Information processing apparatus and information processing method

Abstract

An information processing apparatus according to one embodiment comprises an acquisition unit for acquiring a first S-P table and a second S-P table that indicate respondents which are arranged in a prescribed order of scores and questions which are arranged in a prescribed order of the number of correct answers, a conversion processing unit for reordering the respondents in the second S-P table in accordance with the arrangement of the respondents in the first S-P table, drawing a first S curve and a first P curve of the first S-P table, and converting the first S curve and the first P curve according to the horizontal width of the second S-P table, an analysis processing unit for evaluating learning of the respondents and the questions in the second S-P tables on the basis of the converted first S curve and first P curve and the reordered second S-P table, and an output control unit for outputting the result of the evaluation.

Description

Information Processing Apparatus and Information Processing Method

[0001] This invention relates to an information processing apparatus and an information processing method.

[0002] In an evaluation test of capabilities, there may be cases where external factors are assumed to have an impact, such as when the respondent has attributes related to the amount of the target capabilities to be evaluated. In such cases, when there are favorable external factors, it is assumed that the performance of the evaluation test will be high and the number of correct answers in the test will increase. For example, it is assumed that respondents who score high in a certain English test will also score high in other English tests.

[0003] For example, Non-Patent Document 1 discloses a technique for identifying respondents and questions that deviate from the state of an ideal evaluation test.

[0004] Masahiko Kurata, Takahiro Sato: Examination of the characteristics of the item score list by the S-P table of the linear model, Psychometrika, Vol. 4, No. 1, P11-17, 1976

[0005] However, when there are any problems with the test design or the respondents, there may be a deviation between the results predicted from external factors and the results of the evaluation test. For example, in the case of a test that cannot be solved without memorizing the text used as the subject matter, respondents who have only memorized the original text may be more likely to answer correctly than respondents with favorable attributes. Or, if a respondent with favorable attributes lacks motivation for simple questions, the respondent may answer incorrectly a simple question that can be solved with the ability predicted from the attributes.

[0006] Non-Patent Document 1 has a problem that it cannot detect the problems of questions or respondents where such deviations occur.

[0007] This invention has been made paying attention to the above circumstances, and its object is to provide a technique capable of detecting a deviation from the results assumed from external factors by performing an evaluation considering external factors related to the test in an evaluation test of capabilities.

[0008] To solve the above problems, an information processing device according to one embodiment of the present invention includes: an acquisition unit that acquires a first S-P table and a second S-P table, which are represented by answerers arranged in a predetermined score order and questions arranged in a predetermined number of correct answers order; a conversion processing unit that rearranges the order of answerers in the second S-P table to match the order of answerers in the first S-P table, draws a first S curve and a first P curve of the first S-P table, and converts the first S curve and the first P curve according to the width of the second S-P table; an analysis processing unit that performs learning of the answerers in the second S-P table and evaluation of the questions based on the converted first S curve and the first P curve and the rearranged second S-P table; and an output control unit that outputs the results of the evaluation.

[0009] According to one aspect of this invention, in an ability evaluation test, by performing an evaluation that takes into account external factors related to the test, it becomes possible to detect deviations from results that would be expected from those external factors.

[0010] Figure 1 is a diagram showing an example of an S-P table according to the embodiment. Figure 2 is a block diagram showing an example of the hardware configuration of the information processing device according to the embodiment. Figure 3 is a block diagram showing the software configuration of the information processing device according to the embodiment in relation to the hardware configuration shown in Figure 1. Figure 4 is a flowchart showing an example of the process of detecting problems in the respondent and the question based on a comparison of two S-P tables by the information processing device according to the embodiment. Figure 5 is a flowchart showing a more detailed example of the process in step ST102 according to the embodiment. Figure 6 is a diagram showing an example of the conversion process by the table conversion processing unit according to the embodiment. Figure 7 is a diagram showing another example of the conversion process by the table conversion processing unit according to the embodiment. Figure 8 is a flowchart showing a more detailed example of the process in step ST103 according to the embodiment. Figure 9 is a diagram showing an example of the attention coefficient calculation process by the numerical analysis processing unit and the judgment process using the said attention coefficient according to the embodiment. Figure 10 is a diagram showing an example of the deviation or area calculation process and problem judgment process by the curve analysis processing unit according to the embodiment.

[0011] Next, embodiments of the present invention will be described below with reference to the drawings. In the following embodiments, parts with the same number will be assumed to perform the same function, and therefore repeated explanations will be omitted. For example, when there are multiple identical or similar elements, a common reference numeral may be used to describe each element without distinction, or a sub-number may be used in addition to the common reference numeral to describe each element separately.

