English learning management device and method

Abstract

The present invention relates to an English learning management device and method, wherein the English learning management device may include: a content providing unit that provides selected content selected by a student among a plurality of previously registered English educational contents; an acquisition unit that acquires the student's interpretation answer data regarding instructions within the selected content; an analysis unit that analyzes the interpretation answer data; and a feedback unit that provides feedback generated based on the analysis results.

Description

Technology Field

[0001] The present invention relates to an English learning management device and method. In particular, the present invention relates to an English learning management device and method that can train and assist students to think for themselves about understanding, interpretation, analysis, and application of grammar, sentences (sentence structures), etc., in English phrases by applying question-based learning-inducing (Socratic) AI tutor technology. Background Technology

[0002] Recent English learning focuses on the College Scholastic Ability Test (CSAT) and school school exams, demanding reading comprehension and accurate sentence interpretation skills. However, the majority of lower-to-middle-ranking students struggle to improve their English scores, and this stems from fundamental issues that go beyond a simple lack of vocabulary.

[0003] Although existing educational settings such as private academies and tutoring do teach syntax grammar itself, classes are conducted by rote-learning grammar rules or by providing interpretations of specific sentences, and this method has the following problems.

[0004] 1) Lack of ability to apply and analyze syntactic grammar: Students acquire syntactic grammar (e.g., infinitives, gerunds, participle phrases, etc.) only as individual knowledge, but they fail to develop the ability to apply and analyze complex sentence structures on their own by applying this knowledge to actual sentences.

[0005] 2) Limitations of individual learning process management: There is a problem in that detailed management, in which a teacher identifies a student's interpretation error process in real time and corrects the thinking process through individual questioning (Socratic Questioning), requires a lot of time and cost.

[0006] In other words, the biggest reason students' English scores do not improve is that they do not properly know how to interpret sentences accurately. In this context, "being unable to interpret" does not simply mean a lack of vocabulary, but rather an inability to understand syntax, apply it to actual sentences, and analyze the structural characteristics of sentences based on syntax.

[0007] Consequently, existing English learning methods fail to help students independently master "how to interpret sentences accurately," which acts as a major cause hindering fundamental improvement in students' academic performance. Therefore, there is an urgent need for the development of a new learning management solution that can effectively and efficiently improve learners' ability to proactively (on their own) analyze the sentence structure of English phrases and apply syntactic grammar.

[0008] The technology forming the background of the present invention is disclosed in Korean Registered Patent Publication No. 10-2496608. The problem to be solved

[0009] The present invention aims to solve the problems of the aforementioned prior art and to provide an English learning management device and method that can effectively and efficiently improve a learner's ability to proactively (self-directedly) analyze the sentence structure of English phrases and apply syntactic grammar (i.e., sentence interpretation ability regarding English phrases), thereby helping to improve the student's English grades.

[0010] The present invention aims to provide an English learning management device and method capable of providing an environment (i.e., a thinking-driven learning environment) that supports the thought process of students to independently recall syntax and grammar and analyze sentence structures to derive the correct answer by avoiding the simple presentation of correct answers and providing question-driven feedback regarding points of interpretation error.

[0011] The present invention aims to provide an English learning management device and method that enables the practical application and management of syntactic grammar, and in particular, allows an AI tutor to finely manage the process of identifying the function and role of specific phrases (phrases, clauses) within a sentence and analyzing the structural characteristics of the sentence according to the phrases.

[0012] However, the technical problems that the embodiments of the present invention aim to solve are not limited to the technical problems described above, and other technical problems may exist. means of solving the problem

[0013] As a technical means for achieving the above-mentioned technical task, an English learning management device according to one embodiment of the present invention may include: a content providing unit that provides selected content selected by a student among a plurality of previously registered English educational contents; an acquisition unit that acquires the student's interpretation answer data regarding instructions within the selected content; an analysis unit that analyzes the interpretation answer data; and a feedback unit that provides feedback generated based on the analysis results.

[0014] In addition, each of the above multiple contents may be content designed to train English syntax analysis based on sentence structure recognition.

[0015] In addition, the analysis unit can perform an analysis of the sentence structure of the interpretation answer data based on the instructions.

[0016] In addition, an English learning management device according to one embodiment of the present invention may further include a Socratic guidance unit that provides an answer-leading question or a hint in response to a query when it is detected that a query has been made from a student terminal in relation to instructions after the provision of selected content.

[0017] In addition, an English learning management device according to one embodiment of the present invention may further include a report providing unit that provides a learning report generated based on the analysis results to at least one of a teacher terminal and a parent terminal.

[0018] Meanwhile, an English learning management method by an English learning management device according to an embodiment of the present invention may include: a content providing unit providing selected content selected by a student among a plurality of previously registered English educational contents; an acquisition unit acquiring student's interpretation answer data regarding instructions within the selected content; an analysis unit analyzing the interpretation answer data; and a feedback unit providing feedback generated based on the analysis results.

[0019] The above-described means for solving the problem are merely exemplary and should not be interpreted as intended to limit the invention. In addition to the exemplary embodiments described above, additional embodiments may exist in the drawings and the detailed description of the invention. Effects of the invention

[0020] According to the means for solving the problem of the present invention described above, the present invention provides an English learning management device and method, thereby effectively and efficiently improving the ability of a learner (student) to proactively (on their own) analyze the sentence structure of English phrases and apply syntactic grammar (i.e., sentence interpretation ability regarding English phrases), and thereby helping to improve the student's English grades.

[0021] The present invention provides an English learning management device and method, thereby avoiding the presentation of simple correct answers to students and providing question-driven feedback regarding points of interpretation error. Through this, it is possible to provide an environment (i.e., a thinking-driven learning environment) that supports the thought process so that students can recall syntax and grammar and analyze sentence structures to derive the correct answer on their own.

[0022] The present invention provides an English learning management device and method, thereby enabling the practical application and management of syntactic grammar, and in particular, enables an AI tutor to finely manage the process of identifying the function and role of specific phrases (phrases, clauses) within a sentence and analyzing the structural characteristics of the sentence according to the phrases.

[0023] The present invention provides an English learning management device and method, which can induce fundamental improvement in a student's grades. In particular, by analyzing the student's interpretation error process regarding English syntax, it helps the student internalize the 'method of accurate interpretation' itself rather than simply transmitting knowledge. Through this, it can improve the metacognitive ability of the student to independently analyze and solve complex sentences encountered in exams, thereby leading to actual improvement in grades.

[0024] The present invention provides an English learning management device and method, which can increase learner-led participation. In particular, by managing English learning in a manner where, when a student asks a question, the correct answer is not immediately given but rather a question or hint is provided (i.e., a question / hint provision method), the student's passive learning attitude can be transformed into an active thinking process. Furthermore, by increasing the student's self-efficacy through the process of finding the correct answer, learning motivation can be strengthened.

[0025] The present invention provides an English learning management device and method, thereby enabling an AI tutor to take over the time and effort of teachers that were previously invested in individual student management (especially individual question-inducing management for each student) in conventional field education. This allows teachers, as education providers, to manage a large number of students without time constraints while maintaining the quality of personalized management for each student.

[0026] However, the effects obtainable from the present invention are not limited to those described above, and other effects may exist. Brief explanation of the drawing

[0027] FIG. 1 is a diagram showing the schematic configuration of an English learning management system including an English learning management device according to one embodiment of the present invention. FIG. 2 is a diagram illustrating interpretation answer guide information considered in an English learning management device according to an embodiment of the present invention. FIGS. 3 and FIGS. 4 are drawings for explaining content considered in an English learning management device according to an embodiment of the present invention. FIG. 5 is a diagram showing an example of a screen in which a Socratic guidance unit of an English learning management device according to one embodiment of the present invention provides a correct answer-leading question or hint to a student terminal using Socratic AI. FIG. 6 is a diagram showing an example of a learning management screen within a learning report provided by a report providing unit of an English learning management device according to one embodiment of the present invention. FIGS. 7 to 9 are drawings for explaining the types of content considered in an English learning management device according to an embodiment of the present invention. FIG. 10 is a flowchart of an operation for an English learning management method according to one embodiment of the present invention. Specific details for implementing the invention

[0028] Embodiments of the present invention are described below with reference to the attached drawings so that those skilled in the art can easily implement the invention. However, the present invention may be embodied in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly explain the present invention in the drawings, parts unrelated to the explanation have been omitted, and similar parts throughout the specification are denoted by similar reference numerals.

[0029] Throughout the specification of the present invention, when a part is described as being "connected" to another part, this includes not only cases where they are "directly connected," but also cases where they are "electrically connected" or "indirectly connected" with other elements interposed between them.

[0030] Throughout the specification of the present invention, when a member is described as being located "on," "on the upper," "on the top," "under," "on the lower," or "on the bottom" of another member, this includes not only cases where the member is in contact with the other member, but also cases where another member exists between the two members.

