CN113987019A - Student learning condition analysis method and teaching system based on artificial intelligence - Google Patents

Student learning condition analysis method and teaching system based on artificial intelligence Download PDF

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CN113987019A
CN113987019A CN202111255415.XA CN202111255415A CN113987019A CN 113987019 A CN113987019 A CN 113987019A CN 202111255415 A CN202111255415 A CN 202111255415A CN 113987019 A CN113987019 A CN 113987019A
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彭黎文
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Abstract

The invention discloses a student learning condition analysis method and a teaching system based on artificial intelligence, belonging to the technical field of computer teaching, wherein the method comprises the following steps: extracting test scores of all students studying the same course; clustering and dividing students according to the test scores to obtain the classes of the students which are primarily divided; iteratively searching a new division standard sample according to the preliminarily divided student categories, and re-dividing the student categories to obtain final student categories; and obtaining the learning conditions of all students according to the final student category. The method and the system formed by the method help teachers and students to scientifically and deeply know the learning conditions by intelligently analyzing the learning conditions of the students and assist teachers to make and modify teaching modes according to intelligent analysis results.

Description

Student learning condition analysis method and teaching system based on artificial intelligence
Technical Field
The invention relates to the technical field of computer teaching, in particular to a student learning condition analysis method and a student learning condition teaching system based on artificial intelligence.
Background
The current online teaching lacks a reasonable evaluation system, cannot provide objective evaluation for the learning of learners, the learner independently learns without effective incentive, and a manager cannot know the learning condition of the learner. The network education can not become a mainstream teaching form at present, can not become an evaluation index of the learner for the performance and the promotion, and influences the learning enthusiasm of the learner to a certain extent.
The score is an important standard for the education institution to judge the students, but at present, the education institution only stays at the primary stages of ranking, performance point and the like for the score analysis, and simply divides the students into excellent, passing and failing according to the traditional total score of the end-of-term examination, so that the specific mastering conditions of the knowledge points of the students are not known, the teaching method is not convenient for a teacher to improve, and the instructor cannot be used for pertinently improving different classes of students.
Disclosure of Invention
The invention aims to solve the problem that the learning condition of students is not analyzed in the prior art, and provides an artificial intelligence-based student learning condition analysis method and a teaching system.
The purpose of the invention is realized by the following technical scheme:
the method mainly provides a student learning condition analysis method based on artificial intelligence, and the method comprises the following steps:
extracting test scores of all students studying the same course;
clustering and dividing students according to the test scores to obtain the classes of the students which are primarily divided;
iteratively searching a new division standard sample according to the preliminarily divided student categories, and re-dividing the student categories to obtain final student categories;
and obtaining the learning conditions of all students according to the final student category.
As an option, the test results of the students include the test results of each chapter, wherein the total number of the students is k, the number of the chapters of the course is l, and x is madeiThe test results of section i (i ∈ l) in the course of student x (x ∈ k) are represented, and the test results of all sections in the course of student x are represented as vector (x ∈ k)1,x2,xi,…,xl) Let yiThe test results of section i (i belongs to l) in the class of student y (y belongs to k), and the test results of all sections in the class of student y are expressed as vector (y belongs to l)1,y2,yi,…,yl)。
As an option, the clustering the students according to the test achievement includes:
calculating a knowledge point score difference between student x and student y, wherein the knowledge point score difference is calculated by the formula:
Figure BDA0003323952570000021
according to teaching requirements, students are divided into c categories, among all students, the c students are randomly selected as initial division standard samples, then differences between other students and the c students are calculated, and the different students and the c students with the smallest differences are divided into one category.
As an option, iteratively searching for a new partition criteria sample according to the preliminarily partitioned student categories includes:
suppose that a category of the preliminary classification contains h students, wherein the students are denoted as sv(v ∈ h), the new partition standard sample calculation formula is as follows:
Figure BDA0003323952570000022
wherein m isr(r ∈ c) is the new partition standard sample.
