WO2014147667A1

WO2014147667A1 - System, method and program for evaluating a reflexivity

Info

Publication number: WO2014147667A1
Application number: PCT/JP2013/001961
Authority: WO
Inventors: Sergey TARASENKO; Yuki Kamiya
Original assignee: Nec Corporation
Priority date: 2013-03-22
Filing date: 2013-03-22
Publication date: 2014-09-25

Abstract

Module 12 provides a Yes/No question about reflexivity ability. Module 11 inputs Yes or No answer to the Yes/No question. Module 13 detects Yes or No answer. Module 14,15 provides an open question which is associated with the Yes/No question and is about hypothetical situation close to real one, in case of Yes answer. Module 16,17 inputs free text answer to the open question. Module 18,19 analyzes consistency between the answer to the open question and Y/N question. Module 110,111 judges lack or absence of reflexivity, in case of No answer or inconsistency. Module 112,114,115 provides a learning program corresponding the reflexivity, in case of lack or absence of reflexivity.

Description

SYSTEM, METHOD AND PROGRAM FOR EVALUATING A REFLEXIVITY

The present invention relates to a technology of evaluating a reflexivity.

Reflexivity is ability to image one's own behavior from the third perspective and find mistakes of the self and others. Therefore, reflexivity allows individuals to detect their own mistakes. Consequently, reflexivity plays self-control and prediction function.

Absence or lack of reflexivity can cause serious problems inside social networks. Since big corporations are also big social networks, the absence or lack of reflexivity can cause serious problems.

For example, if person cannot complete an application form (AF) correctly, the incorrectly filled-in form will be returned after several days. Therefore absence of reflexivity (finding a way to fill AF correctly) can cause delays or even failure of the tasks in a big social network within corporations.

Another example of reflexivity problem is when a person considers the problem in organization to be beyond his/her scope of activity. Very often such attitude can cause problems, because a person does not understand his/her involvedness.Or he/she tries to avoid "redundant" problems, because he/she does not feel responsibility for such kind of tasks (Non-Patent Documents 1).

Such behavior is caused by lack or absence of reflexivity.

The approach to improve personal knowledge about a subject (mathematics, physics etc.) was disclosed in Patent Document 1. Users input the answers and their own confidence of their answers. Then the system can evaluate the knowledge and skills, by estimating the level of understanding of users based on the answers and its confidence. The major idea behind is that the system outputs evaluation of individual's knowledge about a particular subject. The inventors assumed that by looking on his/her own results, person becomes aware about the strong and weak sides of his/her knowledge, therefore he/she will put more efforts to study particular fields. In other words, inventors hope that examinee will acquire a habit of self-check to improve knowledge and self-confidence about the knowledge.

[NPL 1] Lefebvre, V.A. Algebra of conscience, 2nd edition, Kluwer Academic Publisher, 2001.

[PTL 1] JP 08-146867A

There are several problems in the previous invention. First of all, the system replies only on the subjective information about an academic subject (mathematics, physics etc.).

Second, the presented approach only suggests usage of self-check. In other words, confidence and reply are provided by the same person. The idea is that person should strive to improve his/her skills. The information about correct answers and confidence should help in understanding what knowledge is weak. The issue of self-check is one of examples of reflexivity (self-reflexivity). On the other hand, there are different types of reflexivity. For example, these are Self-reflexivity; Reflexivity of Others, Systems Thinking Reflexivity, etc.

Third, inventors only assume that providing to examinees their own score will stimulate their learning process in positive way. However, inventors provide no details about how to train and learn the reflexivity abilities.

Finally, problems with reflexivity can cause problem in functioning of social network of workers inside the corporation. Meanwhile the problems with reflexivity are of hidden or implicit nature.

The present invention has been accomplished in consideration of the above-mentioned problems, and an object of the present invention is to provide a technology of solving the above-mentioned problems, namely, a technology of facilitating extraction of problems about reflexivity skills in behavior and reasoning. Therefore it increases a stability and smoothness of the social networks in a company by means of increasing reflexivity.

The present invention for solving the above-mentioned problems is a system for evaluating a reflexivity; comprising: a Yes/No question provision means that provides a Yes/No question about reflexivity ability; a Yes/No answer input means that inputs Yes or No answer to said Yes/No question; a Yes/No answer detection means that detects Yes or No answer; an open question provision means that provides an open question which is associated with said Yes/No question and is about hypothetical situation close to real one, in case said Yes/No answer detection means detects Yes answer; a free answer input means that inputs free text answer to said open question; a consistency analysis means that analyzes consistency between the answer to the open question and the answer to Y/N question; and a reflexivity judgment means that judges lack or absence of reflexivity, in case said Yes/No answer detection means detects No answer or said consistency analysis outputs inconsistency.

