CN113658022A - Big data based teaching mode analysis method - Google Patents
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Abstract
The invention relates to the technical field of teaching mode analysis, in particular to a teaching mode analysis method based on big data. The method comprises the steps of building a network architecture of a teaching mode analysis system, acquiring mass data, processing, intelligently judging and identifying teaching modes in a classroom, analyzing and evaluating the teaching modes, performing collision analysis on student scores, performing comprehensive evaluation and scoring on the teaching modes, generating a comprehensive analysis report and the like. The design of the invention can rapidly carry out teaching evaluation, reduce the workload of workers and improve the efficiency and accuracy of the teaching evaluation; the teaching mode of the classroom can be automatically and accurately judged, and the applied teaching mode is scored aiming at different classes or different subjects by combining with multiple judgment indexes, so that the applicable teaching mode or combination of multiple modes can be better explored; by combining teaching analysis and artificial intelligence technology, the development of professional literacy of teachers is facilitated, and the teaching quality is further improved.
Description
Technical Field
The invention relates to the technical field of teaching mode analysis, in particular to a teaching mode analysis method based on big data.
Background
The teaching mode is a stable structural form of a teaching activity process under the guidance of certain education thought, teaching theory and learning theory, and is the centralized embodiment of the education thought, the teaching theory and the learning theory. The objects of classroom teaching are diversified, so that teaching activities are comprehensive frequently, and therefore, multiple teaching modes are organically combined and applied when classroom teaching is performed frequently. The teacher with strong ability can use various teaching modes with great care in a special teaching, and can neither mechanically take care of the existing teaching modes nor leave the scientific teaching modes and methods to blindly engage in teaching. In practical teaching application, different teaching modes are required to be adopted to improve teaching effects aiming at different teacher personalities, student personalities, different disciplines and the like. With the rapid development of education informatization, teaching activities and artificial intelligence technology are more and more closely fused, but the teaching evaluation link is still in the traditional artificial labeling statistical stage, and cannot be analyzed by combining intuitive teaching effects such as classroom performance, student score and the like with a corresponding teaching mode, so that education workers cannot timely think about teaching behaviors and teaching methods and summarize and correct problems and deficiencies in the teaching link, and further, deep and direct and effective teaching activities are implemented. However, there is no method for effectively analyzing the implementation effect of the teaching mode.
Disclosure of Invention
The invention aims to provide a teaching mode analysis method based on big data to solve the problems in the background technology.
In order to solve the above technical problem, an object of the present invention is to provide a teaching mode analysis method based on big data, which includes the following steps:
s1, building a network architecture of the teaching mode analysis system, and connecting the network architecture with an intelligent campus information management platform;
s2, acquiring mass data related to the teaching mode from multiple aspects, and processing the data;
s3, intelligently judging and identifying the teaching mode of the classroom and recording labels;
s4, analyzing and evaluating the teaching mode of the classroom in combination with classroom performance;
s5, performing collision analysis on student scores and a teaching mode;
s6, comprehensively evaluating and scoring the teaching mode by combining the related projects;
in S6, a weighted average algorithm is used to score the teaching patterns, and the calculation expression is as follows:
in the formula, F represents the evaluation score of a certain teaching mode in a teaching classroom; x is the number of1，x2，…，x3Respectively representing the evaluation scores of different evaluation index items; f. of1+f2+…+fk＝n,(n＝1)，f1，f2，…，f3Called weights, each representing x1，x2，…，x3The proportion weight of the corresponding score in the total evaluation score;
and S7, generating a corresponding comprehensive analysis report, and selecting a correspondingly applicable teaching mode for trial run and application by an educator according to factors such as subject, class structure, student property, teacher performance and the like.
As a further improvement of the present invention, in S2, the method for processing data includes the steps of:
s2.1, acquiring mass relevant data from the intelligent campus information management platform, wherein the mass relevant data comprises school timetable information, teacher lecture materials, teaching audio and video data, student scores and the like;
s2.2, carrying out preprocessing operations such as cleaning, refining and sorting on the data;
s2.3, classifying the data according to a certain rule, such as classes, disciplines and teachers, or according to data types, such as audio, video, tables, characters and the like, and respectively identifying and analyzing different types of data information;
and S2.4, respectively storing the source file and the processed data into corresponding data sets to form a large database.
