 can then generate customized learning content for the learner by storing the customized learning content received from the administrator terminal 300 in the first learning area of ​​the corresponding learner up to the remaining storage limit of the first storage area in priority to the learning content already stored in the service providing server 200, and then repeat the above-mentioned steps S425 to S440.

[0060] Meanwhile, the calculation unit 250 of the service providing server 200 calculates the remaining storage capacity in the second storage area and determines whether the remaining storage capacity in the second storage area has been completely used up (S470). If the remaining storage capacity in the second storage area has been completely used up, the calculation unit 250 stops sending the learning cards stored in the first storage area and starts repeated learning in the second storage area by sequentially sending the learning cards stored in the second storage area to the learner terminal 100 (S475).

[0061] Specifically, it is preferable that the service providing server 200 sequentially transmits the plurality of learning cards that have been cumulatively moved and stored in the second storage area to the learner terminal 100 in the order of storage through the execution of the above-mentioned step S445, so that the repeated learning of the learning card that has been learned earlier is performed first (S480).

[0062] When the learner inputs response information for the learning card stored in the second storage area into the learner terminal 100, the calculation unit 250 of the service providing server 200 executes transfer and storage from the second storage area to the third storage area based on the response information received from the learner terminal 100, and repeats the related procedures in the same manner as the above-mentioned steps S435 to S450 (S485).

[0063] Specifically, when the remaining storage capacity in the third storage area is exhausted, the service providing server 200 stops sending the learning cards stored in the second storage area, and sequentially sends the learning cards stored in the third storage area to the learner terminal 100, thereby allowing repeated learning in the third storage area to begin.

[0064] In this way, when the remaining storage capacity in the Nth storage area is exhausted, the service providing server 200 stops transmitting the learning cards stored in the N-1th storage area and sequentially transmits the learning cards stored in the Nth storage area to the learner terminal 100 so that repeated learning in the Nth storage area is started preferentially, thereby maximizing the learning content that is processed into long-term memory through repeated learning.

[0065] In addition, in implementing the present invention, when the service providing server 200 starts the next learning session after completing a specific learning session, it is preferable to start by storing additional learning content in the first storage area up to the remaining storage capacity of the first storage area until all of the learning content related to the corresponding category stored in the storage unit 230 is used up, and then sequentially transmitting the learning cards stored in the first storage area.However, if all of the learning content related to the corresponding category stored in the storage unit 230 is used up, it is preferable to start by extracting a storage area from the multiple storage areas in which learning cards are stored, and then sequentially transmitting the learning cards stored in the storage area that is arranged earliest among the extracted storage areas.

[0066] The repetitive learning through sequential moving and storing in the multiple storage areas as described above is repeatedly performed until all learning cards related to the relevant category provided by the service providing server 200 are stored in the final storage area, thereby supporting long-term memory of the learning content.

[0067] Meanwhile, in implementing the present invention, the service providing server 200 can also fine-tune the learner's learning environment by resetting the number of storage areas, storage capacity, and repetitive learning cycle set in the above-mentioned step S415 based on the learner's various learning information as described above (S490).

[0068] 4 is a flowchart illustrating a detailed process of a learning characteristic analysis method using AI according to an embodiment of the present invention. Hereinafter, the detailed process of a learning characteristic analysis method using AI according to an embodiment of the present invention will be described with reference to FIG.

[0069] First, the calculation unit 250 of the service providing server 200 can measure and analyze the time required from the time when the learning content stored in the first storage area is first transmitted to the learner terminal 100 to the time when the sequential transmission of the multiple learning cards stored in the first storage area and the reception of the response information for each learning card are all completed (i.e., the time required for the first learning in the first storage area) S491.

[0070] Specifically, if the time required for the first learning session exceeds a predetermined standard required time (e.g., 30 minutes), the calculation unit 250 of the service providing server 200 resets the storage capacity of the second storage area and subsequent storage areas (excluding the last storage area), and resets the storage capacity of each storage area so that it is inversely proportional to the amount by which the time required for the first learning session exceeds the standard required time, thereby making it possible to reduce the amount of repeated learning per session if the learner's learning speed is poor.

[0071] Furthermore, if the time required for the first learning session is less than a predetermined standard time (e.g., 30 minutes), the calculation unit 250 of the service providing server 200 resets the storage capacity of the second storage area and subsequent storage areas (excluding the last storage area), and resets the storage capacity of each storage area so that it is proportional to the amount (absolute value) of the time required for the first learning session that is less than the standard time, thereby making it possible to increase the amount of repetitive learning per session if the learner's learning speed is fast.

[0072] Meanwhile, in carrying out the present invention, the reference time required for each learning session may be included in the learning goal setting information from the learner terminal 100 in step S410.

