JP2010176236A - Information processing method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To (1) develop an information processing method for selecting a short sentence question adapted to the attributes of a user from a database in which short sentence questions are accumulated in the quick translation training system of linguistic learning, and to (2) develop an information processing method for clearly showing a non-English font on a display screen. <P>SOLUTION: (1) Basic attributes made fuzzy (age, sex or the like), time attributes (season or the like), space attributes (region or the like), and difficulty attributes are assigned to each short sentence question. The attributes are assigned also to a user with the same frame, and membership functions are uniquely set. Then, the selection probability of each question is determined based on the degree of the consistency of both sides and information input to a computer by the user while learning. (2) For Chinese language, because there are four types of voice tones in Pinyin, each of Pinyin is assigned by different colors. Then, color-added keys are assigned to a part of a keyboard for English, and when the key is depressed and then a vowel font is depressed, the font is discolored to the color on a display screen. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

The present invention relates to an information processing method for selecting a task nondeterministically from a database that records a learning task (hereinafter referred to as a short sentence task or simply a task) relating to a quick translation of a short sentence and an attribute attached to the task, The present invention also relates to an information processing method for displaying non-English fonts on a display screen in an easy-to-see manner or in a highlighted manner.

In the information processing system of the present invention, autonomous language learning related to short sentence speed translation is supported with the aid of information processing technology. However, the information processing system is characterized by short sentence assignment selection logic and character input / display system in the learning.
In the practice of rapid translation in language learning, there is a learning method in which a short sentence task in a native language (original language) is presented to the learner and promptly answered to improve the ability. Conventionally, printed teaching materials have often been used as a medium for presenting short sentences. However, in the printed teaching materials, the presentation order of the short assignments is fixed, so it has been pointed out that many learners get bored and interrupt if they learn repeatedly. In addition, it was pointed out that it was difficult to keep learning motivation because the content of the task was determined regardless of the interests of the individual learners and the situation where the learners were placed.
However, a computer-based rapid translation practice system can select and present a task using a random number from many short tasks. That is, since it is difficult for the learner to predict which task will be presented following a given task, a game sensation is likely to occur. That is, it is difficult to get bored by fixing the presentation order. However, just using the randomness of random numbers does not make a selection considering the attributes of the learner or assignment, so it cannot be said that it presents an assignment with content that matches the interest of the learner. .
Language learning using computers has been developed in the past. However, it cannot be said that the user's attributes are taken into consideration. There are many systems that are devised to construct educational materials and encourage learning by making use of the multimedia characteristics of the computer rather than creative teaching material presentation logic (for example, Non-Patent Documents 1 and 2). In addition, although probabilistic teaching material presentation logic that takes into account the learner and various attributes of the task has been reported (Non-patent Document 3), re-learning of the task with respect to the task presented during the learning in the logic Teaching material presentation logic with a mechanism for processing information on the content has not yet been reported. Differences between Non-Patent Document 3 and the present invention will be described in detail later.

On the other hand, when registering a short sentence task in the database, the English alphabet and phonetic symbols are entered on the keyboard. However, Pinyin, which is the phonetic symbol of Chinese, and the alphabet used in French and German include fonts (special fonts) that are constructed by adding an original line segment to the top (or bottom) of the English font. . In particular, in Pinyin notation for Chinese main vowels, it is indispensable to add a tone, that is, an intonation that distinguishes the first to fourth voices from the vowel font symbols for English. The “unique line segment” corresponds to tone notation in the case of Chinese. However, it has been pointed out that since the Pinyin notation is usually displayed in a smaller size, the 'original line segment' is displayed smaller, making it difficult to distinguish the tone at a glance. As an alternative, without using “original line segments”, the first, second, third, and fourth voices are respectively 1, 2, 3, and 4 immediately after the vowel font. Measures such as adding the number were sometimes taken. However, it has been pointed out that the measures for adding numbers are also inconvenient in terms of legibility.

Ma Qing and Kelly Peter (2006): Computer Assisted Vocabulary Learning: Design and evaluation, Computer Assisted Language learning, Vol. 19, No. 1, pp. 15-45. St-Jacques C. and Barriere C. (2005): Search by Fuzzy Inference in a Children ’s Dictionary, Computer Assisted Language Learning, Vol.18, No.3, pp.193-215. Kitagaki Ikuo (2008): A presentation system for rapid translation related to foreign language learning. Bulletin of the Graduate School of Education, Hiroshima University, Part 3 (Educational Human Science Related Area) No. 57, pp.335-340.