[0012] (S-P Table) First, let's explain the S-P table. The S-P table is a table used in the S-P analysis method. Here, the S-P analysis method is a method of interpreting data graphically by rearranging a list of item scores, which consists of a group of students (Students) and a series of sub-questions (Problems), using a predetermined procedure (for example, a procedure similar to Guttmann's scaler), and then adding a curve corresponding to the distribution of students' scores (S-curve) and a curve corresponding to the distribution of item pass rates (P-curve) to the rearranged table.

[0013] Therefore, this S-P analysis method can be described as a technique that visualizes the extent to which the test and the test taker's desired properties are reflected in the test answer results, and analyzes the degree of deviation from the ideal state. Here, the properties that a test should possess refer to the property that, for the given questions, test takers with a certain level of ability will answer correctly, and those with lower ability will answer incorrectly. Furthermore, the properties that a test taker should possess refer to the property that, for the given set of questions, they will answer correctly questions below a certain difficulty level and incorrectly answer questions above that difficulty level.

[0014] Figure 1 shows an example of an S-P table according to the embodiment. In the example in Figure 1, the S-P table is represented by arranging the question numbers from left to right in descending order of the number of correct answers and the respondent numbers from top to bottom in descending order of score. That is, the S-P table is a table represented by respondent numbers arranged in a predetermined score order and questions arranged in a predetermined order of the number of correct answers. Furthermore, the S-curve is a line representing the distribution of student scores from the left of the table, and the P-curve is a line representing the distribution of the number of correct answers to the questions from the top of the table. For example, the ideal state in Figure 1 refers to a state where everything to the left of the S-curve is 1 and everything to the right is 0, and everything above the P-curve is 1 and everything below is 0.

[0015] Referring to Figure 1 with the above ideal scenario in mind, question number 1 shows that high-scoring respondents tend to give many incorrect answers, while low-scoring respondents tend to give many correct answers. Therefore, the question design may be inappropriate.

[0016] Furthermore, respondent number 11 made many mistakes on the easy questions and many correct answers on the difficult questions. Therefore, this respondent may have learning difficulties.

[0017] However, for example, Non-Patent Document 1 only uses test results, and therefore cannot detect deviations from results expected from external factors. Here, external factors refer to the attributes of the respondent (including age, ability, experience, etc.). In this embodiment, deviations from results expected from external factors are detected by comparing the S-P table expected from external factors such as the respondent's attributes with the respondent's answer results to the test being evaluated.

[0018] (Configuration) Next, the hardware and software configurations of the information processing device for detecting deviations from expected results due to external factors will be described. Figure 2 is a block diagram showing an example of the hardware configuration of the information processing device 1 according to the embodiment. The information processing device 1 is a computer that analyzes input data, generates output data, and outputs it. For example, the information processing device 1 is installed in any location set by the user who manages the information processing device 1.

[0019] As shown in Figure 2, the information processing device 1 comprises a control unit 10, a program storage unit 20, a data storage unit 30, a communication interface 40, and an input / output interface 50. The control unit 10, the program storage unit 20, the data storage unit 30, the communication interface 40, and the input / output interface 50 are connected to each other via a bus so as to be able to communicate with each other. Furthermore, the communication interface 40 may be connected to an external device so as to be able to communicate with it via a network. In addition, the input / output interface 50 is connected to an input device 2 and an output device 3 so as to be able to communicate with it.

[0020] The control unit 10 controls the information processing device 1. The control unit 10 includes a hardware processor such as a central processing unit (CPU). For example, the control unit 10 may be an integrated circuit capable of executing various programs.

[0021] The program storage unit 20 can use a combination of non-volatile memory that can be written to and read at any time, such as EPROM (Erasable Programmable Read Only Memory), HDD (Hard Disk Drive), and SSD (Solid State Drive), as a storage medium, and non-volatile memory such as ROM (Read Only Memory). The program storage unit 20 stores programs necessary to execute various processes. In other words, the control unit 10 can realize various controls and operations by reading and executing programs stored in the program storage unit 20.

[0022] The data storage unit 30 is a storage device that uses a combination of non-volatile memory, such as an HDD or memory card, which allows for writing and reading at any time, and volatile memory, such as RAM (Random Access Memory), as storage media. The data storage unit 30 is used to store data acquired and generated during the process in which the control unit 10 executes a program and performs various processing.

