JP2018205354A - Learning support device, learning support system, and program - Google Patents

Abstract

To provide a learning support device and the like capable of supporting efficient learning.SOLUTION: A learning support device 1 includes: a collection part for collecting answers to test items by a plurality of learners; a storage part for storing the collected answers of the plurality of learners; an analysis part for analyzing item characteristics of the test items on the basis of the answers of the plurality of learners; and a transmission part for transmitting review questions according to the analyzed item characteristics. The review questions are transmitted according to the analysis results relating to the item characteristics so as to support the efficient learning.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a learning support device, a learning support system, and a program.

  There is known a method of measuring the degree of understanding of a learner by analyzing the result of a test performed on a learner such as a student based on Item Response Theory (IRT). In the following description, the item response theory is described as IRT for the sake of brevity.

  For example, in Patent Document 1, a computer adaptive test using IRT is performed, and an IRT analysis is performed on a test result of each learner to estimate a learner's understanding (scholastic ability). A system that presents a learner with a problem of difficulty according to the degree of understanding is disclosed. According to Patent Document 1, the reliability coefficient of the estimated comprehension level is calculated, and when the reliability coefficient is equal to or higher than a certain threshold, it is determined that sufficient analysis accuracy is secured, and a new problem is presented.

Japanese Patent Laid-Open No. 2005-215023

  However, in the invention according to Patent Document 1, although the reliability of the analysis result is evaluated, the validity of the test problem itself is not evaluated. Therefore, there is a possibility that the learner learns about a test problem that is not valid as an evaluation scale for measuring the degree of understanding of the learner, that is, a poor test problem.

  This invention is made | formed by such a situation, The place made into the objective is to provide the learning assistance apparatus etc. which can support efficient learning.

  The learning support device according to the present invention includes a collection unit that collects answers to test items by a plurality of learners, a storage unit that stores the collected answers of the plurality of learners, and answers of the plurality of learners. An analysis unit that analyzes the item characteristics of the test items and a transmission unit that transmits review questions according to the analyzed item characteristics.

  The learning support apparatus according to the present invention includes a second storage unit that stores the review questions corresponding to the test items, an extraction unit that extracts the review questions according to the item characteristics analyzed by the analysis unit, The transmission unit transmits the review question extracted by the extraction unit.

  In the learning support device according to the present invention, the storage unit stores the answers of the plurality of learners in association with the answer dates and times when each of the learners answered, and whether each of the learners answers correctly, And an estimation unit that estimates a current fixing degree of the test item for each learner based on an elapsed time from the answer date, and the transmission unit transmits the review question at a timing according to the fixing degree. It is characterized by doing.

  The learning support apparatus according to the present invention is characterized in that the estimation unit estimates the fixing degree according to an attribute of the learner.

  The learning support device according to the present invention is characterized in that the estimation unit estimates the fixing degree according to an attribute of the test item.

  The learning support apparatus according to the present invention is characterized in that the estimation unit estimates the fixing degree according to the item characteristic analyzed by the analysis unit.

  A learning support system according to the present invention is a learning support system including a learning support device and a terminal device that communicates with the learning support device, wherein the learning support device performs tests by a plurality of learners from the terminal device. A collection unit that collects answers to items, a storage unit that stores collected answers of the plurality of learners, an analysis unit that analyzes item characteristics of the test items based on the answers of the plurality of learners, And a transmission unit that transmits review questions according to the analyzed item characteristics.

  The program according to the present invention collects answers to test items by a plurality of learners, stores the collected answers of the plurality of learners in a storage unit, and based on the answers of the plurality of learners, the test items The method is characterized by causing the computer to analyze the item characteristics and transmitting a review question according to the analyzed item characteristics.

  According to the present invention, efficient learning can be supported.

It is a schematic diagram which shows the structural example of a learning assistance system. It is a block diagram which shows the structural example of a server. It is explanatory drawing which shows an example of the record layout of learner DB. It is explanatory drawing which shows an example of the record layout of answer DB. It is explanatory drawing which shows an example of the record layout of problem DB. It is explanatory drawing which shows an example of an IRT analysis result. It is explanatory drawing for demonstrating the extraction process of the review problem according to an item characteristic. It is explanatory drawing for demonstrating the transmission timing of a review question. It is explanatory drawing for demonstrating the transmission process of a review question. It is a flowchart which shows an example of the process sequence which a server performs. 6 is a block diagram illustrating a configuration example of a server according to Embodiment 2. FIG. 12 is an explanatory diagram illustrating an example of a record layout of a transmission table according to Embodiment 2. FIG. 10 is an explanatory diagram for explaining transmission timing of a review question according to Embodiment 2. FIG. 10 is a flowchart illustrating an example of a processing procedure executed by a server according to the second embodiment. 10 is an explanatory diagram illustrating an example of a record layout of a transmission table according to Embodiment 3. FIG. 12 is a flowchart illustrating an example of a processing procedure executed by a server according to the third embodiment. FIG. 16 is an explanatory diagram illustrating an example of a record layout of a transmission table according to the fourth embodiment. FIG. 10 is an explanatory diagram for explaining review question transmission processing according to Embodiment 4; 14 is a flowchart illustrating an example of a processing procedure executed by a server according to the fourth embodiment. It is a functional block diagram which shows operation | movement of the server of the form mentioned above.