As an option, the repartitioning of the student categories to obtain a final student category includes:
after the new division standard sample is obtained, calculating the knowledge point score difference between the students and the new standard sample, and re-dividing the classes of the students;
calculating the difference square sum between samples in the category: calculated by the following formula:
Figure BDA0003323952570000031
wherein h is the number of students in a category, and j is a student in a category;
when the integral sum of squares is minimum, the standard sample for division is not changed any more, and the final student class is obtained.
As an option, the repartitioning of the student categories to obtain a final student category further includes:
and solving the intra-class squares and the sums of all the classes, wherein the calculation formula is as follows:
Figure BDA0003323952570000032
wherein totalsquareminRepresents the intra-class square sum of all classes in a dataset.
The invention also provides a teaching system, which comprises a student end, a teacher end, a local database server and an artificial intelligence data analysis module, wherein the student end, the teacher end and the local database server are respectively connected with the artificial intelligence data analysis module;
the local database server is used for storing teaching materials, and the teaching materials comprise test scores of all students in the same course;
the artificial intelligence data analysis module is used for clustering and dividing students according to the test scores to obtain preliminarily divided student categories, iteratively searching new division standard samples according to the preliminarily divided student categories, re-dividing the student categories to obtain final student categories, and finally obtaining the learning conditions of all students according to the final student categories;
the artificial intelligence data analysis module is also used for feeding the learning condition of the student back to the teacher end and the student end.
As an option, the teaching system further comprises a cloud database server, the cloud database server is connected with the artificial intelligence data analysis module, the cloud database server is used for storing teaching materials of the school in the past year, and the teaching materials comprise learning conditions of all students.
As an option, the teacher end includes a teacher's computer and a smart phone, and the student end includes a student's computer and a smart phone.
As an option, access permission is set for the cloud database server, and only teachers with permission above the teaching group leader can call data in the cloud database.
It should be further noted that the technical features corresponding to the above options can be combined with each other or replaced to form a new technical solution.
Compared with the prior art, the invention has the beneficial effects that:
(1) the invention searches out students with similar learning conditions of the knowledge points of the courses by a clustering mode iteration mode, can more scientifically divide the students, can more understand the learning conditions of the knowledge points of the students, is convenient for teachers to improve teaching methods, can pertinently guide and improve different classes of students, and can also request manual data analysis to realize self supervision.
(2) The artificial intelligence data analysis module of the system comprehensively collects information of post-lesson work, knowledge point exercise, test scores and the like of students, analyzes and processes data, and returns analysis results to teachers, so that the teachers can conveniently master own teaching conditions and knowledge point mastering conditions of the students in real time, teaching methods and progress can be adjusted in time, and teaching quality and efficiency are improved.
(3) By utilizing the artificial intelligence data analysis module of the system, students can carry out data analysis on the learning condition of the students through the module and master the learning progress and the knowledge point mastering degree of the students, so that the learning method of the students is improved, the learning state is adjusted, the defects and the shortcomings of the students are seen more clearly, and the learning efficiency is improved.
(4) The local database server and the cloud database server are used separately, so that the storage pressure of mass data can be reduced, and the data access authority in the database is set, so that the data is safer. The data in the local database and the data in the cloud database are combined at will, so that deep longitudinal analysis can be further performed, mining analysis of incremental data is realized, and the constructed model is more accurate.
(5) In the system, the teacher end can be simultaneously arranged on the desktop computer end and the mobile smart phone end, so that the teacher can conveniently carry out teaching design, upload teaching data and input various homework and test information of students, and the information between the desktop computer end and the mobile smart phone end can be synchronously updated, thereby preventing information confusion.
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FIG. 1 is a flow chart of a student learning condition analysis method based on artificial intelligence of the present invention;
FIG. 2 is a schematic diagram of the teaching system of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. 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.
In the description of the present invention, it should be noted that directions or positional relationships indicated by "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", and the like are directions or positional relationships described based on the drawings, and are only for convenience of description and simplification of description, and do not indicate or imply that the device or element referred to must have a specific orientation, be configured and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly stated or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
According to the invention, the learning scores of the students are subjected to data analysis in a clustering mode iteration mode, so that a teacher is helped to know the real learning condition of the students, and the students with similar learning conditions of the students on the knowledge points of the courses are searched out, so that the purposes of more scientifically dividing the students, more knowing the learning conditions of the knowledge points of the students and facilitating the improvement of a teaching method by the teacher are achieved.
