Image learning monitoring device and method

Abstract

Disclosed is an image learning monitoring device 100. The image learning monitoring device 100 may include: a communication unit 170 configured to receive a student's study video captured by a user terminal 200; and a score calculation unit 140 configured to calculate the student's self-directed learning score based on the study video received by the communication unit 170.

Description

[Technical Field]

[0001] The present invention relates to an image learning monitoring device and method, and more specifically, to an image learning monitoring device and method that can strengthen students' self-directed learning abilities, including a good learning attitude, and cultivate good study habits so that they can concentrate more on their study time. [Background technology]

[0002] Research has shown that the effects of the emphasis on grades and the competition for entrance exams extend to the early grades of elementary school, but the reality is that many parents rely heavily on private education, such as advanced learning through academies or private tutors, to ensure that their children do not fall behind in the competition for entrance exams.

[0003] The problem with this excessive private education is that it causes students to lose their self-directed learning ability and interest in learning, leading to a significant decline in academic achievement in the long term. In particular, research data comparing the academic achievement of private education and self-directed learning shows that private education results in a 1.5% success rate, while self-directed learning results in a 4.6% success rate. The importance of self-directed learning is gradually being emphasized at the national level, with educational processes that emphasize self-directed learning being promoted. Educational methods for this type of self-directed learning have long been researched and developed in educationally advanced countries such as Europe and the United States.

[0004] However, today's students spend a considerable amount of time in mobile environments such as smartphones, which tends to gradually reduce their ability to self-directed learning. They sit at their desks with the intention of studying, but when they get there, they waste a lot of time on activities unrelated to self-directed learning, such as using their cell phones, using social media with friends, or watching entertainment video content.

[0005] Therefore, there is a growing social need in the industry, especially in the education industry, for a new type of learning monitoring device, method, and system that can utilize smartphones, which have become an essential part of students' lives, to enhance students' self-directed learning abilities and cultivate good academic habits so that they can concentrate more during study time. Summary of the Invention [Problem to be solved by the invention]

[0006] The present invention has been made to solve the above-mentioned problems, and aims to provide an image learning monitoring device and method that can improve students' self-directed learning abilities through image learning monitoring and evaluation.

[0007] Another object of the present invention is to provide an image learning monitoring device and method that can improve a student's sustainable learning attitude and academic ability by utilizing a self-directed learning score calculated based on the student's study video.

[0008] Another object of the present invention is to provide an image learning monitoring device and method that can provide students with relatively low self-directed learning ability with online learning guidance and customized online / offline integrated teaching services, thereby enabling the students to develop study habits and improve their self-directed learning ability.

[0009] Another object of the present invention is to provide an image learning monitoring device and method that can increase students' interest in an online self-directed learning management system by providing predetermined rewards to students who achieve excellent evaluation results in image learning monitoring.

[0010] The technical problems of the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0011] To solve the above technical problems, an image learning monitoring device according to one embodiment of the present invention may include: a communication unit configured to receive a student's study video captured by a user terminal; and a score calculation unit configured to calculate the student's self-directed learning score based on the study video received by the communication unit.

[0012] The image learning monitoring device may further include an evaluation unit including an environment evaluation unit for evaluating the student's study environment and an attitude evaluation unit for evaluating the student's study attitude.

[0013] The score calculation unit may be further configured to calculate the self-directed learning score using at least one of an evaluation result by the environment evaluation unit and an evaluation result by the attitude evaluation unit.

[0014] Additionally, the communication unit may be further configured to transmit the calculated self-directed learning score to at least one of a parent terminal and the user terminal.

[0015] In addition, an image learning monitoring system according to another embodiment of the present invention for solving the above technical problems may include: the image learning monitoring device; and a user terminal communicatively coupled to the image learning monitoring device.

[0016] In addition, an image learning monitoring method according to a further embodiment for solving the above technical problems may include: receiving a student's study video taken by a user terminal; and calculating the student's self-directed learning score based on the received study video.

[0017] The image-based learning monitoring method may further include the steps of: evaluating the student's study environment; and evaluating the student's study attitude.

[0018] Furthermore, calculating the self-directed learning score of the student may include calculating the self-directed learning score using at least one of the evaluation results of the study environment and the evaluation results of the study attitude.

[0019] The image learning monitoring method may further include transmitting the calculated self-directed learning score to at least one of a parent terminal and the user terminal.

[0020] Furthermore, the computer program for solving the above technical problems may be stored on a computer-readable recording medium so that it can be combined with a computer (hardware) and perform the method. [Effects of the Invention]

[0021] According to the image learning monitoring device and method according to an embodiment of the present invention, students' self-directed learning ability can be improved through image learning monitoring and evaluation.

[0022] In addition, according to an embodiment of the present invention, the image learning monitoring device and method can improve a student's continuous learning attitude and academic ability by utilizing a self-directed learning score calculated based on the student's study video.

[0023] In addition, according to an embodiment of the present invention, the image learning monitoring device and method thereof can provide a customized online / offline integrated teaching service along with online learning guidance to students with relatively low self-directed learning ability, thereby enabling the students to develop study habits and improve their self-directed learning ability.

