Information processing device, information processing method, and information processing program

Abstract

To provide an information processing device, an information processing method, and an information processing program for proposing teaching material data useful for a user's own learning.SOLUTION: An information processing device includes a control unit that extracts information for identifying teaching material data for which results of a test meet predetermined conditions and at least the start-up date of the teaching material data is earlier than the date of the test from learning information including a first history in which start-up dates of the teaching material data for a plurality of users and information for identifying the teaching material data are associated, and a second history in which the implementation date of the test, information for identifying the test, and results of the test for the plurality of users are associated, and outputs the extracted information for identifying the teaching material data.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and an information processing program.

Background Art

[0002] In recent years, electronic devices such as electronic dictionaries are configured to be able to record various teaching material data for assisting learning, such as reference book data, in addition to dictionary data. Also, among this type of information processing apparatus, there are electronic devices having an additional function for teaching material data. Furthermore, there are also electronic devices corresponding to a custom function for selling electronic devices in a state where teaching material data has been added according to the demands of users.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Even in an electronic device corresponding to a custom function for selling an electronic device in a state where teaching material data has been added according to the demands of a user, it may be difficult for the user to determine which teaching material data among a large number of teaching material data is useful for his / her own learning.

[0005] An object of the present invention is to provide an information processing apparatus, an information processing method, and an information processing program for proposing teaching material data useful for a user's own learning.

Means for Solving the Problems

[0006] It should be noted that in Japanese, "

課題を解決するための手段

Means for Solving the Problems

Measures for Solving the Problems

発明が解決しようとする課題

Problems to be Solved by the Invention

課題を解決するための手段

Means for Solving the Problems

[0007] The embodiment can provide an information processing device, an information processing method, and an information processing program for suggesting educational material data useful for a user's own learning. [Brief explanation of the drawing]

[0008] [Figure 1] Figure 1 shows an example of the system configuration according to the embodiment. [Figure 2] Figure 2 shows the detailed configuration of the system. [Figure 3] Figure 3 shows an example of learning information 143. [Figure 4] Figure 4 shows an example of learning information 244. [Figure 5] Figure 5 is a flowchart showing the operation of the server. [Figure 6] Figure 6 is a flowchart showing the operation of an electronic dictionary. [Figure 7] Figure 7 shows an example of the top screen. [Figure 8] Figure 8 is a flowchart showing an example of the proposal process. [Figure 9] Figure 9 shows an example of the proposed top screen. [Figure 10] Figure 10 shows an example of a proposed screen. [Figure 11] Figure 11 is a flowchart illustrating the material extraction process. [Modes for carrying out the invention]

[0009] The embodiments will now be described with reference to the drawings. Figure 1 is a diagram showing an example of the configuration of System 1 according to the embodiment. System 1 includes an electronic dictionary 10 and a server 20. The electronic dictionary 10 and the server 20 are connected to each other via a network 30. The network 30 is, for example, the Internet. Although three electronic dictionaries 10 are shown in Figure 1, the number of electronic dictionaries 10 is not limited to three. In the embodiment, it is assumed that the three electronic dictionaries 10 are used by different users.

[0010] Figure 2 shows a detailed configuration of System 1. In Figure 2, one electronic dictionary 10 is shown as a representative example. The electronic dictionary 10 includes a processor 11, ROM 12, RAM 13, storage 14, input device 15, display device 16, and communication device 17. Each of these is connected to the others via a system bus 18. The electronic dictionary 10 may be an electronic device such as a personal computer (PC), tablet terminal, or smartphone on which an electronic dictionary application is installed.

[0011] Processor 11 is a processor that controls various operations of the electronic dictionary 10. Processor 11 may be an integrated circuit including a CPU (Central Processing Unit), etc. Processor 11 may be a processor other than a CPU, such as an ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), GPU (Graphic Processing Unit), etc.

[0012] ROM12 stores the startup program and other data for the electronic dictionary 10. RAM13 is the main memory for the processor 11 and other components.

