CN110543995A - student cognitive level testing and evaluating system based on fuzzy algorithm - Google Patents
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Abstract
The invention discloses a student cognition level testing and evaluating system based on a fuzzy algorithm, which comprises the following components: the teacher question storage module, the student dynamic question recommendation module, the test result analysis module and the database module; the teacher question storage module is used for storing question information by the teacher; the student testing module is used for testing the cognitive level of students; the dynamic question recommending module is used for arranging the presentation sequence of the questions in the process of the student testing module making the questions; the test result analysis module is used for analyzing the question making result of the student and determining the cognitive level of the student through a fuzzy algorithm; and the database module is used for storing question information and student test result data. The invention can realize the rapid and accurate dynamic recommendation of the test questions according to the grades and the difficulty of the questions and the correctness of the answers of the student questions, reduce the number of questions to be made for judging the cognitive level of the student and improve the accuracy of judging the cognitive level of the student.
Description
Technical Field
The invention relates to a student cognition level testing and evaluating system, in particular to a student cognition level testing and evaluating system based on a fuzzy algorithm.
Background
With the development of network technology and computer technology, Web-based distance education has become a new form of education. The method is not limited by time and space, realizes the sharing and equalization of educational resources, and is a real open type education. The technology relied on by the previous remote teaching tends to be mature, the rapid development of the mobile network and the popularization of the family broadband provide network support for the remote teaching, the popularization of the personal computer and the smart phone and the marketing of the professional mobile learning equipment provide equipment support for the remote teaching, and the network learning software which can be realized by only a few equipment originally can be smoothly operated on the computer and the smart phone which are configured in mainstream.
Network education brings a series of problems while realizing education convenience and the like, and one of the problems is the problem of student cognitive level detection. The testing and evaluation of the cognitive level are used as a part of network remote teaching and are mainly used for detecting and confirming the stage learning condition of students. The detection of the cognitive level is not only helpful for students to know the learning conditions of the students, but also can be used for giving correct learning plans to the students according to the specific cognitive level of the students by some intelligent learning systems (such as self-adaptive learning systems), and the results of the detection of the cognitive level of the students are important for teachers to know the students and arrange teaching plans, so that the accuracy of the detection of the cognitive level is important.
Most of the current remote education platforms adopt a traditional test method in the aspect of detecting the cognitive level of students, whether the students master the knowledge points of a certain chapter or not is detected according to the standard of 60 points, and the unified assessment standard can not reflect the difficult points of courses; and the abilities of the students to be investigated are limited due to the fact that the questions are presented in a static mode or only the order of presenting the questions is changed, the abilities of the students to be investigated are single under the condition that the number of questions tested on the network is small, the real cognitive abilities of the students to the section cannot be correctly reflected, and a teacher cannot obtain an accurate learning state of the students.
Disclosure of Invention
In order to enable a teacher in remote teaching to accurately obtain the learning condition of the student on chapter knowledge through chapter test and provide data support for the student to arrange a subsequent learning plan; meanwhile, the learning time of the students in the learning process of distance education is saved, the learning efficiency is improved, and a student cognitive level testing and evaluating system based on a fuzzy algorithm is provided.
a student cognition level test and evaluation system based on a fuzzy algorithm is characterized in that: deposit question module, student test module, dynamic question recommendation module, test result analysis module and database module including the teacher, the teacher deposits the question module for the teacher deposits the question information in, student test module is used for the test of student's cognitive level, dynamic question recommendation module is used for recommending student test module's question, test result analysis module is used for analyzing student's the question result of doing, confirms student's cognitive level, database module is used for the data storage of question information, student's test result and student's cognitive level.
the database module is linked with the teacher question storage module, the student test module, the dynamic question recommendation module and the test result analysis module, so that the storage of questions, the extraction and presentation of the questions, the information of the student test process and the record of the test results are realized.
The teacher question storage module needs the teacher to store questions, the chapters to which the questions belong, correct answers of the questions, the grades to which the questions belong, the initial accuracy of the questions, the normal question making time of the questions and the longest question making time of the questions into the database module, and the system determines the difficulty of the questions according to the recorded normal question making time and the accuracy of the questions and stores the difficulty into the database module.
the class of the question refers to that when a teacher stores a test question, the class of the question is divided into four classes of comprehension, analysis, synthesis and evaluation according to the degree of understanding of students expecting the question on a certain knowledge point by a Blume education target classification method.
