CN115577940A

CN115577940A - Teaching intervention strategy acquisition method, device and equipment based on big data analysis

Info

Publication number: CN115577940A
Application number: CN202211243109.9A
Authority: CN
Inventors: 孔卓帆
Original assignee: Shenzhen Chengru Technology Co ltd
Current assignee: Shenzhen Chengru Technology Co ltd
Priority date: 2022-10-11
Filing date: 2022-10-11
Publication date: 2023-01-06

Abstract

The invention relates to the field of data analysis, and discloses a teaching intervention strategy acquisition method based on big data analysis, which comprises the following steps: collecting score data of target students and factor data influencing the scores; performing knowledge point cognition analysis on the score data of the target students and performing influence factor characteristic analysis on the influence score factor data, and integrating the cognition level of the knowledge points and the influence factor characteristic analysis result to obtain a learning characteristic analysis result; carrying out comprehensive diagnosis on the learning characteristic analysis result to obtain a learning comprehensive result diagnosis report; and identifying the learning difficulty factors of the students from the learning comprehensive result diagnosis report, and generating a teaching intervention strategy according to the learning difficulty factors. The invention also relates to a block chain technology, and the achievement data and the achievement influencing factor data can be stored in the block chain link points. The invention also provides a teaching intervention strategy acquisition device, equipment and a medium based on big data analysis. The invention can improve the efficiency and accuracy of teaching intervention.

Description

Teaching intervention strategy acquisition method, device and equipment based on big data analysis

Technical Field

The invention relates to the field of data analysis, in particular to a teaching intervention strategy acquisition method, a teaching intervention strategy acquisition device, teaching intervention equipment and a storage medium based on big data analysis.

Background

Teaching intervention refers to a teaching behavior that helps students improve learning effects using a reasonable approach. The traditional teaching intervention method is generally to judge the learning condition of students by collecting the academic data of the practice condition and the examination score of each student in class at ordinary times by teachers. On one hand, the method has two problems that the teaching intelligence is insufficient and the teaching intervention efficiency is low because the academic data needs manual collection and a large amount of manpower is consumed; on the other hand, as the academic data structure is single, the academic barriers and difficulties of students are difficult to be further objectively known only through simple academic data, namely hidden information behind academic performance representation data of the students cannot be mined, so that the provided teaching intervention scheme is relatively single and comprehensive, and the accuracy of teaching intervention is low.

Disclosure of Invention

The invention provides a teaching intervention strategy acquisition method, a device, equipment and a storage medium based on big data analysis, and mainly aims to improve the efficiency and accuracy of teaching intervention.

In order to achieve the above object, the present invention provides a teaching intervention strategy acquisition method based on big data analysis, which comprises:

acquiring score data of target students and acquiring influence score factor data of the target students;

performing knowledge point cognition analysis on the result data to obtain knowledge point cognition level of the target student, performing influence factor characteristic analysis on the influence result factor data by using a preset teaching analysis model to obtain an influence factor characteristic analysis result of the target student, and integrating the knowledge point cognition level and the influence factor characteristic analysis result to obtain a learning characteristic analysis result of the target student;

acquiring preset academic diagnostic items from the learning characteristic analysis results, and performing comprehensive learning characteristic diagnosis on the learning characteristic analysis results according to the academic diagnostic items to obtain a learning comprehensive result diagnosis report of the target student;

and identifying the learning difficulty factors of the target students from the learning comprehensive achievement diagnosis report, and generating a teaching intervention strategy according to the learning difficulty factors.

Optionally, the performing influence factor feature analysis on the influence score factor data by using a preset teaching analysis model to obtain an influence factor feature analysis result of the target student includes:

analyzing the social attribute characteristics, the learning environment characteristics, the learning power characteristics and the learning psychological characteristics of the students of the factor data influencing the achievement by using the teaching analysis model;

and summarizing the student social attribute characteristics, the student learning environment characteristics, the student learning dynamic characteristics and the student learning psychological characteristics to obtain an influence factor characteristic analysis result.

Optionally, the performing, according to the academic diagnosis item, comprehensive diagnosis of learning features on the learning feature analysis result to obtain a learning comprehensive result diagnosis report of the target student includes:

acquiring project information corresponding to the academic diagnostic project from a learning feature analysis result, and extracting a project diagnostic grade to which the project information belongs;

recognizing learning superior items and learning inferior items in the learning feature analysis results according to the item information and the item diagnosis levels;

and integrating the project information, the project diagnosis level, the learning superior project and the learning inferior project according to a preset diagnosis report template and a preset report generating tool to obtain the learning comprehensive achievement diagnosis report.

Optionally, the generating a teaching intervention strategy according to the learning difficulty factor includes:

and determining the learning habits of the target students according to the learning difficulty factors, and generating teaching intervention strategies of the target students aiming at the learning habits by combining a plurality of intervention means, wherein the plurality of intervention means comprise at least two of platform intervention means, teacher intervention means and social intervention means.