[0073] Furthermore, if the time required for the first learning session exceeds a predetermined standard time (for example, 30 minutes), the calculation unit 250 of the service providing server 200 resets the repetitive learning cycle for the learner, so that the repetitive learning cycle is inversely proportional to the amount by which the time required for the first learning session exceeds the standard time, thereby enabling the repetitive learning cycle to be reduced if the learner's learning speed is poor (i.e., if the learner's memory of the repetitive learning is not clear).

[0074] Furthermore, if the time required for the first learning session is less than a predetermined standard time (e.g., 30 minutes), the calculation unit 250 of the service providing server 200 resets the repetitive learning cycle for the learner, so that the repetitive learning cycle is proportional to the amount (absolute value) of the time required for the first learning session that is less than the standard time, thereby enabling the repetitive learning cycle to be increased if the learner's learning speed is fast (i.e., if the learner has a clear memory for repetitive learning).

[0075] In this way, according to the present invention, by analyzing the individual learning characteristics of each learner, it becomes possible to provide an optimized review interval for each individual.

[0076] In addition, the calculation unit 250 of the service providing server 200 can measure and analyze the time required from the time when the learning content stored in the first storage area is first transmitted to the learner terminal 100 to the time when the remaining storage capacity of the second storage area is determined to be 0 in the aforementioned step S470 (i.e., the ``storage area transfer time'', which is the time required from the start of learning in the first storage area to the fulfillment of the learning start requirements in the second storage area) S493.

[0077] Specifically, when the storage area transfer time exceeds a predetermined standard required time (for example, two weeks), the calculation unit 250 of the service providing server 200 resets the storage capacity of the third storage area and subsequent storage areas (excluding the last storage area), and resets the storage capacity of each storage area so that it is inversely proportional to the amount by which the storage area transfer time exceeds the standard required time, thereby making it possible to reduce the amount of repetitive learning per session if the learner's progress in repetitive learning is poor.

[0078] Furthermore, if the storage area transfer time is less than a predetermined standard required time (for example, two weeks), the calculation unit 250 of the service providing server 200 resets the storage capacity of the third storage area and subsequent storage areas (excluding the last storage area), and resets the storage capacity of each storage area so that it is proportional to the amount (absolute value) of the storage area transfer time that remains short of the standard required time, thereby making it possible to increase the amount of repetitive learning per session if the learner's progress in repetitive learning is fast.

[0079] In carrying out the present invention, the reference required time may also be included in the learning goal setting information received from the learner terminal 100 in step S410.

[0080] In addition, the calculation unit 250 of the service providing server 200 resets the repetitive learning cycle of the learner when the storage area movement time exceeds a predetermined standard required time (for example, two weeks), and resets the repetitive learning cycle so that it is inversely proportional to the amount by which the storage area movement time exceeds the standard required time, thereby making it possible to reduce the repetitive learning cycle when the learner's progress in repetitive learning is poor (for example, when the learner makes mistakes when answering the same question repeatedly).

[0081] Furthermore, if the storage area transfer time is less than a predetermined standard required time (for example, two weeks), the calculation unit 250 of the service providing server 200 resets the repetitive learning cycle of the learner, and resets the repetitive learning cycle so that it is proportional to the amount (absolute value) of the storage area transfer time that is not yet reached compared to the standard required time, thereby enabling the repetitive learning cycle to be increased if the learner's progress in repetitive learning is rapid.

[0082] In addition, when determining whether to move and save at the aforementioned step S435, the service providing server 200 calculates the "cumulative number of incorrect answers," which is the number of times the learner's response information for the same learning card is "not fully mastered" or the number of times the response information does not match the correct answer information, and by analyzing this, it can also make detailed adjustments to the learning environment for the learner (S495).

[0083] Specifically, the calculation unit 250 of the service providing server 200 resets the storage capacity of each storage area so that it is inversely proportional to the accumulated number of incorrect answers, so that the more incorrect answers are repeated on the same question, the less repetitive learning there is per session, and resets the repetitive learning period so that it is inversely proportional to the accumulated number of incorrect answers, so that the more incorrect answers are repeated on the same question, the shorter the repetitive learning period becomes.

[0084] In addition, the calculation unit 250 of the service providing server 200 measures the response time required for each learning card, which is the time required from the time when the learning content (specific learning card) is sent in the above-mentioned steps S425 and S480 to the time when the response information (response to the corresponding learning card) is received in the above-mentioned steps S430 and S485, and calculates the average value of these times to analyze the learner's response speed and make detailed adjustments to the learning environment based on this (S497).

[0085] Specifically, the calculation unit 250 of the service providing server 200 resets the storage capacity of each storage area so that it is inversely proportional to the average response time, so that the amount of repetitive learning per session is reduced for learners with longer response times, and resets the repetitive learning period so that it is inversely proportional to the average response time, so that the repetitive learning period is shortened for learners with longer response times.

