Information processing device, information processing method, and program

Abstract

To provide an information processing device, an information processing method and a program for processing log information, etc. acquired from a user terminal so that teachers making online lessons can grasp signs of student problems as soon as possible.SOLUTION: In an information processing device 10, a collection part 11 collects the keying data indicating an operation input amount to a user terminal. An extraction part 12 extracts a keying pattern showing characteristics of the operation input to the user terminal from the keying data. A classification part 13 classifies the approach of the student to the problem based on the keying pattern.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and a program, and more particularly to an information processing device, an information processing method, and a program for processing log information and the like acquired from a user terminal. [Background technology]

[0002] In typical school instruction, after each lesson, teachers prepare a record of the content and progress of the lesson (known as a lesson record) and a report on each student's learning progress (known as a teaching report). When deciding the content of the next lesson, teachers can accurately recall the content of the previous lesson by checking the records they have prepared.

[0003] Private companies such as cram schools and preparatory schools are adopting more advanced ICT (Information Communication Technology) systems.

[0004] For example, Patent Document 1 describes that a management program uses a management database and a curriculum database to create learning instructions to be given to instructors, and also creates instruction reports that include advice to students based on their learning results. Patent Document 2 also discloses a system that enables four parties - classroom director, instructor, student, and parent - to send and receive data to and from a management server using a dedicated menu screen. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2005-182272 [Patent Document 2] Japanese Patent Application Laid-Open No. 2008-009876 Summary of the Invention [Problem to be solved by the invention]

[0006] In the future, with the development of ICT infrastructure, each student will be able to participate in online classes via user devices such as tablets and personal computers. As a result, it is expected that opportunities to use various content such as digital textbooks and educational applications will increase. In online classes, it is difficult for teachers to directly observe how students are doing. Therefore, there is a demand for a system that helps teachers to notice abnormalities in students, i.e., signs of problems, as early as possible, before problems become apparent due to poor grades or other factors.

[0007] The present invention has been made in view of the above-mentioned problems, and its purpose is to provide a technique for supplementing the way students approach assignments in online classes. [Means for solving the problem]

[0008] An information processing device according to one aspect of the present invention comprises a collection means for collecting keystroke data indicating the amount of operational input to a user terminal, an extraction means for extracting keystroke patterns indicating the characteristics of the operational input to the user terminal from the keystroke data, and a classification means for classifying how students approach assignments based on the keystroke patterns.

[0009] In an information control method according to one aspect of the present invention, keystroke data indicating the amount of input to a user terminal is collected, keystroke patterns indicating the characteristics of the input to the user terminal are extracted from the keystroke data, and the student's approach to the assignment is classified based on the keystroke patterns.

[0010] A program according to one aspect of the present invention causes a computer to collect keystroke data indicating the amount of operational input to a user terminal, extract keystroke patterns from the keystroke data that indicate the characteristics of the operational input to the user terminal, and classify how students approach assignments based on the keystroke patterns. [Effects of the Invention]

[0011] According to one aspect of the present invention, a technique can be provided for supplementing students' approaches to assignments in online classes. [Brief explanation of the drawings]

[0012] [Figure 1] 1 is a block diagram showing a configuration of an information processing device according to a first or second embodiment. [Figure 2] 4 is a flowchart showing the operation of the information processing device according to the first or second embodiment. [Figure 3] FIG. 10 is a diagram showing an example of keystroke data collected from a user terminal by the information processing device according to the first or second embodiment. [Figure 4] FIG. 10 is a diagram showing an example of keystroke data for one task, showing the working time and non-working time during one task. [Figure 5] FIG. 1 is a diagram showing an example of classification (types 1 to 4) of how students approach assignments. [Figure 6] FIG. 10 is a diagram schematically illustrating an example of a time series of classification for one student. [Figure 7] FIG. 10 is a diagram for explaining a modified example of the information processing device according to the second embodiment, showing an example of keystroke data for two consecutive tasks. [Figure 8] FIG. 10 is a block diagram showing the configuration of an information processing device according to a third embodiment. [Figure 9] 11 is a flowchart showing the operation of the information processing device according to the third embodiment. [Figure 10] FIG. 10 is a diagram showing a second example of classification of students' approaches to assignments. [Figure 11] This is an example of the combination and evaluation of a student's typing pattern and test results. [Figure 12] 1 is a diagram schematically illustrating an example of the configuration of a communication system including an information processing device according to any one of first to third embodiments. [Figure 13] FIG. 1 is a diagram illustrating an example of a hardware configuration of an information processing device according to first to third embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0013] Several embodiments of the present invention will be described with reference to the drawings. In the following embodiments, the term "student" refers to anyone taking academic classes in general. The term "student" may be a "child" receiving primary education or a "student" receiving higher education. The term "student" may be not only a minor, but also an adult, or may be a member of the workforce (a student taking so-called recurrent education).

[0014] [Embodiment 1] A first embodiment will be described with reference to FIGS.

