CN117892816A - System and method for generating school year knowledge graph based on multiple teaching styles - Google Patents
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Abstract
The invention discloses a system and a method for generating a learning years knowledge graph based on multiple teaching styles, wherein the system comprises a characteristic evaluation unit, a teaching scheme generation unit, a course learning unit and a learning years knowledge graph generation unit; based on the output teaching scheme, entering a course learning unit to perform macroscopic evaluation and microscopic evaluation on the teaching scheme; generating a learning year knowledge graph by entering a learning year knowledge graph generating unit, and generating a multi-teaching-style course of a dedicated individual for each individual to guide students to understand and master knowledge from different angles and modes; through diversified learning experience, students can understand and apply the learning content more deeply, and the learning effect and the memory durability are improved; the method is beneficial to strengthening the generation precision of the multi-teaching-style course and tracking the change of the learning effect of the students under the multi-teaching-style course learning.
Description
Technical Field
The invention relates to the technical field of multi-style learning course generation, in particular to a system and a method for generating a learning years knowledge graph based on multi-teaching style.
Background
Traditionally, education often employs a single teaching method, ignoring individual differences and diversified learning needs of students. However, as research progresses and educational practices develop, it is increasingly becoming appreciated that each student is unique, having different mental types, learning styles, hobbies and competence levels. The multi-teaching style is presented to better meet the diversity needs of students and to provide a more efficient way of learning. It emphasizes personalized education, focuses on individual differences and potential development of students, and aims to improve learning effect and overall development of students. Multiple teaching styles can guide students from different angles and ways to understand and master knowledge. Through diversified learning experience, students can understand and apply the learning content more deeply, and learning effect and memory durability are improved. Has important significance for promoting the comprehensive development of students, improving the education quality and promoting the education innovation.
However, the personalized teaching process only starts from individual differences of students, scientifically displays learning paths suitable for the individual differences or changes teaching speech speed, and increases teaching interaction to improve the attention of the students. But ignores the difference of cognition of students, the acceptance of different teaching styles is different, and the teaching effect is reduced. Therefore, from the individual difference and cognition level of students, it is extremely important to provide a multi-style teaching scheme suitable for different students, and meanwhile, collection of the teaching knowledge graph of students every school year has important significance for long-line tracking of multi-teaching style effects.
Disclosure of Invention
The invention aims to provide a school year knowledge graph generation system based on multiple teaching styles, which comprises the following components:
The feature evaluation unit is used for collecting and recording the physiological feature data of the students and the corresponding learning feature data of the students in the educational administration system, and constructing a priori knowledge base;
The teaching scheme generating unit takes a teaching style library, a knowledge graph of the last school year and information of a priori knowledge library as inputs and outputs a corresponding teaching scheme;
Course learning unit, based on the learning and examination of all knowledge points of the teaching scheme of finishing output, produce macroscopic evaluation and microscopic evaluation;
And the school year knowledge graph generating unit outputs a corresponding school year knowledge graph based on the macro evaluation and micro evaluation results and the corresponding teaching scheme.
Preferably, the construction of the priori knowledge base is specifically as follows:
The students log in the system and are matched with the educational administration system to acquire corresponding student learning characteristic data;
randomly extracting knowledge points to be learned, generating courses according to styles in a teaching style library, and obtaining student physiological characteristic data through the courses generated by learning.
Preferably, the student physiological characteristic data comprises an electroencephalogram characteristic, an electrocardiographic characteristic, a body temperature characteristic, a posture characteristic and a facial expression characteristic; the corresponding student learning characteristics in the educational administration system comprise teacher evaluation, ordinary achievements and rolling achievements of the corresponding students.
Preferably, the teaching style library is constructed as follows: the large language model is adopted to collect the traditional teaching modes, and the courses with the interactive traditional teaching modes are divided into voice and text display types including teaching types and autonomous learning types by analyzing teaching characteristics, auxiliary tools and the presence or absence of the interactive traditional teaching modes.
Preferably, the phonetic text display class comprises a teaching class and an autonomous learning class;
the voice video presentation class comprises a demonstration class, an experiment class, a performance class and a practice class;
the voice interaction presentation class comprises a question and answer class, a heuristic class, a discussion class and a cooperative learning class.
