JP2018163181A - Educational material provision system, educational material provision method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To overcome an existing problem that it is hard to present a proper learning material, which improves a scholastic ability, to each pupil.SOLUTION: An educational material provision system includes a solution characteristic information group storage unit that stores a group of pieces of solution characteristic information including two or more pairs of a problem identifier and solution characteristic information representing naturalness or unnaturalness of a correct or incorrect answer of a problem, a problem determination unit that determines a problem, which satisfies predetermined conditions, for each pupil using the group of pieces of solution characteristic information, an educational material acquisition unit that acquires a learning material associated with the problem determined by the problem determination unit, and a learning material output unit that outputs the learning material acquired by the educational material acquisition unit. Owing to the educational material provision system, a proper learning material that improves a scholastic ability can be presented to each pupil.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a teaching material proposing device that proposes learning materials to students.

  Conventionally, it is possible to solve the problem that uniform teaching materials cannot cope with academic achievement difference, avoid high cost, and reduce the burden of teachers teaching students, There has been a learning material creation system for the purpose of providing learning materials (see, for example, Patent Document 1).

JP 2003-58044 A

  However, in the conventional teaching material proposing device, it is difficult to present appropriate learning materials for improving the academic ability for each student.

  The teaching material proposing apparatus according to the first aspect of the invention provides answer characteristic information having two or more sets of a problem identifier for identifying a problem and answer characteristic information indicating the correctness of an answer to the problem or the unnaturalness or naturalness of an incorrect answer. An answer characteristic information group storage unit in which a group is stored for each student, an answer characteristic information group for each student, a problem determination unit that determines a problem that satisfies a predetermined condition, and a problem determination unit The teaching material proposal device includes a learning material acquisition unit that acquires a learning material corresponding to a problem, and a learning material output unit that outputs the learning material acquired by the learning material acquisition unit.

  With such a configuration, it is possible to present, for each student, appropriate learning materials that improve academic ability using information such as correct answers or unnatural answers.

  Further, in the teaching material proposing device according to the second aspect of the invention, with respect to the first aspect of the invention, the problem determination unit uses answer characteristic information indicating that the problem is an unnatural problem that is unnatural enough to satisfy a predetermined condition. It is a teaching material proposal device that determines a corresponding problem.

  With this configuration, it is possible to efficiently improve scholastic ability by giving a student a learning material corresponding to a problem that is an incorrect answer with an unnatural answer.

  The teaching material proposing device according to the third aspect of the present invention corresponds to the answer characteristic information indicating that the problem determination unit is an unnaturally correct problem that satisfies a predetermined condition with respect to the first aspect. It is a teaching material proposal device that determines the problem to be performed.

  With this configuration, it is possible to efficiently improve scholastic ability by providing students with learning materials corresponding to problems that are unnatural answers.

  In the teaching material proposing device according to the fourth aspect of the present invention, the answer characteristic information is an answer indicating the degree of naturalness or unnaturalness of a correct or incorrect answer to any one of the first to third inventions. It is a teaching material suggestion device that is a characteristic value.

  With this configuration, it is possible to present, for each student, learning materials that efficiently improve academic ability using the answer characteristic values.

  Further, in the teaching material proposing device according to the fifth aspect of the present invention, with respect to the fourth aspect, the problem determination unit acquires a problem identifier of the problem corresponding to the answer characteristic value of the absolute value included in the predetermined range. It is a teaching material suggestion device.

  With this configuration, it is possible to present, for each student, a learning material that efficiently improves academic ability using the absolute value of the answer characteristic value.

  The teaching material proposing device according to the sixth aspect of the present invention is the teaching material in which the answer characteristic value is an ESR calculated by using correct / incorrect information for each question of two or more students in the fourth or fifth aspect. Proposed device.

  With this configuration, it is possible to present, for each student, learning materials that efficiently improve academic ability using ESR.

  Further, the teaching material proposing device according to the seventh aspect of the invention relates to rank information in which rank information, which is information related to the rank to which the student belongs, is stored for each student instead of the answer characteristic information group storage unit. A storage unit, and a correct rate information storage unit that stores correct rate information that is information on the correct rate for each question and each rank, and the problem determination unit determines a problem using the answer characteristic information group Instead, for each student and each problem, the teaching material suggesting apparatus determines the problem that satisfies the predetermined condition based on the rank information corresponding to the student and the correct answer rate information corresponding to the problem.

  With this configuration, it is possible to present, for each student, a learning material that efficiently improves academic ability using the student rank and the correct answer rate.

  Further, in the teaching material proposing device according to the eighth aspect of the invention, the rank information is a rank identifier for identifying the rank to which the student belongs, and the problem determination unit is provided with a student rank identifier for each student. This is a teaching material proposing device that determines a problem in which the correct answer rate information of the rank corresponding to is high enough to satisfy a predetermined condition and the student is an incorrect answer.

  With this configuration, it is possible to present, for each student, a learning material that efficiently improves academic ability using the student rank and the correct answer rate.

  Further, in the teaching material proposing device of the ninth aspect of the invention, the rank information is a rank identifier for identifying the rank to which the student belongs, and the problem determination unit is provided with a student rank identifier for each student. This is a teaching material proposing device that determines a problem in which the correct answer rate information of a rank lower than the rank corresponding to is high enough to satisfy a predetermined condition and the student has an incorrect answer.

  With this configuration, it is possible to present, for each student, a learning material that efficiently improves academic ability using the student rank and the correct answer rate.

  Further, in the teaching material proposing device according to the tenth aspect of the invention, the correct answer rate information is calculated from the correctness / incorrectness information for each question of two or more students using latent rank theory. It is a teaching material suggestion device with an estimated correct answer rate.

  With this configuration, it is possible to present, for each student, a learning material that efficiently improves academic ability using the estimated accuracy rate obtained using the latent rank theory.

  The teaching material proposing device according to the eleventh aspect of the present invention is the first aspect of the invention, in which the problem determination unit includes a rank information storage unit in which rank information, which is information about the rank to which the student belongs, is stored for each student; A correct rate information storage unit that stores correct rate information that is information on the correct rate for each question and each rank, and the problem determination unit determines the problem using the answer characteristic information group For each student and each problem, the teaching material suggesting apparatus determines a problem in which rank information corresponding to the student and correct rate information corresponding to the problem satisfy a predetermined condition.

  With this configuration, the learning material can be presented to each student using both of the two algorithms.

  Further, the teaching material proposing device of the twelfth invention uses the first algorithm for determining a problem using the answer characteristic information group, rank information, and accuracy rate information, relative to the eleventh invention. Priority information indicating priority between the second algorithm for determining the problem is stored, and the problem determination unit learns less than the number satisfying a predetermined condition according to the priority indicated by the priority information. This is a teaching material proposal device that determines a problem so that the teaching material is output.

  With this configuration, it is possible to present a learning material that efficiently improves academic ability for each student using both of the two algorithms.

  According to the teaching material proposing device of the present invention, it is possible to present an appropriate learning material for improving scholastic ability for each student.

Block diagram of teaching material suggestion apparatus 1 in the first embodiment The flowchart explaining the operation example of the teaching material suggestion apparatus 1 A flowchart for explaining an example of the problem determination process Figure showing the learning material management table Figure showing the same errata information management table The figure which shows the same answer characteristic information group management table Block diagram of teaching material suggestion apparatus 2 in Embodiment 2 The flowchart explaining the example of the problem determination process of the teaching material proposal device 2 Figure showing the same errata information management table Figure showing the accuracy rate information management table Figure showing the rank information management table Block diagram of the teaching material proposal device 3 Overview of the computer system in the above embodiment Block diagram of the computer system

  Hereinafter, embodiments of a teaching material suggestion apparatus and the like will be described with reference to the drawings. In addition, since the component which attached | subjected the same code | symbol in embodiment performs the same operation | movement, description may be abbreviate | omitted again.

