CN117689506A - Classroom data processing method, device, equipment and storage medium - Google Patents
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Abstract
The embodiment of the application discloses a classroom data processing method, a device, equipment and a storage medium. The method comprises the following steps: obtaining answer data of a plurality of students in a classroom, wherein the answer data are generated by the students according to self-adjustment and selection of test questions in the classroom; analyzing the answering data of the students to obtain answering options of each student; and comparing the answer options of each student with preset correct options to generate the classroom performance record and the corresponding learning data of each student according to the comparison result. According to the embodiment of the application, teachers can adjust teaching contents according to classroom performance records and generate learning data corresponding to each student, and according to comprehensive learning conditions of students, the teaching efficiency is improved, and the aim of accurate teaching is achieved.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to a classroom data processing method and apparatus, an electronic device, and a computer readable storage medium.
Background
Along with the progress of science and technology, for implementing the teaching reform, promote the integration of information technology and discipline, accelerate the conversion of novel teaching and academic modes, innovate teaching skills, promote the exchange and the study of information technology means between the teacher.
In daily classroom teaching, teachers often want to know how much knowledge students in this section have mastered at the bottom. Then we have to feed back, evaluate their mastery level by the student's correct rate of answering or doing questions. In a traditional classroom, a teacher walks around when a student makes a question, looks at the condition of their question, or calls several students to answer the question. The feedback mode can not enable teachers to timely and comprehensively know the mastering conditions of students, and part of students can answer the questions without thinking, answering the questions or 'following the wind', so that the improvement of the knowledge mastering conditions of the students in the class is a technical problem to be solved urgently.
Disclosure of Invention
In order to solve the technical problems, embodiments of the present application provide a classroom data processing method and apparatus, an electronic device, and a computer readable storage medium.
According to an aspect of an embodiment of the present application, there is provided a classroom data processing method, including: obtaining answer data of a plurality of students in a classroom, wherein the answer data are generated by the students according to self-adjustment and selection of test questions in the classroom; analyzing the answering data of the students to obtain answering options of each student; and comparing the answer options of each student with preset correct options to generate the classroom performance record and the corresponding learning data of each student according to the comparison result.
According to one aspect of the embodiment of the application, the answer data is a reversible two-dimensional code image; analyzing the answer data of the plurality of students to obtain answer options of each student, including: acquiring two-dimensional code images generated by the students according to the questions presented in the class; analyzing the two-dimensional code images corresponding to the students, and obtaining answer options of each student based on analysis results.
According to an aspect of the embodiments of the present application, the analyzing the two-dimensional code images corresponding to the plurality of students, and obtaining answer options of each student based on the analysis result, includes: positioning the two-dimensional code image, and performing image recognition processing on the positioned two-dimensional code image to obtain answer options corresponding to the two-dimensional code image, wherein the image recognition processing comprises: at least one processing mode of image enhancement processing, line segment approximation processing, polygon fitting algorithm and bump detection algorithm.
According to an aspect of the embodiments of the present application, before the obtaining answer data of a plurality of students in a classroom, where the answer data is generated by the students according to self-adjustment selection of questions posed in the classroom, the method further includes: establishing a teacher-student relationship corresponding to the class; acquiring teaching contents uploaded by teachers in the class, detection test questions and correct options corresponding to the detection test questions; and sending two-dimensional code images to each student in the class, wherein the two-dimensional code images can be expressed into different answer data according to different directions.
According to one aspect of embodiments of the present application, the method includes: in the teaching process, starting the detection test questions and acquiring two-dimensional code images corresponding to each student in the class; after the answer is finished, analyzing the two-dimensional code image corresponding to each student, and comparing the analysis result with a preset correct option to generate the classroom performance record and the corresponding learning data of each student according to the comparison result.
According to an aspect of the embodiment of the present application, the analyzing the two-dimensional code image corresponding to each student and comparing the analysis result with a preset correct option includes: and if the number of the detection test questions is multiple, after the answer of each detection test question is finished, analyzing the two-dimensional code image corresponding to each student, comparing the analysis result with the correct options preset for the detection test questions, and determining the accuracy corresponding to the detection test questions and the learning condition of the students according to the comparison result.
According to an aspect of the embodiment of the present application, the answer data includes an identity, and the analyzing the answer data of the plurality of students to obtain answer options of each student includes: analyzing identity marks corresponding to students in the answer data and answer options; binding the identity mark with the answer options to obtain the answer options corresponding to each student.
According to an aspect of an embodiment of the present application, there is provided a classroom data processing device including:
the system comprises an acquisition module, a test question generation module and a test question generation module, wherein the acquisition module is used for acquiring answer data of a plurality of students in a classroom, and the answer data is generated by the students according to self-adjustment and selection of test questions in the classroom; the analysis module is used for analyzing the answer data of the students to obtain answer options of each student; and the comparison module is used for comparing the answer options of each student with preset correct options so as to generate the classroom performance record and the corresponding learning data of each student according to the comparison result.