Optionally, the collecting the achievement data of the target student and collecting the influence achievement factor data of the target student includes:

collecting a test paper image set and a questionnaire image set of the target student, and respectively carrying out feature extraction on the test paper image set and the questionnaire image set by using a volume layer in a preset image recognition model to obtain a test paper feature image set and a questionnaire feature image set;

respectively performing dimension reduction operation on the test paper feature image set and the questionnaire feature image set by using a pooling layer in the image recognition model to obtain a test paper dimension reduction image set and a questionnaire dimension reduction image set;

and respectively outputting the test paper dimension reduction image set and the questionnaire dimension reduction image set by using an activation function in the image recognition model to obtain the achievement data and the factor data influencing the achievement.

Optionally, after generating the teaching intervention strategy according to the learning difficulty factors, the method further includes:

dividing the target students into different intervention groups according to the teaching intervention strategy, wherein the intervention groups comprise a first-level group to be intervened, a second-level group to be intervened and a third-level group to be intervened;

and performing corresponding intervention operation according to the intervention group of the target student.

In order to solve the above problem, the present invention further provides a teaching intervention strategy obtaining apparatus based on big data analysis, where the apparatus includes:

the data acquisition module is used for acquiring the score data of the target students and acquiring the factor data of the influence scores of the target students;

the data analysis module is used for carrying out knowledge point cognition analysis on the achievement data to obtain the knowledge point cognition level of the target student, carrying out influence factor characteristic analysis on the influence achievement factor data by using a preset teaching analysis model to obtain the influence factor characteristic analysis result of the target student, and integrating the knowledge point cognition level and the influence factor characteristic analysis result to obtain the learning characteristic analysis result of the target student;

the comprehensive learning characteristic diagnosis module is used for acquiring preset academic diagnosis items from the learning characteristic analysis results, and performing comprehensive learning characteristic diagnosis on the learning characteristic analysis results according to the academic diagnosis items to obtain a learning comprehensive result diagnosis report of the target students;

and the teaching intervention strategy generation module is used for identifying the learning difficulty factors of the target students from the learning comprehensive result diagnosis report and generating a teaching intervention strategy according to the learning difficulty factors.

In order to solve the above problem, the present invention also provides an electronic device, including:

a memory storing at least one computer program; and

and the processor executes the computer program stored in the memory to realize the teaching intervention strategy acquisition method based on big data analysis.

In order to solve the above problem, the present invention further provides a computer-readable storage medium, in which at least one computer program is stored, and the at least one computer program is executed by a processor in an electronic device to implement the teaching intervention strategy acquisition method based on big data analysis.

In the embodiment of the invention, the efficiency of subsequent teaching intervention is improved conveniently by collecting the score data of the target students and collecting the influence score factor data of the target students without manually collecting the data, and the knowledge point cognitive analysis is further carried out on the score data of the target students and the influence factor characteristic analysis is further carried out on the influence score factor data of the target students, so that more complete score level information is obtained, and the accuracy of the subsequent teaching intervention is improved conveniently; secondly, performing comprehensive diagnosis of the learning characteristics on the learning characteristic analysis result to obtain a learning comprehensive result diagnosis report, and excavating hidden information behind the student score representation data to further know academic barriers and difficulties encountered by students; and finally, learning difficulty factors of the target students are identified from the learning comprehensive result diagnosis report, and a teaching intervention strategy is generated according to the learning difficulty factors, so that the intellectualization of teaching can be realized, different intervention strategies can be provided for different students, and the efficiency and accuracy of teaching intervention are improved. Therefore, the teaching intervention strategy acquisition method, the device, the equipment and the storage medium based on big data analysis provided by the embodiment of the invention can improve the efficiency and the accuracy of teaching intervention.

Drawings

Fig. 1 is a schematic flowchart of a teaching intervention strategy acquisition method based on big data analysis according to an embodiment of the present invention;

fig. 2 is a detailed flowchart illustrating a step in a teaching intervention strategy acquisition method based on big data analysis according to an embodiment of the present invention;

fig. 3 is a schematic flowchart illustrating a step in a teaching intervention strategy acquisition method based on big data analysis according to an embodiment of the present invention in detail;

fig. 4 is a schematic block diagram of a teaching intervention strategy acquisition device based on big data analysis according to an embodiment of the present invention;

fig. 5 is a schematic diagram of an internal structure of an electronic device for implementing a teaching intervention policy obtaining method based on big data analysis according to an embodiment of the present invention;

the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

The embodiment of the invention provides a teaching intervention strategy acquisition method based on big data analysis. The execution subject of the teaching intervention strategy acquisition method based on big data analysis includes but is not limited to at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the application. In other words, the teaching intervention strategy acquisition method based on big data analysis may be executed by software or hardware installed in a terminal device or a server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.

Referring to fig. 1, a schematic flow diagram of a method for obtaining a teaching intervention policy based on big data analysis according to an embodiment of the present invention is shown, in the embodiment of the present invention, the method for obtaining a teaching intervention policy based on big data analysis includes:

s1, acquiring score data of target students and acquiring influence score factor data of the target students.