In the present invention, first, when selecting one task from the short sentence database,
(a) it is difficult for users to predict which issue will be selected,
(b) A task can be selected based on the consistency between the user's attributes and the short task attributes and the information that the user has entered into the computer regarding the re-learning of the presented task during the learning process,
Develop an information processing method that enables
Secondly, we developed a method to express information of non-English fonts with small line segments on part of English fonts using English fonts, and further developed an information processing method that enables the above-mentioned representation methods To do.
Thirdly, in connection with the second problem, an information processing method is developed that emphasizes the distinction of tone by pinyin font display with Chinese tone.

Regarding the first problem, a non-deterministic method is used as an algorithm for realizing the above (a). Here, the non-deterministic method means a relational method in which the past state and the current state are logically related to the future state, but the future state cannot be defined uniquely. Non-deterministic methods include probability theory and chaos theory. Probabilistic methods include the use of random numbers. In addition, the attributes described in (b) include a basic attribute, a time attribute, a spatial attribute, and a difficulty attribute for both the task and the user. Among these, the former three are composed of one or more attributes, and all of them are determined by using a method according to the membership function in fuzzy theory so that the setting can be made as close as possible to human judgment. That is, the independent variable characterizing one attribute is divided into several categories, and a value of [0.0, 1.0] is set for each category. Therefore, it may be said that the membership function is partitioned and a numerical value is given to each partition. Therefore, the attribute characterized by the membership function in the present invention is called a fuzzy attribute. For example, for the attribute of “sex”, two categories of “male” and “female” are provided, and a numerical value of [0.0, 1.0] can be given to both. Therefore, the attribute of the user is the attribute of the user's desire to present, in other words, whether he / she wants to increase or decrease the presentation probability for any attribute issue rather than the attribute attached to the user himself / herself. It may be stated that it is expressed numerically.
The attribute of each task and the attribute of the user are in a one-to-one correspondence. These are collated, and the consistency between each task and the user is obtained. How to find it is as follows.

前記と同じユーザーｕに関し、第j時間属性（j=１，…,J_T）の第k区分（k=１，…,K_T）の値をu_T(j,k)と表記する。第i課題の第j時間属性における第k区分のメンバーシップ関数の値を、s_T(i,j,k)と表記する。このとき、ユーザーｕのアクセス時間が第k₀区分に該当するときは、次式のように設定する。

前記と同じユーザーｕに関し、第j空間属性（j=１，…,J_L）の第k区分（k=１，…,K_L）の値をu_L(j,k)と表記する。第i課題の第j空間属性における第k区分のメンバーシップ関数の値を、s_L(i,j,k)と表記する。このとき、ユーザーｕのアクセス空間が第k₀区分に該当するときは、次式のように設定する。

Define a membership function for the jth basic attribute (j = 1,..., J _B ) of the i-th task (i = 1,..., I). For this purpose, the independent variable of the membership function is expressed by k sections (k = 1,..., K _B ), and the value of the membership function for the kth section is expressed by s (i, j, k). To do. On the other hand, for the user u, the value of the membership function for the kth division of the jth basic attribute is expressed as u (j, k).

It relates same user u and the j-th time attributes (j = 1, ..., J T) k-th segment of the (k = 1, ..., K T) is denoted the value of u _T (j, k) and. The value of the membership function of the kth section in the jth time attribute of the ith task is expressed as s _T (i, j, k). At this time, when the access time of the user u corresponds to the k ₀ segment is set as follows.

Relates same user u and the j-th spatial attributes (j = 1, ..., J L) k-th segment of the (k = 1, ..., K L) representation of the value of u _L (j, k) and. The value of the membership function of the k-th section in the j-th space attribute of the i-th task is expressed as s _L (i, j, k). At this time, when the access space user u corresponds to the k ₀ segment is set as follows.