[0015] (Information processing device 10) The configuration of an information processing device 10 according to the first embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing the configuration of the information processing device 10. As shown in Fig. 1, the information processing device 10 includes a collection unit 11, an extraction unit 12, and a classification unit 13. Each component of the information processing device 10 will be described below.

[0016] The collection unit 11 collects keystroke data indicating the amount of operation input to the user terminal 100. The collection unit 11 is an example of a collection means.

[0017] In one example, the collection unit 11 collects log information from the user terminal 100 (FIG. 12). For example, the collection unit 11 accesses the user terminal 100 through an arbitrary communication network and requests the user terminal 100 to transmit the log information. Alternatively, the user terminal 100 may be configured so that the log information is periodically transmitted from the user terminal 100 to the information processing device 10. The collection unit 11 collects the log information transmitted from the user terminal 100.

[0018] The collection unit 11 extracts, from the log information, keystroke data indicating the amount of operation input to the user terminal 100. The amount of operation input to the user terminal 100 includes, for example, the number of keystrokes on the keyboard, the number of mouse clicks and scrolls, the number of touches on the touch panel, and the contact time on the touchpad.

[0019] The log information includes the keystroke data described above. Furthermore, the log information may include at least one of an operation log, an authentication log, an access log, a communication log, a call log, and an event log.

[0020] Alternatively, the log information of the user terminal 100 may be temporarily stored in the management server 200 (FIG. 12). In this case, the collection unit 11 can indirectly collect the log information transmitted from the user terminal 100 from the management server 200, instead of collecting the log information from the user terminal 100.

[0021] The collection unit 11 outputs the collected keystroke data to the extraction unit 12.

[0022] The extraction unit 12 extracts, from the keystroke data, a keystroke pattern that indicates the characteristics of an operation input to the user terminal 100. The extraction unit 12 is an example of an extraction means.

[0023] In one example, the extraction unit 12 receives keystroke data collected from the user terminal 100 from the collection unit 11. The extraction unit 12 extracts keystroke data corresponding to one task from the keystroke data. The keystroke data corresponding to one task is keystroke data during a time period when a certain task was being performed. The extraction unit 12 extracts keystroke patterns that indicate characteristics of operation inputs to the user terminal 100 from the keystroke data corresponding to one task.

[0024] In one example, the extraction unit 12 extracts the non-task time rate and the effort time as keystroke patterns from the keystroke data. The non-task time rate represents the ratio of the time during which no operation input was made to the user terminal 100 relative to the time limit for the task (FIG. 4). The effort time represents the time from the start of the task to the time when the last operation input was made to the user terminal 100.

[0025] The way in which students use the user terminal 100 to input operations into the user terminal 100 differs. Therefore, both the non-working time rate and the working time represent the individual characteristics of the student using the user terminal 100. For example, a good student can answer questions immediately after the assignment begins, and can therefore finish the assignment quickly. Therefore, both the non-working time and the working time are short. On the other hand, a student who is diligent but not very good will answer questions little by little, using trial and error, and therefore both the non-working time and the working time will be long.

[0026] The keystroke pattern is not limited to the non-task time rate and the effort time. In another example, the keystroke pattern may be a combination of the operation input amount, the non-task time, and the effort time.

[0027] The extraction unit 12 outputs information indicating the keystroke pattern extracted from the keystroke data to the classification unit 13.

[0028] The classification unit 13 classifies how the student approaches the assignment based on the keystroke pattern. The classification unit 13 is an example of a classification means.

[0029] In one example, the classification unit 13 receives information indicating a keystroke pattern from the extraction unit 12. The classification unit 13 analyzes how the student using the user terminal 100 is working on the assignment based on the keystroke pattern.

[0030] In a first example, the classification unit 13 compares the non-task time rate (an example of a keystroke pattern) with a first threshold to determine whether the non-task time rate exceeds the first threshold. The first threshold is determined, for example, based on the average non-task time rate of all students in the class. The classification unit 13 also compares the working time (an example of a keystroke pattern) with a second threshold to determine whether the non-task time rate exceeds the first threshold. The second threshold is determined, for example, based on the average working time of all students in the class.

[0031] The classification unit 13 classifies the student's approach to the assignment based on the magnitude relationship between the non-task time rate and the first threshold value, and the magnitude relationship between the working time and the second threshold value. In this example, there are four types of student approach to the assignment. A more detailed example will be described in embodiment 2.

[0032] In the second example, the classification unit 13 classifies the characteristics of keystroke patterns into one of the classes by using classification conditions. Each class corresponds to a type of student's approach to the task. The classification conditions are generated by machine learning the characteristic patterns of many students.

[0033] In a third example, the classification unit 13 classifies how a student approaches an assignment based on log information such as an operation log, an authentication log, an access log, a communication log, a call log, and an event log in addition to keystroke patterns.

[0034] For example, when a student uses a call function or chat function of the user terminal 100 to consult with a teacher or an assistant about an assignment, a communication log or call log is left in the user terminal 100. The classification unit 13 acquires the log information from the collection unit 11 and analyzes the log information to confirm whether or not there was communication or a call during the time spent working on the assignment.

