Management system and management method

Abstract

To provide a management system that can more properly identify an attribute of a learning content an achievement test targets.SOLUTION: A management system comprises: an acquisition unit 11 that acquires correct answer information indicative of a correct answer of an achievement test; an identification unit 12 that identifies an attribute of a learning content based on the correct answer information; and a registration unit 13 and database 201 that serve as a management unit managing the achievement test and the attribute in association of the achievement test with the attribute.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present disclosure relates to a management system and a management method. [Background technology]

[0002] In recent years, "individually optimized learning" has become a popular theme in the educational field. To achieve this, it is desirable to accurately assess each individual's learning situation. To support this, the Ministry of Education, Culture, Sports, Science and Technology has established curriculum codes that represent the attributes of learning content. By using attributes such as curriculum codes, evaluations can be made that are independent of the type of teaching material. For example, analyses can be performed on a curriculum item-by-item basis to confirm students' understanding, solidify knowledge in students, and improve lessons.

[0003] Academic ability tests are administered to students to confirm their level of understanding and knowledge retention. In order to evaluate each individual's learning information using the academic ability test and attributes, it is necessary to link the academic ability test with the attributes. JP 2020-177507 A (Patent Document 1) discloses a technology that extracts words from test questions, assigns numerical values ​​to each element of a vector for the extracted words to generate vector data, and determines the attributes of the test questions based on the vector data. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2020-177507 Summary of the Invention [Problem to be solved by the invention]

[0005] The technology described in Patent Document 1 uses the question text to determine the attributes of the learning content targeted by the test question. However, when using the question text, there are cases where the attributes of the learning content cannot be properly determined. For example, in the case of a question whose question text is "Write the kanji for 'kyo' in 'Today I went to a temple in the capital.'", the text 'kyo' indicating the learning content is not included in the question text, so the attributes of the learning content targeted by the test question cannot be properly determined.

[0006] The present disclosure has been made to solve the problems described above, and its purpose is to provide a management system and management method that can more appropriately determine the attributes of the learning content targeted by academic ability tests. [Means for solving the problem]

[0007] According to one aspect, the management system includes an acquisition unit that acquires answer information indicating answers to an academic ability test, a discrimination unit that discriminates attributes of the learning content based on the answer information, and a management unit that associates and manages the academic ability test with the attributes.

[0008] Preferably, the academic ability test includes a plurality of questions, the answer information indicates an answer to each of the plurality of questions, and the determination unit determines an attribute for each of the plurality of questions.

[0009] Preferably, the management unit manages the academic ability test and the attributes determined for each of the plurality of questions in association with each other.

[0010] Preferably, the management unit manages each of the plurality of questions in association with the attribute determined for that question.

[0011] Preferably, the attribute indicates at least one of a unit and an item of a curriculum guideline. Also, preferably, the answer information indicates at least one of a model answer and an answer of the student.

[0012] Preferably, the determination unit extracts an answer text indicating the answer from the answer information, and determines the attribute based on the answer text.

[0013] Preferably, the answer information indicates an image of an answer sheet with the answers written in. The determination unit extracts the answer text from the image using optical character recognition technology.

[0014] Preferably, the answer information indicates an image of an answer sheet with the answer written in. The determination unit recognizes figures included in the image using image recognition technology, and uses text identifying the recognized figures as the answer text.

[0015] Preferably, the determination unit extracts the answer text from a highlighted portion of the image.

[0016] Preferably, the discrimination unit identifies a portion of the data indicating at least one of the curriculum guidelines and the teaching materials that has the highest degree of agreement with the answer text, and determines a classification corresponding to the identified portion as an attribute.

[0017] Preferably, the answer information indicates answers of a plurality of students. The management unit manages the attributes determined based on the most popular answer among the answers of the plurality of students in association with the academic ability test.

[0018] Preferably, the answer information indicates the student's answer and a result of a judgment as to whether the student's answer is correct. The judgment unit judges the attribute based on the answer that is judged to be correct.

[0019] Preferably, the determination unit further determines the attribute based on the answer determined to be incorrect, and the management unit further manages incorrect answer information that associates the attribute determined based on the answer determined to be incorrect with the academic ability test.

[0020] According to another aspect, the management method includes the steps of: a computer acquiring answer information indicating answers to an academic ability test; a computer determining attributes of the learning content based on the answer information; and a computer managing the academic ability test in association with the attributes. [Effects of the Invention]

[0021] According to the present disclosure, the attributes of the learning content targeted by the academic achievement test can be more appropriately determined. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a diagram illustrating a schematic configuration of a management system according to an embodiment of the present disclosure. [Figure 2] FIG. 10 is a diagram showing an example of answer information. [Figure 3] FIG. 10 is a diagram showing an example of blank data. [Figure 4] FIG. 2 is a diagram illustrating an example of a hardware configuration of a server device according to the present embodiment. [Figure 5] FIG. 2 is a diagram illustrating an example of a functional configuration of a server device according to the present embodiment. [Figure 6] FIG. 10 is a diagram showing an example of question data. [Figure 7] FIG. 10 is a diagram showing an example of a screen for receiving a command to assign an attribute to an academic ability test. [Figure 8] FIG. 2 is a diagram illustrating an example of a database. [Figure 9] 10 is a flowchart illustrating an example of a processing flow of a server device. [Figure 10] 10 is a flowchart showing the process flow of a subroutine in step S3 of FIG. 9. [Figure 11] 10 is a flowchart showing an example of the process flow of a subroutine in step S4 of FIG. 9. [Figure 12] 10 is a flowchart showing another example of the process flow of the subroutine in step S4 of FIG. 9. [Figure 13] 10 is a flowchart showing another example of the processing flow of the server device. [Figure 14] FIG. 10 is a diagram showing another example of answer information. DETAILED DESCRIPTION OF THE INVENTION

[0023] Hereinafter, a management system according to an embodiment of the present disclosure will be described with reference to the drawings. In the following description, the same parts and components are denoted by the same reference numerals. Their names and functions are also the same. Therefore, detailed descriptions thereof will not be repeated. Note that the embodiments and modifications described below may be selectively combined as appropriate.