Preferably, the teaching mode classification criteria are:
；
Wherein n is the number of participants in the lesson, , and n=1 indicates that only teachers participate and teachers and students have no interaction; when n=2, the teacher and the student participate together, and the teacher and the student interact;
m is the number of the teaching aids, , and when m=1, the teaching aids are written on the blackboard only; m=2, indicating that the lesson aid is blackboard writing and multimedia; m=3, the teaching aid means blackboard writing, multimedia and teaching aid.
Preferably, the macroscopic evaluation is: calculating average score of all students in multiple courses in the academic period, wherein the macro evaluation MA of the course i is expressed as: ; wherein p is the number of students, g is the score of the students.
Preferably, the microscopic evaluation is: calculating all the achievements of a student in a plurality of courses in the academic year; where the microscopic evaluation MI of student k at each course can be expressed as: ;
where j=1, 2,..n, n represents the j-th lesson, n represents the number of lessons; r is the score of student k at each course.
Preferably, in the school year knowledge graph generating unit, the school year knowledge graph is constructed by using courses as entities, learning sequences among the courses as relations, and macro evaluation and micro evaluation of students as course attributes, wherein the learning sequence relations comprise a front-back relation, a parallel relation and an independent relation.
The invention also discloses a school year knowledge graph generation method based on the multiple teaching styles, which comprises the following steps:
s1, logging in a knowledge graph generation system of the school year, and judging whether the user is a new user or not;
if the user is a new user, entering a characteristic evaluation unit;
if the user is not a new user, entering a teaching scheme generating unit;
s2, acquiring a priori knowledge base of a user, entering a teaching scheme generating unit, taking a teaching style base, a last school year knowledge graph and priori knowledge base information as inputs, and outputting to obtain a corresponding teaching scheme;
s3, entering a course learning unit based on the output teaching scheme, completing learning of all knowledge points of the course, performing examination, and performing macroscopic evaluation and microscopic evaluation on the teaching scheme;
and S4, entering a knowledge graph generation unit of the school year, and generating the knowledge graph of the school year according to macro evaluation and micro evaluation results and course related attributes for generating a teaching scheme of the next school year.
The beneficial effects are that: the personalized characteristics of each student are given through acquiring the physiological information and the forward learning information of the student, so that personalized and accurate evaluation of the student is realized; by constructing a teaching style library, the traditional teaching mode is mapped into a new generation teaching style, so that a multi-style teaching foundation is realized, and the method is easier to realize; by generating multiple teaching style courses for each individual, the students are guided to understand and master knowledge from different angles and modes; through diversified learning experience, students can understand and apply the learning content more deeply, and the learning effect and the memory durability are improved; and the construction and recording of the knowledge graph of the school year are beneficial to strengthening the generating precision of the multi-teaching-style course and tracking the change of the learning effect of the students under the multi-teaching-style course learning.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
In the drawings:
FIG. 1 is a schematic diagram of a knowledge graph generation system architecture for school years;
FIG. 2 is a schematic diagram of the prior knowledge base and construction process of the present invention;
FIG. 3 is a schematic diagram of a teaching style library construction process of the present invention;
FIG. 4 is a diagram of the generation of the knowledge graph of the school year teaching of the present invention;
FIG. 5 is a schematic flow chart of the knowledge graph generation method in the school year.
Detailed Description
Embodiments of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention. The terminology used in the description of the embodiments of the invention herein is for the purpose of describing particular embodiments of the invention only and is not intended to be limiting of the invention.
The invention discloses a constitution diagram of a knowledge graph generation system of the academic year, which comprises a characteristic evaluation unit, a teaching scheme generation unit, a course learning unit and a knowledge graph generation unit, wherein the constitution diagram is shown in figure 1; the feature evaluation unit collects and records student physiological feature data and corresponding student learning feature related data in the educational administration system, and builds a priori knowledge base; the teaching scheme generating unit takes a teaching style library, a knowledge graph of the last school year and information of a priori knowledge library as inputs and outputs a corresponding teaching scheme; the course learning unit generates macroscopic evaluation and microscopic evaluation based on learning and examination of all knowledge points of the output teaching scheme; the school year knowledge graph generating unit outputs a corresponding school year knowledge graph based on the macro evaluation and micro evaluation results and the corresponding teaching schemes.
