Learning effect analysis program, information processing device, learning effect analysis method, and education program

Abstract

To provide a learning effect analysis program, an information processing device, a learning effect analysis method, and an education program that analyze learning effect while taking into account personality of a user.SOLUTION: An information processing device 1 includes: counting means 100 for counting personality characteristic data 111 derived from a response to a personality characteristic survey, consciousness characteristic data 112 derived from the response to a disaster prevention consciousness survey, and learning effect data 113 derived from the response to a disaster prevention behavior survey after education provided after the response to the disaster prevention consciousness survey; classification means 101 for classifying respondents on the basis of the responses of the respondents to the disaster prevention consciousness survey; allocation means 102 for allocating, on the basis of the personality characteristics of the group of the classified respondents, personality characteristics to the classified respondent group; and correlation means 103 for correlating, on the basis of the response to the disaster prevention behavior survey of the group of the respondents to which the personality characteristics have been allocated, the consciousness characteristic, the personality characteristic, and the learning effect.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a learning effect analysis program, an information processing device, a learning effect analysis method, and an educational program for analyzing learning effects, and more particularly to a learning effect analysis program, an information processing device, a learning effect analysis method, and an educational program for analyzing learning effects related to disaster prevention. [Background technology]

[0002] As a conventional technique, an information processing device that predicts learning effects has been proposed (see, for example, Patent Document 1).

[0003] The information processing device disclosed in Patent Document 1 records the user's learning history, including the user's physical and mental state during learning and the learning environment, and performs an analysis process of the learning effect using user attributes, learning time, learning location, learning state, and learning environment as learning elements, generates a model that predicts the learning effect as the learning elements, and performs learning information management process, learning effect management process, and learning effect analysis process to predict the learning effect based on the user's learning history. [Prior art documents] [Non-patent literature]

[0004] [Patent Document 1] Patent No. 6655643 Summary of the Invention [Problem to be solved by the invention]

[0005] However, while the above-mentioned information processing devices predict learning effects based on the user's learning history, they have the problem of not taking the user's personality into consideration. In situations where education is required to enable people to act independently, education that takes into account each individual's personality traits is necessary, and an analysis of learning effects based on personality traits is required.

[0006] An object of the present invention is to provide a learning effect analysis program, an information processing device, a learning effect analysis method, and an education program that analyze learning effect while taking into account the user's personality. [Means for solving the problem]

[0007] In order to achieve the above object, one aspect of the present invention provides the following learning effect analysis program, information processing device, learning effect analysis method, and education program.

[0008] [1] Computer, a counting means for counting a personality trait derived from the answers to a first predetermined question, an awareness trait derived from the answers to a second predetermined question, and a learning effect derived from the answers to a third predetermined question after an education provided after the answers to the second predetermined question; classification means for classifying respondents based on their answers to the second predetermined questions; an allocation means for allocating personality traits to the classified respondent groups based on the personality traits of each respondent in the classified respondent groups; a learning effect analysis program that functions as a matching means for matching the awareness trait, the personality trait, and the learning effect based on the answer to the third predetermined question of each respondent in the group of respondents to which the personality trait is assigned. [2] The learning effect analysis program according to [1], wherein the classification means classifies the respondents based on a correlation coefficient of each answer to the second predetermined question. [3] The learning effect analysis program according to [1], further functioning as an output means for evaluating the education based on the result of associating the personality traits with the learning effects. [4] The learning effect analysis program according to [3], wherein the output means evaluates the education based on the number of learning effect items included in the correspondence result between the personality traits and the learning effect. [5] A compilation means for compiling personality traits derived from answers to a first predetermined question, awareness traits derived from answers to a second predetermined question, and learning effects derived from answers to a third predetermined question after education provided after answers to the second predetermined question; classification means for classifying respondents based on their answers to the second predetermined questions; an allocation means for allocating personality traits to the classified respondent groups based on the personality traits of each respondent in the classified respondent groups; an information processing device having a matching means for matching the awareness trait, the personality trait, and the learning effect based on the answer to the third predetermined question of each respondent in the group of respondents to which the personality trait is assigned. [6] a step of aggregating personality traits derived from the answers to the first predetermined question, consciousness traits derived from the answers to the second predetermined question, and learning effects derived from the answers to the third predetermined question after the education provided after the answers to the second predetermined question; categorizing respondents based on their responses to each of the second predetermined questions; assigning personality traits to the classified groups of respondents based on the personality traits of each respondent in the classified groups of respondents; and associating the awareness traits, the personality traits, and the learning effects based on answers to the third predetermined question of each respondent in the group of respondents to which the personality traits are assigned. [7] Computer, a counting means for counting a personality trait derived from the answers to a first predetermined question, an awareness trait derived from the answers to a second predetermined question, and a learning effect derived from the answers to a third predetermined question after an education provided after the answers to the second predetermined question; an education means for carrying out the education using educational materials prepared in advance; classification means for classifying respondents based on their answers to the second predetermined questions; an allocation means for allocating personality traits to the classified respondent groups based on the personality traits of each respondent in the classified respondent groups; a correlation means for correlating the awareness trait, the personality trait, and the learning effect based on an answer to the third predetermined question by each respondent in the respondent group to which the personality trait is assigned; and causing the device to function as an output means for evaluating the education based on the result of associating the personality traits with the learning effects and outputting the evaluation result to the education means; The educational means is an educational program that carries out the education or additional education using teaching materials corresponding to the evaluation results. [8] The educational program described in [7], wherein the educational means asks at least one of the first predetermined question, the second predetermined question, and the third predetermined question when carrying out the education. [9] Computer, a counting means for counting personality traits derived from answers to a first predetermined question, awareness traits regarding disaster prevention derived from answers to a second predetermined question, and a learning effect regarding disaster prevention behavior derived from answers to a third predetermined question after an education regarding disaster prevention that is conducted after the answers to the second predetermined question; classification means for classifying respondents based on their answers to the second predetermined questions; an allocation means for allocating personality traits to the classified respondent groups based on the personality traits of each respondent in the classified respondent groups; A learning effect analysis program that functions as a matching means for matching the awareness characteristics regarding disaster prevention, the personality characteristics, and the learning effects regarding disaster prevention based on the answers to the third predetermined question of each respondent in the group of respondents to which the personality characteristics are assigned. [Effects of the Invention]

