JP7775680B2 - Management system and management method - Google Patents

Description

This disclosure relates to a management system and a management method.

In recent years, "individually optimized learning" has become a popular theme in the educational field. To achieve this, it is desirable to accurately evaluate 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 not dependent on the type of teaching material. For example, analyses can be carried out on a curriculum item basis to confirm students' understanding, ensure students have solidified knowledge, and improve lessons.

Academic ability tests are administered to assess students' level of understanding and knowledge retention. To evaluate each individual's learning information using academic ability tests and attributes, it is necessary to link the academic ability tests to the attributes. JP 2020-177507 A (Patent Document 1) discloses technology that extracts words from test questions, generates vector data by assigning numerical values to each element of a vector for the extracted words, and determines the attributes of the test questions based on the vector data.

Japanese Patent Application Laid-Open No. 2020-177507

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 with the text "In 'Today I went to a temple in the capital,' please write the kanji for 'kyo'," 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.

This 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 achievement tests.

In 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.

Preferably, the academic ability test includes a plurality of questions. The answer information indicates the answer to each of the plurality of questions. The discrimination unit discriminates the attributes for each of the plurality of questions.

Preferably, the management unit manages the academic ability test in association with the attributes determined for each of the multiple questions.

Preferably, the management unit manages each of the multiple questions in association with the attributes determined for that question.

Preferably, the attribute indicates at least one of a unit and a curriculum item. Also, preferably, the answer information indicates at least one of a model answer and a student's answer.

Preferably, the discrimination unit extracts answer text indicating the answer from the answer information and discriminates the attributes based on the answer text.

Preferably, the answer information indicates an image of an answer sheet on which the answers are written. The determination unit extracts the answer text from the image using optical character recognition technology.

Preferably, the answer information indicates an image of an answer sheet on which the answer has been written. The discrimination unit recognizes figures contained in the image using image recognition technology, and uses text identifying the recognized figures as the answer text.

Preferably, the discrimination unit extracts the answer text from the highlighted portion of the image.

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

Preferably, the answer information indicates answers from multiple students. The management unit associates and manages the attributes determined based on the most common answer among the answers from the multiple students with the academic ability test.

Preferably, the answer information indicates the student's answer and the result of a judgment as to whether the student's answer is correct. The discrimination unit discriminates the attributes based on the answer that is judged to be correct.

Preferably, the discrimination unit further discriminates attributes based on answers that have been judged to be incorrect. The management unit further manages incorrect answer information that associates the attributes determined based on answers that have been judged to be incorrect with the academic ability test.

In 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.

This disclosure makes it possible to more appropriately determine the attributes of the learning content targeted by academic achievement tests.

1 is a diagram illustrating a schematic configuration of a management system according to an embodiment of the present disclosure. FIG. 10 is a diagram showing an example of answer information. FIG. 10 is a diagram showing an example of blank data. FIG. 2 is a diagram illustrating an example of a hardware configuration of a server device according to the present embodiment. FIG. 2 is a diagram illustrating an example of a functional configuration of a server device according to the present embodiment. FIG. 10 is a diagram showing an example of question data. FIG. 10 is a diagram showing an example of a screen for receiving a command to assign an attribute to an academic ability test. FIG. 2 is a diagram illustrating an example of a database. 10 is a flowchart illustrating an example of a processing flow of a server device. 10 is a flowchart showing the process flow of a subroutine of step S3 of FIG. 9. 10 is a flowchart showing an example of the process flow of a subroutine of step S4 of FIG. 9 . 10 is a flowchart showing another example of the process flow of the subroutine of step S4 of FIG. 9 . 10 is a flowchart showing another example of the processing flow of the server device. FIG. 10 is a diagram showing another example of answer information.

A management system according to an embodiment of the present disclosure will now be described with reference to the drawings. In the following description, identical parts and components are designated by the same reference numerals. Their names and functions are also the same. Therefore, detailed descriptions of these will not be repeated. Note that the embodiments and variations described below may be selectively combined as appropriate.

<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.

The server device 100, storage device 200, terminal 300, and multifunction device 400 can communicate 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 VPN (Virtual Private Network).

The server device 100 is a computer, such as a cloud server, that manages the attributes of the learning content covered 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 covered 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.

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.