In the embodiment of the present invention, the target students include all students registered in the teaching management system. The result data refers to learning condition data of target students, and comprises course result data (such as a classroom test paper result, an operation result and the like) and result data (such as a middle examination paper, an end examination paper and the like), such as an operation result, a classroom test result, an examination result and the like; the score influencing factor data refers to data influencing the scores of target students, such as learning abilities of the students, academic levels of the students, living modes of the students, academic efficiency feelings, academic attributions, companion relations, teacher-student relations and the like.

According to the embodiment of the invention, by collecting the score data of the target students and collecting the influence score factor data of the target students, the data can be collected without manual work, the data collection efficiency is improved, and the subsequent improvement of the teaching intervention efficiency is facilitated.

As an embodiment of the present invention, the collecting the achievement data of the target student and the collecting the influence achievement factor data of the target student include:

collecting a test paper image set and a questionnaire image set of the target student, and respectively carrying out feature extraction on the test paper image set and the questionnaire image set by using a volume layer in a preset image recognition model to obtain a test paper feature image set and a questionnaire feature image set; respectively performing dimension reduction operation on the test paper feature image set and the questionnaire feature image set by using a pooling layer in the image recognition model to obtain a test paper dimension reduction image set and a questionnaire dimension reduction image set; and respectively outputting the test paper dimension reduction image set and the questionnaire dimension reduction image set by using an activation function in the image recognition model to obtain the achievement data and the influence learning factor data.

The system comprises a web crawler, an online exercise system, an online examination system, an intelligent review system or an intelligent photo system, wherein the web crawler can be used for acquiring a test paper image set from the online exercise system, the online examination system, the intelligent review paper system or the intelligent review paper system, and also can be used for acquiring scales or questionnaires filled by students from a teaching database to obtain a questionnaire image set; the test paper image set can comprise data such as on-line/off-line homework test papers or on-line/off-line examination test papers of students; the questionnaire image set can comprise data of student learning ability, student academic level, living mode, academic effectiveness sense, academic attribution, peer relationship, teacher-student relationship and the like of students.

In an embodiment of the present invention, the preset image recognition model may be a CNN or RNN neural network. The characteristic extraction is a characteristic image obtained by calculating pixel matrixes of the test paper image set and the questionnaire image set; performing pooling operation on the test paper feature image set and the questionnaire feature image set by using the pooling layer is to perform average region division on the feature images, and replace pixel values in a region with an average value of the sum of all pixel values in the region, so that the features extracted from the feature images are not lost and dimension reduction operation is performed.

Preferably, the activation function may be a ReLU function.

S2, performing knowledge point cognition analysis on the result data to obtain knowledge point cognition level of the target student, performing influence factor characteristic analysis on the influence result factor data by using a preset teaching analysis model to obtain an influence factor characteristic analysis result of the target student, and integrating the knowledge point cognition level and the influence factor characteristic analysis result to obtain a learning characteristic analysis result of the target student.

In the embodiment of the invention, the knowledge point cognitive level refers to the knowledge point condition mastered by a target student, wherein the knowledge point cognitive level comprises the overall cognitive level of the student and the cognitive level of the student on each subject; the analysis result of the influence factor characteristics is obtained by analyzing factors influencing the achievement of the target students, and the analysis is obtained by analyzing factors indirectly influencing the learning conditions of the target students, such as the living mode of the students, the effectiveness sense of the academic industry, the academic attribution, the companion relationship, the teacher-student relationship and the like.

In an embodiment of the present invention, the knowledge point cognitive analysis may be performed by using a preset analysis rule, where the preset analysis rule is a rule customized based on different analysis requirements, and specifically, the knowledge point cognitive analysis may be performed on a target student by using a student grade distribution rule, a student score segment distribution rule, a student score advance and retreat distribution rule, a distribution rule of knowledge points in test questions, and the like.

For example, the monthly exam score of a student a is 550 points, the end-of-term score is 480 points, the total score is 610 points, the student a is divided into a plurality of levels (such as A, B, C, D) according to a preset student score level division rule, the knowledge point cognitive level corresponding to each level is from high to low, namely a level one, a level two and a level three, if the end-of-term exam of the student a is divided into a level B according to the rule, the last exam is divided into a level A according to the rule, and the comparison between the end-of-term exam of the student a and the last exam is obviously stepped back. And then, according to the cognitive level of the knowledge points, the step back of the student a is diagnosed to be obviously reflected in which the cognitive level of the knowledge points is low, for example, the student a is horizontal three on the trigonometric function knowledge point of the subject, namely, the knowledge point of the student a is represented as a disadvantaged item of the student.

In an embodiment of the present invention, the preset teaching analysis model may be a DSMS model, and the model is mainly used for analyzing the influence score factor data from a student social attribute dimension (i.e., demographic Characteristics), a student learning environment dimension (i.e., S, support Characteristics), a student learning dynamic dimension (i.e., R, readiness Characteristics), and a student learning Strategy dimension (i.e., S, strategic Characteristics). The learning characteristic analysis result refers to an analysis result combining knowledge point cognition level and influence factor characteristic analysis result, namely the learning characteristic analysis result comprises a cognition level characteristic analysis result directly influencing the learning achievement of the student and a factor characteristic analysis result indirectly influencing the learning achievement of the student.