[0024] In addition, according to an embodiment of the present invention, the image learning monitoring device and method thereof can increase students' interest in the online self-directed learning management system by providing predetermined rewards to students who achieve excellent evaluation results in image learning monitoring.

[0025] A brief description of each of the drawings referred to in the detailed description of the present invention is provided to provide a more complete understanding of the drawings. [Brief explanation of the drawings]

[0026] [Figure 1a] FIG. 1 is a schematic block diagram of an image learning monitoring system 1000 according to an embodiment of the present invention. [Figure 1b] FIG. 1 is a schematic block diagram of an image learning monitoring system 1000 according to an embodiment of the present invention. [Figure 1c] FIG. 10 is a detailed block diagram of an evaluation unit 130. [Figure 2] (a) is an illustrative diagram for explaining the recording of a student's study video according to one embodiment of the present invention, and (b) and (c) are illustrative diagrams for explaining various interfaces through which the student's study video can be viewed on the image learning monitoring device 100 side according to one embodiment of the present invention. [Figure 3] 1 is a flowchart illustrating an image learning monitoring method (S300) according to an embodiment of the present invention. [Figure 4] FIG. 10 is a schematic block diagram of an image learning monitoring system 1000′ according to a further embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0027] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. When adding reference numerals to components in each drawing, it should be noted that identical components are denoted by the same numerals whenever possible, even if they appear in different drawings. Furthermore, when describing embodiments of the present invention, if a detailed description of related known structures or functions is deemed to hinder understanding of the embodiments of the present invention, such detailed description will be omitted. Furthermore, although embodiments of the present invention will be described below, the technical concept of the present invention is not limited thereto and can be modified and implemented in various ways by those skilled in the art.

[0028] Throughout this specification, when a part is said to be "connected" to another part, this includes not only "directly connected" but also "indirectly connected" through another element therebetween. Throughout this specification, when a part is said to "comprise" another component, this does not mean that the part may exclude the other component, but may further include the other component, unless otherwise specified. Furthermore, when describing components of an embodiment of the present invention, terms such as "first," "second," "A," "B," "(a)," and "(b)" may be used. These terms are used merely to distinguish the component from other components, and do not limit the nature, order, or sequence of the components.

[0029] 1a and 1b are schematic block diagrams of an image learning monitoring system 1000 according to one embodiment of the present invention, and FIG. 1c is a detailed block diagram of an evaluator 130. As shown in FIG.

[0030] As shown in FIG. 1 a , an image learning monitoring system 1000 according to an embodiment of the present invention may include an image learning monitoring device 100 and a user terminal 200 .

[0031] For reference, a service for improving online self-directed learning ability can be installed on a user terminal 200 in the form of an application (also referred to as an "app" or "app") and provided to users, including students. For convenience, in the following specification, an application that provides such a self-directed learning service to users will be referred to as an "image learning monitoring application."

[0032] The image learning monitoring device 100 according to an embodiment of the present invention is a device for managing and operating an image learning monitoring application, and may be embodied as, for example, an application server. As shown in FIG. 1A, the image learning monitoring device 100 according to an embodiment of the present invention may include a control unit 110, an input unit 120, an evaluation unit 130, a score calculation unit 140, a feedback unit 150, a matching unit 160, a communication unit 170, a storage unit 180, and a notification unit 190.

[0033] For reference, the components 110, 120, 130, 140, 150, 160, 170, 180, and 190 of the image learning monitoring device 100 shown in FIG. 1a are merely exemplary components for explaining the operation, functions, etc. of the image learning monitoring device 100 according to one embodiment of the present invention, and therefore it is clear that the image learning monitoring device 100 according to one embodiment of the present invention may further include other components (e.g., a power supply unit, a display unit, an output unit, etc.) other than the illustrated components 110, 120, 130, 140, 150, 160, 170, 180, and 190.

[0034] In addition, the image learning monitoring device 100 according to one embodiment of the present invention corresponds to a proprietary computer program-based device produced and operated by an individual or private company, and is referred to by various terms such as device, equipment, server, platform, etc. according to various expression methods in the related technical field.

[0035] For example, the visual learning monitoring device 100 according to an embodiment of the present invention can receive a student's video captured by the user terminal 200 in real time and calculate the student's self-directed learning score based on the video. To this end, the visual learning monitoring device 100 can be implemented as an application or widget, where a widget may include a mini-application that allows immediate use of content or functions without going through an application. Therefore, the term "application" in the following description can be interpreted as a comprehensive concept including a widget, and a user can download a visual learning monitoring application operated and managed by the visual learning monitoring device 100 according to the present invention from an online market, etc., and install and use the application on the user terminal 200.

[0036] A user (e.g., a student) of the user terminal 200 can download and install an image learning monitoring application from an online market, etc., where the user may further have to go through a procedure to subscribe to the application service, and if the application service is operated on a paid basis, may have to go through a payment procedure for the fee (e.g., 10,000 won / month, 20,000 won / month, etc.), and for this purpose, further communication with a payment server (not shown) may also be implemented.

