WO2019168101A1 - Learning assist system and method - Google Patents

Abstract

A learning assist method according to one aspect of the present disclosure includes: setting a first problem selected from among a plurality of problems through a display device; updating the learning level data of a learner that is stored by a storage device on the basis of the addressing of the first problem through an input device by the learner; and selecting a second problem to be newly set from the plurality of problems on the basis of the learning level data of the learner. The learning level data indicates the learning level of the learner pertaining to each of a plurality of learning elements. The updating of the learning level data includes updating the learning levels of one or more learning elements including at least one learning element included in the first problem.

Description

Learning support system and method Cross-reference of related applications

This international application claims priority based on Japanese Patent Application No. 2018-037528 filed with the Japan Patent Office on March 2, 2018, and is based on Japanese Patent Application No. 2018-037528. The entire contents are incorporated by reference into this international application.

This disclosure relates to a learning support system.

There is already known a learning support system for giving a question to a learner through an electronic device (for example, see Patent Document 1). The learning support system is configured to change a new question to be asked according to whether or not the problem is correct by the learner and the correct answer rate.

JP 2004-038033 A

In the conventional technology, when a learner makes a mistake, another question in the same group as the wrong problem is given, thereby supporting learning. However, problems involving thought such as mathematics often require multiple skills to derive correct answers. Therefore, it is not necessarily an appropriate method to ask another question of the same group from the viewpoint of learning efficiency.

Therefore, in one aspect of the present disclosure, it is desirable to be able to provide a learning support system and method capable of realizing efficient learning.

According to one aspect of the present disclosure, a learning support system is provided. The learning support system includes a question part, an update part, and a selection part. The questioning unit is configured to give a first question selected from the plurality of questions through a display device.

The update unit is configured to update the proficiency level data of the learner stored in the storage device based on the effort through the learner's input device for the first question given by the questioning unit. The selection unit is configured to select a second question to be newly given to the questioning unit from a plurality of questions based on the proficiency level data of the learner stored in the storage device.

The proficiency level data indicates the proficiency level of the learner for each of a plurality of learning elements. Each of the plurality of questions may include at least one of the plurality of learning elements. At least some of the plurality of questions may include two or more of the plurality of learning elements. At least some of the plurality of learning elements may be included in two or more of the plurality of problems.

According to one aspect of the present disclosure, the update unit updates the proficiency level of one or more learning elements including at least one learning element included in the first problem based on the learner's approach to the first problem. Thus, the proficiency level data is updated.

This learning support system updates the learner's proficiency level for each learning element. Therefore, when a learner works on a problem that requires multiple skills for correct answers, based on this work, record multiple proficiency levels corresponding to multiple skills in detail to help the learner choose a question Can do. Therefore, according to this system, it is possible to provide a problem that the learner can efficiently learn and to support the learner.

According to one aspect of the present disclosure, the one or more learning elements include at least one learning element included in the first problem and at least one other element related to at least one learning element included in the first problem. And two learning elements. In this case, the update unit can update the proficiency level data based on data indicating relationships between a plurality of learning elements. Extending the learning elements whose proficiency level is to be updated to other than the learning elements included in the problem is useful for improving learning efficiency.

The number of standard exercises may be determined for each of the plurality of learning elements. For each of the learning elements, the update unit sets the proficiency level of the corresponding learning element so that the change in the proficiency level decreases as the number of standard exercises defined for the corresponding learning element increases. May be updated.

The update unit may calculate an experience value corresponding to the learner's approach to the first problem. The update unit converts, for each learning element, an experience value into a proficiency level equivalent value for each learning element, and adds the proficiency level equivalent value to the proficiency level of the corresponding learning element. By doing so, the proficiency level of the corresponding learning element may be updated. The conversion formula may be configured to convert the experience value into the proficiency equivalent value so that the proficiency equivalent value decreases as the number of standard exercises determined for the corresponding learning element increases.

The update unit may be configured to update the learner's proficiency level data based on at least one of the learner's answer behavior and correct answer confirmation behavior through the input device.

The update unit determines, for each learning element, an addition amount with respect to the proficiency level of the corresponding learning element for the one or more learning elements based on whether the answer is correct or not, and determines the determined addition amount based on the proficiency level of the corresponding learning element. The proficiency level of the corresponding learning element may be updated by adding to.

The update unit may determine the addition amount further based on at least one of the learner's answer time and the degree of browsing by the learner of the question comment displayed by the display device after the answer.

The learning support system may be provided with a determination unit that determines the degree of fixation among learners of skills corresponding to each of the one or more learning elements. The update unit may be configured to update each of the proficiency levels of the one or more learning elements within a range equal to or less than an upper limit value corresponding to a corresponding skill fixing level. The upper limit value can be larger as the fixing degree is higher.

∙ Based on the relationship between memory and forgetting, the establishment of skills includes time factors. Therefore, by providing an upper limit value according to the degree of fixation in the proficiency level, it is possible to calculate an appropriate proficiency level that takes into account the forgetfulness of the learner's learning experience.

According to an aspect of the present disclosure, the selection unit selects a second problem so that a problem including a learning element with a low learner's proficiency among a plurality of problems is preferentially selected. It may be configured to.

When the learner answers the first question incorrectly, the selection unit selects a problem including a learning element having a lower proficiency level from among a plurality of learning elements related to the first problem. The second problem may be selected.

The selection unit has a corrected proficiency level based on the corrected proficiency level obtained by correcting the proficiency level of each of the multiple learning elements based on the proficiency level of the multiple learning elements related to the corresponding learning element. The second question may be selected so that a problem including a low learning element is selected more preferentially. According to this problem selection, learning by the learner can be supported more efficiently.

