JP2012068572A - E-learning system and method with question extraction function, taking into account frequency of appearance in tests and learner's weak points - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an e-learning system, taking into account not only a learner's weak points but also frequency of question appearance in tests.SOLUTION: The system has question data, question classification data, learner data, state data for each classification as learning result of each learner, a file holding parameters, and a file for temporally storing question tendency determined from the registered data. At an initial stage of a series of a learning course, weak point analysis questions are extracted for determining a learner's initial state, and the initial state is determined through scoring the answers to these weak point analysis questions. A tendency in each classification is analyzed based on the classification data and the question data registered in the system, and a coefficient for evaluating each learner is calculated by using the analyzed tendency. When repeat learning is conducted, weak point learning questions are extracted for each learner based on the evaluation result at the time, the evaluation result of each learner at the time is updated by scoring the answers to the weak point learning questions, and the latest learning state and questions or answers are shown to each learner.

Description

  In the self-supporting learning system using the Internet environment, the present invention automatically extracts a problem based on a function of analyzing a learner's weaknesses in order to increase learning efficiency and the number of registered weaknesses and the number of registered problems. The present invention relates to a learning system that gives questions to learners.

As IT technology advances, e-learning systems that perform learning on personal computers are becoming common learning methods. The advantage of the e-learning system is that you can study by yourself without the need for a special lecturer. There are already various systems for learning problems as e-learning.
There are many methods that the system issues, in the form of tests, to let users learn. There are some questions that are fixed at each learning, but the e-learning system analyzes the weak points and extracts the problems along the weak points to improve the learning effect. There are many systems that let you answer.
In the case of these technologies, the problem data and the question content data are managed separately, and it is possible to search the registered question content according to the content judged as a weak point. It is a mechanism to extract problems. (Patent Document 1, Patent Document 2)

Japanese Patent Laid-Open No. 2005-066222 JP 2005-164965 A

The above-described technique is characterized by analyzing weak point classification based on a wrong problem and performing learning along the weak point. Considering the wrong problem as a weak point of the learner and supplementing its classification can be considered as an effective means to improve the quality of learning.
However, even if the classification determined to be a weak point is learned intensively and the weak point is overcome, if the classification with a low question frequency in the actual examination is a weak point, the learning effect may be reduced.
In addition, in the case of the above technology, when adding a problem, not only the problem data, but also data for recording which problem is presented in the learning as data that associates the problem with the learning content data must be registered. I must. For this reason, when it is necessary to replace a problem, it is considered that not only the problem data but also the data of the question contents must be paid attention.
The problem to be solved by the present invention is to provide a learning support system that not only considers the weaknesses of learners but also considers the frequency of questions.

As means for solving the above problems, a system according to the present invention includes an area for storing problem data, an area for storing classification data in the problem, an area for storing data of learners who use the system, An area for storing the status data of each classification as a learning result of each learner, a file holding parameters necessary for learning of this system, and the tendency of questions from data registered in this system Have a file to save,
The ability to extract weakness analysis questions to determine the learner's initial state at the beginning of a series of learning flows;
A function for scoring answers to weak point analysis questions to determine the initial state and determining the learner's initial state;
A function that analyzes the trends of each classification based on the classification data and problem data registered in the system,
A function to calculate a coefficient for evaluating a learner using the analyzed tendency;
A function to extract learner's weak point learning problem based on the evaluation result at the time of repeated learning,
An output device that has the function of scoring the answers to weak point learning questions and updating the learner's current evaluation results, and displaying the latest learning status, questions, or answers to the learner, and the learner An input device capable of inputting an answer to the displayed question and instructing execution of scoring is provided.

  The feature is that it has a function that can analyze weak points taking into account information on the number of registered questions (probing questions by continuing to include only questions).

  According to the above invention, at the beginning of a series of learning, a student who considers the frequency of classification questions by analyzing the registered test questions and determining a standard value for evaluation based on the number of questions for each classification It becomes possible to evaluate the problem, and the problem for effective learning can be extracted by using this evaluation and the evaluation standard.

  In addition, the problem is extracted dynamically based on the current evaluation, so new questions can be automatically targeted by adding problem data, and the frequency of questions is updated. Will also be able to do.