[0037] The input unit 120 of the image learning monitoring device 100 according to an embodiment of the present invention may provide an input interface through which the user terminal 200 can input a study plan, for example, study time. The user terminal 200 can input study time (e.g., 10:00 PM to 11:00 PM) through the input unit 120, and the input study time is stored in the memory unit 180 by user category and can be used for evaluation by the evaluation unit 130 and for notification by the notification unit 190.

[0038] In the following specification, study time is described as a representative example of a study plan input by the user via input unit 120, but according to a further embodiment of the present invention, an interface can be further provided that allows the user to input study subjects (e.g., mathematics), study materials (e.g., mathematical formulas), study scope (e.g., pages 103 to 107) in addition to study time.

[0039] For reference, the study time input to the user terminal 200 via the input unit 120 may be (i) input individually for each day, (ii) input as a fixed time during a predetermined period (e.g., 10:00 PM to 11:00 PM every day from December 20th to 31st), or (iii) input as a variable time depending on the day of the week (e.g., 10:00 PM to 11:00 PM Monday through Friday, 2:00 PM to 3:00 PM Saturday through Sunday, etc.). These are merely examples for easier understanding of the present invention, and the present invention is not limited to such study time input methods. The image learning monitoring device 100 according to the present invention functions as a so-called study planner for the student's study time input in this manner, allowing the user to register and manage the study time. For example, the user can check, using a score chart or graph by date, whether the self-directed learning score is gradually improving as targeted by date, whether there is any significant improvement, or even whether the self-directed learning score is decreasing.

[0040] Furthermore, the input unit 120 of the image-based learning monitoring device 100 according to an embodiment of the present invention may further comprise an interface that allows a student to input a change to a previously input study time. For example, if a student has registered a study time of 10:00 PM to 11:00 PM, but another schedule comes up during that time, the student can execute the image-based learning monitoring application and input the changed study time (e.g., 8:00 PM to 9:00 PM on the current day, or 9:00 AM to 11:00 AM the next day) via the input unit 120. Here, the image-based learning monitoring system 1000 may be embodied such that the change to the study time is subject to confirmation from the parent terminal 300 (see FIG. 4).

[0041] Alternatively, the control unit 110 of the image learning monitoring device 100 according to a further embodiment of the present invention may be configured to suggest a customized study time to the user terminal 200 based on user information stored in the storage unit 180 and / or the accumulated study results of such user information. For example, the user terminal 200 may input user information, such as a student's arrival time at school, departure time from school, school hours, after-school schedule, weekend schedule, etc., via the input unit 120, and the control unit 110 may suggest a customized study time to the user terminal 200 based on such user information, the evaluation results by the evaluation unit 130, and the self-directed learning score calculated by the score calculation unit 140. For example, the control unit 110 may statistically analyze times when the self-directed learning score is relatively high and suggest the analyzed study time to the user terminal 200 or the parent terminal 300, thereby improving the student's study efficiency and encouraging the user or parent to take a greater interest in studying.

[0042] When the user terminal 200 registers the student's study time and executes the visual learning monitoring application on the user terminal 200, the student's study video captured by the user terminal 200 can be transmitted in real time to the visual learning monitoring device 100 via the communication unit 170. For accurate and fair evaluation, particularly for AI-based video analysis, the user terminal 200 can be informed in advance via the visual learning monitoring application about a method for capturing the study video (e.g., angle, placement, etc.). For example, as shown in FIG. 2(a), the student can be instructed to hold the user terminal 200 horizontally on the front or side of a desk so that the student's upper body, including their face, and the learning materials placed on the desk are clearly captured. If it is determined that the received video does not meet predetermined requirements, the feedback unit 150 can request the user terminal 200 to reposition the user terminal 200, adjust the angle, etc., by voice or text. The feedback unit 150 can continue to request adjustments until the video requirements are met.

[0043] According to a further embodiment of the present invention, a student's study video captured by the user terminal 200 can be transmitted in real time not only to the visual learning monitoring device 100 but also to the parent terminal 300. The simultaneous transmission of such captured study video to the parent terminal 300 can be subject to approval by, for example, a visual learning monitoring administrator, which has the advantage of allowing parents to easily check their child's study attitude, situation, etc., even from a remote location without direct contact with the child. When the student's study video is also transmitted to the parent terminal 300, the notification unit 190 according to an embodiment of the present invention can provide the parent terminal 300 with a notification to start studying before the study time, for example, about 10 minutes before.

[0044] The evaluation unit 130 of the image learning monitoring device 100 may be configured to evaluate the study environment and study attitude of the student based on the study video received from the user terminal 200 via the communication unit 170. To this end, as shown in Fig. 1c, the evaluation unit 130 may be configured with an environment evaluation unit 131 and an attitude evaluation unit 132.

[0045] The environment evaluation unit 131 can evaluate the study environment before the student begins studying, and the evaluation of the study environment may include at least an evaluation of whether or not there was a timely connection. For example, if a student registers a study time from 10:00 PM to 11:00 PM, the environment evaluation unit 131 can determine whether the student's user terminal 200 executed an image learning monitoring application and connected to the image learning monitoring device 100 at 10:00 PM or a few minutes before. To this end, the image learning monitoring device 100 according to an embodiment of the present invention can notify the user terminal 200 of remote connection conditions to the image learning monitoring system a predetermined time before the registered study time, for example, 10 minutes before, and the user terminal 200 can connect to the image learning monitoring device 100 according to the notified remote connection conditions, for example, using the image learning monitoring application.