According to one aspect of the present disclosure, a learning support method may be provided. The learning support method is a method in which a first problem selected from a plurality of problems is presented through a display device and learning stored in a storage device based on an approach through a learner's input device for the first problem. Updating the learner's proficiency data, and selecting a second question to be newly set out of a plurality of questions based on the learner's proficiency data stored in the storage device. Good. The proficiency level data may indicate a learner's proficiency level for each of the plurality of learning elements. Updating updates the proficiency data to update the proficiency of one or more learning elements, including at least one learning element included in the first problem, based on the learner's approach to the first problem Can include.

According to one aspect of the present disclosure, the learning support method may be executed by a computer. The learning support method may include a procedure executed by the learning support system described above.

A computer program for causing a computer to realize the functions as the learning support system described above may be provided. A computer program for causing a computer to function may be provided as at least one of the questioning unit, the updating unit, the selection unit, and the determination unit.

A computer program for causing a computer to execute the learning support method described above may be provided. A computer program for causing a computer to perform at least one of setting a question, updating, and selecting may be provided. The computer program may be recorded on a computer-readable non-transitory recording medium.

It is a block diagram showing the structure of a learning assistance system. It is a figure explaining the data which a database system memorizes. It is a figure showing the structure of problem data. It is a figure which shows the example of a problem. It is the figure which showed the graph regarding a learning element. It is a figure showing the structure of learning element data. It is a figure explaining the relationship between learning elements. It is a figure showing the structure of learner data. It is a flowchart showing the learning assistance process which a processing device performs. It is a flowchart showing the experience value calculation process which a processing device performs. It is a flowchart showing the proficiency level update process which a processing device performs. It is a figure showing the structure of an upper limit table. It is a flowchart showing the next problem selection process which a processing device performs.

DESCRIPTION OF SYMBOLS 1 ... Learning support system, 10 ... User terminal device, 20 ... Control device, 21 ... CPU, 23 ... RAM, 30 ... Storage device, 40 ... Communication device, 50 ... Display device, 55 ... Input device, 60 ... Server device, 70: Processing device, 71: CPU, 73: RAM, 80 ... Storage device, 90 ... Communication device, 100 ... Database system.

Hereinafter, exemplary embodiments of the present disclosure will be described with reference to the drawings.

The learning support system 1 of the present embodiment shown in FIG. 1 includes a plurality of user terminal devices 10, a server device 60, and a database system 100. The user terminal device 10 is configured to be able to communicate with the server device 60 via the wide area network NT, and the server device 60 is configured to be able to communicate with the database system 100 on the back end side.

The user terminal device 10 is configured to give a learning problem to the user in cooperation with the server device 60. Examples of the user terminal device 10 include electronic devices such as a personal computer, a tablet, and a smartphone.

The user terminal device 10 shown in FIG. 1 includes a control device 20, a storage device 30, a communication device 40, a display device 50, and an input device 55.

The control device 20 includes a CPU (Central Processing Unit) 21 and a RAM (Random Access Memory) 23, and performs overall control of the user terminal device 10. The CPU 21 executes processing according to the computer program stored in the storage device 30. The RAM 23 is used as a working memory when the CPU 21 executes processing. Below, the process performed by CPU21 is demonstrated as a process which the control device 20 performs.

The storage device 30 stores various computer programs and data. The storage device 30 is configured by, for example, a flash memory or a hard disk device. In the user terminal device 10, an application program for asking questions for learning in cooperation with the server device 60 is installed and stored in the storage device 30.

The control device 20 is configured to execute a process according to the application program, thereby to give a question designated by the server device 60 through the display device 50 and to accept an answer operation from the user through the input device 55. Is done. The control device 20 is configured to transmit this operation content to the server device 60 through the communication device 40.

The communication device 40 is configured to be able to communicate with devices in the wide area network NT including the server device 60. The display device 50 is configured by, for example, a liquid crystal display or an organic EL display. The display device 50 is controlled by the control device 20 and displays various screens for the user. The various screens include a question screen including a question sentence and an answer column, and a comment screen including a question commentary.

The input device 55 is configured to accept an input operation from the user and input a corresponding operation signal to the control device 20. The input device 55 may be a touch panel configured integrally with the display device 50. The input device 55 receives a touch operation and a writing operation on the screen displayed on the display device 50 and inputs the operation signal to the control device 20.

1 includes a processing device 70, a storage device 80, and a communication device 90. The processing device 70 includes a CPU 71 and a RAM 73. The CPU 71 executes processing according to the computer program stored in the storage device 80. The RAM 73 is used as a working memory when the CPU 71 executes processing.

Hereinafter, processing executed by the CPU 71 will be described as processing executed by the processing device 70. The communication device 90 is configured to be able to communicate with the user terminal device 10 through the wide area network NT, and further to be able to communicate with the database system 100 on the back end side.

When the application program is activated in the user terminal device 10, the processing device 70 identifies the user corresponding to the user terminal device 10 based on the user identification information transmitted from the user terminal device 10, and the user terminal device 10 is established. Thereafter, the processing device 70 refers to the database system 100 to determine a problem to be presented to the user. The processing device 70 gives the determined question to the user through the user terminal device 10, acquires the content of the answer operation from the user terminal device 10, and determines whether the problem is correct or not.

As shown in FIG. 2, the database system 100 includes a plurality of problem data, a plurality of learning element data, a plurality of learner data, and an upper limit value table. The plurality of problem data includes one problem data for each problem. As shown in FIG. 3, each piece of question data includes question sentence data, correct answer data, commentary sentence data, hint data, and question element data for the corresponding problem.

The question sentence data represents a problem sentence to be displayed on the display device 50. The correct answer data represents the correct answer of the corresponding problem. The correct answer data is used to determine whether the user's answer is correct. The comment sentence data is a problem comment sentence that explains how to solve the corresponding problem, important points, and the like, and represents a problem comment sentence that is displayed on the display device 50 after the user answers the problem. The hint data represents a hint on how to solve the problem displayed on the display device 50 in response to a request from the user in the process of solving the problem.