It is a system configuration figure showing an embodiment of the present invention. 2 is a flowchart of learning performed by the system shown in FIG. 1. It is a flowchart of the process in initial setting (23 of FIG. 2) of scoring and a learner's data. It is a flowchart of the process in extracting the problem of weak point learning and making a learner perform learning (2 of FIG. 2). It is a flowchart of the process in a scoring and a learner's data update (25 of FIG. 2). 4 is a configuration diagram of problem data stored in a problem data storage area 41. FIG. 4 is a configuration diagram of classification data stored in a classification data storage area 42. FIG. 4 is a configuration diagram of learner data stored in a learner data storage area 43. FIG. 4 is a configuration diagram of learning result data of a learner stored in a learning result data storage area 44. FIG. 4 is a configuration diagram of data stored in a learning history storage area 45. FIG. 4 is a configuration diagram of data stored in an answer data storage area 46. FIG. FIG. 2 is a data configuration diagram of a system setting file 47 that stores data necessary for operating the system of FIG. 1. FIG. 5 is a data structure diagram of a question trend information temporary storage file 48, which is a file that is temporarily saved when a question tendency is determined from registered problem data in learning.

  Hereinafter, an embodiment of a learning system to which the present invention is applied will be described.

FIG. 1 is a system configuration diagram showing an embodiment of the present invention. In the present specification, elements displayed as XX part and XX means indicate that a CPU of a computer reads a necessary program and executes processing.
This system includes a data processing unit 1 that mainly performs processing at the time of learning, an input device 2 that is a means for a learner to input a correct answer to the system, processing results in the data processing unit, and learner input Is output to the outside and a data storage unit 4 for storing the problem data for learning and the learning result of the learner, which are physically connected as shown in the figure.
In this configuration example, everything is physically connected, but when implementing this embodiment, even if a network is used to exchange each function and storage area data, etc. May be used, or each function may be distributed.

The data processor 1 has a weak point analysis problem extraction unit 11 that outputs a test (hereinafter referred to as “weak point analysis problem”) for measuring the learner's learning ability at the start of learning, and the learner has solved. A weak point analysis result calculation unit 12 for scoring the result of the weak point analysis problem and judging the ability; a question tendency analysis processing unit 13 for analyzing the question tendency based on the problem data registered in the system after storing the result of the weak point analysis; An achievement rate increase value calculation unit 14 that calculates an increase rate of achievement rate per question of each classification based on the analyzed question tendency, and weak point learning problem extraction that automatically extracts questions that are given according to the state of the learner And a weak point learning result calculation unit 16 for scoring the result of the weak point learning problem solved by the learner and determining the ability.
The input device 2 includes a mouse, a keyboard, a touch panel, and the like, which are devices suitable for inputting a learner's answer to the system.
The output device 3 includes a display, a projector, and the like, which are devices suitable for displaying the result processed by the data processing unit for the learner.

  The data storage unit 4 records a problem data storage area 41 for recording problems held by the system, a classification data storage area 42 for recording classification data registered in the system, and a learner's name and ID. Learner data storage area 43, learning result data storage area 44 that records the situation of each class of learners, learning history storage area 45 that records the degree of achievement of the learner for each learning of the learner, and learning Answer data storage area 46 for recording the answer results at the time, a system setting file 47 for recording the number of learnings and the evaluation criteria of the learning situation, and a question trend information temporary storage file for temporarily recording the question tendency 48.

  Specific data examples of these data storage units 4 have contents as shown in FIGS. The contents of the data storage unit 4 are expressed as a storage area or a file. However, if the data can be stored as shown in the figure, there is no problem even if other storage methods such as a database are used. There is no.

  As shown in FIG. 6, the problem data storage area 41 in the data recording unit 4 has a problem classification ID 601, problem ID 602, difficulty 603, problem sentence 604, correct answer 605, explanation sentence 606, type 607, and learning performed at the beginning of learning. It is composed of a weak point analysis flag 608 for determining whether or not to give a question as a weak point analysis of a person, and a problem date 609, and is registered so as to be uniquely identified by a classification ID and a problem ID. Among these data, data having a weak point analysis flag 608 of “1” is data set as a weak point analysis test. In order to identify whether or not the problem data is an actual test question, a question date 609 is set. If this item is set, it is an actual test question, and if not, it is not a test question. Identified.

As shown in FIG. 7, the classification data storage area 42 includes a classification ID 701 and a classification name 702 of the classification ID, and the classification ID of the problem data is registered as information that links the classification data storage area 42.
The learner data storage area 43 is an area for holding data of learners who use the system as shown in FIG. 8, and includes learner IDs 801, surnames 802, first names 803, and learning progress 804 of learners. It is uniquely identified by ID801. The learning progress 804 is an item for storing the current number of times of learning, and is an item that is updated every time after completion of learning.