[0024] <Overall management system> 1 is a diagram illustrating a schematic configuration of a management system according to an embodiment of the present disclosure. As shown in FIG. 1, the management system 1 includes a server device 100, a storage device 200, a terminal 300, and a multifunction peripheral 400.

[0025] The server device 100, the storage device 200, the terminal 300, and the multifunction device 400 can communicate with each other via a network. The network may be a public network such as the Internet, a public line, or a public wireless LAN (Local Area Network), or a private network such as a LAN or a VPN (Virtual Private Network).

[0026] The server device 100 is a computer, such as a cloud server, that manages the attributes of the learning content targeted by the academic ability test. The server device 100 acquires answer information indicating the answers to the academic ability test from the terminal 300 or the multifunction device 400, and determines the attributes of the learning content targeted by the academic ability test based on the answer information. The server device 100 and the storage device 200 manage the academic ability test in association with the attributes. Specifically, the server device 100 generates attribute information that associates the academic ability test with the attributes, and registers the generated attribute information in a database 201 stored in the storage device 200.

[0027] Academic ability tests include regular tests (midterm and final tests), various mock tests, quizzes, and oral tests. Quizzes include tests administered in daily classes, homework confirmation tests, kanji tests, and English vocabulary tests. Oral tests are tests in which students answer questions asked orally by the teacher on an answer sheet. For this reason, there are no question papers for oral tests.

[0028] Attributes represent classifications of learning content, such as units specified in textbooks or curriculum items. Curriculum guidelines are educational curriculum standards established by the Ministry of Education, Culture, Sports, Science and Technology. Each school implements education in accordance with the curriculum guidelines. Curriculum guidelines are established for each school type and stipulate what should be learned in each subject for each grade. A curriculum code is assigned to every curriculum item for every school type. Therefore, the curriculum code can identify the school type, grade, subject, content, area, content treatment, perspective, etc. Below, we will explain an example of using curriculum guidelines codes as attributes. However, attributes are not limited to curriculum guidelines codes and may also be codes identifying units in each subject, codes identifying items included in digital content (such as digital workbooks), or codes identifying items written on report cards. Alternatively, attributes may be codes identifying words and phrases (e.g., English words, English idioms, proverbs, idiomatic expressions, etc.).

[0029] The storage device 200 stores a database 201. The database 201 is a collection of data that associates academic ability tests with attributes.

[0030] The terminal 300 and the multifunction device 400 are used to generate answer information. For example, the terminal 300 and the multifunction device 400 generate, as answer information, image data in which answers are entered in answer columns of an academic ability test.

[0031] Terminal 300 is used by, for example, a teacher. The teacher uses terminal 300 to create an academic ability test. Terminal 300 generates answer information in accordance with the input. For example, terminal 300 generates, as answer information, image data showing an image of an academic ability test with model answers written in the answer columns.

[0032] The multifunction device 400 has functions of a scanner, printer, copier, etc., as well as a communication function. The multifunction device 400 generates answer information from image data obtained by scanning an academic ability test form on which answers (model answers or answers handwritten by the student) have been written in the answer columns.

[0033] Fig. 2 is a diagram showing an example of answer information. The answer information shown in Fig. 2 shows image 50 of a kanji test. Image 50 includes title 54 located near the beginning of the question area and title 58 located near the beginning of the answer area.

[0034] The question area starting from the title 54 contains question numbers 51a to 51c and question sentences 52a to 52c corresponding to the question numbers 51a to 51c, respectively. The question sentences 52a to 52c each include a line 53a to 53c for highlighting the parts that should be rewritten in kanji or hiragana.

[0035] The answer area starting from the title 58 contains question numbers 56a to 56c and answer columns 57a to 57c corresponding to the question numbers 56a to 56c, respectively. The answer columns 57a to 57c contain model answers by the teacher or answers by the students.

[0036] The terminal 300 and the multifunction device 400 generate answer information corresponding to the academic ability test in response to input instructions from a user (e.g., a teacher), and add identification information (hereinafter referred to as a "test ID") that identifies the academic ability test to the generated answer information. The terminal 300 and the multifunction device 400 transmit the answer information with the test ID added to it to the server device 100.

[0037] Furthermore, the terminal 300 and the multifunction device 400 may generate blank data showing an image of an academic ability test in which no answers have been written, in response to input instructions from a user (e.g., a teacher), and may attach the generated blank data to the answer information and send it to the server device 100.

[0038] Fig. 3 is a diagram showing an example of blank data. The blank data shown in Fig. 3 corresponds to the answer information shown in Fig. 2, and shows image 50A of a kanji test. Image 50A differs from image 50 shown in Fig. 2 in that no answers are written in answer fields 57a to 57c.

[0039] The terminal 300 generates a command instructing the assignment of an attribute to the academic ability test in accordance with an input by a user (e.g., a teacher), and transmits the generated command to the server device 100. In response to the command, the server device 100 determines the attribute based on the answer information for the instructed academic ability test.

[0040] <Server hardware configuration> 4 is a diagram illustrating an example of a hardware configuration of a server device according to the present embodiment. As illustrated in FIG. 4, the server device 100 includes a central processing unit (CPU) 101, which is a hardware processor, a random access memory (RAM) 102, a read only memory (ROM) 103, a hard disk drive (HDD) 104, a memory interface 105, and a network controller 106.

