Patent Publication Number: US-2021166581-A1

Title: Learning support system, method and program

Description:
TECHNICAL FIELD 
     The present invention relates to a learning support system, method and program for changing a display style of a phrase to be learned depending on a user who learns phrases of a foreign language. 
     BACKGROUND ART 
     Hitherto, as an apparatus for supporting learning of a foreign language, for example, there has been known a learning apparatus that enhances a learning effect by increasing the number of learning times of a foreign word as the number of incorrect answers given by a user to a prepared question about the foreign language word increases (see, for example, Patent Literature 1). 
     The learning apparatus described in Patent Literature 1 controls the frequency of iterative learning based on a variable indicating the number of learning times about a question or an elapsed time, a variable indicating the number of correct and incorrect answers given by a user to questions, and the like, so that a question to which an incorrect answer was given previous time is likely to be asked next time and thereafter. 
     This learning apparatus determines, to enhance a learning effect, the frequency of iterative learning depending on a function called a forgetting function indicating that the amount of memory of a human exponentially decreases as time passes. 
     CITATION LIST 
     Patent Literature 
     [PTL1] 
     
         
         
           
             JP-A-H6(1994)-289766 
           
         
       
    
     SUMMARY OF INVENTION 
     Technical Problem 
     However, the learning apparatus described in Patent Literature 1 has a disadvantage that the similarity between a phrase of a language to be learned by a user and the corresponding phrase of the user&#39;s native language is not taken into consideration. 
     That is, the difficulty in learning is different between, for example, a case where a user whose native language is Japanese learns an English phrase and a case where a user whose native language is English learns a Japanese phrase. 
     In particular, an understanding level or memory retention of a user greatly varies depending on whether or not a phrase to be learned is a loanword in the native language of the user, whether or not the type of written characters, such as alphabet, kana, or kanji, is familiar to the user, whether or not a phrase to be learned and the corresponding phrase of the user&#39;s native language are similar to each other in terms of spelling, or whether or not a phrase to be learned and the corresponding phrase of the user&#39;s native language are similar to each other in terms of pronunciation, and thus a learning effect is not uniform. 
     The foregoing learning apparatus determines the frequency of iterative learning without taking these circumstances into consideration, and thus it is not possible to obtain a favorable learning effect depending on phrases of a foreign language to be learned. 
     The present invention has been made to solve these problems, and an object of the present invention is to provide a learning support system, method and program that determine a predetermined period of time until next learning based on the linguistic similarity of a phrase to be learned by a user to the user&#39;s native language, thereby providing an opportunity for iterative learning at more appropriate timing for each phrase to be learned depending on a user, and enhancing an effect of learning of a foreign language. 
     Solution to Problem 
     The learning support system described in claim  1  comprises native language setting means for setting a native language of a user;
         learning phrase storage means for storing phrase information to be learned by the user together with a learning stage;   predetermined period of time calculation means for calculating a predetermined period of time based on linguistic similarity of the phrase information to be learned by the user to the native language input by the native language setting means;   storage control means for storing the learning stage stored in the learning phrase storage means as a first memory state if learning has not been completed, storing the learning stage as a second memory state if learning has been completed, and storing the learning stage as the first memory state again if the predetermined period of time has elapsed after learning was completed; and   output means for outputting the phrase information to be learned by the user in a first output style and a second output style depending on the first memory state and the second memory state, respectively, stored in the learning phrase storage means by the storage control means.       

     The learning support system described in claim  2  further comprises learning stage input means for inputting the learning stage from the user for the phrase information to be learned by the user. 
     An input by the learning stage input means is stored as the first memory state by the storage control means if learning has not been completed, and is stored as the second memory state by the storage control means if learning has been completed. 
     Accordingly, a phrase that has not been completely memorized by the user and that is to be learned becomes clear, and thus an opportunity for iterative learning can be provided more effectively. 
     The learning support system described in claim  3  further comprises phrase information input means for inputting the phrase information to be learned by the user; an phrase-related information storage unit for storing related information that is related to a phrase, the related information including the similarity; and phrase matching means for determining whether or not the phrase information input by the phrase information input means and stored in the learning phrase storage means is stored in the phrase-related information storage unit. 
     The storage control means stores the learning stage as the first memory state regarding phrase information that is not stored in the phrase-related information storage unit based on a matching result obtained by the phrase matching means. 
     Accordingly, in a case where phrases to be learned by the user include a new word that has not been learned, the new word is made clear, and thus an opportunity to effectively learn the new word can be provided. 
     In the learning support system described in claim  4 ,
         the similarity includes any of a variable indicating whether or not the phrase information to be learned is a loanword in the native language,   a variable indicating whether or not a type of characters representing the phrase information to be learned is similar to a type of characters of the native language,   a variable indicating whether or not spelling of the phrase information to be learned is similar to spelling of the native language, and   a variable indicating whether or not pronunciation of the phrase information to be learned is similar to pronunciation of the native language, and   the predetermined period of time calculation means determines the predetermined period of time depending on the similarity.       

     Accordingly, a predetermined period of time until the next learning is accurately calculated based on specific similarity between a phrase to be learned by the user and a corresponding phrase of the user&#39;s native language, and thus an opportunity for iterative learning can be provided more appropriately. 
     In the learning support system described in claim  5 , the predetermined period of time calculation means determines the predetermined period of time based on difficulty of a phrase in a language to be learned by the user. 
     Accordingly, an opportunity for iterative learning of a difficult phrase such as a phrase to be learned in upper grades or a phrase with a long spelling can be provided more appropriately. 
     In the learning support system described in claim  6 , the predetermined period of time calculation means determines the predetermined period of time based on a learner level of the user for a language of a phrase to be learned. 
     Accordingly, a learning support system for enhancing an effect of learning of a foreign language can be provided to learners in a wide age range. 
     The learning support method described in claim  7  is a method in which a learning support system executes
         a native language setting step of setting a native language of a user;   a learning phrase storage step of storing phrase information to be learned by the user together with a learning stage;   a predetermined period of time calculation step of calculating a predetermined period of time based on linguistic similarity of the phrase information to be learned by the user to the native language input by the native language setting step;   a storage control step of storing the learning stage stored in the learning phrase storage step as a first memory state if learning has not been completed, storing the learning stage as a second memory state if learning has been completed, and storing the learning stage as the first memory state again if the predetermined period of time has elapsed after learning was completed;   and an output step of outputting the phrase information to be learned by the user in a first output style and a second output style depending on the first memory state and the second memory state, respectively, stored in the learning phrase storage step by the storage control step.       

     The program described in claim  8  causes a computer to function as a learning support system comprising
         native language setting means for setting a native language of a user;   learning phrase storage means for storing therein phrase information to be learned by the user together with a learning stage;   predetermined period of time calculation means for calculating a predetermined period of time based on linguistic similarity of the phrase information to be learned by the user to the native language input by the native language setting means;   storage control means for storing the learning stage stored in the learning phrase storage means as a first memory state if learning has not been completed, storing the learning stage as a second memory state if learning has been completed, and storing the learning stage as the first memory state again if the predetermined period of time has elapsed after learning was completed; and   output means for outputting the phrase information to be learned by the user in a first output style and a second output style depending on the first memory state and the second memory state, respectively, stored in the learning phrase storage means by the storage control means.       