[0031] Throughout the specification of the present invention, when a part is described as "comprising" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0032] Throughout the specification of the present invention, some of the operations or functions described as being performed by a terminal, device, or device may instead be performed by a server connected to said terminal, device, or device. Likewise, some of the operations or functions described as being performed by a server may also be performed by a terminal, device, or device connected to said server.

[0033] Throughout the specification of the present invention, the term "at least one" may be defined as a term including both singular and plural forms, and it will be obvious that even if the term "at least one" is not present, each component may exist in a singular or plural form and may mean singular or plural. Furthermore, whether each component is provided in a singular or plural form may be changed according to the embodiment.

[0034] FIG. 1 is a diagram showing the schematic configuration of an English learning management system (100) including an English learning management device (10) according to one embodiment of the present invention.

[0035] For convenience of explanation, the English learning management device (10) according to one embodiment of the present invention will be referred to as the device (10), and the English learning management system (100) according to one embodiment of the present invention will be referred to as the system (100). In addition, the details shown (described) in the drawings of the present invention, including FIG. 1, may be applied equally to the description of the device (10), even if they are omitted below.

[0036] Referring to FIG. 1, the present system (100) may include the present device (10), an administrator terminal (20), a teacher terminal (30), a student terminal (40), and a parent terminal (50).

[0037] The device (10) may be referred to as an English learning management device, an English phrase learning management device, an English learning management device with question-based learning guidance (socratic) AI tutor technology applied, etc.

[0038] The device (10) may be a device or server that provides at least one of a webpage (homepage), program, application (app), service, and platform related to English learning management. At this time, the webpage (homepage), program, application, service, and platform related to English learning management provided by the device (10) may be referred to as the webpage (homepage), the program, the app, the service, and the platform, respectively, for convenience of explanation below. In the example illustrated in FIG. 1, the device (10) is described as being provided in the form of a server capable of transmitting and receiving data through a network (5) with each terminal (i.e., administrator terminal, teacher terminal, and student terminal).

[0039] Users of the device (10) may include administrators, teachers, and students. Users may use the service by accessing the program, the app, the website, etc., using their own terminals (especially accessing after installation or without installation), and may also use the service without registering (logging in).

[0040] The administrator terminal (20) may refer to a terminal possessed by the administrator. The administrator may be a person who develops and distributes the device (10) and operates and manages the device (10). The administrator may access the app, etc. through the administrator terminal (20) and, for example, upload and manage multiple English education contents to the device (10).

[0041] The teacher terminal (30) may refer to a terminal possessed by a teacher using the device (10). The teacher may be a person who performs (conducts, provides) and manages English education (especially education / lessons related to English sentence structure) for at least one student. For example, the teacher may access the app, etc. through the teacher terminal (30) and conduct English education / lessons for a student using at least one of the multiple contents provided by the device (10), and may also manage the student's learning (e.g., homework) through the app, etc. The teacher may include a private academy teacher, a private tutor, a school teacher, a professor, etc., but is not limited thereto, and any person who teaches students may be included.

[0042] The student terminal (40) may refer to a terminal possessed by a student using the device (10). The student may be someone who wishes to learn English (especially English sentence structure) through the device (10) and may perform English learning by using at least one of the multiple contents provided through the device (10). The student may be referred to by other terms such as learner. The student may include elementary school students, middle school students, high school students, etc., but is not limited thereto. The age of the student using the device (10) is not specifically restricted, and anyone of any age, regardless of gender, may use the service provided by the device (10), and accordingly, the student may include adults, etc.

[0043] The parent terminal (50) may refer to a terminal possessed by the parent of the student of the student terminal (40). For example, the parent can access the app, etc. through the parent terminal (50) and receive and check the learning report generated for the student through this.

[0044] In the example illustrated in FIG. 1, the present system (100) is illustrated as including one teacher terminal, one student terminal (40), and one parent terminal (50), but this is merely an example to aid in understanding the present invention and is not limited thereto. Preferably, the present system (100) may include a plurality of teacher terminals possessed by each of a plurality of teachers, a plurality of student terminals possessed by each of a plurality of students, and a plurality of parent terminals possessed by each of a plurality of parents.

[0045] At this time, in describing the present invention below, the description of each teacher terminal (30), student terminal (40), or parent terminal (50) may be applied equally to the description of each of the multiple teacher terminals, each of the multiple student terminals, and each of the multiple parent terminals, even if the description is omitted below. That is, for example, the teacher terminal (30) may be a first teacher terminal possessed by one of the multiple teachers (first teacher), the student terminal (40) may be a first student terminal possessed by one of the multiple students (first student), and the parent terminal (50) may be a first parent terminal possessed by one of the multiple parents (first parent).

[0046] In the present invention, the terminal (in particular, each of the administrator terminal (20), teacher terminal (30), student terminal (40) and parent terminal (50)) may include, for example, all types of wired and wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communication), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (WCode Division Multiple Access), Wibro (Wireless Broadband Internet) terminal, smartphone, smartpad, tablet PC, laptop, wearable device, desktop PC, etc., but is not limited thereto.

[0047] In addition, the device (10) can transmit and receive data by being linked with each terminal (i.e., administrator terminal, teacher terminal, student terminal, and parent terminal, respectively) through a network (5), and can control the operation of each terminal (e.g., screen display operation).

[0048] Here, the network (5) may include, for example, a 3GPP (3rd Generation Partnership Project) network, an LTE (Long Term Evolution) network, a WIMAX (World Interoperability for Microwave Access) network, the Internet, a LAN (Local Area Network), a Wireless LAN (Wireless Local Area Network), a WAN (Wide Area Network), a PAN (Personal Area Network), a Bluetooth network, a NFC (Near Field Communication) network, a satellite broadcasting network, an analog broadcasting network, a DMB (Digital Multimedia Broadcasting) network, but is not limited thereto and may include various wired / wireless communication networks. A more detailed description of the device (10) is as follows.

[0049] The device (10) may include a content providing unit (101), an acquisition unit (102), an analysis unit (103), a feedback unit (104), a database unit (105), a Socratic guidance unit (106), a report providing unit (107), and a control unit (108).

[0050] The content providing unit (101) can provide selected content selected by the student among multiple registered English educational contents. The content providing unit (101) can provide one of the multiple contents registered in the database unit (105) selected by the student as the selected content to the student terminal (40).

[0051] At this time, multiple contents may be stored (registered) in advance in the database section (105). These multiple contents stored in the database section (105) may be created (produced) by an administrator and uploaded in advance through an administrator terminal (20) to be stored (registered).

[0052] Each of the above multiple contents may be content designed to train English syntax analysis based on sentence structure recognition (English educational content). That is, each content may refer to English educational content (material) designed for the purpose of improving syntax grammar learning and sentence structure analysis skills, and specifically designed to enable students to understand the syntax grammar of a sentence, analyze the sentence structure step-by-step, and derive the final interpretation themselves in English syntax reading. In describing the present invention below, the description of any one of the multiple contents (e.g., selected content, first content) may be applied equally to the description of each of the multiple contents, even if such content is omitted below.

[0053] In the present invention, the content may be in the form of, for example, a video (video), an image, or a file (for example, PDF, Word, etc.). According to this, when a student selects one of a plurality of contents through a student terminal (40), the content providing unit (101) can provide the selected content to the student terminal (40) by outputting a video, an image, or a file corresponding to the selected content, which is the selected content selected by the student, to the screen of the student terminal (40).

[0054] However, it is not limited to this, and for example, the above-mentioned optional content may be provided to the student in the form of a printed material (i.e., a physical book, textbook, or printed material) in which content data corresponding to the optional content (i.e., data included within the optional content) is printed on paper, etc. Accordingly, the 'content' considered in the present invention may be information that can be provided not only in digital forms such as video, images, and files, but also in physical forms (analog form, physical form) such as physical books, textbooks, and paper.

[0055] When the content provider (101) detects that a student has selected one of the multiple contents, the selected content is not displayed immediately on the screen of the student terminal (40) in response to the selection, but rather, for example, a pre-set interpretation answer guide information (60) is first provided to the student terminal (40) so that the student can check the information first, and then, when a confirmation response indicating that the interpretation answer guide information (60) has been checked is made, the selected content can then be displayed on the screen of the student terminal (40). The explanation of the interpretation answer guide information (60) can be more easily understood by referring to FIG. 2.

[0056] FIG. 2 is a drawing for explaining interpretation answer guide information (60) considered in an English learning management device (10) according to one embodiment of the present invention.

[0057] Referring to FIG. 2, the interpretation answer guide information (60) may be as shown in FIG. 2, for example, and may include information on the method of writing (61) and information on examples of writing (62).