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 these 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, and perspective. Below, we will explain an example of using curriculum codes as attributes. However, attributes are not limited to curriculum codes, and can also be codes identifying units in each subject, codes identifying items included in digital content (such as digital drills), or codes identifying items written on report cards. Alternatively, attributes can be codes identifying words and phrases (such as English words, English idioms, proverbs, and idiomatic expressions).

The storage device 200 stores the database 201. The database 201 is a collection of data that associates academic achievement tests with attributes.

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 image data in which answers have been entered into answer fields for an academic ability test as answer information.

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

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

Figure 2 shows an example of answer information. The answer information illustrated in Figure 2 shows image 50 of a kanji test. Image 50 includes a title 54 located near the beginning of the question area and a title 58 located near the beginning of the answer area.

The question area beginning with title 54 contains question numbers 51a-51c and question statements 52a-52c corresponding to question numbers 51a-51c, respectively. Question statements 52a-52c each include a line 53a-53c to highlight the parts that should be rewritten in kanji or hiragana.

The answer area, beginning with title 58, contains question numbers 56a-56c and answer columns 57a-57c corresponding to question numbers 56a-56c, respectively. Answer columns 57a-57c contain model answers by the teacher or answers by the students.

In response to input instructions from a user (e.g., a teacher), the terminal 300 and multifunction device 400 generate answer information corresponding to the academic ability test 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 multifunction device 400 then send the answer information with the added test ID to the server device 100.

Furthermore, in response to input instructions from a user (e.g., a teacher), 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, and add the generated blank data to the answer information and transmit it to the server device 100.

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

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

<Server device 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.

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

Memory interface 105 includes a driver circuit to which storage medium 107 is removably 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 through electrical, magnetic, optical, mechanical, or chemical action so that the information can be read by CPU 101 or other devices or machines.

The network controller 106 includes circuits such as a NIC for communicating with external devices (e.g., the storage device 200, the terminal 300, and the multifunction device 400) via the network.

<Functional configuration of server device>
Fig. 5 is a diagram schematically illustrating an example of the functional configuration of a server device according to the present 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.

(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.

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

When the answer information 14 indicates a model answer, the memory unit 10 stores one piece of answer information 14 for the academic ability test. When the answer information 14 indicates a student's answer, the memory unit 10 stores multiple pieces of answer information 14 corresponding to multiple students for the academic ability test.

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

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

For each unit of the corresponding teaching material, digital teaching material data 16 indicates text and diagrams that represent the content of that unit, as well as the curriculum guideline code that corresponds to that unit.

Curriculum guideline data 17 indicates, for each item in the curriculum guideline, a string representing the content of that item and the curriculum guideline code assigned to that item.

(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.

(Discrimination part)
The discrimination unit 12 discriminates the attributes of the learning content targeted by the academic ability test based on the answer information 14. Specifically, the discrimination unit 12 provides a screen for receiving a command instructing the assignment of attributes to the academic ability test, and discriminates the attributes of the learning content targeted by the academic ability test according to the command input on the screen.

Figure 7 shows an example of a screen for receiving a command to assign attributes to an academic ability test. Screen 60 shown in Figure 7 is displayed, for example, on the display of terminal 300. As shown in Figure 7, screen 60 includes selection fields 61 to 65 and button 66.

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 memory unit 10 is displayed. The user (e.g., a teacher) selects the desired test ID from the pull-down list.

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.

Selection field 63 is used to select the target grade for the academic ability test identified by the test ID selected in selection field 61. When 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.

Selection field 64 is used to select the subject of the academic ability test identified by the test ID selected in selection field 61. When 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.

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

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).

When the discrimination unit 12 receives the command, it generates question data 15 from the answer information 14 to which the test ID included in the command has been added. Details of how the question data 15 is generated will be described later.

The discrimination 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. Details of how the attributes are determined will be described later.

(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 the storage device 200 .

Figure 8 is a diagram showing an example of a database. As shown in Figure 8, the 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.

<Example of processing flow of server device>
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, the CPU 101 of the server device 100 acquires answer information 14 indicating the answers to the academic ability test from the terminal 300 or the multifunction device 400 (step S1).

Next, the CPU 101 displays the screen 60 shown in FIG. 7 and receives a command via the screen 60 to assign attributes to the academic ability test (step S2). The command includes information indicating the test ID, answer type, target grade, target subject, and target details.

Next, in accordance with the command, CPU 101 executes steps S3 and S4 to determine the attributes of the learning content. 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 has been 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.