According to an aspect of an embodiment of the present application, there is provided an electronic device including: one or more processors; and a storage means for storing one or more programs which, when executed by the one or more processors, cause the electronic device to implement the classroom data processing method as described above.
According to an aspect of the embodiments of the present application, there is provided a computer-readable storage medium having stored thereon computer-readable instructions which, when executed by a processor of a computer, cause the computer to perform the classroom data processing method as described above.
In the technical scheme that this application provided, through obtaining the answer data that a plurality of students were adjusted by oneself according to the detection test questions on the classroom and are generated, with the answer option of going on analyzing to each student's answer data and obtaining each student in the class, avoided the incomplete to student's study condition understanding, the answer follow-up condition of student that avoids, and according to the classroom student's answer condition generation corresponding classroom performance record, so that the teacher can be according to classroom performance record adjustment teaching content, and generate the study data that each student corresponds, according to comprehensive study condition of knowing the student, be favorable to improving teaching efficiency, reach accurate teaching's target.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application. It is apparent that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art. In the drawings:
FIG. 1 is a schematic diagram of an implementation environment for classroom data processing during teaching in accordance with an exemplary embodiment of the present application;
FIG. 2 is a flow chart illustrating a classroom data processing method according to an exemplary embodiment of the present application;
fig. 3 is a schematic diagram of an answer data image shown in an exemplary embodiment of the present application;
FIG. 4 is a schematic diagram of an answer option A image shown in an exemplary embodiment;
FIG. 5 is a flow chart illustrating a classroom data processing method in accordance with another exemplary embodiment of the present application;
FIG. 6 is a flow chart illustrating a method of classroom data processing according to another exemplary embodiment of the present application;
FIG. 7 is a flow chart illustrating a method of classroom data processing according to another exemplary embodiment of the present application;
FIG. 8 is a schematic diagram showing an exemplary embodiment after the test questions are completed;
FIG. 9 is a flow chart illustrating a classroom data processing method according to another exemplary embodiment of the present application;
FIG. 10 is a schematic diagram showing binding of an identity with a student two-dimensional code image in accordance with an exemplary embodiment;
FIG. 11 is a block diagram of a classroom data processing device shown in an exemplary embodiment of the present application;
Fig. 12 shows a schematic diagram of a computer system suitable for use in implementing the electronic device of the embodiments of the present application.
Reference numerals:
an image acquisition device-110; server-120.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
The block diagrams depicted in the figures are merely functional entities and do not necessarily correspond to physically separate entities. That is, the functional entities may be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
Reference to "a plurality" in this application means two or more than two. "and/or" describes an association relationship of an association object, meaning that there may be three relationships, e.g., a and/or B may represent: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
First, in the new era of digital wisdom economy, lifelong learning has become a trend of human society. How to improve the learning efficiency is naturally a great importance in the learning process. The higher learning efficiency can lead the learner to have more harvest and achievement, thereby making greater contribution to the country and society.
In the phase of continuously acquiring knowledge, each student or each class of students has great difference in their learning routes, but the traditional learning way is the same learning route for all students at present, and the learning of all knowledge points by students is not uniform, for example, a the students have poor foundation, recommendation of some basic questions is required during arrangement work, b the students have good foundation, recommendation of some difficult questions is required during arrangement work, and after a period of time, in order to avoid the students forgetting knowledge, some former knowledge points must be repeated for the students. For students of different levels, different states and different classes, a learning route belonging to the students needs to be related. So that the learning efficiency of students is greatly improved.
In daily classroom teaching, teachers often want to know how much knowledge students in this section have mastered at the bottom. Then we have to feed back, evaluate their mastery level by the student's correct rate of answering or doing questions. In a traditional classroom, a teacher walks around when a student makes a question, looks at the condition of their question, or calls several students to answer the question. The feedback mode can not enable teachers to timely and comprehensively know mastering conditions of students, and part of students can answer questions without thinking, answering questions or following the wind, so that the learning conditions of the students in the class are required to be effectively tracked, and the learning conditions of each student and the teaching conditions of the teachers are obtained.
Fig. 1 is a schematic diagram of an implementation environment for collecting and processing data in a classroom according to an exemplary embodiment of the present application. As shown in fig. 1, in a teaching process, or in a classroom, answer data of a plurality of students aiming at detection test questions put forward in the classroom are obtained through a camera device 110 in the classroom, and then the camera device 110 sends the answer data of the plurality of students to a data processing end server 120, so that the answer data of the plurality of students are analyzed through the server 120 to obtain answer options of each student, and the answer options of each student are compared with preset correct options, so that classroom performance records of the classroom class and learning data corresponding to each student can be generated according to the comparison results, and further learning conditions of each student can be fully mastered.
