KR100453430B1 - E-book generating system for learning according to the order and method thereof - Google Patents

Abstract

The present invention relates to a learning material creation system for learning to provide a textbook suitable for the level of the learner. This custom teaching material generation system is to provide to the learner who performed the distance learning through the Internet, the learning that stores the data analyzed by the prototype problem, principle for the learning result performed by the learner by difficulty level through the Internet A situation database, a problem database that stores the problem data that the subject has learned and the problem data that has not been learned, and a principle data that the subject has understood and a principle data that has not been understood among those corresponding to the problem data. Principle database and the understanding database based on the principle problem for each one of the subjects, the principle from the learning situation database and the learning situation database, and according to the result of the discrimination algorithm From the custom problem and the principle database above An E-BOOK generator for performing a learning content extraction algorithm for extracting a plurality of custom principles, and an output means for printing the custom problem, the custom principle, the standard evaluation problem, its principles, and solutions; The E-BOOK generator includes generating a plurality of standard evaluation problems from the problem database according to a problem-producing algorithm, and extracting principles and solutions from the problem database. Accordingly, by analyzing the understanding level by level, the principle of understanding the lacking part is solved and the problem is solved, thereby causing interest and interest in learning and maximizing the learner's learning ability.

Description

E-book generating system for learning according to the order and method approximately}

The present invention relates to a system for creating a customized textbook for learning, and more particularly, to a system for creating a customized textbook for learning to provide a textbook suitable for a learner's level.

In general, textbooks often contain the principles and problems of learning that meet the average learning level for a large number of learners in order to improve learners' education. Such learning materials are almost impossible to always have a high level of education for learners with high or low levels of education, and there is a problem that it is almost impossible to improve learning ability even for learners focused on specific areas by field of study.

In order to solve these problems, publishers compiling learning materials publish textbooks of the same subject by multiple levels according to their levels, and randomly select learners' levels and apply them uniformly.

However, the customized textbooks are difficult to match the learner's individual learning ability, achievement and level, and because the textbook is edited in a situation where the understanding of individual learning tendencies and learning contents is ignored, There is a decisive problem because the learning of the unit does not understand the principle integrated with the next unit and thus does not complete the lesson.

In particular, the question of integrative interests of two or more units is often a problem that cannot be solved because of poor understanding of the principle, and the learning is less efficient and the subject is far from the attention, and thus the progress of learning cannot be met and the grades fall. A problem occurs.

The present invention has been made to solve the above problems, analyzes the understanding of each level of the problem, and focuses on the principle of the learner's lack of learning part to induce his problems to be recognized according to the fundamental solution, thus lacking The purpose of this study is to provide a system for creating customized textbooks for learning that improves the overall educational ability by collecting the problems of the learning part to the individual learners according to the learning level and the educational level.

1 is a block diagram showing a system for creating a customized teaching material according to the present invention,

Figure 2 is a flow chart showing the overall custom learning material generation system according to the present invention,

3a and 3b is a flowchart showing a custom principle extraction according to the present invention,

Figures 4a to 4c is a flowchart showing a custom circular problem extraction according to the present invention.

Explanation of symbols on the main parts of the drawing

100; Learning database 200; Problem Database

300; Principle Database 400; E-BOOK Generator

500; Output means

According to the present invention, according to the present invention, in the system for creating a customized teaching material for learning to provide to the learner who performed the remote learning over the Internet, for each learning problem performed by the learner for each difficulty over the Internet by circular problem, A learning situation database that stores the data analyzed by the principle, a problem database storing the problem data that the learner has learned and the problem data that have not been learned, and the learner understands among the problem data. Principle database that stores one principle data and one principle data that are not understood, and the understanding discrimination algorithm for each prototype problem and principle corresponding to any one learner from the learning situation database, and the result of the discrimination algorithm A plurality of custom statements from the problem database according to And an E-BOOK generator that performs a learning content extraction algorithm for extracting a plurality of custom principles from the principle database, and an output means for printing the custom problem, the custom principle, the standard evaluation problem, its principles and solutions. Includes; The E-BOOK generator is achieved by a learning material generation system for learning that extracts a plurality of standard evaluation questions from the problem database according to a problem-producing algorithm and extracts the principles and solutions from the principle database.