ここに、

である。数式８は基本的整合度を表す。数式９は時間的整合度を表す。数式１０は空間的整合度を表す。数式１１は難度的整合度を表す。また数式において、∧、∨は、それぞれ最小値演算、最大値演算を表す。数式７で定義された全整合度のうち、特定の一課題については、該ユーザーの任意で、数式１２にしたがって値をある程度増減させることができる。特定の一課題とは、直前に提示された課題（第i_C課題）である。すなわち、ユーザーが該課題の再学習を希望するか否かを判断し、必要に応じて、該課題に対する全整合度を若干増減させるという方法を採る。εの値は、システム設計者またはユーザーが設定する。 It is assumed that the difficulty attribute can be set for both the original language and the rapid translation language of the i-th task, and the respective values are denoted as d _A (i) and d _B (i). On the other hand, it represents the difficulty set by the user in d _U. All of d _A (i), d _B (i), and d _U are [0, 1].
At this time, the total matching degree μ _i for the i-th problem of the user is defined by the following equation.

here,

It is. Equation 8 represents the basic matching degree. Equation 9 represents the degree of temporal matching. Equation 10 represents the degree of spatial matching. Equation 11 represents the degree of difficulty matching. In the mathematical expressions, ∧ and ∨ represent the minimum value calculation and the maximum value calculation, respectively. Of all the matching degrees defined by Equation 7, the value can be increased or decreased to some extent according to Equation 12 for a specific problem. The specific task is the task (i- _C task) presented immediately before. That is, a method is adopted in which it is determined whether or not the user wishes to relearn the task, and if necessary, the total matching degree for the task is slightly increased or decreased. The value of ε is set by the system designer or user.

全整合度をもとにして、数式１３で表現される規格化全整合度を求める。そして、数式１３より数式１４の確率分布関数Fを求める。

ここに、

である。また便宜上、

としておく。そして、乱数の値r₀（r₀ =[0,1]）に対し、

となるような第i₀課題を選択する。
数式１７の不等式において、最左項を下限確率分布関数と呼び、最右項を上限確率分布関数と呼ぶ。以上に述べた手順を、課題選択アルゴリズムと呼ぶことにする。数式７，８，９，１０，１１で、演算記号∧は最小値演算のかわりにより一般的なt-ノルムを用いてもよい。同じく、演算記号∨は最大値演算のかわりにt-コノルムを用いてもよい。
なお、非特許文献３では、学習の事中に難度を変更できるような学習システムを報告している。そこでの学習課題の提示論理によれば、難度を変更すると、新たな難度との整合性の高い課題の選択確率が高まる傾向を呈する。一方、本発明における数式１２では、提示されている課題の再学習を希望するか否かに関するユーザー判断に基づいて前記課題の全整合度を変更しているので、難度変更とは内容を異にする。これらの相違点を具体例で述べるならば、ユーザーが、学習の事中に、ユーザー属性としての難度の値を高めると、高める前の難度よりずっと高い難度を有する課題の提示確率がすべて上がりずっと低い難度を有する課題の提示確率がすべて下がる傾向を呈するのに対し、当該学習課題の再学習を希望すると、その後に前記学習課題が再提示される確率が高まり相対的に前記学習課題以外のすべての課題の提示確率が低まる傾向を呈する。明らかに、難度に関する入力情報と再学習に関する入力情報とは、内容的にも機能的にも異なる。
「発明が解決しようとする第二の課題」のための具体的手段として、中国語の場合は声調と色を対応させ、英語用キーボードの一部のキーを色付加キーに割り当てておく。そして、声調付きピンインフォントをディスプレーさせるときは、当該フォントに対応する英語フォントで表現するとともに、前記英語フォントを当該声調に対応する色に変色させる。フランス語（アクサン・テギュー、アクサン・グラーヴ等）やドイツ語（ウムラウト等）等の場合も、ほぼ同様の方式を採る。
「発明が解決しようとする第三の課題」のための具体的手段として、中国語の場合は声調と色を対応させておく。また中国語用キーボードの一部のキーを色付加キーに割り当てておく。そして、前記中国語用キーボードで声調付きフォントをディスプレーするとともに前記フォントを当該の色に変色させて、声調性を強調するという方法を採る。フランス語やドイツ語の場合もほぼ同様の方法を採る。

Based on the total matching degree, the normalized total matching degree expressed by Equation 13 is obtained. Then, the probability distribution function F of Expression 14 is obtained from Expression 13.

here,

It is. For convenience,

Keep it as And for the random value r ₀ (r ₀ = [0,1])