The problem element data represents one or more learning elements included in the corresponding problem. Here, the learning elements will be described based on the example shown in FIG. The problem shown in FIG. 4 is the subtraction of decimals and fractions. In order to solve this subtraction, a skill to convert the mixed number into an improper fraction, a skill to convert a decimal number into a fraction, and a skill to subtract a fraction with a different denominator are required. Furthermore, subtraction of fractions with different denominators requires skill for sharing and skill for subtracting fractions.

In this embodiment, it is assumed that the problem includes a learning element corresponding to the skill, and the problem support data representing the learning element included in the problem is prepared by the designer of the learning support system.

Specifically, problem element data is prepared assuming that the following learning elements E1, E2, E3, E4, E5, and EG are included in the problem.
・ Learning element E1 for acquiring skills to convert mixed numbers into improper fractions
・ Learning element E2 to acquire skills to convert decimals to fractions
・ Learning element E3 to acquire skills to subtract fractions with different denominators
・ Learning element E4 to acquire skill to share
・ Learning element E5 to acquire the skill to subtract fractions
・ Learning element EG to acquire skills to subtract decimals and fractions
The learning element EG is a main purpose learning element for this problem. FIG. 5 shows a graph in which learning elements E1, E2, E3, E4, E5, and EG included in this question are arranged along the flow of thought that leads the question to the correct answer. The graph shown in FIG. 5 may be understood as a directed graph having a direction from top to bottom along the flow of thought.

In the following, the main purpose learning element EG included in one problem is expressed as a goal element, and in this problem, learning elements E1, E2, E3, E4 corresponding to the skills required for learning the goal element. E5 is expressed as a usage element. As described above, the goal element corresponds to a learning element that the user wants to newly learn with the corresponding problem.

The problem element data includes information that can identify one or more learning elements included in the problem, and information that can graph one or more learning elements included in the problem as shown in FIG. For example, as shown in FIG. 3, the problem element data includes utilization element data indicating an ID of a learning element corresponding to a utilization element included in the problem, and an ID of a learning element corresponding to one goal element included in the problem. Can be included.

In the following, the ID of a learning element is also expressed as an element ID. It should be noted that each of a plurality of problems corresponding to a plurality of problem data stored in the database system 100 includes one goal element as a learning element, but does not necessarily include a utilization element.

The plurality of learning element data (see FIG. 2) stored in the database system 100 includes one learning element data for each learning element included in at least one of the plurality of problems corresponding to the plurality of problem data. At least some of the plurality of learning elements corresponding to the plurality of learning element data are included in two or more of the plurality of problems.

As shown in FIG. 6, each of the learning element data defines one or more learning elements related to the attention element in association with the element ID of one corresponding learning element (hereinafter referred to as the attention element). Data, specifically parent element data, derivation source element data, and dependency element data. Further, each of the learning element data includes standard exercise number data.

Parent element data represents the ID of a learning element that is conceptually higher than the element of interest. Hereinafter, the second learning element that is higher than the first learning element is expressed as a parent element of the first learning element. According to FIG. 7, the parent element of the learning elements C11 and C12 is the learning element C1. In this case, when viewed from the learning element C1, the learning elements C11 and C12 are child elements.

For example, the learning element C1 related to “the magnitude of the number” includes a learning element C11 related to “number larger than P” and a learning element C12 related to “number smaller than P” as child elements. P and Q here are arbitrary natural numbers.

As described above, the learning element C1 related to the “number of magnitudes” includes learning elements C11 and C12 as learning elements of a smaller classification or learning elements of lower concepts. The learning element C1 can be decomposed into learning elements C11 and C12 having a smaller classification or lower concept.

Derivation source element data represents the ID of the learning element of the derivation source for the element of interest. According to FIG. 7, the learning element that is the derivation source for the learning elements C21, C22, and C23 is the learning element C1. According to an example, the learning element C21 that is the derivation destination is a learning element related to “the magnitude of a positive integer and a positive integer” with respect to the learning element C1 related to “number of magnitudes”. The learning element C22 is a learning element related to “the magnitude of a positive integer and a negative integer”. The learning element C <b> 23 is a learning element related to “a positive integer and a negative decimal”. In this way, the learning elements C21, C22, and C23 of the derivation destination with respect to the learning element C1 of the derivation correspond to learning elements that are derived due to a difference in parameters or the like.

Dependent element data represents the ID of a learning element that is dependent on the element of interest. According to FIG. 7, the learning element having a dependency relationship with the learning element C1 is the learning element C3. A learning element C3 having a dependency relationship with respect to the learning element C1 related to “number of magnitudes” is a learning element related to “number line”. Here, the learning element C1 depends on the learning element C3 in the sense that in order to learn the learning element C1, the learner needs to make the learning element C3 sufficiently. The first learning element and the second learning element that are in the dependency relationship are learning elements that the learner needs to fully understand in order to learn the second learning element. Correspond.

The standard exercise number data represents the standard value of the number of exercises of the problem including the attention element necessary for learning the attention element. Hereinafter, this standard value is expressed as the number of standard exercises. The number of standard exercises is set by the designer of the learning support system 1.

The plurality of learner data stored in the database system 100 includes one learner data for each user corresponding to the learner. As shown in FIG. 8, the learner data represents the proficiency level of the corresponding user for each of the plurality of learning elements. Specifically, the learner data includes, for each learning element, data representing the proficiency level of the corresponding learning element and exercise history data of a problem including the corresponding learning element.

The exercise history data includes the number of exercises and correct answers by the user of the problem including the corresponding learning element. The exercise history data further includes information on the date and time when the user first corrects the problem including the corresponding learning element. The exercise history data may include information that can specify the date and time of each time point when the user answers the question including the corresponding learning element and whether the answer is correct. The exercise history data is used to determine the degree of fixation of the corresponding skill.