According to another aspect of the present invention, according to another aspect of the present invention, in the method for generating a customized teaching material for learning to provide to a subject who has performed a distance learning through the Internet, the subject of the subject to learn by determining whether the subject Searching the identification number, and based on the search, among the learning groups corresponding to one class of individuals and small groups, the learning groups of the school year, the learning groups of the region, and the learning groups of the whole country. Determining which one belongs to, Determining the learning progress of the learner to determine the learning range of the customized teaching material, Comprehension discrimination algorithm for the prototype problem and principle of the learning range for the learner Analysis of understanding and classification that analyzes understanding and classifies principle and prototype problems according to grade and level accordingly Principle and problem extraction step of extracting a specific number of principles and problem from the principle and problem database according to the degree of understanding, the standard evaluation problem for the evaluation of learning and the solution and principle Drawing out a predetermined number from the step, preparing the printed material by sorting the custom principle and the custom problem in a predetermined order along with the standard evaluation problem and its solution and principle, and extracting the principle and the prototype problem. It is achieved by a method for generating a customized teaching material for learning that includes visually printing on paper.

The comprehension analysis and classification step may include classifying the entire learner's understanding percentage distribution for each individual principle among a plurality of principles corresponding to a predetermined learning range, and understanding one learner's understanding of the individual principles. Classifying the percentage distribution into a plurality of degrees, and understanding the percentage distribution of the total learners classified into the plurality, and the understanding percentage distribution of the learners classified into the plurality, from the individual understanding. Comparing the plurality of principles by a plurality of grades and rankings in the order of lower individual comprehension, and the percentage of the overall learner's understanding of each individual prototype problem among a plurality of prototype problems that fall within a predetermined learning range. Classifying the distribution into a plurality of distributions; Classifying the understanding percentage distribution of one learner into a plurality, the understanding percentage distribution of the entire learners classified into the plurality, and the understanding percentage distribution of the learners classified into the plurality, It is preferable to include the step of classifying the plurality of prototype problems by a plurality of grades and ranks in the order of lower learning objectives relatively lower than the overall understanding from the diagram. In addition, the custom principle extraction step for the learner Sorting from the highest rank to the lower rank among the principles and problems classified by rank, and counting sequentially from the sorted top rank to the lower rank to extract a group corresponding to a certain number of fits, and the same rank If the number of principles belonging to exceeds the above number, Preferably, the method further includes extracting the prototype questions in order of high frequency. The custom problem extracting step includes sequentially sorting the prototype questions sorted by rank for the learner from the highest to the lowest. And extracting a group corresponding to a certain custom circular number by sequentially counting circular problems from the sorted top rank to a lower rank, and sorting the top rank among the circle problems of the extracted group. Arranging in order of low understanding of a plurality of problems corresponding to a prototype problem, counting problems by the sorted circles in order of high frequency of answering, and determining distribution problems by counting up to the maximum number of customized problems; Teacher recommendation and student recommendation It is desirable to include the step of determining the distribution problem by counting up to the maximum in the highest order.

Hereinafter, with reference to the accompanying drawings will be described in detail a custom teaching material generation system according to the present invention. 1 is a block diagram showing a system for creating a customized teaching material according to the present invention, FIG. 2 is a flowchart showing an overall system for creating a customized teaching material according to the present invention, and FIGS. 3A and 3B illustrate a customized principle extraction according to the present invention. 4A to 4C are flowcharts illustrating a custom circular problem extraction according to the present invention.

The system for creating a customized textbook for learning focuses on the areas where the understanding of the principles of learning is weak, based on the results of the learning by the subjects who have performed one-to-one personalized learning through the Internet. If you want to learn as to distribute the customized textbooks by level to the learner. Here, the learner may be an individual (hereinafter referred to as an individual learner), or may be an entire class (hereinafter referred to as a half-learner) of various classes in one school, or the class may be It may be the entire school year (hereinafter referred to as a grade learner), and may be the whole area to which the school belongs (hereinafter referred to as a local learner), and the whole including the area (hereinafter referred to as a full learner) May be

As illustrated in FIG. 1, a learning material generation system for providing learning objects for providing learners (1 learners, semi-learners, grade learners, local learners, and all learners) who have performed remote learning through the Internet is the subject learns the Internet. Learning situation (learning achievement) database (100) for storing the data analyzed by the circular problem, principle based on the results of the learning achievement performed by the difficulty according to the evaluation learning through the problem data and learning learned by the subject The problem database 200 which stores the problem data that is not stored, and the principle data that the subject learns and understands and the principle data that are not understood by the subject, among those corresponding to the problem data as corresponding to the problem data. Principle database (300), which stores solution data for problems, and the learning situation database (1). 00) According to the learning achievement data of 00), the understanding problem determination algorithm is performed according to the circular problem and principle corresponding to any one learner, and according to the result of the discrimination algorithm, a plurality of customized problems and the principle from the problem database 200 are determined. It has an E-BOOK generator 400 that performs a learning content extraction algorithm to extract a plurality of custom principles from the database 300.