[0035] The classification unit 13 subtracts the call time or communication time from the time when there was no operation input to the user terminal 100. If the student is typing or the like during a call and there is operation input to the user terminal 100, the classification unit 13 adds back the call time during which the student was typing to the time when there was no operation input to the user terminal 100. Then, the classification unit 13 regards the remaining time after subtracting the call time or communication time as the net non-work time. The classification unit 13 calculates the ratio of the net non-work time to the time limit for the assignment as the non-work time rate.

[0036] Thereafter, the classification unit 13 classifies the student's approach to the assignment based on the non-task time rate and the working time, using the same procedure as in the first example.

[0037] As in the several examples described above, the classification unit 13 classifies how students approach tasks.

[0038] Thereafter, the classification unit 13 may transmit the information indicating the type of each student to the management server 200 (FIG. 12) via the communication network. The information indicating the type of each student is stored in the management server 200.

[0039] For example, the information processing device 10 may create a list of types for each student based on information acquired from the management server 200 and display the list on the screen of the display unit of the user terminal 100, or may generate screen data for displaying the list and output the screen data to an external monitor, etc. This allows the teacher to easily refer to or acquire information stored in the management server 200 by connecting the user terminal 100 to a network.

[0040] (Operation of information processing device 10) An example of the operation of the information processing device 10 according to the first embodiment will be described with reference to Fig. 2. Fig. 2 is a flowchart showing the flow of processing executed by each unit of the information processing device 10.

[0041] 2, the collection unit 11 collects keystroke data indicating the amount of operation input to the user terminal 100 (S1). The collection unit 11 outputs the keystroke data collected from the user terminal 100 to the extraction unit 12.

[0042] The extraction unit 12 receives keystroke data collected from the user terminal 100 from the collection unit 11. The extraction unit 12 extracts a keystroke pattern indicating a feature of an operation input to the user terminal 100 from the keystroke data (S2). The extraction unit 12 outputs information indicating the keystroke pattern to the classification unit 13.

[0043] The classification unit 13 receives information indicating the keystroke patterns from the extraction unit 12. The classification unit 13 classifies the students' approaches to the assignments based on the keystroke patterns (S3). Thereafter, the classification unit 13 may transmit information indicating the type of each student to the management server 200 (FIG. 12) via a communication network. The information indicating the type of each student is stored in the management server 200.

[0044] This completes the operation of the information processing device 10 according to the first embodiment.

[0045] (Effects of this embodiment) According to the configuration of this embodiment, the collection unit 11 collects keystroke data indicating the amount of operation input to the user terminal 100. The extraction unit 12 extracts keystroke patterns indicating the characteristics of the operation input to the user terminal 100 from the keystroke data. The classification unit 13 classifies how the student approaches the assignment based on the keystroke patterns.

[0046] The types of students' approach to assignments reflect not only their abilities and qualities, but also the influence of their physical condition and lack of sleep. By providing technology to supplement students' approach to assignments in online classes, teachers can understand students' abilities and qualities, such as their concentration and level of understanding, and provide instruction that is appropriate for each student. Furthermore, by understanding the types of students' approach to assignments, teachers can quickly supplement any changes in students' behavior, even in online classes.

[0047] [Embodiment 2] A second embodiment will be described with reference to FIGS. 3 to 7. In this second embodiment, an example of the classification described in the first embodiment will be described. Classification here means sorting students' approaches to assignments into one of several types (classes). In addition, in this second embodiment, an example of the types of lesson content described in the first embodiment will be described.

[0048] The configuration of the information processing device 20 (FIG. 1) according to the present embodiment 2 is the same as the configuration of the information processing device 10 according to the above-described embodiment 1. In the present embodiment 2, the description of the configuration and operation of the information processing device 10 according to the above-described embodiment 1 will be cited, and the description of the common configuration and operation of the information processing device 20 will be omitted.

[0049] (Example of keystroke data) Fig. 3 shows an example of keystroke data indicating the amount of operation input to the user terminal 100. Fig. 3 shows three sets of keystroke data with different time scales. In Fig. 3, the time axis is represented by a horizontal line, and operation inputs are represented by black dots on the horizontal line.

[0050] In Figure 3, the keystroke data in the top row represents the amount of operation input from 8:00 to 15:00 on a certain day. The keystroke data in the middle represents the amount of operation input during the first period from 11:15 to 12:00 on the same day. Referring to Figure 3, an arithmetic class is held from 11:15 to 12:00.

[0051] In Figure 3, the keystroke data in the bottom row represents the amount of input during one task from 11:28 to 11:38 on the same day. Referring to Figure 3, from 11:28 to 11:38, a drill on a web page within a website identified by the URL http: / / drill.*** / was used.

[0052] The classification target of the classification unit 13 (FIG. 1) of the information processing device 20 is any one of the three types of keystroke data shown in FIG. 3. However, the shorter the time span of the keystroke data, the more clearly the characteristics of the student's qualities and abilities, such as the student's concentration and level of understanding, tend to be revealed. In the present embodiment 1, the classification unit 13 classifies the student's approach to a task based on the keystroke data that indicates the amount of operation input during one task.