     Advantageous Effects of Invention 
     According to the present invention, it is possible to provide a learning support system, method and program that calculate a predetermined period of time until next learning based on the linguistic similarity of phrase information to be learned by a user, thereby providing an opportunity for iterative learning at more appropriate timing for each phrase to be learned depending on a user, and enhancing an effect of learning of a foreign language. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a diagram illustrating an overall configuration of a learning support system according to Embodiment 1 of the present invention. 
         FIG. 2  is a diagram schematically illustrating an example data structure in a storage unit according to Embodiment 1 of the present invention. 
         FIG. 3  is a diagram illustrating a specific example data structure of a learning phrase storage unit according to Embodiment 1 of the present invention. 
         FIG. 4  is a diagram illustrating a specific example data structure of a phrase-related information storage unit according to Embodiment 1 of the present invention. 
         FIG. 5  is a flowchart illustrating a procedure from the start, reading phrases to be learned and to extracting phrases in the learning support system according to Embodiment 1 of the present invention. 
         FIG. 6  is a flowchart illustrating a processing procedure when a user learns in the learning support system according to Embodiment 1 of the present invention. 
         FIG. 7  is a correspondence table showing predetermined periods of time corresponding to learning levels of a phrase in the learning support system according to Embodiment 1 of the present invention. 
         FIG. 8  is a flowchart illustrating a processing procedure of calculating a predetermined period of time and an elapsed time for a phrase to be learned by a user in the learning support system according to Embodiment 1 of the present invention. 
         FIG. 9  is a correspondence table showing coefficient 1 corresponding to phrase difficulty levels in the learning support system according to Embodiment 1 of the present invention. 
         FIG. 10  is a correspondence table showing coefficient 1 corresponding to learner levels of users in the learning support system according to Embodiment 1 of the present invention. 
         FIG. 11  is a correspondence table showing coefficient 1 corresponding to difficulty levels in the learning support system according to Embodiment 1 of the present invention. 
         FIG. 12  is a flowchart illustrating a processing procedure of calculating coefficient 1 of a predetermined period of time for a phrase to be learned by a user in the learning support system according to Embodiment 1 of the present invention. 
         FIG. 13  is a flowchart illustrating a processing procedure of calculating coefficient 2 of a predetermined period of time for a phrase to be learned by a user in the learning support system according to Embodiment 1 of the present invention. 
         FIG. 14  is a correspondence table showing coefficient 2 corresponding to similarity sums of a phrase to be learned by a user to a native language in the learning support system according to Embodiment 1 of the present invention. 
         FIG. 15  illustrates an example sample showing the similarity of a phrase to be learned by a user to a native language in the learning support system according to Embodiment 1 of the present invention. 
         FIG. 16  illustrates an example sample showing the similarity of a phrase to be learned by a user to a native language in the learning support system according to Embodiment 1 of the present invention. 
         FIG. 17  is a diagram illustrating an overall configuration of a learning support system according to Embodiment 2 of the present invention. 
         FIG. 18  is a diagram schematically illustrating an example data structure in a storage unit according to Embodiment 2 of the present invention. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, embodiments of the present invention will be described in detail with reference to the attached drawings. 
       FIGS. 1 to 18  are figures illustrating the embodiments of the present invention. In these figures, parts denoted by the same reference signs are identical to each other, and the basic configurations and operations thereof are similar to each other. 
     Embodiment 1 
       FIG. 1  is a diagram illustrating an overall configuration of a learning support system according to Embodiment 1 of the present invention. 
     The learning support system  1  is comprised of a main control unit (CPU)  10  that controls the overall system in a centralized manner, a storage unit  11  that stores therein various pieces of information, an input unit  13  made up of a keyboard and a mouse or the like, an output (display) unit  14  such as a display, a communication unit  15  connected to a network  4 , and a bus line  12  that connects the individual units. 
     The main control unit (CPU)  10  implements phrase extraction means  101 , phrase matching means  102 , predetermined period of time calculation means  103 , storage control means  104 , and various types of processing means and determination means or the like, via a program such as an operating system, a program specifying a procedure of this learning system, and the like. 
     The storage unit  11  is comprised of a volatile or non-volatile semiconductor device such as a RAM or ROM, and an auxiliary storage device such as a hard disk or optical disc. 
     The storage unit  11  comprises a user setting storage unit  111 , a learning phrase storage unit  112 , a phrase-related information storage unit  113 , a learning file storage unit  114 , a correspondence table storage unit  115 , and the like. 
     The input unit  13  comprises a keyboard, a mouse or a pointing device such as a trackball. 
     The input unit  13  includes native language etc. setting means  131  for setting and inputting a native language, a learner level, and so forth of a user, phrase information input means  132  for inputting a phrase to be learned by the user, and learning stage input means  133  for inputting whether or not the user has completed learning an phrase, which are used by the user to input various pieces of information. 
     A phrase input through the phrase information input means  132  includes file data read from the learning file storage unit  114  and electronic data read from the network  4  via the communication unit  15 . 
     The output (display) unit  14  is used to monitor data input by the user and to display phrases or the like to be learned. 
     The communication unit  15  ensures exterior communication via the network  4 , and is used, for example, to receive phrase data to be learned from an external site using a protocol such as a TCP/IP. 
     The phrase extraction means  101  extracts automatically a portion of a phrase stored in the learning phrase storage unit  112  by using a morphological analysis method or the like. 
     The phrase extraction means  101  is implemented as a program running on the main control unit (CPU)  10 . 
     Where, a phrase includes a word, an idiom, an idiomatic phrase, a sentence, sentences, and the like. 
     An extracted portion of a phrase, that is, an extracted phrase, includes a word, an idiom, an idiomatic phrase, and the like. 
     The phrase matching means  102  determines whether or not an extracted phrase extracted by the phrase extraction means  101  is stored in the phrase-related information storage unit  113 . 
     The phrase matching means  102  is implemented as a program running on the main control unit (CPU)  10 . 
     The predetermined period of time calculation means  103  calculates a predetermined period of time from when learning is completed to when an opportunity for learning is provided to the user again, for an extracted phrase that the user has once completed learning. 
     The predetermined period of time calculation means  103  is implemented as a program running on the main control unit (CPU)  10 . 
     The storage control means  104  controls storage of data in the learning phrase storage unit  112 , the data indicating whether each of extracted phrases, extracted by the phrase extraction means  101  from phrases to be learned by the user, has been memorized by the user and learning has been completed, or has not been memorized by the user and learning has not been completed. 
     The storage control means  104  is implemented as a program running on the main control unit (CPU)  10 . 
       FIG. 2  is a diagram schematically illustrating an example data structure in the storage unit according to Embodiment 1 of the present invention. 
     The user setting storage unit  111  holds, mainly in the RAM, native language data of the user, learner level data indicating the number of years of learning experience or academic ability in a language to be learned, and so forth. 
     The learning phrase storage unit  112  holds phrases to be learned by the user. 
     The learning phrase storage unit  112  holds, mainly in the RAM, appearance order data indicating the order in which the extracted phrase appears in the entire phrase, extracted phrase data, registration order data indicating the order in which the extracted phrase was registered in the phrase-related information storage unit  113  in a case where the phrase matching means  102  determines that the extracted phrase is stored in the phrase-related information storage unit  113 , and memory state data indicating whether or not the user has memorized the extracted phrase, for each of extracted phrases extracted by the phrase extraction means  101 . 
       FIG. 3  illustrates a specific example data structure of the learning phrase storage unit according to Embodiment 1 of the present invention. 
     The data of extracted phrases is stored in units of rows. 
     The first column from the left gives the appearance order in the entire phrase, that is, a number increasing one by one like 1, 2, 3 is automatically assigned and stored as an appearance ID, which indicates the extraction order in the morphological analysis method or the like. 
     The second column stores extracted phrases, for example, “Hi”, “Michael”, and so forth. 
     The third column gives registration IDs, each indicating the order in which the extracted phrase was registered in the phrase-related information storage unit  113 . 
     For example, in the case of “Hi” in the first row, “2” is stored because “hi” is stored as a phrase base form corresponding to the second row in the example data structure of the phrase-related information storage unit in  FIG. 4 , which will be described below. 