Here, the E-BOOK generator 400 questions about 20 to 30 questions from the problem database 100 according to the problem-probing algorithm, and the principles and solutions are solved from the principle database 300. And output means 500 for printing the alignment problem, the alignment principle, the standard evaluation problem, the principle and the solution thereof. Here, the output means 500 is a printer or a fax.

The operation of the customized teaching material generation system according to the present invention as described above, first, if the learner who has learned through the Internet to apply for a textbook for custom learning at a desired time or period, search whether there is a learner applied for the period Is done later.

As shown in FIG. 2, the applicants determine whether the applicant has applied for personalized learning and search until there is an applicant (S1). If there is an applicant in the search, the applicant's identification number is recognized (S2). In this case, the identification number designates a learner, and learners for any one of individual learners, half learners, grade learners, local learners, and total learners are selected (S3).

As such, when the subject of the personalized teaching material is determined according to the selection of the subject for the identification number, the E-BOOK generator 400 exports the determined subject's learning progress from the learning situation (learning achievement) database 100. Figure out (S4). Here, the learning progress status is information that collectively represents the results of learning that the learner has performed so far through the Internet, that is, the learning comprehension, the achievement, and the learning achievement information on the poor part of the learning. According to the learning progress situation, a unit for determining a learning range and a learner's personalized textbook is determined (S5), and an algorithm for determining an understanding of principles and problems learned through the Internet for the unit (S6) is performed. Here, the problem is divided into one principle and a plurality of principles, and the above principle is also used as a problem of various questions. In order to understand the principle as described above, there are a number of questions and the set of questions is called a prototype problem.

0≤Student≤25 25≤Student≤50 50≤Student≤75 75≤student≤100 0≤total≤25 3 rank 4th rank 5th rank 7th rank 25≤total≤50 3 rank 4th rank 5th rank 7th rank 50≤all≤75 1st priority 2nd rank 6th rank 8th rank 75≤overall≤100 1st priority 2nd rank 6th rank 8th rank

In other words, the understanding discrimination algorithm is shown in Table 1, which is applied to the prototype problem and the principle at the same time. Understanding of Principle Analysis shows each individual principle as the understanding of the whole learner among the plurality of principles that fall within the learning range, and classifies these principles into percentage distribution. With respect to these principles, one individual learner's understanding of the learner is classified as a percentage distribution. Principle of any of the above-mentioned principles is the percentile distribution of understanding for all learners (hereinafter referred to as "understood") and the percentile understanding of subjects (hereinafter referred to as "understand"). Classify multiple ranks and rankings in order of relatively low level of understanding. This applies not only to the principle but also to the prototype problem.

For example, in determining the rank of the subject for the prototype problem and the principle, if the subject is the individual learner, by setting the order of ascending order in the order of relatively low comprehension of the individual learner to the overall understanding 1 When the ranking is ranked from the lowest rank (lowest comprehension) to the eighth rank (highest comprehension), the individual learner may determine a principle and a prototype problem and a principle and a prototype problem with low understanding as individual rankings.

As described above, the E-BOOK generator 400 solves the principle and the prototype problem at the top by sorting the principle and the prototype problem in the order of the highest rank, that is, the lowest comprehension according to the understanding rank determined by each prototype problem and principle. A custom principle and prototype problem extraction algorithm for extracting a predetermined number from the database 200 and the principle database 300 is performed (S7, S8).

In this way, after extracting the customized principle and the customized circular problem, the standard evaluation problem, correct answer, commentary, and principle for evaluating their learning are almost equally difficult from the problem database 200 and the principle database 300. Draw a predetermined number so that is distributed (S9). Such a custom principle, a custom circular problem and a standard evaluation problem are sorted according to a predetermined learning order (S10) and output through a printer which is an output means (S11).

As shown in FIGS. 3A and 3B, the custom principle extraction algorithm is first prepared with a plurality of learning principles up to nth (S20), and the i = 1th learning principle is extracted from the principle database 300. (S21) determine the ranking according to the understanding determination algorithm as shown in Table 1 (S22), and repeat steps 21 and 22 until reaching the last principle (S23, S24). As such, when all the principles are determined in the comprehension ranking, they are stored in a customized teaching material database (not shown) and sorted in ascending order (S25). Subsequently, the principle selection algorithm shown in FIG. 3B is performed (S26).