[0023] The communication interface 40 includes one or more wired or wireless communication modules. For example, the communication interface 40 includes a communication module that connects to an external device via a network, either wired or wirelessly. The communication interface 40 may also include a wireless communication module that connects to an external device wirelessly, such as a Wi-Fi access point and a base station. Furthermore, the communication interface 40 may include a wireless communication module that connects to an external device wirelessly using short-range wireless technology. In other words, the communication interface 40 can be any general communication interface that can communicate with an external device and send and receive various types of information under the control of the control unit 10.

[0024] The input / output interface 50 is connected to the input device 2 and the output device 3, etc. The input / output interface 50 is an interface that enables the transmission and reception of information between the input device 2 and the output device 3. The input / output interface 50 may be integrated with the communication interface 40. For example, the information processing device 1 and at least one of the input device 2 and the output device 3 may be wirelessly connected using short-range wireless technology, and information may be transmitted and received using said short-range wireless technology.

[0025] The input device 2 may include, for example, a keyboard or pointing device for the user to input various information to the information processing device 1. The input device 2 may also include a reader for reading data to be stored in the program storage unit 20 or data storage unit 30 from a memory medium such as a USB memory, or a disk device for reading such data from a disk medium.

[0026] The output device 3 includes a display that shows the results calculated by the control unit 10, such as the S-P table and the evaluation results of the respondents and questions. The output device 3 also includes a printer that prints the information displayed on the display.

[0027] Figure 3 is a block diagram showing the software configuration of the information processing device 1 according to the embodiment, in relation to the hardware configuration shown in Figure 1. The control unit 10 comprises an input information acquisition unit 101, a conversion processing unit 102, an analysis processing unit 103, and an output control unit 104.

[0028] The input information acquisition unit 101 is an acquisition unit that acquires input information. The input information includes at least two (i.e., multiple) S-P tables. Details of the S-P tables to be acquired will be described later.

[0029] The conversion processing unit 102 comprises an S-P table reading unit 1021, a difference detection unit 1022, a conversion method selection unit 1023, and a table conversion processing unit 1024. The conversion processing unit 102 is a processing unit that converts at least one of the two S-P tables. Details of the S-P table conversion method will be described later.

[0030] The S-P table reading unit 1021 is a reading unit that reads S-P tables stored in the input information storage unit 301, which will be described later. For example, the S-P table reading unit 1021 reads two S-P tables stored in the input information storage unit 301.

[0031] The difference detection unit 1022 is a detection unit that detects whether there are differences between two S-P tables. The S-P tables read by the S-P table reading unit 1021 may have different numbers of questions or different sizes. The difference detection unit 1022 detects such differences in the size of the S-P tables.

[0032] The conversion method selection unit 1023 is a selection unit that selects a conversion method for the S-P table. For example, conversion methods include methods that graphically convert the S-P table, and methods that convert the change rules of the S curve and P curve. The conversion method selection unit 1023 selects which method to use to convert the S-P table.

[0033] The table conversion processing unit 1024 is a processing unit that performs the conversion of the S-P table using the conversion method selected by the conversion method selection unit 1023. Details of the S-P table conversion process will be described later.

[0034] The analysis processing unit 103 comprises a converted S-P table reading unit 1031, an analysis and judgment unit 1032, a numerical analysis processing unit 1033, a curve analysis processing unit 1034, and a problem presence / absence determination processing unit 1035. The analysis processing unit 103 is an analysis unit that compares and analyzes two S-P tables. The analysis processing unit 103 analyzes the S-P tables converted by the conversion processing unit 102 and performs evaluation of the respondent and the question.

[0035] The converted S-P table reading unit 1031 is a reading unit that reads the converted S-P table from the converted S-P table storage unit 302, which will be described later.

[0036] The analysis and determination unit 1032 is a determination unit that determines whether or not to analyze the distribution of values ​​in the converted S-P table. Furthermore, the analysis and determination unit 1032 determines whether or not to analyze the relationships between curves in the converted S-P table.

[0037] The numerical analysis processing unit 1033 is a processing unit that performs value analysis on an S-P table. For example, the numerical analysis processing unit 1033 determines the S curve and P curve for an ideal situation for one S-P table and evaluates the degree of deviation between the ideal S curve and P curve and the actual S curve and P curve. Details of the evaluation method will be described later. Then, the numerical analysis processing unit 1033 determines the trends of the respondent and the question based on the degree of deviation.

[0038] The curve analysis processing unit 1034 is a processing unit that analyzes the relationships between curves in an S-P table. For example, the curve analysis processing unit 1034 evaluates the degree of deviation between the S curve corresponding to another S-P table converted according to the converted S-P table and the original S curve of the converted S-P table. Details of the evaluation method will be described later. Then, the curve analysis processing unit 1034 determines whether there is a problem with the respondent or the question based on whether the degree of deviation exceeds a predetermined threshold.