The student testing module can record the question making time of students, the correctness of answers of the students and the accumulated number of the questions making people when the questions are presented, the question making time and the correctness of the students in the current period can be obtained when the accumulated number of the questions making people reaches a certain number, the dynamic updating of the question making time and the question correctness of the questions is realized, the corresponding question difficulty can be updated according to the question making time and the question correctness of the questions, an option without thinking is added at the end of the questions, when the students select the option, the question making time of the students is the longest default time of the system, and the default record of the answers is wrong.
The dynamic question recommending module determines the grade and the difficulty of the current question according to the grade and the difficulty of the previous question and the answer of the student, takes the grade and the difficulty as the recommending standard of the question, and calls the question from the database module to be presented to the student testing module.
The test result analysis module adopts a fuzzy algorithm to determine the cognitive grade, divides the subject into four grades of comprehension, analysis, synthesis and evaluation according to a brucm education target classification method, and respectively represents four degrees of knowledge understanding of students, so that the cognitive grade of the knowledge of the students is divided into the four grades of comprehension, analysis, synthesis and evaluation corresponding to the grade of the subject; after the test is finished, the current cognition level of the student is determined according to the membership degree of the cognition level of the student through a fuzzy algorithm according to the integral question making condition, and the understanding degree of the student on the current program is determined through the method.
the invention has the following beneficial effects:
1. The student cognitive level testing and evaluating system based on the fuzzy algorithm can accurately test the stage learning achievement of the learner and make important reference for the learning plan of the learner;
2. according to the invention, the optimal question dynamic recommendation path is combined, the questions are classified, and the system can give out a proper next question according to the correctness of the current question of the question maker, so that the cognitive level of the student can be accurately judged by a small number of questions, and the method is convenient and rapid;
3. Through recording and storing the cognitive level test results of each chapter of the student, the learner can conveniently make a targeted review plan, and meanwhile, the teacher can conveniently know the learning condition of the student and arrange a teaching plan.
drawings
FIG. 1 is a flowchart illustrating the operation of the present invention in the e-learning system.
FIG. 2 is an image using the F function of the present invention.
FIG. 3 is a flowchart illustrating the operation of the dynamic topic recommendation module of the present invention.
FIG. 4 is a flowchart illustrating operation of the dynamic topic recommendation module of the present invention.
Detailed Description
the technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. The invention is not limited to the specific embodiments shown and described, but is capable of other modifications and equivalent arrangements.
As shown in FIG. 1, the invention comprises a teacher question storage module, a student test module, a dynamic question recommendation module, a test result analysis module and a database module.
in the embodiment, the teacher question storage module is used for recording questions, chapters to which the questions belong, correct answers to the questions, the grades to which the questions belong, the initial correct rate of the questions, the normal question making time of the questions and the longest question making time of the questions and storing the questions into the database module, and the system determines the difficulty of the questions according to the question making time and the correct rate of the recorded questions. The grades of the subjects are divided according to the brucm education target classification method.
according to the bloom education goal classification method, the cognitive level is divided into the following four grades:
(1) It is to be appreciated that concepts can be memorialized but not flexibly employed;
(2) analyzing the formula, namely analyzing the formula and solving the problem by combining multiple formulas;
(3) The method can be applied and innovated in actual problems;
(4) And (4) evaluating, namely making convincing judgment on the essence of the things.
Correspondingly, the subjects are divided into four grades of comprehension, analysis, synthesis and evaluation according to the content of investigation, and the understanding degree of the students on the concepts and formulas is judged by testing the subjects of the four grades, so that the cognitive level of the students is obtained.