[0009] According to the inventions of claims 1, 5, 6 and 9, the learning effect can be analyzed taking into account the user's personality. According to the invention of claim 2, respondents can be classified based on the correlation coefficient of each answer to the second predetermined question. According to the invention of claim 3, education can be evaluated based on the result of associating personality traits with learning effects. According to the invention of claim 4, education can be evaluated based on the number of learning effect items included in the result of associating personality traits with learning effects. According to the invention of claim 7, it is possible to analyze the learning effect taking into consideration the user's personality, and to educate the user based on the analysis results. According to the invention of claim 8, at least one of the first predetermined question, the second predetermined question, and the third predetermined question can be asked when the education is carried out. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic diagram illustrating an example of the configuration of a learning effect analysis system according to an embodiment. [Figure 2] FIG. 2 is a block diagram illustrating an example of the configuration of an information processing device according to an embodiment. [Figure 3] FIG. 3 is a flowchart illustrating the learning process. [Figure 4] FIG. 4 is a diagram showing an example of the configuration of the personality characteristic data, the awareness characteristic data, and the learning effect data. [Figure 5] 5(a) to 5(c) are diagrams showing examples of the personality trait survey, disaster prevention awareness survey, and question items and answer configurations for the disaster prevention awareness survey. [Figure 6] FIG. 6 is a diagram for explaining the operation of analyzing the personality characteristic data. [Figure 7] 7(a) and (b) are diagrams for explaining the operation of analyzing the awareness characteristic data. [Figure 8] FIG. 8 is a diagram illustrating an example of the configuration of analysis result data. [Figure 9] FIG. 9 is a flowchart for explaining the analysis operation of the information processing device. [Figure 10] FIG. 10 is a diagram showing the structure of the analysis result data in comparison with the analysis result when the grouping is further divided into three cases. [Figure 11] FIG. 11 is a table showing the analysis results of disaster prevention awareness that influences disaster prevention behavior when personality traits are not included. [Figure 12] FIG. 12 is a table showing the relationship between the teaching materials and the output results of the output means. DETAILED DESCRIPTION OF THE INVENTION

[0011] Conventional methods for evaluating learning effectiveness have mainly used outcome indicators such as test scores, and have not fully taken into account multifaceted factors such as the learner's personality traits and awareness. Focusing on this point, the embodiment of the present invention provides a new learning effectiveness analysis method that uses two elements as evaluation axes: the learner's personality traits and awareness of the subject area (e.g., strengths / weaknesses, interest / disinterest, interest / disinterest, etc.).

[0012] As an example of the present invention, a demonstration experiment was conducted in which a learning program utilizing virtual reality (VR) technology was applied to elementary school students in disaster prevention education. In this educational program, proactive disaster prevention behavior (e.g., checking hazard maps and preparing emergency supplies) was positioned as the final outcome, and analysis was conducted using two evaluation axes: the learner's personality traits and their awareness of disasters. As a result, significant differences were observed in the implementation status of disaster prevention behavior between learners grouped according to these two evaluation axes and other learners. This demonstrated that the learning effectiveness analysis method of the present invention is useful in disaster prevention education and other educational fields, not only for simple knowledge acquisition but also for promoting learners' proactive behavioral change and enabling multifaceted evaluation of that change. Furthermore, the analysis method proposed in this invention can be used to evaluate the learning effectiveness of various educational materials. For example, when disaster prevention behavior implementation was evaluated using nine items, the following results were obtained when evaluating learning effectiveness using the analysis method based on the present invention. a When the analysis method of the present invention was applied, it was confirmed that all nine disaster prevention actions were implemented. on the other hand, b When a simple multivariate analysis method was used, 5 out of 9 items were implemented, and a certain degree of behavioral change was confirmed, but it was difficult to analyze in detail the factors behind the change. c When the analysis method did not take into account the features of the present invention, only two of the nine items were performed, and almost no change was observed in the learners' behavior.