[0053] (Non-work time rate; an example of a typing pattern) The non-working time rate described in the first embodiment will be explained with reference to Fig. 4. Fig. 4 is a diagram showing an example of a keystroke pattern indicating the amount of operation input during one task. As described in the first embodiment, the non-working time rate represents the ratio of the time during which no operation input was made to the user terminal 100 (hereinafter, may be referred to as non-working time) to the time limit for the task.

[0054] In Figure 4, T represents the amount of time a student spent on a task. SN represents a period of time during which no operation input has been made to the user terminal 100 for a certain period of time or more. N (N=1~n) is T SN is divided by T. The method for determining the fixed time is arbitrary, but the teacher may determine the fixed time based on the difficulty of the task, the grade of the student, the type of subject being taught, and so on.

[0055] In one example, the non-work time rate is the non-work time S N The non-work time rate is calculated by the classification unit 13 of the information processing device 20 according to the following formula:

[0056]

number

[0057] For example, suppose a task is given with a time limit of 10 minutes, and the first student thinks for the first 5 minutes and answers in the last 5 minutes. In this case, the non-work time rate for the first student is 0.5(=√(5 2) / 10). On the other hand, suppose the second student takes two 2.5-minute breaks (total of 5 minutes). In this case, the non-working time rate for the second student is 0.35(=√(2.5 2 ×2) / 10).

[0058] In this example, the first student and the second student spend the same amount of time working on a task, but the idle time rate of the first student and the second student is different. The idle time rate (which is an example of a typing pattern) represents the characteristics and individuality of each student in terms of how they approach a task.

[0059] (An example of how students approach tasks) An example of classifying how students approach tasks will be described with reference to Figure 5. Figure 5 shows four types.

[0060] Let's assume that students are given a 10-minute assignment. The average time it takes for all students in the class to complete the assignment is 7 minutes. In this example, the students' approach to the assignment is classified into one of four types based on the amount of input, the percentage of non-task time, and the amount of time spent on the assignment.

[0061] As shown in Figure 5, Type 1 has a high non-task time rate and average working time. In the keystroke data shown in the upper left, which is an example of Type 1, operation inputs are concentrated in the latter half of the time period. For example, it is thought that students responded after thoroughly considering the solution in the first half of the time period.

[0062] In the second type, the non-task time rate is small and the working time is short. In the typing data on the bottom left, which is an example of the second type, there is almost no non-task time. For example, it is thought that the student quickly came up with the solution and answered immediately.

[0063] In the third type, the non-task time rate is high and the working time is long. In the typing data in the upper right corner, which is an example of the third type, most of the time is non-task time. For example, it is thought that the student was unable to concentrate on the task.

[0064] In the fourth type, the non-work time rate is low and the working time is long. In the typing data shown in the lower right corner as an example of the fourth type, most of the time is working time. For example, it is thought that the student started to think of an answer but did not reach the point of coming up with a solution.

[0065] Please note that the classification explained here is merely an example. Furthermore, it is not always possible to accurately judge how a student will approach a task based on the classification. Rather, teachers should use the classification as a reference and provide support to students at the appropriate time. Teachers should also be aware of any changes in the student's behavior by understanding how the same student's type changes over time.

[0066] (Variation 1) In one variation, the classification unit 13 outputs information indicating a time series of types of how students approach tasks.

[0067] An example of information output by the classification unit 13 will be described with reference to FIG. 6. The map shown in FIG. 6 shows how a student's approach to an assignment changes over time. In the map shown in FIG. 6, the horizontal axis represents the amount of time spent working on the assignment, and the vertical axis represents the percentage of non-working time. Each box that divides the map corresponds to one type.

[0068] In this example, the students' approaches to the assignments are classified into four types. The star marks in Figure 6 represent the chronological positioning of each student on the map. The arrows connecting the star marks indicate the direction of time.

[0069] According to the configuration of this variation 1, teachers can grasp changes in the type of each student, so they can quickly notice any abnormalities in the student and provide support to the student at the appropriate time.

[0070] (Variation 2) In one variation, we will explain how the non-working time rate can be calculated when two or more assignments are performed consecutively in the same lesson and in the same teaching subject. As explained in the first embodiment, the non-working time rate represents the ratio of the time during which no operation input was made to the user terminal 100 relative to the time limit for the assignment.

[0071] FIG. 7 is a diagram showing an example of a keystroke pattern indicating the amount of operation input while two tasks A and B are being performed consecutively.

[0072] In Figure 7, T A represents the amount of time the student spent working on task A. Also, T B represents the amount of time the student spent working on task B. X SN (X represents A and B) represents the time during task X when no operation input was made to the user terminal 100 for a certain period of time or more. S X N (N=1~n) is T X SN T X The method for determining the fixed time is arbitrary, but the teacher may determine the fixed time based on the difficulty of the task, the grade of the student, the type of subject being taught, and the like.