The learning result data storage area 44 is an area for holding the current learning result in each classification for each learner. As shown in FIG. 9, the learner ID 901, the classification ID 902, the achievement rate increase value 903, the achievement rate 904, and the achievement The degree 905 is uniquely identified by the learner ID 901 and the classification ID 902. Among these items, the achievement rate 904 is a value for recording a learner's level of proficiency as an internal value of the system, and is a value that increases as learning progresses. The achievement rate 904 increases by a certain numerical value only in the relationship between the number of questions and the number of correct answers in the initial state confirmation test, and is set to the achievement rate increase value 903 of the corresponding field every time one question is correctly answered in subsequent repeated learning. To rise. The achievement rate increase value 903 represents the value of the achievement rate that increases when one question is correctly answered during weak point learning. The achievement rate increase value 903 is an item to be calculated based on the number of registered exam questions, that is, the test frequency of the tests up to the present, and the increasing value is calculated based on the test frequency. Learning can be performed with the frequency added to the viewpoint. The achievement level 905 is a value calculated using an achievement rate 904 based on a value of the system setting file 45 described later, and is a value indicating the level of the learner.
The learning result data is initially set at the beginning of learning using this system, and the achievement rate 904 and the achievement level 905 are updated each time learning is repeated. The history data of the individual learner is uniquely identified by the learner ID 901 as in the range 906.

  The learning history data storage area 45 is an area for storing a result of learning performed by the learner, and includes a learner ID 1001, a learning count 1002, a classification ID 1003, and an achievement degree 1004 as shown in FIG. This data is added each time the learner learns, and one learning result is uniquely identified by the number of learning times 1002 as in the range 1005. Further, the entire history data of the learner is uniquely identified by the learner ID 1001 as in the range of 1006.

  As shown in FIG. 11, the answer data storage area 46 includes a learner ID 1101, a learning count 1102, a classification ID 1103, a question ID 1104, a question number 1105, a learner's answer 1106 for the question, and a correct / incorrect 1107 for the answer. The answer data is uniquely identified by a learner ID 1101, a learning count 1102, a classification ID 1103, and a problem ID 1104. The answer data in each learning is identified by whether the number of learnings 1102 is the same as in the range 1108, and the question number is identified by the question number 1105. The entire answer of the learner is identified by the learner ID 1101 as in the range 1109.

  As shown in FIG. 12, the system setting file 47 is a file that holds parameters necessary for calculation during learning. The system setting file 47 stores a learning possible number of times 1201 for storing the number of times of repeated learning, and per learning when performing repeated learning. The number of questions for one learning 1202 that stores the number of questions of the learning, the upper limit 1203 for increasing the achievement rate in one learning, and the upper limit 1203 for increasing the achievement rate in one learning, Among them, an assumed achievement rate maximum value 1204 for saving the assumed maximum value of the achievement rate 904, a weak point analysis achievement rate 1205 in which correspondence between the number of questions and the number of correct answers in the initial state confirmation test is saved, and a maximum value of the achievement degree 905 Is stored, and the achievement degree corresponding value 1207 for recording the correspondence between the achievement degree maximum value 1206 and the achievement ratio 904 for determining the achievement degree 905.

As shown in FIG. 13, the question trend information temporary recording file 48 includes a classification ID 1301, a problem number 1302 currently registered, and a problem number ratio calculated based on the number of problems registered in the problem data storage area 41. 1303 and a question tendency coefficient 1304 used in the calculation reflecting the question tendency in the system. The question trend information temporary recording file is generated for temporary storage when the learning result data storage area 44 is initialized after the weak point analysis.
As points to note when developing this system, the number of problems registered with the problem date 609 stored in the problem data storage area 41, that is, the number of data that are test questions, is included in each category. One question must exist. (Because it is a classification where there is no problem, it is contrary to the direction of the system that reinforces the weak point for each classification)
In addition, it is necessary that data having the weak point analysis flag 608 of “1” is not registered more than three questions for each classification. This is because, as shown in the system setting file 47, the number of problems in each classification in the weak point analysis is not assumed to be 3 or more.

According to the learning system of the present embodiment, the following effects can be expected.
(1) It is possible to evaluate weak points in consideration of the frequency of questions by determining the increase value of the achievement rate, which is the standard for judging the state of the learner, based on the number of registered exam questions. It becomes like this.
(2) Since the achievement rate is calculated based on the number of registered problems, weak points can be automatically evaluated in consideration of the frequency of questions even when the number of problems increases or decreases.

  Hereinafter, the flow of processing in the embodiment configured as described above will be described.