The upper limit value table stored in the database system 100 is configured as a table that defines the upper limit value of the proficiency level for each combination of the fixing degree and the number of standard exercises (see FIGS. 2 and 12).

Next, details of the learning support process executed by the processing device 70 of the server device 60 will be described with reference to FIG. The learning support process is executed for each user. When the learning support process is started, the processing device 70 selects a question to be presented to the user from a plurality of questions corresponding to the plurality of question data stored in the database system 100 (S110).

In S110, the processing device 70 can select a question to be presented to the user according to the curriculum. For example, the processing device 70 can select the problem to be asked next according to the curriculum as the question to be given to the user, based on the problem that the user answered last. The database system 100 can store data defining a curriculum. The processing device 70 may select the same problem or the same kind of problem as the erroneously answered problem when the problem that was last answered by the user is an incorrect answer.

Subsequently, the processing device 70 executes a process for giving the selected question to the user (S120). Specifically, the processing device 70 transmits to the user terminal device 10 data for displaying a question screen including a question sentence and an answer column on the display device 50 of the corresponding user terminal device 10. As a result, a problem screen related to the problem selected by the processing device 70 is displayed on the display device 50.

Thereafter, the processing device 70 accepts the user's answer operation through the input device 55 (S130). The content of the answer operation is transmitted from the user terminal device 10. When the user's answer operation is completed, the processing device 70 can proceed to S140.

In S130, the processing device 70 can display the hint on the display device 50 of the user terminal device 10 based on the corresponding problem hint data when the user performs a hint request operation. The processing device 70 may operate so as to measure the answer time from the display of the question sentence to the end of the answer operation. Alternatively, the processing device 70 may acquire answer time information from the user terminal device 10.

After shifting to S140, the processing device 70 determines whether or not the user has correctly answered the problem based on the content of the user's answer operation (S140). According to an example, the content of the answer operation transmitted from the user terminal device 10 may include user handwriting information for the answer field on the question screen. The processing device 70 can recognize the character of the handwriting and determine whether or not the user has correctly answered the problem based on the recognized character information.

When the processing device 70 determines that the user has correctly answered the problem (Yes in S140), the processing device 70 proceeds to S150. On the other hand, if it is determined that the answer is incorrect (No in S140), the process proceeds to S145, and it is determined whether or not to request redo (S145). If the processing device 70 determines that a redo is requested (Yes in S145), the processing device 70 proceeds to S130 and accepts the user's answer operation for the same question again. At this time, the processing device 70 may operate to display a hint on how to solve the problem on the display device 50 together with a message indicating an incorrect answer.

If the user continues to answer incorrectly even after redoing a predetermined number of times (for example, once), the processing device 70 determines in S145 that no redoing is required, and can proceed to S150.

In S150, the processing device 70 executes processing for causing the display device 50 to display an explanation screen including the problem explanation text based on the comment text data of the corresponding problem (S150). When the user terminal device 10 notifies that the operation for closing the explanation screen has been performed through the input device 55, the process proceeds to S160. In S150, the time from when the comment screen is displayed on the display device 50 until the comment screen is closed is measured.

In S160, the processing device 70 performs an experience value calculation process shown in FIG. 10 to calculate an experience value regarding the user's approach to the problem presented in S120.

In the experience value calculation process, the processing device 70 initializes the experience value X to zero (S210). Thereafter, the processing device 70 determines whether or not the user's answer is a correct answer (S220). If the answer is correct (Yes in S220), the process proceeds to S230. If the answer is incorrect (No in S220), the process proceeds to S280.

In S230, the processing device 70 determines whether or not the user's answer time for the question is equal to or less than the reference value T1. When it is determined that the answer time is equal to or less than the reference value T1 (Yes in S230), the experience value X is added by a predetermined value A1 (S240). On the other hand, when it is determined that the answer time is greater than the reference value T1 (No in S230), the experience value X is added by a value A2 different from the value A1 (S245). The values A1 and A2 are determined by the designer, and the value A2 can be determined to be larger than the value A1 (A2> A1). According to an example, the value A1 is the value 10 and the value A2 is the value 50.

After the addition in S240 and S245, the processing device 70 determines whether or not the answer operation has been performed again (S250). That is, it is determined whether or not an affirmative determination is made in S145.

Here, if it is determined that there has been a redo (Yes in S250), the processing device 70 further adds the experience value X by a predetermined value A3 (S255), and proceeds to S260. If the processing device 70 determines that there is no redo (No in S250), the processing device 70 proceeds to S260 without adding the experience value X. According to one example, the value A3 is the value 10.

In S260, the processing device 70 determines whether the user has seen the hint in the answering process. Here, it is determined whether or not the hint has been displayed for a predetermined time or more. The predetermined time may be set to be longer than the standard time required for the user to read while understanding the hint. The user terminal device 10 operates to stop the display of hints by the display device 50 according to the user's operation.

When it is determined that the user has seen the hint (Yes in S260), the processing device 70 adds the experience value X by the predetermined value A4 (S265), and proceeds to S270. If the processing device 70 makes a negative determination in S260, the processing device 70 proceeds to S270 without adding the experience value X. According to an example, the value A4 is the value 10.

In S270, the processing device 70 determines whether or not the user has seen the problem commentary. Here, it is determined whether or not a problem commentary (comment screen) has been displayed for a predetermined time or more. The predetermined time can be set to be longer than the standard time required for the user to read while understanding the problem commentary. The user terminal device 10 operates to close the explanation screen displayed on the display device 50 in accordance with a user operation.