[0073] In one example, the non-working time rate is calculated by the classification unit 13 of the information processing device 20 according to the following formula:

[0074]

number

[0075] According to the configuration of the present modified example 2, even when two or more assignments are performed consecutively in the same lesson and in the same teaching subject, the non-task time rate can be calculated.

[0076] (Effects of this embodiment) According to the configuration of this embodiment, the collection unit 11 collects keystroke data indicating the amount of operation input to the user terminal 100. The extraction unit 12 extracts keystroke patterns indicating the characteristics of the operation input to the user terminal 100 from the keystroke data. The classification unit 13 classifies how the student approaches the assignment based on the keystroke patterns.

[0077] The types of students' approach to assignments reflect not only their abilities and qualities, but also the influence of their physical condition and lack of sleep. By providing technology to supplement students' approach to assignments in online classes, teachers can understand students' abilities and qualities, such as their concentration and level of understanding, and provide instruction that is appropriate for each student. Furthermore, by understanding the types of students' approach to assignments, teachers can quickly supplement any changes in students' behavior, even in online classes.

[0078] [Embodiment 3] A third embodiment will be described with reference to Figures 8 and 9. In the third embodiment, a configuration will be described in which reference information is provided for a teacher to evaluate the qualities or abilities of a student.

[0079] (Information processing device 30) The configuration of an information processing device 30 according to the third embodiment will be described with reference to Fig. 8. Fig. 8 is a block diagram showing the configuration of the information processing device 30. As shown in Fig. 8, the information processing device 30 includes a collection unit 11, an extraction unit 12, and a classification unit 13. The information processing device 30 further includes an evaluation unit 34. Each component of the information processing device 30 will be described below.

[0080] The evaluation unit 34 evaluates the qualities or abilities of the student based on the type of approach the student takes to the task. The evaluation unit 34 is an example of an evaluation means.

[0081] In one example, the evaluation unit 34 receives information indicating the type of each student from the classification unit 13. Here, the type of a certain student represents the student's approach to a task. The evaluation unit 34 calculates an index as a reference for teachers to evaluate the qualities or abilities of students. However, the index may simply be reference information for teachers to evaluate students.

[0082] In a first example, the evaluation unit 34 calculates an index showing the student's level of understanding based on the type of approach the student took to the assignment and the correctness of the answer. In this example, the evaluation unit 34 acquires information showing the correctness of the student's answer to the assignment from the management server 200. Alternatively, the evaluation unit 34 may acquire information showing the correctness of the answer entered by the teacher into the user terminal 100 from the teacher's user terminal 100.

[0083] The evaluation unit 34 then calculates an index showing the student's level of understanding from a combination of the type of approach the student has taken to the assignment and the correctness or incorrectness of the answer. For example, the index showing the student's level of understanding is a function of a first parameter corresponding to the type of approach the student has taken to the assignment and a second parameter corresponding to the student's behavior.

[0084] In a second example, the evaluation unit 34 calculates an index showing the concentration level of the student based on the keystroke pattern and the behavior of the student. In this example, the evaluation unit 34 acquires data of the student's face image captured by the user terminal 100 from the student's user terminal 100.

[0085] The evaluation unit 34 performs image analysis of the student's facial image data to detect, for example, whether the student is looking away or whether the student is absent. The evaluation unit 34 can use existing technology, such as gaze detection or eyelid opening / closing rate detection, to perform image analysis of the student's facial image data. Alternatively, the evaluation unit 34 may acquire, from the teacher's user terminal 100, information indicating the student's behavior that the teacher has entered into the user terminal 100.

[0086] The evaluation unit 34 then calculates an index showing the student's concentration level from a combination of the task time indicated by the keystroke pattern and the student's behavior. For example, the index showing the student's concentration level is a function of a first parameter corresponding to the task time indicated by the keystroke pattern and a second parameter corresponding to the student's behavior.

[0087] Furthermore, the evaluation unit 34 may generate a report to be submitted to an educational institution or educator based on the information indicating the type of student's approach to the assignment. For example, the report may include indicators for each subject taught. This allows the educational institution or educator to obtain information indicating the student's evaluation that is not based on the teacher's subjective judgment.

[0088] (Operation of information processing device 30) An example of the operation of the information processing device 30 according to the third embodiment will be described with reference to Fig. 9. Fig. 9 is a flowchart showing the flow of processing executed by each unit of the information processing device 30.

[0089] 9, the collection unit 11 collects keystroke data indicating the amount of operation input to the user terminal 100 (S301). The collection unit 11 outputs the keystroke data collected from the user terminal 100 to the extraction unit 12.

[0090] The extraction unit 12 receives keystroke data collected from the user terminal 100 from the collection unit 11. The extraction unit 12 extracts a keystroke pattern indicating a feature of an operation input to the user terminal 100 from the keystroke data (S302). The extraction unit 12 outputs information indicating the keystroke pattern to the classification unit 13.