Example 1
In an exemplary embodiment, there is provided an artificial intelligence-based student learning condition analysis method, as shown in fig. 1, the method including:
extracting test scores of all students studying the same course;
clustering and dividing students according to the test scores to obtain the classes of the students which are primarily divided;
iteratively searching a new division standard sample according to the preliminarily divided student categories, and re-dividing the student categories to obtain final student categories;
and obtaining the learning conditions of all students according to the final student category.
Furthermore, the test scores of the students comprise the test scores of each chapter, wherein the total number of the students is k, the number of the chapters of the course is l, and x is madeiThe test results of section i (i ∈ l) in the course of student x (x ∈ k) are represented, and the test results of all sections in the course of student x are represented as vector (x ∈ k)1,x2,xi,…,xl) Let yiThe test results of section i (i belongs to l) in the class of student y (y belongs to k), and the test results of all sections in the class of student y are expressed as vector (y belongs to l)1,y2,yi,…,yl)。
Further, the clustering and dividing the students according to the test scores comprises:
calculating a knowledge point score difference between student x and student y, wherein the knowledge point score difference is calculated by the formula:
Figure BDA0003323952570000071
according to teaching requirements, students are divided into c categories, among all students, the c students are randomly selected as initial division standard samples, then differences between other students and the c students are calculated, the different students and the c students with the smallest differences are divided into one category, and therefore the c primarily divided student categories are obtained.
Further, the iteratively searching for a new partition standard sample according to the preliminarily partitioned student categories includes:
suppose that a category of the preliminary classification contains h students, wherein the students are denoted as sv(v ∈ h), the new partition standard sample calculation formula is as follows:
Figure BDA0003323952570000072
wherein m isr(r ∈ c) is the new partition standard sample.
Further, the re-classifying the student categories to obtain a final student category includes:
after the new division standard sample is obtained, calculating the knowledge point score difference between the students and the new standard sample according to a knowledge point score difference formula, and re-dividing the classes of the students;
calculating the difference square sum between samples in the category: calculated by the following formula:
Figure BDA0003323952570000073
wherein h is oneThe number of students in a category, j is one student in a category;
in fact, in the process of continuously changing and continuously iterating the division standard samples, the total square sum is smaller and smaller, and mathematics can be used for proving that when the total square sum is minimum, the division standard samples do not change any more, and therefore the solving process becomes an optimization problem.
Further, the re-classifying the student categories to obtain a final student category further includes:
and solving the intra-class squares and the sums of all the classes, wherein the calculation formula is as follows:
Figure BDA0003323952570000081
wherein totalsquareminRepresents the intra-class square sum of all classes in a dataset. In order to effectively classify student classes, a minimum sum of squares within classes needs to be found. When the integral sum of squares is minimum, the standard sample for division is not changed any more, and the final student class is obtained.
Through the clustering mode, students are searched for students with similar learning conditions of the course knowledge points, the students are not simply divided into excellent, passing and failing according to the traditional total score of the end-of-period examination, the students can be divided more scientifically, the learning conditions of the student knowledge points are better known, a teacher can conveniently improve a teaching method, and guidance improvement is pertinently performed on different classes of students.
Example 2
In order to solve the problems that the teaching effect is poor and a teacher cannot deeply master the specific learning condition of students in the existing computer teaching, the invention also provides a teaching system, as shown in fig. 2, the teaching system comprises a student end, a teacher end, a local database server and an artificial intelligence data analysis module, wherein the student end, the teacher end and the local database server are respectively connected with the artificial intelligence data analysis module, and the local database server is also respectively connected with the student end and the teacher end;
the local database server is used for storing teaching materials, and the teaching materials comprise test scores of all students in the same course;
the artificial intelligence data analysis module is used for clustering and dividing students according to the test scores to obtain preliminarily divided student categories, iteratively searching new division standard samples according to the preliminarily divided student categories, re-dividing the student categories to obtain final student categories, and finally obtaining the learning conditions of all students according to the final student categories;
the artificial intelligence data analysis module is also used for feeding the learning condition of the student back to the teacher end and the student end.