Hereinafter, the present invention will be described in detail with reference to the drawings illustrating embodiments thereof.
(Embodiment 1)
FIG. 1 is a schematic diagram illustrating a configuration example of a learning support system. In this embodiment, a learning support system that distributes review questions for reviewing test items will be described as an example. The learning support system includes a learning support device 1 and terminal devices 2, 2, 2,. Each device is connected for communication via a network N such as the Internet.

  The learning support apparatus 1 is an information processing apparatus that performs various types of information processing and transmits and receives information, such as a server apparatus and a personal computer. In the present embodiment, the learning support device 1 is assumed to be a server device, and will be read as the server 1 below for simplicity. The server 1 accumulates test results taken by the learner in a database, performs IRT analysis on the test results, and performs a process of evaluating the academic ability (understanding level) of each learner. The test is a test that is regularly performed in, for example, an elementary school, a junior high school, and the like, and is a test that includes a plurality of test items (small questions). The server 1 collects the results of tests performed at each school, performs IRT analysis on the collected test results, and measures the academic ability of each learner. The server 1 distributes a later-described individual vote indicating the analysis result and a test review question according to the analysis result to each learner (see FIG. 9).

  The terminal device 2 is a terminal device used by an instructor who teaches a learner, and is, for example, a personal computer or a tablet terminal. In the present embodiment, it is assumed that the terminal device 2 is a personal computer. The instructor is, for example, a teacher at each school, and grades the test taken by the learner. The terminal device 2 transmits the answer data already scored by the instructor to the server 1. The server 1 performs IRT analysis based on the answer data transmitted from the terminal device 2.

  For example, the terminal device 2 may be a terminal operated by each learner, and the server 1 may provide a digital test and allow each learner to take an examination, as in e-learning.

FIG. 2 is a block diagram illustrating a configuration example of the server 1. The server 1 includes a control unit 11, a storage unit 12, a communication unit 13, and a mass storage device 14.
The control unit 11 includes an arithmetic processing unit such as a CPU (Central Processing Unit) and an MPU (Micro-Processing Unit), and reads and executes the program P stored in the storage unit 12 to thereby execute various information related to the server 1. Processing, control processing, etc. are performed. In FIG. 2, the control unit 11 is illustrated as a single processor, but the control unit 11 may be a multiprocessor. The storage unit 12 includes memory elements such as a RAM (Random Access Memory) and a ROM (Read Only Memory), and stores a program P or data necessary for the control unit 11 to execute processing. The storage unit 12 temporarily stores data and the like necessary for the control unit 11 to execute arithmetic processing. The communication unit 13 includes a processing circuit for performing processing related to communication, and transmits and receives information to and from the terminal devices 2, 2, 2,.

  The large-capacity storage device 14 is a large-capacity storage device including, for example, a hard disk. The large-capacity storage device 14 stores a learner DB 141, an answer DB 142, and a question DB 143. The learner DB 141 stores data related to learners who take test items. The answer DB 142 stores data related to answers to test items by each learner. The question DB 143 stores review question data distributed to learners according to the test results.

In the present embodiment, the storage unit 12 and the mass storage device 14 may be configured as an integrated storage device. Further, the mass storage device 14 may be constituted by a plurality of storage devices. The mass storage device 14 may be an external storage device connected to the server 1.
In the present embodiment, the server 1 is not limited to the above configuration, and may include, for example, an input unit that receives an operation input, a display unit that displays information related to the server 1, and the like.

  FIG. 3 is an explanatory diagram showing an example of the record layout of the learner DB 141. The learner DB 141 includes a learner ID column, name column, school column, grade column, class column, and number column. The learner ID column stores identification information for identifying each learner. The name column, school column, school year column, class column, and number column store the name, school name, school year, class, and attendance number of each learner in association with the learner ID.

  FIG. 4 is an explanatory diagram showing an example of the record layout of the answer DB 142. The answer DB 142 includes a test ID column, a subject column, a learner ID column, an answer date / time column, an answer column, a score column, a correct answer rate column, and an understanding level column. The test ID column stores identification information for identifying each test performed periodically. The subject column stores the subject of each test in association with the test ID. The learner ID column stores the learner ID of the learner who has received each test in association with the test ID. The answer date column stores the date when each learner took the test, that is, the answer date when the answer was made, in association with the test ID and the learner ID. The answer string stores the answer for each question given in the test and the scoring result of the answer in association with the test ID and the learner ID. In FIG. 4, the correct answer is shown as “1”, and the incorrect answer is shown as “0”. The score sequence and the correct answer rate sequence respectively store the score and the correct answer rate related to the test result of each learner in association with the test ID and the learner ID. The understanding level column stores the level of understanding of each learner analyzed by an IRT analysis process described later in association with the test ID and the learner ID.

  FIG. 5 is an explanatory diagram showing an example of the record layout of the problem DB 143. The problem DB 143 includes a problem ID, a test ID, a test item string, and a data string. The question ID stores identification information for identifying the review question. The test ID column stores a test ID of a test corresponding to each review question in association with the question ID. The test item string stores information related to each test item presented in the test in association with the test ID. Specifically, the test item string stores the problem number and type of each test item. The data string stores review question data corresponding to each test item in association with the question ID, test ID, and test item.

  FIG. 6 is an explanatory diagram illustrating an example of an IRT analysis result. In FIG. 6, a graph showing the analysis result by IRT, a so-called item characteristic curve (ICC) is shown. In the graph shown in FIG. 6, the horizontal axis represents the learner's level of understanding, and the vertical axis represents the test item accuracy rate.