(Embodiment 1)
In the present embodiment, answer characteristic information indicating an incorrect answer of a student's answer or an unnaturalness or naturalness of a correct answer is stored for each of one or more questions, and the appropriate information is used for each student using such information. A teaching material proposing device for proposing learning materials will be described.

  FIG. 1 is a block diagram of a teaching material proposal device 1 according to the present embodiment. The teaching material proposal device 1 includes a storage unit 11, a processing unit 12, and an output unit 13.

  The storage unit 11 includes a learning material storage unit 111, an answer characteristic information group storage unit 112, and a correct / incorrect information storage unit 113. The processing unit 12 includes a problem determination unit 121, a learning material acquisition unit 122, and an answer characteristic value calculation unit 123. The output unit 13 includes a learning material output unit 131.

  The storage unit 11 can store various types of information. The various information includes, for example, a learning material described later, an answer characteristic information group described later, correctness information described later, one or more determination conditions, and the like. The determination condition is a condition for the problem determination unit 121 described later to determine a problem. The determination condition is a condition using the answer characteristic information. It is preferable that the determination condition is a condition using an expression having answer characteristic information as a parameter. The determination condition is, for example, “−0.84 <= answer characteristic value <= − 2.0”.

  The learning material storage unit 111 may store one or more learning materials. The learning material is usually stored in association with the problem. A question is a question answered by a student. The learning material is stored, for example, in pairs with a problem identifier for identifying a problem. The learning material may be stored, for example, in pairs with a group identifier that identifies the problem group. In such a case, it is stored in association with the problem. The learning material may be a problem or an explanatory material. The learning material may be anything as long as it is information to be learned.

  In the answer characteristic information group storage unit 112, an answer characteristic information group is stored for each student. Usually, an answer characteristic information group is stored in a pair with a student identifier for identifying a student. The answer characteristic information group includes, for example, two or more sets of question identifiers and answer characteristic information. The answer characteristic information is information indicating unnaturalness or naturalness of a correct answer or an incorrect answer to a question. The answer characteristic information may be an answer characteristic value, information indicating that it is natural (for example, “0”), or information indicating that it is unnatural (for example, “1”). The answer characteristic information may be information regarding the unnaturalness of the answer, and the content, acquisition method, etc. are not limited. For example, it is preferable that the answer characteristic information includes information indicating whether the answer to the question is a correct answer or an incorrect answer. The answer characteristic value is information indicating the degree of naturalness or degree of unnaturalness of a correct or incorrect answer. The answer characteristic value is preferably, for example, ESR described later. The ESR is a value calculated from correct / incorrect information for each question of two or more students. The ESR is, for example, a value calculated by an answer characteristic value calculation unit 123 described later. Note that ESR may be referred to as a standard residual. The answer characteristic value may be information given manually by a person. A positive characteristic value indicates that the answer is correct, a negative value indicates that the answer is incorrect, and an answer characteristic value that indicates that the larger the absolute value is, the more unnatural the answer characteristic value is. .

  The correct / incorrect information storage unit 113 stores correct / incorrect information for each student and each problem. The correct / incorrect information is information indicating that the answer is correct (for example, “1”) or information indicating that the answer is incorrect (for example, “0”). In the correct / incorrect information storage unit 113, correct / incorrect information associated with the student identifier and the problem identifier is stored.

  The processing unit 12 performs various processes. The various types of processes are, for example, processes of the problem determination unit 121, the learning material acquisition unit 122, the answer characteristic value calculation unit 123, and the like.

  For each student, the problem determination unit 121 determines a problem that satisfies a predetermined condition using the answer characteristic information group corresponding to the student. The algorithm of the problem determination unit 121 is an algorithm for determining a problem using the answer characteristic information group, and is referred to as a first algorithm.

  The question determination unit 121 determines a question from one or more questions corresponding to the answer characteristic information group stored in the answer characteristic information group storage unit 112. That is, the question determination unit 121 determines a question from one or more questions answered by the student. In addition, determining a problem is equivalent to detecting a problem. Usually, a problem identifier is acquired, but information on a link to a problem may be acquired. For example, when the answer characteristic value included in the answer characteristic information group paired with the student identifier of interest is a value satisfying a predetermined condition, the question determination unit 121 is one or more paired with the answer characteristic value. Are obtained from the answer characteristic information group storage unit 112.

  For example, for each student, the problem determination unit 121 determines a problem corresponding to answer characteristic information indicating that the problem is an unnatural incorrect answer that satisfies a predetermined condition.

  For example, for each student, the problem determination unit 121 determines a problem corresponding to answer characteristic information indicating that the degree of unnaturalness is an incorrect answer that satisfies a predetermined condition.

  The question determination unit 121 is, for example, information indicating that the answer characteristic information included in the answer characteristic information group paired with the student identifier of interest is information indicating an incorrect answer, and satisfies the condition that the degree of the incorrect answer is predetermined. Detects large problems. The question determination unit 121 is, for example, information indicating that the answer characteristic information included in the answer characteristic information group paired with the student identifier of interest is information indicating an incorrect answer, and an answer characteristic value indicating the degree of being an incorrect answer. If the absolute value is greater than or greater than the threshold, one or more question identifiers that are paired with the answer characteristic value are acquired.

  In addition, the question determination unit 121 is, for example, the answer characteristic that the answer characteristic information included in the answer characteristic information group that is paired with the focused student identifier is information indicating an incorrect answer and is the degree of being an incorrect answer. When the absolute value of the value is within a predetermined range, one or more question identifiers that are paired with the answer characteristic value are acquired.

  The problem determination unit 121 determines that the answer characteristic information included in the answer characteristic information group paired with the student identifier of interest is information indicating an incorrect answer, and the degree of the incorrect answer is in a predetermined range. It is preferable to detect. The question determination unit 121 is, for example, information indicating that the answer characteristic information included in the answer characteristic information group paired with the student identifier of interest is information indicating an incorrect answer, and an answer characteristic value indicating the degree of being an incorrect answer. If the absolute value is in a predetermined range, one or more question identifiers that are paired with the answer characteristic value are acquired. If the answer characteristic value, which is the degree of unnaturalness, is greater than or equal to or greater than the threshold value, it is determined that the student has made a careless mistake for the problem corresponding to the answer characteristic value. It is preferable that the corresponding learning material is not selected. That is, it is preferable that a learning material corresponding to a problem of incorrect answer with a certain degree of unnaturalness is selected. The question determination unit 121 acquires, for example, one or more question identifiers of a question whose answer characteristic value (here, ESR) satisfies “−0.4 <= ESR <= − 0.8”.

  The problem determination unit 121 determines a problem corresponding to answer characteristic information indicating that the problem is an unnatural answer that satisfies a predetermined condition for each student. The predetermined condition is, for example, that the absolute value of the answer characteristic value is greater than or equal to the threshold value. In such a case, for example, it is suspected that the correct answer has been made due to a chance such as cheating or wisdom.

  For example, for each student, the question determination unit 121 acquires a question identifier of a question corresponding to an absolute answer characteristic value included in a predetermined range.

  The learning material acquisition unit 122 acquires one or more learning materials corresponding to the problem determined by the problem determination unit 121. The learning material acquisition unit 122 acquires, for example, one or more learning materials that are paired with one or more problem identifiers acquired by the problem determination unit 121 from the learning material storage unit 111. The learning material acquisition unit 122 may acquire learning materials from an external server device (not shown).