[0058] The information (61) regarding the above-mentioned writing method may refer to information regarding the method of writing an interpretation answer (interpretation answer data) for instructions within the content provided to the student. This information (61) regarding the above-mentioned writing method may include: i) step-by-step writing order information (611) including step 1 corresponding to finding a verb, step 2 corresponding to distinguishing verb forms, step 3 corresponding to analyzing sentence structure according to the verb forms, step 4 corresponding to writing the interpretation order, and step 5 corresponding to interpreting the entire sentence; and ii) notation definition information (612) which defines rules for notation by role. The above-mentioned step-by-step writing order information (611) may be referred to by other terms such as step-by-step interpretation procedure guidance information.

[0059] For example, the above notation definition information (612) may include information guiding that in a sentence given within the content, i) parts corresponding to phrases and clauses that function as nouns (i.e., noun phrases and noun clauses) should be marked with a first notation form (e.g., [ ]), ii) parts corresponding to phrases and clauses that function as adjectives (i.e., adjective phrases and adjective clauses) should be marked with a second notation form (e.g., ( )), and iii) parts corresponding to phrases and clauses that function as adverbs (i.e., adverb phrases and adverb clauses) should be marked with a third notation form (e.g., < >). As such, the above notation definition information (612) may refer to information that defines rules for notation by role for multiple roles corresponding to nouns, adjectives, and adverbs, and may be referred to by other terms such as notation rule information.

[0060] Additionally, the information (62) regarding the above-mentioned example of writing may refer to information regarding an example of writing an interpretation answer (interpretation answer data) for the provided content.

[0061] Meanwhile, FIGS. 3 and 4 are drawings for explaining content considered in an English learning management device (10) according to an embodiment of the present invention. In particular, FIG. 3 shows an example of one of a plurality of contents (e.g., first content, selected content), and FIG. 4 shows an example of another of a plurality of contents (e.g., second content).

[0062] Referring to FIGS. 3 and 4, a database unit (105) may store a plurality of contents including the contents shown in FIGS. 3 and 4. Based on this, the content providing unit (101) may provide at least one of the plurality of contents to a student terminal (40). For convenience of explanation, the structure of the content will be described below based on the first content shown in FIG. 3 as an example.

[0063] For example, the above content (first content, selected content) (70) may include a problem display section (71), a first interpretation answer writing section (72), and an unknown word writing section (73).

[0064] The above problem display section (71) may include an instruction (711) corresponding to the problem, a problem number (712), a sentence-unit example sentence (713) corresponding to the instruction, a structural analysis instruction (714) instructing the analysis of the sentence structure of the example sentence, an interpretation writing section (715), and a second interpretation answer writing section (716). For example, a sentence corresponding to the example sentence (713) within the problem display section (71) may be displayed in the area of ​​the second interpretation answer writing section (716).

[0065] Here, the structural analysis instruction (714) may refer to an instruction that instructs an analysis (detailed analysis) of a sentence structure including verbs, sentence patterns, parts of speech, modifiers, etc. Such a structural analysis instruction (714) may include multiple detailed instructions. According to an example illustrated in FIG. 3, the multiple detailed instructions may include a first detailed instruction corresponding to 'find and write the be verb,' a second detailed instruction corresponding to 'write the interpretation of the be verb,' and the like. Additionally, according to an example illustrated in FIG. 4, the multiple detailed instructions may include a first detailed instruction corresponding to 'find, write, and interpret the verb,' a second detailed instruction corresponding to 'write the number and tense of the verb,' a third detailed instruction corresponding to 'write the verb form among the 1st to 5th forms,' and the like.

[0066] The interpretation writing section (715) above may refer to an area provided so that when a student writes interpretation answer data for content (70), the student can write (record, mark, input) the entire interpretation data (i.e., data in which the entire English sentence is interpreted into Korean) for the sentence given as an example sentence (713). At this time, the interpretation writing section (715) may be automatically controlled to an input-enabled state so that the student can write the entire interpretation data if, for example, the instructions include an instruction to interpret, and may be automatically controlled to an input-deactivated state so that the student cannot write the entire interpretation data if the instructions do not include an instruction to interpret.

[0067] The above second interpretation answer writing section (716) may refer to an area provided for a student to write (record, mark, input) role-specific notation data and sentence analysis notation data (in particular, sentence analysis notation data including sentence structure analysis notation data and interpretation order analysis notation data) when the student writes interpretation answer data for the content (70), for example.

[0068] Here, the above-mentioned role-specific notation data may refer to data directly written by the student on the example text (713) within the content (70) according to the role-specific notation, based on (referencing) the notation definition information (612) included in the interpretation answer guide information (60) (i.e., analysis data written by the student regarding the role-specific notation).

[0069] Additionally, the above sentence analysis notation data may include i) sentence structure analysis notation data, which is data directly written on the example sentence (713) in the content (70) based on (referencing) the information on the writing method (61) included in the interpretation answer guide information (60) (i.e., information on the third stage corresponding to analyzing the sentence structure according to the form of the verb), and ii) interpretation order analysis notation data, which is data directly written on the example sentence (713) in the content (70) based on (referencing) the information on the writing method (61) included in the interpretation answer guide information (60) (i.e., information on the fourth stage corresponding to writing the interpretation order).

[0070] For example, the information (62) regarding the writing example illustrated in FIG. 2 can be explained as an example. The sentence structure analysis notation data may refer to data in which a student underlines each of the constituent components (e.g., subject (S), verb (V) (e.g., 5-form verb 5V), object (O), object complement (OC), etc.) that make up the sentence of the example sentence, and then writes (notates) the abbreviation of the constituent component (component) corresponding to the underlined part below each underlined part. For example, the sentence structure analysis notation data may include data in which the part corresponding to the subject is underlined and S is written underneath, and data in which the part corresponding to the object is underlined and O is written underneath.

[0071] In addition, for example, the information (62) regarding the writing example illustrated in FIG. 2 can be explained as follows: the interpretation order analysis notation data may refer to data in which a student writes (notifies) sequence numbers for the components according to the interpretation order of the sentence, targeting the components corresponding to the underlines marked as the sentence structure analysis notation data. For example, if the student determines that the interpretation of the components is carried out in the order of 'subject → object → object complement → 5th form verb', the interpretation order analysis notation data may include sequence number 1 (①) written in the part corresponding to the subject (S), sequence number 2 (②) written in the part corresponding to the object (O), sequence number 3 (③) written in the part corresponding to the object complement (OC), and sequence number 4 (④) written in the part corresponding to the 5th form verb (5V).

[0072] Meanwhile, the above-mentioned first interpretation answer writing section (72) may refer to an area provided so that, when a student writes interpretation answer data for the content (70), answer data for each detailed instruction is written (recorded, marked, entered) for a plurality of detailed instructions given in the above-mentioned structure analysis instruction (714), for example.

[0073] The above unknown word writing area (73) may refer to an area provided for a student to write unknown words in a sentence given as an example sentence (713) when writing interpretation answer data for the content (70).

[0074] In one example of the present invention, the case is described by way of example in which one problem (one problem, such as the one shown in FIG. 3, problem 1) is included per content, but this is merely an example to help understand the present invention and is not limited thereto. In another example, one content may include at least one problem (for example, multiple problems).

[0075] Meanwhile, once the selected content is provided to the student terminal (40) by the content provider (101), the student can then use the student terminal (40) to perform an interpretation (solution) of a problem corresponding to an instruction within the selected content based on the provided selected content. At this time, the student can input data for the interpretation (solution) process (i.e., interpretation process data) in real time through the student terminal (40), and can input interpretation answer data containing such interpretation process data into the student terminal (40). The interpretation answer data input by the student into the student terminal (40) may refer to the answer written by the student regarding the given selected content.

[0076] The acquisition unit (102) can acquire (receive input) interpretation answer data (especially interpretation answer data including interpretation process data) for instructions within the selected content in real time from the student terminal (40). The acquisition unit (102) can acquire the student's interpretation answer data entered on the selected content from the student terminal (40) in response to the click of the submission button, for example, when the student clicks a submission button provided in a specific area of ​​the screen after entering interpretation answer data on the selected content.

[0077] The analysis unit (103) can analyze the interpretation answer data obtained from the acquisition unit (102) in real time.

[0078] At this time, the interpretation answer data may refer to data regarding interpretation answers written by a student in relation to the selected content. Specifically, the interpretation answer data may include at least one of: i) answer data by detailed instruction written by a student in the first interpretation answer writing section (72) within the selected content; ii) notation data by role and sentence analysis notation data (in particular, sentence analysis notation data including sentence structure analysis notation data and interpretation order analysis notation data) written by a student in the second interpretation answer writing section (716) within the selected content; and iii) overall interpretation data written by a student in the interpretation writing section (715) within the selected content.

[0079] The analysis unit (103) can perform an analysis of the sentence structure of the interpretation answer data obtained from the student terminal (40) based on the instructions included in the selected content.

[0080] The analysis unit (103) can check for errors in the interpretation answer data in real time by comparing the interpretation answer data obtained from the acquisition unit (102) with the interpretation correct answer data corresponding to the selected content already registered in the database unit (105) in real time. Here, an error refers to an incorrect answer, and in particular, may refer to an error in the application of syntax and grammar written by the student.