The present invention for solving the above-mentioned problems is a method for evaluating a reflexivity, comprising the step of: providing a Yes/No question about reflexivity ability; inputting Yes or No answer to said Yes/No question; detecting Yes or No answer; providing an open question which is associated with said Yes/No question and is about hypothetical situation close to real one, in cases of detecting Yes answer; inputting free text answer to said open question; analyzing consistency between the answer to the open question and the answer to Y/N question; and judging lack or absence of reflexivity, in cases of detecting No answer to said Yes/No question or outputting inconsistency between the answer to the open question and the answer to Y/N question.

The present invention for solving the above-mentioned problems is a program for evaluating a reflexivity, causing a computer to execute: a Yes/No question provision process of providing a Yes/No question about reflexivity ability; a Yes/No answer input process of inputting Yes or No answer to said Yes/No question; a Yes/No answer detection process of detecting Yes or No answer; an open question provision process of providing an open question which is associated with said Yes/No question and is about hypothetical situation close to real one, in cases of detecting Yes answer; a free answer input process of inputting free text answer to said open question; a consistency analysis process of analyzing consistency between the answer to the open question and the answer to Y/N question; and a reflexivity judgment process of judging lack or absence of reflexivity, in cases of detecting No answer to said Yes/No question or outputting inconsistency between the answer to the open question and the answer to Y/N question.

The present invention makes it possible to facilitate extraction of problems about reflexivity skills.

The present invention can work on different types of reflexivity. So it can identify problems with a particular type of reflexivity for each individual.

The present invention can provide each individual with appropriate learning/training program, including special training procedures, which help to increase conscious understanding of reflexivity problems and avoid from doing these mistakes again.

The present invention helps to provide common understanding and cultivates common culture. It allows to increase stability and smoothness of the social networks and corporations.

Fig. 1 is a detailed schema of the system relating to a first exemplary embodiment of the present invention. Fig. 2 is a view explaining the example operation ( in cases of answer "NO"). Fig. 3 is a view explaining the example operation ( in cases of answer "YES"). Fig. 4 is a view illustrating Natural Language Processing Module (NLPM). Fig. 5 is a general schema of the invention. Fig. 6 is detailed schema of the system relating to a second exemplary embodiment of the present invention.

(Overview on concept)
There are different types of reflexivity as follows. Types of reflexivity: 1) self-perception (self-reflexivity); 2) reflexivity of others; 3) system thinking reflexivity; etc.

Different types of reflexivity should be detected and trained in different ways. We propose a particular classification of the types of reflexivity and corresponding learning/training courses. Further we propose particular features to identify problems with a particular type of reflexivity and compose appropriate learning/training program.

To identify these problems we suggest to use special sets of questions:
1. Self-perception (Self-reflexivity): 1) do you have image of yourself?; 2) can you understand duties and tasks?; 3) can you understand your own mistakes?; 4) do you always correct your own mistakes?; 5) can you publicly accept you mistakes?; 6) can you publicly apologize?, etc.
2. Reflexivity of Others: 1) can you understand mistakes of other people?; 2) can you publicly accuse other people?; etc.
3. Systems thinking reflexivity (social reflexivity): 1) do you understand that you are a part of a big social system?; 2) can you completely understand consequences of your own actions?; etc.

Both types of reflexivity and corresponding questions are pre-defined and stored in the databases.

It is possible a situation when person has incorrect knowledge, but high self-confidence that the answer is correct. In order to avoid such situation, we suggest to use the questions, which can help to verify the true ability of the person.

We consider one example of analysis as follows.

A person is asked the question "Can you understand your own mistake?" and the answer is "YES", then we can test person's behavior in a situation close to real one. We can provide him/her with a hypothetical situation in which this person collides with another person and we ask "Whom would you blame and why?".

Usually, in such situation both people have some fault, except for some extraordinary cases. If the answer is, for example, "I blame the other person, because he/she was looking in a different direction", then we can consider the answer of the person to be inconsistent with the answer to the question "Can you understand your own mistakes?". Therefore, we doubt person's "YES" answer to the question "Can you understand your own mistake?". This means that the person should be trained to really understand his/her own mistakes.