The embodiment of the invention obtains the knowledge point cognitive level of the target student by carrying out knowledge point cognitive analysis on the achievement data, obtains the influence factor characteristic analysis result of the target student by carrying out influence factor characteristic analysis on the influence achievement factor data by using a preset teaching analysis model, integrates the knowledge point cognitive level and the influence factor characteristic analysis result to obtain the learning characteristic analysis result of the target student, can obtain not only the direct factor directly influencing the achievement data, but also the factor characteristic indirectly influencing the achievement data to obtain more complete achievement characteristic information, and realizes the analysis on the influence achievement factor data from a plurality of different dimensions by using the teaching analysis model.

As an embodiment of the present invention, referring to fig. 2, the performing influence factor characteristic analysis on the influence achievement factor data by using a preset teaching analysis model to obtain an influence factor characteristic analysis result of the target student includes:

s21, analyzing the social attribute characteristics, the learning environment characteristics, the learning power characteristics and the learning psychological characteristics of the students of the factor data influencing the achievement by using the teaching analysis model;

and S22, summarizing the social attribute features of the students, the learning environment features of the students, the learning dynamic features of the students and the learning psychological features of the students to obtain the characteristic analysis result of the influence factors.

The social attribute Characteristics (i.e., demographic Characteristics) of the student can include physiological Characteristics (such as sex, age, physical health status, etc.) and social Characteristics (such as family and economic status, educational status, etc.) of the student; the student learning environment characteristics (i.e. Support characteristics) may include internal environment Support characteristics (e.g. learning experience, learning adaptability, learning behavior tendency, learning habit, etc.) and external environment Support characteristics (e.g. family conditions, teachers' conditions, learning time, etc.); the student learning dynamic characteristics (namely, the Readiness characteristics) may include original knowledge learning experience (such as knowledge level), internal learning motivation (such as learning objective, etc.), and external learning motivation (such as academic performance sense, academic attribution, etc.); the learning psychology Characteristics (i.e., strategic Characteristics) of the students may include learning cognitive strategies (e.g., cognitive psychology strategies) and learning resource management strategies (e.g., student effort levels).

And S3, acquiring preset academic diagnosis items from the learning characteristic analysis results, and carrying out comprehensive diagnosis on the learning characteristics of the learning characteristic analysis results according to the academic diagnosis items to obtain a learning comprehensive result diagnosis report of the target student.

In the embodiment of the invention, the study comprehensive achievement diagnosis report is a report describing student academic information conditions, and comprises study superiority information and study inferiority information expressed by each student in target students in factors directly influencing achievements and factors indirectly influencing achievements.

According to the embodiment of the invention, the learning characteristic analysis result is subjected to comprehensive diagnosis of the learning characteristic to obtain the diagnosis report of the comprehensive learning result, so that hidden information behind the student academic achievement representation data can be mined, and further the academic obstacles and difficulties encountered by students can be known.

As an embodiment of the present invention, referring to fig. 3, the performing a comprehensive diagnosis of learning features on the learning feature analysis result according to the academic diagnostic item to obtain a learning comprehensive result diagnosis report of the target student includes:

s31, acquiring project information corresponding to the academic diagnostic project from a learning feature analysis result, and extracting a project diagnostic grade to which the project information belongs;

s32, recognizing learning superior items and learning inferior items in the learning feature analysis results according to the item information and the item diagnosis levels;

and S33, integrating the project information, the project diagnosis level, the learning superior project and the learning inferior project according to a preset diagnosis report template and a preset report generating tool to obtain the learning comprehensive achievement diagnosis report.

The academic diagnosis items can comprise examination scores of each subject, job scores of each subject, score ranking, cognitive level, learning ability, academic attribution and the like; the item information is information obtained based on the diagnosis items, and may include a ranking corresponding to each subject examination score and score, a ranking corresponding to each subject work score and a cognitive level grade, and the like.

In the embodiment of the invention, the project diagnosis grades can comprise three grades of excellent, good and poor; when the project diagnosis level is excellent, the project is a superior project, when the project diagnosis level is good or not good, the project is a inferior project, and the project with the poor project level can be highlighted in the diagnosis report, so that the follow-up teaching intervention can be facilitated.

In an embodiment of the invention, if the academic diagnosis item is the examination score of each subject of the student, the study comprehensive result diagnosis report including the examination score of each subject is output, the study disadvantage items are displayed and marked in the study comprehensive result diagnosis report, and the information corresponding to all the study disadvantage items is described by adding characters at the end of the study comprehensive result diagnosis report.