     Finally, the fourth column stores, for example, a flag such as “LEARNING COMPLETED: 1” or “LEARNING NOT COMPLETED: 0”, as memory state data indicating whether or not the user has memorized the extracted phrase. 
     The phrase-related information storage unit  113  holds, for each phrase stored therein, registration order data, type-of-language data, phrase/inflection data, part-of-speech data, learning level data indicating the number of years of learning experience of the user, linguistic similarity data relative to a native language, phrase difficulty data indicating the difficulty of the phrase based on the number of years of experience desired for learning or the number of characters in spelling, last learning time data indicating the time when the user finally completed learning last time, and so forth. 
     The phrase-related information storage unit  113  is a database established mainly in the auxiliary storage device such as a hard disk or optical disc, and the data stored therein, such as words, is able to undergo predetermined operations such as search, addition, update, and deletion. 
       FIG. 4  illustrates a specific example data structure of the phrase-related information storage unit according to Embodiment 1 of the present invention. 
     The data in the phrase-related information storage unit is stored in a unit of a row for each phrase. 
     The first column from the left gives registration IDs, that is, a number increasing one by one like 1, 2, 3, is automatically assigned and stored as the storage order in the phrase-related information storage unit  113 . 
     The second column represents the types of language of registered phrases. 
     For example, in the case of “door” in the first row, “English” is stored. 
     The third column stores the phrase base forms, and the following fourth to sixth rows (columns) store inflection data. 
     For example, in the case of a noun “door” in the first row, “door” is stored as the phrase base form in the third column, and the plural form “doors” is stored as inflection data in the fifth column. 
     In the case of a verb “meet” in the third row, “meet” is stored as the phrase base form in the third column, and the past tense “met” is stored in the fourth column. 
     Although not illustrated in  FIG. 4 , various inflection data can be stored, for example, a past participle, a third person singular present tense, a present participle, and so forth for a verb, and a comparative for an adjective or adverb. 
     The seventh column from the left gives part-of-speech data. 
     Numeric data corresponding to a part of speech is stored, for example, “noun: 1” in the case of “door” in the first row, “verb: 2” in the case of “meet” in the third row, and “adjective: 3” in the case of “nice” in the fifth row. 
     The eighth column represents learning levels indicating the number of learning experience of the user. 
     For example, “0” is stored for a phrase that has never been learned, whereas any one of “1” to “9” in nine ranks of levels is stored for a phrase that has ever been learned. 
     For example, a learning level “1” is stored in the case of the phrase base form “door” in the first row, and a learning level “0” is stored in the case of the phrase base form “Michael” in the tenth row because this is a new phrase. 
     The ninth to twelfth columns represent linguistic similarity data relative to the native language. 
     Whether or not the phrase is a loanword (Y: 1, N: 0), whether or not the type of character is similar (similar: 1, not similar: 0), whether or not the spelling is similar (similar: 2, predictable: 1, not similar: 0), and whether or not the pronunciation is similar (similar: 2, not similar: 0) are stored in order as numeric data. 
     For example, in the case of the phrase base form “door” in the first row, loanword “Y: 1”, similarity in type of character “not similar: 0”, similarity in spelling “predictable: 1”, and similarity in pronunciation “similar: 2” are stored. 
     The second column from the right gives phrase difficulty levels each indicating the number of years of experience desired for learning, which is stored, for example, any one of “seventh grade: 1” to “university: 7” in seven ranks of levels. 
     For example, in the phrase base form “door” in the first row, “eighth grade: 2” is stored because this phrase is to be learned in the eighth grade. 
     The rightmost column gives last learning time data indicating the times when the user finally completed learning, and is stored as a date and time, for example. 
     For example, in the phrase base form “door” in the first row, “2016/9/20 10:05” is stored. 
     The type of language, inflection data, part-of-speech data, linguistic similarity data relative to the native language in the ninth to twelfth columns, phrase difficulty data, and so forth that are stored in the phrase-related information storage unit  113  may be obtained by the main control unit (CPU)  10  by automatically collecting data from an electronic dictionary file (not illustrated) in the learning support system or from an online electronic dictionary service on the network  4  via the communication unit  15  and by storing the data, for example. 
     In addition, the user may input necessary information or edit the data through the input unit  13 . 
     The learning file storage unit  114  holds file data of various languages that stores in advance phrases to be learned by the user. 
     The learning file storage unit  114  holds, for example, English file data, French file data, Chinese file data, Japanese file data, and the like, for individual languages. 
     The individual pieces of file data may be about different matters or may include corresponding translations or the like about the same matter. 
     The learning file storage unit  114  is a database established mainly in the auxiliary storage device such as a hard disk or optical disc, and the file data stored therein is able to undergo predetermined operations such as search, addition, update, and deletion. 
     The correspondence table storage unit  115  holds various pieces of correspondence table data, such as a predetermined period of time correspondence table that is used to determine a predetermined period of time until next time when the user is to learn an phrase, an phrase difficulty level correspondence table, a learner level correspondence table, a difficulty level correspondence table, and a similarity sum correspondence table. 
     The correspondence table storage unit  115  may be held in the RAM or may be stored in the auxiliary storage device such as a hard disk or optical disc. 
     Next, the following describes an operation of the learning support system according to Embodiment 1 of the present invention configured as described above with reference to  FIGS. 3 to 15 , in the order of (1) a procedure from the start, reading a phrase to be learned and to extracting phrases, (2) a processing procedure when the user learns, and (3) a processing procedure of calculating a predetermined period of time and an elapsed time for a phrase to be learned by the user. 
     (1) Procedure from the Start, Reading a Phrase to be Learned and to Extracting Phrases 
     First, a description will be given with reference to the flowchart in  FIG. 5  by using the specific example data structures in  FIGS. 3 and 4  as an example. 
       FIG. 5  is a flowchart illustrating a procedure from the start of learning, reading a phrase to be learned and to extracting phrases in the learning support system according to Embodiment 1 of the present invention. 
     In step S 1 , the main control unit (CPU)  10  receives setting information including a native language and a learner level from the user through the bus line  12 , using the native language etc. setting means  131 . 
     The main control unit (CPU)  10  stores the received setting information as native language data and learner level data in the user setting storage unit  111 . 
     The native language data is, for example, “Japanese”, “English”, or the like, and the learner level (data) is, for example, “seventh grade”, “eleventh grade”, or the like. 
     In step S 2 , the main control unit (CPU)  10  stores phrase information to be learned by the user in the RAM of the storage unit  11  through the bus line  12 , using the phrase information input means  132 . 
     The main control unit (CPU)  10  may receive an instruction from the user via the phrase information input means  132 , read the file data recorded in advance in the learning file storage unit  114  as phrase information to be learned, and store the file data in the RAM of the storage unit  11 . 
     The main control unit (CPU)  10  stores, for example, “Hi, Michael, nice to meet you!” in the RAM as a phrase to be learned. 
     In step S 3 , the main control unit (CPU)  10  sequentially extracts portions of the phrase stored in the RAM of the storage unit  11  by using, for example, the morphological analysis method or the like, the portions serving as extracted phrases. 
     The main control unit (CPU)  10  stores appearance order data and the extracted phrase data in the learning phrase storage unit  112 . 
     The main control unit (CPU)  10  stores, for example, an appearance ID “1”, an extracted phrase “Hi”, an appearance ID “2”, an extracted phrase “Michael”, and so forth of the foregoing phrase in the learning phrase storage unit  112 , as in the example data structure in  FIG. 3 . 
     In step S 4 , the main control unit (CPU)  10  matches each of the extracted phrases with the phrase/inflection data stored in the phrase-related information storage unit  113 . 
     If both match each other (match), the main control unit (CPU)  10  stores the registration order data stored in the phrase-related information storage unit  113  in the learning phrase storage unit  112  as reference data, and the process proceeds to step S 5 . 
     If both do not match (no match), the process proceeds to step S 6 . 
     The main control unit (CPU)  10  matches, for example, the foregoing extracted phrase “Hi” with the phrase base form “hi” stored in association with the registration ID “2” in the data structure of the phrase-related information storage unit illustrated in  FIG. 4 , and stores the registration ID “2” as the registration ID (reference) in  FIG. 3 . 
     In step S 5 , the main control unit (CPU)  10  calculates a predetermined period of time and an elapsed time for a word or the like that has been determined to match by using the predetermined period of time calculation means  103 , and then the process proceeds to step S 7 . 