The consistency of the "YES" answer, which states a particular ability with description of actions in hypothetical situation can be evaluated as follows: the description of hypothetical situation is compared with expected result. The expected result is represented as a structure with key elements. The analyzer of description is searching for match between the key elements of the expected results and the content of the description.

Next we consider another example of analysis. System thinking reflexivity is reflexivity about how the work or business processes are performed in the social groups (teams co-workers, project teams, etc.).

A person is asked the question "Do you consider yourself to be a member of your unit?". The answer is "YES". Next the person is asked a question "What do you think about electricity saving in your unit?".

The answer is "I think that people waste a lot of electricity by not switching off the light or other power greedy devices like air-conditioners, TVs, etc. They should be more economical." The expected result is that a person will speak about his/her unit as one team. But Person says "people" (they, excluding me) instead of we" (all people, including me).

Person should show the involvedness, by suggesting how he/she him/herself could participate in improving the situation. But Person only says "They should be more economical." He/she shows no involvedness.

Therefore analysis of the open question allows it to show, that person thinks differently from what he/she answers to the previous question.

In order to detect the inconsistency between the answers of participants and description of their behavior in hypothetical situation close to real, we propose to use methods of Natural Language Processing (NLP). For example, approach to analysis of the text based on ontological semantics can be used.

Further, in accordance with types of reflexivity, we suggest learning/training courses of three types.

Type 1 course is a course to train self-reflexivity. This course includes learning/training about self-perception. During this course a person learns to constantly monitor his/her own behavior in order to comply with some rules and reduce the number of mistakes done by oneself.

Type 2 course is a course about reflexivity of others. During this course person learns to monitor behavior of others to comply with some rules and reduce the number of mistakes done by others.

Type 3 course is a course about system thinking reflexivity (or social reflexivity). During this course person learns to reflect about the situation within a group of people. The training includes exercises to make a big picture of relationship between the self and others, monitoring the overall of goal of the group and monitor possible mistakes done by self and others while trying to achieve the goal.

Learning courses are assembled into Learning Program. A single Learning Program consists several (one is also possible) learning courses.

The first feature of this invention is to detect the inconsistency between the answer to Yes/No question and the answer of corresponding open question. The answer to open question is about person's action in a hypothetical situation close to real one.

The second feature of this invention is to provide the learning courses by matching the category marker, while Yes/No question is associated with a category marker corresponding to each types of reflexivity. The learning program including the learning courses is provided.

Hereinafter, the exemplary embodiments of the present invention will be illustratively explained in details by referencing the accompanied drawings. However, the constituent element described in the following exemplary embodiments is only an exemplification, and there is no intention of limiting the technological scope of the present invention to hereto.

(First exemplary embodiment)
<Configuration>
The detailed schema of the invention is presented in Fig. 1. System includes unit for Yes-No question provision(module 12,116,117), unit for Yes-No answer input(module 11), unit for Yes-No answer detection(module 13), unit for open question provision (module 14,15), unit for free answer input (module 16,17), unit for consistency analysis (module 18,19), unit for reflexivity judgment (module 110,111), unit for learning program provision (module 112,114,115), and unit for repeat judgment (module 113).

Module 11 takes a question from list 12 of selected Y/N (yes/no) questions. User gives "YES" or "NO" answers to the question.

The list12 of selected Y/N questions is generated by module 116. Module 116 has preset criteria (refer to the Second Exemplar Embodiment) to select Y/N questions from database 117 of Y/N questions about type of reflexivity (module 117). Each question is processed one by one. The database 117 contains lists of questions, which are marked to correspond to a particular type of reflexivity (category markers). Also each Y/N question in modules 12 and modules 117 is related to the set of open questions in module 15 by question marker.

Module 13 detects Yes or No answer. After the user has answered to the single question, module 11 sends the answer to module 13. Module 13 checks the answer to the question.

If the answer is "YES", module 13 sends "YES" signal to module 14. If the answer is "NO", module 13 sends "NO" signal to module 110. Corresponding category of reflexivity is automatically added into the list 111 of problems by module 110.

Module 14 has two inputs from module 13 and module 11. When Module 14 receives a question marker from module 11, it sends question marker to module 15.