As a further improvement of the present technical solution, in S2.3, the method for respectively identifying and analyzing different types of data information includes the following steps:
s2.3.1, aiming at classroom audio data, detecting active tone and mute part in audio through a Gaussian mixture model GMM, distinguishing voices of teachers and students through a voice recognition technology, and respectively counting speaking frequency and speaking duration of the teachers and the students;
s2.3.2, tracking and judging the behavior of teachers in the classroom and respectively counting the time length and frequency, such as explanation, blackboard writing, PPT demonstration, question asking, question solving and the like, by aiming at classroom video data and through an artificial intelligence AI technology;
s2.3.3, tracking and judging the behaviors of students in the classroom and respectively counting the frequency, such as listening and speaking, distracting, raising hands, standing up, interacting with a teacher and the like, aiming at classroom video data through an artificial intelligence AI technology;
s2.3.4, directly acquiring the judgment and evaluation of the teaching mode and acquiring the corresponding classroom performance evaluation according to the recorded files of lectures and teaching research.
Wherein in S2.3.1, the active sound is the effective audio with human voice, and the mute is the audio without effective human voice; the time length of the activity voice in the classroom, the speaking time length of the teacher, the conversation frequency of the teacher and the students and the like can be used as indexes for judging and identifying the teaching mode.
As a further improvement of the present technical solution, in S2.3.1, an arithmetic calculation expression of a gaussian mixture model is as follows:
with a random variable X, the mixed Gaussian model can be represented by:
wherein N (x | mu)kΣ k) is called the kth component in the hybrid model;
in addition, for the case of two clusters, which can be represented by two-dimensional gaussian distributions, the component number K is 2kIs a mixing coefficient and satisfies:
0≤πk≤1；
in fact, it can be said thatkIs that each component N (x | mu)kΣ k).
As a further improvement of the technical solution, in S3, the method for intelligently identifying the teaching mode of the classroom includes the following steps:
s3.1, drawing a corresponding polygonal grid attribute distribution map according to the items such as the explanation time length of a teacher in a classroom, the blackboard writing/PPT demonstration time length, the questioning times, the teacher-student interaction and the student performance condition by combining the data analysis and statistics in the step S2;
s3.2, drawing the polygon mesh attribute distribution map expressed in all classes;
s3.3, setting a grade threshold value of each attribute;
and S3.4, comparing each attribute in the classroom polygon mesh attribute distribution graph with a corresponding grade threshold value, so as to judge the teaching mode of the classroom and mark a label.
The type labels of the teaching modes mainly comprise a transmission-reception type, a self-learning-assistance type, a research type teaching, a concept acquisition mode, a Butler learning mode, an anchoring type teaching, a paradigm teaching mode, a phenomenon analysis mode, a Ganne mode, an Olympe mode, a cooperative learning mode and a discovery type mode; in the specific application process, the type labels of the teaching modes are simplified into an explanation transmission type, an interactive conversation type, a self-learning guidance type, a multivariate mixed type and the like.
As a further improvement of the technical solution, in S5, the method for performing collision analysis on the student achievement and the payment mode includes the following steps:
s5.1, taking the class as a unit, acquiring subject scores of the class students in different teaching modes, or taking the subject as a unit, acquiring scores of the class students of the subject in different teaching modes;
s5.2, calculating the average values of the result data of the students of the same class in different teaching modes respectively, or calculating the average values of the result data of the students of the same class in different teaching modes respectively;
s5.3, calculating the subject result data mean square deviation values of students in the same class in different teaching modes respectively, or calculating the result data mean square deviation values of students in the same subject in different classes in different teaching modes respectively;
s5.4, acquiring subject result data extremum of students in the same class in different teaching modes, or acquiring result data extremum of students in different classes in the same subject in different teaching modes, and respectively calculating result data full-range values in each teaching mode in corresponding projects;
s5.5, calculating subject scores and lattice rates of students in the same class in different teaching modes, or calculating scores and lattice rates of students in the same subject in different classes in different teaching modes;
and S5.6, performing collision analysis on multiple teaching modes by combining the calculated result mean value, result mean variance value, full range value and passing rate of the same class or the same subject in different teaching modes.
As a further improvement of the technical solution, in S5.2, a calculation expression of the student achievement data mean value is as follows:
wherein the content of the first and second substances,
is the average value of the score data in the data set, m is the number of the score data in the data set, y1，y2，y3,...,ymThe score of each achievement data.