     Where, the predetermined period of time is a period of time from when the user once completed learning to when an opportunity for learning is provided again. 
     The elapsed time is a period of time from when the user once completed learning to the present. 
     The time when the user once completed learning is stored as last learning time data in the data structure of the phrase-related information storage unit illustrated in  FIG. 5 , in the form of “2016/9/20 10:05”, for example. 
     A detailed method for calculating a predetermined period of time and an elapsed time will be described below in the following (3) a processing procedure of calculating a predetermined period of time and an elapsed time for a phrase to be learned by the user. 
     In step S 6 , the main control unit (CPU)  10  stores the extracted phrase that does not match the phrase/inflection data stored in the phrase-related information storage unit  113  as a new word at the end of the phrase/inflection data stored in the phrase-related information storage unit  113 . 
     Subsequently, the main control unit (CPU)  10  stores the registration order data stored in the phrase-related information storage unit  113  as reference data in the learning phrase storage unit  112 , and the process proceeds to step S 9 . 
     For example, the appearance ID “2” or the extracted phrase “Michael” extracted in step S 3  does not match, and thus the main control unit (CPU)  10  adds a registration ID “10” in the tenth row at the bottom of the data structure of the phrase-related information storage unit illustrated in  FIG. 4 , and stores the extracted phrase “Michael” as the phrase base form. 
     Subsequently, the main control unit (CPU)  10  stores the added registration ID “10” as the registration ID (reference) “10” in  FIG. 3 . 
     Subsequently, in step S 7 , the main control unit (CPU)  10  compares the predetermined period of time obtained by the predetermined period of time calculation means  103  in step S 5  with an elapsed time. 
     If the elapsed time does not exceed the predetermined period of time (N), the process proceeds to step S 8  with “learning completed” being left as is. 
     If the elapsed time exceeds the predetermined period of time (Y), the process proceeds to step S 9  with “learning not completed”. 
     In step S 8 , the main control unit (CPU)  10  keeps the memory state stored in the phrase-related information storage unit  113  “learning completed: 1”, and the process proceeds to step S 10 . 
     In step S 9 , the main control unit (CPU)  10  stores the memory state stored in the phrase-related information storage unit  113  as “learning not completed: 0”, and the process proceeds to step S 10 . 
     In step S 10 , the main control unit (CPU)  10  determines whether or not there is a portion to be extracted from the phrase stored in the RAM of the storage unit  11 . 
     If there is any portion to be extracted, the process returns to step S 3  and the extraction process is continued. 
     If all portions of the phrase have been extracted, the extraction process is finished. 
     (2) Processing Procedure when the User Learns 
     Next, a description will be given with reference to the flowchart in  FIG. 6  by using the specific data structures in  FIGS. 3 and 4  as an example. 
       FIG. 6  is a flowchart illustrating a processing procedure when the user learns in the learning support system according to Embodiment 1 of the present invention. 
     It is assumed that an extraction process for the phrase to be learned by the user has finished, for example, the example data structure of the learning phrase storage unit illustrated in  FIG. 3  is stored in the learning phrase storage unit  112 , and the example data structure of the phrase-related information storage unit illustrated in  FIG. 4  is stored in the phrase-related information storage unit  113 . 
     In step S 21 , the main control unit (CPU)  10  determines the value stored as the memory state of an extracted phrase stored in the phrase-related information storage unit  113 . 
     In the case of “learning not completed: 0”, the process proceeds to step S 22 . 
     In the case of “learning completed: 1”, the process proceeds to step S 23 . 
     In step S 22 , the main control unit (CPU)  10  outputs (displays) the extracted phrase stored in the learning phrase storage unit  112  to (on) the output (display) unit  14  in display color  1 . 
     In step S 23 , the main control unit (CPU)  10  outputs (displays) the extracted phrase to (on) the output (display) unit  14  in display color  2 . 
     In a case where a plurality of extracted phrases identical to each other are output to the output (display) unit  14 , all the identical extracted phrases are output (displayed) in the same display color. 
     After the output, the process proceeds to step S 24 . 
     For example, in the case of the extracted phrase “Michael” in  FIG. 3 , “learning not completed: 0” is stored as the memory state, and thus the extracted phrase “Michael” is output to (displayed on) the output (display) unit  14  in display color  1 . 
     As display color  1 , various highlighting styles may be applied to the phrase to be learned. 
     For example, the color of the characters of “Michael” may be red or purple, the characters of “Michael” may be enclosed, bold, or italic, the characters of “Michael” may be underlined with a double line or a wavy line, or the background color of the characters of “Michael” may be yellow or red. 
     On the other hand, in the case of the extracted phrase “Hi” in  FIG. 3 , “learning completed: 1” is stored as the memory state, and thus “Hi” is output to (displayed on) the output (display) unit  14  in display color  2 . 
     Display color  2  represents a normal display style, and thus only an extracted phrase for which learning has not been completed is highlighted, leaving a strong impression on the user. 
     In step S 24 , the main control unit (CPU)  10  determines whether or not all the extracted phrases stored in the learning phrase storage unit  112  have been output. 
     If there is any extracted phrase that has not been output (N), the process returns to step S 21 , and the main control unit (CPU)  10  continues the output operation. 
     If all the extracted phrases have been output (Y), the main control unit (CPU)  10  causes the process to proceed to step S 25 . 
     That is, for example, the main control unit (CPU)  10  continues the output operation so that the extracted phrase “Hi” in the first row to the extracted phrase “you” in the bottom row of the example data structure of the learning phrase storage unit in  FIG. 3  are sequentially output to (displayed on) the output (display) unit  14 . 
     In step S 25 , the main control unit (CPU)  10  determines whether or not an input for finishing learning has been received from the user via the learning stage input means  133 , the phrase information input means  132 , or the like. 
     If an input for finishing learning has been received from the user (Y), learning is finished. 
     If an input has not been received (N), the process proceeds to step S 26 . 
     In step S 26 , the main control unit (CPU)  10  determines whether or not the user has selected the extracted phrase that is output (displayed) using the learning stage input means  133 . 
     If the user has selected the extracted phrase (Y), the process proceeds to S 27 . 
     If the user has not selected anything (N), the process returns to step S 25 . 
     In step S 27 , the main control unit (CPU)  10  waits for input of a learning stage from the learning stage input means  133 . 
     In the case of “learning completed: 1”, the process proceeds to step S 28 . 
     In the case of “learning not completed: 0”, the process proceeds to step S 31 . 
     In step S 28 , the main control unit (CPU)  10  adds 1 to the learning level data that corresponds to the selected extracted phrase and that is stored in the phrase-related information storage unit  113  and stores the sum. 
     If the sum exceeds 9, the learning level data is stored as “9”. 
     Subsequently, the process proceeds to step S 29 . 
     For example, the learning level of the phrase base form “Michael” stored in the tenth row of the example data structure of the phrase-related information storage unit in  FIG. 4  is “0”. 
     Thus, 1 is added to it and “1” is stored as a new learning level. 
     In step S 29 , the main control unit (CPU)  10  stores “learning completed: 1” as the memory state that corresponds to the selected extracted phrase and that is stored in the learning phrase storage unit  112 . 
     Subsequently, the process proceeds to step S 30 . 
     In step S 30 , the main control unit (CPU)  10  stores the system date and time of the learning support system  1  as the last learning time data stored in the phrase-related information storage unit  113 . 
     Subsequently, the process returns to step S 21 , and the process continues. 
     On the other hand, in step S 31  that is performed in the case of “learning not completed: 0” in step S 27 , the main control unit (CPU)  10  subtracts 1 from the learning level data, that corresponds to the selected extracted phrase and that is stored in the learning phrase storage unit  112 , and stores the difference. 
     If the difference is less than 1, “1” is stored as the learning level data. 
     Subsequently, the process proceeds to step S 32 . 
     For example, the learning level of the phrase base form “nice” stored in the fifth row of the example data structure of the phrase-related information storage unit in  FIG. 4  is “5”. 
     Thus, 1 is subtracted from it and “4” is stored as a new learning level. 
     In step S 32 , the main control unit (CPU)  10  stores “learning not completed: 0” as the memory state that corresponds to the selected extracted phrase and that is stored in the learning phrase storage unit  112 . 
     Subsequently, the process returns to step S 21  and the process continues. 
     (3) Processing Procedure of Calculating a Predetermined Period of Time and an Elapsed Time for a Phrase to be Learned by the User 
     Before describing the processing procedure of calculating a predetermined period of time and an elapsed time, an outline of a method for calculating the predetermined period of time will be described. 
     The forgetting curve, developed by a psychologist Hermann Ebbinghaus and describing that the amount of memory of a human decreases as time passes, is expressed by the following logarithmic function. 
       Amount of memory  b (%)=100×coefficient  k /[(log period of time  t ) coefficient c +coefficient  k ]  (1)
 