In an optional implementation of the invention, the study comprehensive achievement diagnosis report can comprise one or more of school grade, subject, teacher, class, student and parent diagnosis reports, and particularly, for a school grade manager, a general teaching quality diagnosis report can be obtained, so that follow-up measures can be taken in time for problems, medicines can be given according to symptoms, and the problem can be solved quickly; for teachers, whether the setting of a teaching target is reasonable or not, whether the application of a teaching method and means is proper or not, whether key points and difficult points of teaching are completely covered or not can be known, the learning conditions and problems of students can be mastered, the follow-up adjustment of the teaching method is facilitated, the teaching measures are improved, the teaching level and the teaching efficiency are improved, and the actual problems in teaching are pertinently solved; for students, the information such as the score, the ranking, the knowledge point mastering condition and the like of each examination and homework can be consulted through the big data acquisition and analysis system, the self knowledge mastering condition including weak subject and knowledge blind spot can be known, meanwhile, the students can start from the relevant factors influencing the score, the follow-up targeted medication can be facilitated, and the comprehensive healthy development of the students can be promoted; for parents, the study condition and the development conditions of physical and mental aspects of children can be mastered and tracked at any time, the auxiliary function of families can be better played in the follow-up process, and the study of students can be promoted by cooperating with schools and teachers.

And S4, identifying the learning difficulty factors of the target students from the learning comprehensive result diagnosis report, and generating a teaching intervention strategy according to the learning difficulty factors.

In the embodiment of the invention, the student difficulty factor refers to the reason why a student has a certain learning disadvantage item, such as unstable attention, infirm subject foundation, immature teaching strategy, poor learning efficiency, low learning interest and the like. The teaching intervention strategy may be pre-set.

As an embodiment of the present invention, the generating a teaching intervention strategy according to the learning difficulty factor includes:

and determining the learning habits of the target students according to the learning difficulty factors, and combining a plurality of intervention means with the learning habits to generate a teaching intervention strategy of the target students, wherein the plurality of intervention means comprise at least two of platform intervention means, teacher intervention means and social intervention means.

According to the embodiment of the invention, the learning difficulty factors of the target students are identified from the learning comprehensive achievement diagnosis report, and the teaching intervention strategy is generated according to the learning difficulty factors, so that the teaching intellectualization can be realized, different intervention strategies can be provided for different students, and the teaching intervention efficiency and accuracy can be improved.

For example, when the learning difficulty factor is low learning efficiency, the learning habit is relatively poor by analyzing that the online reading time of the learning behavior does not reach 30 minutes, the homework is not completed on time or the number of times of answering in a classroom is zero, and the learning habit of the student is relatively poor, and an intervention strategy capable of improving the learning efficiency is made according to different learning habits.

In an embodiment of the present invention, the platform intervention means may include means for increasing resource interest, adjusting learning difficulty, learning tracking and early warning, and the like in a teaching system (such as a teaching electronic whiteboard); the teacher intervention means can comprise means such as promotion, reward, warning and punishment; the social intervention means is a social entity which considers the target students as having a cooperation and competition relationship, and the social intervention means can comprise means for promoting cooperation and competition among the students.

For example, the learning habit of the target student is a teaching intervention strategy that the attention of the classroom is not concentrated, the homework is not completed on time and the knowledge point is difficult to understand, and the learning tracking early warning means in the platform intervention means (for example, sending homework completion time early warning to the target student at regular time every day to remind the student to complete homework) and the learning difficulty means (for example, refining the learning target of the target student and repeatedly emphasizing the knowledge point) can be adjusted; the teacher intervention means comprises a praise and reward means (such as consciously encouraging target students to actively share the harvest of learning and helping the target students to establish learning confidence), and a warning and punishment means (such as timely warning in class patrol, punishment on students who do not finish homework on time and strengthening the active learning consciousness of the students); in the social intervention means, cooperation among students and competition means (such as arranging students who take progress in the same group to exchange learning experience and developing classroom answering activities) are promoted to be combined to serve as a teaching intervention strategy.

Further, after generating the teaching intervention strategy according to the learning difficulty factors, the method further comprises:

dividing the target students into different intervention groups according to the teaching intervention strategy, wherein the intervention groups comprise a first-level group to be intervened, a second-level group to be intervened and a third-level group to be intervened; and performing corresponding intervention operation according to the intervention group of the target student.

Wherein, the first-level group to be intervened comprises the learning disadvantage items which are mainly items indirectly influencing the learning achievement and the condition that the knowledge points of individual subjects are not mastered by the students with excellent learning achievement; the secondary to-be-intervened group comprises students with good learning scores in target students, and learning disadvantage items and learning advantage items of the students account for half of items directly influencing the learning scores and items indirectly influencing the learning scores; the three levels of groups to be intervened comprise students with learning scores in the downstream of target students, and learning disadvantage items are mainly items directly influencing the learning scores.

In one embodiment of the invention, for the first-level group to be intervened, the knowledge points which are difficult to understand can be made into related homework test questions aiming at the learning habits (such as the knowledge points which are difficult to understand) of part of students, and the inferior items can be pushed to test questions in a personalized manner for the students, so that the mastery of the knowledge points by the part of student groups is promoted; for the secondary group to be intervened, a learning group is established, and experimental and animation links are added in the classroom teaching content for intervention, so that the interest of students in the class is improved; for the three-level intervention category, the student individual can timely feed back the learning condition of the student by keeping contact with the student parents, supervise the student to complete the intervention, realize the joint supervision of the parents and the teacher, improve the learning condition of the student and improve the learning score of the student.

