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, and a service providing server used therein, which not only enables precise analysis of individual learning characteristics of a learner, such as learning ability and learning achievement level during repetitive learning, through artificial intelligence, but also enables active setting of the learning environment of the learner by reflecting the individual learning characteristics of the learner analyzed through artificial intelligence. [Background technology]

[0002] Ebbinghaus's forgetting curve is empirical evidence that shows how important it is to review learned content, and it is widely known that cyclical repetitive learning is more effective than anything else for long-term retention of learned content.

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

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

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

[0006] However, even with this method, technical limitations remain, such as the fact that learners cannot directly participate in setting their learning environment, such as the repetitive learning cycle and amount of repetitive learning, that it is not possible to analyze the individual learning characteristics of learners, such as their learning ability and level of achievement as they proceed with repetitive learning, and that it is not possible to actively set up the learning environment to reflect the individual learning characteristics of such learners. 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 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 through artificial intelligence, but also enables active setting of the learner's learning environment by reflecting the learner's individual learning characteristics analyzed through artificial intelligence, and a service providing server used therein.

[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-mentioned objective, the method for analyzing learning characteristics and adjusting learning environment using artificial intelligence according to the present invention includes a step 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 or not 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 learning characteristics of the learner based on the learner's response information.

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

[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 learning characteristics of the learner based on the learner's response information.

[0012] Preferably, the calculation unit adjusts at least one of a repetitive learning period of the learner and an amount of repetitive learning per session based on the analysis result of the learning characteristics of the learner. Effect 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 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 a person having ordinary skill in the art to which the present invention pertains from the following description. [Brief description of the drawings]

[0015] [Figure 1] 1 is a configuration diagram of a learning characteristic analysis and learning environment adjustment system using artificial intelligence according to one embodiment of the present invention. [Diagram 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. [Diagram 3]4 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. [Figure 4] 4 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 PREFERRED EMBODIMENTS

[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. Detailed descriptions of known functions and configurations that may unnecessarily obscure the gist of the present invention will be omitted.

[0017] 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. Referring to FIG. 1, the learning characteristic analysis and learning environment adjustment system using artificial intelligence 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 a smartphone owned by a learner who uses the learning characteristics analysis and learning environment adjustment service using artificial intelligence according to the present invention, and such a learner terminal 100 can be installed with a predetermined learner application program required to use the learning characteristics 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 providing a learning characteristic analysis and learning environment adjustment service using artificial intelligence according to the present invention, and such a service providing server 200 sequentially transmits learning contents stored in a first storage area among a plurality of storage areas to the learner terminal device 100, receives learner response information regarding the learning contents from the learner terminal device 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 period and the amount of repetitive learning per session based on the analysis result of the learner's learning characteristics.

[0020] The administrator terminal 300 is a wireless communication terminal such as a PC or a 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 application program for administrators required for using 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 through 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 for performing 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 for performing 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 receives from the learner terminal 100 information on the selection of a learning category and response information to the learning contents, and receives from the administrator terminal 300 learning contents by category.

[0027] The calculation unit 250 of the service providing server 200 determines whether or not to move and save the learning content from the first storage area to the second storage area based on the learner's response information regarding 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 period 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 assigned 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 an execution process of a learning characteristic analysis and learning environment adjustment method using artificial intelligence according to an embodiment of the present invention. Hereinafter, an execution process of a learning characteristic analysis and learning environment adjustment method using artificial intelligence according to an embodiment of the present invention will be described with reference to FIG. 1 to FIG. 3.

[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 may 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 correct answer information corresponding to the questions recorded on the corresponding question-type learning card in association therewith.

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

[0034] In addition, the learner's terminal 100 can transmit learning goal setting information, including a target learning period and a 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 multiple storage areas in the individual storage space allocated to the learner's account, the storage capacity of each storage area, and the repetitive learning period 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) associated with the learning subject category and the target learning period P, each storage capacity (e.g., the storage limit of the learning card), 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 multiple 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 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 associated with the learning subject categories and the target learning amount S, each storage capacity (e.g., the storage limit of learning cards), and the repetitive learning period T, as shown in Table 2 below.

[0040] [Table 2]

[0041] In this way, 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 amount of learning 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 study start time (e.g., 5 p.m.) that the service providing server 200 received and stored from the learner's terminal 100 in accordance with the learner's selection along with the study category in the above-mentioned step S410 arrives, the service providing server 200 sequentially transmits the study 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 an 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., one minute), or by selecting the ``Mastery Incomplete'' button if he / she determines that he / she has not fully mastered the contents 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 a 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 transmitted 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 of the service providing server 200 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 manner, the first learning is completed by sequentially transmitting the multiple learning cards stored in the first storage area and receiving the response information for each learning card.