As a further improvement of the technical solution, in S5.3, a calculation expression of the student achievement data mean square deviation value is as follows:
wherein d is the mean variance value of the achievement data in the data set.
As a further improvement of the technical solution, in S5.4, a calculation expression of the student achievement data overall distance value is as follows:
s＝ymax-ymin；
wherein s is the overall distance value of the score data in the data set, ymaxMaximum value of score data in dataset, yminIs the minimum value of the score of the performance data in the data set.
As a further improvement of the technical solution, in S6, a weighted average algorithm is used for scoring the teaching patterns, and a calculation expression thereof is as follows:
in the formula, F represents the evaluation score of a certain teaching mode in a teaching classroom; x is the number of1，x2，…，x3Respectively representing the evaluation scores of different evaluation index items; f. of1+f2+…+fk＝n,(n＝1)，f1，f2，…，f3Called weights, each representing x1，x2，…，x3The corresponding score is weighted proportionally in the overall score of the assessment.
The evaluation index items include, but are not limited to, class student achievement average, student classroom performance, teacher teaching effect, teaching and research evaluation, and the like.
As a further improvement of the technical scheme, the method comprises the following steps.
Another object of the present invention is to provide a teaching mode analysis system based on big data and an operating device thereof, including a processor, a memory and a computer program stored in the memory and running on the processor, wherein the processor is used to implement any of the above steps of the teaching mode analysis method based on big data when executing the computer program.
It is a further object of the present invention to provide a computer-readable storage medium, which stores a computer program, which when executed by a processor implements the steps of any of the above-mentioned big data based teaching pattern analysis methods.
Compared with the prior art, the invention has the beneficial effects that:
1. the big data-based teaching mode analysis method is connected with the intelligent campus information management platform, mass data related to teaching modes are obtained, deep mining, refining and analysis are carried out on classroom teaching audio and video data, teaching evaluation can be carried out rapidly, manual workload is reduced, and efficiency and accuracy of teaching evaluation are improved;
2. according to the teaching mode analysis method based on the big data, the polygon grid attribute distribution map of classroom teaching is automatically drawn by combining classroom performance and student scores, so that the teaching mode of a classroom can be automatically and accurately judged, and the applied teaching mode is graded aiming at different classes or different subjects by combining multiple judgment indexes, so that the applicable teaching mode or multiple mode combinations can be better explored;
3. according to the big data-based teaching mode analysis method, teaching analysis and an artificial intelligence technology are combined, so that the limitation existing in the traditional teaching mode analysis method is overcome, the goals of improving the professional ability of teachers and promoting teaching quality are met, the development of professional literacy of teachers is facilitated, and the teaching quality is further improved.
Drawings
FIG. 1 is an exemplary product architecture diagram of the present invention;
FIG. 2 is a diagram of an exemplary polygon mesh attribute distribution according to the present invention;
FIG. 3 is an overall method flow diagram of the present invention;
FIG. 4 is a flow chart of a partial method of the present invention;
FIG. 5 is a second flowchart of a partial method of the present invention;
FIG. 6 is a third flowchart of a partial method of the present invention;
FIG. 7 is a fourth flowchart of a partial method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
As shown in fig. 1 to fig. 7, an object of the present embodiment is to provide a teaching mode analysis method based on big data, which includes the following steps:
s1, building a network architecture of the teaching mode analysis system, and connecting the network architecture with an intelligent campus information management platform;
s2, acquiring mass data related to the teaching mode from multiple aspects, and processing the data;
s3, intelligently judging and identifying the teaching mode of the classroom and recording labels;
s4, analyzing and evaluating the teaching mode of the classroom in combination with classroom performance;
s5, performing collision analysis on student scores and a teaching mode;
s6, comprehensively evaluating and scoring the teaching mode by combining the related projects;
in S6, the teaching mode is scored using a weighted average algorithm, and the calculation expression is:
in the formula, F represents the evaluation score of a certain teaching mode in a teaching classroom; x is the number of1，x2，…，x3Respectively representing the evaluation scores of different evaluation index items; f. of1+f2+…+fk＝n,(n＝1)，f1，f2，…，f3Called weights, each representing x1，x2，…，x3The proportion weight of the corresponding score in the total evaluation score;
and S7, generating a corresponding comprehensive analysis report, and selecting a correspondingly applicable teaching mode for trial run and application by an educator according to factors such as subject, class structure, student property, teacher performance and the like.