The teacher needs to define the lowest passing level for each section, and the teacher sets different lowest passing levels according to different degrees of importance of the section on the course and the association degree with the context. For example, in the actual learning process, the introduction is to let the students generally understand the contents and key points to be learned in the course, and the students only need to understand the contents and key points, so the lowest passing level can be determined as comprehension. Different minimum pass grades are set according to chapter difference, compare in most and the ruled line (60 minutes) as minimum pass grade in current network learning, can make the course wholly stand out the key point of course through categorizing student's cognitive grade, avoid student's study to find the key point of course and study blindly, improve student's learning efficiency.
The boundaries between the topic difficulty ratings are in a fuzzy state.
In the embodiment, the difficulty of the questions is judged through the time and the accuracy of the students in making the questions. Therefore, the difficulty D of the specified topic is determined by the passing rate P of the topic and the average time F for making the topic.
the time of the student doing the test questions stipulates three time periods: the shortest question making time alpha, the normal question making time beta, the longest question making time gamma and the actual question making time x. Expressed as function F, as follows:
（1）
The image of the F function is shown in fig. 3.
Compared with the average time F, the accuracy P of the questions can reflect the reverberation of the questions in students, and the difficulty coefficient of the questions can be displayed more clearly.
The difficulty of the topic D is expressed as a weighted function with respect to P and F. The expression form of the formula is:
(2)
Wherein alpha is a weight factor representing the preference of P or F, and the value of alpha is most reasonable as proved by experiments and is 0.7. Therefore, the formula for the topic difficulty determination can be determined as follows:
(3)
The topic difficulty is realized by a piecewise function, as follows:
(4)
At the moment, the difficulty of a certain topic is represented by D, and the topic is automatically generated and stored in the database after the topic is added with the topic making time and the passing rate.
The student testing module has the main function of presenting questions extracted from the database by the dynamic question recommending module according to grades and difficulty, assisting students in completing tests and obtaining cognitive grades. In the embodiment, a series of improvement works are made on the interface. The method is mainly characterized in that the time of making questions and the correctness of answers of students are recorded, and the dynamic updating of the question difficulty is realized according to the accumulated number of the questions made.
An option of 'no thought' is added at the end of each question, so that error deviation caused by random guessing of the questions due to no question making thought is reduced. When the student clicks the option, the time is recorded according to the longest question making time in the statistics of question making time, and answers answered by the student are recorded as errors. The existence of the option can improve the accuracy degree of the dynamic change of the question difficulty and is beneficial to obtaining the accurate question difficulty.
In an embodiment, the student testing module records the question making time and the correctness of answers of students, and when the number of the questions making people of a certain question reaches 500, the new question making time and the question correctness rate are obtained through the calculation p of a formula. And obtaining the new difficulty of the topics through the cooperative operation of the formula (1), the formula (3) and the formula (4), and updating the difficulty of the topics into a database. In the embodiment, the complete answer flow and the answers of the student test are recorded on the interface, so that the student can look up wrong questions during review, the review efficiency of the student during the end-of-term examination is improved, and the test result analysis module is also provided with the calculated data support.
In an embodiment, the dynamic topic recommendation module runs code for the system background. The student test system is mainly used for providing the question contents stored by the teacher question storage module for the student test module from the database. The choice of the subject is distinguished by the subject grade and the subject difficulty and is influenced by the answer of the last subject tested by the students. In order to facilitate a dynamic topic recommendation module to extract topic contents from a database according to topic grades and difficulty, four grades of topics are extracted: the acquisition, analysis, synthesis and evaluation are indicated by the numbers 1,2,3,4, respectively. The following is a specific recommendation flow of the dynamic topic recommendation module in an embodiment.
In an embodiment, the recommendation principle of the dynamic topic recommendation module is to gradually recommend the topic from easy to difficult, low level to high level.
In an embodiment, the next topic recommended by the dynamic topic recommendation module is determined by the difficulty level of the current topic, the grade of the current topic, and the correctness of the student's answer.
as shown in FIG. 4, in this case that the answer is correct, the ranks of the topics are gradually increased upward by the topic with the lowest rank and the lowest difficulty. When the student completes the question and clicks the next question, the dynamic question recommending module is triggered to determine the grade and the difficulty of the next question according to the difficulty and the grade of the current question and the correctness of the answer of the student, and the specific rule is as follows:
And in the case that the answer is correct, judging the trend of the question according to the difficulty D of the question.