     Where, the amount of memory b is an amount of memory (%) after learning, and each of k and c is a coefficient. 
     Typically, a forgetting curve is approximately derived from the function and is expressed by the following negative exponential function, for example. 
       Degree of memory  R =Exp(−period of time  t /strength of memory  S )  (2)
 
     The strength of memory S in equation (2) is proportional to the number of learning times, for example, and maintains a constant degree of memory R even after the period of time t elapses. 
     In other words, the period of time t, during which iterative learning is performed to maintain the same degree of memory, gradually increases in an exponential manner as the number of learning times increases. 
     A predetermined period of time for an extracted phrase from when learning is completed to when an opportunity for learning is provided to the user again is calculated by using the following calculation formula. 
       Predetermined period of time=coefficient 1×Exp(coefficient 2×learning level)   (3)
 
     Where, coefficient 1 and coefficient 2 are adjustment coefficients, and the standard of each coefficient is 1. 
     The following describes a processing procedure of calculating a predetermined period of time and an elapsed time for a phrase to be learned by the user with reference to the flowchart in  FIG. 8  by using the correspondence table in  FIG. 7 . 
       FIG. 7  is a correspondence table that shows predetermined period of times corresponding to learning levels of a phrase in the learning support system according to Embodiment 1 of the present invention and that is stored in the correspondence table storage unit  115  in  FIG. 2 . 
     The top row represents the learning levels in the example data structure of the phrase-related information storage unit described with reference to  FIG. 4 , indicated in nine ranks from 1 to 9. 
     The bottom row represents predetermined period of times, each being obtained through calculation based on equation (3) when both coefficient 1 and coefficient 2 are 1, which is the standard. 
       FIG. 8  is a flowchart illustrating a processing procedure of calculating a predetermined period of time and an elapsed time for a phrase to be learned by the user in the learning support system according to Embodiment 1 of the present invention. 
     When the process proceeds to the predetermined period of time calculation process in step S 5  in  FIG. 5 , the subroutine process in  FIG. 8  starts. 
     In step S 41 , the main control unit (CPU)  10  performs a coefficient 1 calculation process for the extracted phrase. 
     Subsequently, in step S 42 , the main control unit (CPU)  10  performs a coefficient 2 calculation process. 
     A detailed calculation method for coefficient 1 will be described below with reference to the flowchart in  FIG. 12 , and a detailed calculation method for coefficient 2 will be described below with reference to the flowchart in  FIG. 13 . 
     In step S 43 , the main control unit (CPU)  10  calculates a predetermined period of time based on coefficient 1 obtained in step S 41 , coefficient 2 obtained in step S 42 , and the learning level data stored in the phrase-related information storage unit  113 . 
     For example, in the case of “Internet” in the fourth row of the example data structure of the phrase-related information storage unit illustrated in  FIG. 4 , the learning level is “4”. 
     Thus, the predetermined period of time is calculated to be “55” hours if both coefficient 1 and coefficient 2 are 1. 
     In step S 44 , the main control unit (CPU)  10  calculates, as an elapsed time, the difference between the last learning time data stored in the phrase-related information storage unit  113  and the current date and time of the system. 
     After that, the subroutine process in  FIG. 8  is finished, and the process proceeds to step S 7  in  FIG. 5 . 
     &lt;Coefficient 1 Calculation Process&gt; 
     The following describes a calculation processing procedure for coefficient 1 with reference to the flowchart in  FIG. 12  by using the correspondence tables in  FIGS. 9 to 11 . 
       FIG. 9  is a correspondence table that shows coefficients 1 corresponding to phrase difficulty levels in the learning support system according to Embodiment 1 of the present invention and that is stored in the correspondence table storage unit  115  in  FIG. 2 . 
     The top two rows represent phrase difficulty. 
     The phrase difficulty may be represented by, for example, a learner&#39;s grade indicating the number of years of experience desired for learning or the length of characters of the phrase. 
     In the example data structure of the phrase-related information storage unit described with reference to  FIG. 4 , a learner&#39;s grade indicating the number of years of experience desired for learning is used as a phrase difficulty level. 
     The middle row represents phrase difficulty levels corresponding to phrase difficulty, indicated in seven ranks from 1 to 7. 
     The phrase difficulty level is used to calculate coefficient 1 in consideration of another element. 
     The bottom row represents the values of coefficient 1 corresponding to the phrase difficulty levels in the middle row. 
     The value of coefficient 1 is 1 in a standard state as described in equation (3), decreases to be smaller than 1 at 0.05 intervals as the phrase difficulty increases so as to provide an opportunity for iterative learning at more appropriate timing, and is 0.7 at the minimum. 
     Coefficient 1 in the bottom row is used in a case where the phrase difficulty level is independently used as the difficulty level described below. 
       FIG. 10  is a correspondence table that shows coefficients 1 corresponding to learner levels of users in the learning support system according to Embodiment 1 of the present invention and that is stored in the correspondence table storage unit  115  in  FIG. 2 . 
     The top row represents learner&#39;s grades indicating the number of years of learning experience or academic ability of a learner. 
     The middle row represents learner levels corresponding to the learner&#39;s grades in the top row, indicated in seven ranks from 1 to 7 in the same way as for  FIG. 9 . 
     The learner level is used to calculate coefficient 1 in consideration of another element. 
     The bottom row represents the values of coefficient 1 corresponding to the learner levels in the middle row. 
     The value of coefficient 1 is 1 in a standard state, decreases to be smaller than 1 at 0.05 intervals as the learner level increases, and is 0.7 at the minimum in the same way as for  FIG. 9 . 
     The values in the bottom row are used in a case where the learner level is independently used as the difficulty (level) described below. 
       FIG. 11  is a correspondence table that shows coefficients 1 corresponding to difficulty levels, each of which is a combination of a word (phrase) difficulty level and a learner level in the learning support system according to Embodiment 1 of the present invention and that is stored in the correspondence table storage unit  115  in  FIG. 2 . 
     The top row represents difficulty levels, each being a difference obtained by subtracting the learner level illustrated in  FIG. 10  from the phrase difficulty level illustrated in  FIG. 9 . 
     Both the phrase difficulty level and the learner level are indicated in seven ranks from 1 to 7, and thus the difference takes a value from −6 to +6. 
     However, all negative values are regarded as 0, and thus the difficulty level is indicated in seven levels from 0 to 6. 
     The bottom row represents the values of coefficient 1 corresponding to the difficulty levels in the top row. 
     The value of coefficient 1 is 1 in a standard state, decreases to be smaller than 1 at 0.05 intervals as the difficulty increases, and is 0.7 at the minimum. 
       FIG. 12  is a flowchart illustrating a processing procedure of calculating coefficient 1 of a predetermined period of time for a phrase to be learned by the user in the learning support system according to Embodiment 1 of the present invention. 
     When the process proceeds to the coefficient 1 calculation process in step S 41  in  FIG. 8 , the subroutine process in  FIG. 12  starts. 
     In step S 51 , the main control unit (CPU)  10  calculates a difficulty level by subtracting a learner level from a phrase difficulty level for the extracted phrase. 
     In particular, the main control unit (CPU)  10  calculates the difference between the phrase difficulty level stored in the phrase-related information storage unit  113  and the learner level stored in the user setting storage unit  111 . 
     If the difference is a negative value, the main control unit (CPU)  10  also performs a process of setting the value to 0, although not illustrated. 
     For example, when the user is an eighth grader, and in the case of “Internet” in the fourth row of the example data structure of the phrase-related information storage unit illustrated in  FIG. 4 , the learner level in the correspondence table in  FIG. 10  is “2” and the phrase difficulty level in the correspondence tables in  FIGS. 4 and 9  is “4”, and thus the difficulty level is “4−2=2”. 
     In step S 52 , the main control unit (CPU)  10  obtains coefficient 1 from the correspondence table in  FIG. 11  based on the difficulty level obtained in step S 51 . 
     For example, in the foregoing example of “Internet”, “0.9” corresponding to the difficulty level “2” is obtained as coefficient 1. 
     Subsequently, the subroutine process in  FIG. 12  is finished, and the process proceeds to step S 42  in  FIG. 8 . 
     &lt;Coefficient 2 Calculation Process&gt; 
     The following describes a calculation processing procedure for coefficient 2 with reference to  FIGS. 13 to 16 . 
       FIG. 13  is a flowchart illustrating a processing procedure of calculating coefficient 2 of a predetermined period of time for a phrase to be learned by the user in the learning support system according to Embodiment 1 of the present invention. 
     As described regarding the example data structure of the phrase-related information storage unit in  FIG. 4 , the linguistic similarity of an phrase to a native language is determined in accordance with, for example, whether or not the phrase is a loanword, whether or not the type of character is similar, whether or not the spelling is similar, whether or not the pronunciation is similar, and the like. 
     The similarity of the phrase is calculated based on these four items stored in the phrase-related information storage unit  113 , and coefficient 2 is adjusted to provide an opportunity for iterative learning earlier as the similarity decreases. 
     When the process proceeds to the coefficient 2 calculation process in step S 42  in  FIG. 8 , the subroutine process in  FIG. 13  starts. 
     In step S 61 , the main control unit (CPU)  10  determines whether or not the extracted phrase is a loanword. 
     The main control unit (CPU)  10  refers to the loanword data stored in the phrase-related information storage unit  113 , stores level 1 as “1” in the RAM if the extracted phrase is a loanword (Y) (step S 62 ), and stores level 1 as “0” in the RAM if the extracted phrase is not a loanword (N) (step S 63 ). Subsequently, the process proceeds to step S 64 . 
     For example, regarding “Internet” in the fourth row of the example data structure of the phrase-related information storage unit in  FIG. 4 , the column of loanword gives “Y:1”, and thus level 1 is stored as “1” in the RAM. 