[0013] Thus, in result a, all disaster prevention actions were taken, whereas in result b, the number of actions taken was reduced, and in result c, very few actions were taken. This clear difference confirmed that the analysis method of the present invention is an effective method for promoting proactive behavioral change in learners. Based on this, the learning effect analysis method of the present invention has a higher accuracy of evaluating behavioral change than conventional methods, and is expected to be applied in various fields of education.

[0014] The field of education and learning described in this embodiment relates to disaster prevention for elementary and junior high school students, but the field is not limited to this. It may also relate to disaster prevention for medical professionals such as nurses and public health nurses, which are fields requiring proactive action, disaster prevention for the general public, disaster prevention for disaster prevention leaders such as neighborhood associations and disaster prevention specialists, and disaster prevention for professionals such as police and firefighters. It may also relate to fields other than disaster prevention, such as school studies of Japanese language or mathematics, or learning of attitudes and skills required for occupations. Proactive action here means "thinking and making decisions independently in response to the situation one finds oneself in, and acting of one's own volition." This does not mean acting independently under someone's instructions or following pre-established rules. Furthermore, education that fosters proactive action requires education that takes into account individual personality traits. Previous research has shown that people with a high tendency toward neuroticism tend to make emotional decisions and evacuate more blindly during fire evacuation (Wang, Guanning, et al. 2023), and that learning services tailored to each learner's personality traits can have a positive impact on their motivation to learn (Hirabayashi, 2019).

[0015] On the other hand, education requires teaching materials that can provide appropriate learning effects (fostering proactive behavior).The teaching material evaluation method using the learning effect analysis method of the present invention takes personality traits into account when determining changes in awareness traits before and after learning the teaching materials and learning effects (for example, disaster prevention behavior).As a result, by using teaching materials that produce favorable teaching material evaluation results of the present invention, it is expected that proactive behavior will be fostered.

[0016] [Embodiment Mode] FIG. 1 is a schematic diagram illustrating an example of the configuration of a learning effect analysis system according to an embodiment.

[0017] This learning effect analysis system is configured by connecting an information processing device 1 and terminals 2a, 2b, etc. via a network 3 so that they can communicate with each other. Terminals 2a, 2b, etc. are operated by users 4a, 4b, etc., respectively. User 4a is the person who provides education, and users 4b, etc. are the people who receive education, i.e., learners. Although not shown, the learning effect system further includes teaching materials, which will be described later, VR (Virtual Reality) goggles for playing the teaching materials, an information processing device, a display device, etc.

[0018] The field of education and learning described in this embodiment relates to disaster prevention for elementary and junior high school students, but the field is not limited to this and may be disaster prevention for medical professionals such as nurses and public health nurses, disaster prevention for the general public, disaster prevention leaders such as neighborhood associations and disaster prevention officers, disaster prevention for professionals such as police and fire departments, or fields other than disaster prevention.

[0019] The information processing device 1 is a server-type information processing device for analyzing learning effects, and operates in response to requests from terminals 2a, 2b, etc., and is equipped with electronic components such as a CPU (Central Processing Unit) and flash memory that have the function of processing information within the main body.

[0020] Terminals 2a, 2b, etc. are information processing devices such as PCs (Personal Computers) that are mainly used by trainees to input survey responses so that trainees can communicate with information processing device 1 and analyze the learning effects, and are equipped with electronic components such as a CPU and flash memory that have functions for processing information within the main body. Note that survey responses may be entered on paper in the form of a mark sheet or free-form entry, without using terminal 2b, and the trainer may input the responses using terminal 2a.

[0021] The network 3 is a communication network capable of high-speed communication, and is, for example, a wired or wireless communication network such as the Internet or a LAN (Local Area Network).

[0022] (Configuration of information processing device) FIG. 2 is a block diagram showing an example of the configuration of the information processing device 1 according to the embodiment.

[0023] The information processing device 1 comprises a control unit 10 which is composed of a CPU and the like and controls each part and executes various programs, a memory unit 11 which is composed of a storage medium such as a flash memory and stores information, and a communication unit 12 which communicates with the outside via a network.

[0024] The control unit 10 executes a learning effect analysis program 110, which will be described later, to function as a tallying means 100, a classification means 101, an allocation means 102, a correlation means 103, an output means 104, and the like.

[0025] The aggregation means 100 aggregates the responses to the personality trait survey, disaster prevention awareness survey, and disaster prevention behavior survey described below, and stores them in the memory unit 11 as personality trait data 111, awareness trait data 112, and learning effect data 113, respectively.

[0026] The classification means 101 classifies respondents (users receiving education, learners) based on the awareness characteristic data 112. Details of the classification method will be described later.

[0027] The allocation means 102 allocates personality traits to the respondent groups classified by the classification means 101 based on the personality trait data 111 .