FIG. 2 is a flow of learning in the embodiment.
First, the learner data of this system is registered in the learner data storage area 43 of this system. (Step 21)
After registering the learner data, the system uses the weak point analysis problem extraction unit 11 to extract the weak point analysis problem in order to evaluate the learner's ability. The problems extracted here are those registered in the problem data storage area 41 for which the weak point analysis flag 608 is “1”. At this time, problem numbers are assigned in order from the top of the extraction result. (Step 22)
The extracted problem is displayed to the learner by the output device 3. The learner who sees the displayed problem inputs an answer to the system using the input device 2.
When the learner inputs a scoring instruction to the system after inputting the answers to all the questions, the system starts the initial setting of the learner's data by using the scoring of the weak point analysis question and the result. (Step 23)

The details of the scoring and learner data initial setting process are as shown in FIG.
In FIG. 3, from 301 to 305, the result of weak point analysis is calculated.
First, the system setting file 47 is read. (Step 301)
Next, the weak point analysis result calculation unit 12 performs correct / incorrect determination on the learner's data input by the input device 2 based on the answer 605 in the question data storage area. The determination is made in the order of the problem numbers given in step 22. The result of correct / incorrect determination is recorded in the answer data storage area 46. At the time of recording, “0” learning is performed so that the item of learning count 1102 can be determined to be a record of weak point analysis. (Step 302)
Thereafter, based on the classification data stored in the classification data storage area 42, the number of questions and the number of correct answers in each classification in the weak point analysis test are totalized. (Step 303)
After the number of correct answers for each category is completed, the initial achievement rate for each category is calculated based on the number of correct answers. The initial achievement rate is set based on the value 1205 stored in the system setting file 47. For example, the initial achievement rate when the number of correct answers in the classification of three questions in the weak point analysis is 2 is “25” set to 1205-n−2. Perform this procedure for all classifications to determine the initial achievement rate. (Step 304)

Next, the achievement level is calculated from the previously determined achievement rate. The achievement level is calculated using the achievement level corresponding value 1207 stored in the system setting file 47. In the setting, when the achievement rate is less than the value of 1207-a that is the first value of the achievement rate achievement rate corresponding value 1207, the achievement rate is 0, and thereafter, the value is equal to or greater than the value of 1207-a and less than the value of 1207-b. If there is a degree of achievement 1, a value of 1207-b or more and less than 1207-c, the degree of achievement 2 is judged. If the last degree of achievement is a value of 1207-n or more, the final achievement degree is obtained. It becomes 5. In the case of this example, when the achievement rate of a certain classification is 20, it is 15 or more and less than 30, so the achievement level is judged as 1. (Step 305)
From 306 to 309, the achievement rate increase value is calculated in consideration of the question tendency and stored in the learning result recording area.

First, the question tendency analysis unit 13 analyzes the question tendency.
The problem data in which the problem date 609 exists is extracted from the problem data registered in the problem data storage area. As described above, since all questions having the problem date 609 of the problem data are registered as previous test questions, only the test questions are extracted here. In this case, it is also possible to allow the learner to input a date and target only questions that have been given as a test during a predetermined period. (Step 306)
The extracted problems are tabulated for each classification ID 601 and the number of test questions for each classification is calculated. In this way, the number of test questions for each category registered in the system can be tabulated. (Step 307)

The necessary information is stored in the question trend information temporary storage file 48 using the number of questions thus tabulated. At the time of saving, the classification ID 1301 and the number of problems 1302 are saved as they are in step 307. The question number ratio 1303 stores “number of questions 1302 for each class” ÷ “total number of test questions extracted in step 306”.
The question tendency coefficient 1304 is obtained by first obtaining “the number of possible learnings 1201” × “the number of questions 1202 once learned” + “twice the number of questions presented in the weakness analysis” in the system setting file read in step 301. The total number of questions that can be answered in a series of studies. The reason why the number of questions presented in the weak point analysis is doubled is that the increase in the achievement rate by the weak point analysis in this system is a fixed value, which is larger than in normal learning. Then, the product of the total number of questions that can be answered and the question number ratio 1303 is calculated to obtain a question tendency coefficient 1304. The meaning of the question tendency coefficient is an approximate number that indicates how much the classification problem can be solved in a series of learning. (Step 308)
Then, the achievement rate increase value calculation unit 14 is used to calculate the achievement rate increase value of each classification based on the data stored in the question trend information temporary storage file 48. This calculation is obtained by “maximum assumed achievement rate 1204” ÷ “question tendency coefficient 1304”. (Step 309)

Finally, all the values obtained in steps 301 to 309 are stored in the learning result storage area 44, and the initial setting of the scoring and learning result recording file is completed.
After scoring the weak point analysis and initial setting of the learner's data, a problem for repeated learning called weak point learning is extracted and the learner is made to learn. (Step 24)