When the current subject difficulty D =0.25, judging the grade of the current subject: if the rank of the current topic is 4 (rating), a topic with D =0.5 is extracted from the database as the next topic recommended. If the level of the topic is the first three levels (comprehending, analyzing and integrating), extracting the topic with unchanged difficulty from the database, and taking the topic with the level added by 1 as the next recommended topic.
When the current topic difficulty D =0.5, the grade of the topic is not required to be judged, the next topic is to select the topic difficulty D =0.75 from the database, and the topic with the unchanged grade is used as the next recommended topic.
And when the current subject difficulty D =0.75, stopping reading the subject of the database, and transmitting the whole test result of the student to the test result analysis module for calculating the cognitive grade. And after the calculation is finished, the cognitive grade of the student is stored in the database, the calculation result is compared with the lowest passing grade stored in the teacher question storage interface by the teacher, and the conclusion that the student passes the test or needs to learn again is obtained according to the comparison result.
As shown in FIG. 4, when a topic is wrong, the rank will become the first criterion for the topic recommendation. The specific path is as follows:
And when the grade of the subject is 1 (comprehended), finishing reading the subject from the database, and transmitting the whole test result of the student to the test result analysis module to calculate the cognitive grade. And after the calculation is finished, the cognitive grade of the student is stored in the database, the calculation result is compared with the lowest passing grade stored in the teacher question storage interface by the teacher, and the conclusion that the student passes the test or needs to learn again is obtained according to the comparison result.
when the grades of the topics are other three grades (analysis, integration and evaluation), judging the difficulty D of the topics, namely if the difficulty D =0.25 of the topics, selecting the next topic with grade minus 1 and D =0.5 from the database as the dynamic recommendation; if the difficulty D of the subject is =0.5, selecting a dynamically recommended next subject with grade minus 1 and unchanged subject difficulty D from the database; if topic D =0.75, the next dynamically recommended topic with a grade minus 1 and difficulty minus 0.25 (select D = 0.5) is selected from the database.
When the student clicks the option of 'no thought', the student can be considered not to have the question of solving the grade and difficulty of the book during testing, but in order to eliminate the contingency of the test question, the dynamic question recommending module can record the frequency of clicking the module of 'no thought'. When a student clicks the option for the first time, the dynamic topic recommendation module can recommend the topic with unchanged topic grade and topic difficulty and submit the topic to the student test module, when the student clicks the option without thought again, the student can be determined to have no ability of answering the topic with the grade and difficulty, after clicking the option without thought for the second time, the student can recommend the question according to the answer error, the two times of answering time are recorded into the database according to the longest answering time, and the student answering record is also recorded according to the error. If the student answers correctly in the recommended questions of the second thought-free question, the question recommendation recommends the questions according to the recommendation path with the correct answers, the question making time and the answers of the first question are not recorded in the database, and only the question making time and the answers of the second question are recorded.
in the embodiment, the test result analysis module is also used as a background running code to perform calculation. The data input of the test result analysis module has two schemes, wherein the first scheme is to input the whole test result of the student into the calculation module as the input of the test result analysis module; the second scheme is that a test result analysis module is embedded in a dynamic question recommendation module, and a test result is input into the test result analysis module for calculation after each question is done. In this embodiment, we adopt the first scheme, and input the whole test result of the student as the input of the test result analysis module into the calculation module.
in the test result analysis module, a fuzzy algorithm is adopted for calculating the cognitive grade of the student. The method is mainly characterized in that the cognition grade membership degree of the student is obtained according to the correctness of the student in making questions, the grade and the difficulty attribute of the questions, the membership degree value of the cognition grade of the student is stored in an array, and finally the highest membership degree value is the cognition grade with the highest possibility of the cognition grade of the student.