[0028] The matching means 103 matches the personality traits in the personality trait data 111 with the learning effects in the learning effect data 113 based on the responses to the disaster prevention behavior survey of the group of respondents to which personality traits have been assigned, and stores the resulting analysis result data 114 in the memory unit 11.

[0029] The output means 104 outputs part or all of the analysis result data 114 directly or after editing it. Specific examples of output methods will be described later.

[0030] The storage unit 11 stores a learning effect analysis program 110 that causes the control unit 10 to operate as each of the above-mentioned means 100-104, personality characteristic data 111, awareness characteristic data 112, learning effect data 113, analysis result data 114, and the like.

[0031] FIG. 4 is a diagram showing an example of the configuration of the personality characteristic data 111, the awareness characteristic data 112, and the learning effect data 113.

[0032] The personality trait data 111a includes answer numbers (for example, "1: No," "2: Can't say," and "3: Yes") as the answers of each respondent to questions (first predetermined questions) in the personality trait survey.

[0033] In addition, the awareness characteristic data 112a1, 112a2, and 112a3 have answer numbers (for example, "1: Not at all," "2: Not really," "3: Don't know," "4: Somewhat," and "5: Very much") as the answers given by each respondent to the questions (second predetermined questions) in the disaster prevention awareness survey.

[0034] The learning effect data 113 includes answer numbers (for example, "1: ×" and "2: ○") as the answers of each respondent to the questions (third predetermined questions) in the disaster prevention awareness survey.

[0035] 5(a) to 5(c) are diagrams showing examples of the personality trait survey, disaster prevention awareness survey, and question items and answer configurations for the disaster prevention awareness survey.

[0036] The personality trait survey uses the "Five-Factor Personality Test for Elementary School Students" developed by Soga (1999), one of the well-known personality tests for elementary school students. The personality trait survey includes multiple questions and response options of "1: No," "2: Neutral," and "3: Yes." For example, personality traits such as "Agreeableness," "Control," "Emotionality," "Openness," and "Extroversion" are associated with each question. A personality trait is determined to be present if the number of "3: Yes" responses to questions corresponding to that trait is high. For example, "Agreeableness" is associated with question numbers (1, 9, 19, 35, etc.). "Control" is associated with question numbers (2, 10, 22, 31, etc.). "Emotionality" is associated with question numbers (3, 11, 25, 32, etc.). "Openness" is associated with question numbers (4, 12, 28, 38, etc.). "Extraversion" is associated with question numbers (5, 13, 30, 36, etc.) Alternatively, the "Five-Factor Personality Test for Elementary School Students" developed by Murakami (2010) can also be used, but although the names of the five factors differ from those of Soga's test (extraversion, agreeableness, conscientiousness (Soga's control), emotional stability (Soga's emotionality), and intellectual curiosity (Soga's openness), and the cutoff values ​​in Figure 6, which will be described later, are different, it can be used in the same way.

[0037] The disaster prevention awareness survey has multiple questions, and the response items for each question are "1: Not at all," "2: Not really," "3: Don't know," "4: A little," and "5: A lot." A five-point Likert scale is used, but if a three-point Likert scale of "1: Not think," "2: Don't know," and "3: Agree" is used, the personality traits that affect disaster prevention awareness will be limited to only two of the five factors, "control" and "cooperativeness," which would blur the relationship between disaster prevention awareness and personality traits. Therefore, a five-point Likert scale is preferable. (See the five-point grouping method and three-point grouping method in Figure 10 below.)

[0038] The learning effect survey has a plurality of questions and answer items for each question, such as "1: No" or "2: Good."

[0039] (Operation of information processing device) Next, the operation of this embodiment will be explained by dividing it into (1) investigation operation and (2) analysis operation. In order to analyze the learning effect, first, various investigations are carried out before and after the training as explained below.

[0040] (1) Investigative operation FIG. 3 is a flowchart illustrating the learning process.

[0041] First, the user 4a who provides the education conducts a personality trait survey (S1) on the users 4b who receive the education (S3) before the education.

[0042] Next, the user 4a who provides the education conducts a disaster prevention awareness survey (first time) (S2) on the users 4b who receive the education (S3) before the education.

[0043] Next, the user 4a who provides the education uses the teaching materials to the users 4b who receive the education, and the users 4b learn (S3). The educational content may be, for example, learning using VR (personal experience) or classroom learning.

[0044] Next, the user 4a who provides the education conducts a disaster prevention awareness survey (second time) (S4) for the users 4b who receive the education (S3).

[0045] Next, the user 4a who provides the education conducts a disaster prevention awareness survey (third time) and a disaster prevention action survey (S5) for the users 4b who receive the education three months after the education (S3). Note that the interval between the education (S3) and the disaster prevention awareness survey (third time) and the disaster prevention action survey is an example and is not limited to three months and may be changed based on the content of the education, the content of the curriculum, the purpose of the survey, etc.