[0091] The classification unit 13 receives information indicating the keystroke patterns from the extraction unit 12. The classification unit 13 classifies the students' approaches to the assignments based on the keystroke patterns (S303). Thereafter, the classification unit 13 may transmit information indicating the type of each student to the management server 200 (FIG. 8) via a communication network. The information indicating the type of each student is stored in the management server 200. The classification unit 13 outputs the information indicating the type of each student's approach to the assignments to the evaluation unit 34.

[0092] The evaluation unit 34 receives information indicating the type of approach the student takes to the assignment from the classification unit 13. The evaluation unit 34 evaluates the student's qualities or abilities based on the type of approach the student takes to the assignment (S304). For example, the evaluation unit 34 calculates an index indicating an evaluation of the student's qualities or abilities.

[0093] This completes the operation of the information processing device 30 according to the third embodiment.

[0094] (Second example of categorizing students' approaches to tasks) A second example of classification of students' approaches to tasks will be described with reference to Figure 10. Figure 10 shows seven types.

[0095] Compared to the types shown in Figure 5, the types shown in Figure 10 include three additional types. Specifically, the seven types shown in Figure 10 include the four types shown in Figure 5, plus an "average type." Furthermore, in the seven types shown in Figure 10, types 1 and 3 shown in Figure 5 are each divided into two types depending on how students approach the class.

[0096] Both the "partial participation type" and the "stopping type" shown in Figure 10 have the same keystroke pattern as the first type shown in Figure 5. Also, both the "at a loss" and the "non-participating type" shown in Figure 10 have the same keystroke pattern as the third type shown in Figure 5.

[0097] The "quick-start" type (corresponding to the second type in Figure 5) indicates that the student completed the task quickly.

[0098] "Partial participation" (corresponding to Type 1 in Figure 5) indicates that students are not concentrating on the task.

[0099] "Stopping" (corresponding to the first type in Figure 5) indicates that the student has given sufficient thought to the solution.

[0100] The "struggle type" (corresponding to the fourth type in Figure 5) indicates that the student went through trial and error.

[0101] "Non-participation" (corresponding to the third type in Figure 5) indicates that students have barely started on the assignment.

[0102] "At a loss" (corresponding to the third type in Figure 5) indicates that the student has not found a solution.

[0103] The "average type" (no corresponding type in Figure 5) represents the average typing pattern and task time of students in a class or group. The average typing pattern is obtained by performing statistical processing on multiple typing data.

[0104] Please note that the interpretation of the student characteristics (i.e., individuality regarding how they approach tasks) represented by each type is merely an example.

[0105] (Variation) In this modification, the evaluation unit 34 evaluates the qualities or abilities of a student based on a combination of the student's type represented by the student's keystroke pattern and the test results.

[0106] Figure 11 shows an example of the combination of a student's typing pattern types and test results, and their evaluation. In the table shown in Figure 11, the vertical axis represents the test result types, and the horizontal axis represents the typing pattern types. The test results shown in Figure 11 represent the student's evaluation points (scores) for tasks described in digital textbooks, etc. The seven types shown in Figure 11 are the same as those shown in Figure 10.

[0107] In the table shown in FIG. 11, the test results are classified into three categories: "good," "average," and "bad." The keystroke pattern types are classified into seven categories, as in FIG. 10. The data in the table shown in FIG. 11 is stored, for example, in the management server 200 (FIG. 12). Note that the classification of the test results and keystroke patterns is not limited.

[0108] In the table shown in Figure 11, the blank box where the row corresponding to one test result type and the column corresponding to one keystroke pattern type intersect is where the teacher writes comments about the student's approach to the assignment.

[0109] For example, in each blank cell, a teacher writes in the user terminal 100 information such as their experience of how students often approach assignments, and knowledge (perspectives) gained from teaching students. The knowledge, experience, and other information written in the teacher's user terminal 100 is transmitted to the management server 200 via the network. The management server 200 then stores information for understanding the meaning of combinations of keystroke pattern types and test result types. Any teacher other than the teacher who transmitted the information to the management server 200 can access the information stored in the management server 200 from any teacher's user terminal 100. In this way, the knowledge and experience gained by each teacher can be shared among teachers.

[0110] In one example, the evaluation unit 34 receives information indicating how the student approached the assignment from the classification unit 13, and also acquires data on the student's answers to the assignment from the student's user terminal 100. The evaluation unit 34 calculates the student's evaluation score for the assignment by analyzing the data on the answers to the assignment. Based on the calculated evaluation score, the evaluation unit 34 classifies the test results into one of three types: "good," "average," or "poor."

[0111] The evaluation unit 34 evaluates how the student approaches the assignment based on the previously calculated classification of the test results and the student type identified by the classification unit 13. For example, the evaluation unit 34 acquires information indicating the knowledge and experience entered by the teacher from the management server 200 by referring to the data in the table shown in Fig. 11. The evaluation unit 34 outputs the information indicating the knowledge and experience entered by the teacher as an evaluation result that directly or indirectly indicates the qualities or abilities of the student.