[0041] The CPU 101 loads a program stored in a storage device such as the HDD 104 into the RAM 102 and executes the loaded program. The RAM 102 includes an area for storing various types of information and a work area for the CPU 101 to execute the program. The ROM 103 stores the programs and data executed by the CPU 101. The HDD 104 stores a system program 110 including an OS and a management program 112 executed under the system program 110.

[0042] Memory interface 105 includes a driver circuit to which storage medium 107 is detachably attached, and which reads data or programs from or writes data or programs to storage medium 107. Storage medium 107 is a medium that stores information such as programs recorded therein by electrical, magnetic, optical, mechanical, or chemical action so that the information can be read by CPU 101 or other devices, machines, etc.

[0043] The network controller 106 includes a circuit such as a NIC for communicating with external devices (for example, the storage device 200, the terminal 300, and the multifunction peripheral 400) via a network.

[0044] <Server device functional configuration> Fig. 5 is a diagram schematically illustrating an example of the functional configuration of a server device according to this embodiment. As shown in Fig. 5, the server device 100 includes a storage unit 10, an acquisition unit 11, a determination unit 12, and a registration unit 13. The storage unit 10 is realized by the RAM 102, the ROM 103, and the HDD 104 shown in Fig. 3. The acquisition unit 11, the determination unit 12, and the registration unit 13 are realized by the CPU 101 shown in Fig. 3 executing a management program 112.

[0045] (Storage part) The storage unit 10 stores answer information 14 for each academic ability test, question data 15, digital teaching material data 16, and curriculum guideline data 17.

[0046] As described above, the answer information 14 is information indicating answers to an academic ability test, and is, for example, image data in which answers have been entered in the answer fields of the academic ability test. A test ID that identifies the corresponding academic ability test is added to the answer information 14. Furthermore, blank data indicating an image of the academic ability test in which no answers have been written may be added to the answer information 14.

[0047] When the answer information 14 indicates a model answer, the storage unit 10 stores one answer information 14 for the academic ability test. When the answer information 14 indicates a student's answer, the storage unit 10 stores multiple answer information 14 corresponding to multiple students for the academic ability test.

[0048] The question data 15 is generated from the answer information 14, and associates a question number, a character string of the question (hereinafter referred to as "question text"), and a character string of the answer (hereinafter referred to as "answer text"). The answer type indicates either a model answer or an answer by a student.

[0049] FIG. 6 is a diagram showing an example of question data. FIG. 6 shows question data 15 generated from the answer information representing image 50 in FIG. 2. As shown in FIG. 6, question data 15 is in a table format and includes a field 15a in which a test ID identifying the academic ability test corresponding to question data 15 and an answer type are written, and a record 15b for each question. The answer type indicates either a model answer or an answer by the student. Each record 15b has a field 15c in which a question number is entered, a field 15d in which question text is entered, and a field 15e in which answer text is entered.

[0050] The digital teaching material data 16 indicates, for each unit of the corresponding teaching material, character strings and diagrams that represent the content of the unit, and the curriculum guideline code that corresponds to the unit.

[0051] The curriculum guideline data 17 indicates, for each item of the curriculum guideline, a character string representing the content of the item and a curriculum guideline code assigned to the item.

[0052] (Acquisition Department) The acquisition unit 11 acquires answer information 14 from the terminal 300 or the multifunction device 400 for each academic ability test. A test ID that identifies the corresponding academic ability test is added to the answer information 14. Furthermore, blank data indicating an image of the academic ability test where no answers have been written may be added to the answer information 14. The acquisition unit 11 stores the acquired answer information 14 in the storage unit 10.

[0053] (Discrimination part) The discrimination unit 12 discriminates the attributes of the learning content that is the target of the academic ability test based on the answer information 14. Specifically, the discrimination unit 12 provides a screen for receiving a command to instruct the assignment of attributes to the academic ability test, and discriminates the attributes of the learning content that is the target of the academic ability test according to the command input on the screen.

[0054] Fig. 7 is a diagram showing an example of a screen for receiving a command to assign an attribute to an academic ability test. Screen 60 shown in Fig. 7 is displayed, for example, on the display of terminal 300. As shown in Fig. 7, screen 60 includes selection fields 61 to 65 and a button 66.

[0055] The selection field 61 is used to select a test ID. When the selection field 61 is clicked, a pull-down list showing a list of test IDs added to the answer information 14 stored in the storage unit 10 is displayed. The user (e.g., a teacher) selects a desired test ID from the pull-down list.

[0056] The selection field 62 is used to select the type of answer indicated by the answer information 14 to which the test ID selected in the selection field 61 is added. When the selection field 62 is clicked, a pull-down list is displayed for selecting either "Model Answer" or "Student's Answer." The user (e.g., a teacher) selects the type corresponding to the answer information 14 from the pull-down list.

[0057] The selection field 63 is used to select the target grade for the academic ability test identified by the test ID selected in the selection field 61. When the selection field 63 is clicked, a pull-down list showing a list of grades is displayed. The user (e.g., a teacher) selects the target grade for the academic ability test from the pull-down list.

[0058] The selection field 64 is used to select the subject of the academic ability test identified by the test ID selected in the selection field 61. When the selection field 64 is clicked, a pull-down list showing a list of subjects is displayed. The user (e.g., a teacher) selects the subject of the academic ability test from the pull-down list.

[0059] The selection field 65 is used to select the subject details of the academic ability test identified by the test ID selected in the selection field 61. When the selection field 65 is clicked, a pull-down list showing a list of the subjects selected in the selection field 64 is displayed. The user (e.g., a teacher) selects the subject details of the academic ability test from the pull-down list.