[0046] The counting means 100 receives and counts the responses to the personality trait survey, disaster prevention awareness survey, and disaster prevention behavior survey, and stores them in the storage unit 11 as personality trait data 111, awareness trait data 112, and learning effect data 113, respectively. The information processing device 1 uses this information to analyze the learning effect as will be described below.

[0047] (2) Analysis operation Fig. 9 is a flowchart for explaining the analysis operation of the information processing device 1. Fig. 6 is a diagram showing cutoff values ​​used to explain the analysis operation of the personality trait data 111. The cutoff values ​​in Fig. 6 are average values ​​obtained by Soga (1999) when developing the scale for the "Five-Factor Personality Test for Elementary School Students."

[0048] Next, the aggregation means 100 of the information processing device 1 divides the personality trait data 111 into two groups, high and low, for each of "Agreeableness," "Control," "Emotionality," "Openness," and "Extroversion," using cutoff values, and classifies the high group as users who have these personality traits (S10). By this operation, one or more personality traits are assigned to each user.

[0049] Next, the classification means 101 classifies the respondents based on the awareness characteristic data 112 (S11). Specifically, the classification is performed as described below.

[0050] 7(a) and (b) are diagrams for explaining the operation of analyzing the awareness characteristic data 112. FIG.

[0051] These figures show the Spearman's correlation coefficients calculated between the 10 disaster prevention awareness items, and items with a correlation coefficient of 0.4 or higher are selected and displayed. This evaluation method makes it possible to visualize the relationships between each disaster prevention awareness item and obtain useful insights for analyzing the effectiveness of education.

[0052] The reason for adopting 0.4 as the correlation coefficient threshold is that 0.4 is widely recognized as a standard indicating a "moderate correlation" in practical interpretation. 0.3 is often considered a "weak correlation" and may be insufficient to ensure the accuracy and reliability of classification. On the other hand, setting a value of 0.5 or higher poses the risk of not extracting relevant items, so 0.4 was determined to be a moderate and reasonable threshold. Thus, in this invention, 0.4 is adopted to achieve both the validity of the correlation and the practicality of classification. However, the threshold can be flexibly changed depending on the number of users, the purpose, and the nature of the data.

[0053] For example, as shown in FIG. 7(a), if many users give high-point answers to items such as "I'm afraid of disasters," "I'll get hurt," "I can do something about it myself," "I'll tell my family about it," and "I'll make promises," the classification means 101 groups these users as it is determined that there is a strong correlation between these items. Note that "r" in the figure is an example of a calculated correlation coefficient. The correlation coefficient is calculated by Spearman, for example. The example in the figure extracts question items with a correlation coefficient of 0.4 or more.

[0054] Similarly, the classification means 101 groups users who have given high-point answers to the item "areas where disasters occur" as shown in Figure 7(b), and who have given high-point answers to both items, "getting injured" and "telling family about the situation," as being strongly correlated.

[0055] As a grouping method for the classification means 101, in addition to Spearman, a method based on cluster analysis (hierarchical or non-hierarchical), a method based on principal component analysis, or a method based on factor analysis may be used.

[0056] Cluster analysis uses methods such as Ward's method and k-means to group learners based on data on disaster prevention awareness and behavioral characteristics. The grouping criteria typically involve an index measuring the similarity between items (e.g., Euclidean distance) or focusing on items with a correlation coefficient of 0.4 or higher. The optimal number of clusters is determined using visual analysis of the dendrogram or the elbow method. This analysis allows learners with similar awareness and behavioral characteristics to be classified, making it possible to evaluate the impact of each group on disaster prevention behavior.

[0057] Principal component analysis extracts principal components that maximize data variance while compressing high-dimensional data into low-dimensional data from the 10 disaster prevention awareness items and behavioral characteristic data. The number of principal components to adopt is determined based on the contribution rate and cumulative contribution rate. For example, principal components with a contribution rate of 10% or more and those with a cumulative contribution rate of 80% or more are often selected. Utilizing principal component analysis makes it possible to identify the main factors that influence disaster prevention awareness and behavior and understand the structure of learning effects.

[0058] The aim of factor analysis is to clarify the latent factor structure among the 10 disaster prevention awareness items, and items are grouped based on their covariance structure. The number of factors to be extracted is determined using a scree plot or based on factors with an eigenvalue of 1 or greater. Furthermore, the factor to which each item is associated is determined based on the factor loading value (e.g., 0.4 or greater). This can reveal latent characteristics that influence disaster prevention behavior, which can be useful for improving and evaluating the effectiveness of educational programs.

[0059] These analytical methods can be applied not only to disaster prevention education but also to other educational fields, and are positioned as effective methods for evaluating the diversity of learner characteristics and behaviors. For example, discriminant analysis can classify students into high and low disaster prevention awareness groups and clarify the characteristics of the disaster prevention behavior items answered by each group. In this case, the allocation criteria use a pre-set threshold based on the total score of the disaster prevention awareness survey or specific important items. For example, scores of 50 or above can be classified as the high group, and scores below 50 as the low group.