[0039] The problem determination processing unit 1035 is a processing unit that determines whether or not there are problems with the respondent or the question. For example, the problem presence / absence determination processing unit 1035 determines whether or not there are problems with the respondent or the question according to the pattern of combinations of analysis results from the numerical analysis processing unit 1033 or the curve analysis processing unit 1034. Details of the problem presence / absence determination process will be described later.

[0040] The output control unit 104 is a control unit that controls the display of the analysis results on the display of the output device 3. The output control unit 104 controls the display to show the analysis results stored in the analysis result storage unit 303, which will be described later. This allows the user of the information processing device 1 to understand the problems with the answerer and the question, taking external factors into consideration.

[0041] The data storage unit 30 comprises an input information storage unit 301, a converted S-P table storage unit 302, and an analysis result storage unit 303. The input information storage unit 301 is a storage unit used to store input information acquired by the input information acquisition unit 101. The converted S-P table storage unit 302 is a storage unit used to store the converted S-P table converted by the conversion processing unit 102. The analysis result storage unit 303 is a storage unit used to store the analysis results based on the converted S-P table by the analysis processing unit 103.

[0042] [Operation] Figure 4 is a flowchart showing an example of the process by which the information processing device 1 according to the embodiment detects problems with the respondent and the question based on a comparison of two S-P tables. The operation of this flowchart is realized when the control unit 10 of the information processing device 1 reads and executes a program stored in the program storage unit 20.

[0043] This operation is initiated by a user managing the information processing device 1, or when the user inputs information via the input device 2.

[0044] In step ST101, the input information acquisition unit 101 acquires input information. The input information includes at least two S-P tables. One is an S-P table created based on the test results of a predetermined respondent. The other is an S-P table estimated considering the attributes of the predetermined respondent. Note that the method of creating the S-P table based on the attributes of the respondent may be any general method. Therefore, detailed description here is omitted. Then, the input information acquisition unit 101 stores the acquired multiple S-P tables in the input information storage unit 301.

[0045] In step ST102, the conversion processing unit 102 converts at least one of the two S-P tables. That is, the conversion processing unit 102 may convert both of the two S-P tables or only one of them. Also, the conversion processing unit 102 stores the converted converted S-P table in the converted S-P table storage unit 302.

[0046] FIG. 5 is a flowchart showing an example of more detailed processing in step ST102 according to the embodiment. In step ST201, the S-P table reading unit 1021 reads two S-P tables. For example, the S-P table reading unit 1021 reads two S-P tables from the S-P tables stored in the input information storage unit 301. The read S-P tables are one S-P table created based on the results of testing a predetermined respondent and the other S-P table estimated considering the attributes of the respondent.

[0047] In step ST202, the difference detection unit 1022 detects whether there is a difference between the two S-P tables. For example, the read S-P tables may have different numbers of questions. In this case, the sizes of the S-P tables are different, and the control unit 10 cannot compare the two S-P tables. Therefore, the difference detection unit 1022 detects the difference between the two S-P tables. If it is determined that there is a difference between the two S-P tables, the process proceeds to step ST203. On the other hand, if it is determined that there is no difference between the two S-P tables, the process ends and proceeds to step ST103.

[0048] In step ST203, the conversion method selection unit 1023 selects a conversion method. The conversion method selection unit 1023 selects a conversion method for making the two S-P tables comparable. For example, the conversion method selection unit 1023 selects whether to apply a conversion method for graphically converting the S-P table or a conversion method for converting the change rules of the S curve and the P curve.

[0049] In step ST204, the table conversion processing unit 1024 converts the two S-P tables. The table conversion processing unit 1024 converts the two S-P tables using the conversion method selected by the conversion method selection unit 1023. As described above, the table conversion processing unit 1024 may convert only one of the two S-P tables, or may convert both of the two S-P tables.

[0050] FIG. 6 is a diagram showing an example of the conversion processing by the table conversion processing unit 1024 according to the embodiment. In the example of FIG. 6, an example of a conversion method for graphically converting the S curve and the P curve is shown. First, the table conversion processing unit 1024 extracts the S curve and the P curve of one of the two S-P tables (referred to as table A) as a figure. For example, the left diagram in FIG. 6 is a diagram showing an example of table A.

[0051] Next, the table conversion processing unit 1024 rearranges the order of the respondents in the other table (table B) to be the same as that of table A. Further, the table conversion processing unit 1024 enlarges or reduces the S curve and the P curve of table A so that the horizontal widths are the same and overlaps them on the rearranged table B. The middle diagram in FIG. 6 is an example of a diagram in which the S curve and the P curve of table A are enlarged and overlapped on table B.