In this embodiment, in S2, the method for processing data includes the following steps:
s2.1, acquiring mass relevant data from the intelligent campus information management platform, wherein the mass relevant data comprises school timetable information, teacher lecture materials, teaching audio and video data, student scores and the like;
s2.2, carrying out preprocessing operations such as cleaning, refining and sorting on the data;
s2.3, classifying the data according to a certain rule, such as classes, disciplines and teachers, or according to data types, such as audio, video, tables, characters and the like, and respectively identifying and analyzing different types of data information;
and S2.4, respectively storing the source file and the processed data into corresponding data sets to form a large database.
Further, in S2.3, the method for performing identification analysis on different types of data information respectively includes the following steps:
s2.3.1, aiming at classroom audio data, detecting active tone and mute part in audio through a Gaussian mixture model GMM, distinguishing voices of teachers and students through a voice recognition technology, and respectively counting speaking frequency and speaking duration of the teachers and the students;
s2.3.2, tracking and judging the behavior of teachers in the classroom and respectively counting the time length and frequency, such as explanation, blackboard writing, PPT demonstration, question asking, question solving and the like, by aiming at classroom video data and through an artificial intelligence AI technology;
s2.3.3, tracking and judging the behaviors of students in the classroom and respectively counting the frequency, such as listening and speaking, distracting, raising hands, standing up, interacting with a teacher and the like, aiming at classroom video data through an artificial intelligence AI technology;
s2.3.4, directly acquiring the judgment and evaluation of the teaching mode and acquiring the corresponding classroom performance evaluation according to the recorded files of lectures and teaching research.
Wherein, in S2.3.1, the active sound is the effective audio with human voice, and the mute is the audio without effective human voice; the time length of the activity voice in the classroom, the speaking time length of the teacher, the conversation frequency of the teacher and the students and the like can be used as indexes for judging and identifying the teaching mode.
Specifically, in S2.3.1, the arithmetic calculation expression of the mixture gaussian model is as follows:
with a random variable X, the mixed Gaussian model can be represented by:
wherein N (x | mu)kΣ k) is called the kth component in the hybrid model;
in addition, for the case of two clusters, which can be represented by two-dimensional gaussian distributions, the component number K is 2kIs a mixing coefficient and satisfies:
0≤πk≤1；
in fact, it can be said thatkIs that each component N (x | mu)kΣ k).
In this embodiment, in S3, the method for intelligently identifying the teaching mode of the classroom includes the following steps:
s3.1, drawing a corresponding polygonal grid attribute distribution map according to the items such as the explanation time length of a teacher in a classroom, the blackboard writing/PPT demonstration time length, the questioning times, the teacher-student interaction and the student performance condition by combining the data analysis and statistics in the step S2;
s3.2, drawing the polygon mesh attribute distribution map expressed in all classes;
s3.3, setting a grade threshold value of each attribute;
and S3.4, comparing each attribute in the classroom polygon mesh attribute distribution graph with a corresponding grade threshold value, so as to judge the teaching mode of the classroom and mark a label.
The type labels of the teaching modes mainly comprise a transmission-reception type, a self-learning-assistance type, a research type teaching, a concept acquisition mode, a Butler learning mode, an anchoring type teaching, a paradigm teaching mode, a phenomenon analysis mode, a Ganne mode, an Olympe mode, a cooperative learning mode and a discovery type mode; in the specific application process, the type labels of the teaching modes are simplified into an explanation transmission type, an interactive conversation type, a self-learning guidance type, a multivariate mixed type and the like.
In this embodiment, in S5, the method for performing collision analysis on the student achievement and the payment mode includes the following steps:
s5.1, taking the class as a unit, acquiring subject scores of the class students in different teaching modes, or taking the subject as a unit, acquiring scores of the class students of the subject in different teaching modes;
s5.2, calculating the average values of the result data of the students of the same class in different teaching modes respectively, or calculating the average values of the result data of the students of the same class in different teaching modes respectively;
s5.3, calculating the subject result data mean square deviation values of students in the same class in different teaching modes respectively, or calculating the result data mean square deviation values of students in the same subject in different classes in different teaching modes respectively;
s5.4, acquiring subject result data extremum of students in the same class in different teaching modes, or acquiring result data extremum of students in different classes in the same subject in different teaching modes, and respectively calculating result data full-range values in each teaching mode in corresponding projects;
s5.5, calculating subject scores and lattice rates of students in the same class in different teaching modes, or calculating scores and lattice rates of students in the same subject in different classes in different teaching modes;
and S5.6, performing collision analysis on multiple teaching modes by combining the calculated result mean value, result mean variance value, full range value and passing rate of the same class or the same subject in different teaching modes.