[0060] Button 66 is used to start sending a command to assign attributes to the academic ability test. When button 66 is clicked, the device (e.g., terminal 300) on which screen 60 is displayed sends the command to server device 100. The command includes the information selected in selection fields 61 to 65 (test ID, answer type, target grade, target subject, target details).

[0061] Upon receiving the command, the determination unit 12 generates question data 15 from the answer information 14 to which the test ID included in the command has been added. The method for generating the question data 15 will be described in detail later.

[0062] The determination unit 12 determines the attributes of the learning content targeted by the academic achievement test based on the question data 15 and the target grade, target subject, and target details included in the command. The method for determining the attributes will be described in detail later.

[0063] (Registration Department) The registration unit 13 generates attribute information that associates the academic ability test with the attributes, and registers the generated attribute information in a database 201 stored in a storage device 200.

[0064] Fig. 8 is a diagram showing an example of the database. As shown in Fig. 8, database 201 includes, for each academic ability test, attribute information that associates a test ID that identifies the academic ability test, the number of a question included in the academic ability test (question number), and an attribute that indicates the learning content corresponding to the question.

[0065] <An example of the processing flow of the server device> Fig. 9 is a flowchart showing an example of the processing flow of the server device. The flowchart shown in Fig. 9 is applied when the answer information indicates a model answer. As shown in Fig. 9, CPU 101 of server device 100 acquires answer information 14 indicating the answer to the academic ability test from terminal 300 or multifunction device 400 (step S1).

[0066] 7, and receives a command to assign attributes to the academic ability test via the screen 60 (step S2). The command includes information indicating the test ID, the type of answer, the target grade, the target subject, and the target details.

[0067] Next, CPU 101 executes steps S3 and S4 to determine the attributes of the learning content in accordance with the command. In step S3, CPU 101 generates question data 15 from answer information 14 to which the test ID selected in selection field 61 on screen 60 is added. In step S4, CPU 101 determines the attributes of the learning content (curriculum guideline code) based on question data 15 and the answer type, target grade, target subject, and target details included in the command.

[0068] Next, CPU 101 generates attribute information that associates the academic ability test with the determined curriculum code, and registers the generated attribute information in database 201 (step S5).

[0069] <Subroutine of Step S3> The method for generating the question data 15 will be described in detail with reference to Fig. 10. Fig. 10 is a flowchart showing the flow of the processing of the subroutine of step S3 in Fig. 9.

[0070] 10, CPU 101 uses optical character recognition technology to recognize characters included in the image indicated by answer information 14 to which the test ID included in the command has been added, and the positions of the characters in the image (step S11). Specifically, CPU 101 executes known OCR (Optical Character Recognition) software or ICR (Intelligent Character Recognition) software.

[0071] Next, CPU 101 extracts the question number, question text, and answer text for each question from the recognition results obtained by executing step S11 (step S12).

[0072] Question numbers are usually written as "1.", "(1)", "Question 1", or "Q.1". Rules for writing question numbers are pre-registered in the management program 112. For example, the rules indicate combinations of numbers and periods, combinations of numbers in parentheses, and combinations of the characters "Question" or "Q." and numbers. The CPU 101 extracts a character string that satisfies any of these rules as a question number.

[0073] Academic achievement tests typically include an answer area where answers are written and a question area where questions are written. The answer area typically includes a boxed or underlined answer column. The answer area and the question area may also have titles (e.g., "Question" and "Answer") written on them.

[0074] Therefore, the CPU 101 distinguishes between the answer area and the question area, for example, according to one of the following methods (a) to (c): Alternatively, the CPU 101 may distinguish between the answer area and the question area by combining a plurality of methods selected from the following methods (a) to (c).

[0075] (a) CPU 101 extracts the character strings "Question" and "Answer." CPU 101 determines the question area and the answer area from the positions of the extracted character strings "Question" and "Answer." These character strings are placed near the beginning of the corresponding area. For example, in the case of an academic ability test written vertically, the title is placed in the upper right corner of the corresponding area. In the case of an academic ability test written horizontally, the title is placed in the upper left corner of the corresponding area. Furthermore, each area is usually rectangular. Therefore, CPU 101 can determine the question area and the answer area based on the arrangement direction of the character strings and the positions of the character strings "Question" and "Answer."

[0076] For example, in the case of image 50 shown in Fig. 2, CPU 101 extracts titles 54 and 58. Because the character strings included in image 50 are arranged vertically, CPU 101 determines that the image is a vertically written academic ability test. CPU 101 then determines, as the question area, a rectangular area that includes title 54 in the upper right corner but does not include title 58. Similarly, CPU 101 determines, as the answer area, a rectangular area that includes title 58 in the upper right corner but does not include title 54.

[0077] (b) If blank data is added to the answer information 14, the CPU 101 determines the answer area using the blank data. No answer is written in the image shown by the blank data (see image 50A in FIG. 3). Therefore, the CPU 101 identifies the difference between the image shown by the blank data and the image shown by the answer information 14. The difference indicates the location where the answer is written. Therefore, the CPU 101 determines the area of ​​the difference as the answer area, and determines the area other than the answer area as the question area.

[0078] (c) CPU 101 extracts a box or an underline, and determines the area within the extracted box or the area above the underline as an answer area, and determines the area outside the answer area as a question area.

[0079] For example, in the case of the image 50 shown in FIG. 2, the CPU 101 may extract the frames of the answer columns 57a to 57c and determine the area within the extracted frames as the answer area.