In the embodiment of the invention, the efficiency of subsequent teaching intervention is improved conveniently by collecting the score data of the target students and collecting the influence score factor data of the target students without manually collecting the data, and the knowledge point cognitive analysis is further carried out on the score data of the target students and the influence factor characteristic analysis is further carried out on the influence score factor data of the target students, so that more complete score level information is obtained, and the accuracy of the subsequent teaching intervention is improved conveniently; secondly, performing comprehensive diagnosis of the learning characteristics on the learning characteristic analysis result to obtain a learning comprehensive result diagnosis report, and excavating hidden information behind the student score representation data to further know academic barriers and difficulties encountered by students; and finally, learning difficulty factors of the target students are identified from the learning comprehensive result diagnosis report, and a teaching intervention strategy is generated according to the learning difficulty factors, so that the intellectualization of teaching can be realized, different intervention strategies can be provided for different students, and the efficiency and accuracy of teaching intervention are improved. Therefore, the teaching intervention strategy acquisition method based on big data analysis provided by the embodiment of the invention can improve the efficiency and accuracy of teaching intervention.

The teaching intervention strategy acquisition device 100 based on big data analysis can be installed in electronic equipment. According to the realized functions, the teaching intervention strategy acquisition device based on big data analysis may include a data acquisition module 101, a data analysis module 102, a comprehensive diagnosis module 103 for learning characteristics, and a teaching intervention strategy generation module 104, where the modules of the present invention may also be referred to as units, and refer to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, and are stored in a memory of the electronic device.

In the present embodiment, the functions regarding the respective modules/units are as follows:

the data acquisition module 101 is used for acquiring the score data of the target students and acquiring the influence score factor data of the target students.

In an embodiment of the present invention, the target students include all students registered in the teaching management system. The result data refers to learning condition data of target students, and comprises course result data (such as a classroom test paper result, an operation result and the like) and result data (such as a middle examination paper, an end examination paper and the like), such as an operation result, a classroom test result, an examination result and the like; the score influencing factor data refers to data influencing the scores of target students, such as learning abilities of the students, academic levels of the students, living modes of the students, academic effectiveness feelings, academic attributions, peer relations, teacher-student relations and the like.

As an embodiment of the present invention, the data collection module 101 collects the achievement data of the target student and collects the influence achievement factor data of the target student by performing the following operations, including:

Preferably, the activation function may be a ReLU function.

The data analysis module 102 is configured to perform knowledge point cognition analysis on the achievement data to obtain knowledge point cognition levels of the target students, perform influence factor feature analysis on the influence achievement factor data by using a preset teaching analysis model to obtain influence factor feature analysis results of the target students, and integrate the knowledge point cognition levels and the influence factor feature analysis results to obtain learning feature analysis results of the target students.

In the embodiment of the invention, the knowledge point cognitive level refers to the knowledge point condition mastered by a target student, wherein the knowledge point cognitive level comprises the overall cognitive level of the student and the cognitive level of the student on each subject; the analysis result of the influence factor characteristics is obtained by analyzing the factors influencing the achievement of the target student, and the analysis is obtained by analyzing the factors indirectly influencing the learning condition of the target student, such as the living mode of the student, the effectiveness sense of the academic industry, the attribution of the academic industry, the relationship of the fellow and the relationship of the teachers and students.

As an embodiment of the present invention, the data analysis module 102 performs influence factor characteristic analysis on the influence achievement factor data by using a preset teaching analysis model by performing the following operations, so as to obtain an influence factor characteristic analysis result of the target student, including:

and summarizing the student social attribute characteristics, the student learning environment characteristics, the student learning dynamic characteristics and the student learning psychological characteristics to obtain the influence factor characteristic analysis result.

The student social attribute Characteristics (i.e. Demographic Characteristics) may include physiological Characteristics (such as sex, age, physical health status, etc.) and social Characteristics (such as family and economic status, educational condition, etc.) of the student; the student learning environment characteristics (i.e. Support characteristics) may include internal environment Support characteristics (such as learning experience, learning adaptability, learning behavior tendency, learning habit, etc.) and external environment Support characteristics (such as family conditions, teacher and resource conditions, learning time, etc.); the student learning dynamic characteristics (namely, the Readiness characteristics) may include original knowledge learning experience (such as knowledge level), internal learning motivation (such as learning objective, etc.), and external learning motivation (such as academic performance sense, academic attribution, etc.); the learning psychology Characteristics (i.e., strategic Characteristics) of the students may include learning cognitive strategies (e.g., cognitive psychology strategies) and learning resource management strategies (e.g., student effort levels).

The learning characteristic comprehensive diagnosis module 103 is configured to obtain a preset academic diagnosis item from the learning characteristic analysis result, and perform learning characteristic comprehensive diagnosis on the learning characteristic analysis result according to the academic diagnosis item to obtain a learning comprehensive result diagnosis report of the target student.