Specifically, in S5.2, the calculation expression of the student achievement data mean value is as follows:
wherein the content of the first and second substances,
is the average value of the score data in the data set, m is the number of the score data in the data set, y1,y2,y3,...,ymThe score of each achievement data.
Specifically, in S5.3, the calculation expression of the student achievement data mean square deviation value is as follows:
wherein d is the mean variance value of the achievement data in the data set.
Specifically, in S5.4, the calculation expression of the student achievement data overall distance value is as follows:
s＝ymax-ymin；
wherein s is the overall distance value of the score data in the data set, ymaxMaximum value of score data in dataset, yminIs the minimum value of the score of the performance data in the data set.
In this embodiment, in S6, a weighted average algorithm is used to score the teaching patterns, and the calculation expression is as follows:
in the formula, F represents the evaluation score of a certain teaching mode in a teaching classroom; x is the number of1，x2，…，x3Respectively representing the evaluation scores of different evaluation index items; f. of1+f2+…+fk＝n,(n＝1)，f1，f2，…，f3Called weights, each representing x1，x2，…，x3The corresponding score is weighted proportionally in the overall score of the assessment.
The evaluation index items include, but are not limited to, class student achievement average, student classroom performance, teacher teaching effect, teaching and research evaluation, and the like.
The embodiment also provides a teaching mode analysis system based on big data and an operation device thereof, wherein the device comprises a processor, a memory and a computer program which is stored in the memory and operated on the processor.
The processor comprises one or more processing cores, the processor is connected with the memory through the bus, the memory is used for storing program instructions, and the teaching mode analysis method based on big data is realized when the processor executes the program instructions in the memory.
Alternatively, the memory may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
In addition, the present invention also provides a computer readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the teaching mode analysis method based on big data.
Optionally, the present invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the steps of the above-described big data based tutorial pattern analysis method.
It will be understood by those skilled in the art that all or part of the steps of implementing the above embodiments may be implemented by hardware, or may be implemented by hardware related to instructions of a program, which may be stored in a computer-readable storage medium, such as a read-only memory, a magnetic or optical disk, and the like.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and the preferred embodiments of the present invention are described in the above embodiments and the description, and are not intended to limit the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. A teaching mode analysis method based on big data is characterized in that: the method comprises the following steps:
s1, building a network architecture of the teaching mode analysis system, and connecting the network architecture with an intelligent campus information management platform;
s2, acquiring mass data related to the teaching mode from multiple aspects, and processing the data;
s3, intelligently judging and identifying the teaching mode of the classroom and recording labels;
s4, analyzing and evaluating the teaching mode of the classroom in combination with classroom performance;
s5, performing collision analysis on student scores and a teaching mode;
s6, comprehensively evaluating and scoring the teaching mode by combining the related projects;
in S6, a weighted average algorithm is used to score the teaching patterns, and the calculation expression is as follows:
wherein F represents a certain teachingThe evaluation score of the mode in the teaching class; x is the number of1，x2，…，x3Respectively representing the evaluation scores of different evaluation index items; f. of1+f2+…+fk＝n，(n＝1)，f1，f2，…，f3Called weights, each representing x1，x2，…，x3The proportion weight of the corresponding score in the total evaluation score;
and S7, generating a corresponding comprehensive analysis report, and selecting a correspondingly applicable teaching mode for trial run and application by an educator according to factors such as subject, class structure, student property, teacher performance and the like.
2. The big-data-based teaching pattern analysis method of claim 1, wherein: in S2, the method for processing data includes the steps of:
s2.1, acquiring mass relevant data from the intelligent campus information management platform, wherein the mass relevant data comprises school timetable information, teacher lecture materials, teaching audio and video data, student scores and the like;
s2.2, carrying out preprocessing operations such as cleaning, refining and sorting on the data;
s2.3, classifying the data according to a certain rule, such as classes, disciplines and teachers, or according to data types, such as audio, video, tables, characters and the like, and respectively identifying and analyzing different types of data information;
and S2.4, respectively storing the source file and the processed data into corresponding data sets to form a large database.