[0046] If the environment evaluation unit 131 evaluates that the user terminal 200 connected in a timely manner, this acts as a positive factor when the score calculation unit 140 calculates the self-directed learning score. Conversely, if the environment evaluation unit 131 evaluates that the user terminal 200 did not connect in a timely manner, for example, if the user terminal 200 connected to the system 20 minutes late, this can act as a negative factor when the score calculation unit 140 calculates the self-directed learning score. In particular, if the user terminal 200 does not connect at all during the registered study time, the evaluation by the evaluation unit 130 and the score by the score calculation unit 140 can be all zero points, and the fact of such non-connection and the zero score can be immediately notified to the parent terminal 300 and / or the user terminal 200.

[0047] In addition to or as an alternative to evaluating whether or not there is a timely connection, the environment evaluation unit 131 according to an embodiment of the present invention may evaluate whether or not the student's surrounding environment is suitable for studying based on study video received in real time from the user terminal 200. For example, the environment evaluation unit 131 may evaluate, based on artificial intelligence (AI), from the captured video, whether study materials, writing implements, etc. are prepared on the desk, whether other items that may interfere with studying (e.g., a portable game console, toys, etc.) are placed on the desk, and whether the lighting illuminance of the study environment is appropriate. For such AI learning and evaluation, the memory unit 180 may store reference video (or images) of study materials, tablets, etc., reference video (or images) of lighting illuminance, related image data, etc.

[0048] The attitude evaluation unit 132 can evaluate the student's study attitude using artificial intelligence (AI) based on the video information during the registered study time. For example, the attitude evaluation unit 132 can evaluate the student's study attitude by comprehensively determining whether the student remains seated during the registered study time, whether the student is absent (or withdrawn) during the study time, whether the student is sleeping face down, whether the student is in a twisted position, etc. For such AI learning and evaluation, the memory unit 180 can further store reference information such as reference videos and images, reference values, etc. for various situations that the student may be in during study time, such as sitting, being absent (or withdrawn), and sleeping.

[0049] Here, to immediately meet the needs of students who want to study beyond the previously entered study time even during or before starting their studies, an image learning monitoring application according to a further embodiment of the present invention can monitor their studies by increasing the time in the student's pre-registered study schedule when the student studies beyond the study time. Thus, a student can increase their study time at any time before starting or during their studies, and such a request for an increase in study time can be notified to the parent terminal 300 in real time. The attitude evaluation unit 132 and the score calculation unit 140 can consider the request for an increase in study time received from the user terminal 200 as a positive factor in attitude evaluation and in calculating the self-directed learning score.

[0050] Similarly, the image learning monitoring application may further include an interface that allows a student to request a study break in the middle of study time. When a situation arises in which a student is forced to stop studying in the middle of study time, the control unit 110 may be configured to add the remaining time resulting from the study break to future study time (e.g., the next day or a date entered by the student), or to register a separate study time in the schedule and set and save the study time in the schedule so that further study can be done according to the time registered in the schedule.

[0051] Once the evaluation by the evaluation unit 130, more specifically, the evaluation of the study environment by the environment evaluation unit 131 and the evaluation of the study attitude by the attitude evaluation unit 132, is completed, the score calculation unit 140 of the image learning monitoring device 100 according to an embodiment of the present invention can calculate a self-directed learning score based on at least one of the environment evaluation and the attitude evaluation evaluated by the evaluation unit 130. For example, the self-directed learning score can be set to a maximum of 100 points, and can be scored as a specific numerical value by comprehensively evaluating the presence or absence of timely connection, the study environment before starting to study, the state of study preparation, the study attitude during study time, etc., and the scoring of the self-directed learning environment and attitude can be automatically implemented using AI-based learning.

[0052] Here, if the study time registered by the user terminal 200 exceeds one hour, i.e., if a student registers a study time exceeding one hour, the image-based learning monitoring device 100 according to a further embodiment of the present invention can set a break time, for example, approximately halfway through the study time, and notify the user terminal 200 and / or parent terminal 300. The set break time may be approximately 10 to 20 minutes. The image-based learning monitoring system 1000 can also be embodied so that the attitude evaluation unit 132 does not evaluate the study attitude and the score calculation unit 140 does not calculate the self-directed learning score during the notified break time. Here, a maximum daily study time (e.g., four hours) that the user terminal 200 can register can be set in advance, and any study time entered that exceeds the set maximum study time can be corrected to the set maximum study time value and returned to the user terminal 200 and / or parent terminal 300.

[0053] For reference, the self-directed learning score calculated by the score calculation unit 140 may be implemented as a score based on the environmental assessment and a score based on the attitude assessment, respectively, or as a single score that combines the score based on the environmental assessment and the score based on the attitude assessment (e.g., a score obtained by averaging two items, a score obtained by applying different weights to two items, etc.), but the method of evaluating the self-directed learning score according to one embodiment of the present invention is not limited thereto.