[0080] The CPU 101 labels each character in the question area. If the academic ability test includes multiple questions, the question text is divided for each question. Therefore, the CPU 101 labels each character in the question area, for example, based on the character spacing. For example, the CPU 101 assigns different labels to two characters that are separated by a certain distance or more. As a result, text that has been assigned the same label constitutes the question text for a certain question. The CPU 101 determines that the character string that is placed in the question area and assigned the same label is the question text.

[0081] For example, in the case of image 50 shown in FIG. 2, CPU 101 assigns different labels to the text "I went to Tokyo today," the text "The rain continued for a week," and the text "It's my turn to clean the classroom."

[0082] Note that the question text may be highlighted with a line or other highlighting. Such highlighting is applied to the most important parts of the question text. Therefore, CPU 101 adds information indicating the highlighted characters in the question text (hereinafter referred to as "highlighting information") to the question text. For example, in the case of image 50 shown in FIG. 2, a line 53a is added to "kyo" in question text 52a. Therefore, CPU 101 adds highlighting information indicating "kyo" to the question text "I went to Tokyo today." Similarly, a line 53c is added to "kyo" in question text 52c. Therefore, CPU 101 adds highlighting information indicating "kyo" to the question text "It's my turn to clean the classroom."

[0083] The CPU 101 labels each character in the answer area. As a result, each character with the same label forms the answer to a certain question. The CPU 101 determines the character string that is placed in the answer area and has the same label as the answer text.

[0084] The answer information 14 corresponding to the oral test does not include the question text, and in this case, the CPU 101 only needs to extract the question number and the answer text.

[0085] Next, the CPU 101 links the extracted question number, question text, and answer text (step S13).

[0086] In an academic ability test, question numbers are placed near the question text and answer box of the corresponding question. Therefore, for each question text, CPU 101 identifies the question number closest to the position of the question text and associates the identified question number with the question text. Similarly, for each answer text, CPU 101 identifies the question number closest to the position of the answer text and associates the identified question number with the answer text.

[0087] Next, CPU 101 generates question data 15 based on the result of step S13 (step S14). Specifically, CPU 101 generates question data 15 by entering the mutually linked question number, question text, and answer text in fields 15c to 15e of the same record 15b. Furthermore, CPU 101 enters the test ID and answer type included in the command in field 15a. If question text has not been extracted, field 15d is blank.

[0088] <Example of the process flow of the subroutine in step S4> A method for determining the attributes of learning content (curriculum guideline code) will be described in detail with reference to Fig. 11. Fig. 11 is a flowchart showing an example of the flow of the subroutine processing of step S4 in Fig. 9. As shown in Fig. 11, steps S21 to S25 are repeatedly performed for each question. That is, CPU 101 executes steps S21 to S25 for each question indicated by question data 15 in which the test ID included in the command is written in field 15a.

[0089] In step S21, CPU 101 determines whether there is a curriculum item corresponding to the answer text for the i-th question. Specifically, CPU 101 reads the answer text from field 15e of i-th record 15b of question data 15. CPU 101 specifies a search range from curriculum data 17 based on the command. That is, CPU 101 specifies, as the search range, a range of curriculum data 17 that corresponds to the target grade, target subject, and target item included in the command. For example, in the case of the target grade being "second grade of elementary school," the target subject being "Japanese," and the target item being "kanji," CPU 101 specifies, as the search range, a range of curriculum data 17 that indicates kanji learned in second grade of elementary school. CPU 101 determines whether there is an item matching the answer text in the search range.

[0090] If there is a curriculum item corresponding to the answer text of the i-th question (YES in step S21), CPU 101 proceeds to step S23. In step S23, CPU 101 assigns the curriculum code corresponding to the item matching the answer text to the i-th question.

[0091] If there is no curriculum guideline item corresponding to the answer text of the i-th question (NO in step S21), CPU 101 determines whether there is a curriculum guideline item corresponding to the question text of the i-th question (step S22). Specifically, CPU 101 reads the question text from field 15d of i-th record 15b of question data 15. CPU 101 specifies a search range in curriculum guideline data 17 based on the command. That is, CPU 101 specifies a range in curriculum guideline data 17 corresponding to the target grade, target subject, and target sub-item included in the command as the search range. CPU 101 determines whether there is an item matching the question text in the search range. Note that if emphasis information is added to the question text, CPU 101 determines whether there is an item matching the characters indicated by the emphasis information in the search range.

[0092] If there is a curriculum item corresponding to the question text of the i-th question (YES in step S22), CPU 101 proceeds to step S23. In step S23, CPU 101 assigns the curriculum code corresponding to the item matching the question text to the i-th question.

[0093] If there is no curriculum item corresponding to the question text of the i-th question (NO in step S22), CPU 101 assigns the code "NG" to the i-th question, indicating that an attribute cannot be assigned (step S24).

[0094] After step S23 or step S24, CPU 101 increments i by 1 (step S25), whereby steps S21 to S25 are executed for the next (i+1)th question.

[0095] According to the flowchart shown in FIG. 11, for the answer information 14 showing the image 50 shown in FIG. 2, a curriculum code is assigned to the question as follows.

[0096] Since the answer text corresponding to question number 51a is "Kyoto", CPU 101 determines in step S21 that an item indicating "Kyoto" exists within the search range (the range indicating kanji learned in the second grade of elementary school) of curriculum guideline data 17. As a result, in step S23, CPU 101 assigns curriculum guideline code "8210020120300000" corresponding to the item to the question with question number 51a.

[0097] On the other hand, since the answer text corresponding to question number 51c is "kyo," CPU 101 determines in step S21 that there is no item indicating "kyo" within the search range of curriculum guideline data 17 (the range indicating kanji learned in the second grade of elementary school). Therefore, CPU 101 reads out question text "It's my turn to clean the classroom" corresponding to question number 51c from question data 15. Since emphasis information indicating "teaching" is added to the question text, CPU 101 determines in step S22 that there is an item indicating "teaching" within the search range of curriculum guideline data 17. As a result, in step S23, CPU 101 assigns the curriculum guideline code "8210020120320000" corresponding to the item to the question with question number 51c.