  Below, the outline | summary of the process which the server 1 performs is demonstrated. The server 1 collects the answer of the test by each learner from the terminal devices 2, 2, 2,. Specifically, the server 1 collects answer data already scored by each instructor. The server 1 stores the collected answer data in the answer DB 142. For example, when the accumulated number of answers reaches a predetermined number necessary for ensuring the reliability of the analysis result, the server 1 analyzes the collected answers by IRT.

  IRT is a technique for analyzing the characteristics of an evaluation item or the characteristics of a subject according to the response of the subject to the evaluation item group. In this embodiment, the evaluation items are each test item in the test, and the subject is a learner. The server 1 analyzes each learner's answer (response) to each test item, and evaluates the characteristics of the test item and the characteristics of the learner.

  Specifically, the server 1 performs the analysis assuming that the analysis result applies to a certain mathematical model. For example, the server 1 applies a two-parameter logistic model (2PLM) to the answer data and applies the analysis to the following equation (1) for analysis.

D is a constant, θi is an understanding level of each learner, aj is a test item discrimination power, bj is a test item difficulty level, and Pj is a test item accuracy rate. The symbol i indicates a code for identifying a learner, and the symbol j indicates a code for identifying a test item.
The server 1 specifies the discriminating power aj, the difficulty bj, and the comprehension degree θi that satisfy Expression (1) from the data group of answers by a plurality of answerers. For example, the server 1 linearizes discrete answer data using the PROX method, and specifies each parameter using a maximum likelihood estimation method, a Bayes estimation method, or the like. Since the algorithm is publicly known, detailed description thereof is omitted.

  Through the IRT analysis, the server 1 derives the probability Pj that the learner with the understanding degree θi correctly answers the j-th test item. FIG. 6 is a graph showing an example of the derived analysis result of the IRT analysis. “Question 1” is calculated with aj = 1 and bj = −1, “Question 2” is calculated with aj = 0.5 and bj = −1, and “Question 3” is calculated with aj = 1 and bj = 1. .

  According to the IRT, the item characteristic curve indicating the item characteristic of the test item ideally draws an S-shaped curve as shown in FIG. Specifically, the increase rate of the correct answer rate Pj is small when the understanding level θi is low, but the increase rate of the correct answer rate Pj increases when the understanding level θi approaches the difficulty level bj, and the correct answer when the understanding level θi further increases. The increasing rate of the rate Pj becomes smaller again. That is, the correct answer rate Pj rises when the understanding degree θi is close to the difficulty level bj.

  The discriminating power aj represents good discrimination of the test item as an evaluation scale. Specifically, the higher the discriminating power aj, the more rapidly the correct answer rate Pj rises when the understanding level θi is closer to the difficulty level bj. For example, as shown in FIG. 6, the correct answer rate Pj of “question 1” with aj = 1 is higher than the correct answer rate Pj of “question 2” with aj = 0.5. The increase rate is close to -1. Thereby, the item characteristic curve of “Q1” draws a steep S-curve. Therefore, a learner with a high degree of understanding θi and a learner with a low degree of understanding θi can be well identified. Generally, the discriminating power aj takes a value of about 0.3 to 2.0.

  The difficulty bj represents the difficulty of the test item. The higher the difficulty level bj, the higher the numerical value of the understanding level θi at which the correct answer rate Pj rises. For example, as shown in FIG. 6, the correct answer rate Pj of “Question 3” with bj = 1 rises around θi = 1, but the correct answer rate Pj of “Question 1” with bj = −1 is θi = −. Stand up around 1. That is, the higher the difficulty level bj is, the more the item characteristic curve is located on the right side in the figure, and the analysis result indicates that the problem is more difficult. The difficulty level bj can take either a positive or negative value depending on the difficulty level of the test item.

  As described above, the server 1 performs the IRT analysis so that not only the academic ability (understanding degree θi) of each learner but also the parameters aj and bj indicating the characteristics of each test item (sub-question) related to the test performed. Identify. In the above description, 2PLM is adopted as a mathematical model related to IRT. However, the server 1 may adopt a one-parameter logistic model (1PLM, so-called rush model) that focuses only on the test item difficulty bj. Further, the server 1 may adopt a three-parameter logistic model (3PLM) that takes into account the guesswork in addition to the discrimination power aj and the difficulty level bj.

FIG. 7 is an explanatory diagram for explaining a review question extraction process according to item characteristics. In this embodiment, the server 1 evaluates the validity of the problem according to the item characteristics of each test item (sub-question), provides a review question to the learner only for the test item that is valid for review, and reviews To do.
7A to 7D show item characteristic curves of four patterns. The item characteristic curve in FIG. 7A is an S-shaped curve, similar to the item characteristic curve shown in FIG. That is, a learner with a high correct answer rate Pj and a learner with a low correct answer rate Pj are well identified according to the comprehension degree θi, and the comprehension degree θi and the correct answer rate Pj are ideally correlated.

  On the other hand, in the item characteristic curve of FIG. 7B, since the difficulty bj is extremely low, the correct answer rate Pj is generally high regardless of the understanding degree θi. In other words, because the test item is too simple and many learners have answered correctly, the test item does not function as an evaluation scale.

  Moreover, in the item characteristic curve of FIG. 7C, since the discrimination power aj is extremely small, the correct answer rate Pj is uniform regardless of the level of the understanding degree θi. That is, the test item has poor identification, and the ability difference between learners cannot be properly determined.