In the embodiment of the invention, the study comprehensive achievement diagnosis report is a report for describing student academic information conditions, and comprises study superiority information and study inferiority information which are expressed by each student in target students in factors directly influencing achievements and factors indirectly influencing achievements.

As an embodiment of the present invention, the comprehensive diagnosis module 103 for learning characteristics performs comprehensive diagnosis of learning characteristics on the learning characteristic analysis result according to the academic diagnosis item by performing the following operations to obtain a learning comprehensive result diagnosis report of the target student, including:

acquiring project information corresponding to the academic diagnosis project from a learning feature analysis result, and extracting a project diagnosis grade to which the project information belongs;

The academic diagnosis items can comprise examination scores of each subject, job scores of each subject, score ranking, cognitive level, learning ability, academic attribution and the like; the item information is information obtained based on the diagnostic items, and may include the examination score and the corresponding rank of each subject, the job score of each subject and the corresponding rank and cognitive level grade, and the like.

In the embodiment of the invention, the project diagnosis grades can comprise three grades of excellent, good and poor; when the project diagnosis grade is excellent, the project is a dominant project, when the project diagnosis grade is good or not good, the project is a disadvantaged project, and the project with the poor project grade can be highlighted in the diagnosis report, so that the follow-up teaching intervention is facilitated.

The teaching intervention strategy generation module 104 is configured to identify a learning difficulty factor of the target student from the learning comprehensive result diagnosis report, and generate a teaching intervention strategy according to the learning difficulty factor.

In the embodiment of the invention, the student difficulty factor refers to the reason why a student has a certain learning disadvantage project, such as unstable attention, infirm subject foundation, immature teaching strategy, poor learning efficiency, low learning interest and the like. The teaching intervention strategy may be pre-set.

As an embodiment of the present invention, the teaching intervention strategy generating module 104 generates a teaching intervention strategy according to the learning difficulty factor by performing the following operations, including:

According to the embodiment of the invention, the learning difficulty factors of the target students are identified from the learning comprehensive result diagnosis report, and the teaching intervention strategy is generated according to the learning difficulty factors, so that the teaching intellectualization can be realized, different intervention strategies can be provided for different students, and the teaching intervention efficiency and accuracy are improved.

For example, the learning habit of the target student is a teaching intervention strategy that the attention of the classroom is not concentrated, the homework is not completed on time and the knowledge point is difficult to understand, and the learning tracking early warning means in the platform intervention means (for example, sending homework completion time early warning to the target student at regular time every day to remind the student to complete homework) and the learning difficulty means (for example, refining the learning target of the target student and repeatedly emphasizing the knowledge point) can be adjusted; a promotion and reward means in teacher intervention means (for example, target students are consciously encouraged to actively share the harvest of learning and help the target students to build learning confidence), and a warning and punishing means (for example, timely warning in class patrol, punishment of students who do not complete homework on time, and reinforcement of active learning consciousness of students); in the social intervention means, cooperation among students and competition means (such as arranging students who take progress in the same group to exchange learning experience and developing classroom answering activities) are promoted to be combined to serve as a teaching intervention strategy.

The instructional intervention policy generation module 104 is further operable to:

after a teaching intervention strategy is generated according to the learning difficulty factors, dividing the target students into different intervention groups according to the teaching intervention strategy, wherein the intervention groups comprise a first-level group to be intervened, a second-level group to be intervened and a third-level group to be intervened;

Wherein, the first-level group to be intervened comprises the learning disadvantage items which are mainly items indirectly influencing the learning achievement and the condition that the knowledge points of individual subjects are not mastered by the students with excellent learning achievement; the secondary group to be intervened comprises students with good learning scores in target students, and learning inferior items and learning superior items of the students account for half of items directly influencing the learning scores and items indirectly influencing the learning scores; the three levels of groups to be intervened comprise students with learning scores in the downstream of target students, and learning disadvantage items are mainly items directly influencing the learning scores.

In one embodiment of the invention, for the first-level group to be intervened, the knowledge points which are difficult to understand can be made into related homework test questions aiming at the learning habits (such as the knowledge points which are difficult to understand) of part of students, and the inferior items can be pushed to test questions in a personalized manner for the students, so that the mastery of the knowledge points by the part of student groups is promoted; for the group to be intervened in the second level, a learning group is established, and the intervention is performed by adding experiment and animation links in the classroom teaching content, so that the interest of the group in class hiss is improved; for the three-level intervention category, the student individual can timely feed back the learning condition of the student by keeping contact with the student parents, supervise the student to complete the intervention, realize the joint supervision of the parents and the teacher, improve the learning condition of the student and improve the learning score of the student.