The problem extraction in step 24 is performed in the weak point learning problem extraction unit 15 along the flow of FIG.
First, learner data in the system setting file 47 and the learning result data storage area 44 are read as data necessary for problem extraction. (Step 401)
Then, the lowest achievement level among the achievement levels for each classification registered in the read learning result data storage area 44 is checked, and the number of the lowest achievement levels is stored. (Step 402)
Calculate how many problems you plan to extract in each category of the lowest achievement you have examined. The calculation is obtained by rounding up the decimals of “the number of problems 1202 per learning in the system setting file 47” ÷ “the number of classifications of the minimum achievement level”. However, be sure to have at least one question. For example, when the number of questions 1202 per learning is 20 and the number of classifications of the minimum achievement level is 4, it is assumed that 5 questions are extracted for each classification. (Step 403)
Then, the classification with the lowest achievement rate among the minimum achievement levels is examined, and the number of problems is determined from this classification. (Step 404)
When confirming the number of problems for each category examined, the number of decimals of “achievement rate increase upper limit value 1203 for one learning in system setting file 47” ÷ “achievement rate increase value 903 of learning result data storage area 44” is calculated. A value obtained from the rounded-up result is calculated, and the smaller of this value and step 403 is extracted as the number of problems to be extracted. For example, when the achievement rate increase upper limit 1203 is 20 and the achievement rate increase value 903 is 12, it is limited not to extract two or more questions, and five questions are scheduled to be extracted in step 403. Even in this case, only two questions will be extracted for this classification. The total number of extracted questions is adjusted so as not to exceed the number of questions 1202 per learning.
By doing this, many categories with large achievement rate increase values are extracted, and a large number of achievement rates are prevented from increasing at once. (Step 405)

When the number of problems in the first classification is determined, the total number of problems for which extraction has been determined is examined. If this is not the number of questions 1202 per learning, the classification of the next lowest achievement rate is examined in the learning result data storage area. If the classification is the same as the previous achievement level, the procedure from step 404 is performed. (Steps 406 → 407 → 408 → 404) In the procedure from step 404, the number of problems to be extracted is the same degree of achievement, so the number of problems is determined as the same value as before. If the degree of achievement is different, the procedure from step 402 is performed based on the next degree of achievement. (Step 406 → 407 → 409 → 402) In this case, since the degree of achievement is different from the previous one, the number of scheduled extraction problems is recalculated. In this case, “the number of problems per learning of the system setting file 47” The number of problems is determined by “difference between 1202 and the number of problems that have been extracted” ÷ “the number of classifications of the minimum achievement level”.
If the total number of questions for which extraction has been determined exceeds the number of questions 1202 for one learning, or if the total number of problems for which extraction has been determined in the above-described repetition exceeds the number of problems for which extraction has been determined, the process goes through the repetition and proceeds to step 410.
After the repetition, the problem data is extracted for each category from the problem data storage area according to the determined number of extracted problems for each category, and is used as a weak point learning problem. (Step 410)
After extracting the problem, the extracted problem is displayed on the output device 3 as in the weak point analysis, and the learner's answer is input by the input device 2.
Thereafter, the data of the learner of the weak point learning is updated. (Step 25)

The scoring and updating of the learner's data are performed in the procedure of FIG.
First, the system setting file 47 is read, and then learner data is read from the learner data storage area 43 and the learning result data storage area 44. (Step 501)
Thereafter, the correctness / incorrectness of the input answer is determined based on the answer 605 in the question data storage area 41. After the correct / incorrect determination is completed, the result is stored in the answer data storage area 46. The learning count at the time of storage is a value obtained by adding 1 to the value of the learning progress 804 in the learner data storage area 43. Then, the stored scoring results are totaled for each category, and the number of correct answers for each category is examined. (Step 502, Step 503)

  Finally, the learning result storage area 44 is updated by calculating the achievement rate / achievement degree of each classification after learning based on the number of correct answers of each classification. The achievement rate after completion of the learning is obtained by “the achievement rate so far” + “the number of correct answers in the current learning” × “achievement rate increase value 903” for each classification. The achievement level is determined according to the correspondence of the achievement level achievement value corresponding value 1207 of the system setting file 47 based on the achievement rate calculated previously. Further, the learning progress 804 in the learner data storage area 43 is incremented by 1, and the learning is performed once. Further, the current learning result is stored in the learning history storage area 45 by using the achievement level of the current learning result.

  After scoring and updating the learner's data, it is checked whether the learning progress 804 added earlier exceeds the learning possible number of times 1201 in the system setting file. A series of learning is finished.

  It can be used not only for a stand-alone system but also for a system that distributes processing via a network. It can be used in a system that implements many processes on the server side provided on the web and provides services to users accessing the Internet.