[0013] Storage 14 can operate as a storage unit. Various programs such as an electronic dictionary control program used by processor 11, parameters, etc. are stored in storage 14. Processor 11 controls the operation of electronic dictionary 10 by executing various programs according to input signals from input device 15 and the like. The various programs include, for example, teaching material processing program 141. Teaching material processing program 141 is a program for executing various processes on the content included in teaching material data 142 based on a user's instruction. Teaching material processing program 141 can be, for example, a program for executing a process of displaying the content included in teaching material data 142 on display device 16 and a process of returning a response to an instruction from the user for the displayed content.

[0014] Teaching material data 142 is various content data used for a user's learning, such as dictionary data and reference book data. For example, dictionary data is content data in which words and phrases such as a Japanese dictionary and an English-Japanese dictionary are associated with their meanings and the like and stored. Reference book data is content data in which explanations of textbooks and simple problem set data for confirmation of understanding are stored. Also, teaching material data 142 includes test data. Test data is content data for conducting various tests. Test data may be generated by referring to problem set data. Teaching material data 142 may be added, for example, by being downloaded from server 20.

[0015] Furthermore, in the embodiment, learning information 143 is stored in storage 14. Learning information 143 is information on the learning history of the user's use of electronic dictionary 10. In the embodiment, the learning history includes the startup history of dictionary data, reference book data, problem set data, etc. as learning for improving one's own academic ability and the implementation history of tests by test data as learning for confirming one's own current academic ability.

[0016] FIG. 3 is a diagram showing an example of learning information 143. The learning information 143 includes information on "date" and "content". The "date" is information on the implementation date of learning. The "content" is information indicating the content of learning. The content of learning includes information such as information for specifying teaching material data activated by the user, information for specifying a test by test data implemented by the user, and the result of that test. The information for specifying teaching material data is, for example, the name of the teaching material data. Also, the information for specifying a test is, for example, the name of the test. The learning information 143 is updated when the teaching material data 142 is activated. Or, the learning information 143 is updated when a test is implemented and scoring is completed. For example, when "Teaching Material A" is used as the teaching material data 142, its activation date is recorded as the information of "date", and the name "Teaching Material A" of the activated teaching material data 142 is recorded as the information of "content". Also, for example, when "Test 1" is implemented using the teaching material data 142, its implementation date is recorded as the information of "date", and the name "Test 1" of the implemented test is recorded as the information of "content" together with the scoring result at that time. Here, the learning information 143 may be stored for each user of the electronic dictionary 10. In this case, the learning information 143 is stored in association with, for example, the ID of the user of the electronic dictionary 10.

[0017] The input device 15 includes an input key, a touch panel, etc. Also, the input device 15 may include a voice input device such as a microphone. In response to a user operation via the input device 15, a signal indicating the content of the user operation is input to the processor 11 via the system bus 18.

[0018] The display device 16 is a liquid crystal display, an organic EL display, etc. The display device 16 may be provided integrally with the electronic dictionary 10, or may be provided separately from the electronic dictionary 10. Various images are displayed on the display device 16.

[0019] The communication device 17 includes a circuit for communicating with an external communication network such as the network 30. The communication device 17 can operate as a communication unit.

[0020] Server 20 is an information processing device having a processor 21, ROM 22, RAM 23, storage 24, and communication device 25. Each of these is connected to the others via a system bus 26.

[0021] Processor 21 is a processor that controls various operations of server 20. Processor 21 may be an integrated circuit including a CPU. Processor 21 may also be a processor other than a CPU, such as an ASIC, FPGA, GPU, etc.

[0022] ROM22 stores information used for the operation of the processor 21, etc. RAM23 is the main memory for the operation of the processor 21, etc.

[0023] Storage 24 stores various programs, parameters, etc., such as server control programs used by the processor 21. The processor 21 controls the operation of the server 20 by executing various programs. These programs include, for example, an extraction program 241. The extraction program 241 is a program that performs the process of extracting educational material data useful for the user's learning from the educational material data 142 stored in the electronic dictionary 10 or from educational material data other than the educational material data 142. The processor 21 can operate as a control unit by executing processing according to the extraction program 241.

[0024] Furthermore, the storage 24 stores user information 242, teaching material data 243, and learning information 244.