When it is determined that the user has seen the problem commentary (Yes in S270), the processing device 70 adds the experience value X by the predetermined value A5 (S275). The value A5 can be changed according to the time when the problem commentary is viewed. For example, in S275, a larger value A5 can be added to the experience value X as the commentary is viewed longer. The value A5 may be changed according to the amount of viewing the explanatory text. For example, the processing device 70 determines whether or not the user has watched until the end of the commentary, based on the scroll operation of the commentary screen. When the user sees to the end of the commentary, the processing device 70 is larger than the case where the user has not seen it. The value A5 may be added to the experience value X.

For example, the processing device 70 adds the value A5 = 5 to the experience value X when the time for viewing the comment is short, and when the time for viewing the comment is long, the processing device 70 adds the value A5 = to the experience value X. When 15 is added and the commentary is viewed to the end, the value A5 = 20 may be added to the experience value X.

The processing device 70 ends the experience value calculation process after adding the experience value X in S275. On the other hand, if a negative determination is made in S270, the processing device 70 ends the experience value calculation process without adding the experience value X.

When the user answers the question incorrectly, the processing device 70 determines in S280 whether or not the user has seen the hint in the answering process, as in the processing in S260. If the processing device 70 determines that the user has seen the hint (Yes in S280), the processing device 70 adds the experience value X by the predetermined value A6 (S285), and proceeds to S290. If the processing device 70 makes a negative determination in S280, the processing device 70 proceeds to S290 without adding the experience value X. According to one example, the value A6 is the value 5.

In S290, the processing device 70 determines whether or not the user has seen the problem commentary, as in the processing in S270. When the processing device 70 determines that the user has seen the problem commentary (Yes in S290), the processing device 70 adds the experience value X by the predetermined value A7 (S295). For example, the processing device 70 adds the value A7 = 15 to the experience value X when the time when the commentary is viewed is short, and when the time when the commentary is viewed is long, the value A7 = When 20 is added and the commentary is viewed to the end, the value A7 = 25 may be added to the experience value X.

The processing device 70 ends the experience value calculation process when the experience value X is updated in S295. On the other hand, if a negative determination is made in S290, the processing device 70 ends the experience value calculation process without adding the experience value X.

In this way, the processing device 70 calculates the experience value X according to whether the answer is correct, the answer time, whether or not to redo, the browsing of hints, and the browsing of the question explanation as an experience value corresponding to the approach to the problem. To do.

When the processing device 70 ends the experience value calculation process in S160, the processing device 70 executes the proficiency level update process shown in FIG. In the proficiency level update process, the processing device 70 calculates the proficiency level Ye of the goal element included in the question presented in S120 (S310).

Specifically, the processing device 70 converts the experience value X into a conversion formula C = (Ke · K) based on the experience value X calculated in S160, the standard exercise number Ne of the goal element, and a predetermined coefficient Ke. X / Ne) and converted to a proficiency equivalent value C. The processing device 70 adds the proficiency level equivalent value C to the current proficiency level Ye0 of the user regarding the goal element, thereby calculating the proficiency level Ye of the goal element after tackling the above-mentioned problem (Ye) = Ye0 + Ke · X / Ne).

Here, the coefficient Ke is a value that does not depend on the learning element, for example, Ke = 1. The standard exercise number Ne of the goal element is specified by referring to the learning element data of the corresponding learning element stored in the database system 100. The user's current proficiency level Ye0 is specified by referring to the user's learner data.

After that, the processing device 70 calculates the proficiency level Yh of the use element included in the question that has been asked (S320). Specifically, the processing device 70 converts the experience value X according to the conversion formula C = (Kh · X / Nh) based on the experience value X, the standard practice number Nh of the use elements, and a predetermined coefficient Kh. It is converted into a proficiency equivalent value C. The processing device 70 adds the proficiency level equivalent value C = (Kh · X / Nh) to the current proficiency level Yh0 of the user regarding the usage factor, and masters the usage factor after tackling the above-mentioned problem. The degree Yh is calculated (Yh = Yh0 + Kh · X / Nh). The coefficient Kh is a value that does not depend on the learning element, for example, Kh = 0.3.

When a plurality of usage elements are included in the question, the processing device 70 sets the individual conversion formula C = based on the proficiency level Yh of each of the usage elements based on the standard practice number Nh of the corresponding usage elements. It can be calculated using (Kh · X / Nh). When the utilization element is not included in the problem, the processing device 70 can skip the process of S320.

After completing the process in S320, the processing device 70 calculates the mastery level Yp of the parent element corresponding to the learning element included in the problem in the subsequent S330. Specifically, the processing device 70 converts the experience value X into a proficiency equivalent value C according to the conversion formula C = (Kp · X / Np). Np is the number of standard exercises of the parent element. The parent element is specified by referring to the parent element data of the learning element included in the problem.

The processing device 70 adds the proficiency level equivalent value C = (Kp · X / Np) to the current proficiency level Yp0 of the user regarding the parent element, and the parent element after tackling the above-mentioned problem is processed. A proficiency level Yp is calculated (Yp = Yp0 + Kp · X / Np). Here, the coefficient Kp is a value that does not depend on the learning element, and for example, Kp = 0.1.

When the problem includes a plurality of learning elements, the processing device 70 determines, for each of the plurality of learning elements, an individual conversion formula C = (Kp · X) based on the standard practice number Np of the parent element of the corresponding learning element. / Np) can be used to calculate the proficiency level Yp. Alternatively, in S330, the proficiency level Yp may be calculated for only the parent element of the goal element.

After finishing the process in S330, the processing device 70 calculates the proficiency level Yd of the derivation source element corresponding to the learning element included in the problem in the subsequent S340. Specifically, the processing device 70 converts the experience value X into a proficiency equivalent value C according to the conversion formula C = (Kd · X / Nd). Nd is the number of standard exercises of the derivation source element. The derivation source element is specified by referring to the derivation element data of the learning element included in the problem.