[0054] Furthermore, the feedback unit 150 of the image learning monitoring device 100 can provide real-time feedback to the user terminal 200 during the registered study time based on artificial intelligence (AI). For example, the feedback unit 150 can provide appropriate feedback to the user terminal 200 when a student is not concentrating on their studies and is dozing off, or when a student is absent from their desk for a predetermined period of time (e.g., three minutes) by analyzing captured video information received from the user terminal 200 based on AI. For example, the feedback provided to the user terminal 200 can be embodied in the form of audio (or sound / notification), text, video, or a combination of audio, text, and video. Of course, when a student adheres to self-directed learning, the feedback unit 150 can actively encourage the student's interest in good study habits by providing positive feedback.

[0055] The evaluation operations by the environment evaluation unit 131 and the attitude evaluation unit 132 or the feedback providing operation by the feedback unit 150 can be implemented using AI based on video information analysis, and here, the operator (also called a "master" or "tutor") of the image learning monitoring device 100 can also assist the evaluation operations by the evaluation unit 130 or the feedback operations by the feedback unit 150.

[0056] According to the image learning monitoring system 1000 as exemplarily shown in Fig. 1a, a single user terminal 200 can connect to the image learning monitoring device 100 and receive image learning monitoring services, and an interface through which such one-to-one learning monitoring video is viewed on the image learning monitoring device 100 side is exemplarily shown in Fig. 2(b). As shown in Fig. 2(b), the image learning monitoring device 100 monitors only the study video received from the single user terminal 200 on a one-to-one basis, enabling more focused and specialized learning monitoring and online learning guidance.

[0057] Furthermore, according to the image learning monitoring system 1000 as illustrated in FIG. 1b, multiple user terminals 200-1, 200-2, ..., 200-N (where N is a natural number greater than or equal to 2) can connect to the image learning monitoring device 100 and receive image learning monitoring services, and an interface through which such one-to-many learning monitoring images can be viewed on the image learning monitoring device 100 side is illustrated in FIG. 2(c) as an example.

[0058] 2(c), the feedback unit 150 may be configured to communicate (e.g., converse, chat, etc.) one-to-one with only one selected user terminal 200 among the multiple user terminals 200 connected to the image learning monitoring device 100, and the content of the one-to-one communication is not transmitted to the other unselected user terminals 200. Thus, even in a one-to-many learning monitoring environment, targeted or customized feedback can be provided to a specific student or specific students. For example, the one-to-one customized feedback provided to the user terminal 200 may include at least content related to online learning instruction.

[0059] Furthermore, according to other embodiments of the present invention, the feedback unit 150 may be configured to communicate with all of the multiple user terminals 200 simultaneously (e.g., for public disclosure), and for this purpose, the image learning monitoring device 100 may further be provided with a mode switching interface that can switch between one-to-one communication mode and one-to-many communication mode.

[0060] 1b and 2c, when a plurality of user terminals 200 simultaneously connect to the image-based learning monitoring device 100 and receive the image-based learning monitoring service, the display of each of the plurality of user terminals 200 may display only the user's own image, or may also display at least one other student's image. To this end, an interface that allows a student to select a mode in which to hide the other students' study videos and a mode in which to display the other students' study videos may be provided to the user terminal 200 via the image-based learning monitoring application. Thus, each student can select, according to their own preference, whether to display the images of other students who are currently studying at the same time as them.

[0061] In particular, in the latter case, by displaying the study videos of other students who are studying at the same time on the user terminal 200, it is possible to visually confirm the learning attitudes of the other students and further stimulate motivation for self-directed study. Here, the other students displayed on the user terminal 200 may be a randomly selected portion of multiple students who connected to the image learning monitoring device 100 at the same time, or may be students or a portion thereof grouped based on the user's age (grade), gender, region, school, score, etc.

[0062] In connection with the calculation of the self-directed learning score by the score calculation unit 140 according to an embodiment of the present invention, a standard score (e.g., 50 points) for customized online / offline joint instruction may be preset. For example, if the self-directed learning score calculated by the score calculation unit 140 does not reach the standard score, or if the average of the self-directed learning scores for a certain period (e.g., one week) does not reach the standard score, the matching unit 160 according to an embodiment of the present invention may match the student with a tutor who can provide customized online / offline joint instruction, or the control unit 110 may allocate additional (or supplemental) study time by referring to the student's schedule and provide it to the user terminal 200 and / or parent terminal 300, thereby providing additional time for the student to improve their self-directed learning ability.

[0063] The matching unit 160 may perform customized student-tutor matching based on information about students and information about registered tutors stored in the storage unit 180, and may provide the matched information to the user terminal 200, the parent terminal 300, the tutor terminal 400 (see FIG. 4), etc. For reference, the tutor terminal 400 may be communicably coupled to the image learning monitoring device 100 according to an embodiment of the present invention, and the term "tutor" used herein may refer to a specialized teacher who is able to provide instruction on a specific subject as well as self-directed learning not only online but also offline, i.e., face-to-face instruction with students.