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

<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.

As shown in FIG. 10, CPU 101 uses optical character recognition technology to recognize the characters contained in the image indicated by answer information 14 to which the test ID included in the command has been added, and the position of those characters within the image (step S11). Specifically, CPU 101 executes publicly known OCR (Optical Character Recognition) software or ICR (Intelligent Character Recognition) software.

Next, the 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).

Question numbers are usually written as "1.", "(1)", "Question 1", or "Q.1". Rules regarding the writing of 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, or combinations of the letters "Question" or "Q." and numbers. The CPU 101 extracts character strings that satisfy any of these rules as question numbers.

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 question area may also have titles (e.g., "Question" and "Answer") written on them.

The CPU 101 therefore 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 multiple methods selected from the following methods (a) to (c).

(a) CPU 101 extracts the character strings "Question" and "Answer." CPU 101 determines the question area and 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 typically rectangular. Therefore, CPU 101 can determine the question area and answer area based on the arrangement direction of the character strings and the positions of the character strings "Question" and "Answer."

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

(b) If blank data is added to answer information 14, CPU 101 uses the blank data to determine the answer area. No answer is written in the image represented by the blank data (see image 50A in Figure 3). Therefore, CPU 101 identifies the difference between the image represented by the blank data and the image represented by answer information 14. The difference indicates the location where the answer is written. Therefore, CPU 101 determines the area of the difference as the answer area, and determines the area outside the answer area as the question area.

(c) The CPU 101 extracts a frame or underline. The CPU 101 determines the area within the extracted frame or the area above the underline as the answer area, and determines the area outside the answer area as the question area.

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

The CPU 101 labels each character in the question area. If the academic ability test includes multiple questions, the question text is divided into separate questions. Therefore, the CPU 101 labels each character in the question area based on, for example, 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 single question. The CPU 101 determines that the character strings that are placed in the question area and that have been assigned the same label are the question text.

For example, in the case of image 50 shown in Figure 2, the 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."

Note that questions may be highlighted with a line or other highlighting. Such highlighting is applied to the most important parts of the question. 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 Figure 2, a line 53a is added to "kyo" in question sentence 52a. Therefore, CPU 101 adds emphasis information indicating "kyo" to the question text "I went to Tokyo today." Similarly, a line 53c is added to "kyo" in question sentence 52c. Therefore, CPU 101 adds emphasis information indicating "kyo" to the question text "It's my turn to clean the classroom."

The CPU 101 labels each character in the answer area. As a result, each character with the same label constitutes the answer to a single question. The CPU 101 determines that the character string placed in the answer area and assigned the same label is the answer text.

Note that the answer information 14 corresponding to the oral test does not include the question text. In this case, the CPU 101 only needs to extract the question number and answer text.

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

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

Next, the CPU 101 generates question data 15 based on the results of step S13 (step S14). Specifically, the CPU 101 generates question data 15 in which the linked question number, question text, and answer text are entered in fields 15c to 15e of the same record 15b. Furthermore, the CPU 101 enters the test ID and answer type included in the command in field 15a. Note that if no question text has been extracted, field 15d is blank.

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

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

If a curriculum guideline item exists that corresponds to the answer text for the i-th question (YES in step S21), the CPU 101 proceeds to step S23. In step S23, the CPU 101 assigns the curriculum guideline code corresponding to the item that matches the answer text to the i-th question.

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 the i-th record 15b of question data 15. CPU 101 specifies the search range within curriculum guideline data 17 based on the command. That is, CPU 101 specifies the range of 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 in the search range that matches the question text. Note that if emphasis information is added to the question text, CPU 101 determines whether there is an item in the search range that matches the characters indicated by the emphasis information.

If a curriculum guideline item corresponding to the question text of the i-th question exists (YES in step S22), the CPU 101 proceeds to step S23. In step S23, the CPU 101 assigns the curriculum guideline code corresponding to the item that matches the question text to the i-th question.

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

After step S23 or step S24, the CPU 101 increments i by 1 (step S25). This causes steps S21 to S25 to be executed for the next (i+1)th question.

According to the flowchart shown in Figure 11, a curriculum code is assigned to the question for the answer information 14 representing the image 50 shown in Figure 2 as follows:

Because 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 of curriculum guideline data 17 (the range indicating kanji learned in second grade of elementary school). As a result, in step S23, CPU 101 assigns the curriculum guideline code "8210020120300000" corresponding to that item to question number 51a.