     In step S 64 , the main control unit (CPU)  10  determines whether or not the type of character is similar. 
     The main control unit (CPU)  10  refers to the similarity in type of character stored in the phrase-related information storage unit  113 , stores level 2 as “1” in the RAM if the type of character is similar (step S 65 ), and stores level 2 as “0” in the RAM if the type of character is not similar (step S 66 ). 
     Subsequently, the process proceeds to step S 67 . 
     For example, regarding “Internet” in the fourth row of the example data structure of the phrase-related information storage unit in  FIG. 4 , the similarity in type of character is “not similar: 0”, and thus level 2 is stored as “0” in the RAM. 
     In step S 67 , the main control unit (CPU)  10  refers to the similarity in spelling stored in the phrase-related information storage unit  113 , stores level 3 as “2” in the RAM if the spelling is similar (step S 69 ), stores level 3 as “1” in the RAM if the spelling is predictable (step S 68 ), and stores level 3 as “0” in the RAM if the spelling is not similar (step S 70 ). 
     Subsequently, the process proceeds to step S 71 . 
     For example, regarding “Internet” in the fourth row of the example data structure of the phrase-related information storage unit in  FIG. 4 , the similarity in spelling is “predictable: 1”, and thus level 3 is stored as “1” in the RAM. 
     In step S 71 , the main control unit (CPU)  10  determines whether or not the pronunciation is similar. 
     The main control unit (CPU)  10  refers to the similarity in pronunciation stored in the phrase-related information storage unit  113 , stores level 4 as “2” in the RAM if the pronunciation is similar (step S 72 ), and stores level 4 as “0” in the RAM if the pronunciation is not similar (step S 73 ). 
     Subsequently, the process proceeds to step S 74 . 
     For example, regarding “Internet” in the fourth row of the example data structure of the phrase-related information storage unit in  FIG. 4 , the similarity in pronunciation is “similar: 2”, and thus level 4 is stored as “2” in the RAM. 
     In step S 74 , the main control unit (CPU)  10  calculates a similarity sum by calculating the sum of level 1 to level 4 stored in the RAM and adding 1 to the sum. 
     For example, regarding “Internet” in the fourth row of the example data structure of the phrase-related information storage unit in  FIG. 4 , level 1 is “1”, level 2 is “0”, level 3 is “1”, and level 4 is “2”. 
     Thus, the similarity sum is “5”. 
     In step S 75 , the main control unit (CPU)  10  obtains coefficient 2 based on the obtained similarity sum by referring to the correspondence table for the similarity sum and coefficient 2 in  FIG. 14  described below. 
     For example, in the foregoing case of “Internet”, “0.9” corresponding to the similarity sum “5” in the correspondence table in  FIG. 14  is obtained as coefficient 2. 
     Subsequently, the subroutine process in  FIG. 13  is finished, and the process proceeds to step S 43  in  FIG. 8 . 
       FIG. 14  is a correspondence table that shows coefficients 2 corresponding to similarity sums of a phrase to be learned by the user to the native language in the learning support system according to Embodiment 1 of the present invention and that is stored in the correspondence table storage unit  115  in  FIG. 2 . 
     The top row represents similarity sums obtained in step S 74  in the flowchart illustrated in  FIG. 13 , indicated in seven ranks from 1 to 7. 
     The bottom row represents the values of coefficient 2 corresponding to the similarity sums. 
     The value is 1 in a standard state, decreases to be smaller than 1 at 0.05 intervals as the similarity decreases so as to provide an opportunity for iterative learning at more appropriate timing, and is 0.7 at the minimum. 
       FIGS. 15 and 16  illustrate example samples showing the similarity of a phrase to be learned by the user to the native language in the learning support system according to Embodiment 1 of the present invention. 
     In  FIGS. 15 and 16 , the top two rows show a phrase to be learned by the user and its pronunciation. 
     On the left side, items of the user&#39;s native language, English, French, Japanese, and Chinese in order from the top row, are provided, and their characters and pronunciations in individual native languages corresponding to the phrase to be learned are shown in the third and fourth columns from the left. 
     In the fifth to eighth columns from the left, four items for evaluating the similarity of the phrase to be learned to the native languages, that is, loanword, type of character, spelling, and pronunciation, are provided in order, and the values of individual levels based on the flowchart of calculating coefficient 2 in  FIG. 13  are shown. 
     In addition, similarity sums used in the correspondence table in  FIG. 14  are shown in the rightmost column. 
       FIG. 15  illustrates similarity sums for individual native speakers when learning English phrase “Internet”. 
     For example, in the case of a learner whose native language is Japanese, the third row is referred to. 
     The similarity sum is “5”, with loanword “y: 1”, t y pe of character “not similar: 0”, spelling “predictable: 1”, and pronunciation “similar: 2”. 
     On the other hand, in the case of a learner whose native language is English or French, the similarity sum is “7”. 
     The similarity sum for a learner whose native language is Chinese is “1”. 
       FIG. 16  illustrates similarity sums for individual native speakers when learning a Japanese phrase “ ”. 
     For example, in the case of a learner whose native language is English, the first row is referred to. 
     The similarity sum is “4”, with loanword “y: 1”, type of character “not similar: 0”, spelling “not similar: 0”, and pronunciation “similar: 2”. 
     On the other hand, in the case of a learner whose native language is Japanese, the similarity sum is “7”. 
     The similarity sum for a learner whose native language is Chinese is “1”. 
     Coefficient 2 varies from 0.7 to 1 based on the correspondence table in  FIG. 14  depending on a similarity sum. 
     Thus, even in the case of learning the same phrase, the predetermined period of time is different according to a native language, and an opportunity for iterative learning can be provided at more appropriate timing for each phrase to be learned depending on a user. 
     A description has been given above of (3) a processing procedure of calculating a predetermined period of time and an elapsed time for a phrase to be learned by the user, with reference to the flowcharts in  FIGS. 8, 12, and 13 . 
     For example, in the case of “Internet” in the fifth row of the example data structure of the phrase-related information storage unit in  FIG. 4 , the predetermined period of time is 55 hours depending on equation (3), that is, in step S 43  in  FIG. 8 , when both coefficient 1 and coefficient 2 are 1 as the standard. 
     On the other hand, as described above, since coefficient 1 is 0.9 in a case where the phrase difficulty and the learner level (in the example, eighth grade) are taken into consideration, a predetermined period of time calculated depending on equation (3), that is, in step S 43  in  FIG. 8 , is shortened to 49 hours. 
     Furthermore, since coefficient 2 is 0.9 in a case where the similarity of a phrase to a native language (for example, Japanese) is taken into consideration, a predetermined period of time is calculated to be more shortened to 33 hours. 
     Thus, with the above matters being taken into consideration, it becomes possible to provide an opportunity for iterative learning at more appropriate timing for each phrase to be learned depending on a user. 
     As described above, according to Embodiment 1 of the present invention, adoption of the above-described configuration makes it possible to provide an opportunity for iterative learning at more appropriate timing for each phrase to be learned depending on a user. 
     Accordingly, a learning support system, method, and program for enhancing an effect of learning of a foreign language can be provided. 
     Embodiment 2 
       FIG. 17  is a diagram illustrating an overall configuration of a learning support system according to Embodiment 2 of the present invention. 
     The learning support system  1  has a configuration where a server  2  and user terminals  3  are connected to the network  4 . 
     The server  2  is comprised of a main control unit (CPU)  20  that controls the overall system in a centralized manner, a storage unit  21  that stores therein various pieces of information, and a communication unit  22  for performing communication with the user terminals  3  via the network  4 . 
     The main control unit (CPU)  20  implements phrase extraction means  201 , phrase matching means  202 , predetermined period of time calculation means  203 , storage control means  204 , and various types of processing means and determination means or the like, via a program such as an operating system, a program specifying a procedure of this learning system, and the like. 
     The phrase extraction means  201 , the phrase matching means  202 , the predetermined period of time calculation means  203 , and the storage control means  204  have functions similar to those of the phrase extraction means  101 , the phrase matching means  102 , the predetermined period of time calculation means  103 , and the storage control means  104  in  FIG. 1 . 
     The storage unit  21  is comprised of a volatile or non-volatile semiconductor device such as a RAM or ROM, and an auxiliary storage device such as a hard disk or optical disc. 
     The storage unit  21  comprises a user setting storage unit  211 , a learning phrase storage unit  212 , a phrase-related information storage unit  213 , a learning file storage unit  214 , a correspondence table storage unit  215 , and the like. 
     The communication unit  22  ensures communication with the user terminals  3  via the network  4 . 
     The communication unit  22  has a function as native language etc. setting means  221  for receiving setting data of a native language and so forth transmitted from the user terminals  3  using a protocol such as a TCP/IP, phrase information input means  222  for receiving phrase information input data, learning stage input means  223  for receiving input data of a learning stage, and output means  224  for transmitting output information to be output to a display unit of the user terminals  3 . 
     Each user terminal  3  is comprised of an input unit  31 , a display unit  32 , and a communication unit  33 . 
     The input unit  31  comprises a keyboard, a mouse or a pointing device such as a trackball. 
     The input unit  31  is used to input setting information of a native language and so forth of a user, phrase information to be learned by the user, learning stage information about a phrase, or the like. 
     The display unit  32  includes a display or the like, and is used to display information input from the input unit  31  and output information transmitted from the server  2 . 
     The communication unit  33  is used to transmit various pieces of information data input from the input unit  31  to the server  2  or to receive output information from the server  2  via the network  4 . 
       FIG. 18  is a diagram schematically illustrating an example data structure in the storage unit according to Embodiment 2 of the present invention. 