The processing device 70 adds the proficiency level equivalent value C = (Kd · X / Nd) to the user's current proficiency level Yd0 for the derivation source element, and derives the derivation source after tackling the above-mentioned problem. Element proficiency Yd is calculated (Yd = Yd0 + Kd · X / Nd). Here, the coefficient Kd is a value that does not depend on the learning element, for example, Kd = 0.3. The handling of the derivation source element when the problem includes a plurality of learning elements is the same as that of the parent element.

Thereafter, the processing device 70 corrects the calculated proficiency level so that the proficiency level is equal to or lower than the upper limit value for each learning element whose proficiency level is calculated in S310 to S340 (S350). By this correction, the proficiency level recorded in the learner data is corrected to a value corresponding to the skill fixing level corresponding to the user's learning element.

Specifically, in S350, the processing device 70 determines, for each learning element, the degree of fixation of the skill corresponding to the learning element in the user.

¡Fixing degree takes 5 levels from 0 to 4. With respect to a learning element (hereinafter referred to as a determination target element) for a fixing degree determination target, when the user has never answered a problem including the determination target element, the fixing degree of the skill corresponding to the determination target element is 0. Determined.

When the user corrects the problem including the determination target element only once, the degree of fixing of the skill corresponding to the determination target element is determined to be 1. When the user corrects the problem including the determination target element only twice, the fixing degree of the skill corresponding to the determination target element is determined to be 2.

When the user corrects the problem including the determination target element a plurality of times with a period of one day or more from the first correct answer date, and thus corrects the total three times or more, the fixing degree of the determination target element is 3 Is determined.

When a user corrects a problem including a determination target element multiple times over a period of three days or more from the first correct answer date, and the correct answer is made three or more times in total, the fixing degree of the determination target element is 4 is determined.

Based on the upper limit value table stored in the database system 100, the processing device 70 has, for each learning element, the proficiency level of the corresponding learning element is equal to or lower than the upper limit value defined by the combination of the determined fixing degree and the number of standard exercises. Thus, the proficiency level in which the calculated value in S310-S340 exceeds the upper limit value is corrected to the upper limit value.

As shown in FIG. 12, the upper limit value table defines 80 as the upper limit value of the proficiency level of learning elements in which the fixing degree is 0 and the standard exercise number N is 2 or less. Similarly, the upper limit table defines an upper limit value of 60 when the standard exercise number N is 5 or less under the condition that the fixing degree is 0, and an upper limit value when the standard exercise number N is 8 or less. Is defined as 50, an upper limit value is defined as 40 when the standard exercise number N is 12 or less, and an upper limit value is defined as 30 when the standard exercise number N is 13 or more. As described above, the upper limit table defines a lower upper limit for learning elements having a larger number N of standard exercises.

The upper limit value table defines the upper limit value when the fixing degree is 1 as 60 and the upper limit value when the fixing degree is 2 as 80 under the condition that the standard exercise number N is 12 or less, The upper limit value when the fixing degree is 3 is defined as 90, and the upper limit value when the fixing degree is 4 is defined as 100. As described above, the upper limit value table defines the upper limit value higher as the fixing degree increases.

When the processing device 70 corrects the proficiency level calculated in S310 to S340 according to the upper limit table, the processing device 70 proceeds to S360. In S360, for each of the learning elements whose proficiency levels have been calculated in S310-S340, the processing device 70 writes the proficiency level after correction in S350 into the corresponding user's learner data, thereby indicating the learner data. Update the proficiency level of each learning element of the user. Along with the update of the proficiency level, the processing device 70 updates the exercise history data. Thereby, the processing device 70 updates the user's learner data (S360). Thereafter, the proficiency level update process is terminated.

When the proficiency level update process ends in S170, the processing device 70 determines whether or not the end condition is satisfied (S180). The processing device 70 can determine that the end condition is satisfied when the user terminal device 10 notifies the user that the intention to end learning has been displayed.

If the processing device 70 determines that the end condition is satisfied (Yes in S180), the processing device 70 ends the learning support process. If the processing device 70 determines that the end condition is not satisfied (No in S180), the processing device 70 proceeds to S190, and FIG. The next problem selection process shown in FIG.

In the next question selection process, the processing device 70 determines whether or not the user has correctly answered the question presented in S120 (S410). If it is determined that the answer is correct (Yes in S410), the processing device 70 selects the next question according to the curriculum (S415), and ends the next question selection process.

If the processing device 70 determines that the answer is not correct (No in S410), the processing device 70 determines whether to perform problem selection based on the proficiency level (S420). If sufficient problem exercises for problem selection based on the proficiency level are not performed, the processing device 70 makes a negative determination here, and proceeds to S425.

In S425, the processing device 70 selects a standard return destination problem according to a predetermined rule for the given question as the next problem (S425), and ends the next question selection process. The problem to be returned corresponds to the problem that is given before the curriculum rather than the question that has been given.

If the processing device 70 makes a positive determination in S420, the processing device 70 proceeds to S430 and generates an element list. The element list is a list of learning elements included in the given question. Goal elements included in the standard return problem for the question that has been presented may be added to the element list.

In subsequent S440, the processing device 70 calculates the combined proficiency level for each of the learning elements included in the element list. The composite proficiency level corresponds to the proficiency level obtained by correcting the proficiency level of the learning element by the proficiency level of the related learning element. Hereinafter, the learning element for which the composite proficiency level is to be calculated is expressed as a target element.

Specifically, the composite proficiency level Ym is calculated according to the formula Ym = Σ (Yi · Ni) / ΣNi. The denominator value ΣNi corresponds to the sum of the standard exercise numbers Ni of the learning elements Ei belonging to the specific group. The specific group here is a group of child elements of the target element and the learning element of the dependence destination. For example, when the target element is the learning element C1 shown in FIG. 7, the specific group corresponds to a group of learning elements C11 and C12 and a learning element C3. The specific element may include the target element itself.