[0052] Finally, the table conversion processing unit 1024 determines whether the curve overlaps on the cell of the table. If it overlaps, the table conversion processing unit 1024 aligns the curve to either the left or the right of the cell. The alignment method is unified within the table. For example, the vertical line is always aligned to the right. Or, the alignment method is unified such that if the crossing position is shifted to the right from the center of the cell, it is aligned to the right, and if it is shifted to the left, it is aligned to the left. The right diagram in FIG. 6 is a diagram showing an example when the deviation of the S curve and the P curve is corrected to the right.

[0053] Figure 7 shows another example of the conversion process performed by the table conversion processing unit 1024 according to the embodiment. The example in Figure 7 shows an example of a conversion method for converting the change rules of the S curve and the P curve. First, the table conversion processing unit 1024 extracts the S curve and the P curve from one of the two S-P tables (let's call it Table A) as geometric figures. For example, the left side of Figure 7 shows an example of Table A.

[0054] Next, the table conversion processing unit 1024 rearranges the order of the respondents in the other table (Table B) so that it is the same as in Table A. Furthermore, the table conversion processing unit 1024 extracts the rules for the S curve and P curve in Table A. For example, the rules may be as follows: (1) If the score or the number of correct answers in the row or column of the target row in Table A is the same as the score or the number of correct answers in the next row → the position of the vertical line of the S curve or the horizontal line of the P curve in Table B is the same. At this time, as a criterion for determining the candidate position of the vertical / horizontal line in Table B (position candidate 1), one value is set from the maximum, minimum, average, etc. of the scores in Table B of the group of respondents with the same score in Table A, and position candidate 1 is calculated according to that value. (2) If the score or number of correct answers in the target row or column on Table A is n greater than the score or number of correct answers in the next row, then the position of the vertical line of the S curve or the horizontal line of the P curve on Table B is m greater (where m is an arbitrary constant), and position candidate 2 of the vertical line of the S curve or the horizontal line of the P curve is set. Note that the determination of greater or lesser can start from the bottom row and the rightmost column, or from the top row and the leftmost column. (3) Select the larger or smaller of position candidate 1 and position candidate 2. (4) The horizontal direction of the P curve is k times (where k is an arbitrary constant).

[0055] Then, the table transformation processing unit 1024 draws the S curve and P curve on table B according to the extracted rules.

[0056] The central and right-hand figures in Figure 7 show examples of how the rules described above are applied to plot the S-curve and P-curve in Table B. For example, in the example in Figure 7, rule (1) takes the maximum value, rule (2) if Table A is 1 larger, then Table B is 1 larger, and the starting point for calculation is the bottom row and the rightmost column, rule (3) takes whichever is larger, and rule (4) sets k=2, i.e., double.

[0057] For example, according to rule (1), the table conversion processing unit 1024 determines that the vertical lines of the S-curves from the second to the fourth row are placed in the same position. Also, since it is assumed to be the maximum value, the maximum value of the scores from the second to the fourth row in Table B is 4, so candidate position 1 for the vertical lines of the S-curves from the second to the fourth row in Table B is to the right of the fourth cell from the left. Candidate position 1 for each row is listed below (1) on the right side of the central figure in Figure 7. Furthermore, according to rule (2), the table conversion processing unit 1024 determines that since the scores from the second to the fourth row in Table A are 1 (n) greater than the score in the fifth row, the vertical lines of the S-curves from the second to the fourth row in Table B are 1 (m) greater than the vertical line in the fifth row. From this, candidate position 2 for the vertical lines of the S-curves from the second to the fourth row in Table B is 5. In Figure 7, below (2) on the right side of the central figure, the candidate positions 2 for each respondent and each question when n=1 and m=1 are listed. Finally, according to rule (3), the table conversion processing unit 1024 sets the larger of candidate position 1 and candidate position 2 for each row as the vertical line position, thereby drawing the S curve as shown on the right side of Figure 7.

[0058] Similarly, the table transformation processing unit 1024 determines, according to rules (1) and (4), that the horizontal lines of the P curves in the first and second columns, third and fourth columns, and fifth and sixth columns in table B are positioned at the same location. The candidate positions 1 for the horizontal lines of the P curves in each column at this time are shown to the right of (1) at the bottom of the central figure in Figure 7. Furthermore, according to rule (2), the table transformation processing unit 1024 determines that the candidate position 2 for the horizontal lines in the fifth and sixth columns is 2, the candidate position 2 for the horizontal lines in the third and fourth columns is 3, and the candidate position 2 for the horizontal lines in the first and second columns is 5. (Shown to the right of (2) at the bottom of the central figure in Figure 7) Furthermore, according to rule (3), the table transformation processing unit 1024 sets the larger of the candidate positions 1 and 2 for each column as the horizontal line position, thereby drawing the P curves as shown on the right side of Figure 7.