  The answer characteristic value calculation unit 123 calculates an answer characteristic value. The answer characteristic value calculation unit 123 normally calculates the answer characteristic value for each student and each question by using the correct / wrong information for each student and each question in the correct / wrong information storage unit 113 and stores it in the answer characteristic information group storage unit 112. To do. As a process of the answer characteristic value calculation unit 123, a PROX method (Approximation Procedure) in IRT (Item Response Theory) can be used. This method was published by Leslie Cohen of the University of Southampton in 1972 and described in the paper “BEST TEST DESIGN (p21-23)” (1979) by Benjamin D. Wright of the University of Chicago.

For example, the answer characteristic value calculation unit 123 calculates a standard residual (ESR) that is an example of the answer characteristic value by the following (1) to (12).
(1) The answer characteristic value calculation unit 123 uses the correct / incorrect information in the correct / incorrect information storage unit 113 to calculate the correct answer rate and the incorrect answer rate of each student (hereinafter also referred to as a subject).
(2) The answer characteristic value calculation unit 123 calculates LC. For each subject according to the arithmetic expression “LC. = LN (correct answer rate / incorrect answer rate)”. (Logit collect) is calculated. The answer characteristic value calculation unit 123 also calculates “LC ^ 2” which is the square value of logit collect. The logit collect is information for estimating the ability (θ) of the subject. LN is a natural logarithm. In addition, the answer characteristic value calculation unit 123 is usually LC. Etc. are not calculated.
(3) The answer characteristic value calculation unit 123 uses the correct / incorrect information in the correct / incorrect information storage unit 113 to calculate a correct answer rate and an incorrect answer rate for each item. The item is a problem.
(4) The answer characteristic value calculation unit 123 calculates the L.I.c. = LN (incorrect answer rate / correct answer rate) for each item according to the arithmetic expression “L. Ic. (Logit Incorrect) is calculated. The logit collect is information for estimating the item difficulty (b) which is an index of the difficulty of the problem.
(5) The answer characteristic value calculation unit 123 is an L.A. Ic. (Ave. (L. Ic.)) Is calculated.
(6) For each item, the answer characteristic value calculation unit 123 calculates I.I.C. = L.Ic.-Ave. (L.Ic.) From I. C. (Initial item difficulty) is calculated. The answer characteristic value calculation unit 123 also calculates a square value (I.I.C. ^ 2) of the initial item difficulty level for each item.
(7) The answer characteristic value calculation unit 123 calculates the average of LC and the sum of LC ^ 2. The answer characteristic value calculation unit 123 also calculates the square value of the average LC value (LC average ^ 2).
(8) The answer characteristic value calculation unit 123 calculates the unbiased unbiased variance “V” by the arithmetic expression “V = (sum of LC ^ 2) −number of subjects × LC average ^ 2 / (number of subjects−1)”. .
(9) The answer characteristic value calculation unit 123 calculates the item unbiased variance “U” using the arithmetic expression “sum of U = I.I.C. ^ 2 / (number of items−1)”.
(10) The answer characteristic value calculation unit 123 uses the capability unbiased variance “V” and the item unbiased variance “U” to calculate the equation “θEF = ((1 + U / 2.89) / (1−UV / 8.35)”. )), And the answer characteristic value calculation unit 123 uses the capability unbiased variance “V” and the item unbiased variance “U” to calculate the equation “I.EF = The item expansion element (I.EF) is calculated by “(1 + V / 2.89) / (1−UV / 8.35)) square root.” 2.89 is a square value of 1.7. Yes, 8.35 is the fourth power of 1.7, and 1.7 is an adjustment constant in IRT (Item Response Theory), which corresponds to the symbol “D”.
(11) The answer characteristic value calculation unit 123 calculates the ability value (Fθ) for each subject using the arithmetic expression “Finalθ (Fθ) = LC × θEF”. Moreover, the answer characteristic value calculation unit 123 calculates the item difficulty (Fb) for each item by the arithmetic expression “Fb = IIC × IEF”.
(12) The answer characteristic value calculation unit 123 uses the arithmetic expression “(2 * correction information-1) * EXP (((2 * correction information-1) * (Fb−Fθ)) / 2))” for each subject. And ESR is calculated for each item. Here, “1” is substituted for correct / incorrect information, and “0” is substituted for incorrect.

  The output unit 13 outputs various types of information. The various types of information are, for example, learning materials. Here, output refers to display on a display, projection using a projector, printing with a printer, sound output, transmission to an external device, storage in a recording medium, and output to other processing devices or other programs. It is a concept that includes delivery of processing results.

  The learning material output unit 131 outputs one or more learning materials acquired by the learning material acquisition unit 122.

  The storage unit 11, the learning material storage unit 111, the answer characteristic information group storage unit 112, and the correct / incorrect information storage unit 113 are preferably non-volatile recording media, but can also be realized by volatile recording media.

  The process in which information is stored in the storage unit 11 or the like is not limited. For example, information may be stored in the storage unit 11 or the like via a recording medium, information transmitted via a communication line or the like may be stored in the storage unit 11 or the like, Alternatively, information input via the input device may be stored in the storage unit 11 or the like.

  The processing unit 12, the problem determination unit 121, the learning material acquisition unit 122, and the answer characteristic value calculation unit 123 can be usually realized by an MPU, a memory, or the like. The processing procedure of the processing unit 12 or the like is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The output unit 13 and the learning material output unit 131 may or may not include an output device such as a display or a speaker. The output unit 13 and the like can be realized by output device driver software, or output device driver software and an output device.

  Next, an operation example of the teaching material proposal device 1 will be described with reference to the flowchart of FIG.

  (Step S201) The processing unit 12 substitutes 1 for a counter i.

  (Step S <b> 202) The processing unit 12 determines whether there is an i-th student who should provide the teaching material. If the i-th student exists, the process goes to step S203, and if the i-th student does not exist, the process ends. Whether or not the i-th student exists is determined, for example, based on whether or not the i-th student identifier exists in the storage unit 11.

  (Step S <b> 203) The question determination unit 121 reads the answer characteristic information group that is paired with the student identifier of the i-th student from the answer characteristic information group storage unit 112.

  (Step S204) The problem determination unit 121 performs a problem determination process. An example of the problem determination process will be described with reference to the flowchart of FIG.

  (Step S205) The learning material acquisition unit 122 substitutes 1 for the counter j.

  (Step S206) The learning material acquisition unit 122 determines whether or not the j-th problem identifier exists among the problem identifiers acquired in step S204. If the jth problem identifier exists, the process goes to step S207, and if the jth problem identifier does not exist, the process goes to step S209.

  (Step S207) The learning material acquisition unit 122 acquires one or more learning materials that are paired with the j-th problem identifier from the learning material storage unit 111.

  (Step S208) The learning material acquiring unit 122 increments the counter j by 1. The process returns to step S206.

  (Step S209) The learning material acquisition unit 122 selects one or more learning materials to be output from the learning materials acquired in step S207. Here, the learning material acquisition unit 122 may select all learning materials acquired in step S207 as learning materials to be output. Further, it is preferable that the learning material acquisition unit 122 selects a predetermined number of learning materials. In addition, the learning algorithm selection algorithm does not matter. For example, the learning material acquisition unit 122 selects a learning material corresponding to a problem with a low correct answer rate in preference to a learning material corresponding to a problem with a high correct answer rate. The learning material acquisition unit 122 selects, for example, a learning material having a priority attribute value based on the attribute value (problem, description, etc.) of the learning material. For example, when giving priority to the explanatory material over the problem, the educational material acquisition unit 122 selects, for example, the learning material having the explanatory attribute value with priority.

  (Step S210) The learning material output unit 131 outputs one or more learning materials selected in step S209. The learning material to be output is a learning material for the i-th student. Here, the output is, for example, printing, transmission to the i-th student's terminal, or the like.

  (Step S211) The processing unit 12 increments the counter i by one. The process returns to step S202.

  In the flowchart of FIG. 2, for example, the process in step S209 may not be performed.

  Next, an example of the problem determination process in step S204 will be described using the flowchart of FIG.