  Moreover, in the item characteristic curve of FIG. 7D, since the discrimination power aj takes a negative value, the higher the comprehension degree θi, the lower the correct answer rate Pj. In this case, for example, the test item is a so-called trapping problem, and it is considered that the more competent learner is wrong. In the analysis result shown in FIG. 7D, the original purpose of the test for measuring the learner's academic ability is lost.

  As described above, there are cases where the problem is poor and the learner's academic ability cannot be measured appropriately. There is a high possibility that even if the test item is reviewed by the learner, it is not possible to appropriately support the improvement of academic ability. Therefore, the server 1 selects test items to be reviewed by the learner according to the item characteristics of the test items specified by the IRT analysis. For example, the server 1 selects only test items whose discrimination power aj and difficulty bj are in appropriate numerical ranges as test items to be reviewed. For example, the server 1 selects test items having a discrimination power aj of 0.3 or more and 2.0 or less. For example, the server 1 selects test items having a difficulty level bj of −3.0 or more and 3.0 or less.

  The server 1 extracts the review questions for the selected test items from the problem DB 143 and distributes them to each learner. For example, the server 1 distributes, to each learner, a review question for a test item that has been mistaken by each learner among the test items for which appropriate analysis results have been obtained.

  FIG. 8 is an explanatory diagram for explaining the transmission timing of the review question. FIG. 8 shows a graph of the fixing degree Qi that represents the ratio of each learner who remembers the test contents in time series. In the graph shown in FIG. 8, the horizontal axis indicates the elapsed time t (weeks) from the answer date and time, and the vertical axis indicates the fixing degree Qi. The server 1 transmits the review questions extracted from the question DB 143 to each learner at the timing when the fixing degree Qi shown in FIG.

  The curve shown in FIG. 8 is a curve that approximates the forgetting curve of Ebbinghouse. The forgetting curve is a time-series curve representing the forgetting of the medium term memory or the long term memory, and it is empirically known that the stored contents are attenuated exponentially. According to the Ebbinghouse experiment, human memory decays exponentially, but by re-storing (reviewing) when it has been forgotten to some extent, the memory content becomes difficult to forget.

  In the present embodiment, the server 1 estimates the fixing degree Qi by reflecting information of individual learners in a function approximating the above-mentioned forgetting curve. Specifically, the server 1 calculates a fixing degree Qi for each learner according to the following equation (2) according to each learner.

  k represents a constant of 0 or more and less than 1, and t represents an elapsed time from the answer date to the present. αi is a coefficient corresponding to the correctness of each learner's answer, and is, for example, a correct answer rate obtained by dividing the number of correct answers in the entire test by the number of test items (number of small questions).

  Based on Expression (2), the server 1 multiplies the approximate expression k ^ t related to the forgetting curve by a coefficient αi corresponding to the correctness of each learner's answer, thereby estimating the fixing degree Qi for each learner. . For example, the server 1 sets the correct answer rate of the test by each learner as the coefficient αi. In addition, the server 1 refers to the answer date stored in the answer DB 142 and calculates the elapsed time t for each learner. The server 1 calculates the fixing degree Qi for each learner from Equation (2) based on the coefficient αi and the elapsed time t related to each learner.

  FIG. 8 shows the fixing degrees Qi of the learners A, B, and C, respectively. In the graph shown in FIG. 8, the calculation was performed with k = 0.5. In addition, the calculation was performed with αi = 0.9 for learner A, αi = 0.6 for learner B, and αi = 0.7 for learner C. Further, learners A and B are assumed to have been tested at t = 0, and learner C is assumed to have been tested at t = 2.

  As shown in FIG. 8, the server 1 estimates that the fixing degree Qi attenuates exponentially according to the elapsed time t. In this case, the server 1 estimates the fixing degree Qi according to each learner's grade (correctness of answer). For example, in FIG. 8, when learners A and B are compared, the fixing degree Qi of the learner A with good results is higher than the fixing degree Qi of the learner B with poor results than the learner A. As described above, the server 1 determines the coefficient αi according to whether each learner's answer is correct or not, thereby calculating the fixing degree Qi according to each learner's grade.

  Further, the server 1 calculates the fixing degree Qi based on the answer date and time when each learner took the test. For example, in FIG. 8, when learners A and C are compared, the server 1 calculates the fixing degree Qi for learner A according to the elapsed time t from t = 0. On the other hand, for the learner C, the server 1 calculates the fixing degree Qi according to the elapsed time t-2 from t = 2, which is two weeks behind the learner A.

  For example, the server 1 calculates the fixing degree Qi related to each learner every other week, and determines whether or not the fixing degree Qi is equal to or less than a predetermined value. The predetermined value is, for example, 0.4. The server 1 transmits a review question at a timing when the fixing degree Qi of each learner becomes 0.4 or less. For example, in the example of FIG. 8, the server 1 transmits a review question to the learner A at a timing of t = 3 when the fixing degree Qi becomes 0.4 or less. Similarly, the server 1 transmits review questions to learners B and C at timings t = 2 and 4, respectively.

  As described above, the server 1 transmits the review questions at the timing when the student's fixing degree Qi is lowered, that is, at the timing when the test content is almost forgotten. According to the experiment of Ebbinghouse, by letting the learner review at the timing when the test content (memory content) is about to be forgotten, the memory can be efficiently fixed in a short review time.