Specifically, the local database is mainly used for storing information of students and teachers, including teaching materials, student scores and other teaching-related data.
The artificial intelligence analysis module can analyze the learning data of the students and return the data analysis result to the teacher end, so that the data analysis is effectively carried out on the learning scores of the students, the teachers are helped to know the real learning conditions of the students, the teaching method of the teachers is effectively adjusted and improved, the teaching method and progress can be adjusted in time, and the teaching quality and efficiency are improved.
The student side can also request the artificial data analysis from the artificial intelligent analysis module to analyze individual learning data, such as statistical analysis of individual examination scores, analysis of subject knowledge point mastering conditions and the like, and supervise and manage the student side. The student can carry out data analysis to the study condition of oneself through this module, masters the study progress of oneself and knowledge point mastery degree to improve the study method of oneself, adjust the study state, more clear see oneself shortcoming and not enough, improve learning efficiency.
Example 3
Based on embodiment 2, a teaching system is provided, the teaching system further comprises a cloud database server, the cloud database server is connected with the artificial intelligence data analysis module, the cloud database server is used for storing teaching materials of schools in the past years, and the teaching materials comprise learning conditions of all students.
Specifically, with the increase of the number of students every year, the data amount of teaching-related data is continuously increased, the local database server does not have the storage capacity, and all data in the past year are not needed to be stored in the local database, so that a large amount of data in the past year need to be stored in the cloud, and required data are called from the cloud when the data are needed to be used. The cloud database is mainly used for storing information of students and teachers, and comprises teaching data, student scores and other teaching-related data, and the data are stored according to the year.
The artificial intelligence data analysis module can be to the analysis of getting of high in the clouds data, can count the contrastive analysis with the student data of this year with the student data of the past year, for example according to the condition of examination knowledge score of the past year, analyzes out the difficult point in this year's student knowledge point study to and take the effect after the improvement measure. The data in the local database and the data in the cloud database can be combined at will, differences among students in any year can be analyzed, and score influence factor analysis can be carried out. In addition, the method has an important function of realizing mining analysis of incremental data, and with the continuous increase of student information data in the past, a data analysis model can be updated according to the increased data, so that the accuracy of the model is higher and higher, and the more accurate the analysis result is.
Furthermore, the teacher end is mainly installed in teacher's office computer, perhaps installs in the smart mobile phone through the APP, and the teacher of being convenient for carries out the teaching design, uploads the teaching data and types into various homework of student, test information. And no matter the operation of the computer end or the mobile end of the mobile phone can be synchronously updated to the local database, so that the problem of disordered data information can be avoided. Similarly, the student end is mainly installed in the student's learning computer, or is installed in individual smart mobile phone through APP.
Furthermore, access permission is set for the cloud database server, and only teachers with permission above the teaching group leader can call data in the cloud database.
The above detailed description is for the purpose of describing the invention in detail, and it should not be construed that the detailed description is limited to the description, and it will be apparent to those skilled in the art that various modifications and substitutions can be made without departing from the spirit of the invention.

Claims (10)

1. A student learning condition analysis method based on artificial intelligence is characterized by comprising the following steps:
extracting test scores of all students studying the same course;
clustering and dividing students according to the test scores to obtain the classes of the students which are primarily divided;
iteratively searching a new division standard sample according to the preliminarily divided student categories, and re-dividing the student categories to obtain final student categories;
and obtaining the learning conditions of all students according to the final student category.