The image capturing apparatus 110 shown in fig. 1 may be any terminal device supporting image capturing, such as a smart phone, a vehicle-mounted computer, a tablet computer, a notebook computer, or a wearable device, but is not limited thereto. The data processing end server 120 shown in fig. 1 may be, for example, an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or may be a cloud server that provides a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network service, cloud communication, a middleware service, a domain name service, a security service, a CDN (Content Delivery Network, a content delivery network), and basic cloud computing services such as big data and an artificial intelligence platform, which are not limited herein. The image pickup device 110 may communicate with the server 120 via a wireless network such as 3G (third generation mobile information technology), 4G (fourth generation mobile information technology), 5G (fifth generation mobile information technology), and the like, and this is not limited thereto.
In daily classroom teaching, teachers often want to know how much knowledge students in this section have mastered at the bottom. Then we have to feed back, evaluate their mastery level by the student's correct rate of answering or doing questions. In a traditional classroom, a teacher walks around when a student makes a question, looks at the condition of their question, or calls several students to answer the question. The feedback mode can not lead teachers to know the mastering conditions of students timely and comprehensively, and partial students can answer the questions without thinking, answering the questions or 'following the wind'.
The problems indicated above have general applicability in general-purpose classroom scenarios, and in order to solve these problems, embodiments of the present application respectively propose a classroom data processing method, a classroom data processing device, an electronic device, a computer-readable storage medium, and a computer program product, and these embodiments will be described in detail below.
Referring to fig. 2, fig. 2 is a flowchart illustrating a classroom data processing method according to an exemplary embodiment of the present application, which may be applied to the implementation environment shown in fig. 1 and executed by the server 120 in the implementation environment. It should be understood that the method may be adapted to other exemplary implementation environments and be specifically executed by devices in other implementation environments, and the implementation environments to which the method is adapted are not limited by the present embodiment.
As shown in fig. 2, in an exemplary embodiment, the specific implementation method of the classroom data processing method at least includes steps S210 to S230, which are described in detail as follows:
and S210, obtaining answer data of a plurality of students in a classroom, wherein the answer data is generated by the students according to self-adjustment and selection of detection test questions in the classroom.
Specifically, in a classroom, students can represent answers to test questions detected in the classroom through answer data or corresponding pictures recorded on electronic equipment in hands, and can collect the answer data made by the students through corresponding answer collecting devices or image collecting devices in the classroom.
Optionally, if the students express the answer data of the test questions detected in the classroom through the pictures, the answer data corresponding to each student can be respectively acquired through a camera or other image acquisition devices in the classroom. The answer data can be words; options such as "a", "B", "C", etc. are also possible.
Optionally, in some possible embodiments, the answer data may also be generated by the student according to the adjustment made by the teacher in the classroom to detect the answer sheet in the opponent of the test question, that is, the answer sheet in the student's hand may rotate in different schemes or different rotation angles, which represents different answers. As shown in fig. 3, wherein four different directions represent four different answers, for example north for a, east for B, south for C, and west for D; of course, in different application scenarios, the image not only has four directions, but also can be provided with a plurality of different directions to represent different answers according to actual needs.
Step S220, analyzing the answer data of the students to obtain answer options of each student.
Specifically, a plurality of students in a classroom are collected to answer test questions provided by teachers, answer data made by each student in the classroom are collected through an image collecting device in the classroom, and answer options corresponding to each student in the classroom can be obtained through analyzing the answer data.
Optionally, in the foregoing embodiment, if the student expresses the answer data of the test questions through the images, the answer options of the student may be determined according to the content expressed on the answer images by collecting and analyzing the answer image provided by each student in the education room.
For example, in some possible embodiments, if the answer provided by the student is expressed by a, B, C and D in the answer image provided by the student, the option may be determined as the answer option corresponding to the student by which option in the answer image provided by the student is up. If the "a" option of the four options is upward, as shown in fig. 4, the answer option made by the student is determined to be "a".
Optionally, in some realizable embodiments, different answer options may also be determined according to different directions presented in the answer image of the student, or the answer options corresponding to the student may be determined through characters in the answer image, or the like. In addition, in order to avoid the situation that students follow the wind mutually so far, resulting in inaccurate generated classroom performance records and learning data of the students, patterns with little pattern difference in four equally divided parts can be set as answer images, for example, answer patterns in four directions only have slight differences and are difficult to distinguish by naked eyes, and answer options corresponding to the students can be determined through an image recognition technology.