Module 15 is a database of open questions about types of reflexivity. Each question in module 12 is associated with open question in module 15 by means of question marker. After module 15 receives the question marker from module 14, it sends back a set of open questions associated with this particular question marker.

Module 14 randomly selects a single open question and sends this question to module 16. The open question is organized in a way to request free text description of action, which user would take in a hypothetical situation close to real.

Module 16 is a dialog interface with user. User input free text answer to the open question. Module 16 outputs free text description 17. Then open text description 17 is processed by module 18.

Module 18 compares the content of the free text description with some template (set of statements). The set of statements is pre-defined for each type of reflexivity. Module 18 calculates a level of consistency (LC) between the free text description and the template. Next, it sends LC value to module 19.

Module 19 compares LC value with preset threshold. If LC value is higher or equal to a threshold value, module 19 sends a signal to module 113. If LC value is low than threshold, module 19 sends a signal to module 110.

Modules

16, 17, 18 and 19 together are called Natural Language Processing Module (NLPM).

Module 110 takes three inputs from module 11, module 13 and module 19. Module 110 receives category marker from module 11. When module 110 receives the signal from module 13, by using category marker, it adds corresponding type of Reflexivity to the List 111 of the problems. Also when module 110 receives the signal from module 19, by using category marker, it adds corresponding type of Reflexivity to the List 111 of the problems. Module 110 also sends signal to module 113.

Module 114 takes two inputs from module 111 and module 112. Module 114 receives a list of problems (types of reflexivity, which a user has low ability about) from module 111. Module 114 receives a list of Learning Courses from database 112 of Learning Courses. The learning courses in module 112 have corresponding category marker.

Since problem list in module 111 contains the list of category marker, the learning courses in module 111 can be selected easily by matching the category marker. Module 114 selects the learning course for a given category marker and inserts it into a Learning Program 115 for the particular user.

Module 113 judges whether process repeats or not. After each question from module 12 is processed, module 114 can add a new learning course into the Learning program 115. After all questions from module 12 are processed, module 113 sends a signal to module 115, and the creation of learning program is completed. Otherwise, module 113 sends a signal to module 11 and process repeats.

By the way, module 110 adds category marker in both cases when a person answers "YES" or "NO". In other words, module 110 takes inputs from module 13 and module 19. To distinguish these cases, module 110 adds 0 value to category marker to indicate "NO" answer, and 1 value otherwise, namely 0+"category marker" or 1+"category marker". This information can be used further to prioritize learning courses.

The inconsistency between answers (input from module 19) means the unawareness of absence or lack of reflexivity. So, "1+category maker" is more important than "0+category marker". Module 114 assigns higher priority to the learning courses of "1+category maker" than prioritize to the learning courses of "0+category marker".

<Operation 1>
Here the example of implementation of the invention in the case when Y/N question is answered "NO" is presented. The example operation is presented in Fig. 2.

First, system provides user with Yes-No question "Do you realize how you imagine yourself?" This question is taken from database in module 12. The Category marker for this question is "Self-reflexivity". The user inputs answer "NO" in the module 11.

Module 13 detects answer "NO" and sends a signal to module 110. Module 110 receives the signal from module 13 and category marker "Self-reflexivity" from module 11.

Therefore, module 110 adds category marker "Self-reflexivity" to the list 111 of problems. The learning course regarding Self-Reflexivity from module 112 will be included into the Learning program 115 by module 114.

<Operation 2>
Other example of implementation of the invention in the case when Y/N question is answered "YES" is presented. The example operation is presented in Fig. 3. Social reflexivity is about human relationships in the social groups.

First, system provides user with Yes-No question "Do you feel yourself as a member of a team?". This question is taken from database in module 12. The Category marker for this question is "Social reflexivity". The question marker for this question is "question5". The user inputs answer "YES" in the module 11.

Module 13 detects answer "YES" and sends a signal to module 14. Module 14 receives the signal from module 13 and question marker "question5".from module 11.

Module 14 selects a set of questions, which correspond to the question marker, from module 15 and selects randomly a single question. The selected question is "What do you think about electricity saving in your unit?". This question is send to module 16.

A user input an answer to the question in module 16. The output of the module 16 is a free text description of user's opinion stored in module 17. Module 18 receives input from module 17 and calculates LC between the user's answer and template of the answer.

The detailed analysis of the free text description is presented in Fig. 4.