[0064] The communication unit 170 may be configured to communicate with the user terminal 200, the parent terminal 300, the tutor terminal 400, a payment server (not shown), etc. For example, the student's self-directed learning score calculated by the score calculation unit 140 may be transmitted to at least one of the student's user terminal 200 and the student's parent terminal 300 via the communication unit 170, and student-tutor matching information performed by the matching unit 160 for a student who has a score below the standard may be transmitted to at least one of the student's user terminal 200, the student's parent terminal, and the matched teacher's tutor terminal 400 via the communication unit 170.

[0065] For reference, the communication unit 170 is provided for direct connection to the outside or connection via a network, and may be a wired and / or wireless communication unit 170. Specifically, the communication unit 170 may transmit data from the control unit 110, the input unit 120, the evaluation unit 130, the score calculation unit 140, the feedback unit 150, the matching unit 160, the notification unit 190, etc. via a wired or wireless connection, or may receive data from the outside via a wired or wireless connection, and transmit the data to the control unit 110, the input unit 120, the evaluation unit 130, the score calculation unit 140, the feedback unit 150, the matching unit 160, the notification unit 190, or store the data in the memory unit 180. The data may include content such as text, images, and videos.

[0066] The communication unit 170 may communicate via LAN, Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), Wireless Broadband Internet (WiBro), Radio Frequency (RF) communication, Wireless LAN, Wireless Fidelity, Near Field Communication (NFC), Bluetooth, infrared communication, etc. However, these are merely examples, and various wired and wireless communication technologies applicable in the art may be used depending on the embodiment to which the present invention is applied.

[0067] The storage unit 180 may store any data related to the image learning monitoring device 100, image learning monitoring system 1000, and image learning monitoring service according to an embodiment of the present invention. For example, the data stored in the storage unit 180 may include, but is not limited to, data related to the image learning monitoring application, data related to user information, data related to registered study time, data related to filmed study videos, data related to environment assessment results, data related to attitude assessment results, data related to self-directed learning scores, data related to artificial intelligence algorithms for real-time feedback coaching, and data related to student-tutor matching.

[0068] For reference, the storage unit 180 may be implemented as various types of storage devices capable of inputting and outputting information, such as a hard disk drive (HDD), read-only memory (ROM), random access memory (RAM), electrically erasable and programmable read-only memory (EEPROM), flash memory, a compact flash (CF) card, a secure digital (SD) card, a smart media (SM) card, a multimedia (MMC) card, or a memory stick, as known to those skilled in the art, and may be provided inside the image learning monitoring device 100 as shown in Fig. 1 or in a separate external device. Alternatively, the storage unit 180 may be replaced by web storage that performs a storage function on the Internet.

[0069] 1, the image-based learning monitoring device 100 according to an embodiment of the present invention may further include a display unit (not shown) and may display, for example, a video of a student on the display unit as shown in (b) and (c) of FIG. 2. The display unit may display various information processed by the image-based learning monitoring device 100 on a display means, which may include at least one of a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT LCD), an organic light-emitting diode (OLED), a flexible display, and a 3D display.

[0070] The notification unit 190 can provide various types of notifications to the user terminal 200. For example, for a user terminal 200 that has registered 10:00 PM to 11:00 PM as the study time, the notification unit 190 can provide a predetermined notification to the user terminal 200 in advance (for example, 9:50 PM), and can also provide a predetermined notification to the parent terminal 300 in advance, thereby significantly improving the convenience and satisfaction of users who receive this image learning monitoring service.

[0071] The control unit 110 may generally control the operation of the image learning monitoring device 100 according to an embodiment of the present invention. For example, the control unit 110 may be embodied as a processor, a controller, a microprocessor, a microcontroller, etc. That is, the control unit 110 may operate at least one or more components included in the image learning monitoring device 100 in combination with each other to run an application program.

[0072] As described above, the user terminal 200 can be communicatively coupled to the image learning monitoring device 100, and a one-to-one learning monitoring environment can be established as shown in Figures 1a and 2(b), or a one-to-many learning monitoring environment can be established as shown in Figures 1b and 2(c). In this specification, the user of the user terminal 200 is described as a student who wants to improve his or her self-directed learning ability, but it goes without saying that the user of the user terminal 200 can also include ordinary people who want to cultivate a self-directed attitude and behavior.

[0073] Furthermore, although this specification describes a service that monitors a user's autonomous learning using the image learning monitoring device 100 as a representative example, it is clear that the image learning monitoring device 100 and image learning monitoring system 1000 can also be applied to various activities that can cultivate self-directed lifestyle habits, such as reading, writing, speaking a foreign language, home training, cooking, and hobbies, in addition to studying, and various types of content can also be stored in the memory unit 180 to support services for such various activities.

[0074] For reference, user terminal 200 may also be referred to as a subscriber unit, subscriber station, mobile station, mobile terminal, remote station, remote terminal, mobile device, access terminal, terminal, wireless communication device, user agent, user device, or user equipment (UE). An access terminal may be a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA), a handheld device with wireless connectivity capabilities, a computing device, or other processing device connected to a wireless modem.

[0075] FIG. 3 is a flowchart illustrating an image learning monitoring method (S300) according to an embodiment of the present invention.