DESCRIPTION OF SYMBOLS 1 Data processing part 2 Input device 3 Output device 4 Data storage part 11 Weak point analysis problem extraction part 12 Weak point analysis result calculation part 13 Question tendency analysis processing part 14 Achievement rate increase value calculation part 15 Weak point learning problem extraction part 16 Weak point learning result calculation Part

Claims (5)

Calculated from problem data storing question sentences, answers, commentary, and classification information, learner data storing personal information of the learner, and learner's learning status, achievement, and number of questions at the start of learning A recording device having learning result recording data for storing the weights,
An output device for displaying questions, answers, and explanations that the learner learns;
An input device for the learner to input information;
An e-learning system comprising: a data processing device for extracting a problem for the learner, calculating a learning result and storing the result in a recording device;
The data processing device includes:
A weak point analysis problem extraction unit that outputs a test for measuring the learning ability of the learner at the start of learning (hereinafter referred to as “weak point analysis problem”);
A weak point analysis result calculation unit for scoring the result of the weak point analysis problem solved by the learner and judging the ability;
A question trend analysis processing unit that analyzes a question tendency based on problem data registered in the system after storing the weak point analysis result calculated by the weak point analysis result calculation unit;
An achievement rate increase value calculation unit for calculating an achievement rate increase value per question of each classification based on the question tendency analyzed by the question tendency analysis processing unit;
A weak point learning problem extraction unit that extracts a question to be given to the learner based on a result calculated by the achievement rate increase value calculation unit;
A weak point learning result calculation unit for scoring the result of the learner's solving of the weak point learning problem extracted by the learning problem extraction unit and determining the ability, and for initial setting of the score of the weak point analysis problem and the learner's data An e-learning system characterized by extracting the problem for repeated learning and allowing the learner to perform weak point learning. An e-learning system according to claim 1,
The problem data stored in the recording device is
A weakness analysis flag that determines whether or not to give a question as a weakness analysis problem for the learner at the beginning of learning,
And a question date indicating that the question is actually a question from a previous exam,
The recording device includes:
The number of possible learnings to store the number of repeated learning,
The number of questions per learning that stores the number of questions per learning when performing repeated learning,
An upper limit of increase in achievement rate for one learning that sets an upper limit for increasing the achievement rate in one learning,
An assumed achievement rate maximum value that stores the assumed maximum value of the achievement rate in a series of learning flows;
Weakness analysis achievement rate that preserves the correspondence between the number of questions and the number of correct answers in the initial state confirmation test,
The maximum achievement level that stores the maximum achievement level, and
A system setting file that is a file that holds parameters related to the evaluation criteria of the learning status necessary for computation during learning, including the achievement achievement correspondence value that records the achievement achievement correspondence that determines the achievement;
Have
The question tendency analysis processing unit
A main examination question extraction means for reading all of the problem data with the problem date;
For the questions extracted by the main examination question extraction means, the number of test questions for each category for counting the number of test questions for each classification based on the classification information of the problem data;
Based on the number of test questions for each category totaled by the number of test questions for each category, a problem number ratio calculating means for calculating a problem number ratio obtained by dividing the number of test questions for each category by the total number of test questions;
Calculate the total number of questions that can be answered, based on the number of learnable times stored in the system setting file, the number of questions per learning, and the number of questions extracted by the weak point analysis question extraction unit Means,
By multiplying the total number of solvable questions calculated by the total number of solvable questions and the number of questions calculated by the number-of-questions ratio calculation means, how many questions of the classification can be answered in a series of learning And a question tendency coefficient calculation means for calculating a question tendency coefficient which is an approximate number indicating whether or not
The achievement rate increase value calculation unit,
An achievement rate increase value calculating means for calculating an achievement rate increase value for each classification, which is a value obtained by dividing the assumed achievement rate maximum value stored in the system setting file by the question tendency coefficient calculated by the question tendency coefficient calculation means; ,
An e-learning system comprising: initial setting completion means for storing each value obtained by the processing after scoring the weak point analysis problem as the learning result recording data and completing initial setting. An e-learning system according to claim 1,
The problem data stored in the recording device is
A weakness analysis flag that determines whether or not to give a question as a weakness analysis problem for the learner at the beginning of learning,
And a question date indicating that the question is actually a question from a previous exam,
The weak point analysis problem extraction unit
Problem extracting means for extracting, from among the problem data recorded in the recording device, the weak spot analysis flag being 1,
Problem number assigning means for assigning a problem number to the extraction result extracted by the problem extracting means;
Problem output means for outputting to the output device in the order of numbers assigned by the problem number assignment means,
The weak point analysis result calculation unit,
System setting file reading means for reading the system setting file;
Correctness determination means for performing correctness determination based on the answers stored in the question data in the order of the question numbers with respect to the result of the learner answering the problem output by the problem output means;
The result of determination by the correctness determination means is set to 0 as the number of learning times, and the correctness / incorrectness determination result recording means for recording as a weak point analysis record;
Based on the classification data stored in the recording device, the number of correct answers for each category for counting the number of correct answers for each category for the weak point analysis problem,