[0086] Furthermore, in carrying out the present invention, the calculation unit 250 of the service providing server 200 can reset each of the standard required times in the steps S491 and S493 so as to be proportional to the cumulative number of incorrect answers in the step S495 and the average value of the required response times in the step S497, thereby increasing the standard required time for a learner who repeatedly gives incorrect answers to the same question and a learner who takes a long time to respond (S499).

[0087] In addition, in implementing the present invention, the service providing server 200 may repeatedly perform the detailed adjustment process of the learning environment as described above when each learning session is completed or when the learning start requirements in each storage area are met, and may also limit the number of times such detailed adjustment of the learning environment is performed depending on the learning subject category.

[0088] Specifically, the service providing server 200 receives and stores the maximum number of detailed adjustments of the learning environment for each learning category along with the learning content for each learning category from the administrator terminal 300 in step S405, and based on this, can limit the number of adjustments (e.g., 5 times) for each learning category.

[0089] For this purpose, the storage unit 230 of the service providing server 200 may store information on the number of times adjustment is permitted for each learning category as shown in Table 3 below.

[0090] [Table 3]

[0091] In this way, in the present invention, by differentiating the number of times that detailed adjustments of the learning environment are permitted depending on the characteristics of the learning subject category, such as the difficulty level, it becomes possible to fine-tune each learning environment to reflect the characteristics of the learning subject category.

[0092] In addition, in implementing the present invention, the calculation unit 250 of the service providing server 200 applies the detailed adjustment results of the learning environment for a specific learning category (e.g., intermediate English A) as in steps S491 to S499 to the learning environment for other learning categories (e.g., intermediate English B) of the corresponding learner in the same way, and initializes the number of storage areas, storage capacity, and repetitive learning period for other learning categories in step S415, so that the analysis results of the learning characteristics of the learner as shown in Figure 4 can be initially applied to the learning of the corresponding learner for other categories.

[0093] Meanwhile, in setting up a learning environment for another learning target category by applying the analysis results of the learner's previous learning characteristics, the service providing server 200 can send a learning environment automatic setting approval request message (e.g., "Do you want to apply the learning environment based on the analysis results of the learning characteristics in the previous learning?") to the learner terminal device 100, and when an approval message is received from the learner terminal device 100, can apply the analysis results of the previous learning characteristics to set up a learning environment for another learning target category.

[0094] In addition, in implementing the present invention, the calculation unit 250 of the service providing server 200 can also set a learning environment for another learning subject category by applying the analysis results of the previous learning characteristics of the corresponding learner by applying the adjustment ratios (e.g., 10% increase or decrease) of the number of storage areas, storage capacity, and repetitive learning cycle based on the detailed adjustment of the learning environment described through Figure 4 to the number of storage areas, storage capacity, and repetitive learning cycle set based on Tables 1 and 2 in the above-mentioned step S415.

[0095] In this way, according to the present invention, not only can the individual learning characteristics of a learner, such as learning ability and learning achievement level, during repetitive learning be precisely analyzed through artificial intelligence, but also the learning environment of the learner can be actively set up to reflect the individual learning characteristics of the learner analyzed through artificial intelligence.

[0096] Specifically, in the present invention, the service providing server 200 can use machine learning to analyze the individual learning characteristics of a learner through artificial intelligence, and for this purpose, the storage unit 230 of the service providing server 200 can store a learning model for analyzing the time required for learning, the time required to move between storage areas, the learner's response information, and the response speed.

[0097] In addition, the learning model in the present invention can be composed of a calculation algorithm that analyzes the learner's learning characteristics based on the learning time required in the calculation unit 250, the time required to move to the storage area, the learner's response information and response speed as described above, and adjusts the learner's repetitive learning period and the amount of repetitive learning per session based on the analysis results of the learner's learning characteristics, and can also be formed by a neural network of various structures.

[0098] Specifically, such a learning model can be machine-learned based on the learning time, storage area movement time, learner response information and response speed accumulated and stored in the storage unit 230, the repetitive learning period generated based thereon, and the adjustment value of the amount of repetitive learning per session.

[0099] Meanwhile, in implementing the present invention, the service providing server 200 may also generate compensation information for the learner based on the total study period or total number of times of study required until all study cards related to the relevant category are stored in the final storage area (S500).

[0100] Specifically, the calculation unit 250 of the service providing server 200 can generate learning compensation points in inverse proportion to the total number of learning sessions, thereby encouraging learners who have completed repetitive learning for long-term memory within a short period of time.