  (Step S301) The problem determination unit 121 acquires the determination conditions stored in the storage unit 11.

  (Step S302) The problem determining unit 121 assigns 1 to a counter i.

  (Step S303) The question determination unit 121 determines whether or not the i-th question exists among the questions answered by the students. When the i-th problem exists, the process goes to step S304, and when the i-th problem does not exist, the process returns to the upper process. Whether or not the i-th problem exists is determined, for example, based on whether or not the i-th problem identifier exists in the learning material storage unit 111.

  (Step S304) The question determination unit 121 acquires answer characteristic information that is paired with the student identifier of the student of interest and the question identifier of the i-th question from the answer characteristic information group storage unit 112. Note that the student identifier of the student of interest is the student identifier of the i-th student in the flowchart of FIG.

  (Step S305) The question determination unit 121 determines whether or not the answer characteristic information acquired in step S304 matches the determination condition acquired in step S301. If the determination condition is met, the process goes to step S306. If the determination condition is not met, the process goes to step S307.

  (Step S306) The problem determination unit 121 temporarily stores the i-th problem identifier in a buffer (not shown).

  (Step S307) The problem determination unit 121 increments the counter i by one. The process returns to step S302.

  Hereinafter, a specific operation of the teaching material suggesting apparatus 1 in the present embodiment will be described.

  Now, it is assumed that the learning material storage unit 111 stores the learning material management table shown in FIG. The learning material management table is a table for managing information on learning materials to be proposed to students when the problem determination unit 121 acquires a problem identifier. The learning material management table manages records having problem identifiers and one or more learning materials. The question identifier is information for identifying a question answered by the student. The learning material is information of the learning material that is output when the problem identifier to be paired is selected by the problem determination unit 121. The learning material includes, for example, information on a problem similar to the problem identified by the problem identifier, information for acquiring knowledge necessary for solving the problem identified by the problem identifier, and the like. The learning material information here is the file name of the file in which the learning content is stored, but it goes without saying that the information format of the learning material does not matter. In addition, here, it is assumed that the file storing the learning content is stored in the storage unit 11 or an external server device, for example.

  Further, it is assumed that the correct / incorrect information storage unit 113 stores the correct / incorrect information management table shown in FIG. The correct / incorrect information management table is a result of the test, and is a table in which correct / incorrect information for each student and each question is managed. This correct / incorrect information management table shows correct / incorrect results obtained by each of five subjects answering five questions. The test subject has the same meaning as “student”. The problem may be referred to as “item” or “Item”. The errata information management table is a two-axis table of subjects and problems. The attribute values in the correct / incorrect information management table in FIG. 5 indicate that the questions “Item 1”, “Item 2”, “Item 3”, “Item 4”, and “Item 5” of each subject identified by the student identifiers “1” to “5” were correct answers. Information indicating whether the answer was incorrect. Here, the correct / incorrect information when the answer is correct is “1”, and the correct / incorrect information when the answer is incorrect is “0”. That is, the test subject “1” has an incorrect answer only for the “Item 3” problem.

  Further, the answer characteristic value calculation unit 123 calculates the answer characteristic information value (ESR) for each student and for each item by using the above-described correct / wrong information management table of FIG. Accumulate in the storage unit 112. A table calculated by the answer characteristic value calculation unit 123 and accumulated in the answer characteristic information group storage unit 112 is shown in FIG. 6 is referred to as an answer characteristic information group management table. The answer characteristic information group management table is a table for managing ESR of each question of each subject identified by “1” to “5”. In FIG. 6, the ESR of Item 1 of the subject “1” is “0.182158”, and the ESR of Item 3 is “−2.09434”. In FIG. 6, when the value of ESR is positive, a correct answer is indicated, and when it is negative, an incorrect answer is indicated. The absolute value of ESR indicates unnaturalness, and the larger the absolute value, the more unnatural. In FIG. 6, incorrect answers (here, “−0.8” or less) that are unnatural enough to satisfy the conditions are shaded. An incorrect answer with a large unnatural degree may be a careless mistake, for example. Moreover, the correct answer with a large unnatural degree can be considered, for example, cheating or intuition.

  Further, it is assumed that the storage unit 11 stores the determination condition “−0.84 <= ESR <= − 2.5”. In other words, in this specific example, in order to propose a learning material with a high learning effect to the subject, an unnatural wrong answer problem is determined to some extent, and the learning material corresponding to the problem identifier of the determined problem is output. I will do it. The determination condition “−0.84 <= ESR <= − 2.5” is a condition for selecting a question of an incorrect answer that is somewhat unnatural.

  Further, it is assumed that the storage unit 11 stores the maximum learning material number “3” as the learning material output condition. That is, the teaching material proposing device 1 does not propose four or more learning materials to one student at a time.

  In such a situation, the teaching material proposal device 1 operates as follows. That is, the problem determination unit 121 applies each ESR of the subject “1” to the determination condition “−0.84 <= ESR <= − 2.5”, and the problem identifier “Item 3” is applied to the subject “1”. To get. Then, the learning material acquisition unit 122 acquires the learning material identifiers “basic problem 31.txt” and “application problem 31.txt” of the learning material to be paired with the problem identifier “Item3” from the learning material management table. Then, the learning material acquiring unit 122 acquires a file identified by the file names “basic problem 31.txt” and “application problem 31.txt” from the storage unit 11. Then, the learning material output unit 131 transmits two files to the subject “1”. Here, it is assumed that the learning material output unit 131 stores the email address of each subject. Here, the output is transmission, but it may be displayed or printed.

  In addition, the problem determination unit 121 applies each ESR of the subject “2” to the determination condition, and acquires the problem identifier “Item 3” for the subject “2”. Then, the learning material acquisition unit 122 acquires the learning material identifiers “basic problem 31.txt” and “application problem 31.txt” of the learning material to be paired with the problem identifier “Item3” from the learning material management table. Then, the learning material acquiring unit 122 acquires a file identified by the file names “basic problem 31.txt” and “application problem 31.txt” from the storage unit 11. Then, the learning material output unit 131 transmits two files to the subject “2”.

  In addition, the problem determination unit 121 applies each ESR of the subject “3” to the determination condition, and does not acquire a problem identifier for the subject “3”. Therefore, the learning material is not transmitted to the subject “3”.

  Further, the problem determination unit 121 applies each ESR of the subject “4” to the determination condition, and does not acquire a problem identifier for the subject “4”. Therefore, the learning material is not transmitted to the subject “4”. Note that the ESR of Item 2 of the subject “4” is “−7.42235” indicates that it is very unnatural for the subject “4” to mistake the Item 2 problem. And here, the learning teaching material corresponding to the problem of an unnatural incorrect answer is not proposed for the efficiency of learning.

  Furthermore, the problem determination unit 121 applies each ESR of the subject “5” to the determination condition, and acquires the problem identifiers “Item1” and “Item4” for the subject “5”. Then, the learning material acquisition unit 122 sets the learning material identifiers “basic problem 11.txt”, “basic problem 12.txt”, “reference book 11.jpeg”, and “applied problem 41.” which are paired with the problem identifiers “Item1” and “Item4”. txt ”is acquired from the learning material management table. Next, the learning material acquisition unit 122 selects three learning materials from the four learning materials so as to satisfy the learning material output condition “3 or less”. Here, for example, it is assumed that the learning material acquisition unit 122 has selected “basic problem 11.txt”, “basic problem 21.txt”, and “reference book 11.jpeg”. Then, the learning material acquisition unit 122 acquires, from the storage unit 11, a file identified by the file names of “basic problem 11.txt”, “basic problem 21.txt”, and “reference book 11.jpeg”. Then, the learning material output unit 131 transmits three files to the subject “5”.