In the examples, we take the following calculation scheme:
After the title is finished, an array variable is set to store the membership degree corresponding to each cognitive grade in the background. And calculating the membership degree of each rating level according to the correctness of the student answers and the ascending and descending principle. The formula used for the calculation is:
Ascending principle:
(5)
the principle of descent:
(6)
description of variables:
m (m belongs to [1,4 ]) is the grade of the current test question;
i is the grade of the cognitive level of the student, i = {1,2,3,4} = { comprehend, analyze, synthesize, evaluate };
qi is the membership degree of the i grade;
The membership set of the cognitive level is Q;
q = { Q1, Q2, Q3, Q4} = { membership to be grasped, membership to be analyzed, membership to be integrated, membership to be evaluated };
D is the difficulty value of the current topic.
As can be seen from the ascending principle and the descending principle, in this system, the difficulty D of the topics is different, and the ascending weight and the descending weight are different. The difficulty of the subject increases, and the degree of change in the degree of membership of the cognitive level increases.
Claims (7)
1. A student cognition level test and evaluation system based on a fuzzy algorithm is characterized in that: the teacher question storage module, the student test module, the dynamic question recommendation module, the test result analysis module and the database module are included;
The teacher question storage module is used for storing question information by the teacher;
The student testing module is used for testing the cognitive level of students;
the dynamic question recommending module is used for recommending questions in the student testing module;
The test result analysis module is used for analyzing the question making result of the student and determining the cognitive level of the student;
And the database module is used for storing question information, student test results and data of student cognitive level.
2. the fuzzy algorithm-based student cognition level test assessment system according to claim 1, wherein: the teacher question storage module needs the teacher to store questions, the chapters to which the questions belong, correct answers of the questions, the grades to which the questions belong, the initial accuracy of the questions, the normal question making time of the questions and the longest question making time of the questions into the database module, and the system determines the difficulty of the questions according to the recorded normal question making time and the accuracy of the questions and stores the difficulty into the database module.
3. The fuzzy algorithm-based student cognition level test assessment system according to claim 2, wherein: the class of the question refers to that when a teacher stores a test question, the class of the question is divided into four classes of comprehension, analysis, synthesis and evaluation according to the degree of understanding of students expecting the question on a certain knowledge point by a Blume education target classification method.
4. the fuzzy algorithm-based student cognition level test assessment system according to claim 1, wherein: the student testing module can record the question making time of students, the correctness of answers of the students and the accumulated number of the questions when the questions are presented, the question making time and the correctness of the students in the current period can be obtained when the accumulated number of the questions reaches a certain number, the dynamic updating of the question making time and the correctness of the questions is realized, the corresponding question difficulty can be updated according to the question making time and the correctness of the questions, an option without thought is added at the end of the questions, when the students select the option, the question making time of the students is the longest default time of the system, and the default record of the answers is wrong.
5. the fuzzy algorithm-based student cognition level test assessment system according to claim 1, wherein: the dynamic question recommending module determines the grade and the difficulty of the current question according to the grade and the difficulty of the previous question and the answer of the student, takes the grade and the difficulty as the recommending standard of the question, and calls the question from the database module to be presented to the student testing module.
6. The fuzzy algorithm-based student cognition level test assessment system according to claim 1, wherein: the test result analysis module adopts a fuzzy algorithm to determine the cognitive grade, divides the subject into four grades of comprehension, analysis, synthesis and evaluation according to a brucm education target classification method, and respectively represents four degrees of knowledge understanding of students, so that the cognitive grade of the knowledge of the students is divided into the four grades of comprehension, analysis, synthesis and evaluation corresponding to the grade of the subject; after the test is finished, the current cognition level of the student is determined according to the membership degree of the cognition level of the student through a fuzzy algorithm according to the integral question making condition, and the cognition degree of the student to the current program is determined through the method.
7. The fuzzy algorithm-based student cognition level test assessment system according to claim 1, wherein: the database module is linked with the teacher question storage module, the student test module, the dynamic question recommendation module and the test result analysis module, so that the storage of questions, the extraction and presentation of the questions, the information of the student test process and the record of the test results are realized.
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| CN112651623A (en) * | 2020-12-23 | 2021-04-13 | 贵州树精英教育科技有限责任公司 | Academic ability level testing system and algorithm |
| CN113779396A (en) * | 2021-09-10 | 2021-12-10 | 平安科技（深圳）有限公司 | Topic recommendation method and device, electronic equipment and storage medium |
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Application publication date: 20191206 |