  In the above description, the review question is simply transmitted when the fixing degree Qi becomes equal to or less than the predetermined value. However, the present embodiment is not limited to this. For example, the server 1 may transmit the review question at a timing when the decay rate (first derivative of Qi) of the fixing degree Qi becomes equal to or greater than a predetermined value and the decay becomes moderate. That is, the server 1 only needs to be able to transmit review questions at a timing according to the fixing degree Qi, and the transmission timing is not limited to the timing when the fixing degree Qi becomes a predetermined value or less.

  FIG. 9 is an explanatory diagram for explaining review question transmission processing. The server 1 transmits the data shown in FIG. 9 toward the learner at the timing determined above. Specifically, the server 1 transmits an individual vote indicating the test result (upper part of FIG. 9) and a review question (“recommendation sheet” at the lower part of FIG. 9). Note that the server 1 may transmit individual votes and review questions to the terminal device 2 of the instructor, or may distribute it directly to each learner. As shown in FIG. 9, in addition to comments automatically generated according to each student's test scoring results and grades, the individual vote is an instruction that the review problem should be tackled (the part indicated by an ellipse in FIG. 9) including.

  For example, the server 1 provides a reviewer with three review questions per test. Specifically, as described above, the server 1 provides review questions corresponding to test items (sub-questions) that the learner has made a mistake. For example, in the example shown in FIG. 9, the server 1 provides review questions for questions 8 and 10 that the learner has made a mistake. In addition, since the question 8 and the question 10 alone do not reach three questions, the server 1 further provides a review question “B1” as an expansion problem.

  On the other hand, in the example shown in FIG. 9, since it is determined that the question 4 is not in the appropriate numerical range, the server 1 does not provide a review question regarding the question 4. As described above, the server 1 appropriately selects the test items to be reviewed according to the item characteristics, and provides the review questions.

FIG. 10 is a flowchart illustrating an example of a processing procedure executed by the server 1. Based on FIG. 10, the processing content which the server 1 performs is demonstrated.
The control unit 11 of the server 1 collects answers to the test items by a plurality of learners from the terminal devices 2, 2, 2,... (Step S11). For example, the control unit 11 collects answer data already scored by each instructor. The control unit 11 stores the collected answers of each learner in the answer DB 142 in association with the answer date and time when each learner answered (Step S12).

  Based on the collected answers of the plurality of learners, the control unit 11 performs an IRT analysis that analyzes the item characteristics of the test items and the understanding level of each learner (step S13). Specifically, as described above, the control unit 11 applies a mathematical model such as 2PLM to analyze the scoring results, and analyzes the item characteristics such as the discriminating power aj and the difficulty bj of the test item, and the understanding degree θi of the learner. To do.

  The control unit 11 extracts a review question to be provided to the learner from the problem DB 143 in accordance with the item characteristics of the analyzed test item (step S14). For example, the control unit 11 determines that the test item should be reviewed when the discrimination power aj is within a predetermined numerical range. For example, when the degree of difficulty bj is within a predetermined numerical range, the control unit 11 determines that the test item should be reviewed. Thereby, the control part 11 specifies the test item in which an item characteristic curve draws an ideal S-shaped curve, and extracts a review question only about the specified test item.

  The control unit 11 estimates the current degree of fixation of the test item for each learner according to the correctness of each learner's answer and the elapsed time from each learner's answer date (step S15). Specifically, as described above, the control unit 11 calculates the fixing degree Qi for each learner by multiplying the approximate expression related to the forgetting curve of the Ebbing House by a coefficient αi corresponding to the correctness of the answer.

  The control unit 11 determines whether it is time to transmit the review question according to the current fixing degree estimated in Step S15 (Step S16). For example, the control unit 11 determines whether or not the fixing degree Qi is equal to or less than a predetermined value. If it is determined that it is not the timing to transmit (S16: NO), the control unit 11 returns the process to step S15. When it determines with it being the timing to transmit (S16: YES), the control part 11 transmits the review question extracted by step S14 (step S17), and complete | finishes a series of processes.

  In addition, although the server 1 shall acquire the answer data scored by each leader from the terminal device 2 above, this Embodiment is not limited to this. For example, the server 1 may perform scoring by checking the answers collected from the terminal device 2 with the model answer. That is, the server 1 may collect unscored answer data.

  Moreover, although the server 1 provided the review question only about the test item which the learner made a mistake above, you may provide the review question also about the test item which the learner answered correctly.

  Further, in the above description, a review problem is prepared in the problem DB 143 separately from the questions presented in the test, and the review problem is distributed to the terminal device 2. It is not limited. For example, the server 1 may distribute the question given in the test as it is as a review question. That is, the server 1 determines whether or not the question is an ideal problem by IRT analysis. If the problem that the learner has made is an ideal problem, the fixing degree Qi is reduced for the data of the problem. It is delivered to the terminal device 2 at the timing. Thus, the test questions and review questions may be the same.

  As described above, according to the first embodiment, according to the item characteristics of the test items, it is possible to select appropriate problems for review and allow the learner to review, thereby supporting efficient learning. Can do.

  In addition, according to the first embodiment, review problems are prepared in the problem DB 143 separately from the test questions, and more efficient learning is supported by solving the review problems. Can do.

  Further, according to the first embodiment, by estimating the fixing degree Qi for each learner, review at an appropriate timing can be promoted, and more efficient learning contents can be fixed.