[0098] <Another example of the processing flow of the subroutine in step S4> Fig. 12 is a flowchart showing another example of the processing flow of the subroutine of step S4 in Fig. 9. As shown in Fig. 12, steps S31 to S36 are repeatedly performed for each question. That is, CPU 101 executes steps S31 to S36 for each question indicated by question data 15 in which the test ID included in the command is written in field 15a.

[0099] In step S31, CPU 101 determines the curriculum item corresponding to the answer text for the i-th question and the degree of match between the answer text and the item. Specifically, CPU 101 reads the answer text from field 15e of i-th record 15b of question data 15. CPU 101 specifies a search range in curriculum data 17 based on the command. That is, CPU 101 specifies a range of curriculum data 17 corresponding to the target grade, target subject, and target item included in the command as the search range. For example, in the case of the target grade being "second grade of elementary school," the target subject being "Japanese," and the target item being "kanji," CPU 101 specifies a range of curriculum data 17 indicating kanji learned in second grade of elementary school as the search range. CPU 101 extracts an item corresponding to the answer text from the search range. For example, CPU 101 extracts an item that matches the answer text, an item that shares at least a portion of the reading with the answer text, etc. The CPU 101 determines the degree of match for the extracted items. For example, the CPU 101 determines the degree of match for an item that matches the answer text to be "high." The CPU 101 determines the degree of match for an item that does not match the answer text and shares a reading with the answer text to be "medium." The CPU 101 determines the degree of match for an item that does not match the answer text and shares a part of a reading with the answer text to be "low."

[0100] Next, in step S32, the CPU 101 determines the curriculum item corresponding to the question text of the i-th question and the degree of match between the question text and the item. Specifically, the CPU 101 reads the question text from field 15d of the i-th record 15b of the question data 15. The CPU 101 specifies a search range within the curriculum data 17 based on the command. That is, the CPU 101 specifies a range within the curriculum data 17 corresponding to the target grade, target subject, and target sub-item included in the command as the search range. The CPU 101 extracts items corresponding to the question text from the search range. For example, the CPU 101 extracts items that match the question text, items that share the same reading as the question text, etc. The CPU 101 determines the degree of match for the extracted items. For example, the CPU 101 determines the degree of match for items that match the question text to be "high." The CPU 101 determines the degree of match as "medium" for an item that does not match the question text and has the same reading as the question text. The CPU 101 determines the degree of match as "low" for an item that does not match the question text and has the same reading as the question text. Note that if emphasis information is added to the question text, the CPU 101 may extract from the search range items that match characters indicated by the emphasis information, items that have at least a part of the same reading as the characters indicated by the emphasis information, and the like.

[0101] Next, CPU 101 determines whether or not there is an item having a degree of match equal to or higher than a criterion (step S33). The criterion degree of match is, for example, "medium."

[0102] If NO in step S33, CPU 101 assigns a code "NG" to the ith question, indicating that an attribute cannot be assigned (step S34).

[0103] If the answer is YES in step S33, CPU 101 assigns the curriculum code corresponding to the item with the highest degree of match to the ith question (step S35).

[0104] After step S34 or step S35, CPU 101 increments i by 1 (step S36), whereby steps S31 to S36 are executed for the next (i+1)th question.

[0105] According to the flowchart shown in FIG. 12, for the answer information 14 showing the image 50 shown in FIG. 2, a curriculum code is assigned to the question as follows.

[0106] Since the answer text corresponding to question number 51a is "Kyoto", in step S31, CPU 101 determines the item "Kyoto" from the search range of curriculum data 17 (the range indicating kanji learned in the second grade of elementary school) and the degree of match for that item as "high".

[0107] Furthermore, since emphasis information indicating "kyo" is added to the question text corresponding to question number 51a, in step S32, CPU 101 determines the items "kyo" and "kyo" from the search range of curriculum guideline data 17 and the degree of match for these items as "medium."

[0108] Then, in step S34, CPU 101 assigns the course of study code "8210020120300000" corresponding to the item "Kyoto" with the highest degree of coincidence to the question with question number 51a.

[0109] On the other hand, since the answer text corresponding to question number 51c is "kyo," in step S31, CPU 101 determines the items "kyo," "tsuyo," and "kyo" from the search range of curriculum guideline data 17 and the degree of match for these items as "medium."

[0110] Furthermore, since emphasis information indicating "teaching" is added to the question text corresponding to question number 51c, in step S32, CPU 101 determines the item "teaching" from the search range of curriculum guideline data 17 and the degree of match for that item as "high."

[0111] Then, in step S34, CPU 101 assigns the course of study code "8210020120320000" corresponding to the item "teaching" with the highest degree of coincidence to the question with question number 51c.

[0112] <Another example of the processing flow of the server device> FIG. 13 is a flowchart showing another example of the processing flow of the server device. The flowchart shown in FIG. 13 is applied when the answer information indicates a student's answer. When the answer information indicates a student's answer, that answer does not necessarily mean that the answer is correct. Therefore, by taking statistics of the answers of multiple students, attributes for the question (e.g., curriculum code) are determined. The flowchart shown in FIG. 13 can also be applied to an oral test without a question statement.

[0113] 13, the CPU 101 of the server device 100 acquires answer information 14 indicating the students' answers to the academic ability test from the terminal 300 or the multifunction device 400 (step S41). Note that in step S41, multiple pieces of answer information 14 corresponding to multiple students are acquired for each academic ability test.