[0081] At this time, the interpretation answer data refers to the correct answer to the question corresponding to the selected content, and may be data that the administrator has pre-registered in the database unit (105) through the administrator terminal (20). In the database unit (105), interpretation answer data, which is the correct answer to the question corresponding to each content, may be stored together for each of the multiple contents.

[0082] The analysis unit (103) can perform an analysis of the sentence structure of the interpretation answer data through, for example, the above comparison. When performing the analysis of the sentence structure, it can perform, for example, verb search, distinction of verb forms, recognition of part of speech functions, and sentence structure analysis. In particular, it can perform an analysis (analysis of sentence structure) that includes whether the answer data for each detailed instruction matches, whether the notation by role within the sentence (notation data by role) matches, whether the sentence structure analysis notation data matches, whether the interpretation order analysis notation data matches (i.e., whether the interpretation order matches), and whether the entire interpretation data (i.e., the entire interpretation sentence) matches.

[0083] In other words, the analysis unit (103) can automatically perform an analysis of structural elements based on the obtained interpretation answer data when performing the analysis of the above sentence structure, including i) deriving the core verb of the sentence, ii) determining the verb form (forms 1 to 5), iii) mapping the constituent components (e.g., subject, object, complement, etc.), iv) analyzing the syntactic marking of noun phrases / adjective phrases / adverb phrases, etc., iv) analyzing the tense / voice / clause structure, and v) evaluating the logical appropriateness of the entire sentence structure.

[0084] According to this, for example, when performing analysis on the above interpretation answer data, the analysis unit (103) can perform analysis on the function and positional relationship of the constituent components within the sentence by interpreting (analyzing) the role-specific notation data within the interpretation answer data entered by the student (i.e., syntactic structures indicated as noun phrases / clauses ([ ]), adjective phrases / clauses (( )), and adverb phrases / clauses (< >).

[0085] The analysis unit (103) can check (judge, detect) errors related to the interpretation answer data, such as errors in sentence pattern judgment, misjudgment of part of speech function, interpretation of modification relationships, structure (position) arrangement errors, and interpretation (translation) logic errors, by performing the above comparison / analysis. The analysis unit (103) can evaluate (analyze) the accuracy of all intermediate stages, such as 'verb → sentence pattern → part of speech → structure analysis → interpretation,' rather than simply evaluating the final interpretation sentence. That is, the analysis unit (103) can enable checking / evaluation based on the thought process rather than simply evaluating correct / incorrect answers.

[0086] The analysis unit (103) can control the error check for interpretation answer data to be performed automatically (i.e., automatically checked) using, for example, a pre-trained AI model, or can control the error check to be performed manually by the teacher (i.e., manually checked) by providing the interpretation answer data to the teacher terminal (30). The analysis unit (103) can reduce the burden of grading on the teacher by allowing the error check to be performed automatically using a pre-trained AI model.

[0087] At this time, the aforementioned pre-trained AI model (AI, artificial intelligence) used for error checking may refer to various neural network models that have already been previously disclosed or will be developed in the future, such as deep learning models, machine learning models, neural network models (artificial neural network models), neuro-fuzzy models, convolutional neural networks (CNN), recurrent neural networks (RNN), and deep neural networks, but is not limited thereto and may also refer to the Socratic AI described below.

[0088] Subsequently, the feedback unit (104) can provide feedback generated based on the result of the analysis by the analysis unit (103) (i.e., the analysis result) to the student terminal (40). The analysis result may include the result of checking for errors in the interpretation answer data. The feedback unit (104) can provide the feedback to the student terminal (40) in real time. The feedback may include, for example, information indicating the point (part) where an error was found within the interpretation answer data.

[0089] The feedback unit (104) can provide feedback, thereby controlling (inducing) the student to think once again about the point of error discovery based on the feedback and to re-enter (re-enter) the corrected interpretation answer.

[0090] The Socratic Guidance Department (106) may provide a question or hint to guide the answer in response to the question when it detects that a question has been made from the student terminal (40) regarding the instructions after the above-mentioned selection content has been provided.

[0091] That is, the Socratic Guidance Department (106) may provide a question or hint to the student terminal (40) in response to the question, so that the student can analyze and interpret the sentence structure of the syntax of the instructions themselves, when it is detected that a question has been made from the student terminal (40) of a student who is performing an interpretation of the instructions in relation to the instructions within the selected content after the selected content has been provided to the student terminal (40) by the content provision department (101).

[0092] Additionally, the Socratic Guidance Unit (106) can provide a question or hint to the student terminal (40) in response to the question (question after feedback) even when it is detected that a question (question after feedback) has been made from the student terminal (40) during the process of inputting the corrected interpretation answer from the student terminal (40) after feedback has been provided to the student terminal (40) by the feedback unit (104).

[0093] The Socratic guidance unit (106), when the above-mentioned question is made from the student terminal (40), may not immediately provide (present) the correct answer to the question, but instead provide the above-mentioned question or hint to guide the answer, thereby helping the student to find (find) the correct answer on their own through the provided question or hint. That is, the Socratic guidance unit (106) does not directly present the correct answer to the question, but instead provides step-by-step questions / hints, thereby guiding the student to solve it on their own and helping to improve thinking skills and grammatical analysis abilities.

[0094] At this time, the above-mentioned answer-leading questions are questions that induce the derivation of the correct answer, and may be arranged to include multiple questions (especially step-by-step questions).

[0095] The Socratic leadership unit (106) can provide the answer-leading questions or hints using an AI model that has been pre-trained by, for example, a manager, and can provide the answer-leading questions or hints in the form of, for example, a messenger (chat).

[0096] Here, the aforementioned pre-trained AI model may refer to, for example, a generative AI based on the Socratic method of questioning. For reference, while a general generative AI such as ChatGPT is a passive method of responding to user requests, the aforementioned Socratic AI applied to the device (10) may refer to a model that has an active method of first asking a question to the user and continuously asking additional questions based on the student's answer. Accordingly, the aforementioned pre-trained AI model considered in the device (10) may be referred to by other terms such as Socratic AI (AI model), question-based learning-inducing (Socratic) AI tutor, AI tutor, etc.

[0097] In other words, the Socratic guidance unit (106) can provide the student terminal (40) with an AI Socratic tutoring function (tutoring function using Socratic AI) that, when a student inputs a question, provides step-by-step questions or hints so that the student can recognize the structural error on their own, instead of directly presenting the correct answer to the question.

[0098] For example, when the content provider (101) provides the selected content to the student terminal (40), it may also provide a question input button in a specific area of ​​the screen of the student terminal (40) where the selected content is displayed. At this time, if the Socratic guidance unit (106) detects that a student has entered a question after clicking the question input button while performing interpretation (solution) of the selected content (i.e., before inputting the interpretation answer data), for example, the entered question may be applied as an input value to the Socratic AI, and subsequently, an answer-guiding question or hint generated to induce the correct answer corresponding to the entered question may be obtained as an output value from the Socratic AI, and subsequently, the obtained output value may be provided to the student terminal (40).

[0099] At this time, the above query input from the student terminal (40) may be input in the form of characters (text) or voice. In addition, the output value (i.e., the answer-leading question or hint) provided to the student terminal (40) may likewise be provided in the form of characters (text) or voice. According to the above description, the query input button may be provided together, for example, when selection content is provided, and may also be provided after feedback is provided.

[0100] FIG. 5 is a diagram showing an example of a screen in which a Socratic guidance unit (106) of an English learning management device (10) according to an embodiment of the present invention provides a question or hint guiding the correct answer to a student terminal using a Socratic AI (AI tutor). Referring to FIG. 5, when the Socratic guidance unit (106) detects that a question input button has been clicked on the student terminal (40), it may respond to the click and provide a chat room to the student terminal (40) where chatting (conversation) between the Socratic AI and the student is possible, as shown in FIG. 5, and enable the student to perform question-and-answer with the Socratic AI through the chat room. At this time, the Socratic AI may be configured (set) to provide only a question or hint guiding the correct answer to the question made by the student, without presenting the correct answer.

[0101] In the present invention, information regarding feedback from the feedback unit (104) provided to the student terminal (40) or information provided by the Socratic guidance unit (105) (e.g., questions leading to the correct answer or hints, etc.) may be provided directly to the student terminal (40) using, for example, the AI ​​model of the device (10) (i.e., Socratic AI, etc.), but is not limited thereto and may be provided to the student terminal (40) via a teacher as another example. That is, in the latter case, the device (10) may provide the above-described information to the teacher terminal (30), and through this, the teacher may provide information regarding the feedback, questions leading to the correct answer, hints, etc. to the student based on the received information. According to this, the device (10) enables the teacher to educate (guide) the student by referring to the information regarding the feedback, questions leading to the correct answer, hints, etc. provided by the device (10). That is, the functions described for Socratic AI in this invention may also be performed by a teacher as needed.