On the other hand, because 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 second grade of elementary school). Therefore, CPU 101 reads out the question text "It's my turn to clean the classroom" corresponding to question number 51c from question data 15. Because 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 that item to question number 51c.

<Another example of the processing flow of the subroutine in step S4>
Fig. 12 is a flowchart showing another example of the process 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, the CPU 101 executes steps S31 to S36 for each question indicated by the question data 15 in which the test ID included in the command is written in field 15a.

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 the i-th record 15b of question data 15. CPU 101 identifies the search range within curriculum data 17 based on the command. That is, CPU 101 identifies the 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," the target subject being "Japanese," and the target item being "kanji," CPU 101 identifies the range of curriculum data 17 indicating kanji learned in second grade as the search range. CPU 101 extracts items corresponding to the answer text from the search range. For example, CPU 101 extracts items that match the answer text, items that share 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 items that match the answer text to be "high." The CPU 101 determines the degree of match for items that do not match the answer text and share a reading with the answer text to be "medium." The CPU 101 determines the degree of match for items that do not match the answer text and share part of a reading with the answer text to be "low."

Next, in step S32, 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, CPU 101 reads the question text from field 15d of the i-th record 15b of question data 15. CPU 101 specifies the search range within curriculum data 17 based on the command. That is, CPU 101 specifies the range within curriculum data 17 that corresponds to the target grade, target subject, and target item included in the command as the search range. CPU 101 extracts items that correspond to the question text from the search range. For example, CPU 101 extracts items that match the question text, items that share the same reading as the question text, etc. CPU 101 determines the degree of match for the extracted items. For example, 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 to be "medium" for items that do not match the question text and share the same reading as the question text. The CPU 101 determines the degree of match to be "low" for items that do not match the question text and share part of 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 share at least part of the reading with characters indicated by the emphasis information, etc.

Next, the CPU 101 determines whether there are any items with a matching degree equal to or greater than a criterion (step S33). The criterion matching degree is, for example, "medium."

If the answer is NO in step S33, the CPU 101 assigns the code "NG" to the i-th question, indicating that an attribute cannot be assigned (step S34).

If the answer is YES in step S33, the CPU 101 assigns the curriculum code corresponding to the item with the highest degree of match to the i-th question (step S35).

After step S34 or step S35, the CPU 101 increments i by 1 (step S36). This causes steps S31 to S36 to be executed for the next (i+1)th question.

According to the flowchart shown in Figure 12, a curriculum code is assigned to the question for the answer information 14 representing the image 50 shown in Figure 2 as follows:

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

Furthermore, because emphasis information indicating "today" has been added to the question text corresponding to question number 51a, in step S32, CPU 101 determines the items "teaching" and "strength" from the search range of curriculum guideline data 17 and the degree of match for these items to be "medium."

Then, in step S34, the CPU 101 assigns the curriculum guideline code "8210020120300000" corresponding to the item with the highest degree of match, "Kyoto," to question number 51a.

On the other hand, since the answer text corresponding to question number 51c is "Kyou," in step S31, the CPU 101 determines the items "Kyo," "Tsuyoshi," and "Kyoto" from the search range of the curriculum guideline data 17, and determines the degree of match for these items to be "Medium."

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

Then, in step S34, the CPU 101 assigns the curriculum guideline code "8210020120320000" corresponding to the item "teaching" with the highest degree of match to question number 51c.

<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, the 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.

As shown in FIG. 13, the CPU 101 of the server device 100 acquires answer information 14 indicating 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.

Next, the CPU 101 displays the screen 60 shown in FIG. 7 and receives a command via the screen 60 to assign attributes to the academic ability test (step S2). The command includes information indicating the test ID, answer type, target grade, target subject, and target details.

Next, the 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 has been added. As described above, multiple pieces of answer information 14 corresponding to multiple students are obtained. Therefore, steps S42, S3, S4, and S43 are repeatedly executed for each student.

In step S42, the CPU 101 selects the answer information 14 of the jth student as the target. The CPU 101 then executes steps S3 and S4 using the target answer information 14. Details of steps S3 and S4 are as described above.

After step S4, the CPU 101 increments j by 1 (step S43). This causes steps S42, S3, S4, and S43 to be executed for the answer information 14 of the next j+1th student.