[0025] User information 242 is information for identifying the user of the electronic dictionary 10. User information may include, for example, information representing the user's attributes such as the user's ID, the ID of the electronic dictionary 10, and the user's age.

[0026] The teaching material data 243 consists of various content data that can be downloaded and used on the electronic dictionary 10, such as dictionary data, reference book data, problem set data, and test data. The teaching material data 243 may contain more content data than the teaching material data 142 that is pre-stored on the electronic dictionary 10.

[0027] Learning information 244 is learning information collected from multiple electronic dictionaries 10. Figure 4 shows an example of learning information 244. Each piece of learning information collected from the electronic dictionary 10 as learning information 244 is assigned an ID (identification information) for management purposes. Figure 4 shows an example where three pieces of learning information are stored. Figure 4 also shows that the IDs of the three pieces of learning information are "Learning Information 1," "Learning Information 2," and "Learning Information 3," respectively. The information collected as learning information may be the same as the learning information 143 stored in the electronic dictionary 10. Here, learning information 244 may be stored for each user of the electronic dictionary 10. In this case, different IDs are assigned to learning information of different users collected from the same electronic dictionary 10. Furthermore, information of the aggregated results of learning information collected from multiple electronic dictionaries 10 may also be stored as learning information 244. The aggregated results information may include, for example, the number of times the same learning material data was used within a certain period, or the average score of the same test administered within a certain period.

[0028] The communication device 25 includes a circuit for communicating with an external communication network such as network 30. The communication device 25 can operate as a communication unit.

[0029] Next, we will explain the operation of System 1. Figure 5 is a flowchart showing the operation of Server 20. The process shown in Figure 5 is started periodically while Server 20 is running.

[0030] In step S1, the processor 21 of the server 20 determines whether or not it has received learning information from the electronic dictionary 10. If it is determined in step S1 that it has received learning information, the process proceeds to step S2. If it is not determined in step S1 that it has received learning information, the process proceeds to step S3.

[0031] In step S2, the processor 21 stores the learning information received from the electronic dictionary 10 in the storage 24. If the corresponding learning information is already stored in the storage 24, the processor 21 overwrites the original learning information with the learning information received from the electronic dictionary 10. On the other hand, if the corresponding learning information is not stored in the storage 24, the processor 21 newly stores the learning information received from the electronic dictionary 10. The learning information 244 stored in the storage 24 may be erased after a certain period of time. Of course, the learning information 244 stored in the storage 24 does not have to be erased after a certain period of time. Furthermore, as mentioned above, the learning information may be managed on a per-user basis. In this case, the processor 21 stores the learning information for each user based on the user ID associated with the learning information received from the electronic dictionary 10 and the user ID stored in the user information 242.

[0032] In step S3, the processor 21 determines whether or not it has received a request for material data suggestions. The request for material data suggestions is sent, for example, from the electronic dictionary 10. The request for material data suggestions may also be sent from an electronic device other than the electronic dictionary 10, i.e., an electronic device that does not have an electronic dictionary function. If it is determined in step S3 that a request for material data suggestions has been received, the process proceeds to step S4. If it is not determined in step S3 that a request for material data suggestions has been received, the process returns to step S1.

[0033] In step S4, the processor 21 performs the material extraction process. After the material extraction process, the process returns to step S1. The material extraction process is the process of extracting material data that is useful for the user's learning. The material extraction process will be explained later.

[0034] Figure 6 is a flowchart illustrating the operation of the electronic dictionary 10. The operation shown in Figure 6 starts, for example, each time the electronic dictionary 10 is powered on. In step S101, the processor 11 of the electronic dictionary 10 displays the top screen of the electronic dictionary 10 on the display device 16.

[0035] Figure 7 shows an example of the top screen. Other buttons and elements may be displayed on the top screen besides those shown in Figure 7.

[0036] In one example of the top screen, a list 161 of the educational materials 142 included in the electronic dictionary 10 is displayed. The user can select the educational materials they wish to use from the list 161. Also in one example of the top screen, a list 162 of tests that can be taken based on the educational materials 142 of the electronic dictionary 10 is displayed. The user can select the test they wish to take from the list 162. Furthermore, in one example of the top screen, a suggestion button 163 is displayed. The suggestion button 163 is a button that the user can select when the electronic dictionary 10 suggests educational materials that are useful for the user's learning.