Modules

The free text is compared with a particular template. In this example, template contains three key elements (statements):
Statement1: speaker should associate him/herself with other people as one intact (or whole, entire) team;
Statement2: speaker should show his/her desire to be involved into finding solution of the problem;
Statement3: speaker should show his/her social responsibility.
Here the key elements are statements, which should be found in the free text answer, if person's social reflexivity (system thinking reflexivity) level is high. The statements corresponding to each type of reflexivity are pre-defined as well as types of reflexivity and corresponding questions. The statements are defined from the dictionary of psychological practice or ontological semantics.

For example, many people tend to think that if some number of individuals is gathered at the same place, these individuals are one team. In fact, such statement is false. People at the stadium for soccer game are not one team. The group of people to be called a team should have some attributes like: one common goal, mutual understanding, each team member consider about behavior of other team members, etc. So Statement1 is one of the key among the template.

The variables a1, a2 and a3 correspond to statement1, statement2 and statement3. Each variable ai takes value 1, if the corresponding statement is found in the free text, and 0 otherwise.

In this case, a1, a2 and a3 equal 0, because none of the statements has been found in the free text, and LC value is 0. The threshold value corresponds to the number of statements (threshold = 3). Therefore, module 19 sends a signal to module 110.

Module 110 also receives category marker "Social Reflexivity" from module 11. Module 110 adds category marker "Social Reflexivity" to the list 111 of problems. The learning course regarding Social -Reflexivity from module 112 will be included into the Learning program 115 by module 114.

(Second exemplary embodiment)
The general schema of the invention is presented in Fig. 5. Second embodiment adds new function to the system described in first embodiment. This new function is to detect the people suspected in low reflexivity ability.

The general schema includes two modules of larger scale. Module I includes

modules

21 and 22. Module II includes only module 23. The first exemplar embodiment can be used as Module II. In other words, Module I is additional function.

First, module 21 analyzes arbitrary question and free text answer to this question. Then module 21 calculates LC regarding various types of reflexivity (General Reflexivity). Module 22 compares LC with threshold value. The threshold value is received by module 22 from module 21. If LC is not smaller than a threshold, then investigation about suspected person is terminated. Otherwise, person should undergo more detailed testing in module 23.

Therefore this exemplar embodiment provides two level verification of the person's skill in reflexivity. On the first level, a suspect person is chosen on the basis of analysis of his/her answer to open question. On the second level, the person undergoes detailed inspection of weak skills in General Reflexivity. The first level is used to spot a suspect person's weak General Reflexivity skills, on the second level the detailed analysis of person's skills are performed (refer to the First Exemplar Embodiment).

Important difference of our method from simple feature matching, is that we not only look for the key elements of information (features) in the text, but we also consider what features (statements) were not detected. The detected features are then subject for intensive testing as described in the First Exemplar Embodiment section. The features which were not found indicate that the types of reflexivity they refer to are obligatory subjects to train a person.

The detailed schema of the second exemplar embodiment is presented in Fig. 6.

Module 31 contains an arbitrary open question (OQ). Module 32 takes the arbitrary OQ from module 31 and detects what categories of reflexivity this OQ refers to. Module 32 outputs the list of statements 18a with corresponding category markers, which correspond to the categories of reflexivity related to the OQ from module 31.

Next, the second exemplar embodiment employs

modules

17, 18 and 19 of the first exemplar embodiment. In order to distinguish between these module in the first and the second exemplar embodiments, the modules in the second exemplar embodiment are assigned additional index "C". Therefore the modules are referred as

module

17C, 18C and 19C.

The module 18a, which is a part of module 18C, contains list of the statements produced by module 32. A special generation method is needed to produce the statements. As example of such method, we suggest ontological semantics or other methods of natural language processing. Ontological semantics considers concepts. Each concept is the list of attributes. Each attribute have a particular value. The statements express the attribute and its value. For example, in the case of social reflexivity, free text "I don't' care about other team members" means "person associates him/herself with entire team = no" ([attribute = value]).

Module 17C contains free text answer to the question contained in module 31. Module 18C calculates the LC of the answer with detected categories of reflexivity in the OQ.

Module 19C received two inputs from

modules

32 and 18C. Module 32 sends to module 19C a total number of statements. The total number ( =5 in Fig. 6.) is used as a threshold value in module 19C. Module 19C receives LC value from module 18C. If LC value is less than a threshold value, module 19C sends a signal to module 33. Modules 33 receives two inputs from

modules

18b and 18a. Module 18b sends to module 33 a list of variables ai, where i = 1,2,3,..., while module 18a contains the statements with corresponding category markers. Module 33 selects the category markers which correspond to variables a1, a2, a3, etc., which equal zero.