The value of the numerator corresponds to the sum of the values (Yi · Ni) of the learning elements Ei belonging to the specific group. Yi corresponds to the proficiency level of the learning element Ei, and Ni corresponds to the standard number of exercises of the learning element Ei. That is, the value (Yi · Ni) corresponds to the product of the proficiency level Yi related to the learning element Ei and the standard exercise number Ni.

As can be understood from this, the composite proficiency level Ym corresponds to a weighted average of the proficiency level Yi of a specific group consisting of learning element groups related to the target element. When the target element has no child element and dependent learning element, the composite proficiency matches the proficiency of the target element itself.

Thereafter, the processing device 70 edits the element list so as to exclude a part of the learning elements included in the element list from the list (S445). Specifically, the processing device 70 edits the element list such that the ratio of the number of actual exercises of the user to the number of standard exercises is greater than the reference and the error rate is higher than the reference, and other learning elements are excluded. To do. Further, the processing device 70 edits the element list so as to exclude learning elements having a combined proficiency level equal to or higher than the reference. As a result, the processing device 70 edits the element list so that the learning element with a high level of proficiency and the learning element at the beginning of learning are excluded from the element list.

In S445, the processing device 70 further edits the element list so as to leave the learning elements included in the problems up to a predetermined number in the curriculum and exclude other learning elements based on the questions that have been presented. Also good. Thereby, the element list may be edited so that only learning elements that are not good at learning elements that the user has recently learned remain in the element list.

After the processing in S445, the processing device 70 sorts the learning elements in the edited element list in ascending order of the combined proficiency level (S450). Then, among the learning elements in the unprocessed element list for the processes after S470, the learning element with the lowest combined proficiency is selected as the processing target (S460), and the learning element to be processed is included in the goal element. A group of questions before the curriculum is extracted from the questions presented in (S470).

After that, the processing device 70 arranges the extracted problem groups in the order of the problems according to the curriculum, and separates the arranged problem groups according to the chapter division in the curriculum while maintaining the arrangement, thereby separating the problem groups into a plurality of clusters. To separate. The processing device 70 selects, from among the plurality of clusters, the first problem of the cluster closest in the curriculum to the problem erroneously answered in S120 as a candidate for the next problem (S480).

Subsequently, the processing device 70 determines whether or not the candidate for the next problem selected in S480 is a problem that has recently been asked (S490). The determination of whether or not the question has recently been asked is realized by determining whether or not the candidate for the next question is a question that has been given before a predetermined time (for example, within 10 minutes) from the current time. Can do.

If the processing device 70 determines that the candidate for the next question is a recently-issued question (Yes in S490), the processing device 70 proceeds to S460, selects the learning element with the next lowest composite proficiency as a processing target, and performs S470. The subsequent processing is executed. If the processing device 70 determines that the candidate for the next question is not a question that has recently been asked (No in S490), the processing device 70 selects that candidate as the next question (S495), and ends the next question selection process.

When the processing device 70 completes the next question selection process in S190, the processing device 70 proceeds to S120 and presents the next question selected in S190 to the user through the display device 50 of the user terminal device 10. Thereafter, the processing after S130 is executed.

The learning support system 1 according to the present embodiment described above updates the proficiency level of one or more learning elements including at least one learning element included in the given question based on the user's approach to the given question. Thus, the user's learner data is updated.

Specifically, the learning support system 1 updates the user's proficiency level based on at least one of the user's answer behavior and correct answer confirmation behavior for the question that has been presented. More specifically, the learning support system 1 updates the user's proficiency level based on whether the answer is correct or not, the answer time, and whether or not the hint is viewed as parameters related to the answer behavior. Furthermore, the learning support system 1 updates the user's proficiency level based on the degree of browsing the problem commentary as a parameter related to correct answer confirmation behavior.

Therefore, according to the learning support system 1 of the present embodiment, it is possible to provide a problem that the user can efficiently learn and support the learning by the user. Specifically, when a user tackles a problem that requires multiple skills for correct answers, such as mathematics and mathematics, based on this approach, the user's multiple proficiency levels are recorded in detail and presented to the user. Can be used to select the problem to be performed.

In particular, according to the present embodiment, when a user tackles a problem, not only the learning elements included in the tackled problem, but also learning elements related to the learning element, specifically, a parent-child relationship, a dependency relationship, and It also updates the proficiency level of learning elements that have derivation relationships. Therefore, the proficiency level of each learning element in the learner data can be updated so as to match the actual state of improvement of the proficiency level of the user.

According to the present embodiment, the learning amount Y increases as the number of standard exercises N increases by adjusting the addition amount C of the learning level Y of each learning element based on the standard exercise number N of learning elements. Update so that becomes smaller. Specifically, based on the coefficient K based on the type of learning element, the standard number of exercises N for that learning element, and the experience value X related to the problem approach, = Calculated according to K · X / N. Therefore, it is possible to appropriately update the proficiency level of each learning element having different learning difficulty levels.

According to this embodiment, the upper limit value of the proficiency level based on the skill fixing level is further determined, and the proficiency level is corrected to the upper limit value or less. Accordingly, it is possible to calculate an appropriate level of proficiency in consideration of the user's forgetting about the learning experience.

Further, according to the present embodiment, among the learning elements included in the problem that the user has answered incorrectly, a problem including a learning element with a low proficiency level is preferentially selected as the next problem and the question is given. Therefore, learning of learning elements that the user is not good at can be intensively supported.

It goes without saying that the present disclosure is not limited to the above-described embodiment, and can take various forms. The technology of the present disclosure can be used for learning support of various subjects regardless of arithmetic and mathematics. The specific formula for the proficiency level is not limited to the above embodiment, and is calculated using other parameters other than the above parameters such as correctness of answer, answer time, and degree of browsing of question commentary. Also good. Some of the above parameters may not be used for the proficiency level calculation.