[0037] In step S102, the processor 11 determines whether or not to launch the teaching material data. For example, if one teaching material data is selected by the user from the list of teaching material data 161, it is determined that the teaching material data should be launched. If it is determined in step S102 that the teaching material data should be launched, the process proceeds to step S103. If it is not determined in step S102 that the teaching material data should be launched, the process proceeds to step S105.

[0038] In step S103, the processor 11 starts the learning material data selected by the user. The processor 11 then performs processing corresponding to the started learning material data. After the start of the learning material data is finished, the process moves to step S104. For example, if dictionary data is started, the processor 11 displays the top screen of the started dictionary on the display device 16, then searches the dictionary data for terms corresponding to the search term entered by the user, and displays the search results on the display device 16. If reference book data is started, the processor 11 displays explanations of textbooks stored as reference book data according to the user's operation. Furthermore, if problem set data is started, the processor 11 displays problems for various subjects according to the user's operation, and when the user enters an answer to a problem, it displays it compared with the correct answer stored in advance for that problem, and also displays an explanation. After receiving a command from the user to end the display of the learning material data, the process moves to step S104.

[0039] In step S104, the processor 11 updates the learning information 143. Then, the process moves to step S110. Specifically, the processor 11 registers the name of the launched learning material data along with the launch date in the learning information 143.

[0040] In step S105, the processor 11 determines whether or not to perform a test. For example, if one test is selected by the user from the list of tests 162, it is determined that the test should be performed. If it is determined in step S105 that the test should be performed, the process proceeds to step S106. If it is not determined in step S105 that the test should be performed, the process proceeds to step S108.

[0041] In step S106, the processor 11 launches the test data included in the learning material data selected by the user and conducts the test. After the test is completed, the process moves to step S107. For example, the processor 11 displays the test questions along with the answer fields on the display device 16 based on the selected test data. After the test time is up or the user instructs the end of the test, the processor 11 performs scoring. Scoring may be performed by an external device of the electronic dictionary 10, such as a server 20. After scoring, the processor 11 displays the scoring results on the display device 16. Upon receiving a subsequent instruction from the user to end the test, the process moves to step S107.

[0042] In step S107, the processor 11 updates the learning information 143. Then, the process moves to step S110. Specifically, the processor 11 registers the name of the test that was administered, the scoring result at that time, and the date it was administered in the learning information 143.

[0043] In step S108, the processor 11 determines whether or not to suggest teaching material data. For example, if the suggestion button 163 is selected by the user, it is determined that teaching material data should be suggested. If it is determined in step S108 that teaching material data should be suggested, the process proceeds to step S109. If it is not determined in step S108 that teaching material data should be suggested, the process proceeds to step S110.

[0044] In step S109, the processor 11 performs a suggestion process. After the suggestion process is completed, the process moves to step S110. The suggestion process is the process of suggesting the teaching material data extracted by the server 20 as recommended teaching material data to the user. The suggestion process will be explained later.

[0045] In step S110, the processor 11 determines whether or not to turn off the power to the electronic dictionary 10. For example, if the user presses the power button on the electronic dictionary 10, it is determined to turn off the power to the electronic dictionary 10. If it is determined in step S110 to turn off the power to the electronic dictionary 10, the process proceeds to step S111. If it is not determined in step S110 to turn off the power to the electronic dictionary 10, the process returns to step S101.

[0046] In step S111, the processor 11 sends the learning information to the server 20. After that, the process shown in Figure 6 ends. Here, the transmission of the learning information does not necessarily have to occur immediately before the electronic dictionary 10 is turned off. The transmission of the learning information may occur, for example, when the learning information is updated, at a predetermined time such as each evening, or immediately after the electronic dictionary 10 is turned on.

[0047] Figure 8 is a flowchart showing an example of the suggestion process. In step S201, the processor 11 of the electronic dictionary 10 displays the suggestion top screen on the display device 16.