[0102] The report providing unit (107) can provide a learning report generated based on the results of the analysis (analysis results) from the analysis unit (103) to at least one of a teacher terminal (30) and a parent terminal (50). Additionally, the report providing unit (107) can provide the learning report to a student terminal (40).

[0103] For example, the report providing unit (107) may include: i) a learning data analysis unit (1071) that analyzes at least one of a student's weakness, repetitive error patterns, and learning tendencies using the analysis results; ii) a learning roadmap generation unit (1072) that generates a learning roadmap optimized for the student using the results of the analysis by the learning data analysis unit; and iii) a homework progress management unit (1073) that manages at least one of the student's homework performance status (homework completion status), content learning progress (content learning status), interpretation answer submission status, feedback reception history, and question / hint reception history through Socratic AI.

[0104] In addition, the above-mentioned learning data analysis unit (1071) can generate a profile by analyzing, for example, each student's sentence type accuracy, syntax structure analysis pattern, repetition error, hint usage, learning time / speed, etc.

[0105] For example, the above-mentioned learning roadmap generation unit (1072) can automatically adjust at least one of the learning order and the difficulty level of the content provided to the student terminal (40) by comprehensively considering the student's incorrect answer type, learning speed, usage of hints through Socratic AI, and reaction tendency to specific sentence patterns. That is, the above-mentioned learning roadmap generation unit (not shown) can also automatically adjust the difficulty level of the content, the number of sentences, and the amount of repeated learning suitable for the student's level based on the results of the learning data analysis unit.

[0106] For example, if the learning roadmap generation unit (1072) analyzes that a student repeatedly makes errors in sentences of five forms, it can automatically select content focused on the sentence patterns in which the student repeatedly makes errors from the database unit (105) and recommend it to the student terminal (40), and if the student analyzes that the student has difficulty interpreting adjective phrases, it can automatically select content including sentences specialized in adjective phrases and recommend it to the student terminal (40).

[0107] According to this, the above learning report may include, for example, analysis results from the analysis unit (103), analysis results from the learning data analysis unit, a learning roadmap generated by the learning roadmap generation unit (1072), information on whether homework is performed managed by the homework progress management unit (1073), information on the progress of content learning, and information on whether interpretation answers have been submitted. Additionally, for example, the learning report may include the accuracy of the student's sentence structure recognition, the completeness of the step-by-step interpretation process, changes in the ability to analyze sentence patterns and parts of speech, proficiency by sentence pattern, changes in the accuracy of syntactic structure analysis, information on the decreasing trend of error types, statistical information on learning participation, and records of weaknesses and improvements.

[0108] FIG. 6 is a diagram showing an example of a learning management screen within a learning report provided by a report providing unit (107) of an English learning management device (10) according to an embodiment of the present invention. Referring to FIG. 6, the learning report may include a learning management screen that includes information such as that shown in FIG. 6. The learning management screen may include, for example, a total homework completion rate, the number of completed and uncompleted items for recent homework and past homework, and graph information regarding completed and uncompleted items in relation to the monthly homework completion status.

[0109] The control unit (108) can control the operation of each unit included in the device (10) (e.g., content provision unit, acquisition unit, analysis unit, feedback unit, database unit, Socratic guidance unit, report provision unit, etc.). Additionally, the control unit (108) can control the operation (e.g., screen display operation) of each terminal connected to the device (10) via a network (e.g., administrator terminal, teacher terminal, student terminal, parent terminal). The database unit (105) may store multiple contents and interpretation answer data corresponding to each content in a linked manner, and may also store various data (transmission and reception data) considered by the device (10).

[0110] According to the above description, the device (10) may be an English learning management device that enables sentence analysis training using content designed to recognize the structure of parts of speech / clause units step by step, particularly focusing on verb forms (forms 1 to 5) within English sentences.

[0111] Existing English grammar textbooks are structured around the memorization of fragmentary grammar rules, which has limitations in that students (learners) cannot grasp the structural relationships of entire sentences. Consequently, there are many students who understand grammar theory but lack the ability to interpret actual sentences. To solve this problem, the present invention provides the technology of the device (10).

[0112] To solve this problem, each of the plurality of contents considered in the present invention may be, for example, content designed to enable English syntax analysis training based on sentence structure recognition.

[0113] Specifically, each of the multiple contents may be a content that enables a student to sequentially (step by step) perform multiple execution steps, including 1) a sentence pattern recognition step, 2) a part of speech recognition step, 3) a sentence transformation step, and 4) a syntax analysis step, during the process of interpreting (problem interpretation) a sentence in a given content.

[0114] Here, the sentence recognition step of 1) above may mean a step in which the student distinguishes the form of a given sentence based on the verb as one of the 1st to 5th forms.

[0115] The above-mentioned part-of-speech recognition step in 2) may refer to a step in which the student recognizes the structure (sentence structure) by visually indicating the role of each part of speech and the object of modification.

[0116] 3) The sentence transformation stage may refer to a stage where students directly perform sentence structure transformations based on changes in tense / voice / number (singular, plural).

[0117] 4) The syntactic analysis stage may refer to a stage in which, when interpreting a sentence, the student performs the analysis in the order of 'verb-centered → part of speech → overall structure'.

[0118] According to this, the content provider (101) can enable the student to perform syntax analysis training (especially syntax analysis training that enables accurate recognition of sentence structure) by providing content of the above-described structure to the student terminal (40), thereby allowing the student to sequentially (step by step) perform a plurality of execution steps including 1) sentence pattern recognition execution step, 2) part of speech recognition execution step, 3) sentence transformation execution step, and 4) syntax analysis execution step through the content (e.g., selected content).

[0119] That is, the content provider (101) provides content of the structure described above (i.e., content prepared to include multiple execution steps of 1) to 4) to a student terminal (40), thereby enabling the student to learn English (especially English sentence structure interpretation learning based on sentence structure recognition) through the provided content, and in particular i) enable the student to accurately recognize sentence structure by distinguishing 1 to 5 forms centered on the core verb of the sentence, ii) enable the student to learn the roles of parts of speech (adjectives / adverbs / prepositions, etc.) and positional relationships within the sentence step by step, iii) enable the student to understand sentence transformations according to changes in the structure of tense, number, voice, and clause, and iv) improve the accuracy of sentence interpretation by training the student to interpret sentences in structural units rather than in word units.

[0120] In addition, the device (10) can obtain interpretation answer data for content provided to the student terminal (40) in real time from the student terminal (40), analyze the obtained interpretation answer data in real time, and provide feedback (error check result) based on the analysis result to the student terminal (40) in real time. At this time, the provided feedback may include information on errors by grammar unit (check information) and check information on interpretation accuracy.

[0121] This device (10) enables a student to perform syntactic analysis training based on the content of the structure described above, thereby improving the student's ability to recognize sentence patterns, strengthening the understanding of part-of-speech functions and positional relationships, enabling integrated learning of tense / voice / clause structures, training in logical thinking during the analysis process, and enabling various applications such as textbooks and online systems.

[0122] The device (10), by providing this service, enables English syntax interpretation training based on sentence structure recognition and, in particular, can provide a creative English syntax education method that allows for the integrated analysis of verb forms, parts of speech functions, tenses, and clause structures. The device (10) can be extended and applied not only to textbooks but also to AI analysis systems, app-based learning systems, etc.

[0123] The device (10) adopts a learning method (i.e., a method of leading the answer by providing a question or hint using Socratic AI, rather than immediately providing the correct answer when a student asks a question while learning English using content, thereby guiding the student to find the correct answer on their own (i.e., a method of leading the answer by a question-leading approach). This helps the student's thinking process and allows for effective management of the application of syntax and grammar and the analysis of sentence structure, thereby helping to improve actual grades. In other words, the learning method provided by the device (10) can provide a question-leading learning method that 'makes the student understand and analyze the problem' rather than making them solve the problem. As a result of directly applying the technology provided by the device (10) (the technology of the service) in the actual field, the applicant confirmed that the students' grades actually improved and that the students' ability to interpret sentences improved.

[0124] The device (10) can provide a function that provides hints or answer-leading questions instead of the correct answer using Socratic AI, and can also automate training on the application of syntax grammar and sentence structure analysis through the linkage of content provision / real-time analysis / feedback provision, and can analyze individual student weaknesses through analysis, present a customized learning roadmap, provide a progress management function for homework and learning, and provide a function that can automatically provide learning reports to parents, etc.

[0125] The consumer base using this device (10) may include B2C (e.g., middle / high school students with lower-to-middle English scores, students lacking self-directed learning ability, parents who want to check their child's learning process in detail), B2B (private tutoring and small-scale academy sites), and B2G (middle / high schools in small and medium-sized cities and rural areas).

[0126] The device (10) can be used (applied) in, for example, as an auxiliary learning tool in private tutoring and small academies (individual English tutoring students, Haeu English tutoring), a mobile / PC app for individual students, an app / web for parent monitoring, and in middle / high schools in small provincial cities and rural areas where educational conditions are inferior compared to other cities.