(Embodiment 2)
In the present embodiment, a mode is described in which the timing for transmitting review questions is determined according to the attributes of the learner. In addition, about the content which overlaps with Embodiment 1, the same code | symbol is attached | subjected and description is abbreviate | omitted.
FIG. 11 is a block diagram illustrating a configuration example of the server 1 according to the second embodiment. Server 1 according to the present embodiment stores transmission table 144. The transmission table 144 is a table used when determining the timing for transmitting review questions.

  FIG. 12 is an explanatory diagram illustrating an example of a record layout of the transmission table 144 according to the second embodiment. The transmission table 144 includes a class understanding level column and a coefficient column. The class comprehension degree column stores the comprehension degree θc for the entire class of the school to which the learner belongs. For example, the class understanding level θc is the level of understanding of all learners (students) belonging to the class. The coefficient sequence stores a calculation method of the coefficient αi defined according to the class understanding level θc in association with the class understanding level θc. For example, the transmission table 144 defines a class α having a class comprehension θc that is equal to or smaller than a certain value (θc is 0 or less in the present embodiment) such that the coefficient αi decreases as the class comprehension θc decreases. That is, the transmission table 144 specifies that the coefficient αi of the class with poor results is small.

  FIG. 13 is an explanatory diagram for explaining the transmission timing of the review problem according to the second embodiment. In this Embodiment, the server 1 determines the timing which transmits a review question according to a learner's attribute, in addition to the right / wrong of each learner's answer, the elapsed time from an answer date.

  The attribute of the learner is information on a group such as a class, grade, school, etc. to which the learner belongs, for example. The server 1 refers to the learner DB 141 to determine the group to which each learner belongs. The server 1 weights the fixing degree Qi according to which group each learner belongs to.

  For example, the server 1 refers to the answer DB 142 and calculates the average value of the correct answer rate Pj in the entire class to which the learner belongs. The server 1 refers to the analyzed IRT analysis result (item characteristic curve) and identifies the class understanding degree θc from the average value of the correct answer rate Pj. The server 1 estimates the current fixing degree Qi of each learner based on the specified class understanding degree θc. For example, the server 1 refers to the transmission table 144 and calculates the coefficient αi of the fixing degree Qi according to the class understanding degree θc. Specifically, the server 1 calculates a coefficient αi by multiplying a numerical value corresponding to the class understanding level θc by the correct answer rate of each learner when the class understanding level θc is equal to or less than a certain value. More specifically, the server 1 calculates the coefficient αi to be smaller as the class understanding degree θc is lower (see FIG. 12). As a result, the server 1 calculates so that the degree of fixing Qi is low for learners in a class with low grades.

  FIG. 13 is a graph comparing the fixing degree Qi of the learner A belonging to a class having a high class understanding degree θc and the fixing degree Qi of a learner B belonging to a class having a lower class understanding degree θc than the class of the learner A. Indicates. In the graph of FIG. 13, it is assumed that learners A and B have the same grade, and for learner A, αi = 1.0 (a numerical value corresponding to class comprehension degree θc) × 0.7 (learner A The correct answer rate for the learner B is calculated as αi = 0.6 × 0.7.

  As shown in FIG. 13, learners A and B have the same grade, but learner B's fixing degree Qi belongs to a class whose class comprehension θc is a certain value or less. Higher than. Thereby, the timing for transmitting the review question to the learner B is earlier than the transmission timing for the learner A. In the example of FIG. 13, the server 1 transmits a review question to the learner B one week after the test is performed and to the learner A two weeks later. In this way, the server 1 changes the timing for distributing the review questions according to the attributes of each learner. Thereby, the learning delay caused by the learner's learning environment is prevented.

FIG. 14 is a flowchart illustrating an example of a processing procedure executed by the server 1 according to the second embodiment. After extracting the review questions according to the IRT analysis result (step S14), the control unit 11 of the server 1 executes the following processing.
The control unit 11 estimates the current fixing degree of the test item for each learner according to the attribute of each learner (step S201). For example, the control unit 11 estimates the fixing degree Qi according to the class, grade, school, or the like group to which each learner belongs. Specifically, the control unit 11 refers to the answer DB 142 and specifies the class understanding level θc for the entire class to which the learner belongs. The control unit 11 refers to the transmission table 144 and calculates the coefficient αi according to the specified class understanding degree θc. Specifically, the control unit 11 performs calculation so that the coefficient αi is small for learners belonging to a class having a low class understanding degree θc. Thereby, the control part 11 estimates low the fixing degree Qi of the learner of a class with a low grade. The control part 11 transfers a process to step S16.

  In the above description, the fixing degree Qi is estimated according to the class understanding degree θc, but the present embodiment is not limited to this. For example, the server 1 may estimate the fixing degree Qi to be lower by decreasing the coefficient αi as the learner's grade becomes higher. Thus, the review timing is advanced for learners (students) in the upper grades, and the review timing is delayed for learners in the lower grades. Thus, the server 1 only needs to be able to estimate the fixing degree Qi according to the learner's attribute, and the method for estimating the fixing degree Qi is not particularly limited.

  In the above description, the fixing degree Qi is weighted only for a class having a class comprehension degree θc of a certain value or less, that is, a class having a poor grade, but the fixing degree Qi is also weighted for a class having a class comprehension degree θc of a certain value or more. You may go.

  As described above, according to the second embodiment, it is possible to adjust the review timing according to the learning environment and the situation by estimating the fixing degree Qi according to the learner's attribute.