And step S230, comparing the answer options of each student with preset correct options to generate the classroom performance record and the corresponding learning data of each student according to the comparison result.
Specifically, in the embodiment, after the answer options of each student in the class are determined, the answer options corresponding to each student are compared with the preset correct options, so that the completion condition of each student on the test questions in the class of the current section can be obtained, and the corresponding class performance record and the learning data of each student in the class can be generated.
Optionally, comparing answer options of each student in the class aiming at the test questions proposed by the teacher in the class with preset correct options, so as to obtain the correct rate of the selected test questions, for example, the correct options of the test questions are 'C', 30 students are in the class, wherein the answers of 22 students aiming at the test questions are 'C', two students selecting 'A', 3 students selecting 'B', and 3 students selecting 'D', and then the corresponding detection results of the test questions can be generated, wherein a corresponding pie chart, a bar chart and the like can be generated and sent to terminal equipment corresponding to the teacher, and the terminal equipment corresponding to the test questions is stored at knowledge points corresponding to the test questions.
Then, on the one hand, according to the answer options of each student in the class for the test questions in the class, the corresponding test result in the class can be obtained, for example, the accuracy rate of the test question-1 is 75%; the accuracy of test question-2 was 62%, the accuracy of test question-3 was 72%. The accuracy of test question-n was 50%; therefore, a teacher can determine the knowledge mastering situation of students according to the accuracy of each test question according to the students in the class, further improve the teaching plan, and generate a corresponding review plan aiming at the knowledge points corresponding to the test questions with the accuracy lower than the preset accuracy threshold so as to consolidate the knowledge mastering situation of the students.
In another aspect, for each student in a class, corresponding learning data is generated for each completion condition of test questions for the student, for example, error question labeling is performed for knowledge points where the student frequently fails, the completion conditions of the student for different test questions are sent to corresponding terminal devices of parents, so that the parents know the learning conditions of the child in the school, and corresponding department teacher can teach the completion conditions of each student according to the material, so that the teaching quality is improved, and the knowledge grasping conditions of the students are more sufficient.
In addition, in some realizable embodiments, the teacher can readjust the situation of the equipping class according to the performance of each student on the classroom and the accuracy of the students on the test questions, so that the teaching efficiency is improved, and the aim of accurate teaching is achieved.
In the embodiment, the generated answer data are automatically adjusted by a plurality of students according to the detection test questions in the class, so that the answer options of each student in the class are obtained by analyzing the answer data of each student, incomplete learning condition knowledge of the students is avoided, the answering follow-up condition of the students is avoided, corresponding class performance records are generated according to the answering condition of the students in the class, teachers can adjust teaching according to the class performance records, learning data corresponding to each student are generated, learning conditions of the students are comprehensively known, teaching efficiency is improved, and the aim of accurate teaching is fulfilled.
Further, referring to fig. 5, in an exemplary embodiment provided in the present application, the answer data is a reversible two-dimensional code image, the specific implementation process of analyzing the answer data of the plurality of students to obtain answer options of each student may further include step S510 and step S520, which are described in detail below:
step S510, obtaining two-dimensional code images generated by the students according to the questions presented in the class;
and step S520, analyzing the two-dimensional code images corresponding to the students, and obtaining answer options of each student based on the analysis result.
Specifically, each student in a classroom can distribute a two-dimensional code image, wherein the two-dimensional code in the two-dimensional code image is partially the same, but the identification codes used for representing the identity information of the students are different, so that the students can start to detect test procedure through the direction of the two-dimensional code in the hands, for example, the teacher only displays one test question on the teaching terminal each time, each student makes corresponding selection through adjusting the two-dimensional code image in the hands according to own judgment, for example, when the student judges the test question selection A, the two-dimensional code in the hands can be adjusted to the direction of representing the item A, the remote distance acquisition of the two-dimensional code corresponding to each student in the classroom can be performed through the camera device in the classroom, answer options corresponding to each student are further identified, the answer options corresponding to each student are further compared with the preset correct options, thus the corresponding mastering conditions of the test questions are generated according to the comparison result, for each student in the test procedure can be sequentially recorded under the condition of judging the test question of each student, and the answer data corresponding to each test question in the test class can be recorded for each test question.
Optionally, in step S520, the collected two-dimensional code image corresponding to each student in the plurality of students is parsed, and the answer options corresponding to each student may be identified by identifying the two-dimensional code image corresponding to each student. After the answer is finished, analyzing the collected two-dimensional code image of each student to obtain answer options which are not corresponding to the student, for example, aiming at the test question-1, the answer option corresponding to the student-xx 1 is 'A', the answer option corresponding to the student-xx 2 is 'B', the answer option corresponding to the student-xx 3 is 'A' …, and the answer option corresponding to the student-xxn is 'C', so that detection data corresponding to the test question-1 is obtained; and similarly, the detection data corresponding to the rest of other detection test questions can be obtained, and then the classroom performance record of the current classroom is generated based on the detection data corresponding to each detection test question.