[0101] In addition, when the calculation unit 250 of the service providing server 200 resets the number of storage areas and storage capacity in the aforementioned step S415 for a learner who will resume the repetitive learning process later, it sets the storage capacity inversely proportional to the total number of times the learner has studied in the previous repetitive learning process, sets the number of storage areas proportional to the total number of times the learner has studied, and sets the storage capacity in proportion to the total percentage of correct answers in the previous repetitive learning process, and sets the number of storage areas in inversely proportional to the total percentage of correct answers, thereby enabling the provision of a customized long-term support service that takes into account the learner's learning ability.

[0102] Meanwhile, in implementing the present invention, the calculation unit 250 of the service providing server 200 generates billing information for the learner in proportion to the number of learning cards stored in the last storage area at the time of termination of use of the service according to the present invention, thereby enabling the learner to pay a reasonable usage fee in proportion to his / her learning results (S500).

[0103] In addition, in implementing the present invention, the calculation unit 250 of the service providing server 200 generates the learner's billing information in proportion to the total study period or the total number of times of study required until all study cards related to the relevant category are stored in the final storage area, thereby charging a usage fee in proportion to the service usage period and the number of times of use and at the same time encouraging the learner to complete repetitive learning early through diligent study.

[0104] In addition, in implementing the present invention, a program for executing the learning characteristic analysis and learning environment adjustment method using artificial intelligence according to the present invention can be installed in the service providing server 200 according to the present invention, recorded on various computer-readable recording media, or stored in a server that transfers the corresponding program via a network.

[0105] In addition, the program according to the present invention can be distributed among computer systems connected via a network, and computer-readable code can be stored and executed in a distributed manner. Functional programs, codes, and code segments for embodying the present invention can be easily understood by those skilled in the art to which the present invention pertains.

[0106] Meanwhile, the order of the steps described above in the present invention is merely an example and is not limited thereto, that is, the order of the steps described above may be changed, and some steps may be performed simultaneously or deleted.

[0107] The terms used in the present invention are merely used to describe specific embodiments and are not intended to limit the present invention. Singular expressions include plural expressions unless otherwise clearly intended in the context. In this application, terms such as "comprise" or "have" are intended to specify the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, and should be understood not to preclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0108] While the preferred embodiments and application examples of the present invention have been illustrated and described above, the present invention is not limited to the specific embodiments and application examples described above, and various modifications may be made by a person having ordinary skill in the art to which the invention pertains without departing from the gist of the present invention as claimed in the claims, and such modifications should not be understood separately from the technical ideas and perspectives of the present invention.

Claims

1. (a) transmitting learning content stored in a first storage area among a plurality of storage areas from a service providing server to a learner's terminal; (b) the service providing server receiving, from the learner's terminal, information on the learner's response to the learning content; (c) the service providing server determines whether to move and store the learning content from the first storage area to the second storage area based on the response information of the learner; and (d) analyzing the learning characteristics of the learner based on the response information of the learner by using a learning model machine-learned based on the learning time, storage area movement time, response information and response speed of the learner, which are cumulatively stored in the storage unit, the repetitive learning period generated based on the response speed, and an adjustment value of the amount of repetitive learning for one time; Including, In (c), Calculate the cumulative number of incorrect answers, which is the number of times the response information does not match the correct answer information; (d) By resetting the storage capacity of each storage area so as to be inversely proportional to the cumulative number of incorrect answers, a learning environment is actively set up so that the amount of repetitive learning per session decreases as the number of repeated incorrect answers to the same question increases. A method for analyzing learning characteristics and adjusting the learning environment using artificial intelligence.

2. (e) The method for analyzing learning characteristics and adjusting a learning environment using artificial intelligence according to claim 1, further comprising a step in which the service providing server adjusts at least one of the learner's repetitive learning period and the amount of repetitive learning per session based on the analysis results of the learner's learning characteristics.

3. a transmitting unit for transmitting the learning content stored in a first storage area among the plurality of storage areas to the learner's terminal; a receiving unit for receiving, from the learner's terminal, information on the learner's response to the learning content; a service providing server that determines whether to move and save the learning content from the first storage area to the second storage area based on the response information of the learner, and a calculation unit that analyzes the learning characteristics of the learner based on the response information of the learner using a learning model that is machine-learned based on the learning time, storage area movement time, the response information and response speed of the learner that are cumulatively saved in the storage unit, the repetitive learning cycle generated based on the response speed, and an adjustment value of the amount of repetitive learning for one time. Including, The calculation unit Calculate the cumulative number of incorrect answers, which is the number of times the response information does not match the correct answer information; By resetting the storage capacity of each storage area so as to be inversely proportional to the accumulated number of incorrect answers, a learning environment is actively set up so that the amount of repetitive learning per session decreases as the number of repeated incorrect answers to the same question increases. Service provider server.

4. 4. The service providing server according to claim 3, wherein the calculation unit adjusts at least one of the repetitive learning period and the amount of repetitive learning per session of the learner based on the analysis result of the learning characteristics of the learner.

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