The i _0th task is selected such that
In the inequality of Expression 17, the leftmost term is called a lower limit probability distribution function, and the rightmost term is called an upper limit probability distribution function. The procedure described above is called a task selection algorithm. In Equations 7, 8, 9, 10, and 11, the operation symbol ∧ may use a general t-norm instead of the minimum value operation. Similarly, t-conorm may be used for the calculation symbol ∨ instead of the maximum value calculation.
Note that Non-Patent Document 3 reports a learning system in which the difficulty level can be changed during learning. According to the presentation logic of the learning task there, when the difficulty level is changed, the selection probability of the task having high consistency with the new difficulty level tends to increase. On the other hand, in Formula 12 in the present invention, since the total consistency of the task is changed based on the user's judgment regarding whether or not to relearn the presented task, the content differs from the difficulty change. To do. If these differences are described by specific examples, if the user increases the difficulty level as a user attribute during learning, the presentation probability of tasks with a much higher difficulty level will be increased. While all the presentation probabilities of tasks with low difficulty tend to decrease, if you wish to re-learn the learning task, then the probability that the learning task will be re-presented increases, and all other than the learning task is relatively Presents a tendency to reduce the probability of presenting the task. Obviously, the input information related to the difficulty level and the input information related to the relearning are different in content and function.
As a specific means for “the second problem to be solved by the invention”, in the case of Chinese, a tone and a color are associated with each other, and some keys of the English keyboard are assigned to the color-added keys. When displaying a pinyin font with a tone, it is expressed in an English font corresponding to the font, and the English font is changed to a color corresponding to the tone. In French (Aksan Tegu, Aksan Grave, etc.) and German (Umlaut, etc.), the same method is used.
As concrete means for “the third problem to be solved by the invention”, in the case of Chinese, the tone and the color are made to correspond. In addition, some keys of the Chinese keyboard are assigned to the color addition keys. Then, a method is used in which the font with tone is displayed on the Chinese keyboard and the tone is emphasized by changing the font to the corresponding color. The same method is used for French and German.

As described above, the present invention relates to an information processing method related to rapid translation practice, and when selecting one problem from a database of short sentence tasks for rapid translation,
(i) Since it is difficult for the user to predict the problem to be selected, a game sensation is likely to occur.
(ii) Consistency between user attributes and task attributes, consistency between user-accessed time attributes and task time attributes, consistency between user-accessed spatial attributes and task spatial attributes, difficulty set by user Since the task is selected depending on the consistency between the attribute and the difficulty attribute of the task and the information that the user has input during the course of learning regarding the re-selection of one task, the task that matches the user's situation is selected. Is likely
Because the algorithm of the nature is used, the practice effect is great. In addition, when inputting Chinese into a computer, according to the present invention, it is possible to distinguish between colors using English fonts or display fonts with Chinese tones emphasized by distinguishing colors. That is, there is an effect that the user can distinguish the tone by color on the display screen.

(a) is the figure which showed the process of selecting one subject from the subject database using the subject selection algorithm of this invention. (b) is a diagram showing an example in which each tone and color in Chinese are associated with each other, and an example in which each tone is changed in color by assigning some keys of the English keyboard to color-added keys. It is the figure which showed the structure of the assignment database. (a) is the figure which showed the structure regarding an area | region. (b) is the figure which showed the structure regarding a theme. (c) is the figure which showed the structure regarding a subject. It is the figure which showed the example of a setting of a subject and a user's attribute. (a) is a setting example of basic attributes. (b) is a setting example of a time attribute. Use the internal timer of the computer for the season and day and night. (c) is an example of setting a spatial attribute. The area is set automatically using a measuring device such as GPS, or set by the user. (d) is a difficulty level attribute. FIG. 2 is a diagram in which only the task selection algorithm part of FIG. It is the figure which showed what kind of structure one subject has in a database. It is the figure which showed what kind of screen this system shows sequentially when it sees from a user's standpoint. It is the figure which showed the example of a screen display at the time of the short sentence task and the correct answer of a translated sentence being shown.