[0048] Figure 9 shows an example of the proposed top screen. Other buttons and elements may be displayed on the proposed top screen besides those shown in Figure 9.

[0049] In one example of the top screen for suggestions, a list of 164 subjects for which the user wishes to receive suggestions for learning materials is displayed. The user can select the subject for which they wish to receive suggestions from the list of 164 subjects. Here, Figure 9 shows that the list of 164 subjects is displayed. Instead of the list of 164 subjects, a list of tests may be displayed. In this case, the user can select the test for which they wish to improve their score. Furthermore, if a list of subjects is selected, a list of specific learning items for which the user wishes to receive suggestions for learning materials may be displayed. For example, if "Mathematics" is selected as the subject, a list of learning items for each subject, such as "Quadratic Functions" and "Factorization," may be displayed. In this case, the user can select the subject for which they wish to receive suggestions from the list of 164 subjects, and then select the learning items.

[0050] Furthermore, on one example of the suggested top screen, a selection field 165 for the user's learning goals is displayed. The user can select a goal that is close to their own learning goal from the goal selection field 165. For example, in Figure 9, the user can select the one that is closest to their goal from two options: "I want to improve my score" and "I want to get a high score." Here, the goals displayed in the goal selection field 165 are not limited to the two shown in Figure 9.

[0051] Furthermore, on one example of the top screen for a proposal, the "Execute Proposal" button 166 is displayed. The "Execute Proposal" button 166 is a button that the user selects to instruct the execution of the proposed educational material data.

[0052] In step S202, the processor 11 determines whether or not to execute the suggestion. For example, if the suggestion execution button 166 is selected, it is determined that the suggestion should be executed. If it is not determined in step S202 that the suggestion should be executed, the process returns to step S201. During this time, the user may select the subject for which they wish to receive suggestions from the list of subjects 164, and also select a goal that is close to their learning goal from the goal selection field 165. If it is determined in step S202 that the suggestion should be executed, the process proceeds to step S203.

[0053] In step S203, the processor 11 determines whether the information necessary for executing the proposal has been entered. For example, if the subject information and learning objective information have been entered on the proposal top screen in Figure 9, it is determined that the information necessary for executing the proposal has been entered. If it is determined in step S203 that the information necessary for executing the proposal has been entered, the process proceeds to step S204. If it is not determined in step S203 that the information necessary for executing the proposal has been entered, the process returns to step S201. At this time, the processor 11 may display a message on the display device 16 to prompt the user to enter information.

[0054] In step S204, the processor 11 sends a suggestion request to the server 20. The suggestion request includes, for example, information to identify the electronic dictionary 10 or the user, and information on the subject and learning objectives entered by the user.

[0055] In step S205, the processor 11 determines whether or not it has received suggestion information from the server 20. The suggestion information includes information for identifying the teaching material data extracted by the server 20. In step S205, the processor 11 waits until it receives suggestion information from the server 20. If it is determined in step S205 that it has received suggestion information from the server 20, the process proceeds to step S206.

[0056] In step S206, the processor 11 displays a suggested screen for teaching material data on the display device 16 based on the suggested information received from the server 20.

[0057] Figure 10 shows an example of a proposal screen. Other buttons and elements not shown in Figure 10 may be displayed on the proposal screen.

[0058] In one example suggestion screen, a list 167 of teaching material data extracted by the server 20 is displayed. As will be explained later, the teaching material data extracted by the server 20 may include teaching material data already included in the electronic dictionary 10 and teaching material data that is not already included. For teaching material data that is not already included in the electronic dictionary 10, an add button 168 is displayed. The add button 168 is a button that the user selects when adding teaching material data.

[0059] In addition, one example of a suggestion screen displays an exit button 169. The exit button 169 is a button that the user can select to end the display of the suggestion screen.

[0060] In step S207, the processor 11 determines whether the exit button 169 has been selected. If it is determined in step S207 that the exit button 169 has been selected, the process in Figure 8 ends. In this case, the process proceeds to step S110 in Figure 6. If it is not determined in step S207 that the exit button 169 has been selected, the process proceeds to step S208.