Module 34 output list of category markers, which correspond to zero value variables ai, where i=1,2,3,... This list of categories serves as input for module 116 of the first exemplar embodiment.

By default, module 116 randomly selects a category, then randomly selects a question. The selected category and question are excluded from the original list to avoid repetition. On the other hand, it is possible to specify how to select question in accordance with particular goal. For example, if we want to test only Self-reflexivy, we fix category to be reflexivity, and randomly select a several questions.

Module 116 has set criteria. Procedure to select questions is called criteria. Module 116 selects Y/N questions, which correspond to the list of category markers in module 34 from the databases of Y/N questions in module 117.

From this point, the processing follows the schema of the first exemplar embodiment.

(Supplementary note)
Further, the content of the above-mentioned exemplary embodiments can be expressed as follows.

A system for evaluating a reflexivity; comprising:
a Yes/No question provision means that provides a Yes/No question about reflexivity ability;
a Yes/No answer input means that inputs Yes or No answer to said Yes/No question;
a Yes/No answer detection means that detects Yes or No answer;
an open question provision means that provides an open question which is associated with said Yes/No question and is about hypothetical situation close to real one, in case said Yes/No answer detection means detects Yes answer;
a free answer input means that inputs free text answer to said open question;
a consistency analysis means that analyzes consistency between the answer to the open question and the answer to Y/N question; and
a reflexivity judgment means that judges lack or absence of reflexivity, in case said Yes/No answer detection means detects No answer or said consistency analysis outputs inconsistency.

The system for evaluating a reflexivity;
further comprising: a learning program provision means that provides a learning program including a learning course corresponding the reflexivity, in case said reflexivity judgment means judges lack or absence of reflexivity.

The system for evaluating a reflexivity; wherein

said Yes/No question is associated with a category marker corresponding to each types of reflexivity,

said learning program provision means provides the learning course by matching the category marker.

The system for evaluating a reflexivity; further comprising:
an reflexivity extraction means that extracts at least one reflexivity from a plurality of types of reflexivity,
wherein said Yes/No question provision means provides the Yes/No question about extracted reflexivity by said reflexivity extraction means.

The system for evaluating a reflexivity; wherein
said reflexivity judgment means judges lack or absence of reflexivity as priority, in case said consistency analysis means outputs inconsistency.

A method for evaluating a reflexivity, comprising the step of:
providing a Yes/No question about reflexivity ability;
inputting Yes or No answer to said Yes/No question;
detecting Yes or No answer;
providing an open question which is associated with said Yes/No question and is about hypothetical situation close to real one, in cases detecting Yes answer;
inputting free text answer to said open question;
analyzing consistency between the answer to the open question and the answer to Y/N question; and
judging lack or absence of reflexivity, in cases of detecting No answer to said Yes/No question or outputting inconsistency between the answer to the open question and the answer to Y/N question.

The method for evaluating a reflexivity; further comprising
the step of: providing a learning program including a learning course corresponding the reflexivity, in case said reflexivity judgment means judges lack or absence of reflexivity.

The method for evaluating a reflexivity; wherein
said Yes/No question is associated with a category marker corresponding to each types of reflexivity,
providing the learning course by matching the category marker.

The method for evaluating a reflexivity; further comprising the step of:
extracting at least one reflexivity from a plurality of types of reflexivity,
wherein providing the Yes/No question about extracted reflexivity.

The method for evaluating a reflexivity; wherein
judging lack or absence of reflexivity as priority, in cases of outputting inconsistency.

A program for evaluating a reflexivity, causing a computer to execute:
a Yes/No question provision process of providing a Yes/No question about reflexivity ability;
a Yes/No answer input process of inputting Yes or No answer to said Yes/No question;
a Yes/No answer detection process of detecting Yes or No answer;
an open question provision process of providing an open question which is associated with said Yes/No question and is about hypothetical situation close to real one, in cases of detecting Yes answer;
a free answer input process of inputting free text answer to said open question;
a consistency analysis process of analyzing consistency between the answer to the open question and the answer to Y/N question; and
a reflexivity judgment process of judging lack or absence of reflexivity, in cases of detecting No answer to said Yes/No question or outputting inconsistency between the answer to the open question and the answer to Y/N question.