  Then, each terminal of the subjects “1”, “2”, and “5” receives and outputs the learning material. Thus, the subjects “1”, “2”, and “5” can effectively learn.

  As described above, according to the present embodiment, an appropriate learning material for improving academic ability can be presented for each student using information such as correct answer or unnatural answer.

  Further, according to the present embodiment, it is possible to efficiently improve academic ability by giving a student a learning material corresponding to a problem that is an incorrect answer of an unnatural answer.

  Further, according to the present embodiment, it is possible to efficiently improve academic ability by giving a student a learning material corresponding to a problem that is an unnatural answer of an answer.

  Further, according to the present embodiment, an appropriate learning material for improving academic ability can be presented for each student using the answer characteristic value.

  Further, according to the present embodiment, an appropriate learning material that improves academic ability can be presented for each student using the absolute value of the answer characteristic value.

  Furthermore, according to the present embodiment, an appropriate learning material for improving academic ability can be presented for each student using ESR.

  Note that the processing in the present embodiment may be realized by software. Then, this software may be distributed by software download or the like. Further, this software may be recorded and distributed on a recording medium such as a CD-ROM. This also applies to other embodiments in this specification. In addition, the software which implement | achieves the teaching material proposal apparatus 1 in this Embodiment is the following programs. In other words, for example, in a recording medium accessible by a computer, this program includes a set of a problem identifier that identifies a problem and answer characteristic information that indicates whether the answer to the problem is correct or incorrect or unnatural or natural. An answer characteristic information group storage unit that stores two or more sets of answer characteristic information groups for each student is provided, and the computer determines a problem that satisfies a predetermined condition using the answer characteristic information group for each student. A program for causing the problem determination unit to function as a learning material acquisition unit that acquires a learning material corresponding to the problem determined by the problem determination unit, and a learning material output unit that outputs the learning material acquired by the learning material acquisition unit. is there.

  Further, in the above program, the problem determination unit is a program that causes a computer to function as determining a problem corresponding to answer characteristic information indicating that the problem is an unnatural incorrect answer that satisfies a predetermined condition. It is preferable to be.

  In the above program, the problem determination unit is a program that causes a computer to function as determining a problem corresponding to answer characteristic information indicating that it is an unnatural answer that satisfies a predetermined condition. That is preferred.

(Embodiment 2)
In the present embodiment, a teaching material suggesting apparatus that determines teaching materials to be proposed to a student using information related to the rank of the student, a correct answer rate for each rank, and correct / incorrect information about the student will be described. The rank is usually a student's academic ability level. The rank may be information that classifies students.

  In the present embodiment, in the rank to which the student belongs, the problem is such that the correct answer rate is high enough to satisfy a predetermined condition, but the student determines the problem that is incorrect and uses the information of the problem, A teaching material proposing device that determines teaching materials to be proposed to students will be described.

  In this embodiment, the correct answer rate of the lower rank student is a problem that is high enough to satisfy a predetermined condition, but the student determines an incorrect answer and uses the information of the problem Next, a teaching material proposing device for determining teaching materials to be proposed to the student will be described.

  FIG. 7 is a block diagram of the teaching material suggestion apparatus 2 in the present embodiment. The teaching material proposal device 2 includes a storage unit 21, a processing unit 22, and an output unit 13. The storage unit 21 includes a learning material storage unit 111, a rank information storage unit 211, a correct answer rate information storage unit 212, and a correct / incorrect information storage unit 113. The processing unit 22 includes a problem determination unit 221, a learning material acquisition unit 122, a correct answer rate calculation unit 223, and a rank determination unit 224.

  The storage unit 21 can store various types of information. The various information includes, for example, learning materials, rank information described later, accuracy rate information described later, correct / incorrect information, one or more determination conditions, and the like. The determination condition is a condition for the problem determination unit 221 to determine a problem. The determination condition is a condition using accuracy rate information. The determination condition is, for example, “the correct answer rate information of the corresponding problem of the rank to which the subject belongs is equal to or greater than the threshold and the subject is incorrect” “the correct answer rate of the corresponding problem in the class immediately below the rank to which the subject belongs. The information is equal to or greater than the threshold and the subject is incorrect. ”“ The correct answer rate information of the corresponding problem in at least one of the classes below the rank to which the subject belongs is greater than or equal to the threshold, and the subject is It must be correct. "

  The rank information storage unit 211 stores rank information for each student for each student. The rank information is information related to the rank to which the student belongs. The rank information is, for example, a rank identifier that identifies the rank to which the student belongs. The rank information may be, for example, probability information for each rank by the student. The rank information storage unit 211 includes, for example, a set of student identifiers and rank identifiers. The number of ranks is less than the number of students.

  The correct answer rate information storage unit 212 stores correct answer rate information that is information on the correct answer rate for each question and each rank. The correct answer rate information storage unit 212 stores, for example, two or more pieces of individual correct answer rate information, which are sets of a problem identifier, a rank identifier, and correct answer rate information. Here, the correct answer rate information is preferably information on the estimated correct answer rate estimated for each problem and for each rank. It is preferable that the correct answer rate information is an estimated correct answer rate calculated using latent rank theory from correct / incorrect information for each question of two or more students. For the latent rank theory, see the paper “Shojima, K. (2011) Japanese Journal of Applied Statistics, 40, 141-156 .:“ Local dependence model in latent rank theory ””. The estimated correct answer rate may be referred to as an estimated correct answer rate. Further, as described above, the correct answer rate information is preferably information on the estimated correct answer rate, but may be an actual correct answer rate (actual correct answer rate) for each question and each rank.

  The processing unit 22 performs various processes. The various processes are processes performed by, for example, the problem determination unit 221, the learning material acquisition unit 122, the correct answer rate calculation unit 223, and the like.

  The problem determination unit 221 determines, for each student, a problem that satisfies a predetermined condition using the correct answer rate information of each problem. The problem determination unit 221 determines a problem that satisfies a predetermined condition using the student rank information and the correct answer rate information of each question. The algorithm of the problem determination unit 221 that determines a problem using rank information and accuracy rate information is referred to as a second algorithm.

  For example, for each student, the problem determination unit 221 determines a problem in which the correct answer rate information of the rank corresponding to the rank identifier of the student satisfies a predetermined condition, and the student is incorrect.

  For example, for each student, the problem determination unit 221 determines a problem in which the correct answer rate information of a rank lower than the rank corresponding to the student rank identifier satisfies a predetermined condition and the student is incorrect. For example, the problem determination unit 221 determines, for each student, a problem in which the correct answer rate information of one rank lower than the rank corresponding to the student rank identifier satisfies a predetermined condition and the student is incorrect. .

  The problem determination unit 221 determines a problem using at least two algorithms, and two or more algorithms have priorities, and less than the number of learning materials satisfying a predetermined condition are output using the priorities. As is, the problem is determined using two or more algorithms. The two algorithms are, for example, the problem determination algorithm (first algorithm) using the answer characteristic information group described in the first embodiment and the correct answer rate information of the rank described in the present embodiment. This is a problem determination algorithm (second algorithm). Note that determining a problem is usually acquiring a problem identifier.

  The correct answer rate calculation unit 223 calculates correct answer rate information. The correct answer rate calculation unit 223 normally calculates the estimated correct answer rate by rank for each question using the correct / incorrect information for each student and each question in the correct / incorrect information storage unit 113 and accumulates it in the correct answer rate information storage unit 212.