[0127] The device (10) can provide: i) a Socratic AI tutoring function that enables students to analyze syntax / sentences themselves through questions and hints rather than simple solving; ii) a syntax analysis specialized management function in which the AI ​​can automatically check and provide feedback on the process of applying / analyzing syntax / grammar; iii) a personalized learning path design function that analyzes the student's incorrect answer patterns / learning habits based on data; iv) a learning management automation function that enables homework completion confirmation, automatic generation and sharing of weekly / monthly reports; v) a motivation function through visualization of learning achievement and linkage with a mission-based learning system.

[0128] The device (10) is differentiated from conventional technologies in that it includes a configuration that combines a Socratic (question-inducing) AI and specialized analysis management for syntax and grammar, rather than a simple answer-providing AI; a configuration that induces students to analyze sentence structures themselves and allows the AI ​​to check and provide feedback in real time; a configuration that enables automatic design of learning paths and automatic provision of parent reports based on data generated during the learning process; and a configuration that can provide an innovative platform that supports thinking skills and self-directed learning, unlike existing English learning management services which are limited to simple grading and feedback.

[0129] For example, the device (10) can enable users such as teachers, students, and parents to use the service at a price of about 30,000 to 50,000 won per month, but is not limited to this, and the price of the service can be set and changed in various ways by the administrator.

[0130] The device (10) can be described as a technology specialized in thought guidance and sentence analysis management, and enables learning based on question-based Socratic tutoring and syntactic grammar structure analysis, and can effectively help improve the grades of lower-to-middle-ranking students. The device (10) can integrate the technology of the service into the context of entrance exams. The device (10) can provide a smart education platform (app, etc.) where students and teachers are connected through content-based classes, and AI (Socratic AI) assists students in their learning.

[0131] Additionally, for example, the plurality of contents considered in the device (10) may be classified into types such as syntax, syntax grammar, problem solving, reading comprehension practice, incorrect word answers, incorrect question answers, incorrect sentence answers, etc. For example, among the plurality of contents, the content of the syntax type may be content prepared to include the data shown in FIGS. 3 and FIGS. 4.

[0132] FIGS. 7 to 9 are drawings for explaining the types of content considered in an English learning management device (10) according to an embodiment of the present invention. Referring to FIGS. 7 to 9, among the plurality of contents, the content of each type, such as syntax grammar, problem solving, reading comprehension practice, word incorrect answer, problem incorrect answer, and sentence incorrect answer, may be content provided to include data such as that illustrated in FIGS. 7 to 9. Meanwhile, the device (10) may further provide various functions such as the following.

[0133] < First Function Regarding Real-time Concentration and Thought Process Analysis Based on Brainwave / Eye Tracking >

[0134] The device (10) may further include, for example, a biosignal acquisition unit (109) and an accident analysis unit (110) to provide the first function.

[0135] The biosignal acquisition unit (109) can acquire the student's brainwave data and / or the student's eye movement data in real time while the student performs the interpretation process of instructions within the selected content by using a brainwave sensor (EEG sensor) and / or an eye-tracking sensor connected to or integrated with the student terminal (40). In particular, the brainwave data may include an indicator representing the student's attention level or cognitive load.

[0136] Subsequently, the thought analysis unit (110) can analyze brainwave data and / or eye movement data obtained from the biosignal acquisition unit (109) to identify real-time patterns of concentration change during the student's interpretation process and points of thought cessation (blockage) in specific sentence components or syntactic structures. At this time, the points of thought cessation may include points where the student's gaze lingers abnormally long on a specific word or phrase (i.e., lingers longer than a standard time preset by the administrator), or where a rapid increase in cognitive load is detected in a specific frequency band of brainwaves (e.g., Theta / Beta ratio) (i.e., an increase exceeding a preset increase amount by the administrator).

[0137] At this time, the thought analysis unit (110) can control the operation of the Socratic guidance unit (106) so that when a point of thought cessation or a sharp drop in concentration is detected at a specific point during the student’s interpretation process through the analysis of acquired brainwave data and / or eye movement data, the Socratic AI of the Socratic guidance unit (106) automatically generates a question or hint for the correct answer related to the sentence structure or grammatical elements of the specific point (i.e., the point of thought cessation or the specific point where a sharp drop in concentration is detected) at the time the detection occurs, even without the student’s explicit input of a question (i.e., even if the student has not entered a question), and provides it to the student terminal (40).

[0138] That is, the thought analysis unit (110) can automatically generate and provide a question or hint for the correct answer related to the sentence structure or grammatical element corresponding to the thought stop point at the time of detection of the thought stop, even if no question is entered by the student, when it is determined that a thought stop has occurred at a specific point while the student is performing interpretation (solution) of the selected content (i.e., the occurrence of a thought stop point is detected). Through this, the student's thinking can be actively resumed (re-induced).

[0139] The present device (10), by providing the first function, can quantitatively analyze real-time concentration and cognitive block points (i.e., points where thinking stops) by utilizing brainwave sensors or eye tracking sensors while a student performs an interpretation process, and use this to adjust the timing and difficulty of providing questions and hints by Socratic AI. The present device (10) can analyze the student's actual thinking process rather than providing specific information (e.g., feedback, hints, or questions leading to the correct answer) at the time when an answer is entered or a question is entered by the student, and can immediately and automatically provide hints or questions leading to the correct answer to the student when a point where thinking stops is detected during the thinking process (i.e., while performing interpretation / solution), thereby helping to actively resume the student's thinking that had stopped.

[0140] < Second feature related to providing interpretation prediction and incorrect answer simulation based on syntax structure visualization >

[0141] The device (10) may further include, for example, a structure visualization unit (111) and an error prediction unit (112) to provide the second function.

[0142] The structural visualization unit (111) can generate a syntactic structure tree diagram in real time representing the provisional sentence structure recognized by the student of the corresponding sentence of the selected content based on the role-specific notation data (phrase / clause notation) and sentence structure analysis notation data (S / V / O / C / M, etc.) entered by the student in the second interpretation answer writing section (716), and can visually display it on the screen of the student terminal (40), thereby helping the student immediately check whether their syntactic analysis is appropriate according to English grammar rules.

[0143] Subsequently, the error prediction unit (112) can compare the provisional sentence structure generated by the structure visualization unit (111) with the error type data (pre-learned error patterns) for the corresponding sentence type (sentence type of the sentence within the selected content) stored in the database unit (105) to predict potential errors that may occur in the student's current analysis state in real time and output the prediction result (predicted potential error simulation result). For example, if the student wrote a to-infinitive as a noun phrase but the order of interpretation is similar to the order of adverbial usage, an 'interpretation logic error' can be predicted. The error prediction unit (112) can calculate potential errors by matching with pre-registered error type data and using either a pre-defined standard or a machine learning-based prediction model.

[0144] Subsequently, the feedback unit (104) can generate and provide preventive feedback to the student terminal (40), including not only the analysis results (actual errors) of the analysis unit (103) but also the prediction results (potential error simulation results) predicted by the error prediction unit (112). At this time, the preventive feedback may include probability and type information, such as, for example, "If you proceed with interpretation according to the current syntax analysis (tree diagram), there is a 70% chance of falling into the most common type of error A." By providing this preventive feedback to the student terminal (40), the feedback unit (104) can actively induce the student to review their thought process before finally submitting the interpretation answer (interpretation answer data).

[0145] This device (10) can provide the second function, thereby visualizing the student's expected interpretation process in the form of a syntactic structure tree diagram before the student inputs (completes input) interpretation answer data, and can show in advance the simulation results of possible incorrect answers based on the role-specific notation (phrase / clause notation) and sentence structure analysis (S / V / O / C / M) data that the student inputs in real time during the interpretation process, and thereby induce the student to realize and correct their own analysis errors in advance.

[0146] < Third function related to 'learner persona' customized question generation based on psychological language models >

[0147] The device (10) may further include a learning persona analysis unit (113) and a question style adjustment unit (114) to provide the third function.

[0148] The learning persona analysis unit (113) can dynamically generate (identify) and update the student's 'language learning psychological persona' by comprehensively analyzing the student's past learning history, namely i) the repetition pattern of specific sentence structure / part of speech errors, ii) the frequency and type of hint usage during question-and-answer sessions with the Socratic guidance unit (106), iii) the speed of input of interpretation answer data, and iv) linguistic characteristics used during question-and-answer sessions (e.g., specificity of questions, presence or absence of emotional expression). At this time, the persona can be classified into multiple types, such as 'logical / analytical type', 'intuitive / emotional type', 'rule-dependent type', and 'experiential learning type'.