[0076] First, the user terminal 200, which has downloaded and installed the image learning monitoring application via an online market or the like, can register the study time via the input unit 120 (S310). The study time registered via the input unit 120 is stored in the memory unit 180 and can be used for evaluation by the evaluation unit 130, calculation of the self-directed learning score by the score calculation unit 140, notification by the notification unit 190, etc.

[0077] When the input unit 120 inputs the student's study time (e.g., 10:00 PM to 11:00 PM), the environment evaluation unit 131 of the evaluation unit 130 can determine whether the user terminal 200 is connected in a timely manner (S320). As already described, if a student is determined to have connected in a timely manner, this will be a positive factor in the self-directed learning score, and if a student is not determined to have connected in a timely manner, this will be a negative factor in the self-directed learning score.

[0078] The presence or absence of a timely connection is determined, and when the registered study time arrives, the student's study video captured by the user terminal 200 can be received in real time via the communication unit 170 (S330). In a one-to-one learning monitoring environment, as shown in FIG. 2(b), the student's study video is displayed on the visual learning monitoring device 100, whereas in a one-to-many learning monitoring environment, as shown in FIG. 2(c), multiple study videos may be displayed on the visual learning monitoring device 100, and as already described, the user terminal 200 can select a display mode so that the study videos of other students may or may not be displayed together.

[0079] The environment evaluation unit 131 can evaluate the study environment (S340), the attitude evaluation unit 132 can evaluate the study attitude (S350), and the score calculation unit 140 can calculate a self-directed learning score based on the evaluation result of the study environment by the environment evaluation unit 131 and / or the evaluation result of the study attitude by the attitude evaluation unit 132 (S360).

[0080] When the self-directed learning score is calculated, the communication unit 170 may transmit the calculated self-directed learning score to at least one of the user terminal 200 and the parent terminal 300 (S370), and the matching unit 160 may provide customized online / offline collaborative instruction to students who have a self-directed learning score below the reference score by performing student-tutor matching (S380). For reference, the image learning monitoring system 1000 according to an embodiment of the present invention may further include regional education locations for offline collaborative instruction, and may assist matched students and tutors to meet at the education locations to conduct offline learning instruction.

[0081] Furthermore, according to another embodiment of the present invention, a predetermined reward may be provided to a student who has an excellent self-directed learning score. For example, the reward provided to an excellent student may include, but is not limited to, a discount / waiver on service fees, point accumulation, etc.

[0082] In addition to transmitting the calculated self-directed learning score to the user terminal 200 and / or parent terminal 300, the image learning monitoring device 100 according to a further embodiment of the present invention can further transmit to the user terminal 200 and / or parent terminal 300 specific items related to items such as the environment evaluation results and attitude evaluation results evaluated by the evaluation unit 130, and online learning guidance provided by the feedback unit 150.

[0083] FIG. 4 is a schematic block diagram of an image learning monitoring system 1000' according to a further embodiment of the present invention.

[0084] As described above, the self-directed learning score calculated by the score calculation unit 140 of the image learning monitoring device 100 according to one embodiment of the present invention can be transmitted to the parent terminal 300 via the communication unit 170, and for this purpose, the image learning monitoring system 1000' according to a further embodiment of the present invention may further include a parent terminal 300 communicatively coupled to the image learning monitoring device 100 and the user terminal 200.

[0085] In addition, the matching unit 160 of the image learning monitoring device 100 according to one embodiment of the present invention can provide customized online / offline collaborative tutoring services by performing student-tutor matching for students who have self-directed learning scores below a reference score, and for this purpose, the image learning monitoring system 1000′ according to a further embodiment of the present invention may further include a tutor terminal 400 communicatively coupled to the image learning monitoring device 100.

[0086] For reference, the parent terminal 300 and the tutor terminal 400 may also be referred to as subscriber units, subscriber stations, mobile stations, mobile terminals, remote stations, remote terminals, mobile devices, access terminals, terminals, wireless communication devices, user agents, user devices, or user equipment (UE). While FIG. 4 illustrates a single parent terminal 300 and a single tutor terminal 400, it will be apparent that multiple parent terminals 300 and multiple tutor terminals 400 may be provided.

[0087] As described above, the image learning monitoring device and method according to an embodiment of the present invention can improve students' self-directed learning ability through image learning monitoring and evaluation.

[0088] In addition, according to an embodiment of the present invention, the image learning monitoring device and method can improve a student's continuous learning attitude and academic ability by utilizing a self-directed learning score calculated based on the student's study video.

[0089] In addition, according to an embodiment of the present invention, the image learning monitoring device and method thereof can provide a customized online / offline integrated teaching service along with online learning guidance to students with relatively low self-directed learning ability, thereby enabling the students to develop study habits and improve their self-directed learning ability.

[0090] In addition, according to an embodiment of the present invention, the image learning monitoring device and method thereof can increase students' interest in the online self-directed learning management system by providing predetermined rewards to students who achieve excellent evaluation results in image learning monitoring.

[0091] It should be noted that various embodiments described herein may be implemented using hardware, middleware, microcode, software, and / or combinations thereof. For example, various embodiments may be implemented using one or more custom application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processor devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to perform the functions presented herein, or combinations thereof.