[0059] Returning to Figure 4, in step ST103, the analysis processing unit 103 compares and analyzes the S-P tables. The analysis processing unit 103 compares and analyzes the two converted S-P tables and stores the analysis results in the analysis result storage unit 303.

[0060] Figure 8 is a flowchart showing a more detailed example of the process in step ST103 according to the embodiment. In step ST301, the converted S-P table reading unit 1031 reads the converted S-P table. For example, the converted S-P table reading unit 1031 reads the converted S-P table from the converted S-P table storage unit 302.

[0061] In step ST302, the analysis determination unit 1032 determines whether to analyze the value distribution of the converted S-P table. For example, if the user has entered an instruction to analyze the value distribution, the analysis determination unit 1032 determines to analyze the value distribution of the converted S-P table. In this case, the process proceeds to step ST303. On the other hand, if it is determined not to analyze the value distribution of the converted S-P table, the process proceeds to step ST305.

[0062] In step ST303, the numerical analysis processing unit 1033 measures the degree of deviation from the ideal situation.

[0063] First, let's assume that the following conditions are defined as ideal in the transformed S-P table: (1) The left side of the transformed S curve is all 1 and the right side is all 0. (2) The upper side of the transformed P curve is all 1 and the lower side is all 0.

[0064] Next, the numerical analysis processing unit 1033 evaluates the degree of deviation of the converted S-P table from the ideal situation. For example, the numerical analysis processing unit 1033 uses the attention coefficient as a method for evaluating the degree of deviation. The attention coefficient is a numerical representation of the heterogeneity between the tendency of a problem or student and the overall tendency. For example, the numerical analysis processing unit 1033 calculates the attention coefficient of a certain student A as follows: (1) Find the "0"s to the left of student A's S curve and find the sum of the number of correct answers for those problems. (2) Find the "1"s to the right of student A's S curve and find the sum of the number of correct answers for those problems. (3) When student A's score is p points, find the sum of the number of correct answers for the first p items from the left in the number of correct answers column. (4) Calculate student A's attention coefficient using the following formula. (Attention coefficient for student A) = ((1) - (2)) / ((3) - (Student A's score rate) × (Total score of all students)) When calculating the attention coefficient for a question, the numerical analysis processing unit 1033 calculates the attention coefficient by replacing the upper S curve with a P curve, the left with the bottom, the right with the top, and the number of correct answers with the score.

[0065] Furthermore, if necessary, the numerical analysis processing unit 1033 corrects the attention coefficient. For example, if the attention coefficient (1) is 0 (i.e., the score / correct answer rate that can be assumed to be answered is met), the attention coefficient is corrected to 0.

[0066] Figure 9 shows an example of the attention coefficient calculation process by the numerical analysis processing unit 1033 and the judgment process using the said attention coefficient according to the embodiment. As shown in Figure 9(a), the score of respondent 1 is 3, and the values ​​of (1) to (3) described above are 6, 0, and 18, respectively. Also, the score rate is 3 / 7 ≈ 0.43, so the attention coefficient is calculated to be 0.58. The numerical analysis processing unit 1033 performs the same calculation for the other respondents. Note that for respondent 3, the value of (1) described above is 0, so the numerical analysis processing unit 1033 corrects the attention coefficient to 0.

[0067] Furthermore, the numerical analysis processing unit 1033 may classify each respondent based on their score and attention coefficient, as shown on the right in Figure 9. In the example in Figure 9(b), the responses are classified as good (5 < score and 0 ≤ attention coefficient < 0.5), average (2 < score ≤ 5 and 0 ≤ attention coefficient < 0.5), incompetent (score ≤ 2 and 0 ≤ attention coefficient < 0.5), careless (5 < score and 0.5 ≤ attention coefficient < 1), need attention (2 < score ≤ 5 and 0.5 ≤ attention coefficient < 1), and haphazard (score ≤ 2 and 0.5 ≤ attention coefficient < 1). Here, the values ​​of the attention coefficient used for the divisions and the scores used for the divisions can be arbitrarily determined, and are not limited to this example.

[0068] For example, the numerical analysis processing unit 1033 determines, based on each respondent's score and attention coefficient, that respondent 3 and respondent 5 are classified as normal, respondent 1 and respondent 4 are classified as requiring attention, and respondent 2 is classified as random.