[0112] (Effects of this embodiment) According to the configuration of this embodiment, the collection unit 11 collects keystroke data indicating the amount of operation input to the user terminal 100. The extraction unit 12 extracts keystroke patterns indicating the characteristics of the operation input to the user terminal 100 from the keystroke data. The classification unit 13 classifies how the student approaches the assignment based on the keystroke patterns.

[0113] The types of students' approach to assignments reflect not only their abilities and qualities, but also the influence of their physical condition and lack of sleep. By providing technology to supplement students' approach to assignments in online classes, teachers can understand students' abilities and qualities, such as their concentration and level of understanding, and provide instruction that is appropriate for each student. Furthermore, by understanding the types of students' approach to assignments, teachers can quickly supplement any changes in students' behavior, even in online classes.

[0114] Furthermore, according to the configuration of this embodiment, the evaluation unit 34 evaluates the qualities or abilities of students based on the type of approach the students take to the assignments. Teachers can use the evaluation results by the evaluation unit 34 as a reference for their own evaluation of the qualities or abilities of their students.

[0115] [Embodiment 4] A fourth embodiment will be described with reference to Fig. 12. In the fourth embodiment, a communication system including any one of the information processing devices 10, 20, and 30 according to the first to third embodiments will be described. In the fourth embodiment, the same components as those in the first to third embodiments will be denoted by the same reference numerals, and the description thereof will be omitted.

[0116] (Communication System 1) Fig. 12 is a diagram schematically illustrating an example of the configuration of a communication system 1 according to the fourth embodiment. As shown in Fig. 12, the communication system 1 includes an information processing device 10 (20, 30), a plurality of user terminals 100, and a management server 200. Here, the "information processing device 10 (20, 30)" means any one of the information processing devices 10, 20, 30 according to the first to third embodiments.

[0117] The multiple user terminals 100 include a first user terminal used by the teacher and a second user terminal used by each of the students. For example, each user terminal 100 is a personal computer or a tablet terminal.

[0118] A plurality of user terminals 100 and the information processing devices 10 (20, 30) are communicatively connected via any communication network. Various data is transmitted and received between each user terminal 100 and the information processing devices 10 (20, 30). In particular, log information is transmitted from each user terminal 100 to the information processing devices 10 (20, 30).

[0119] The log information includes information indicating the content used on the user terminal 100. Furthermore, the log information may include at least one of an operation log, an authentication log, an access log, a communication log, a call log, and an event log.

[0120] The information processing device 10 (20, 30) analyzes the log information received from each user terminal 100 to generate information indicating the classification of the lesson and information regarding the content used, as described in the first to third embodiments. Then, the information processing device 10 (20, 30) records the generated information in the management server 200.

[0121] Furthermore, the information processing device 10 (20, 30) may create a report (FIG. 7) including information indicating the classification of the lesson and information related to the content, as described in the third embodiment. The information processing device 10 (20, 30) may store the created report data in the management server 200. This allows educational institutions or educational personnel to easily refer to or obtain the report data stored in the management server 200 by connecting to the network using the first user terminal 100.

[0122] For security reasons, access restrictions may be set on the management server 200 so that the second user terminal 100 used by the student cannot access the management server 200. This is because students should be prevented from viewing reports about other students from the perspective of protecting personal information and privacy. Furthermore, because reports are confidential information of the school, students should not be able to view or obtain report data, even if the report is related to them.

[0123] (Effects of this embodiment) According to the configuration of this embodiment, the collection unit 11 collects keystroke data indicating the amount of operation input to the user terminal 100. The extraction unit 12 extracts keystroke patterns indicating the characteristics of the operation input to the user terminal 100 from the keystroke data. The classification unit 13 classifies how the student approaches the assignment based on the keystroke patterns.

[0124] The types of students' approach to assignments reflect not only their abilities and qualities, but also the influence of their physical condition and lack of sleep. By providing technology to supplement students' approach to assignments in online classes, teachers can understand students' abilities and qualities, such as their concentration and level of understanding, and provide instruction that is appropriate for each student. Furthermore, by understanding the types of students' approach to assignments, teachers can quickly supplement any changes in students' behavior, even in online classes.

[0125] (About hardware configuration) Each of the components of the information processing devices 10, 20, and 30 described in the first to third embodiments is represented by a functional block. Some or all of these components are realized by an information processing device 900 as shown in Fig. 13. Fig. 13 is a block diagram showing an example of the hardware configuration of the information processing device 900.

[0126] As shown in FIG. 13, the information processing device 900 includes the following configuration, for example.