The correct answer rate calculation unit 223 calculates the estimated correct answer rate for each rank by the following (1) to (??), for example. The accuracy rate calculation unit 223 calculates the estimated accuracy rate for each rank using, for example, latent rank theory.
(1) Give the rank number. The number of ranks is less than the number of items. Note that a reception unit (not shown) of the teaching material proposal device 2 receives the number of ranks.
(2) A rank reference matrix (referred to as “RRM” where appropriate) is given an initial value. Note that the correct rate calculation unit 223 may calculate the initial value of each rank of the RRM using the number of ranks.
The correct rate calculation unit 223 calculates an RRM initial value for each item of each rank, using an arithmetic expression “RRM initial value = numerical value for identifying a rank / (total rank number + 1)”. The RRM initial value of each item having the same rank is the same value. When the number of ranks is 3, the numerical values for identifying the rank of each rank are, for example, “1”, “2”, and “3”.
(3) The correct answer rate calculation unit 223 calculates the Euclidean distance between the “reference vector” that is a vector for each rank and the “input vector” that is correct / incorrect information of each student for each subject and for each item. Then, the correct rate calculation unit 223 determines a winner rank (w) that is a rank corresponding to a reference vector having the smallest Euclidean distance (closest to the input vector). Note that the reference vector is the RRM initial value only for the first time. From the second time onward (for the second and subsequent subjects), the reference vector is a vector whose element is the value of the immediately preceding Euclidean distance. The Euclidean distance is calculated by “(input vector−reference vector) ^ 2.”
(4) The accuracy rate calculation unit 223 acquires the neighborhood function as follows. That is, the correct answer rate calculation unit 223 calculates a “learning rate coefficient” that brings the reference vector closer to the input vector in order to obtain a neighborhood function. Specifically, the learning rate coefficient is a coefficient (α) for controlling the magnitude of the learning amount and a coefficient (σ) for controlling the influence range of the learning amount. The correct answer rate calculation unit 223 calculates α by an arithmetic expression “α _t = (T−t) α ₁ + (t−1) α _T / T−1” (T = total number of updates, t = update times). To do. In addition, the correct rate calculation unit 223 calculates σ by an arithmetic expression “σ _t = (T−t) σ ₁ + (t−1) σ _T / T−1” (T = total number of updates, t = update times). Is calculated. If α is too small, the data cannot be sufficiently learned. On the other hand, if α is too large, the learning amount remains large and the model is not stable. On the other hand, if σ is too small, learning becomes local and LRs are difficult to be ordered. On the other hand, if σ is too large, learning becomes global and LR becomes uniform. Then, the correct rate calculation unit 223 repeats the above calculation 10 times from t = 1 to t = 10 (that is, T = 10), and calculates “α _t ” and “σ _t ”. For example, if “α ₁ = 1” and “σ ₁ = 1” are used as initial values, α _t is calculated with “α _T = 0.01” and “σ _T = 0.12” as constants. Used when

Next, the correct rate calculation unit 223 calculates the neighborhood function expression “h (t) = αt * Q / N * exp ((− (q−w) ^ 2 / (2 * Q ^ 2 * σt ^ 2))). ”Is substituted for each t-th α and σ to obtain a neighborhood function. N = total number of subjects, Q = number of set ranks. It should be noted that the trend of the input vector is sequentially reflected in the RRM by the neighborhood function.
(5) The correct answer rate calculation unit 223 updates the RRM using the acquired neighborhood function. More specifically, the accuracy rate calculation unit 223 obtains an updated RRM by an arithmetic expression “reference vector + neighbor function (input vector−reference vector)”.
(6) The correct answer rate calculation unit 223 repeats the RRM update described above, and acquires the set TRM (for example, T = 10) RRM as a definite RRM (an estimated correct answer rate for each item by rank). Then, the correct answer rate calculation unit 223 accumulates the acquired confirmed RRM in the correct answer rate information storage unit 212. The accumulated confirmed RRM is, for example, FIG. 10 described later.

  The rank determination unit 224 determines the rank of the one student using the accuracy rate information for each question and each rank in the accuracy rate information storage unit 212 and the correct / incorrect information for each question of one student. More specifically, the rank determination unit 224 uses the correct answer rate information for each question and each rank in the correct answer rate information storage unit 212 and the correct / incorrect information for each question of the one student, to determine the rank of the one student. Get information. Here, the rank information acquired by the rank determination unit 224 may be a rank identifier that identifies the rank of the student, or may be an affiliation probability of the one student for each rank.

The rank determination unit 224 acquires the rank information of one student by, for example, the latent rank theory. More specifically, the rank determination unit 224 acquires rank information of one student by the following (1) and (2), for example.
(1) The rank determination unit 224 calculates rank-specific affiliation probabilities that are student affiliation probabilities for each rank by maximum likelihood estimation from the confirmed RRM (estimated correct answer rate by item for each rank). The rank-specific affiliation probability may be referred to as RMP (rank membership profile). More specifically, the rank determination unit 224 acquires the estimated correct answer rate from the correct rate information storage unit 212 for each item and for each rank, and the estimated correct answer rate (RRE) for the item for which the one student is correct. ) And the one student obtains “1-estimated correct answer rate” (1-RRE) for an incorrect answer item. Then, for each rank, the product of the numerical values (“RRE” or “1-RRE”) of the number of items (this is assumed to be S) is calculated. Next, the sum of S of all ranks (this is set as SW) is calculated. Next, “S / SW” is calculated for each rank. This “S / SW” is the affiliation probability relating to the probability that one student belongs to each rank.
(2) The rank determination unit 224 acquires the rank identifier of the rank having the highest affiliation probability calculated in (1). The rank identified by this rank identifier is the rank to which a student belongs.

  The storage unit 21, the rank information storage unit 211, and the correct answer rate information storage unit 212 are preferably non-volatile recording media, but can also be realized by volatile recording media.

  The process of storing information such as the storage unit 21 is not limited. For example, information may be stored in the storage unit 21 or the like via a recording medium, information transmitted via a communication line or the like may be stored in the storage unit 21 or the like, Alternatively, information input via the input device may be stored in the storage unit 21 or the like.

  The processing unit 22, the problem determination unit 221, the correct answer rate calculation unit 223, and the rank determination unit 224 can be usually realized by an MPU, a memory, or the like. The processing procedure such as the processing unit 22 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  Next, the operation of the teaching material proposal device 2 will be described. The operation of the teaching material proposal device 2 and the operation of the teaching material proposal device 1 differ only in the problem determination process in step S204. The problem determination process of the teaching material proposal device 2 will be described with reference to the flowchart of FIG.

  (Step S <b> 801) The problem determination unit 221 acquires one or more determination conditions stored in the storage unit 21.

  (Step S802) The problem determination unit 221 acquires the student identifier of the student of interest and the rank identifier of the student.

  (Step S803) The problem determination unit 221 substitutes 1 for a counter i.

  (Step S804) The question determination unit 221 determines whether or not the i-th question exists among the questions answered by the students. If the i-th problem exists, the process goes to step S805, and if the i-th problem does not exist, the process returns to the upper process.

  (Step S805) The problem determination unit 221 substitutes 1 for the counter j.

  (Step S806) The problem determination unit 221 determines whether or not the jth determination condition exists. If the jth determination condition exists, the process goes to step S807. If the jth determination condition does not exist, the process goes to step S810.

  (Step S807) The problem determination unit 221 acquires accuracy rate information of ranks that are correct rate information corresponding to the i-th question and can be used under the j-th determination condition.

  (Step S808) The question determination unit 221 acquires correct / incorrect information that is paired with the student identifier of the student of interest and the question identifier of the i-th question, and uses the correct / incorrect information and the correct rate information acquired in step S807. , Applied to the jth determination condition to determine whether or not the jth determination condition is met. If the jth determination condition is met, the procedure goes to step S809, and if the jth determination condition is not met, the procedure goes to step S811.

  (Step S809) The problem determination unit 221 temporarily stores the problem identifier of the i-th problem in a buffer (not shown).

  (Step S810) The problem determination unit 221 increments the counter i by 1. The process returns to step S804.

  (Step S811) The problem determination unit 221 increments the counter j by 1. The process returns to step S806.