In the embodiment of the invention, the efficiency of subsequent teaching intervention is improved conveniently by collecting the score data of the target students and collecting the influence score factor data of the target students without manually collecting the data, and the knowledge point cognitive analysis is further carried out on the score data of the target students and the influence factor characteristic analysis is further carried out on the influence score factor data of the target students, so that more complete score level information is obtained, and the accuracy of the subsequent teaching intervention is improved conveniently; secondly, performing comprehensive diagnosis on the learning characteristic analysis result to obtain a learning comprehensive result diagnosis report, and mining hidden information behind student score expression data to further understand academic obstacles and difficulties encountered by students; and finally, learning difficulty factors of the target students are identified from the learning comprehensive result diagnosis report, and a teaching intervention strategy is generated according to the learning difficulty factors, so that the intellectualization of teaching can be realized, different intervention strategies can be provided for different students, and the efficiency and accuracy of teaching intervention are improved. Therefore, the teaching intervention strategy acquisition device based on big data analysis provided by the embodiment of the invention can improve the efficiency and accuracy of teaching intervention.

Fig. 5 is a schematic structural diagram of an electronic device for implementing a teaching intervention policy obtaining method based on big data analysis according to the present invention.

The electronic device may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may further include a computer program stored in the memory 11 and executable on the processor 10, such as a teaching intervention strategy acquisition program based on big data analysis.

The memory 11 includes at least one type of media, which includes flash memory, removable hard disk, multimedia card, card type memory (e.g., SD or DX memory, etc.), magnetic memory, local disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as codes of a teaching intervention strategy acquisition program based on big data analysis, etc., but also to temporarily store data that has been output or will be output.

The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, teaching intervention strategy acquisition programs based on big data analysis, etc.) stored in the memory 11 and calling data stored in the memory 11.

The communication bus 12 may be a PerIPheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The communication bus 12 is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like. For ease of illustration, only one thick line is shown, but this is not intended to represent only one bus or type of bus.

Fig. 5 shows only an electronic device with components, and those skilled in the art will appreciate that the structure shown in fig. 5 does not constitute a limitation of the electronic device, and may include fewer or more components than shown, or some components may be combined, or a different arrangement of components.

For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.

Optionally, the communication interface 13 may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices.

Optionally, the communication interface 13 may further include a user interface, which may be a Display (Display), an input unit (such as a Keyboard (Keyboard)), and optionally, a standard wired interface, or a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.

It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.

The big data analysis-based teaching intervention strategy acquisition program stored in the memory 11 of the electronic device is a combination of a plurality of computer programs, and when running in the processor 10, can realize that:

acquiring preset academic diagnosis items from the learning characteristic analysis results, and performing learning characteristic comprehensive diagnosis on the learning characteristic analysis results according to the academic diagnosis items to obtain a learning comprehensive result diagnosis report of the target students;

Specifically, the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.

Further, the electronic device integrated module/unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a computer readable medium. The computer readable medium may be non-volatile or volatile. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM).

Embodiments of the present invention may also provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program may implement:

and identifying the learning difficulty factors of the target students from the learning comprehensive result diagnosis report, and generating a teaching intervention strategy according to the learning difficulty factors.

Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.

In the several embodiments provided in the present invention, it should be understood that the disclosed medium, apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.

The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.

The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a string of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, which is used for verifying the validity (anti-counterfeiting) of the information and generating a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.

Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A teaching intervention strategy acquisition method based on big data analysis is characterized by comprising the following steps:

2. The big data analysis-based teaching intervention strategy acquisition method of claim 1, wherein the performing influence factor characteristic analysis on the influence achievement factor data by using a preset teaching analysis model to obtain the influence factor characteristic analysis result of the target student comprises:

3. The big data analysis-based teaching intervention strategy acquisition method according to claim 1, wherein the comprehensive diagnosis of the learning characteristics is performed on the learning characteristic analysis result according to the academic diagnostic item, so as to obtain a learning comprehensive achievement diagnosis report of the objective student, and the method comprises the following steps:

4. The big data analysis-based teaching intervention strategy acquisition method according to claim 1, wherein the generating a teaching intervention strategy according to the learning difficulty factors comprises:

5. The big data analysis-based teaching intervention strategy acquisition method of any one of claims 1 to 4, wherein the collecting of achievement data of a target student and the collecting of achievement factor data of the target student comprise:

collecting a test paper image set and a questionnaire image set of the target student, and respectively performing feature extraction on the test paper image set and the questionnaire image set by utilizing a volume layer in a preset image recognition model to obtain a test paper feature image set and a questionnaire feature image set;

and respectively outputting the test paper dimension reduction image set and the questionnaire dimension reduction image set by using an activation function in the image recognition model to obtain the result data and the influence result factor data.

6. The big data analysis-based teaching intervention strategy acquisition method of any one of claims 1-4, wherein after generating a teaching intervention strategy according to the learning difficulty factors, the method further comprises:

7. A teaching intervention strategy acquisition device based on big data analysis is characterized in that the device comprises:

8. An electronic device, characterized in that the electronic device comprises:

at least one processor; and (c) a second step of,

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the big data analysis based instructional intervention strategy acquisition method of any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the big data analysis-based teaching intervention policy acquisition method according to any one of claims 1 to 6.