As shown in fig. 2, a prior knowledge base construction process is schematically shown; the priori database comprises student physiological characteristics and corresponding student learning characteristics in the educational administration system; the physiological characteristics of students are collected through an intelligent classroom, and the flow is as follows: randomly extracting knowledge points to be learned and generating a course according to styles in a teaching style library; the method comprises the steps of providing a head-mounted electroencephalogram device, a patch type electrocardio device and a suspension type monitoring device for students to collect physiological data of the students, wherein the physiological data comprise electroencephalogram signals, electrocardio signals and video signals; carrying out key feature extraction processing on the collected data signals to obtain student key data features including brain electrical features, electrocardio features, body temperature features, posture features and facial expression features;
The student study characteristic acquisition flow is as follows: the students register systematically through the student number, enter the educational administration system knowledge base, and carry on the student and study the record and match according to the number, get the student and study the characteristic, including teacher's evaluation, ordinary score and rolling up the face score of the corresponding student.
As shown in fig. 3, a schematic diagram of a teaching style library construction process is shown; the method for collecting the traditional teaching modes by adopting the large language model comprises the following steps: the teaching class, the autonomous learning class, the demonstration class, the experiment class, the performance class, the exercise class, the exploratory learning class, the question-answer class, the heuristic class, the discussion class and the cooperative learning class are 11 classes; the courses are divided into voice and text display types by analyzing teaching features, auxiliary tools and interaction, wherein the voice and text display types comprise a teaching type, an independent learning type and a voice and video display type, the demonstration type, the experiment type, the performance type, the exercise type, the exploring learning type and the voice interaction display type comprise three types of question and answer type, heuristic type, discussion type and cooperative learning type; the specific classification criteria are as follows:
defining n as the number of participants in the lesson, , and when n=1, indicating that only teachers participate and teachers and students have no interaction; when n=2, the teacher and the student participate together, and the teacher and the student interact;
m is the number of the teaching aids, , and when m=1, the teaching aids are written on the blackboard only; m=2, indicating that the lesson aid is blackboard writing and multimedia; m=3, the teaching aid means writing on blackboard, multimedia and teaching aid;
The number of n and m statistical analysis can be obtained by carrying out the statistical analysis on the 11 types of teaching videos,
；
Correspondingly, the course of the voice and text display class is displayed to students for learning by adopting voice and text; the voice video presentation class presents voice and animation to students for learning; the voice interactive display class displays the voice, animation and online interactive questions to students for learning.
The teaching style library, the knowledge graph of the last school year and the prior knowledge base information are taken as inputs to obtain a teaching scheme; based on the output teaching scheme, the learning of all knowledge points is completed, examination is carried out, and macroscopic evaluation and microscopic evaluation are carried out on the teaching scheme;
Wherein, macroscopic evaluation is: calculating average scores of all students in a plurality of courses in the academic period, wherein p students in the academic period participate in learning of the course i, the score of each student is represented by g, and the macro evaluation MA of the course i can be represented as: ;
Microscopic evaluation was: calculating all the achievements of a student in a plurality of courses in the academic year; where n courses are defined in the academic year, where the performance of student k in each course is denoted by r, the microscopic evaluation MI of student k in each course can be expressed as: ;
where j=1, 2,..n, represents the j-th lesson.
And generating the knowledge graph of the subject year according to the macro evaluation and the micro evaluation results and the corresponding teaching schemes.
FIG. 4 is a diagram showing the generation of a knowledge graph of the school year; the course is taken as an entity, the learning sequence among courses is taken as a relation, the macro evaluation and the micro evaluation of students are taken as course attributes to construct a knowledge graph of the school year, and the learning sequence relation comprises a front-back relation, a parallel relation and an independent relation.