[0069] The numerical analysis processing unit 1033 stores the S-P table, which includes the attention coefficient as shown in Figure 9(a), and the table shown in Figure 9(b) as analysis results in the analysis result storage unit 303.

[0070] In the example described above, the numerical analysis processing unit 1033 calculates both the attention coefficient for students and the attention coefficient for questions. However, it is certainly possible for the numerical analysis processing unit 1033 to calculate only one of either the attention coefficient for students or the attention coefficient for questions.

[0071] Returning to Figure 8, in step ST304, the analysis determination unit 1032 determines whether to analyze the relationships between curves in the converted S-P table. For example, if the user has input an instruction to analyze the relationships between value curves, the analysis determination unit 1032 determines to analyze the relationships between value curves in the converted S-P table. In this case, the process proceeds to step ST305. On the other hand, if it is determined not to analyze the relationships between value curves in the converted S-P table, the process proceeds to step ST306.

[0072] In step ST305, the curve analysis processing unit 1034 measures the degree of divergence in the relationships between the curves. First, it plots the S curve and P curve on the converted S-P table using the values ​​from the original S-P table (table B in the example above) (these curves will be referred to as the S' curve and P' curve below). Next, the curve analysis processing unit 1034 compares the curves, such as the S curve and the S' curve, and the P curve and the P' curve, detects the divergent parts, and classifies the degree of divergence.

[0073] As one example, the curve analysis processing unit 1034 calculates the horizontal deviation between the S curve and the S' curve, and the vertical deviation between the P curve and the P' curve for each respondent or question. Then, if the deviation is greater than or equal to a threshold α, the curve analysis processing unit 1034 determines that there is a high probability that there is a problem with the respondent's learning or the design of the question. Here, α is an arbitrary constant and may have different values ​​for the S curve and the P curve.

[0074] As another example, the curve analysis processing unit 1034 calculates the difference in the area enclosed by the S curve and the S' curve, and the P curve and the P' curve. If this difference is greater than or equal to a threshold β, the curve analysis processing unit 1034 determines that there is a high probability that there is a problem with the respondent's learning or the design of the question. Here, β is an arbitrary constant and may have different values ​​for the S curve and the P curve.

[0075] Figure 10 shows an example of the deviation or area calculation process and problem determination process by the curve analysis processing unit 1034 according to the embodiment. As shown in Figure 10(a), the vertical deviation between the S curve and the S' curve is 7, which is greater than or equal to the threshold α (=3). Therefore, the curve analysis processing unit 1034 determines that this is a pattern that is highly likely to have a learning problem. Also, the difference in the area enclosed by the S curve and the S' curve is 10, which is less than the threshold β (=15). Therefore, the curve analysis processing unit 1034 determines that this is a pattern that is highly likely to have no problems for the respondent as a whole.

[0076] Similarly, as shown in Figure 10(b), the vertical difference between the P curve and the P' curve is 2, which is greater than or equal to the threshold α (=2). Therefore, the curve analysis processing unit 1034 determines that this is a pattern in which there is a high probability that there is a problem with the design of the question. Also, the difference in the area enclosed by the P curve and the P' curve is 7, which is greater than or equal to the threshold β (=7). Therefore, the curve analysis processing unit 1034 determines that this is a pattern in which there is a high probability that there is a problem with the test as a whole.

[0077] Furthermore, the curve analysis processing unit 1034 stores these determination results, as well as the S-P table with the S-curve and S'-curve drawn, and the S-P table with the P-curve and P'-curve drawn, as analysis results in the analysis result storage unit 303.

[0078] In the example described above, the curve analysis processing unit 1034 is shown to perform processing on both the S curve and the S' curve, and the P curve and the P' curve. However, it is of course possible for the curve analysis processing unit 1034 to perform processing on only the S curve and the S' curve, or on only the P curve and the P' curve.

[0079] In step ST306, the problem presence / absence determination processing unit 1035 determines whether there are any problems with the respondent or the question, according to the pattern of combinations of analysis results from the numerical analysis processing unit 1033 or the curve analysis processing unit 1034. For example, it determines that there are no problems only if the result from the numerical analysis processing unit 1033 is "good" or "average" and the result from the curve analysis processing unit 1034 is "a pattern that is highly likely to have no problems with learning," and determines that there are problems in all other cases.

[0080] Based on the above-described process with reference to Figure 8, the analysis processing unit 103 may analyze the distribution of values ​​and the relationships between the value curves in the converted S-P table. Alternatively, the analysis processing unit 103 may analyze only one of the following: the distribution of values ​​and the relationships between the value curves in the converted S-P table. These analysis processes may be performed according to the results instructed by the user.