That is, in order to select four learning principles, prepare learning principles sorted from 1st to 8th ranks (S27), search the number of learning principles of 1st rank which is the highest rank (S28), and the number is less than 4 In the case (S29) extracts X learning principles as much as the searched number from the principle database 300 (S30), calculates the number of extractions corresponding to the next rank (S31) and sets the second rank, which is the next rank (S32). ).

Then, the number of learning principles of the second rank is searched (S28), and compared to the number of extractions, the number is continuously moved to the next rank until it is the same to search for and extract whether there are more than (N-X) learning principles (S29). (NX) When more than one learning principle is extracted, the frequency of questions of the previous questions corresponding to the principle is searched, sorted in order of ranking and frequency (S33), and finally, four are extracted in order of frequency of questions (S34). .

On the other hand, the circular problem extraction algorithm, as shown in Figures 4a to 4c, should extract 40 seconds.

First, the algorithm for extracting the 15-question question is extracted by searching the frequency of the question and the year of the question for the round question of the ranking determined in Table 1 (S40), and among the extracted prototype problems. Enter to select a total of 15 questions (N questions) (S41). Then, X questions are calculated by extracting the highest-priority problem, that is, the lowest rank, that is, the first rank. It is determined whether the X pre-rounded problems are 15 or more in total (S43), and in the case of 15 or less rounds, X problems are extracted by extracting one problem for each round (S44).

The next rank, that is, the second rank and the 15 questions are set to the remaining questions (N items) by subtracting the X items (S45), and the X-numbers are extracted by extracting the original-priority problem of the second rank (S42). If the second-priority X-issued questions are more than the rest of the questions, a total of fifteen questions are finally extracted in order of the highest frequency of questions among the questions that have been asked in the last three years or less (S46).

For example, if there are more than fifteen questions in the first rank, one question is extracted from each question in the previous three years, and 15 questions are finally extracted in the order of the highest frequency. However, if there are 7 questions with 15 or less priority questions, 1 question is extracted from each question and 7 questions from 15 questions are subtracted from the 8 questions. If there are more than 8 questions in the 2nd priority, select more than 15 questions out of the 2nd priority questions, and if it is less than 8, select the 15 more questions. The relevant question should be extracted. At this time, if more than 15 questions are selected, the 15 questions are finally selected from the items with less than 3 years in the order of the highest frequency.

In addition, the algorithm for extracting a 20-question fit question is set to extract a total of 20 questions (N) based on the ranking according to the comprehension determined in Table 1 (S50, S51). At this time, the number X of questions is set to zero. By setting the maximum number of items (MAX = 4) extracted from each prototype problem among a plurality of prototype problems corresponding to each ranking (S52), the item of any one of the highest ranking questions, that is, the first-order prototype problems ( j = 0) is selected (S53). When the selected number of items j is less than or equal to the maximum number of items (S54), the first priority is set to K = 1 th circle (S55) and the items are searched in the circle (S56). It is determined whether the item is extracted from the searched K-th circle (S56), and finally one item is extracted (S57). It is determined that one item (X = X + 1) is extracted (S58), and it is determined whether the number of extracted items (X) reaches the total number of questions (N) (S59), and when the total number of questions is reached, the process ends. If not reached, set the next (K = K + 1) prototype problem (S60), and determine whether K = 2 is higher than the number of the first rank (S61). S62) If j = 2 is lower than MAX = 4 (S54), K = 1 is set again (S55), and the item is extracted from the first-order K-th round problem (S56, S57). Here, the MAX for each rank is different, and can be changed according to the content and range of the customized teaching material. For example, MAX in the 1st and 2nd ranks is 4 questions, MAX in the 3rd, 4th, and 5th ranks is 3 questions, and the number of items gradually decreases as the lower rank is reached.

When the MAX = 4 item is exceeded in this way, the next rank item, that is, the second rank item, must be extracted (S63). Here, a total of 20 questions of the personalized questions are extracted by performing the above repeated loop.

In addition, the algorithm for extracting the five-tailored non-answering problem is set to extract a total of five questions based on the ranking according to the understanding determined in Table 1 as shown in FIG. 4C (S71). That is, a total of 40 questions are subtracted from 15 questions and 20 items of personalized answers, and the remaining 5 questions (n) are set to be extracted from the first rank.

The number of prototype questions (x) of the first rank is extracted (S72) to determine whether the number of prototype questions is more than five questions of the customized non-answer questions (S73), and if there are many, five questions are extracted as priorities (S76). However, if there are not many, one is selected for each prototype problem from the number of prototype problems (x) to extract the total x items (S74). Set the remaining number of questions in the next rank (S75) and repeat until a total of five questions.