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, the CPU 101 executes steps S45 and S46 for each question indicated by the question data 15 in which the test ID included in the command is written in field 15a.

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 multiple student answers is likely to be the correct answer. Therefore, the curriculum code most frequently assigned corresponds to the answer most likely to be the correct answer. This allows CPU 101 to appropriately assign a curriculum code indicating the learning content to a question, even when using answer information 14 indicating the student's answer.

Next, the CPU 101 increments i by 1 (step S46). This causes step S45 to be executed for the next (i+1)th question.

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

<Modification 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 a learning content attribute (curriculum guideline code) to the question based on the result of the judgment on whether the student's answer is correct or incorrect.

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

The processing flow of the server device according to Variation 1 is similar to the flowchart shown in Figure 13. However, in step S3, the CPU 101 operating as the discrimination unit 12 determines whether the mark 59 for each question indicates a correct or incorrect answer. The CPU 101 then adds the determination result to the record 15b corresponding to that question in the question data 15.

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. In other words, the CPU 101 determines that the answer is correct if the similarity with the first shape group is higher than the similarity with the second shape group. Conversely, the CPU 101 determines that the answer is incorrect if the similarity with the second shape group is higher than the similarity with the first shape group.

Furthermore, the server device according to Variation 1 executes the following steps instead of step S45. That is, the CPU 101 operating as the discrimination unit 12 references question data 15 generated from the answer information 14 of multiple students and identifies a record 15b containing a judgment result indicating a correct answer for the i-th question. The CPU 101 determines the attribute (curriculum guideline code) determined using the identified record 15b as the attribute (curriculum guideline code) of the learning content corresponding to the i-th question.

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

The CPU 101 operating as the registration unit 13 may identify, for each question, a record 15b containing a determination result indicating an incorrect answer, and use the identified record 15b to register in the database 201 incorrect answer information that associates the attribute (curriculum guideline code) determined using the identified record 15b with the question. By checking the incorrect answer information, it is possible to analyze error trends.

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

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

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, calculates the degree of similarity between the answer text and the text included in the curriculum guideline data 17 using known techniques such as natural language processing and clustering, and then discriminates the attributes of the learning content based on the calculated degree of similarity. By using known natural language processing and clustering, the degree of similarity is calculated taking into account the meaning and content of the text. For example, the CPU 101 calculates a high degree of similarity 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 similarity between the curriculum guideline item "There are various organs inside the body that maintain life activities" and the answer text "skin" because there is a low correlation between "skin" and "inside the body" and "organ." In this way, the CPU 101 calculates the degree of match with the answer text for each item in 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.

<Modification 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 multiple questions. When assigning one attribute (curriculum guideline code) to multiple questions, it is preferable to use the digital teaching material data 16 rather than the curriculum guideline data 17.

If answer information 14 indicates "Shiretoko," "Tokachi," and "Obihiro" as the answers to three sub-questions, and "6th 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:

The CPU 101 extracts from the digital teaching material data 16 the portion (e.g., unit) that matches most closely with "Shiretoko," "Tokachi," and "Obihiro," and assigns the curriculum guideline code corresponding to the extracted portion to the three sub-questions with "Shiretoko," "Tokachi," and "Obihiro" as the answers. For example, the CPU 101 assigns a "high" degree of match to a portion that includes all of "Shiretoko," "Tokachi," and "Obihiro," a "medium" degree of match to a portion that includes two of "Shiretoko," "Tokachi," and "Obihiro," and a "low" degree of match to a portion that includes one of "Shiretoko," "Tokachi," and "Obihiro."

<Modification 4>
An academic achievement test may have an answer section in which multiple options are written in advance. In this case, a student highlights one of the multiple options written in the answer section 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.

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

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 Images, 51a-51c, 56a-56c Question number, 52a-52c Question statement, 53a-53c Bar line, 54, 58 Title, 57a-57c Answer column, 59, 59a-59c Mark, 60 Screen, 61-65 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 program, 200 storage device, 201 database.

Claims (14)

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 . 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. The management system described in claim 2, wherein the management unit manages the academic ability test in association with the attributes determined for each of the plurality of questions. The management system described in claim 2, wherein the management unit manages each of the plurality of questions in association with the attributes determined for that question. 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. 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. 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. 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. The management system according to claim 7 , wherein the determining unit extracts the answer text from a highlighted portion of the image. 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. 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. 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. 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. 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 .