[0149] The question style adjustment unit (114) can adjust in real time the style and expression method (e.g., tone, whether metaphors are used, difficulty increase curve) of the answer-leading questions or hints provided by the Socratic Guidance Unit (106) to correspond to the student's language learning psychological persona generated (identified) by the learning persona analysis unit (113). For example, the question style adjustment unit (114) can control the application of a question style that presents a grammar rule number at the point of error in interpretation and asks for the definition of the rule first when the type of the student's language learning psychological persona is generated (identified) as a 'rule-dependent' persona, and ii) control the application of a question style that leads to the correct answer by presenting a short metaphorical example sentence with a structure similar to the point of error when the type of the student's language learning psychological persona is generated as an 'intuitive / emotional' persona.

[0150] The question style adjustment unit (114) can provide information (adjustment information) regarding the style and expression method adjusted in real time to the Socratic guidance unit (106) as a persona-tailored question style guide.

[0151] Afterward, the Socratic guidance unit (106) can generate a question or hint to guide the correct answer according to the persona-customized question style guidelines provided by the question style adjustment unit (114) and provide it to the student terminal (40), thereby enabling the implementation and provision of a Socratic question-and-answer method optimized for the student's cognitive characteristics.

[0152] The device (10) can maximize learning immersion and efficiency by providing the third function, analyzing the student's past learning history (type of incorrect answers, hint usage, conversation style with Socratic AI), constructing the student's language learning psychological model (Learning Persona), and adjusting the AI ​​tutor's question style, tone, level of humor, and motivation method in real time according to this persona. For example, the device (10) can provide definition-based sharp questions (answer-leading questions) to logical / analytical students, and metaphor / storytelling questions to emotional / intuitive students.

[0153] < Fourth Function Related to Calculation and Management of Metacognition-Based 'Interpretation Confidence Index' >

[0154] The device (10) may further include a confidence index acquisition unit (115) and a metacognitive analysis unit (116) to provide the fourth function.

[0155] The confidence index acquisition unit (115) can acquire an ‘interpretation confidence index (Confidence Score)’ that indicates the degree of subjective confidence of the student regarding the interpretation result, by receiving input through the student terminal (40) just before the student finally submits interpretation answer data for instructions within the selected content or while inputting step-by-step interpretation process data. Here, the interpretation confidence index refers to data that quantifies the student’s metacognitive judgment and may be data that is input based on a scale from 1 to 5 points.

[0156] Subsequently, the metacognitive analysis unit (116) can calculate a ‘metacognitive error index’ that indicates the degree of discrepancy between subjective confidence and objective accuracy by comparing and analyzing the information of the interpretation confidence index (subjective judgment) obtained from the confidence index acquisition unit (115) with the analysis result of the analysis unit (103) (objective accuracy of interpretation answer data).

[0157] Afterward, the feedback unit (104) can generate metacognitive reinforcement feedback based on the metacognitive error index calculated by the metacognitive analysis unit (116) and provide it to the student terminal (40). At this time, the metacognitive reinforcement feedback may include information regarding the student's metacognitive error type (e.g., overconfidence type, underestimation type, etc.).

[0158] At this time, the calculated metacognitive error index ranges from 0 to 1, and the closer it is to 0, the more the student's metacognitive judgment aligns with objective accuracy. For example, if a student's interpretation confidence index is 5 points (normalized 1.0) and actual accuracy is 30% (0.3), the metacognitive error index is calculated as |1.0-0.3|=0.7, which can be judged (evaluated) as an overconfidence type.

[0159] According to this, the feedback unit (104) can generate metacognitive reinforcement feedback information, such as [the interpretation confidence index was 5 points, but the actual accuracy was 30%. Therefore, the student belongs to the overconfidence type], or [the interpretation confidence index was 1 point, but the actual accuracy was 100%. Therefore, the student belongs to the underestimation type], and provide it to the student terminal (40).

[0160] The feedback unit (104) can help the student correct their judgment on their cognitive ability by providing the above-mentioned metacognitive reinforcement feedback, and help the student manage their learning in a way that reduces the metacognitive error index in the long term.

[0161] By providing the fourth function, the device (10) can induce the student to input a metacognitive judgment (degree of confidence / certainty) about their interpretation process or result before or after inputting the interpretation answer, and analyze the gap between this subjective confidence index and the actual correct answer (objective accuracy) to enable training and management of the student's metacognitive ability itself.

[0162] In particular, the device (10) can quantitatively measure and provide the discrepancy between a student's subjective judgment (confidence) and actual interpretation accuracy by providing the fourth function, thereby enabling the student to recognize their cognitive judgment tendency (overconfidence / underestimation), and thereby strengthen self-monitoring ability during the learning process. Furthermore, since the system (the device (10)) can automatically detect the student's metacognitive error type (overconfidence type / underestimation type, etc.) beyond simple error analysis, it can provide high-dimensional cognitive analysis functions that were not provided by existing English learning systems. Moreover, instead of providing feedback based solely on objective accuracy, it can provide sophisticated customized feedback that reflects 'where the student feels overconfidence or uncertainty,' thereby significantly improving the efficiency of AI-based customized learning that takes into account individual learning tendencies.

[0163] < Fifth Function Related to 'Sentence Decomposition' Analysis for Context-Based Automatic Difficulty Scaling >

[0164] The device (10) may further include, for example, a context complexity analysis unit (117) and a sentence decomposition adjustment unit (118) to provide the fifth function.

[0165] The above contextual complexity analysis unit (117) can calculate the contextual complexity score of an example sentence (713) within selected content by quantitatively analyzing factors such as the depth of overlap between subordinate clauses and relative clauses within the main clause, the degree of insertion of participial phrases, and the distance between syntactic elements (Dependency Distance), beyond simple word counts or sentence patterns. At this time, the contextual complexity (contextual complexity score) may represent an indicator of how easily the sentence can be understood through 'grammatical parsing'.

[0166] The sentence degradability adjustment unit (118) can perform 'adjustment of sentence degradability' by separating complex subordinate clause parts within the sentence into logical units and presenting them, or by automatically generating simplified parallel sentences that clearly show the subordinate structure, when the contextual complexity calculated by the contextual complexity analysis unit (117) exceeds a preset threshold, before providing the example sentence (713) to the student.

[0167] Afterward, the Socratic guidance unit (106) can generate and provide a question to guide the answer based on a separated or simplified sentence structure adjusted by the sentence decomposition adjustment unit (118) when the student's question occurs in a complex sentence, thereby helping the student to think by focusing on the separated structures in stages rather than the most complex structure.

[0168] This device (10) can determine the complexity based on the depth of the contextual dependent structure rather than the simple 'length' or 'number of difficult words' when a single English sentence is intertwined with multiple complex phrases by providing the fifth function, and can automatically adjust the sentence degradability of the content according to this complexity to enable setting the question level of the Socratic AI.

[0169] < Function 6 related to multi-mode integrated feedback and correct / incorrect judgment >

[0170] The device (10) may further include, for example, a semantic accuracy analysis unit (119) and a reverse translation evaluation unit (120) to provide the sixth function.

[0171] The above semantic accuracy analysis unit (119) can analyze the semantic similarity between the entire interpretation data and the interpretation answer data using natural language processing (NLP) technology, going beyond simply comparing the entire interpretation data entered by the student in the interpretation writing section (715) with the interpretation answer data of the corresponding sentence stored in the database unit (105).

[0172] The semantic accuracy analysis unit (119) can evaluate the student's flexible vocabulary selection ability by calculating a quantified score (e.g., a value between 0 and 1) that determines whether the student's interpretation sentence (i.e., the entire interpretation data) is semantically identical to the correct answer even if the vocabulary is different, through the analysis of the semantic similarity.

[0173] Afterward, the back-translation evaluation unit (120) can automatically perform the process of back-translating the entire interpretation data entered by the student back into the original English text, and calculate a back-translation quality score that measures the degree of agreement in syntactic structure and meaning between the back-translated English sentence and the original example sentence (713). At this time, if the student's Korean interpretation distorts the meaning of the original sentence, the back-translated English sentence will also differ significantly from the original text, and in this case, the back-translation quality score may be calculated as low.

[0174] The above back-translation quality score may represent a value that quantifies the semantic and syntactic consistency between the sentence back-translated into English using a machine translation model from the entire interpretation data (Korean sentence) entered by the student and the original English example sentence (original text). The above back-translation quality score may be calculated as a value between 0 and 1, for example, and the closer it is to 1, the higher the consistency between the back-translated sentence and the original text in both semantic and syntactic structure. The back-translation evaluation unit (120) may ensure that the calculated back-translation quality score is reflected in the error check of the analysis unit (103).

[0175] That is, when the analysis unit (103) checks for errors in the interpretation answer data obtained from the acquisition unit (102), it can calculate a final multi-mode interpretation quality index for the student's entire interpretation data by considering the semantic similarity score calculated by the semantic accuracy analysis unit (119) and the back-translation quality score calculated by the back-translation evaluation unit (120) as weights, and provide this to the feedback unit (104).

[0176] By providing the sixth function, this device (10) can analyze the semantic accuracy, syntactic accuracy, and pragmatic flow of the interpretation order in multiple modes when a student writes an answer (interpretation answer data), and can utilize a back-translation quality score to quantitatively evaluate the quality of the entire interpretation data entered by the student.