The present invention has the effect of inducing interest and interest in learning and maximizing the learner's ability to learn because it analyzes the level of understanding for each level to understand the principle of the lacking part and solves the problem.

In addition, by providing individual learners with customized textbooks and learning ability according to their learning level, they can fully understand the lack of learning principle, so they can set the direction of learning by themselves and provide the amount of learning that satisfies each educational level. It is effective to achieve a new era of learning.

Claims (11)

Claims [1] A system for creating a customized teaching material for learning to provide to a subject who has performed distance learning through the Internet; A learning situation database for storing the data analyzed by the prototype problem and the principle for the learning result performed by the learner on the difficulty level through the Internet; A problem database for storing the problem data learned by the learner and the problem data not learned; A principle database for storing the principle data understood by the learner and the non-understood principle data among those corresponding to the problem data; From the learning situation database, a comprehension discrimination algorithm is performed for each one of the subjects corresponding to one of the subjects, based on the principle, and according to the result of the discriminating algorithm, a plurality of customized problems from the problem database and a plurality of problems from the principle database. An E-BOOK generator for performing a learning content extraction algorithm to extract the custom principle of the; Output means for printing the fitting problem, the fitting principle, the standard evaluation problem, the principle thereof, and the solution; The E-BOOK generator generates a plurality of standard evaluation questions from the problem database according to a problem-producing algorithm, and extracts the principles and solutions from the principle database. Claims [1] A method for creating a customized teaching material for learning to provide to a subject who has performed distance learning through the Internet; Retrieving an identification number of the learner by determining whether the learner is a grantee of a customized teaching material; Selecting, based on the search, whether the learner belongs to one of a learning group corresponding to one class of an individual and a small group, a learning group of a grade level, a learning group of a region, and a learning group of a country; Wow; Determining the learning range of the customized teaching material by grasping the learning progress of the learner; An understanding analysis and classification step of performing an understanding degree discrimination algorithm for the prototype problem and the principle of the learning range for the learner to analyze the comprehension and classifying the principle and the prototype problem according to grade and level; A principle and problem extraction step of extracting a custom principle and a problem according to the understanding from a principle and a problem database by a specific number; Drawing out a standard number of questions for evaluation of learning, solving and principles thereof from a principle and problem database; Preparing to print the alignment principle and the alignment problem along with the standard evaluation problem and its solution and principle according to a predetermined order of the teaching material; And a step of visually printing the extracted principles and circular problems on the paper. The method of claim 2; The understanding analysis and classification step, Classifying the total learner's understanding percentage distribution for each individual principle among a plurality of principles corresponding to a predetermined learning range into a plurality; Classifying a learner's understanding percentage distribution into a plurality of individual principles; The understanding percentage distribution of the total learners classified into the plurality of students (understandability) and the understanding percentage distribution of the learners classified into the plurality of students (individual understanding) in the order of lower individual understanding than the overall understanding Classifying the principles into a plurality of grades and ranks; Classifying the total learner's comprehension percentage distribution for each individual prototype problem among a plurality of prototype problems corresponding to a predetermined learning range into a plurality; Classifying a student's understanding percentage distribution into a plurality of individual prototype problems; The understanding percentage distribution (understandability) of the total learners classified into the plurality and the understanding percentage distribution (understandability) of the learner classified into the plurality, in order of lower comprehension compared to the overall comprehension. And classifying the plurality of prototype questions by a plurality of grades and rankings. The method of claim 2; The custom principle extraction step, Sorting from the highest rank to the lower rank among the principles and problems classified by rank for the learner; Counting sequentially from the sorted top rank to the bottom rank to extract a group corresponding to a certain fit number; And if the number of principles belonging to the same rank exceeds the number of fittings, extracting the number of questions of the prototype problem corresponding to the principle in the order of high order. The method of claim 2; The custom problem extraction step, Sequentially arranging the circular questions sorted by rank with respect to the learner from a top rank to a bottom rank among them; Counting a circle problem sequentially from the ordered top rank to the bottom rank and extracting a group corresponding to a predetermined number of rounded circles; Sorting from the highest order among the prototype problems of the extracted group in order of low understanding of a plurality of problems corresponding to each prototype problem; Determining distribution problems by counting problems by the sorted circles up to a maximum number of customized problems in order of high frequency; If the number of the customized problem is not reached, the method for generating a customized teaching material for learning, comprising the step of determining the distribution problem by sequentially counting up to the maximum value in order of high teacher recommendation and student recommendation. delete delete delete delete delete delete