Optionally, in some possible embodiments, answer options corresponding to each test question by each student in the class and accuracy rate of each test question can be collected, and mastering degree of the student on a knowledge point corresponding to each test question can be recorded, so that learning situations of the student can be generated, a corresponding learning scheme can be formed for the student, and a corresponding teacher can comprehensively master knowledge learning situations of the student.
That is, the learning condition corresponding to each student can be generated according to the answering condition of the student in the classroom, so that the learning condition of the student is evaluated according to the accuracy of the students in the questions, the teaching quality is improved, and the aim of accurate teaching is achieved.
Further, based on the foregoing embodiment, in one exemplary embodiment provided in the present application, the specific implementation process of analyzing the two-dimensional code images corresponding to the plurality of students and obtaining the answer options of each student based on the analysis result may further include the following steps, which are described in detail below:
positioning the two-dimensional code image, and performing image recognition processing on the positioned two-dimensional code image to obtain answer options corresponding to the two-dimensional code image, wherein the image recognition processing comprises: at least one processing mode of image enhancement processing, line segment approximation processing, polygon fitting algorithm and bump detection algorithm.
Specifically, the two-dimensional code image can be positioned, wherein the image positioning method can be used for keeping the original image direction confirmation function and facilitating the image positioning under low resolution by taking three positioning graphs in the two-dimensional code image as effective positioners. Specifically, image recognition processing can be performed on the aligned two-dimensional code image so as to obtain corresponding answer options through the two-dimensional code image after image processing, wherein the performing image recognition on the two-dimensional code image comprises: at least one processing mode of image enhancement processing, line segment approximation processing, polygon fitting algorithm and bump detection algorithm. (image enhancement refers to the processing of degraded certain image features, such as edges, contours, contrast, etc., by some image processing method to improve the visual effect of the image, enhance the sharpness of the image, or highlight certain "useful" information in the image, compress other "useless" information, and convert the image into a form more suitable for human or computer analysis processing.
Among them, it should be noted that image enhancement can be classified into two types: spatial domain methods and frequency domain methods. The spatial domain method refers to the spatial domain, that is, the image itself, directly performs various linear or nonlinear operations on the image, and performs enhancement processing on the pixel gray value of the image. The frequency domain law is to treat an image as a two-dimensional signal in the transform domain of the image, and to perform signal enhancement based on a two-dimensional fourier transform on the image.
The space domain method is divided into two main types, namely point operation and template processing. The point operation is a processing method applied to a single pixel neighborhood and comprises the technologies of image gray level transformation, histogram correction and pseudo-color enhancement; template processing is a processing method acting on the pixel field and comprises the technologies of image smoothing, image sharpening and the like. Common methods for frequency domain methods include low-pass filtering, high-pass filtering, homomorphic filtering, and the like. )
The line segment approximation method is a method for detecting and approximating line segments in an image. This approach is typically used to simplify complex parts in the image for better analysis and processing. In implementing line segment approximations, it is often necessary to use an image processing library or toolkit, such as OpenCV. The image is first read and converted into a gray scale image. Edges in the image are then detected using a Canny edge detection algorithm. Next, a straight line in the image is detected using a hough transform algorithm, and the detected straight line is drawn onto the original image. Finally, the resulting image is displayed.
The Oxford (OTSU) binarization threshold algorithm is an algorithm widely applied in image segmentation, and the basic principle is to divide the gray value of an image into two parts, namely a background and a foreground, and determine the optimal threshold by calculating the maximization of the inter-class variance. The main advantage of this method is that it can automatically select the optimal threshold according to the gray distribution of the image, thereby effectively reducing the influence of color, light and background on image recognition. In the concrete implementation, the Ojin algorithm firstly divides the image into a background part and a foreground part according to the gray characteristic of the image. Then, the inter-class variance of the two parts is calculated. The inter-class variance is a measure of the uniformity of the gray distribution and its size reflects the difference between the two parts that make up the image. When a part of the foreground is divided into a background or a part of the background is divided into a foreground, the difference between the two parts becomes smaller. Thus, a segmentation that maximizes the inter-class variance means that the probability of misclassification is minimal. The algorithm has the advantages of simple and quick calculation, no influence of image brightness and contrast, and wide application in digital image processing.
In the step, any one or more image processing modes can be adopted to process the acquired images for expressing the answering data of the students so as to analyze and distinguish the answering data to be expressed by the students.