Hereinafter, the present invention will be described in detail with reference to the drawings.
Regarding the first embodiment of the “problem to be solved by the invention”, the above-described problem selection algorithm will be described with reference to FIG.
[s01] is an issue database. The database includes task sentences for quick translation. In the issue database, a hierarchy called “area” is provided under the root directory, and a hierarchy called “subject” is provided therebelow. The detailed structure is as shown in FIG. The assignment database records an assignment [s02] and an assignment attribute [s03] corresponding to the assignment. As the task attributes, four attributes [s04], that is, a basic attribute, a time attribute, a space attribute, and a difficulty attribute are recorded.
[s07] is a user database. The database includes information on users who practice rapid translation. The user attribute [s06] corresponding to the user [s08] is recorded in the user database. The user attribute has the same structure as the task attribute. In setting the value for the time attribute related to the user, the access time is used using the internal timer [s15]. The spatial attribute related to the user may be automatically set by using a device such as GPS [s16], which identifies the user's whereabouts, or may be set by the user at his / her own discretion. In the latter case, the spatial attribute can be regarded as a part of the basic attribute.

[s14] is the core part of the task selection algorithm. In [s10], the task attribute [s03] and the user attribute [s06] are collated, and the basic consistency (Formula 8), temporal consistency (Formula 9), spatial consistency (Formula 10), and difficulty matching Degree (Formula 11) is obtained. Next, the total matching degree [s11] (Formula 7) is obtained using the four types of matching degrees, and the normalized total matching degree (Formula 13) obtained by standardizing the total matching degree is obtained. At that time, the user [s08] can refer to the information input by the computer regarding the task selection using the user button [s09], and based on this, the total matching degree for the specific task can be increased or decreased to some extent (Formula 12). A probability distribution function [s12] (Formula 14) is obtained based on the normalized total consistency. The lower limit / upper limit probability distribution function for the subject is obtained from Equation 14 (Equation 17). Then, a random number [0, 1] is generated, and one task is selected from among the tasks existing in the “subject” set in advance by the user based on the condition of Expression 17 [s17].
FIG. 2 shows the structure of the issue database. FIG. (A) is an area table, and one area key is assigned to one area name expressed in each language. FIG. 2B is a theme table, and one theme key is assigned to one theme name expressed in each language. FIG. 3C is an assignment table showing the subject key range within which the assignment key belongs. Information included in one assignment key is shown in FIG. 5 later.

FIG. 3 shows an example of assignment of task attributes. Fig. (A) is an example of basic attributes (age, gender, etc.), Fig. (B) is an example of time attributes, and Fig. (C) is an example of spatial attributes. In either case, an example of setting a category is shown for each attribute. Although FIG. (D) shows the difficulty level attribute, the difficulty level itself is set in several stages without having a division.
FIG. 4 is a table showing the relationship between the variables shown in [s10], [s11], and [s12] in the task selection algorithm [s14] of FIG. For each issue key, the basic attribute, the time attribute, the spatial attribute, and the difficulty attribute are determined for the consistency with the user, the total consistency is obtained by collecting information on the consistency, and the normalized total is calculated from the total consistency. The relationship is shown in which the degree of matching is obtained and the lower limit / upper limit probability distribution function is obtained from the normalized total matching degree.
In FIG. 4, the total number of tasks is assumed to be 100. In FIG. 4, assuming that the normalized total matching degree is the numerical value shown in the table, the lower limit probability distribution function and the upper limit probability distribution function shown in Expression 17 are obtained based on this value. In this state, it is assumed that the value r ₀ of the random number r is 0.18. Then, since the lower limit / upper limit probability distribution functions of the task key 0005 are 0.10 and 0.19, respectively, and 0.10 and 0.19 sandwich 0.18, the task key 0005 is selected. If the user changes the value of the basic attribute (related to the user) in FIG. 3 in the middle of learning, the lower limit / upper limit distribution function is obtained again by recalculating FIG.

FIG. 5 shows information of one task key. In field 1, an assignment key is recorded. Field 2 represents the subject key symbol containing the task. Field 3 represents information on whether or not to display an image corresponding to the original language. A field 4 represents information on whether or not to display an image corresponding to the rapid translation language. Field 5 represents information on whether or not to display a hint corresponding to the original language. A field 6 represents information on whether or not to display a hint corresponding to the rapid translation language. In field 8, a quick translation task sentence expressed in Japanese is recorded. In the field 7, a sentence is recorded that explains the situation of the background of the rapid translation task sentence. In the field 10, path information of a quick translation task sentence expressed in Japanese speech is recorded. In field 9, there is recorded Japanese audio path information explaining the background of the rapid translation task sentence. In the field 11, path information of image information related to the Japanese task sentence and the Japanese situation explanation is recorded. In field 12, hints in Japanese are recorded. In the field 13, pronunciation symbols for Japanese task sentences are recorded. In field 14, the rapid translation difficulty of the Japanese sentence is recorded as a numerical value of [0, 1]. After field 15, the same information as field 7 to field 14 is recorded in a foreign language every 8 fields.