     The learning phrase storage unit  212 , the phrase-related information storage unit  213 , the learning file storage unit  214 , and the correspondence table storage unit  215  hold data similar to that in the learning phrase storage unit  112 , the phrase-related information storage unit  113 , the learning file storage unit  114  and the correspondence table storage unit  115  in  FIG. 2 , respectively. 
     The user setting storage unit  211  is different in holding user identification data in addition to the native language data and learner level data held in the user setting storage unit  111 . 
     This is because, in a case where the plurality of users use the server from the plurality of user terminals  3 , it is necessary for each user to store the learning phrase storage unit  212 , the phrase-related information storage unit  213 , and the learning file storage unit  214 . 
     The specific data structures of the learning phrase storage unit  212  and the phrase-related information storage unit  213  are similar to the example data structures in  FIGS. 3 and 4 , respectively. 
     The following describes a basic operation of the learning support system according to Embodiment 2 of the present invention with reference to  FIG. 17 . 
     Each user terminal  3  used by a user connects to the server  2  via the network  4 . 
     The user terminal  3  transmits to the server  2  native language etc. setting information including user identification data, native language data, and learner level data input by the user with the input unit  31 , through the communication unit  33  via the network  4 . 
     The server  2  receives the native language etc. setting information transmitted by the user terminal  3  through the native language etc. setting means  221  of the communication unit  22 , and stores it in the user setting storage unit  211 . 
     The server  2  reserves the learning phrase storage unit  212  in the RAM of the storage unit  21  based on the user identification data stored in the user setting storage unit  211 . 
     And the server  2  specifies the phrase-related information storage unit  213  that has been established through the previous learning by the user. 
     The subsequent operation is similar to that of the learning support system according to Embodiment 1 of the present invention. 
     As described above, according to Embodiment 2 of the present invention, adoption of the above-described configuration makes it possible to provide an opportunity for iterative learning at more appropriate timing for each phrase to be learned depending on each of a plurality of users. 
     Accordingly, a learning support system, method, and program for enhancing an effect of learning of a foreign language can be provided. 
     The present invention is not limited to the above-described embodiments. 
     For example, in a learning support system according to an embodiment of the present invention, only a phrase difficulty level may be used as an index of a difficulty level that is used to calculate coefficient 1. 
     As phrase difficulty, the length of characters of a phrase may be used as an index. 
     Furthermore, as phrase difficulty, an arithmetic mean or the like of a learner&#39;s grade indicating the number of years of experience desired for learning and the length of characters of a phrase may be used. 
     Alternatively, phrase difficulty may have more ranks, and the corresponding value of coefficient 1 may be set from 0.5 to 1, for example. 
     Furthermore, as phrase difficulty, a result of a questionnaire about difficulty in memorizing each phrase may be used as an index, for example. 
     The difficulty in memorizing a phrase significantly varies according to the native language of a learner. 
     Thus, for example, 1000 people speaking various languages as their native language are caused to memorize each of phrases to be learned, and based on a result of a memory test conducted after a certain period of time, the difficulty in memorization for each word is statistically calculated. 
     Memorization difficulty data obtained accordingly may be used as phrase difficulty data. 
     In a learning support system according to an embodiment of the present invention, any one of loanword, type of character, spelling, and pronunciation, or a combination of two or more of them may be used as an index of similarity used to calculate coefficient 2. 
     Furthermore, in a learning support system according to an embodiment of the present invention, the similarity of a phrase to a native language may be used as an index for calculating coefficient 1, and the phrase difficulty or learner level may be used as an index for calculating coefficient 2. 
     In a learning support system according to an embodiment of the present invention, either of coefficient 1 or coefficient 2 may be a constant. 
     Accordingly, it becomes possible to set a predetermined period of time more accurately or more moderately, and an effect of learning of a foreign language can be further enhanced depending on a user. 
     In a learning support system according to an embodiment of the present invention, a difficulty level of an phrase that is too easy and need not be reviewed, or a difficulty level of an phrase that is too difficult and has a little learning effect may be set as learning phrase exclusion level data that is input from a user with the native language etc. setting means  131 , and the learning phrase exclusion level data may be stored in the user setting storage unit  111 . 
     Together with or instead of the learning phrase exclusion level data, learning phrase limitation level data for limiting a learning level may be stored in the user setting storage unit  111 . 
     Furthermore, part-of-speech specification data for limiting or excluding a part of speech of phrases to be learned may be stored in the user setting storage unit  111 . 
     In the flowchart in  FIG. 5 , for example, a determination procedure of excluding or limiting a phrase to be learned may be provided between step S 4  and step S 5 . 
     In the case of a phrase to be excluded, the process may proceed to step S 9 , whereas in the case of an phrase to be limited, the process may proceed to step S 5 . 
     As a result of limiting phrases to be learned, an opportunity for iterative learning can be provided more appropriately. 
     In a learning support system according to an embodiment of the present invention, a third memory state may be provided as memory data stored in the learning phrase storage unit  112 , and display color  3  corresponding to the third memory state may be set. 
     Display color  3  has a highlighting style different from that of display color  1 , for example. 
     As the highlighting style, for example, the character color or background color of an phrase may be different from display color  1 , or characters of an phrase may be outlined, shaded, embossed, or raised. 
     In the flowchart in  FIG. 6 , for example, step S 21  has a branch into “learning not completed this time: 2” as the third memory state, and output in display color  3  is performed in the corresponding case. 
     In addition, at the selection in step S 27 , “learning not completed: 0” may be replaced with “learning not completed this time: 2”. 
     Accordingly, the phrase recognized by the user as not having been completely learned this time becomes clear, and an effect of supporting learning according to a user can be further enhanced. 
     In addition, in a learning support system according to an embodiment of the present invention, the last date and time when the user selected a phrase, for example, may be stored as the last learning time data stored in the phrase-related information storage unit  113 . 
     In the flowchart in  FIG. 6 , the procedure of storing the system date and time in the phrase-related information storage unit  113  in step S 30  may be inserted between step S 26  and step S 27 . 
     Accordingly, regardless of whether or not learning of a phrase has been completed, an elapsed time can be calculated based on the date and time when the phrase was learned last time, and a display style can be determined by comparing the elapsed time with a predetermined period of time. 
     A learning support system according to an embodiment of the present invention can be used as a word book, to view sentences, and to learn terminology or the like. 
     Not only words or sentences of a single language, but also corresponding words or sentences of a plurality of languages may be simultaneously output (displayed). 
     Accordingly, in a case where a user learns a second foreign language, a native language and a first foreign language can also be simultaneously output (displayed), and a learning effect of the foreign languages can be further enhanced. 
     In addition, in a learning support system according to an embodiment of the present invention, in a case where a user learns a second foreign language, a predetermined period of time can be calculated in consideration of the similarity to not only a native language but also a first foreign language that the user has already learned. 
     For example, the first foreign language data that is input from the user with the native language etc. setting means  131 , is stored in the user setting storage unit  111  together with native language data. 
     The processing procedure for coefficient 2 in step S 42  in  FIG. 8  is performed on each of the native language and the first foreign language, and an arithmetic mean of the obtained values of coefficient 2 may be calculated, or the minimum value of the obtained values may be adopted. 
     For a user who has already started learning a plurality of languages, a predetermined period of time can be also calculated in consideration of the similarity to a second foreign language and a third foreign language. 
     In this case, the predetermined period of time may be calculated by weighing depending on the degrees of achievement of the user in the individual foreign languages. 
     For example, the native language may be weighed with ⅔ and a target foreign language may be weighed with ⅓. 
     Accordingly, more appropriate learning support can be provided depending on a user who is learning a plurality of languages. 
     In addition, in a learning support system according to an embodiment of the present invention, if a result obtained by adding 1 to the learning level exceeds “9” in step S 28  of the flowchart in  FIG. 6 , the value as is may be stored as the learning level in the phrase-related information storage unit  113 . 
     At the next learning, a phrase at a learning level exceeding 9 stored in the phrase-related information storage unit  113  may be automatically set as learning phrase exclusion level data. 
     Also, in the case of finishing learning in step S 25 , phrase information whose learning level exceeds 9 may be deleted from the phrase-related information storage unit  113 . 
     Accordingly, a phrase to be learned can be made clearer, and more effective learning support can be provided. 
     In a learning support system according to an embodiment of the present invention, for example, the following n-th order function may be used instead of the exponential function in equation (3) that is used to calculate a predetermined period of time corresponding to the learning level in  FIG. 7 . 
       Predetermined period of time=coefficient 1×(coefficient 2×learning level)  (4)
 