[0114] 7, and receives a command to assign attributes to the academic ability test via the screen 60 (step S2). The command includes information indicating the test ID, the type of answer, the target grade, the target subject, and the target details.

[0115] Next, CPU 101 repeatedly executes steps S42, S3, S4, and S43 for each piece of answer information 14 to which the test ID included in the command is added. As described above, multiple pieces of answer information 14 corresponding to multiple students are acquired. Therefore, steps S42, S3, S4, and S43 are repeatedly executed for each student.

[0116] In step S42, CPU 101 determines answer information 14 of the j-th student as the target. After that, CPU 101 executes steps S3 and S4 using the target answer information 14. Details of steps S3 and S4 are as described above.

[0117] After step S4, CPU 101 increments j by 1 (step S43), whereby steps S42, S3, S4, and S43 are executed for answer information 14 of the next j+1th student.

[0118] After steps S42, S3, S4, and S43 have been executed for all students' answer information 14, steps S45 and S46 are repeatedly executed for each question. That is, CPU 101 executes steps S45 and S46 for each question indicated by question data 15 in which the test ID included in the command is written in field 15a.

[0119] In step S45, CPU 101 determines the curriculum code most frequently assigned to the i-th question as the curriculum code indicating the learning content corresponding to the i-th question. The most frequently assigned answer among the answers of multiple students is likely to be the correct answer. Therefore, the curriculum code most frequently assigned corresponds to the answer that is likely to be the correct answer. In this way, even when using answer information 14 indicating the student's answer, CPU 101 can appropriately assign a curriculum code indicating the learning content to the question.

[0120] Next, the CPU 101 increments i by 1 (step S46), whereby step S45 is executed for the next (i+1)th question.

[0121] When the curriculum code has been determined for all questions, CPU 101 generates attribute information that associates the academic ability test with the determined curriculum code, and registers the generated attribute information in database 201 (step S5). After step S5, CPU 101 ends the process.

[0122] <Variation 1> The answer information 14 may include an image showing the result of the judgment on whether the student's answer is correct or incorrect, along with the student's answer. In this case, the CPU 101 can assign an attribute of the learning content (curriculum guideline code) to the question based on the result of the judgment on whether the student's answer is correct or incorrect.

[0123] Fig. 14 is a diagram showing another example of answer information. The answer information illustrated in Fig. 14 shows image 50B of a student's answer sheet on which the result of the correct / incorrect judgment is written. Compared to image 50 shown in Fig. 2, image 50B includes marks 59 indicating the result of the correct / incorrect judgment. Marks 59a and 59c indicate a correct answer. Mark 59b indicates an incorrect answer.

[0124] The processing flow of the server device according to the first modification is similar to the flowchart shown in Fig. 13. However, in step S3, the CPU 101 operating as the determination unit 12 determines whether the mark 59 for each question indicates a correct answer or an incorrect answer. Then, the CPU 101 adds the determination result to the record 15b corresponding to the question in the question data 15.

[0125] The shape of the mark 59 varies greatly depending on whether the answer is correct or incorrect. A first group of shapes that the mark 59 indicating a correct answer can take and a second group of shapes that the mark 59 indicating an incorrect answer can take are pre-registered in the management program 112. Therefore, the CPU 101 calculates the similarity between the shape of the mark 59 and each of the first and second shape groups, and determines whether the answer is correct or incorrect based on the similarity. That is, if the similarity with the first shape group is higher than the similarity with the second shape group, the CPU 101 determines that the answer is correct. Conversely, if the similarity with the second shape group is higher than the similarity with the first shape group, the CPU 101 determines that the answer is incorrect.

[0126] Furthermore, the server device according to the first modification executes the following steps instead of step S45. That is, CPU 101 operating as determination unit 12 refers to question data 15 generated from answer information 14 of multiple students, and identifies record 15b including a determination result indicating a correct answer for the i-th question. CPU 101 determines the attribute (curriculum guideline code) identified using identified record 15b as the attribute (curriculum guideline code) of the learning content corresponding to the i-th question.

[0127] As a result, the attribute (curriculum guideline code) determined using the correct answer is registered in the database 201.

[0128] The CPU 101 operating as the registration unit 13 may identify, for each question, the record 15b containing the determination result indicating an incorrect answer, and may register incorrect answer information in which the attribute (curriculum guideline code) determined using the identified record 15b is associated with the question in the database 201. By checking the incorrect answer information, it is possible to analyze the tendency of errors.

[0129] <Variation 2> Academic ability tests are not limited to kanji tests, but also include English vocabulary tests, science tests, and social studies tests.

[0130] In science and social studies tests, answers may be given using diagrams. For example, a geography test may have a question such as, "On the map of Japan in the answer section, black out 'Tokyo'." Or, a comprehension test may have a question such as, "On the flower diagram in the answer section, black out the 'pistil'." In such cases, CPU 101 cannot generate answer information 14 using optical character recognition technology. Therefore, CPU 101 may use known image recognition technology to recognize figures included in an image and generate question data 15 including text identifying the recognized figure as answer text.

[0131] Furthermore, in science and social studies tests, there may be cases where the answer text does not completely match the text included in the curriculum guideline data 17. Therefore, the CPU 101 operating as the discrimination unit 12 may use known techniques such as natural language processing and clustering to calculate the degree of match between the answer text and the text included in the curriculum guideline data 17, and discriminate the attributes of the learning content based on the calculated degree of match. By using known natural language processing and clustering, the degree of match is calculated taking into account the meaning and content of the text. For example, the CPU 101 calculates a high degree of match between the curriculum guideline item "There are various organs inside the body that maintain life activities" and the answer text "liver" because "liver" is an example of an "organ" within the "body." On the other hand, the CPU 101 calculates a low degree of match between the curriculum guideline item "There are various organs inside the body that maintain life activities" and the answer text "skin" because the association between "skin" and "inside the body" and "organ" is low. In this way, CPU 101 calculates the degree of match with the answer text for each item of the curriculum guidelines, identifies the item with the highest degree of match, and determines the classification (curriculum guidelines code) corresponding to the identified item as an attribute.