[0061] In step S208, the processor 11 determines whether or not the additional button 168 has been selected. If it is determined in step S208 that the additional button 168 has been selected, the process proceeds to step S209. If it is not determined in step S208 that the additional button 168 has been selected, the process returns to step S207.

[0062] In step S209, the processor 11 processes the addition of teaching material data. After that, the process returns to step S207. Specifically, the processor 11 requests, for example, the server 20 to send the teaching material data corresponding to the add button 168 selected by the user. The request for additional teaching material data may be made after the user has completed payment of the fee. Also, the additional teaching material data does not necessarily have to be stored in the server 20. In this case, the processor 11 requests the storage location where the relevant teaching material data is stored to send the teaching material data. After receiving the additional teaching material data, the processor 11 stores the received teaching material data as new teaching material data 142 in the storage 14. After that, the process returns to step S207.

[0063] Figure 11 is a flowchart illustrating the material extraction process. In step S301, the processor 21 of the server 20 starts the process of suggesting recommended material data based on the suggestion request received from the user's electronic dictionary 10. Specifically, it determines from the information contained in the suggestion request whether the goal selected by the user is to "get a high score" or to "improve their score". If it is determined in step S301 that the goal selected by the user is to "get a high score", the process moves to step S302. If it is determined in step S301 that the goal selected by the user is to "improve their score", the process moves to step S304.

[0064] In step S302, the processor 21 extracts learning information from the learning information 244 of the subject selected by the user in which the test score is high, equal to or above the standard score. If the test selected by the user is Test 1, and the standard for a high score is, for example, 90 points or higher, the processor 21 extracts learning information in which the result of Test 1 is 90 points or higher. For example, if the learning information shown in Figure 4 is stored in storage 24, the processor 21 extracts learning information 1, in which the result of Test 1 is 90 points, as learning information with a high test score. On the other hand, the processor 21 does not extract learning information 2, in which the result of Test 1 is 70 points, and learning information 3, in which the result of Test 1 is 50 points, as learning information with a high test score because the test scores are below the standard score of 90 points. On the other hand, if the standard value for a high score is, for example, 70 points or higher, the processor 21 extracts learning information in which the test result is 70 points or higher. In this case, for example, if the learning information shown in Figure 4 is stored in storage 24, the processor 21 will extract learning information 1, where the result of Test 1 is 90 points, and learning information 2, where the result of Test 1 is 70 points, as learning information with high test scores, but will not extract learning information 3, where the result of Test 1 is 50 points, as learning information with high test scores. The criteria for high scores are set in advance on server 20. The criteria for high scores may also be specified by the user. Furthermore, if the request for proposal also includes information on learning items, the processor 11 may extract learning information based on the scores of the learning items selected by the user.

[0065] In step S303, the processor 21 excludes information about learning materials used during the period after the test from the extracted learning information. Then, the process proceeds to step S306. For example, in learning information 1, the date of the implementation of Test 1 is January 6, 2022. Therefore, the processor 21 excludes information with an activation date of January 7, 2022 from the extracted learning information 1. In other words, the processor 21 extracts information with an activation date earlier than the implementation date of Test 1. This is because it is unclear whether learning material data used after the test contributed to high scores. Even if the activation date is earlier than the implementation date of Test 1, learning material data with a recent activation date more than a predetermined period (e.g., one month) earlier than the implementation date of Test 1 is excluded from extraction. This is because it is unclear whether learning material data with a very old activation date contributed to high scores on the test.

[0066] In step S304, the processor 21 extracts learning information from the learning information 244 of the subject selected by the user that shows an improvement in test scores. When the test of the subject selected by the user is Test 1, the processor 21 extracts learning information that shows an improvement in the score for Test 1. For example, if the learning information shown in Figure 4 is stored in storage 24, the processor 21 extracts learning information 2, where the result of Test 1 improved from 50 points to 70 points, as learning information that shows an improvement in test scores. On the other hand, the processor 21 does not extract learning information 1 where Test 1 has not been taken multiple times, and learning information 3 where the result of Test 1 has not improved from 50 points, as learning information that shows an improvement in test scores. Here, a threshold value for the amount of score improvement may be set in advance in the server 20. In other words, learning information where the amount of score improvement is less than the threshold value does not need to be extracted. The threshold value for the amount of score improvement may also be specified by the user. Furthermore, if the request for proposal also includes information on learning items, the processor 11 may extract learning information based on the scores of the learning items selected by the user.