[0051] In this way, in implementing the present invention, it is preferable for the service providing server 200 to complete the corresponding learning session when the sequential transmission of the learning cards stored in the storage area where the sequential transmission of the learning cards is being performed 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 the movement and storage 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 is completed in this manner, when the repetitive learning period (e.g., 2 days) that the service providing server 200 received and stored from the learner terminal 100 based on the learner's selection along with the learning target category in the above-mentioned step S410 arrives, the service providing server 200 generates a second learning content by additionally storing in the first storage area, among the learning cards stored in the common storage space of the storage unit 230, the learning cards corresponding to the category selected by the learner up to the remaining storage limit of the first storage area, and then repeats the above-mentioned 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 learning cards from the first storage area to the second storage area (S445), and can transmit a guidance message about the calculated remaining storage capacity information (for example, the second storage area can currently store an additional five learning cards, and when the storage capacity of the second storage area is exhausted, the learning in the first storage area is interrupted and the transfer of the learning cards stored in the second storage area is started) to the learner's terminal 100.

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

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

[0057] In addition, in implementing the present invention, the service providing server 200 may also transmit a request for approval of modification of setting information, including the request for modification of setting information 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 corresponding 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 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 may also transmit 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 may then generate customized learning content for the learner by additionally 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 transmitting the learning cards stored in the first storage area and starts repetitive learning in the second storage area by sequentially transmitting 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, which have been cumulatively moved and stored in the second storage area, to the learner terminal 100 in the order of storage through execution of the above-mentioned step S445, so that the repetitive learning of the learning card which 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 storage from the second storage area to the third storage area based on the response information received from the learner terminal 100 and repeatedly executes the same procedures as steps S435 to S450 described above (S485).

[0063] Specifically, when the remaining storage capacity in the third storage area is exhausted, the service providing server 200 stops transmitting the learning cards stored in the second storage area, and sequentially transmits 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 manner, in carrying out the present invention, 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 repetitive learning in the Nth storage area is started preferentially, thereby maximizing the learning content that is processed into long-term memory through repetitive 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 exhausted, and then sequentially transmitting the learning cards stored in the first storage area. However, when all of the learning content related to the corresponding category stored in the storage unit 230 is exhausted, it is preferable to start by extracting a storage area in which learning cards are stored from among the multiple storage areas, 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 movement and storage in the multiple storage areas as described above is repeatedly performed until all learning cards related to the corresponding 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 finely adjust the learner's learning environment by reconfiguring the number of storage areas, storage capacity, and repetitive learning cycle set in the above-mentioned step S415 based on various learning information of the learner as described above (S490).

[0068] 4 is a flow chart illustrating a detailed process of a learning characteristic analysis method using artificial intelligence according to an embodiment of the present invention. Hereinafter, the detailed process of the learning characteristic analysis method using artificial intelligence 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, when the time required for the first learning session exceeds 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) so that the storage capacity of each storage area is inversely proportional to the amount by which the time required for the first learning session exceeds the standard time, thereby making it possible to reduce the amount of repetitive 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 reference 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) so that the storage capacity of each storage area is proportional to the amount (absolute value) of the time required for the first learning session that is short of the reference 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, when the time required for the first learning session exceeds a predetermined standard time (e.g., 30 minutes), the calculation unit 250 of the service providing server 200 resets the repetitive learning period of the learner so that the repetitive learning period 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 period to be reduced when the learner's learning speed is poor (i.e., when the learner's memory of the repetitive learning is not clear).

[0074] Furthermore, when the time required for the first learning session is less than a predetermined reference time (e.g., 30 minutes), the calculation unit 250 of the service providing server 200 resets the repetitive learning period of the learner, so that the repetitive learning period is proportional to the amount (absolute value) of the time required for the first learning session that falls short of the reference time, thereby enabling the repetitive learning period to be increased when the learner's learning speed is fast (i.e., when the learner has a clear memory for the 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 above-mentioned 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 movement time exceeds a predetermined standard required time (e.g., 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) so that the storage capacity of each storage area 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 amount of repetitive learning per session when the learner's progress in repetitive learning is poor.

[0078] Furthermore, if the storage area movement time is less than a predetermined standard required time (e.g., 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) so that the storage capacity of each storage area is proportional to the shortfall (absolute value) of the storage area movement time from the standard required time, thereby making it possible to increase the amount of repetitive learning per session when the learner's progress in repetitive learning is fast.