The method for selecting the next problem is not limited to the above embodiment. Various problem selection methods may be employed in which learning elements that support learning are changed according to the level of proficiency between learning elements. For example, the learning support system may be configured to perform problem selection according to the proficiency level in a probabilistic manner so that a learning element with a low proficiency level increases a question probability of a problem including the learning element.

Some or all of the functions of the server device 60 and the database system 100 may be incorporated in the user terminal device 10. The service by the learning support system 1 may be provided to a user terminal device such as a personal computer through a web browser.

The functions of one component in the above embodiment may be distributed among a plurality of components. Functions of a plurality of components may be integrated into one component. A part of the configuration of the above embodiment may be omitted. At least a part of the configuration of the above embodiment may be added to or replaced with the configuration of another embodiment. Any aspect included in the technical idea specified from the wording of the claims is an embodiment of the present disclosure.

Finally, the correspondence between terms will be explained. The process of S120 executed by the processing device 70 corresponds to an example of a process realized by the questioning unit. The processing of S160 and S170 executed by the processing device 70 corresponds to an example of processing realized by the update unit. The process of S190 executed by the processing device 70 corresponds to an example of a process realized by the selection unit. The process of S350 executed by the processing device 70 corresponds to an example of a process realized by the determination unit.

Claims (14)

A question section configured to present a first question selected from a plurality of questions through a display device;
An update unit configured to update the learner's proficiency data stored in a storage device based on an effort through the learner's input device for the first question presented by the question unit;
Based on the learner's proficiency data stored in the storage device, a selection unit configured to select a second question to be newly given to the questioning unit from the plurality of questions;
With
The proficiency data indicates the proficiency of the learner for each of a plurality of learning elements,
At least some of the plurality of questions includes two or more of the plurality of learning elements;
At least some of the plurality of learning elements are included in two or more of the plurality of questions,
The update unit is configured to update the proficiency level of one or more learning elements including at least one learning element included in the first problem based on the learner's approach to the first problem. Learning support system that updates degree data. The learning support system according to claim 1,
The one or more learning elements include the at least one learning element included in the first problem, another at least one learning element related to the at least one learning element included in the first problem, and Including
The said update part is a learning assistance system which updates the said proficiency level data based on the data which show the relationship between these learning elements. The learning support system according to claim 1 or 2,
Each of the plurality of learning elements has a standard number of exercises,
For the one or more learning elements, the updating unit sets the proficiency level of the corresponding learning element for each learning element, and the change in the proficiency level is smaller as the number of standard exercises determined for the corresponding learning element is larger. Learning support system to be updated. The learning support system according to any one of claims 1 to 3,
The updating unit calculates an experience value corresponding to the learner's approach to the first problem, and learns the experience value using an individual conversion formula for each learning element with respect to the one or more learning elements. A learning support system that converts a proficiency level equivalent value and adds the proficiency level equivalent value to the proficiency level of the corresponding learning element, thereby updating the proficiency level of the corresponding learning element. The learning support system according to claim 4,
The conversion formula is configured to convert the experience value to the proficiency equivalent value so that the more the number of standard exercises defined for the corresponding learning element is, the lower the proficiency equivalent value is. Learning support system. The learning support system according to any one of claims 1 to 5,
The update unit is a learning support system that updates the learner's proficiency data based on at least one of the learner's answer behavior and correct answer confirmation behavior through the input device as the effort. The learning support system according to any one of claims 1 to 6,
The updating unit determines, based on whether the learner's answer is correct or not, for each learning element, an addition amount for the proficiency level of the corresponding learning element, and the determined addition amount is A learning support system that updates the proficiency level of the corresponding learning element by adding to the proficiency level of the corresponding learning element. The learning support system according to claim 7,
The said update part is a learning assistance system which determines the said addition amount further based on at least one of the said learner's answer time and the said student's browsing degree of the question commentary displayed by the said display device after an answer. The learning support system according to any one of claims 1 to 8,
A determination unit that determines a degree of fixation of the skill corresponding to each of the one or more learning elements in the learner, and the update unit fixes each of the proficiency levels of the one or more learning elements. It is configured to update within the range below the upper limit corresponding to the degree,
The learning support system is such that the upper limit value is larger as the fixing degree is higher. The learning support system according to any one of claims 1 to 9,
The learning selection system, wherein the selection unit selects the second problem so that a problem including a learning element having a lower learning level of the learner among the plurality of problems is preferentially selected. The learning support system according to claim 10,
The selection unit gives priority to a problem including a learning element having a lower proficiency among a plurality of learning elements related to the first problem when the learner answers the first problem incorrectly. A learning support system for selecting the second problem to select. The learning support system according to claim 11,
The selection unit is configured to correct the proficiency level after the correction based on the corrected proficiency level corrected based on the proficiency levels of the plurality of learning elements related to the corresponding learning element. A learning support system for selecting the second problem so that a problem including a learning element having a lower degree is selected with higher priority. A computer program for causing a computer to function as the questioning unit, the updating unit, and the selection unit provided in the learning support system according to any one of claims 1 to 12. A learning support method executed by a computer,
The first question selected from multiple questions is given through the display device,
Updating the learner's proficiency data stored in a storage device based on the learner's input device approach to the first problem;
Selecting a second question to be newly set out of the plurality of questions based on the learner's proficiency data stored in the storage device;
Including
The proficiency data indicates the proficiency of the learner for each of a plurality of learning elements,
At least some of the plurality of questions includes two or more of the plurality of learning elements;
At least some of the plurality of learning elements are included in two or more of the plurality of questions,
The updating is based on the learner's approach to the first problem, so as to update the proficiency level of one or more learning elements including at least one learning element included in the first problem. A learning support method including updating proficiency data.

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