[0132] <Variation 3> For example, in a social studies test, multiple sub-questions included in a main question are likely to correspond to one unit. In such a case, the CPU 101 operating as the discrimination unit 12 may assign one attribute (curriculum guideline code) to the multiple questions. When assigning one attribute (curriculum guideline code) to the multiple questions, it is preferable to use the digital teaching material data 16 instead of the curriculum guideline data 17.

[0133] If answer information 14 indicates "Shiretoko," "Tokachi," and "Obihiro" as the answers to three sub-questions, and "sixth grade" and "social studies" are selected as the target grade and target subject, respectively, CPU 101 determines the attributes (curriculum guidelines code) for the three sub-questions as follows.

[0134] The CPU 101 extracts the portion (e.g., unit) that has the highest degree of agreement with "Shiretoko," "Tokachi," and "Obihiro" from the digital teaching material data 16, and assigns the curriculum guideline code corresponding to the extracted portion to three sub-questions with "Shiretoko," "Tokachi," and "Obihiro" as the answer. For example, the CPU 101 may classify the degree of agreement of a portion that includes all of "Shiretoko," "Tokachi," and "Obihiro" as "high," the degree of agreement of a portion that includes two of "Shiretoko," "Tokachi," and "Obihiro" as "medium," and the degree of agreement of a portion that includes one of "Shiretoko," "Tokachi," and "Obihiro" as "low."

[0135] <Variation 4> An academic achievement test may have an answer field in which multiple options are written in advance. In this case, a student highlights one of the multiple options written in the answer field by circling it. In such a case, the CPU 101 operating as the discrimination unit 12 may extract the answer text from the highlighted portion of the answer area of ​​the image indicated by the answer information 14. Note that the highlighting may include decorations, ornaments, and annotations other than circles.

[0136] The embodiments disclosed herein should be considered to be illustrative in all respects and not restrictive. The scope of the present invention is defined by the claims, not by the above description, and is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0137] 1 Management system, 10 Memory unit, 11 Acquisition unit, 12 Discrimination unit, 13 Registration unit, 14 Answer information, 15 Question data, 15a, 15c, 15d, 15e Field, 15b Record, 16 Digital teaching material data, 17 Course of study data, 50, 50A, 50B Image, 51a to 51c, 56a to 56c Question number, 52a to 52c Question statement, 53a to 53c Bar line, 54, 58 Title, 57a to 57c Answer column, 59, 59a to 59c Mark, 60 Screen, 61 to 65 Selection column, 66 Button, 100 Server device, 101 CPU, 102 RAM, 103 ROM, 105 Memory interface, 106 Network controller, 107 Storage medium, 110 System program, 112 Management programs, 200 Storage devices, 201 Databases

Claims

1. an acquisition unit that acquires answer information indicating answers to an academic ability test; a discrimination unit that discriminates attributes of learning content based on the answer information; a management unit that manages the academic ability test and the attributes in association with each other, The attribute indicates at least one of a unit and an item of a curriculum guideline.

2. The achievement test includes a plurality of questions, the answer information indicates an answer to each of the plurality of questions; The management system according to claim 1 , wherein the determining unit determines the attribute for each of the plurality of questions.

3. The management system according to claim 2 , wherein the management unit manages the academic ability test and the attributes determined for each of the plurality of questions in association with each other.

4. The management system according to claim 2 , wherein the management unit manages each of the plurality of questions in association with the attribute determined for that question.

5. The management system according to claim 1 , wherein the answer information indicates at least one of a model answer and an answer of a student.

6. The determination unit extracting an answer text indicating the answer from the answer information; The management system according to claim 1 , wherein the attribute is determined based on the answer text.

7. the answer information indicates an image of an answer sheet on which the answers are written, The management system according to claim 6 , wherein the determining unit extracts the answer text from the image using optical character recognition technology.

8. the answer information indicates an image of an answer sheet on which the answers are written, The determination unit Recognizing figures contained in the image using image recognition technology; The management system of claim 6 , wherein text identifying the recognized figure is used as the answer text.

9. The management system according to claim 7 , wherein the determining unit extracts the answer text from a highlighted portion of the image.

10. The determination unit Identifying the portion of the data indicating at least one of the curriculum guidelines and teaching materials that has the highest degree of agreement with the answer text; The management system according to claim 6 , wherein a classification corresponding to the identified part is determined as the attribute.

11. the answer information indicates answers of a plurality of students; The management system according to claim 5 , wherein the management unit manages the attribute determined based on the most common answer among the answers given by the plurality of students in association with the academic ability test.

12. the answer information indicates the student's answer and a result of determining whether the student's answer is correct or incorrect; The management system according to claim 5 , wherein the determining unit determines the attribute based on an answer for which the determination result is a correct answer.

13. The determination unit further determines the attribute based on the answer for which the determination result is an incorrect answer, The management system according to claim 12 , wherein the management unit further manages incorrect answer information that associates attributes determined based on the answers for which the determination result is incorrect with the academic ability test.

14. A step in which a computer acquires answer information indicating answers to an achievement test; a step in which the computer determines attributes of the learning content based on the answer information; and a step in which the computer manages the academic ability test in association with the attributes, A management method in which the attribute indicates at least one of a unit and an item of a curriculum guideline.

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