[0067] In step S305, the processor 21 excludes information about learning materials used outside the test period between the previous test and the next test from the extracted learning information. The process then proceeds to step S306. For example, in learning information 2, the date of the first test 1 is January 4, 2022, and the date of the second test 1 is January 8, 2022. Therefore, the processor 21 excludes information with an activation date of January 2, 2022, and information with an activation date of January 3, 2022, from the extracted learning information 2. In other words, the processor 21 extracts information with an activation date later than the date of the first test 1, and earlier than the date of the second test 1. This is because it is unclear whether learning material data used outside the test period contributed to improving scores.

[0068] In step S306, the processor 21 refers to the learning information of the electronic dictionary 10 that sent the suggestion request or its user, and excludes information on learning materials already used by the user from the extracted learning information. For example, if the user's learning information for the electronic dictionary 10 is learning information 3, the processor 21 excludes information on "Learning Material C," "Learning Material D," and "Learning Material E" from the extracted learning information. This is because learning materials already used by the user may not contribute to the user achieving a high score or improving their score.

[0069] In step S307, the processor 21 sends suggestion information to the electronic dictionary 10 that sent the suggestion request. After that, the process shown in Figure 11 is completed. The suggestion information includes information for identifying the learning material data that remained from the extracted learning information without being excluded. For example, if the learning goal is "I want to get a high score," the test for the subject selected by the user is Test 1, and the threshold for a high score is, for example, 90 points or higher, then as described above, the processor 21 extracts learning information 1. In this case, the suggestion information includes information for identifying "Learning Material A" and "Learning Material B," respectively. The information for identifying the learning material data may be the name, ID, etc., of the learning material data. According to such suggestion information, the suggestion screen shown in Figure 10 may be displayed in the electronic dictionary 10.

[0070] As described above, according to this embodiment, based on learning information collected from multiple electronic dictionaries 10, information on educational materials useful for the user's learning is output as suggested information. The extraction of educational material information at this time is carried out not only based on test scores but also on the date the test was administered. This makes it possible to efficiently exclude educational material information that is not considered to contribute to improving academic ability. Therefore, the likelihood of extracting educational material information useful for the user's learning increases. Such suggested educational material information helps the user to determine which educational material data among many is useful for their own learning.

[0071] Furthermore, information extraction from the learning materials data is also performed based on learning objectives specified by the user. This increases the likelihood of extracting learning materials data that better matches the user's objectives. In this embodiment, information extraction from learning materials data is shown based on two learning objectives: "I want to improve my score" and "I want to get a high score." Needless to say, for other learning objectives, information extraction from learning materials data will be performed in accordance with each respective learning objective.

[0072] [Differentiation] The following describes some variations of the embodiment. Figure 4 shows an example where only three learning information items are stored in the storage 24 of the server 20. In reality, a large amount of learning information collected from many more electronic dictionaries 10 may be stored in the storage 24. In this case, there is a possibility that learning information containing information about learning materials that resulted in high test scores or improved scores only when used by a specific user may be extracted. Such information about learning materials that improve academic ability only when used by a specific user does not need to be included in the proposed information. In other words, a process to exclude information about learning materials whose usage count among all users does not fall below a threshold may be added to the process in Figure 11. Of course, information about learning materials that improve academic ability only when used by a specific user may also be useful, so a process to exclude information about learning materials whose usage count among all users does not fall below a threshold may not be added to the process in Figure 11.

[0073] Furthermore, in the embodiment described above, the learning information stored in the server 20's storage 24 is assumed to be collected from the electronic dictionary 10. However, if the learning information is associated with user information, it does not necessarily have to be collected from the electronic dictionary 10. For example, the results of a test conducted in an environment other than the electronic dictionary 10 may be sent to the server 20 as learning information associated with the user's ID, etc. Also, the learning information may be entered manually, for example. In this case, the test does not necessarily have to be conducted using electronic devices; for example, it may be conducted using paper.