An initial achievement rate determining means for calculating an initial achievement rate of each classification based on the counting result of the correct number of correctives for each classification;
Based on the initial achievement ratio determined by the initial achievement ratio determination means, the initial achievement degree calculation means for calculating the initial achievement degree of each classification by referring to the achievement degree achievement ratio corresponding value set in the system setting file When,
Have
The weak point learning problem extraction unit,
Referring to the learning result record data, extracting the minimum achievement level classification, counting the number of the minimum achievement level, calculating the minimum achievement level classification extraction means,
Divide the number of problems per learning in the system setting file by the minimum achievement class number calculated by the minimum achievement class extraction means, and how many problems are to be extracted in each classification of the minimum achievement level An extraction problem number guideline calculating means for calculating a guideline of
A minimum achievement rate classifying survey means for examining a class having the lowest achievement rate among the minimum achievement categories extracted by the minimum achievement degree classification extracting means;
Extraction problem number determination means for determining the number of problems to be extracted in order of classification with the lowest achievement rate;
Problem extraction means for extracting a problem for weak point learning according to the number of problems determined by the extraction problem number determination means,
The weak point learning result calculation unit,
In response to the learner inputting an answer to the problem for weak point learning, correct / incorrect determination means for determining whether the answer is correct;
A correctness / incorrectness determination result recording means for adding the number of learnings by 1 to the determination result of the correctness / incorrectness determination means and recording it as a record of weak point learning;
A means for counting the number of correct answers for each category,
Based on the number of correct answers for each category compiled by the number of correct answers for each category, learning result update means for calculating the achievement rate and achievement for each category and updating the learning result record data;
An e-learning system characterized by having. Question sentence / answer / explanation / classification information / weak point analysis flag (a flag that determines whether or not to give a weak point analysis question for the learner at the beginning of learning) / question date (the question is actually given in the previous exam) Date indicating that the problem has been answered), learner data storing the personal information of the learner performing the learning, and the learner's learning status / achievement level / number of problems at the start of learning Learning result recording data for storing the calculated weight, system setting file (number of learnings that can be stored for the number of times of repeated learning, and number of problems for one learning for storing the number of problems per learning for repeated learning) And an upper limit for increasing the achievement rate in one learning, which sets an upper limit for increasing the achievement rate in one learning, and an assumed achievement rate maximum for storing the assumed maximum value of the achievement rate in a series of learning flows Value and initial state Achievement that records the correspondence between the achievement rate that determines the degree of achievement and the achievement rate that determines the degree of achievement, and the achievement rate that stores the maximum degree of achievement, which stores the correspondence between the number of questions and the number of correct answers in the confirmation test A recording device having a file containing parameters related to evaluation criteria of a learning situation necessary for calculation during learning including a degree achievement rate correspondence value,
An output device for displaying questions, answers, and explanations that the learner learns;
An input device for the learner to input information;
A weak point analysis problem extraction unit that outputs a test (weak point analysis problem) for measuring the learner's learning ability at the start of learning, and a weak point that determines the ability by scoring the result of the weak point analysis problem solved by the learner An analysis result calculation unit, a question trend analysis processing unit that analyzes the question tendency based on the problem data registered in the system after storing the weak point analysis result calculated by the weak point analysis result calculation unit, and the question tendency analysis An achievement rate increase value calculation unit that calculates an increase rate of achievement rate per question of each classification based on the tendency of questions analyzed by the processing unit, and the learner based on the results calculated by the achievement rate increase value calculation unit A weak point learning problem extracting unit for extracting a question to be presented, and a weak point learning result calculating unit for scoring a result obtained by solving the weak point learning problem extracted by the learning problem extracting unit and determining the ability, Extract problems for learners A e-learning method in e-learning system and a data processing unit for storing the learning result on the calculated recording apparatus,
The question-taking trend analysis processing unit reads out all questions with question dates from the question data, and a question extraction question extracting step of the test,
For the questions extracted in the main exam question extraction step, the number of test questions per category for totaling the number of test questions for each category based on the classification information of the problem data;
A problem number ratio calculating step for calculating a problem number ratio obtained by dividing the number of test questions for each class by the total number of test questions based on the number of test questions for each class totaled by the number of test questions for each class;
Calculate the total number of questions that can be answered, based on the number of learnable times stored in the system setting file, the number of questions per learning, and the number of questions extracted by the weak point analysis question extraction unit Steps,
How many questions to be answered in a series of learning by multiplying the total number of answerable questions calculated by the total number of answerable question calculation means and the question number ratio calculated in the question number ratio calculation step. A question tendency coefficient calculation step for calculating a question tendency coefficient which is an approximate number indicating whether or not the answer can be achieved, and the achievement rate increase value calculation unit calculates the maximum value of the assumed achievement rate stored in the system setting file as the question tendency coefficient calculation unit. An achievement rate increase value calculating step for calculating an achievement rate increase value of each classification, which is a value divided by the question tendency coefficient calculated by;