3. The big-data-based teaching pattern analysis method of claim 1, wherein: in S2.3, the method for respectively identifying and analyzing different types of data information includes the following steps:
s2.3.1, aiming at classroom audio data, detecting active tone and mute part in audio through a Gaussian mixture model GMM, distinguishing voices of teachers and students through a voice recognition technology, and respectively counting speaking frequency and speaking duration of the teachers and the students;
s2.3.2, tracking and judging the behavior of teachers in the classroom and respectively counting the time length and frequency, such as explanation, blackboard writing, PPT demonstration, question asking, question solving and the like, by aiming at classroom video data and through an artificial intelligence AI technology;
s2.3.3, tracking and judging the behaviors of students in the classroom and respectively counting the frequency, such as listening and speaking, distracting, raising hands, standing up, interacting with a teacher and the like, aiming at classroom video data through an artificial intelligence AI technology;
s2.3.4, directly acquiring the judgment and evaluation of the teaching mode and acquiring the corresponding classroom performance evaluation according to the recorded files of lectures and teaching research.
4. The big-data-based teaching pattern analysis method of claim 1, wherein: in S2.3.1, the arithmetic calculation expression of the Gaussian mixture model is as follows:
with a random variable X, the mixed Gaussian model can be represented by:
wherein N (x | mu)kΣ k) is called the kth component in the hybrid model;
in addition, for the case of two clusters, which can be represented by two-dimensional gaussian distributions, the component number K is 2kIs a mixing coefficient and satisfies:
0≤πk≤1；
in fact, it can be said thatkIs that each component N (x | mu)kΣ k).
5. The big-data-based teaching pattern analysis method of claim 1, wherein: in S3, the method for intelligently identifying the teaching mode of the classroom includes the following steps:
s3.1, drawing a corresponding polygonal grid attribute distribution map according to the items such as the explanation time length of a teacher in a classroom, the blackboard writing/PPT demonstration time length, the questioning times, the teacher-student interaction and the student performance condition by combining the data analysis and statistics in the step S2;
s3.2, drawing the polygon mesh attribute distribution map expressed in all classes;
s3.3, setting a grade threshold value of each attribute;
and S3.4, comparing each attribute in the classroom polygon mesh attribute distribution graph with a corresponding grade threshold value, so as to judge the teaching mode of the classroom and mark a label.
6. The big-data-based teaching pattern analysis method of claim 1, wherein: in S5, the method for performing collision analysis on the student achievement and the payment mode includes the following steps:
s5.1, taking the class as a unit, acquiring subject scores of the class students in different teaching modes, or taking the subject as a unit, acquiring scores of the class students of the subject in different teaching modes;
s5.2, calculating the average values of the result data of the students of the same class in different teaching modes respectively, or calculating the average values of the result data of the students of the same class in different teaching modes respectively;
s5.3, calculating the subject result data mean square deviation values of students in the same class in different teaching modes respectively, or calculating the result data mean square deviation values of students in the same subject in different classes in different teaching modes respectively;
s5.4, acquiring subject result data extremum of students in the same class in different teaching modes, or acquiring result data extremum of students in different classes in the same subject in different teaching modes, and respectively calculating result data full-range values in each teaching mode in corresponding projects;
s5.5, calculating subject scores and lattice rates of students in the same class in different teaching modes, or calculating scores and lattice rates of students in the same subject in different classes in different teaching modes;
and S5.6, performing collision analysis on multiple teaching modes by combining the calculated result mean value, result mean variance value, full range value and passing rate of the same class or the same subject in different teaching modes.
7. The big-data-based teaching pattern analysis method of claim 1, wherein: in S5.2, the calculation expression of the student achievement data mean value is as follows:
wherein the content of the first and second substances,
is the average value of the score data in the data set, m is the number of the score data in the data set, y1，y2，y3，...，ymThe score of each achievement data.
8. The big-data-based teaching pattern analysis method of claim 1, wherein: in S5.3, the calculation expression of the student achievement data mean variance value is as follows:
wherein d is the mean variance value of the achievement data in the data set.
9. The big-data-based teaching pattern analysis method of claim 1, wherein: in S5.4, the calculation expression of the student achievement data overall distance value is as follows:
s＝ymax-ymin；
whereinS is the overall distance value of the score data in the data set, ymaxMaximum value of score data in dataset, yminIs the minimum value of the score of the performance data in the data set.
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