2. The method as claimed in claim 1, wherein the test results of the students include the test results of each chapter, wherein the total number of students is k, the number of chapters in the course is l, let x beiThe test results of section i (i ∈ l) in the course of student x (x ∈ k) are represented, and the test results of all sections in the course of student x are represented as vector (x ∈ k)1,x2,xi,…,xl) Let yiThe test results of section i (i belongs to l) in the class of student y (y belongs to k), and the test results of all sections in the class of student y are expressed as vector (y belongs to l)1,y2,yi,…,yl)。
3. The method for analyzing the learning condition of the students based on the artificial intelligence as claimed in claim 2, wherein the clustering the students according to the test results comprises:
calculating a knowledge point score difference between student x and student y, wherein the knowledge point score difference is calculated by the formula:
Figure FDA0003323952560000011
according to teaching requirements, students are divided into c categories, among all students, the c students are randomly selected as initial division standard samples, then differences between other students and the c students are calculated, and the different students and the c students with the smallest differences are divided into one category.
4. The method for analyzing learning condition of students based on artificial intelligence as claimed in claim 3, wherein said iteratively searching for new partition standard samples according to said preliminarily partitioned student categories comprises:
suppose that a category of the preliminary classification contains h students, wherein the students are denoted as sv(v ∈ h), the new partition standard sample calculation formula is as follows:
Figure FDA0003323952560000021
wherein m isr(r ∈ c) is the new partition standard sample.
5. The method for analyzing learning conditions of students based on artificial intelligence as claimed in claim 4, wherein said re-classifying the student categories to obtain the final student categories comprises:
after the new division standard sample is obtained, calculating the knowledge point score difference between the students and the new standard sample, and re-dividing the classes of the students;
calculating the difference square sum between samples in the category: calculated by the following formula:
Figure FDA0003323952560000022
wherein h is the number of students in a category, and j is a student in a category;
when the integral sum of squares is minimum, the standard sample for division is not changed any more, and the final student class is obtained.
6. The method for analyzing learning conditions of students based on artificial intelligence as claimed in claim 4, wherein said re-classifying the student categories to obtain the final student categories further comprises:
and solving the intra-class squares and the sums of all the classes, wherein the calculation formula is as follows:
Figure FDA0003323952560000023
wherein totalsquareminRepresents the intra-class square sum of all classes in a dataset.
7. A teaching system based on the student learning condition analysis method of any one of claims 1 to 6, wherein the teaching system comprises a student terminal, a teacher terminal, a local database server and an artificial intelligence data analysis module, the student terminal, the teacher terminal and the local database server are respectively connected with the artificial intelligence data analysis module, and the local database server is also respectively connected with the student terminal and the teacher terminal;
the local database server is used for storing teaching materials, and the teaching materials comprise test scores of all students in the same course;
the artificial intelligence data analysis module is used for clustering and dividing students according to the test scores to obtain preliminarily divided student categories, iteratively searching new division standard samples according to the preliminarily divided student categories, re-dividing the student categories to obtain final student categories, and finally obtaining the learning conditions of all students according to the final student categories;
the artificial intelligence data analysis module is also used for feeding the learning condition of the student back to the teacher end and the student end.
8. The teaching system of claim 7, further comprising a cloud database server, wherein the cloud database server is connected to the artificial intelligence data analysis module, and is configured to store teaching materials of school calendar years, and the teaching materials include learning conditions of all students.
9. An instructional system as claimed in claim 7 wherein said instructor end comprises an instructor's computer and a smartphone and said student end comprises a student's computer and a smartphone.
10. The instructional system of claim 8, wherein the cloud database server is configured with access rights such that only teachers with rights above the teaching group can access the data in the cloud database.
CN202111255415.XA 2021-10-27 2021-10-27 Student learning condition analysis method and teaching system based on artificial intelligence Pending CN113987019A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114662995A (en) * 2022-05-19 2022-06-24 山东经贸职业学院 Online learning effect evaluation method and system based on artificial intelligence
CN115829803A (en) * 2023-02-15 2023-03-21 威海海洋职业学院 Remote education information processing platform system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114662995A (en) * 2022-05-19 2022-06-24 山东经贸职业学院 Online learning effect evaluation method and system based on artificial intelligence
CN115829803A (en) * 2023-02-15 2023-03-21 威海海洋职业学院 Remote education information processing platform system

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