(Embodiment 3)
In Embodiment 2, the timing for transmitting the review question is determined according to the attribute of the learner. In the present embodiment, a mode is described in which the timing for transmitting the review question is determined according to the attribute of the test item.
FIG. 15 is an explanatory diagram illustrating an example of a record layout of the transmission table 144 according to the third embodiment. The transmission table 144 according to the present embodiment stores a subject understanding level θs instead of the class understanding level θc. The subject comprehension degree θs is the learner's comprehension degree θs for each subject analyzed from the test of each subject. The transmission table 144 stores a calculation method of the coefficient αi corresponding to the subject understanding level θs in association with the subject understanding level θs. For example, the transmission table 144 defines that when the subject comprehension degree θs is equal to or less than a certain value (θs is equal to or less than 0 in the present embodiment), the coefficient αi decreases as the subject comprehension degree θs decreases. That is, the transmission table 144 defines the coefficient αi to be small with respect to the fixing degree Qi of the weak subject test.

  The server 1 calculates the fixing degree Qi according to the attribute of the test received by the learner. Specifically, the server 1 refers to the transmission table 144 and calculates the fixing degree Qi for each subject. For example, the server 1 calculates the coefficient αi so that the coefficient αi is small for the weak subject (the subject whose θs is 0 or less), and estimates the degree of fixing Qi low. Thereby, the server 1 advances the transmission timing of the review problem related to the weak subject. By transmitting review questions for weak subjects at an early stage, the server 1 tries to prevent learning failures in poor subjects. Since this embodiment is substantially the same as the second embodiment except that the subject comprehension degree θs is adopted instead of the class comprehension degree θc, detailed illustration and description are omitted in the present embodiment.

FIG. 16 is a flowchart illustrating an example of a processing procedure executed by the server 1 according to the third embodiment. After extracting the review questions according to the IRT analysis result (step S14), the control unit 11 of the server 1 executes the following processing.
The control unit 11 estimates the current fixing degree of the test item for each learner according to the attribute of the test item (step S301). For example, the control unit 11 estimates the fixing degree Qi according to the subject of the test received by each learner. Specifically, the control unit 11 refers to the answer DB 142 and specifies the learner's degree of understanding θs for each subject by subject. The control unit 11 refers to the transmission table 144 and calculates the coefficient αi according to the subject understanding degree θs. Specifically, when the subject comprehension degree θs is equal to or less than a predetermined value, the control unit 11 calculates the coefficient αi to be smaller as the subject comprehension degree θs is lower. Thereby, the control part 11 estimates low the fixing degree Qi of a weak subject. The control part 11 transfers a process to step S16.

  In the above description, the fixing degree Qi is estimated for each subject. However, the present embodiment is not limited to this. For example, the server 1 may estimate the fixing degree Qi according to the type of test item (for example, the problem type). Specifically, the server 1 performs IRT analysis for each type of problem, such as a question that asks “knowledge”, a question that asks “skill”, and a question that asks “concept”, and analyzes the understanding degree θi of the learner for each type. To do. The server 1 calculates the coefficient αi based on the understanding degree θi corresponding to the problem type. Thus, the server 1 only needs to be able to estimate the fixing degree Qi according to the attribute of the test item, and the attribute of the test is not limited to the subject.

  As described above, according to the third embodiment, it is possible to prompt the review at the timing according to the learner's ability by estimating the fixing degree Qi according to the attribute of the test item.

(Embodiment 4)
In this embodiment, a mode in which the review timing transmission timing is determined according to the analysis result of the IRT will be described.
FIG. 17 is an explanatory diagram illustrating an example of a record layout of the transmission table 144 according to the fourth embodiment. The transmission table 144 according to the present embodiment defines the coefficient αi according to the item characteristics of the test item. For example, the transmission table 144 defines the coefficient αi according to the discrimination power aj of the test item. Specifically, the transmission table 144 stipulates that the coefficient αi decreases as the discriminating power aj approaches the ideal numerical range of 0.3 to 2.0. Thereby, the server 1 calculates the fixing degree Qi lower as the discriminating power aj is closer to the ideal numerical range, and transmits the review question to the learner at an early timing.

  FIG. 18 is an explanatory diagram for explaining review question transmission processing according to the fourth embodiment. In the first embodiment, the server 1 extracts the review questions only for the test items whose item characteristics are in the ideal numerical range and transmits them to the learner. On the other hand, in the present embodiment, the server 1 performs weighting according to the item characteristics of the test items, and transmits the review questions preferentially from the test items having good item characteristics.

  Specifically, the server 1 performs IRT analysis on the answer of each test item presented in the test, and specifies item characteristics such as discriminating power aj and difficulty bj. Thereby, the server 1 obtains an item characteristic curve of each test item as shown in FIG. Here, as shown in FIG. 18, the discriminating power aj is in an ideal numerical range, the test item in which the curve draws an S-shaped curve (upper left of FIG. 18), and the discriminating power aj is from the ideal numerical range. It is assumed that there are a test item (middle stage on the left in FIG. 18) that is slightly deviated and has a gentle curve, and a test item (lower stage on the left in FIG. 18) that has extremely small discrimination power aj and no discrimination power. In this case, the server 1 determines the transmission timing of the review question and the presence / absence of transmission according to the discrimination power aj of each test item.