FIG. 6 shows a procedure of the rapid translation practice system viewed from the user. [s41] is an initial step. If you are a new user, you can perform new registration at the initial step. If you are a registered user, you can log in or unregister. [s42] is a step of selecting a language, and an original language and a rapid translation language are selected. [s43] is a step in which the user registers or changes user attributes. [s44] is a step in which the user selects one “region” from among several “regions” prepared in advance. [s45] is a step in which the user selects one “theme” from among several “themes” prepared in advance for the selection area. When the user selects one subject in [s45], the system selects one task by the method of FIG. 4 and presents the screen in the original language [s46].

This system is intended for autonomous learning. Therefore, the user who is a learner quickly translates the task presented on the screen and then self-evaluates his / her answer by displaying one correct answer on the screen. If you feel that the task presented on the screen is too difficult or too easy, you can pass and present the next task. In FIG. 6, when it is desired to pass and present the next task, “next task [s48]” is selected. Select "Problem Voice [s49]" if you want to present the assignment by voice. To display the correct answer, select “Character answer [s52]”. Select "Voice Answer [s53]" if you want to present the correct answer by voice. After that, if you want to present the next assignment, select “Next assignment [s48]”. Select “Re-learning hope [s50]” to increase the presentation probability of the presented task in order to re-learn the presented task. Thereby, the total matching degree of the subject is recalculated by Equation 7. To reduce the presentation probability of the presented task in order to inform the computer that the user does not wish to re-learn the presented task, select [Do not re-learn [s51]]. To change the area, select "Area selection [s54]". If you want to change the theme, select "Select theme [s55]". To change user attributes, select “Change Attributes [s56]”. When it is desired to change the difficulty level desired by the user, the desired change is made in “Difficulty level change [s57]”. Select “Logout [s58]” when you want to finish learning.

FIG. 7 shows a computer screen display example when one correct answer for one task is displayed. The underlined part of [s86] is the subject sentence for quick translation. A situation explanation for the task sentence is an underlined portion of [s81]. The underlined part of [s82] is a hint. The underlined part of [s83] is one correct answer. In the case of a language that requires pronunciation symbols for learning, it is displayed on the right side of the portion where “phonetic symbols” are written. The display positions of the original language image information and the rapid translation language image information are [s84] and [s85], respectively. FIGS. 6 [s48] to [s58] described above correspond to FIGS. 7 [s87] to [s97], respectively.
This system can be used overseas with a roaming function if a portable PC or smartphone is used, and the foreign language learning in a foreign language environment becomes possible.

The second embodiment of the “problem to be solved by the invention” is as follows. In this assignment, in order to make it easy to input phonetic symbols and assignment sentences in the multilingual registration in the assignment database, (c) information on non-English fonts with a line segment added to English fonts And (d) to develop an information processing method that enables the expression method.
In order to solve (c), the property that the color display on the display screen of the computer can be performed more easily than the printed teaching material is utilized. Specifically, instead of displaying the desired non-English font, by coloring the English font or the background of the English font, the colored English font becomes a non-English with a line segment attached to a part of the English font. Set rules to mean fonts. Assuming that the Chinese language is now used, when expressing the tone in Pinyin, for example, as shown in the frame of FIG. , Red, purple and green are assigned one-to-one. Pinyin representing `` base '', `` sum '', `` purple '', and `` medium '' described in FIG. (B) includes the main vowels of the first, second, third, and fourth voices, respectively. As shown on the right side of FIG. (B), each is represented by a blue 'i', a red 'e', a purple 'i', a green 'e', and a colored English font.