In the embodiment, the answer data are expressed by the two-dimensional code image, so that the cost input of using the electronic equipment is reduced, the following behavior among students is effectively avoided, the cost of technical realization is reduced, the teaching efficiency is improved, and the teaching quality is ensured.
Further, based on the above embodiment, referring to fig. 6, in one exemplary embodiment provided in the present application, before obtaining answer data of a plurality of students in a classroom, where the answer data is generated by the students according to self-adjustment selection of questions posed in the classroom, a specific implementation process of the classroom data processing method may further include steps S610 to S630, which are described in detail below:
step S610, establishing a teacher-student relationship corresponding to the class.
Specifically, a class relationship is established, where a class relationship may be established, that is, a class is established in the system, and an identity of each student in the class is stored in advance, for example, identity information of each student in the class is input into the established class relationship, and then information of each teacher in each family is input into the class relationship, so that a teacher can prepare lessons in the system.
For example, the class teacher-student relationship may be expressed as "five-grade class three", and if information of all students of "five-grade class three" is input, a corresponding family teacher is added to the class teacher-student relationship, and a corresponding group is generated.
Step S620, acquiring teaching contents uploaded by teachers in the class, detection questions and correct options.
The above embodiment is received, the teaching content, the test questions and the correct options corresponding to the test questions uploaded by the teacher in the class are obtained, wherein the corresponding teacher can prepare lessons based on the established relationship between the teacher and the student, and the teaching content, the test questions and the correct options corresponding to the test questions are uploaded in the corresponding teaching system, so that the teaching is performed based on the teaching content and each student in the class is tested based on the test questions in the teaching process.
Step S630, sending two-dimensional code images to each student in the class, where the two-dimensional code images may be represented as different answer data according to different directions.
Optionally, in some possible embodiments, the two-dimensional code image corresponding to each student in the class may be generated in advance, and the two-dimensional code image may be sent to the corresponding student, where in some possible embodiments, the two-dimensional code image may be made into a physical two-dimensional code image and distributed to each student in the class, and during the test of the test question, the student may represent the two-dimensional code image in the hand as different answer data according to different directions.
For example, the multiple answer data may be represented as corresponding multiple answer data according to the difference of the directions of the two-dimensional code images; the number of directions corresponding to the two-dimensional code image and the answer data corresponding to each direction can be defined according to the number of options corresponding to the detection test questions. In general, four directions of a two-dimensional code image represent four different options, for example: four directions of southeast and northwest represent four options of ABCD respectively, so students can answer test questions in the class by changing the directions of two-dimensional code images in hands.
In this embodiment, through the different answer options of direction expression of two-dimensional code, not only practiced thrift teaching cost, avoided the student to realize the reporting of answer option through electronic equipment, still avoided following the wind action between the student through the two-dimensional code, realized classroom detection purpose, guaranteed teaching quality.
Further, based on the above embodiment, referring to fig. 7, in one exemplary implementation provided in the present application, the specific implementation process of the above classroom data processing method may further include the following step S710 and step S720, which are described in detail below:
step S710, starting the detection test questions in the teaching process, and acquiring two-dimensional code images corresponding to each student in the class;
And step S720, after the answer is finished, analyzing the two-dimensional code image corresponding to each student, and comparing the analysis result with a preset correct option to generate the classroom performance record and the corresponding learning data of each student according to the comparison result.
Specifically, in the course of teaching by a teacher, the teacher can start the test questions on the display device corresponding to the classroom, or in some practical embodiments, the test questions can be written or spoken on the blackboard by the teacher, and then the student can answer the test questions through the two-dimensional code images in the hands, wherein the two-dimensional code images in each student hand in the classroom can be collected through the camera in the classroom.
After the answer is finished, analyzing the two-dimensional code image corresponding to each student, obtaining answer options corresponding to each student according to the analysis result, and comparing the answer options with preset correct options, so that class performance records of the class and corresponding learning data of each student in the class can be generated according to the comparison result.
In the embodiment, the learning data corresponding to each student in the class is obtained through analyzing the answer two-dimensional code provided by the student, so that the learning condition of each student can be determined according to the learning data of each student, thereby being capable of accurately teaching and improving the teaching quality.
Further, based on the foregoing embodiment, in one exemplary embodiment provided in the present application, the specific implementation process of analyzing the two-dimensional code image corresponding to each student and comparing the analysis result with a preset correct option may further include the following steps, which are described in detail below:
and if the number of the detection test questions is multiple, after the answer of each detection test question is finished, analyzing the two-dimensional code image corresponding to each student, comparing the analysis result with the correct options preset for the detection test questions, and determining the correct rate corresponding to the detection test questions and the learning condition of the students according to the comparison result.