[0092] Further, for example, various embodiments may be embodied in or encoded on a computer-readable medium that includes instructions. The instructions embodied in or encoded on the computer-readable medium may cause a programmable processor or other processor, for example, to perform a method when the instructions are executed. Computer-readable media includes computer storage media, which may be any available medium that can be accessed by a computer. For example, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage media, magnetic disk storage media or other magnetic storage devices.

[0093] Such hardware, software, firmware, etc. may be embodied within the same device or within separate devices to support the various operations and functions described herein. Additionally, elements, units, modules, components, etc. described as "units" in the present invention may be embodied together or individually as separate but interoperable logic devices. Depictions of different characteristics for modules, units, etc. are intended to highlight different functional embodiments and do not necessarily imply that they must be realized by separate hardware or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware or software components or integrated within common or separate hardware or software components.

[0094] Although acts are shown in the figures in a particular order, it should not be understood that these acts must be performed in the particular order or sequential order shown, or that all of the acts shown must be performed, to achieve desired results. In certain environments, multitasking and parallel processing may be advantageous. It should be understood that the division of various components in the above-described embodiments should not be understood as requiring such division in all embodiments, and that the described components may generally be integrated into a single software product or packaged into multiple software products.

[0095] As described above, the drawings and specification disclose the preferred embodiment. Although specific terms are used herein, they are used merely for the purpose of describing the present invention and are not used to limit the meaning or the scope of the present invention as described in the claims. Therefore, a person skilled in the art will understand that various modifications and equivalent embodiments are possible. Therefore, the true technical scope of protection of the present invention should be determined by the technical spirit of the appended claims. [Explanation of symbols]

[0096] 100 Image learning monitoring device 110 control section 120 Input section 130 Evaluation Department 131 Environmental Assessment Unit 132 Attitude Assessment Unit 140 Score Calculation Section 150 Feedback section 160 Matching Section 170 Communications Department 180 Storage section 190 Notification Department 200 user terminals 300 Parent terminal 400 Tutoring Terminal 1000, 1000' Image Learning Monitoring System

Claims

1. An image learning monitoring system 1000, comprising: an image learning monitoring device 100; a user terminal 200 communicatively coupled to the image learning monitoring device 100; The image learning monitoring device 100 includes: a communication unit 170 configured to receive a student's study video captured by the user terminal 200 when the study time input by the user terminal 200 arrives and an image study monitoring application is executed on the user terminal 200; a score calculation unit (140) configured to calculate the student's self-directed learning score based on the study video received by the communication unit (170) and the study time input by the user terminal (200); an evaluation unit 130 including an environment evaluation unit 131 for evaluating the student's study environment and an attitude evaluation unit 132 for evaluating the student's study attitude; The environment evaluation unit 131 is configured to evaluate whether or not there is a timely connection for the input study time based on connection information of communication from the user terminal 200 to the image learning monitoring device 100, The attitude evaluation unit 132 is configured to input the study video and use a pre-prepared AI learning model to analyze whether the student is seated, whether there are signs of drowsiness, whether they are absent, whether they are prone, and changes in posture, and to evaluate the student's study attitude during the study time and the student's attitude to study beyond the input study time based on the analysis results; The user terminal 200 is provided with an input interface for inputting the study time and a predetermined notification regarding the input study time, The communication unit 170 is further configured to transmit the calculated self-directed learning score to a parent terminal 300 or the user terminal 200.

2. The image learning monitoring system 1000 of claim 1, wherein the score calculation unit 140 is further configured to calculate the self-directed learning score using at least one of the evaluation results by the environment evaluation unit 131 and the evaluation results by the attitude evaluation unit 132.

3. An image learning monitoring method in an image learning monitoring device, comprising: When the study time input by the user terminal arrives and the visual study monitoring application is executed on the user terminal, a communication unit of the visual study monitoring device receives a study video of the student taken by the user terminal; a step in which an evaluation unit of the image learning monitoring device evaluates the student's study environment and evaluates the student's study attitude; A score calculation unit of the image learning monitoring device calculates a self-directed learning score of the student based on the received study video and the study time input by the user terminal; The communication unit transmits the calculated self-directed learning score to a parent terminal or the user terminal; Evaluating the student's study environment includes outputting, as an evaluation result, whether or not there was a timely connection with respect to the input study time, based on connection information of communication from the user terminal to the image learning monitoring device; Evaluating the student's study attitude includes inputting the study video and using a pre-prepared AI learning model to analyze whether the student is seated, whether there are signs of drowsiness, whether they are absent, whether they are lying face down, and changes in posture, and outputting the student's study attitude during the study time and the student's attitude to study beyond the input study time as evaluation results based on the analysis results; The image learning monitoring method, wherein the user terminal is provided with an input interface for inputting the study time and a predetermined notification regarding the input study time.

4. The image-based learning monitoring method of claim 3 , wherein the step of calculating the self-directed learning score of the student includes the step of calculating the self-directed learning score using at least one of the evaluation results of the study environment and the evaluation results of the study attitude.

5. A computer program stored on a computer-readable recording medium so that the computer program can be combined with a computer that is hardware and can perform the method according to claim 3 or 4.

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