[0074] Furthermore, in the embodiment described above, the suggestion screen is displayed on the display device 16 of the electronic dictionary 10. However, the suggestion screen does not necessarily have to be displayed on the display device 16 of the electronic dictionary 10. For example, the suggestion screen shown in Figure 10 may be displayed on the screen of an online shop that a user considering purchasing an electronic dictionary is viewing using an electronic device such as a smartphone. In this case, the user can decide whether or not to add the suggested teaching material data to the electronic dictionary they are considering purchasing, and can add the teaching material data as needed. Thus, the embodiment can also be applied to the online sale of an electronic dictionary 10 with custom functions.

[0075] Furthermore, in the embodiment described above, the extraction of information on educational material data useful for user learning is generated on the server 20. In contrast, the generation of suggestion information and the display of the suggestion screen may be performed on the electronic dictionary 10. In this case, the electronic dictionary 10 operates as an information processing device. Here, even if the electronic dictionary 10 extracts information on educational material data, the collection of learning information from multiple electronic dictionaries 10 may be performed on the server 20.

[0076] It should be noted that the present invention is not limited to the embodiments described above, and can be modified in various ways during implementation without departing from its essence. Furthermore, each embodiment may be combined as appropriate as possible, and in that case, the combined effects can be obtained. Moreover, the above embodiments include inventions at various stages, and various inventions can be extracted by appropriate combinations of the multiple constituent elements disclosed. For example, if some constituent elements are deleted from all the constituent elements shown in an embodiment, and the problem described in the section on the problem the invention aims to solve is solved and the effects described in the section on the effects of the invention are obtained, then the configuration with these constituent elements deleted can be extracted as an invention. [Explanation of Symbols]

[0077] 1 System, 10 Electronic dictionary, 11 Processor, 12 ROM, 13 RAM, 14 Storage, 15 Input device, 16 Display device, 17 Communication device, 18 System bus, 20 Server, 21 Processor, 22 ROM, 23 RAM, 24 Storage, 25 Communication device, 26 System bus, 30 Network, 141 Educational material processing program, 142 Educational material data, 143 Learning information, 241 Extraction program, 242 User information, 243 Educational material data, 244 Learning information.

Claims

1. From learning information including a first history associated with the activation date of the learning material data and information for identifying the learning material data, and a second history associated with the test implementation date and the test results, the learning material data is extracted where the test results satisfy predetermined conditions and, based on the first and second histories, the activation date of the learning material data is earlier than the test implementation date. Output information to identify the extracted educational material data. An information processing device comprising a control unit.

2. The aforementioned predetermined condition is that the score on the test is equal to or greater than the standard value. The control unit extracts information from the learning information in which the test score is above a certain threshold, to identify the learning material data whose activation date is earlier than the test execution date. The information processing apparatus according to claim 1.

3. The information processing apparatus according to claim 1, characterized in that the control unit extracts information for identifying the teaching material data in which the test results satisfy the predetermined conditions, and the activation date of the teaching material data is earlier than the test implementation date and within a predetermined period from the test implementation date.

4. The aforementioned predetermined condition is that the score on the test is higher than the score on the previous test. The control unit extracts information from the learning information in which the test score has improved compared to the previous test score, to identify the learning material data whose activation date is before the test date and after the previous test date. The information processing apparatus according to claim 1.

5. An information processing method performed by an information processing device, From learning information including a first history associated with the activation date of the learning material data and information for identifying the learning material data, and a second history associated with the test execution date and the test results, extract the learning material data in which the test results satisfy predetermined conditions and the activation date of the learning material data is earlier than the test execution date based on the first and second histories. To output information for identifying the extracted teaching material data, Information processing methods including

6. From learning information including a first history associated with the activation date of the learning material data and information for identifying the learning material data, and a second history associated with the test execution date and the test results, extract the learning material data in which the test results satisfy predetermined conditions and the activation date of the learning material data is earlier than the test execution date based on the first and second histories. To output information for identifying the extracted teaching material data, An information processing program that causes a processor to execute an action.

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