  Hereinafter, a specific operation of the teaching material suggesting apparatus 2 in the present embodiment will be described.

  Now, it is assumed that the learning material storage unit 111 stores the learning material management table shown in FIG.

  In this specific example, it is assumed that three ranks are given “R1”, “R2”, and “R3”.

  Further, it is assumed that the correct / incorrect information storage unit 113 stores the correct / incorrect information management table shown in FIG. This correct / incorrect information management table shows correct / incorrect results obtained by each of five subjects answering five questions. Here, the correct / incorrect information when the answer is correct is “1”, and the correct / incorrect information when the answer is incorrect is “0”. That is, the subject “1” has answered all the questions correctly.

  Further, the correct rate calculation unit 223 calculates the estimated correct rate for each item and each rank from the correct / incorrect information management table shown in FIG. 9 and accumulates it in the correct rate information storage unit 212 based on the latent rank theory described above. To do. FIG. 10 shows a correct rate information management table as such information. The correct answer rate information management table is a table showing the correct answer probability of each rank for each item from “Item 1” to “Item 5”. In the accuracy rate information management table, a frame whose threshold is equal to or greater than the threshold (0.5) is shaded.

  Further, it is assumed that the teaching material proposal device 2 has received correct / incorrect information for each item of the subject 9 from the subject 6. The rank determination unit 224 stores the rank information management table shown in FIG. 11 in the rank information through the processing of the rank determination unit 224 described above using the accuracy rate information management table of FIG. 10 and the correctness / incorrectness information of each subject. It is assumed that the data can be stored in the unit 211. It is assumed that the correctness / incorrectness information of the subject 6 is “1, 0, 1, 1, 0”. That is, it is assumed that the subject 6 has the correct answer for Item1, Item3, and Item4, and that Item2 and Item5 are incorrect.

  Furthermore, the storage unit 21 includes a determination condition (1) “the correct rate information of the corresponding problem of the rank to which the subject belongs is 0.5 or more and the subject is incorrect”, and a determination condition (2) It is assumed that two determination conditions are stored: “Accuracy rate information of the corresponding problem in the class immediately below the rank to which the subject belongs is 0.5 or more and the subject is incorrect”.

  In such a situation, the problem determination unit 221 acquires the rank identifier “R1” of the subject 6 from the rank information management table.

  Next, it is assumed that the problem determination unit 221 has acquired the received correct / incorrect information “1, 0, 1, 1, 0” of the subject 6.

  Next, the problem determination unit 221 determines that neither the determination condition (1) nor the determination condition (2) is satisfied for the Items 2 and 5 for which the subject 6 is an incorrect answer, and does not acquire the problem identifier.

  Next, for example, the problem determination unit 221 acquires the rank identifier “R2” of the subject 7 from the rank information management table.

  Next, it is assumed that the problem determination unit 221 has acquired the correct / incorrect information “1, 1, 1, 0, 0” of the subject 7 received.

  Next, the problem determination unit 221 determines that the estimated correct answer probability of the rank “R1” is “0.589” for Item4 in which the subject 7 is an incorrect answer, and therefore determines that the determination condition (2) is satisfied. Item4 "is acquired. The question determination unit 221 determines that the estimated correct probability of the rank “R2” is “0.7434” for the Item 5 in which the subject 7 is an incorrect answer, and therefore determines that the determination condition (1) is satisfied. Is obtained. As described above, the problem determination unit 221 acquires the problem identifiers “Item 4” and “Item 5” for the subject 7.

  Next, the learning material acquisition unit 122 acquires, from the learning material management table, the learning material identifier “application problem 41.txt” of the learning material that is paired with the problem identifier “Item4”. The learning material acquisition unit 122 acquires the learning material identifiers “application problem 51.txt”, “application problem 51.txt”, and “reference book 51.jpeg” of the learning material that is paired with the problem identifier “Item5” from the learning material management table. .

  Next, the learning material acquisition unit 122 selects three learning materials from the four learning materials so as to satisfy the learning material output condition “3 or less”. Here, for example, the learning material acquisition unit 122 prioritizes the decision condition (2) over the decision condition (1) and selects “application problem 51.txt”, “application problem 51.txt”, and “reference book 51.jpeg”. Suppose that It is assumed that priority order information indicating that the determination condition (2) has priority over the determination condition (1) is stored in the storage unit 21. And the learning material acquisition part 122 acquires a learning material using the said priority information.

  Next, the learning material acquisition unit 122 acquires from the storage unit 11 a file identified by the file names of the learning material identifiers “application problem 51.txt”, “application problem 51.txt”, and “reference book 51.jpeg”.

  Then, the learning material output unit 131 transmits three files to the subject 7.

  Thereafter, the same process is performed for other subjects.

  As described above, according to the present embodiment, it is possible to present, for each student, a learning material that improves scholastic ability using the rank of the student and the correct answer rate for each rank.

  In the present embodiment, the problem determination unit 221 uses the answer characteristic information group described in the first embodiment in addition to determining a problem using rank information and accuracy rate information corresponding to the students. You can also determine the problem. Accordingly, the problem determination process described in Embodiment 1 or Embodiment 2 can be supplemented, and teaching materials can be provided to the student with little leakage.

  FIG. 12 is a block diagram of the teaching material proposal device 3 in such a case. The teaching material proposal device 3 includes a storage unit 31, a processing unit 32, and an output unit 13. The storage unit 31 includes a learning material storage unit 111, an answer characteristic information group storage unit 112, a correct / incorrect information storage unit 113, a rank information storage unit 211, and a correct answer rate information storage unit 212. The processing unit 32 includes a problem determination unit 121, a teaching material acquisition unit 122, an answer characteristic value calculation unit 123, a problem determination unit 221, and a correct answer rate calculation unit 223.

  Moreover, the software which implement | achieves the teaching material proposal apparatus 2 in this Embodiment is the following programs. In other words, in this program, the computer-accessible recording medium is a rank information storage unit in which rank information, which is information related to the rank to which the student belongs, is stored for each student, and information on the accuracy rate for each question and each rank. A correct rate information storage unit for storing the correct rate information, and for each student and each question, rank information corresponding to the student and the correct rate information corresponding to the question are determined in advance. A problem determination unit that determines a problem that satisfies a given condition, a learning material acquisition unit that acquires a learning material corresponding to the problem determined by the problem determination unit, and a learning material output that outputs the learning material acquired by the learning material acquisition unit This is a program for functioning as a part.

  FIG. 13 shows the external appearance of a computer that executes the program described in this specification and realizes the teaching material proposing apparatus according to various embodiments described above. The above-described embodiments can be realized by computer hardware and a computer program executed thereon. FIG. 13 is an overview diagram of the computer system 300, and FIG. 14 is a block diagram of the system 300.

  In FIG. 13, the computer system 300 includes a computer 301 including a CD-ROM drive 3012, a keyboard 302, a mouse 303, and a monitor 304.

  14, the computer 301 includes a CD-ROM drive 3012, an MPU 3013, a bus 3014, a ROM 3015, a RAM 3016, and a hard disk 3017. The ROM 3015 stores programs such as a bootup program. The RAM 3016 is connected to the MPU 3013 and temporarily stores application program instructions and provides a temporary storage space. The hard disk 3017 normally stores application programs, system programs, and data. Although not shown here, the computer 301 may further include a network card that provides connection to a LAN.

  A program that causes the computer system 300 to execute the function of the teaching material suggesting apparatus according to the above-described embodiment may be stored in the CD-ROM 3101, inserted into the CD-ROM drive 3012, and further transferred to the hard disk 3017. Further, the program may be transmitted to the computer 301 via a network (not shown) and stored in the hard disk 3017. The program is loaded into the RAM 3016 at the time of execution. The program may be loaded directly from the CD-ROM 3101 or the network.