[0127] ·CPU(Central Processing Unit)901 ROM (Read Only Memory) 902 ·RAM(Random Access Memory)903 Program 904 loaded into RAM 903 A storage device 905 for storing a program 904 A drive device 907 for reading and writing data from and to the recording medium 906 A communication interface 908 for connecting to a communication network 909 Input / output interface 910 for inputting and outputting data Bus 911 connecting each component Each of the components of the information processing devices 10, 20, and 30 described in the first to third embodiments is realized by the CPU 901 reading and executing a program 904 that realizes the functions of the components. The program 904 that realizes the functions of the components is stored in advance in, for example, the storage device 905 or the ROM 902, and is loaded into the RAM 903 and executed by the CPU 901 as needed. The program 904 may be supplied to the CPU 901 via the communication network 909, or may be stored in advance in the recording medium 906, and the drive device 907 may read out the program and supply it to the CPU 901.

[0128] According to the above configuration, the information processing devices 10, 20, and 30 described in the first to third embodiments are realized as hardware, and therefore, the same effects as those described in any of the first to third embodiments can be achieved.

[0129] (Addendum) One embodiment of the present invention is also described as in the following supplementary notes, but is not limited to the following.

[0130] (Appendix 1) a collection means for collecting keystroke data indicating the amount of operation input to a user terminal; extraction means for extracting, from the keystroke data, a keystroke pattern that indicates a characteristic of an operation input to the user terminal; and a classification means for classifying the student's approach to the task based on the keystroke pattern. Information processing device.

[0131] (Appendix 2) The keystroke pattern includes a length of non-work time during which no operation input is made to the user terminal, and a length of time the student spends working on the assignment. 2. The information processing device according to claim 1,

[0132] (Appendix 3) The system further includes an evaluation means for evaluating the qualities or abilities of the student based on the type of approach the student takes to the task. 3. The information processing device according to claim 1 or 2.

[0133] (Appendix 4) The evaluation means Calculating an index showing the student's level of understanding based on the type of approach the student took to the assignment and the correctness of the answer 4. The information processing device according to claim 3,

[0134] (Appendix 5) The evaluation means Calculating an index indicating the student's concentration level based on the keystroke pattern and the student's behavior 4. The information processing device according to claim 3,

[0135] (Appendix 6) The classification means outputs information indicating a time series of types of approaches of the students to the assignments. 6. The information processing device according to any one of Supplementary Notes 1 to 5.

[0136] (Appendix 7) Collect keystroke data that indicates the amount of input to the user's device; extracting a keystroke pattern indicating a characteristic of an operation input to the user terminal from the keystroke data; Classifying the student's approach to the task based on the keystroke pattern Information processing methods.

[0137] (Appendix 8) Collecting keystroke data indicating the amount of operation input to a user terminal; extracting a keystroke pattern indicating a feature of an operation input to the user terminal from the keystroke data; classifying the student's approach to the task based on the keystroke pattern; and A program that causes a computer to execute the following.

[0138] (Appendix 9) The collecting means collects log information from the user terminal and extracts the keystroke data from the log information. 7. The information processing device according to any one of Supplementary Notes 1 to 6, (Appendix 10) The log information includes at least one of an operation log, an authentication log, an access log, a communication log, a call log, and an event log. 10. The information processing device according to claim 9,

[0139] (Appendix 11) The classification means classifies the student's approach to the assignment into four categories based on two categories: the magnitude of the non-work time rate and the length of the working time. 7. The information processing device according to any one of Supplementary Notes 1 to 6, [Industrial Applicability]

[0140] The present invention can be used, for example, to support online classes in which each student participates through a user terminal, and can also be used to exchange information between teachers and as reference information for evaluating students. [Explanation of symbols]

[0141] 1. Communication Systems 10. Information processing equipment 11 Collection Department 12 Extraction part 13 Classification section 20 Information processing equipment 30 Information processing equipment 34 Evaluation Department

Claims

1. a collection means for collecting keystroke data indicating the amount of operation input to a user terminal; extraction means for extracting, from the keystroke data, a keystroke pattern that indicates a characteristic of an operation input to the user terminal; a classification means for classifying the student's approach to the assignment based on the keystroke pattern; and an evaluation means for evaluating the qualities or abilities of the student based on the type of approach the student takes to the task. Information processing device.

2. The keystroke pattern includes a length of non-work time during which no operation input is made to the user terminal, and a length of time the student spends working on the assignment.

2. The information processing apparatus according to claim 1, wherein:

3. The evaluation means Calculating an index showing the student's level of understanding based on the type of approach the student took to the assignment and the correctness of the answer 3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

4. The evaluation means Calculating an index indicating the student's concentration level based on the keystroke pattern and the student's behavior 3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

5. The classification means outputs information indicating a time series of types of approaches of the students to the assignments.

5. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

6. Collect keystroke data that indicates the amount of input to the user's device; extracting a keystroke pattern indicating a characteristic of an operation input to the user terminal from the keystroke data; Classifying the student's approach to the task based on the keystroke pattern; assessing the student's qualities or abilities based on the type of approach the student takes to the task; Information processing methods.

7. Collecting keystroke data indicating the amount of operation input to a user terminal; extracting a keystroke pattern indicating a feature of an operation input to the user terminal from the keystroke data; classifying the student's approach to the task based on the keystroke pattern; and assessing the student's qualities or abilities based on the type of approach the student takes to the task; A program that causes a computer to execute the following.

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