In order to make it easy to add a color to the English font by inputting an English font, a keyboard having color addition keys shown in FIG. [s26] is a key of alphanumeric characters and special symbols, [s27] is a numeric keypad, and [s28] to [s32] are function keys. Then, [s28] to [s32] are used as color addition keys. For example, [s28] is blue, [s29] is red, [s30] is purple, [s31] is green, [s32] is brown,・ As such as assign. When the alphabet key is pressed after pressing the color addition key, the first alphabet is displayed in the color. There is also a method in which when the alphabet key is pressed while the color addition key is pressed, all of the alphabet keys pressed during that time are displayed in the corresponding color. In addition to the above method, there is a method of assigning a BG (background) key. For example, there is a method of assigning the control key to the BG key, pressing the BG key and the color addition key simultaneously, and then pressing the alphabet key so that the background of the first alphabet is painted in that color.
[S20] in FIG. 1 (c) is an assignment creation screen. The phonetic symbol for the “answer sentence” in the screen of [s20] is [s21] (third voice), [s22] (fourth voice), [s23] (third voice), [s24] The English vowel fonts of (first voice) and [s25] (second voice) are colored purple, green, purple, blue, and red, respectively, using the color addition keys.
The third embodiment of the “problem to be solved by the invention” is as follows. This subject is related to said 2nd subject, and is developing the information processing system which emphasizes the distinction of a tone by the pinyin with a Chinese tone.
For this purpose, the correspondence between the font with the tone and the color is registered in advance with a Chinese keyboard. In actual operation, by performing a normal Pinyin input operation, the background of the simplified font with tone, the traditional font with tone, or the background of the simplified font with tone or the background of the traditional font with tone The screen should be displayed in color.

s01: Issue database
s02: Challenge
s03: Issue attribute
s04: Basic attributes (for assignment), time attributes, space attributes, difficulty attributes
s05: (User) basic attributes, time attributes, spatial attributes, difficulty attributes
s06: User attribute
s07: User database
s08: User
s09: User button
s10: Basic consistency, temporal consistency, spatial consistency, difficulty consistency
s11: Total consistency
s12: Lower / upper probability distribution function
s13: Random number
s14: Task selection algorithm (core part)
s15: Internal timer
s16: GPS etc.
s17: Selected issue
s20 :: Project creation screen
s21: Main vowel 'o' (purple)
s22: Main vowel 'i' (green)
s23: Main vowel 'e' (purple)
s24: Main vowel 'i' (blue)
s25: Main vowel 'e' (red)
s26: Alphanumeric / special character key
s27: Numeric keypad
s28: Color addition key 1
s29: Color addition key 2
s20: Color addition key 3
s31: Color addition key 4
s32: Color addition key 5
s41: Initial screen
s42: Language selection screen
s43: Attribute setting screen
s44: Area setting screen
s45: Theme setting screen
s46: Short text presentation screen
s47: Button selection
s48: “Next Challenge” button
s49: "Problem audio" button
s50: “Re-learning request” button
s51: "Do not re-learn" button
s52: "Character answer" button
s53: "Voice answer" button
s54: "Select area" button
s55: "Select theme" button
s56: "Change attribute" button
s57: "Change difficulty" button
s58: "Logout" button
s81: Situation explanation
s82: Hint
s83: Correct sentence
s84: Original language image information
s85: Quick translation language image information
s86: assignment text
s87: “Next Challenge” button
s88: "Problem audio" button
s89: "Re-learning request" button
s90: "Don't wish to re-learn" button
s91: "Character answer" button
s92: "Voice answer" button
s93: "Select area" button
s94: "Theme selection" button
s95: "Change attribute" button
s96: "Change difficulty" button
s97: "Logout" button

Claims (4)

In a database that records learning tasks and fuzzy attributes attached to the learning tasks,
Consistency between the fuzzy attributes of the learning task recorded in the database and the fuzzy attributes set by the user of the database;
Consistency between the fuzzy time attribute of the learning task and the time accessed by the user;
Consistency between the fuzzy space attribute of the learning task and the space where the user exists,
The consistency between the difficulty attribute of the learning task and the difficulty set by the user;
Information entered by the user regarding the re-learning of the learning task presented to the user; and
An information processing method that non-deterministically selects one learning task from the database and displays it on the screen. An information processing method that displays the position of a font displayed on a computer screen based on the correspondence between a preset font and color. An information processing method in which when the alphabet key is pressed after pressing the color addition key on the keyboard having the color addition key, the font position of the first character is changed to the color assigned to the color addition key and displayed. An information processing method in which when a letter key is pressed while pressing the color addition key on a keyboard having a color addition key, the position of the font of the alphabet is changed to the color attached to the color addition key and displayed.

Images