Optionally, if the number of the test questions in the classroom is multiple, after the answer of each test question is finished, analyzing the two-dimensional code image corresponding to each student collected by the camera to obtain an answer option corresponding to each student, comparing the answer option with a preset correct option of the test question, as shown in fig. 8, obtaining the accuracy corresponding to the test question and the mastering condition of learning, and displaying the preparation rate corresponding to the test question on a terminal corresponding to teaching to finish the test question.
Optionally, in some implementable embodiments, if the number of the test questions in the class is multiple, after the answer of all test questions is finished, the two-dimensional code image corresponding to each student corresponding to each test question collected by the camera is analyzed, so as to obtain the accuracy corresponding to each test question and the learning condition corresponding to each student in the class, where the learning condition includes the test score corresponding to each student, the corresponding error question, the weak knowledge point, and the like, and the mastery degree of the future student on which knowledge point is predicted by the answer result of the student, so that the subsequent teaching of the teacher is convenient, and the accurate teaching is performed.
Optionally, in some possible embodiments, after the teacher issues the test questions during the testing along with the hall, each student in the classroom may express different options through different directions of the two-dimensional code images, so as to collect the two-dimensional code images provided by each student to determine answer options of each student, jin Jiner determines which students answer on the display device corresponding to the teacher, which students answer in error, generates a corresponding error question set for the students answering in error, and marks on knowledge points corresponding to the error questions, so as to generate a learning file corresponding to each student.
In this embodiment, after each test question is answered, the two-dimensional code image corresponding to each student is parsed, and the mastering degree of the student at which knowledge point is predicted according to the result of the student answer, so that the subsequent teaching of the teacher in accordance with the material is convenient, and the teaching is accurate.
Further, based on the above embodiment, referring to fig. 9, in one exemplary embodiment provided in the present application, the answer data includes an identity, the specific implementation process of analyzing the answer data of the plurality of students to obtain answer options of each student may further include a quotation step S910 and a step S920, which are described in detail below:
step S910, analyzing the identity mark and the answer options corresponding to the student in the answer data;
step S920, binding the identity mark with the answer options to obtain the answer options corresponding to each student.
Specifically, as shown in fig. 10, the identity identification area on the two-dimensional code image of each student may be scanned to obtain the identity identification corresponding to each student and the corresponding answer options, and the identity identification and the answer options are bound, so as to obtain the answer options corresponding to each student, for example, the identity identification of each student is determined as a number according to the identity identification area on the two-dimensional code: and 20110111, and binding the student name with the student number 20110111 and the option D by identifying the answer option area of the two-dimensional code image to obtain the corresponding answer option D.
Optionally, in some possible embodiments, the identity of each student in the class may be determined through a seat table in the class, and then the obtained answer data on the two-dimensional code image may be bound with the identities of the students recorded on the seat table, so as to obtain a learning record of each student.
Optionally, in order to realize accurate teaching and improve education quality, the students can customize personalized learning schemes according to the learning condition of each student recorded in each class, for example, weak points of the students, learning and mastering incomplete knowledge points, knowledge points frequently wrong by the students, weak disciplines of the students, deviant conditions of the students and the like, so that the learning condition of the students can be simulated by adopting a machine learning method.
Machine learning (MachineLearning, ML) is a multi-domain interdisciplinary involving multiple disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, and the like. It is specially studied how a computer simulates or implements learning behavior of a human to acquire new knowledge or skills, and reorganizes existing knowledge structures to continuously improve own performance. Among them, machine learning is the core of artificial intelligence, which is the fundamental approach for making computers intelligent, and is applied throughout various fields of artificial intelligence. Machine learning and deep learning typically include techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, induction learning, teaching learning, and the like.
Based on the strong learning capacity of machine learning, the machine learning process aiming at a large number of history tracks can be utilized to realize that a machine learning model learns to master incomplete knowledge points for weak points of students, knowledge points of frequent errors of the students, weak disciplines of the students and estimation of the learning condition of the students by the off-the-shelf condition of the students so as to ensure that the estimated learning condition of the students is more accurate and reliable. By way of example, the machine learning model may include a neural network-based supervisory model, such as a two-class machine learning model, which is trained by using a large number of historical trajectories, so that the machine learning model performs model parameter adjustment during the training process, and the adjusted model parameters have comprehensive predictive performance on all-round features such as weak points of students, learning and mastering incomplete knowledge points, knowledge points of frequent errors of students, weak disciplines of students, deviational situations of students, and the like.
In the embodiment, the learning data of the student is bound with the identity of the student, so that the learning condition of the student can be clearly obtained, and the mastering degree of the student at which knowledge point is predicted according to the answering result of the student, so that the personalized learning scheme is customized for the student, and the improvement of the learning score is facilitated.