An e-learning method, comprising: an initial setting completion step for storing each value obtained by the processing after scoring the weak point analysis problem as the learning result recording data and completing initial setting. Question sentence / answer / explanation / classification information / weak point analysis flag (a flag that determines whether or not to give a weak point analysis question for the learner at the beginning of learning) / question date (the question is actually given in the previous exam) Date indicating that the problem has been answered), learner data storing the personal information of the learner performing the learning, and the learner's learning status / achievement level / number of problems at the start of learning Learning result recording data for storing the calculated weight, system setting file (number of learnings that can be stored for the number of times of repeated learning, and number of problems for one learning for storing the number of problems per learning for repeated learning) And an upper limit for increasing the achievement rate in one learning, which sets an upper limit for increasing the achievement rate in one learning, and an assumed achievement rate maximum for storing the assumed maximum value of the achievement rate in a series of learning flows Value and initial state Achievement that records the correspondence between the achievement rate that determines the degree of achievement and the achievement rate that determines the degree of achievement, and the achievement rate that stores the maximum degree of achievement, which stores the correspondence between the number of questions and the number of correct answers in the confirmation test A recording device having a file containing parameters related to evaluation criteria of a learning situation necessary for calculation during learning including a degree achievement rate correspondence value,
An output device for displaying questions, answers, and explanations that the learner learns;
An input device for the learner to input information;
A weak point analysis problem extraction unit that outputs a test (weak point analysis problem) for measuring the learner's learning ability at the start of learning, and a weak point that determines the ability by scoring the result of the weak point analysis problem solved by the learner An analysis result calculation unit, a question trend analysis processing unit that analyzes the question tendency based on the problem data registered in the system after storing the weak point analysis result calculated by the weak point analysis result calculation unit, and the question tendency analysis An achievement rate increase value calculation unit that calculates an increase rate of achievement rate per question of each classification based on the tendency of questions analyzed by the processing unit, and the learner based on the results calculated by the achievement rate increase value calculation unit A weak point learning problem extracting unit for extracting a question to be presented, and a weak point learning result calculating unit for scoring a result obtained by solving the weak point learning problem extracted by the learning problem extracting unit and determining the ability, Extract problems for learners A e-learning method in e-learning system and a data processing unit for storing the learning result on the calculated recording apparatus,
A problem extraction step in which the weak point analysis problem extraction unit extracts the problem data recorded in the recording device and the weak point analysis flag is 1,
A problem number assignment step for assigning a problem number to the extraction result extracted in the problem extraction step;
A problem output step of outputting to the output device in the order of the numbers assigned by the problem number assigning step;
The weak point analysis result calculation unit reads a system setting file to read the system setting file;
A correct / incorrect determination step for determining correctness based on the answers stored in the question data in the order of the question numbers with respect to the result of the learner answering the problem output in the question output step;
The result determined in the correctness determination step is the number of learning times 0, and a correctness / incorrectness determination result recording step for recording as a record of weak point analysis;
Based on the classification data stored in the recording device, the number of correct answers for each category for totalizing the number of correct answers for each category for the weak point analysis problem;
An initial achievement rate determination step for calculating an initial achievement rate of each classification, based on the counting result of the correct answer count step for each classification;
An initial achievement level calculating step of calculating an initial achievement level of each classification by referring to an achievement level corresponding value set in the system setting file based on the initial achievement rate determined by the initial achievement rate determining means When,
The weak achievement learning problem extracting unit refers to the learning result recording data, extracts the minimum achievement level classification, counts the number of the minimum achievement levels, and calculates the minimum achievement level classification extraction step. When,
Divide the number of problems per learning of the system setting file by the number of classifications of the minimum achievement degree calculated in the minimum achievement degree classification extraction step, and the number of problems to be extracted in each classification of the minimum achievement degree A step for calculating the number of extraction problems to calculate the guideline,
A minimum achievement rate classification survey step for examining a category having the lowest achievement rate among the minimum achievement categories extracted in the minimum achievement degree classification extraction step;
An extraction problem number determination step for determining the number of problems to be extracted in the order of classification with the lowest achievement rate;
A problem extraction step of extracting a problem for weak point learning according to the number of problems determined in the extraction problem number determination step;
In response to the learner inputting an answer to the problem for weak point learning, the weak point learning result calculation unit makes a correct / incorrect determination of the answer; and
A correct / incorrect determination result recording step of adding the number of times of learning to the determination result of the correct / incorrect determination step and recording it as a record of weak point learning;
A step of counting the number of correct answers for each category,
A learning result update step of updating the learning result record data by calculating an achievement rate / achievement degree for each category based on the number of correct answers for each category counted by the number of correct answers for each category,
An e-learning method characterized by comprising:

Images