The program for evaluating a reflexivity; further causing a computer to execute:
a learning program provision process of providing a learning program including a learning course corresponding the reflexivity, in cases of the judgment of lack or absence of reflexivity.

The program for evaluating a reflexivity; wherein
said Yes/No question is associated with a category marker corresponding to each type of reflexivity,
providing the learning course by matching the category marker in said learning program provision process.

The program for evaluating a reflexivity; further causing a computer to execute:
an reflexivity extraction process of extracting at least one reflexivity fr om a plurality of types of reflexivity,
wherein providing the Yes/No question about extracted reflexivity in said Yes/No question provision process.

The program for evaluating a reflexivity;
wherein judging lack or absence of reflexivity as priority in cases of outputting inconsistency in said reflexivity judgment process.

The present invention assists in increasing people ability to control their own mistakes and mistakes of others (reflexivity). Because a matter of detecting mistakes is closely related to the matter of planning, this invention also allows to increase people's ability to plan their actions.

11 module (input Y/N question)
12 module (Yes-No question provision)
13 module (Yes-No answer detection)
14,15 module (open question provision)
16,17 module (free answer input)
18,19 module (consistency analysis)
21,22,23 larger scale module (general schema)
31 module (open question provision)
32 module (categories of reflexivity detection)
33 module (category marker selection)
34 module (list of category markers)
116,117 module (Yes-No question provision)
110,111 module (reflexivity judgment)
112,114,115 module (learning program provision)
113 module (repeat judgment).

Claims

A system for evaluating a reflexivity; comprising:
a Yes/No question provision means that provides a Yes/No question about reflexivity ability;
a Yes/No answer input means that inputs Yes or No answer to said Yes/No question;
a Yes/No answer detection means that detects Yes or No answer;
an open question provision means that provides an open question which is associated with said Yes/No question and is about hypothetical situation close to real one, in case said Yes/No answer detection means detects Yes answer;
a free answer input means that inputs free text answer to said open question;
a consistency analysis means that analyzes consistency between the answer to the open question and the answer to Y/N question; and
a reflexivity judgment means that judges lack or absence of reflexivity, in case said Yes/No answer detection means detects No answer or said consistency analysis outputs inconsistency.
The system for evaluating a reflexivity according to claim 1; further comprising:
a learning program provision means that provides a learning program including a learning course corresponding the reflexivity, in case said reflexivity judgment means judges lack or absence of reflexivity.
The system for evaluating a reflexivity according to claim 2; wherein
said Yes/No question is associated with a category marker corresponding to each types of reflexivity,
said learning program provision means provides the learning course by matching the category marker.
The system for evaluating a reflexivity according to claim 1 to 3; further comprising:
an reflexivity extraction means that extracts at least one reflexivity from a plurality of types of reflexivity,
wherein
said Yes/No question provision means provides the Yes/No question about extracted reflexivity by said reflexivity extraction means.
The system for evaluating a reflexivity according to claim 1 to 3; wherein
said reflexivity judgment means judges lack or absence of reflexivity as priority, in case said consistency analysis means outputs inconsistency.
A method for evaluating a reflexivity, comprising the step of:
providing a Yes/No question about reflexivity ability;
inputting Yes or No answer to said Yes/No question;
detecting Yes or No answer;
providing an open question which is associated with said Yes/No question and is about hypothetical situation close to real one, in cases of detecting Yes answer;
inputting free text answer to said open question;
analyzing consistency between the answer to the open question and the answer to Y/N question; and
judging lack or absence of reflexivity, in cases of detecting No answer to said Yes/No question or outputting inconsistency between the answer to the open question and the answer to Y/N question.
A program for evaluating a reflexivity, causing a computer to execute:
a Yes/No question provision process of providing a Yes/No question about reflexivity ability;
a Yes/No answer input process of inputting Yes or No answer to said Yes/No question;
a Yes/No answer detection process of detecting Yes or No answer;
an open question provision process of providing an open question which is associated with said Yes/No question and is about hypothetical situation close to real one, in cases of detecting Yes answer;
a free answer input process of inputting free text answer to said open question;
a consistency analysis process of analyzing consistency between the answer to the open question and the answer to Y/N question; and
a reflexivity judgment process of judging lack or absence of reflexivity, in cases of detecting No answer to said Yes/No question or outputting inconsistency between the answer to the open question and the answer to Y/N question.