[0081] Returning to Figure 4, in step ST104, the output control unit 104 displays the analysis results. The output control unit 104 retrieves the analysis results stored in the analysis result storage unit 303 and controls the output device 3 to display the retrieved analysis results. The user of the information processing device 1 can see the analysis results displayed on this display, that is, the evaluation results of the respondent and the question according to the comparison of the two S-P tables.

[0082] [Effects] According to this embodiment, by comparing an S-P table estimated from external factor information with an S-P table corresponding to the actual test results, problems with the responders and questions that take external factors into account can be detected. By knowing these detected problems, the user can then design tests more efficiently and provide guidance to the responders.

[0083] [Other Embodiments] Furthermore, in the above embodiment, the information processing device 1 can also be configured to construct the operation of each component as a program, which can then be installed and executed on a computer used as a user device or on a computer used as an external device.

[0084] Furthermore, the method described in the above embodiment can be distributed by storing the program (software means) that can be executed by a computer in a storage medium such as a magnetic disk (floppy disk, hard disk, etc.), an optical disk (CD-ROM, DVD, MO, etc.), or a semiconductor memory (ROM, RAM, flash memory, etc.), and by transmitting it via a communication medium. The program stored on the medium also includes a configuration program that configures the software means (including not only the execution program but also tables and data structures) to be executed by the computer. The computer realizing this device reads the program stored in the storage medium, and, if necessary, constructs the software means using the configuration program, and executes the above-described process by controlling its operation with this software means. The storage medium referred to in this specification is not limited to distribution mediums, but also includes storage mediums such as magnetic disks and semiconductor memories provided inside the computer or in devices connected via a network.

[0085] In short, this invention is not limited to the embodiments described above, and can be modified in various ways during implementation without departing from its essence. Furthermore, each embodiment may be combined as appropriately as possible, in which case the combined effects can be obtained. Moreover, the embodiments described above include inventions at various stages, and various inventions can be extracted by appropriate combinations of the multiple constituent elements disclosed.

[0086] 1...Information Processing Device 10...Control Unit 101...Input Information Acquisition Unit 102...Conversion Processing Unit 1021...Table Reading Unit 1022...Difference Detection Unit 1023...Conversion Method Selection Unit 1024...Table Conversion Processing Unit 103...Analysis Processing Unit 1031...Table Reading Unit 1032...Analysis and Judgment Unit 1033...Numerical Analysis Processing Unit 1034...Curve Analysis Processing Unit 1035...Problem Presence / Absence Judgment Processing Unit 104...Output Control Unit 20...Program Storage Unit 30...Data Storage Unit 301...Input Information Storage Unit 302...Table Storage Unit 303...Analysis Result Storage Unit 40...Communication Interface 50...Input / Output Interface 2...Input Device 3...Output Device

Claims

1. An information processing device comprising: an acquisition unit that acquires a first S-P table and a second S-P table, which are represented by respondents arranged in a predetermined score order and questions arranged in a predetermined number of correct answers order; a conversion processing unit that rearranges the order of respondents in the second S-P table to match the order of respondents in the first S-P table, draws a first S curve and a first P curve of the first S-P table, and converts the first S curve and the first P curve according to the width of the second S-P table; an analysis processing unit that performs learning of the respondents in the second S-P table and evaluation of the questions based on the converted first S curve and the first P curve and the rearranged second S-P table; and an output control unit that outputs the results of the evaluation.

2. The information processing device according to claim 1, wherein the analysis processing unit determines ideal S curves and P curves for the rearranged second S-P table, and evaluates the degree of deviation between the ideal curves and the first S curve or the first P curve using an attention coefficient, thereby performing the learner's assessment of the second S-P table and the evaluation of the questions.

3. The information processing device according to claim 1, wherein the analysis processing unit draws the second S curve and the second P curve of the second S-P table, and determines whether the deviation between the first S curve and the second S curve, or between the first P curve and the second P curve, exceeds a predetermined threshold, thereby performing learner training for the second S-P table and evaluation of the questions.

4. An information processing method executed by the processor of an information processing device, comprising: obtaining a first S-P table and a second S-P table represented by respondents arranged in a predetermined score order and questions arranged in a predetermined number of correct answers order; rearranging the order of respondents in the second S-P table to match the order of respondents in the first S-P table; drawing a first S curve and a first P curve of the first S-P table; transforming the first S curve and the first P curve according to the width of the second S-P table; performing learning of the respondents in the second S-P table and evaluation of the questions based on the transformed first S curve and the first P curve and the rearranged second S-P table; and outputting the results of the evaluation.

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