  The program does not necessarily include an operating system (OS), a third party program, or the like that causes the computer 301 to execute the function of the teaching material suggestion apparatus according to the above-described embodiment. The program need only include the part of the instruction that calls the appropriate module in a controlled manner and achieves the desired result. How the computer system 300 operates is well known and will not be described in detail.

  In the above program, in the step of transmitting information, the step of receiving information, etc., processing performed by hardware, for example, processing performed by a modem or an interface card in the transmission step (only performed by hardware) Processing) is not included.

  Further, the computer that executes the program may be singular or plural. That is, centralized processing may be performed, or distributed processing may be performed.

  Further, in each of the above embodiments, it goes without saying that two or more communication units existing in one apparatus may be physically realized by one medium.

  In each of the above embodiments, each process may be realized by centralized processing by a single device, or may be realized by distributed processing by a plurality of devices. That is, although the teaching material proposal device in each of the above embodiments has been described as a stand-alone device, it may be a server device that communicates with a terminal. In such a case, the output unit 13 communicates with one or more terminals.

  The present invention is not limited to the above-described embodiments, and various modifications are possible, and it goes without saying that these are also included in the scope of the present invention.

  As described above, the teaching material proposing device according to the present invention has an effect that it is possible to present a learning teaching material that efficiently improves academic ability for each student, and is useful as a teaching material proposing device or the like.

1, 2, 3 Teaching Material Proposal Device 11, 21, 31 Storage Unit 12, 22, 32 Processing Unit 13 Output Unit 111 Teaching Material Storage Unit 112 Answer Characteristic Information Group Storage Unit 113 Correct / Incorrect Information Storage Unit 121, 221 Problem Determination Unit 122 Teaching Material Acquisition Unit 123 answer characteristic value calculation unit 131 learning material output unit 211 rank information storage unit 212 accuracy rate information storage unit 223 accuracy rate calculation unit 224 rank determination unit

Claims (16)

An answer characteristic in which an answer characteristic information group having two or more sets of answer characteristic information indicating a problem identifier for identifying a question and an answer answer or an answer answer unnatural or natural for the answer is stored for each student. An information group storage unit;
For each student, using the answer characteristic information group, a problem determination unit that determines a problem that satisfies a predetermined condition;
A learning material acquisition unit for acquiring a learning material corresponding to the problem determined by the problem determination unit;
A teaching material proposal device comprising: a learning material output unit that outputs the learning material acquired by the learning material acquisition unit. The problem determination unit
The teaching material suggestion apparatus according to claim 1, wherein a problem corresponding to answer characteristic information indicating that the problem is an unnatural incorrect answer as long as a predetermined condition is satisfied is determined. The problem determination unit
The teaching material suggestion apparatus according to claim 1, wherein a problem corresponding to answer characteristic information indicating that the problem is an unnatural correct answer that satisfies a predetermined condition is determined. The teaching material suggestion device according to any one of claims 1 to 3, wherein the answer characteristic information is an answer characteristic value indicating a degree of naturalness or unnaturalness of a correct answer or an incorrect answer. The problem determination unit
5. The teaching material proposing device according to claim 4, wherein a problem identifier of a problem corresponding to an answer characteristic value of an absolute value included in a predetermined range is acquired. 6. The teaching material suggestion apparatus according to claim 4, wherein the answer characteristic value is an ESR calculated using correct / incorrect information for each question of two or more students. Instead of the answer characteristic information group storage unit, a rank information storage unit in which rank information, which is information related to the rank to which the student belongs, is stored for each student;
A correct rate information storage unit that stores correct rate information that is information on the correct rate for each question and each rank;
The problem determination unit
Instead of determining a question using the answer characteristic information group, for each student and each question, rank information corresponding to the student and the accuracy rate information corresponding to the question are predetermined conditions. The teaching material suggesting apparatus according to claim 1, wherein a problem that satisfies the above is determined. The rank information is a rank identifier for identifying a rank to which a student belongs,
The problem determination unit
8. The teaching material proposing device according to claim 7, wherein for each student, the correct answer rate information of the rank corresponding to the rank identifier of the student is determined to be high enough to satisfy a predetermined condition, and the problem is determined to be incorrect by the student. The rank information is a rank identifier for identifying a rank to which a student belongs,
The problem determination unit
8. The teaching material proposing device according to claim 7, wherein, for each student, the correct answer rate information of a rank lower than the rank corresponding to the rank identifier of the student is determined to be high enough to satisfy a predetermined condition, and the student determines an incorrect answer. . The accuracy rate information is:
9. The teaching material proposing device according to claim 7 or 8, wherein the teaching correct rate is an estimated correct answer rate calculated using latent rank theory from correct / incorrect information for each question of two or more students. The problem determination unit
A rank information storage unit in which rank information, which is information related to the rank to which the student belongs, is stored for each student;
A correct rate information storage unit that stores correct rate information that is information on the correct rate for each question and each rank;
The problem determination unit
In addition to determining a question using the answer characteristic information group, for each student and each question, rank information corresponding to the student and the accuracy rate information corresponding to the question are predetermined conditions. The teaching material suggesting apparatus according to claim 1, wherein a problem that satisfies the above is determined. Priority information indicating a priority order between a first algorithm for determining a problem using the answer characteristic information group and a second algorithm for determining a problem using the rank information and the accuracy rate information. Stored,
The problem determination unit
12. The teaching material suggesting apparatus according to claim 11, wherein a problem is determined such that less than or equal to a number of learning materials satisfying a predetermined condition are output according to the priority order indicated by the priority information. The recording medium is
An answer characteristic in which an answer characteristic information group having two or more sets of answer characteristic information indicating a problem identifier for identifying a question and an answer answer or an answer answer unnatural or natural for the answer is stored for each student. An information group storage unit,
A teaching material proposal method realized by a problem determination unit, a teaching material acquisition unit, and a learning teaching material output unit constituting the teaching material proposal device,
The problem determination unit determines a problem that satisfies a predetermined condition using the answer characteristic information group for each student; and
The learning material acquisition unit acquires a learning material corresponding to the problem determined in the problem determination step,
A learning material proposal method, wherein the learning material output unit includes a learning material output step of outputting the learning material acquired in the learning material acquisition step. The recording medium is
Instead of the answer characteristic information group storage unit, a rank information storage unit in which rank information, which is information related to the rank to which the student belongs, is stored for each student;
A correct rate information storage unit that stores correct rate information that is information on the correct rate for each question and each rank;
In the problem determination step,
Instead of determining a question using the answer characteristic information group, for each student and each question, rank information corresponding to the student and the accuracy rate information corresponding to the question are predetermined conditions. 14. The teaching material proposing method according to claim 13, wherein a problem satisfying the condition is determined. Computer-accessible recording media
An answer characteristic in which an answer characteristic information group having two or more sets of answer characteristic information indicating a problem identifier for identifying a question and an answer answer or an answer answer unnatural or natural for the answer is stored for each student. An information group storage unit,
Computer
For each student, using the answer characteristic information group, a problem determination unit that determines a problem that satisfies a predetermined condition;
A learning material acquisition unit for acquiring a learning material corresponding to the problem determined by the problem determination unit;
A program for causing the learning material acquisition unit to function as a learning material output unit that outputs the learning material acquired. The recording medium is
Instead of the answer characteristic information group storage unit, a rank information storage unit in which rank information, which is information related to the rank to which the student belongs, is stored for each student;
A correct rate information storage unit that stores correct rate information that is information on the correct rate for each question and each rank;
The problem determination unit
Instead of determining a question using the answer characteristic information group, for each student and each question, rank information corresponding to the student and the accuracy rate information corresponding to the question are predetermined conditions. 16. A program according to claim 15 for causing a computer to function as a problem that satisfies the following condition.

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