  For example, the server 1 refers to the transmission table 144, weights the coefficient αi for the ideal test items shown in the upper part of FIG. 18, and estimates the fixing degree Qi to be low. Thereby, the server 1 transmits the review question at an early timing. On the other hand, for the test items shown in the middle part of FIG. 18, the server 1 calculates the coefficient αi to be larger than the test items in the upper part of FIG. As a result, the server 1 transmits the review question to the learner later than the test item in the upper part of FIG. Further, the server 1 does not adopt the test item in the lower part of FIG. 18 as a test item to be reviewed and does not transmit a review question because there is no discriminating power aj. As described above, the server 1 determines the transmission timing of the review question and the presence / absence of transmission according to the degree of whether or not the item characteristics of the test items are ideal numerical values. Thereby, review is promoted preferentially for test items that are appropriate for review, and more efficient learning can be supported.

FIG. 19 is a flowchart illustrating an example of a processing procedure executed by the server 1 according to the fourth embodiment. After extracting the review questions according to the IRT analysis result (step S14), the control unit 11 of the server 1 executes the following processing.
The control unit 11 estimates the current fixing degree of the test item according to the item characteristic of the test item analyzed in step S13 (step S401). For example, the control unit 11 estimates the fixing degree Qi according to the discrimination power aj of the test item. Specifically, the control unit 11 refers to the transmission table 144 and calculates so that the coefficient αi decreases as the discriminating power aj approaches the ideal numerical range. Thereby, the control unit 11 calculates the fixing degree Qi of the test item having good item characteristics to be low. The control unit 11 determines whether it is time to transmit the review question according to the fixing degree estimated in step S401 (step S402). For example, the control unit 11 determines whether the fixing degree Qi has become a predetermined value or less. If it is determined that it is not the timing to transmit (S402: NO), the control unit 11 returns the process to step S401. When it is determined that it is the transmission timing (S402: YES), the control unit 11 transmits a review question for the test item whose fixing degree is equal to or less than a predetermined value (step S403).

  The control unit 11 determines whether transmission has been completed for all review questions extracted in step S14 (step S404). When it determines with not having completed transmission (S404: NO), the control part 11 returns a process to step S401. As a result, the control unit 11 recalculates the current fixing degree Qi for the test item for which the review question has not been transmitted, and transmits it at any time when the fixing degree Qi becomes a predetermined value or less. When it is determined that transmission of all review questions has been completed (S404: YES), the control unit 11 ends a series of processes.

  As described above, according to the fourth embodiment, it is possible to rank the test items to be reviewed by estimating the fixing degree Qi according to the item characteristics of the test items.

(Embodiment 5)
FIG. 20 is a functional block diagram showing the operation of the server 1 in the above-described form. When the control unit 11 executes the program P, the server 1 operates as follows. The collection unit 201 collects answers to test items by a plurality of learners. The storage unit 202 stores the collected answers of the plurality of learners. The analysis unit 203 analyzes item characteristics of the test items based on the answers of the plurality of learners. The transmission unit 204 transmits review questions according to the analyzed item characteristics.

  The fifth embodiment is as described above, and the other parts are the same as those of the first to fourth embodiments. Therefore, the corresponding parts are denoted by the same reference numerals, and detailed description thereof is omitted.

  The embodiment disclosed this time is to be considered as illustrative in all points and not restrictive. The scope of the present invention is defined by the terms of the claims, rather than the meanings described above, and is intended to include any modifications within the scope and meaning equivalent to the terms of the claims.

1 server (learning support device)
11 Control Unit 12 Storage Unit P Program 13 Communication Unit 14 Mass Storage Device 141 Learner DB
142 Answer DB
143 Problem DB
144 Transmission table 2 Terminal device

Claims (8)

A collection unit that collects answers to test items from multiple learners,
A storage unit for storing the collected answers of the plurality of learners;
Based on the answers of the plurality of learners, an analysis unit that analyzes item characteristics of the test items;
A learning support apparatus comprising: a transmission unit that transmits review questions according to the analyzed item characteristics. A second storage unit storing the review questions corresponding to the test items;
An extraction unit for extracting the review questions according to the item characteristics analyzed by the analysis unit,
The learning support apparatus according to claim 1, wherein the transmission unit transmits the review question extracted by the extraction unit. The storage unit stores the answers of the plurality of learners in association with the answer date and time when each of the learners answered,
Based on the correctness of each learner's answer and the elapsed time from the answer date and time, the estimation unit for estimating the current degree of fixation of the test item for each learner,
The learning support apparatus according to claim 1, wherein the transmission unit transmits the review question at a timing according to the fixing degree. The learning support apparatus according to claim 3, wherein the estimation unit estimates the fixing degree according to an attribute of the learner. The learning support apparatus according to claim 3, wherein the estimation unit estimates the fixing degree according to an attribute of the test item. The learning support device according to claim 3, wherein the estimation unit estimates the degree of fixation according to the item characteristic analyzed by the analysis unit. A learning support system having a learning support device and a terminal device that communicates with the learning support device,
The learning support device includes:
A collection unit for collecting answers to test items by a plurality of learners from the terminal device;
A storage unit for storing the collected answers of the plurality of learners;
Based on the answers of the plurality of learners, an analysis unit that analyzes item characteristics of the test items;
A learning support system comprising: a transmission unit that transmits review questions according to the analyzed item characteristics. Collect answers to test items from multiple learners,
Storing the collected answers of the plurality of learners in a storage unit;
Based on the answers of the plurality of learners, analyze the item characteristics of the test item,
A program that causes a computer to execute a process of sending review questions according to the analyzed item characteristics.

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