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

[0080] In addition, when the storage area movement time exceeds a predetermined standard required time (e.g., two weeks), the calculation unit 250 of the service providing server 200 resets the repetitive learning period of the learner, and resets the repetitive learning period to be inversely proportional to the excess of the storage area movement time over the standard required time, so that the repetitive learning period can be reduced when the learner's progress in repetitive learning is poor (e.g., when the learner makes mistakes when answering the same question repeatedly).

[0081] In addition, if the storage area movement time is less than a predetermined standard required time (e.g., two weeks), the calculation unit 250 of the service providing server 200 resets the repetitive learning period of the learner, and resets the repetitive learning period to be proportional to the shortfall (absolute value) of the storage area movement time from the standard required time, so that the repetitive learning period can be increased if the learner's progress in repetitive learning is fast.

[0082] In addition, when determining whether to move and save at the aforementioned step S435, the service providing server 200 calculates the "accumulated 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 can analyze this to fine-tune 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 in inverse proportion to the accumulated number of incorrect answers, so that the amount of repetitive learning per session decreases as the number of repeated incorrect answers to the same question increases, and resets the repetitive learning period in inverse proportion to the accumulated number of incorrect answers, so that the repetitive learning period becomes shorter as the number of repeated incorrect answers to the same question increases.

[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 transmitted 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, thereby analyzing the response speed of the learner and making detailed adjustments to the learning environment based on the result (S497).

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

[0086] In addition, 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 accumulated 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 above-mentioned detailed adjustment process of the learning environment upon completion of each learning session or upon fulfillment of the learning start requirements in each storage area, 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 contents for each learning category from the administrator terminal 300 in step S405 described above, and can limit the number of adjustments (e.g., 5 times) for each learning category based on the received learning contents.

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

[0090] [Table 3]

[0091] In this way, in the present invention, by differentially setting 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 finely adjust 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 target category (e.g., intermediate English A) as in the above-mentioned steps S491 to S499 to the learning environment for other learning target categories (e.g., intermediate English B) of the corresponding learner in the same way, and initializes the number of storage areas, storage capacity, and repeated learning period for other learning target categories in the above-mentioned 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 previous learning characteristics of the corresponding learner, 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 other learning subject categories 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 to reflect the individual learning characteristics of the learner analyzed through artificial intelligence.

[0096] Specifically, in the present invention, the service providing server 200 may 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 may store a learning model for analyzing the time required for learning, the time required to move between storage areas, and the learner's response information and response speed.

[0097] In addition, the learning model in the present invention may be composed of a calculation algorithm that analyzes the learning characteristics of a learner based on the learning time required in the calculation unit 250, the storage area movement time, 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 may also be formed of 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 an adjustment value of the amount of repetitive learning for one time.

[0099] Meanwhile, in implementing the present invention, the service providing server 200 may also generate compensation information for a learner based on the total learning period or total number of learning times required until all learning cards related to the corresponding 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 inversely proportional to the total number of learning attempts, thereby encouraging a learner who has 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 the storage capacity in the above-mentioned step S415 for a learner who will later start the repetitive learning process again, the calculation unit 250 sets the storage capacity inversely proportional to the total number of times the learner performed the previous repetitive learning process and sets the number of storage areas proportional to the total number of times the learner performed the previous repetitive learning process, and also sets the storage capacity in proportion to the total correct answer ratio in the previous repetitive learning process and sets the number of storage areas inversely proportional to the total correct answer ratio, thereby providing a customized long-term support service that takes into account the learning ability of the learner.

[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 the end of the 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 carrying out the present invention, the calculation unit 250 of the service providing server 200 generates billing information for the learner in proportion to the total study period or the total number of times of study required until all study cards related to the corresponding category are stored in the final storage area, thereby charging a usage fee in proportion to the service use period and the number of times of use, and at the same time, encouraging the learner to complete repetitive study early through serious study.

[0104] In addition, in carrying out 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 transmits the corresponding program via a network.

[0105] In addition, the program according to the present invention can be distributed among network-connected computer systems, and computer-readable code can be stored and executed in a distributed manner. The functional programs, codes, and code segments for embodying the present invention can be easily understood by those of ordinary skill 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 of the 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, the terms "include" 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 presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0108] Although 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 it goes without saying that various modifications can 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 contents stored in a first storage area among a plurality of storage areas by 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 or not 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) the service providing server analyzes the learning characteristics of the learner based on the response information of the learner. A method for analyzing learning characteristics and adjusting a learning environment using artificial intelligence, comprising:

2. (e) 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 result of the learner's learning characteristics, according to the method of claim 1,

3. a transmitting unit for transmitting the learning contents 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; and A calculation unit in which the service providing server analyzes the learning characteristics of the learner based on the response information of the learner. A service providing server including:

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

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