[0177] < Function 7 related to Socratic AI-based 'Error Cause Hypothesis Generation' >

[0178] The device (10) may further include, for example, a cause hypothesis generation unit (121) and a hypothesis verification question unit (122) to provide the seventh function.

[0179] When an error is detected in the interpretation answer data by the analysis unit (103), the above-mentioned cause hypothesis generation unit (121) can automatically generate multiple hypotheses regarding the potential deep cause of the error committed by the student by comprehensively analyzing i) the student's past incorrect answer records, ii) the context in which the current error occurred, and iii) the student's biosignal data (i.e., including brainwave data and / or eye movement data) obtained through the above-mentioned biosignal acquisition unit (109). For example, when the detected error is 'failure to find the verb', the cause hypothesis generation unit (121) can generate multiple hypotheses such as 'Hypothesis A: Confusion of the noun usage of the infinitive with the verb', 'Hypothesis B: Failure to grasp the structure of the compound verb', etc.

[0180] Afterward, the hypothesis testing question unit (122) may construct (generate) a step-by-step question set to verify the most likely hypothesis among the multiple hypotheses generated by the causal hypothesis generation unit (121), and transmit the generated step-by-step question set to the Socratic guidance unit (106).

[0181] At this time, the questions within the step-by-step question set generated by the hypothesis testing question section (122) may be configured not to directly ask the student, 'Is the reason you committed this error A or B?', but to include grammatical concept questions to test hypothesis A, syntactic structure questions to test hypothesis B, etc. (i.e., composed of hypothesis testing questions to test each of the multiple hypotheses generated automatically).

[0182] Subsequently, the Socratic Guidance Department (106) may, in response to the detection, prioritize providing the student with the questions of the step-by-step question set received from the hypothesis testing question department (122) (i.e., hypothesis testing questions generated to verify the hypothesis of the cause of the error) when an error is detected in the analysis department (103), even if no question is entered from the student.

[0183] The Socratic guidance unit (106) can provide the questions of the above-mentioned step-by-step question set (hypothesis-testing questions) to the student terminal (40), thereby enabling guidance to be provided in a way that corrects the student's thinking error path itself, beyond simply guiding the correct answer, and can guide the student to identify the root cause of the error on their own.

[0184] By providing the seventh function, the present device (10) can go beyond simply asking a question that leads to the correct answer (answer-leading question) when a student commits a specific error, and instead synthesize the student's past error patterns, current answers, and biosignal data to generate "multiple hypotheses regarding the potential causes of the error committed by the student," and provide the student with questions to verify these generated hypotheses in reverse, thereby enabling the student to identify the root cause of the error on their own.

[0185] Below, we will briefly examine the operation flow of the present invention based on the details described above.

[0186] FIG. 10 is a flowchart of an operation for an English learning management method according to one embodiment of the present invention.

[0187] The English learning management method illustrated in FIG. 10 can be performed by the device (10) described above. Therefore, even if the content described below is omitted, the description of the device (10) can be equally applied to the description of the English learning management method.

[0188] Referring to FIG. 10, in step S11, the content provider can provide selected content selected by the student among a plurality of previously registered English educational contents.

[0189] Next, in step S12, the acquisition unit can acquire the student's interpretation answer data for the instructions within the selected content.

[0190] Next, in step S13, the analysis unit can analyze the above interpretation answer data.

[0191] Next, in step S14, the feedback unit can provide feedback generated based on the analysis results.

[0192] In the description above, steps S11 to S14 may be further divided into additional steps or combined into fewer steps, depending on an embodiment of the present invention. Additionally, some steps may be omitted as necessary, and the order between steps may be changed.

[0193] An English learning management method according to one embodiment of the present invention may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., either individually or in combination. The program instructions recorded on the medium may be those specifically designed and configured for the present invention, or they may be those known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical recording media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The hardware device may be configured to operate as one or more software modules to perform the operation of the present invention, and vice versa.

[0194] In addition, the aforementioned English learning management method can also be implemented in the form of a computer program or application executed by a computer stored on a recording medium.

[0195] The foregoing description of the present invention is for illustrative purposes only, and those skilled in the art will understand that other specific forms can be easily modified without altering the technical spirit or essential features of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single unit may be implemented in a distributed manner, and components described as distributed may likewise be implemented in a combined form.

[0196] The scope of the present invention is defined by the claims set forth below rather than by the detailed description above, and all modifications or variations derived from the meaning and scope of the claims and equivalent concepts thereof should be interpreted as being included within the scope of the present invention.

Claims

Claim 1 A content providing unit that provides selected content chosen by a student among multiple previously registered English educational contents; an acquisition unit that acquires the student's interpretation answer data regarding instructions within the selected content; and an analysis unit that performs an analysis of the sentence structure regarding the interpretation answer data. It includes a feedback unit that provides feedback generated based on analysis results, and the content providing unit first provides pre-set interpretation answer guide information when the selected content is selected by a student, and then displays the selected content on the screen of the student terminal after a confirmation response regarding the interpretation answer guide information is made. The interpretation answer guide information includes information regarding the writing method and information regarding writing examples, which is information on writing examples of interpretation answers for the provided content. The information regarding the writing method is information regarding the method of writing interpretation answers for instructions within the content provided to the student, and includes step-by-step writing order information including Step 1 corresponding to finding verbs, Step 2 corresponding to distinguishing verb forms, Step 3 corresponding to analyzing sentence structure according to verb forms, Step 4 corresponding to writing interpretation order, and Step 5 corresponding to interpreting the entire sentence, and notation definition information that defines rules for notation by role. The notation definition information is information that defines rules for notation by role for multiple roles corresponding to nouns, adjectives, and adverbs, and in the part corresponding to the phrase and clause that function as a noun in the sentence given within the content, the first It includes information guiding to display in a display form, to display in a second display form in parts corresponding to phrases and clauses functioning as adjectives, and to display in a third display form in parts corresponding to phrases and clauses functioning as adverbs; each of the above multiple contents is content designed to enable training in English syntax interpretation based on sentence structure recognition, and is provided to include a question display section, a first interpretation answer writing section, and an unknown word writing section; the above question display section includes instructions corresponding to the question, a question number,It includes a sentence-unit example sentence corresponding to the instructions, a structural analysis instruction instructing the analysis of the sentence structure of the example sentence, an interpretation writing section, and a second interpretation answer writing section; the sentence corresponding to the example sentence within the problem display section is displayed on the area of ​​the second interpretation answer writing section; the structural analysis instruction is an instruction instructing a detailed analysis of the sentence structure including verbs, sentence patterns, parts of speech, and modifiers, and includes multiple detailed instructions; the interpretation writing section is an area provided for the student to write the entire interpretation data, which is data obtained by interpreting the entire English sentence given as an example sentence into Korean, when the student writes interpretation answer data for the content; the second interpretation answer writing section is an area provided for the student to write role-specific notation data and sentence analysis notation data when the student writes interpretation answer data for the content; the role-specific notation data is data directly written by the student on the example sentence within the content according to the role-specific notation by referring to the notation definition information included in the interpretation answer guide information; and the sentence analysis notation data is information regarding the writing method included in the interpretation answer guide information It includes sentence structure analysis notation data, which is data directly written on the example text within the content by referring to the information in Step 3 corresponding to analyzing sentence structure according to the verb form within, and interpretation order analysis notation data, which is data directly written on the example text within the content by referring to the information in Step 4 corresponding to writing the interpretation order among the writing methods included in the interpretation answer guide information; the above-mentioned first interpretation answer writing section is an area provided to allow the student to write answer data for each detailed instruction regarding multiple detailed instructions given in the above-mentioned structure analysis instructions when writing interpretation answer data for the content, and the above-mentioned interpretation answer data, in which the analysis of sentence structure is performed by the above-mentioned analysis unit, includes answer data for each detailed instruction written by the student in the first interpretation answer writing section within the selected content,An English learning management device comprising at least one of the role-specific notation data and sentence analysis notation data written by a student in the second interpretation answer writing section within the elective content, and the total interpretation data written by a student in the interpretation writing section within the elective content. Claim 2 delete Claim 3 delete Claim 4 An English learning management device according to claim 1, further comprising a Socratic guidance unit that provides an answer-leading question or a hint in response to a query when it is detected that a query has been made from a student terminal in relation to an instruction after the above-mentioned selection content has been provided. Claim 5 An English learning management device according to claim 1, further comprising a report providing unit that provides a learning report generated based on the analysis results to at least one of a teacher terminal and a parent terminal. Claim 6 A method for managing English learning using an English learning management device of claim 1, comprising: a content providing unit providing selected content selected by a student among a plurality of previously registered English educational contents; an acquisition unit acquiring student's interpretation answer data regarding instructions within the selected content; an analysis unit analyzing the interpretation answer data; and a feedback unit providing feedback generated based on the analysis results.

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