[0060] In logistic regression analysis, whether or not disaster prevention behavior was practiced (implemented / not implemented) is used as the dependent variable, and personality traits and disaster prevention awareness items are modeled as independent variables. In this case, the allocation criterion is that if the predicted probability exceeds a certain threshold (e.g., 0.5), it is judged as "implemented," and if it is below that threshold, it is judged as "not implemented." This method makes it clear which traits have the greatest impact on disaster prevention behavior.

[0061] Conjoint analysis allows for the collection of preference data on how important each learner feels each item of disaster prevention behavior is, and makes it possible to prioritize the items that are most important. The allocation criteria use the utility value (preference level) of each item, and items with a utility value above a certain value are prioritized. This method makes it possible to propose personalized learning materials based on the characteristics of each learner.

[0062] Next, the allocation means 102 allocates personality traits to the respondent groups classified by the classification means 101 (S12). Specifically, a chi-square test is performed between two classified respondent groups (for example, the group of respondents who selected "5: strongly agree" for all answers shown in FIG. 7(a) and the group who selected other answers) and between the two groups for each item of the five personality trait factors ("Agreeableness," "Control," "Emotionality," "Openness," and "Extraversion").

[0063] As for the allocation method, the correlation can also be grasped by discriminant analysis, logistic regression analysis, or conjoint analysis.

[0064] Next, based on the result of allocation by the allocation means 102, the association means 103 further associates the personality traits of the personality trait data 111 with the learning effects of the learning effect data 113, and stores the association data as analysis result data 114 in the storage unit 11 (S13). In other words, the contents of the answers given by the respondents of the respondent group as the learning effect data 113 are extracted, and associated with the personality trait data 111.

[0065] Fig. 8 is a diagram showing an example of the configuration of the analysis result data 114. Fig. 10 is a diagram showing the configuration of the analysis result data 114 in comparison with the analysis result when the grouping is further divided into three cases.

[0066] The analysis result data 114 includes questions from a disaster prevention awareness survey, personality traits obtained by a conventional analysis method, personality traits assigned by the assignment means 102, and disaster prevention actions associated by the association means 103.

[0067] In the analysis result data 114, different personality traits were associated with the questions in the disaster prevention awareness survey. Furthermore, disaster prevention behaviors were associated with the learning results of the respondent groups for each personality trait, so it became possible to measure the learning effect that a user with a certain personality trait would obtain (or not obtain) when studying using this teaching material, and thus to evaluate this teaching material.

[0068] On the other hand, when the personality trait data 111 and the awareness trait data 112 were analyzed by multiple regression analysis, the personality traits that corresponded to the questions in the disaster prevention awareness survey were all "controllability," resulting in biased analysis result data.

[0069] Furthermore, when responses to multiple questions in the disaster prevention awareness survey were given on a three-point Likert scale of "1: I don't think so," "2: I don't know," and "3: I think so," the personality traits that influenced disaster prevention awareness were only two of the five factors: "control" and "cooperativeness," resulting in analysis data that blurred the relationship between disaster prevention awareness and personality traits.

[0070] Next, the output means 104 outputs part or all of the analysis result data 114 directly or after editing (S14). Specifically, the output means 104 may output the analysis result data 114a shown in FIG. 8 (with the "influential personality traits derived from multiple regression analysis" omitted) as is, or may output a determination that the teaching material satisfies (does not satisfy) the evaluation criteria, for example, if the number of disaster prevention behavior items is greater than (or less than) a predetermined threshold. Furthermore, if three or more of the five personality trait items are extracted, the teaching material may be determined as teaching material that can be used for a variety of personalities, and if fewer than three items are extracted, the teaching material may be determined as teaching material specialized for that item. Furthermore, the specialized teaching material may be determined as teaching material for people with low scores in that item.

[0071] For example, learners with "control" (a cautious and planned personality) tended to be more likely to take specific disaster prevention actions, such as checking hazard maps and preparing emergency supplies, after receiving disaster prevention education using VR teaching materials. On the other hand, learners with "openness" (an optimistic and responsive personality) were less likely to take disaster prevention actions after the education. These results make it clear that the learning effect differs depending on the learner's personality traits, and can be used as an important indicator for considering the scope of application and areas for improvement of this teaching material.

[0072] For example, if three or more disaster prevention action items are implemented, including "checking evacuation routes in the event of a disaster," "preparing an emergency evacuation bag," and "checking means of communication between family members in the event of a disaster," the educational material can be judged to meet the evaluation criteria. On the other hand, if only one or two of these three items are implemented, it is deemed that the educational material may not be sufficiently effective, and an output will be issued indicating that improvement is required.

[0073] Furthermore, if three of the personality traits "extraversion," "control," and "emotionality" are extracted, the teaching materials can be determined to be suitable for learners with a variety of personality traits. For example, it is thought that materials that promote cooperative group learning would be appropriate for learners with high "extraversion" and medium scores for "control" and "emotionality." On the other hand, if only one or two traits are extracted, the materials can be determined to be specialized for that personality trait, and one possible application would be to provide materials to support planned disaster prevention actions to learners with low "control."