The invention also discloses a school year knowledge graph generation method based on multiple teaching styles, as shown in fig. 5, comprising the following steps:
s1, logging in a knowledge graph generation system of the school year, and judging whether the user is a new user or not;
if the user is a new user, entering a characteristic evaluation unit;
if the user is not a new user, entering a teaching scheme generating unit;
s2, acquiring a priori knowledge base of a user, entering a teaching scheme generating unit, taking a teaching style base, a last school year knowledge graph and priori knowledge base information as inputs, and outputting to obtain a corresponding teaching scheme;
s3, entering a course learning unit based on the output teaching scheme, completing learning of all knowledge points of the course, performing examination, and performing macroscopic evaluation and microscopic evaluation on the teaching scheme;
and S4, entering a knowledge graph generation unit of the school year, and generating the knowledge graph of the school year according to macro evaluation and micro evaluation results and course related attributes for generating a teaching scheme of the next school year.
In one embodiment: the user A logs in the system, judges that the user A is a new user, enters a characteristic evaluation unit, requires the user A to wear head-mounted electroencephalogram equipment, patch type electrocardio equipment and authorized monitoring equipment, generates an evaluation course according to all styles in a teaching style library, and collects the characteristics of the electroencephalogram, the electrocardio, the body temperature, the posture and the facial expression of the user A in the learning process; meanwhile, the login academic information of the user A is utilized to search the annual teacher evaluation, the usual achievements and the rolling achievements of the user A in the educational administration system, and the physiological characteristics and the background characteristics of the user A are integrated to obtain a priori knowledge base, wherein the priori knowledge base is expressed as follows:
Then, entering a teaching scheme generating unit, taking teaching style library and priori knowledge library information as input, outputting 3 styles with the duty ratio, and generating the academic course teaching schemes with different styles in corresponding courses according to the duty ratio; after the study of all courses in the school year is completed, carrying out the macro evaluation and the micro evaluation, and finally, carrying out the generation of a knowledge graph in the school year, wherein the learning sequence among the courses is used as a relation by taking the courses as an entity, and the macro evaluation and the micro evaluation of students are used as course attributes to construct the knowledge graph in the school year, and the learning sequence relation is defined as a front-back relation c, a parallel relation a and an independent relation 0;
In one embodiment: the user B logs in the system, judges that the user B is an old user, enters a teaching scheme generating unit, takes teaching style library, last school year knowledge graph and priori knowledge library information as input, outputs 3 style duty ratios, and generates school year course teaching schemes expressed by different styles in corresponding courses according to the duty ratios; after the study of all courses in the school year is completed, carrying out the macro evaluation and the micro evaluation, and finally, carrying out the generation of a knowledge graph in the school year, wherein the learning sequence among the courses is used as a relation by taking the courses as an entity, and the macro evaluation and the micro evaluation of students are used as course attributes to construct the knowledge graph in the school year, and the learning sequence relation is defined as a front-back relation c, a parallel relation a and an independent relation 0;
While the embodiments of the present invention have been described in detail with reference to the drawings, the present invention is not limited to the above embodiments, and it will be apparent to those skilled in the art that various equivalent changes and substitutions can be made therein without departing from the principles of the present invention, and such equivalent changes and substitutions should also be considered to be within the scope of the present invention.
Claims (10)
1. The utility model provides a school year knowledge graph generation system based on many teaching styles which characterized in that includes:
The feature evaluation unit is used for collecting and recording the physiological feature data of the students and the corresponding learning feature data of the students in the educational administration system, and constructing a priori knowledge base;
The teaching scheme generating unit takes a teaching style library, a knowledge graph of the last school year and information of a priori knowledge library as inputs and outputs a corresponding teaching scheme;
Course learning unit, based on the learning and examination of all knowledge points of the teaching scheme of finishing output, produce macroscopic evaluation and microscopic evaluation;
And the school year knowledge graph generating unit outputs a corresponding school year knowledge graph based on the macro evaluation and micro evaluation results and the corresponding teaching scheme.
2. The multi-teaching-style-based academic year knowledge graph generation system of claim 1, wherein: the construction of the priori knowledge base is specifically as follows:
The students log in the system and are matched with the educational administration system to acquire corresponding student learning characteristic data;
randomly extracting knowledge points to be learned, generating courses according to styles in a teaching style library, and obtaining student physiological characteristic data through the courses generated by learning.
3. The multi-teaching-style-based academic year knowledge graph generation system of claim 2, wherein: the student physiological characteristic data comprise brain electrical characteristics, electrocardio characteristics, body temperature characteristics, posture characteristics and facial expression characteristics; the corresponding student learning characteristics in the educational administration system comprise teacher evaluation, ordinary achievements and rolling achievements of the corresponding students.