     Where, n is a natural number such as 1, 2, or 3, for example. 
     Furthermore, for example, the following fractional function, irrational function, or the like may be used to calculate a predetermined period of time. 
       Predetermined period of time=coefficient 1×(1−coefficient 2/learning level)  (5)
 
       Predetermined period of time=coefficient 1×√(coefficient 2×learning level)  (6)
 
     The learning support system of the present invention has been described above using specific embodiments, but the present invention is not limited to these embodiments. 
     It is possible for a person skilled in the art to add various changes or improvements to the configuration and function of the present invention according to the above embodiments or other embodiments without deviating from the gist of the present invention. 
     The learning support system of the present invention is also implemented by a program that causes a computer to function as a learning support system. 
     This program may be stored in a computer-readable storage medium. 
     The storage medium storing the program therein may be the ROM itself of the learning support system illustrated in  FIG. 1 , or may be a readable CD-ROM or the like that is a storage medium inserted into a program reading device such as a CD-ROM drive provided as an external storage device. 
     Alternatively, the storage medium may be a magnetic tape, a cassette tape, a flexible disk, a hard disk, an MO/MD/DVD or the like, or a semiconductor memory. 
     All publications, patents, and patent applications cited in this description are incorporated herein by reference in their entirety. 
     In addition, the disclosure of JP patent application No. 2016-234721 filed on Dec. 2, 2016 including the description, claims, and drawings is incorporated herein by reference in their entirety. 
     REFERENCE SIGNS LIST 
     
         
           1  learning support system 
           2  server 
           3  user terminal 
           4  network 
           10  main control unit (CPU) 
           11  storage unit 
           12  bus line 
           13  input unit 
           14  output (display) unit 
           15  communication unit 
           20  main control unit (CPU) 
           21  storage unit 
           22  communication unit 
           31  input unit 
           32  display unit 
           33  communication unit 
           101  phrase extraction means 
           102  phrase matching means 
           103  predetermined period of time calculation means 
           104  storage control means 
           111  user setting storage unit 
           112  learning phrase storage unit 
           113  phrase-related information storage unit 
           114  learning file storage unit 
           115  correspondence table storage unit 
           131  native language etc. setting means 
           132  phrase information input means 
           133  learning stage input means 
           201  phrase extraction means 
           202  phrase matching means 
           203  predetermined period of time calculation means 
           204  storage control means 
           211  user setting storage unit 
           212  learning phrase storage unit 
           213  phrase-related information storage unit 
           214  learning file storage unit 
           215  correspondence table storage unit 
           221  native language etc. setting means 
           222  phrase information input means 
           223  learning stage input means 
           224  output means