[0074] FIG. 12 is a table showing the relationship between the teaching materials and the output results of the output means 104.

[0075] Here, as shown in Figure 12, we will explain an example of a disaster prevention class conducted for fifth and sixth graders in an elementary school in Prefecture A using teaching materials α and β as S3 in Figure 3. Teaching materials α and β differ primarily in the way they use VR and whether or not they include group work, but S1, S2, S4, and S5 in Figure 3 all obtained data similar to that of this embodiment. Analysis of the obtained data revealed that teaching materials α extracted four of the five personality trait factors, making it possible to determine that teaching materials α are suitable for learners with diverse personality traits. On the other hand, teaching materials β did not extract any personality trait factors, making them inappropriate for learners with diverse personality traits and unable to be specialized for a specific personality trait.

[0076] Furthermore, it is also possible to present the missing disaster prevention action items and output an output to encourage additional learning for the missing parts. Based on the personality traits and disaster prevention awareness using big data, educational materials (including VR) according to the personality traits may be created in advance, and the user may learn using the educational materials according to the personality traits of each user based on the analysis result data 114.

[0077] For example, if three disaster prevention actions are presented—"checking hazard maps," "stockpiling emergency food and water," and "checking communication methods in the event of a disaster"—and it is determined that a learner has not yet stockpiled emergency food and water, additional learning materials are provided to focus on that item. These materials include a VR simulation that allows participants to experience the impact of a shortage of emergency supplies during a disaster, as well as videos that teach participants how to properly select emergency supplies. Furthermore, as an example of providing materials tailored to personality traits, for learners with high "control," materials that more specifically communicate risks are effective, and therefore materials including simulations of damage predictions in the event of a disaster are provided. On the other hand, for learners with high "openness" who do not readily recognize the importance of preparation, behavioral change can be promoted by providing VR materials that allow participants to experience a simulated disaster situation without emergency supplies.

[0078] (Effects of the embodiment) According to the above-described embodiment, respondents are classified based on the awareness characteristic data 112, personality characteristics are assigned to the group of classified respondents based on the personality characteristic data 111 of the group of respondents, and personality characteristics, awareness characteristics, and learning effects are associated with each other based on the learning effect data 113 of the group of respondents, so that the learning effect can be analyzed taking into account the user's personality.

[0079] [Other embodiments] The present invention is not limited to the above-described embodiment, and various modifications are possible without departing from the spirit of the present invention.

[0080] For example, in the above embodiment, the personality trait survey, disaster prevention awareness survey, and disaster prevention behavior survey were conducted separately from learning, but when using teaching materials such as VR or classroom slides, an educational means may be further provided in the information processing device 1, and each survey may be conducted by asking questions to determine personality traits and disaster prevention awareness in the VR, classroom slides, etc. executed by the educational means. Furthermore, the content of the VR, classroom slides, etc. may be changed in real time using the survey results to suit the personality traits of each user.

[0081] Furthermore, even if the contents of the VR, lecture slides, etc. are not changed in real time, education may be provided by the educational means. The educational means may be configured to change the teaching materials for the user's next education in response to the output of the output means 104. The lecture itself may be conducted by a lecturer, but in this case, the contents of the lecture slides are selected by the educational means.

[0082] Additionally, the survey content uses scales appropriate for the learning topic, such as a disaster prevention awareness survey or health awareness survey. The educational content could be, for example, learning using VR (personal experience) or classroom learning, and the survey items and awareness scales are expected to change depending on the learning content. The timing of the survey can be adjusted to evaluate changes in awareness before and after education for each learning content. For example, in the case of a curriculum that encourages independent learning of subjects such as Japanese language or mathematics, step S5 would survey learning awareness and learning behavior. Because learning behavior is a daily occurrence for children, the survey could be conducted one to two weeks after education (step S2).

[0083] FIG. 11 is a table showing the analysis results of disaster prevention awareness that influences disaster prevention behavior when personality traits are not included.

[0084] The table in Figure 11 shows the results of a conventional analytical method that "verifies the relationship between disaster prevention behavior and disaster prevention awareness" without taking personality traits into account. A logistic regression analysis was performed with disaster prevention behavior as the dependent variable and disaster prevention awareness as the independent variable, and disaster prevention awareness that influences disaster prevention behavior was extracted.

[0085] Here, B represents the regression coefficient. For example, if the desire to continue disaster prevention learning increases by 1, the behavior of deciding on a meeting place increases by 0.12. In other words, the larger the value of B, the greater the influence of consciousness on behavior. Furthermore, β represents the partial regression coefficient. For example, if the desire to continue disaster prevention learning increases by 1, the behavior of deciding on a meeting place increases by 0.226. In other words, the closer the value is to 1 (or -1), the greater the influence of consciousness on behavior. Furthermore, 95% CI represents the 95% confidence interval. In other words, because the 95% CI for the desire to continue disaster prevention learning is 0.002-0.237 and does not include 0, this variable is considered to have a statistically significant influence on disaster prevention behavior of deciding on a meeting place.