4. The multi-teaching-style-based academic year knowledge graph generation system of claim 1, wherein: the teaching style library is constructed by the following steps: the large language model is adopted to collect the traditional teaching modes, and the courses with the interactive traditional teaching modes are divided into voice and text display types including teaching types and autonomous learning types by analyzing teaching characteristics, auxiliary tools and the presence or absence of the interactive traditional teaching modes.
5. The multi-teaching-style-based academic year knowledge graph generation system of claim 4, wherein: the phonetic and text display class comprises a teaching class and an autonomous learning class;
the voice video presentation class comprises a demonstration class, an experiment class, a performance class and a practice class;
the voice interaction presentation class comprises a question and answer class, a heuristic class, a discussion class and a cooperative learning class.
6. The multi-teaching-style-based academic year knowledge graph generation system of claim 4, wherein: the teaching mode classification standard is as follows:
；
wherein n is the number of participants in the lesson, , and n=1 indicates that only teachers participate and teachers and students have no interaction; when n=2, the teacher and the student participate together, and the teacher and the student interact;
m is the number of the teaching aids, , and when m=1, the teaching aids are written on the blackboard only; m=2, indicating that the lesson aid is blackboard writing and multimedia; m=3, the teaching aid means blackboard writing, multimedia and teaching aid.
7. The multi-teaching-style-based academic year knowledge graph generation system of claim 1, wherein: the macroscopic evaluation was: calculating average score of all students in multiple courses in the academic period, wherein the macro evaluation MA of the course i is expressed as:
；
wherein p is the number of students, g is the score of the students.
8. The multi-teaching-style-based academic year knowledge graph generation system of claim 1, wherein: the microscopic evaluation was: calculating all the achievements of a student in a plurality of courses in the academic year; where the microscopic evaluation MI of student k at each course can be expressed as: ;
Where j=1, 2,..n, n represents the j-th lesson, n represents the number of lessons; r is the score of student k at each course.
9. The multi-teaching-style-based academic year knowledge graph generation system of claim 1, wherein: in the school year knowledge graph generating unit, school years knowledge graphs are built by using courses as entities and learning sequences among the courses as relations, wherein the macro evaluation and the micro evaluation of students are course attributes, and the learning sequence relations comprise front-back relations, parallel relations and no relations.
10. The school year knowledge graph generation method based on the multiple teaching styles is characterized by comprising the following steps of:
s1, logging in a knowledge graph generation system of the school year, and judging whether the user is a new user or not;
if the user is a new user, entering a characteristic evaluation unit;
if the user is not a new user, entering a teaching scheme generating unit;
s2, acquiring a priori knowledge base of a user, entering a teaching scheme generating unit, taking a teaching style base, a last school year knowledge graph and priori knowledge base information as inputs, and outputting to obtain a corresponding teaching scheme;
s3, entering a course learning unit based on the output teaching scheme, completing learning of all knowledge points of the course, performing examination, and performing macroscopic evaluation and microscopic evaluation on the teaching scheme;
and S4, entering a knowledge graph generation unit of the school year, and generating the knowledge graph of the school year according to macro evaluation and micro evaluation results and course related attributes for generating a teaching scheme of the next school year.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111753098A (en) * | 2020-06-23 | 2020-10-09 | 陕西师范大学 | Teaching method and system based on cross-media dynamic knowledge graph |
| CN113535982A (en) * | 2021-07-27 | 2021-10-22 | 南京邮电大学盐城大数据研究院有限公司 | Big data-based teaching system |
| CN114020929A (en) * | 2021-11-03 | 2022-02-08 | 北京航空航天大学 | Intelligent education system platform design method based on course knowledge graph |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111753098A (en) * | 2020-06-23 | 2020-10-09 | 陕西师范大学 | Teaching method and system based on cross-media dynamic knowledge graph |
| CN113535982A (en) * | 2021-07-27 | 2021-10-22 | 南京邮电大学盐城大数据研究院有限公司 | Big data-based teaching system |
| CN114020929A (en) * | 2021-11-03 | 2022-02-08 | 北京航空航天大学 | Intelligent education system platform design method based on course knowledge graph |
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