[0086] To summarize the above, as can be seen from the table in Figure 11, when personality traits were not included in the analysis, it was possible to extract disaster prevention awareness that influences disaster prevention behavior, but the analysis results were limited to the conclusion that "education should be provided to increase the extracted disaster prevention awareness."

[0087] In the above embodiment, the functions of the means 100 to 104 of the control unit 10 are realized by a program, but all or part of the means may be realized by hardware such as an ASIC. The program used in the above embodiment may also be provided by storing it on a recording medium such as a CD-ROM. The steps described in the above embodiment may be replaced, deleted, or added without departing from the spirit and scope of the present invention. [Explanation of symbols]

[0088] 1: Information processing equipment 2a, 2b, 2c: Terminal 3: Network 4a, 4b: User 10: Control section 11: Storage section 12: Communications Department 100: Aggregation method 101: Classification means 102: Allocation method 103: Correspondence means 104: Output means 110: Learning effect analysis program 111: Personality trait data 112: Consciousness characteristics data 113: Learning effect data 114: Analysis result data

Claims

1. Computer, a counting means for counting a personality trait derived from an answer to a first predetermined question, an awareness trait derived from an answer to a second predetermined question, and a learning effect derived from an answer to a third predetermined question after an education provided after the answer to the second predetermined question; a classification means for classifying respondents based on their answers to the second predetermined questions; an allocation means for allocating personality traits to the classified respondent groups based on the personality traits of each respondent in the classified respondent groups; a learning effect analysis program that functions as a matching means for matching the awareness trait, the personality trait, and the learning effect based on the answer to the third predetermined question of each respondent in the group of respondents to which the personality trait is assigned.

2. 2. The learning effect analysis program according to claim 1, wherein the classifying means classifies the respondents based on a correlation coefficient of the answers to the second predetermined questions.

3. 2. The learning effect analysis program according to claim 1, further functioning as an output means for evaluating the education based on the result of associating the personality traits with the learning effects.

4. 4. The learning effect analysis program according to claim 3, wherein the output means evaluates the education based on the number of learning effect items included in the result of associating the personality traits with the learning effects.

5. a counting means for counting a personality trait derived from an answer to a first predetermined question, an awareness trait derived from an answer to a second predetermined question, and a learning effect derived from an answer to a third predetermined question after an education provided after the answer to the second predetermined question; a classification means for classifying respondents based on their answers to the second predetermined questions; an allocation means for allocating personality traits to the classified respondent groups based on the personality traits of each respondent in the classified respondent groups; and an information processing device having a matching means for matching the awareness trait, the personality trait, and the learning effect based on the answer to the third predetermined question of each respondent in the group of respondents to which the personality trait is assigned.

6. a step of aggregating a personality trait derived from the answer to a first predetermined question, an awareness trait derived from the answer to a second predetermined question, and a learning effect derived from the answer to a third predetermined question after an education provided after the answer to the second predetermined question; categorizing respondents based on their responses to each of the second predetermined questions; assigning personality traits to the classified groups of respondents based on the personality traits of each respondent in the classified groups of respondents; and associating the awareness traits, the personality traits, and the learning effects based on answers to the third predetermined question of each respondent in the group of respondents to which the personality traits are assigned.

7. Computer, a counting means for counting a personality trait derived from an answer to a first predetermined question, an awareness trait derived from an answer to a second predetermined question, and a learning effect derived from an answer to a third predetermined question after an education provided after the answer to the second predetermined question; an education means for carrying out the education using educational materials prepared in advance; a classification means for classifying respondents based on their answers to the second predetermined questions; an allocation means for allocating personality traits to the classified respondent groups based on the personality traits of each respondent in the classified respondent groups; a correlation means for correlating the awareness trait, the personality trait, and the learning effect based on an answer to the third predetermined question by each respondent in the respondent group to which the personality trait is assigned; and causing the device to function as an output means for evaluating the education based on the result of associating the personality traits with the learning effects and outputting the evaluation result to the education means; The educational means is an educational program that carries out the education or additional education using teaching materials corresponding to the evaluation results.

8. 8. The education program according to claim 7, wherein the education means asks at least one of the first predetermined question, the second predetermined question, and the third predetermined question when the education is carried out.

9. Computer, a counting means for counting personality traits derived from answers to a first predetermined question, awareness traits regarding disaster prevention derived from answers to a second predetermined question, and learning effects regarding disaster prevention behavior derived from answers to a third predetermined question after education regarding disaster prevention is provided after answers to the second predetermined question; a classification means for classifying respondents based on their answers to the second predetermined questions; an allocation means for allocating personality traits to the classified respondent groups based on the personality traits of each respondent in the classified respondent groups; A learning effect analysis program that functions as a matching means for matching the awareness characteristics regarding disaster prevention, the personality characteristics, and the learning effects regarding disaster prevention based on the answers to the third predetermined question of each respondent in the group of respondents to which the personality characteristics are assigned.

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