Fig. 11 is a block diagram of a classroom data processing device shown in an exemplary embodiment of the present application. The apparatus may be applied to the implementation environment shown in fig. 1 and is specifically configured in the server 120. The apparatus may also be adapted to other exemplary implementation environments and may be specifically configured in other devices, and the present embodiment is not limited to the implementation environments to which the apparatus is adapted.
As shown in fig. 11, the classroom data processing device includes:
the obtaining module 1110 is configured to obtain answer data of a plurality of students in a classroom, where the answer data is generated by the students according to self-adjustment and selection of test questions in the classroom;
the parsing module 1120 is configured to parse the answer data of the plurality of students to obtain answer options of each student;
and the comparison module 1130 is configured to compare the answer options of each student with preset correct options, so as to generate the classroom performance record and the learning data corresponding to each student according to the comparison result.
According to an aspect of the embodiments of the present application, the parsing module 1120 further specifically includes:
the acquisition unit is used for acquiring two-dimensional code images generated by the students according to the problems in the class;
And the analysis unit is used for analyzing the two-dimensional code images corresponding to the students and obtaining answering options of each student based on analysis results.
According to an aspect of the embodiments of the present application, the analyzing unit is further specifically configured to locate the two-dimensional code image, and perform image recognition processing on the located two-dimensional code image to obtain answer options corresponding to the two-dimensional code image, where the image recognition processing includes: at least one processing mode of image enhancement processing, line segment approximation processing, polygon fitting algorithm and bump detection algorithm.
According to an aspect of the embodiments of the present application, the classroom data processing device further includes:
the building module is used for building a teacher-student relationship corresponding to the class;
the data acquisition module is used for acquiring teaching contents uploaded by teachers in the class, detection test questions and correct options corresponding to the detection test questions;
and the sending module is used for sending two-dimensional code images to each student in the class, wherein the two-dimensional code images can be expressed into different answer data according to different directions.
According to an aspect of the embodiments of the present application, the classroom data processing device may further include:
The two-dimensional code acquisition module is used for starting the detection test questions in the teaching process and acquiring two-dimensional code images corresponding to each student in the class;
and the result generation module is used for analyzing the two-dimensional code image corresponding to each student after the answer is finished, and comparing the analysis result with a preset correct option so as to generate the classroom performance record and the corresponding learning data of each student according to the comparison result.
According to an aspect of the embodiment of the present application, the comparison module is further specifically configured to analyze the two-dimensional code image corresponding to each student after the answer of each test question is finished if the test questions are multiple, compare the analysis result with the correct options preset for the test questions, and determine the accuracy corresponding to the test questions and the learning situation of the students according to the comparison result.
According to an aspect of the embodiments of the present application, the parsing module 1120 further specifically includes:
the identity analysis unit is used for analyzing the identity identification corresponding to the student and the answer options in the answer data;
and the binding unit is used for binding the identity mark with the answer options to obtain the answer options corresponding to each student.
It should be noted that, the classroom data processing device provided in the above embodiment and the classroom data processing method provided in the above embodiment belong to the same concept, and the specific manner in which each module and unit perform the operation has been described in detail in the method embodiment, which is not repeated here. In practical application, the classroom data processing device provided in the above embodiment may allocate the functions to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above, which is not limited herein.
The embodiment of the application also provides electronic equipment, which comprises: one or more processors; and a storage device for storing one or more programs, which when executed by the one or more processors, cause the electronic device to implement the classroom data processing method provided in each of the above embodiments.
Fig. 12 shows a schematic diagram of a computer system suitable for use in implementing the electronic device of the embodiments of the present application. It should be noted that, the computer system 1200 of the electronic device shown in fig. 12 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present application.
As shown in fig. 12, the computer system 1200 includes a central processing unit (CentralProcessingUnit, CPU) 1201, which can perform various appropriate actions and processes, such as performing the methods described in the above embodiments, according to a program stored in a Read-only memory (ROM) 1202 or a program loaded from a storage section 1208 into a random access memory (RandomAccessMemory, RAM) 1203. In the RAM1203, various programs and data required for the system operation are also stored. The CPU1201, ROM1202, and RAM1203 are connected to each other through a bus 1204. An Input/Output (I/O) interface 1205 is also connected to bus 1204.
The following components are connected to the I/O interface 1205: an input section 1206 including a keyboard, a mouse, and the like; an output portion 1207 including a cathode ray tube (CathodeRayTube, CRT), a liquid crystal display (LiquidCrystalDisplay, LCD), and the like, a speaker, and the like; a storage section 1208 including a hard disk or the like; and a communication section 1209 including a network interface card such as a LAN (local area network) card, a modem, or the like. The communication section 1209 performs communication processing via a network such as the internet. The drive 1210 is also connected to the I/O interface 1205 as needed. A removable medium 1211 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on the drive 1210 so that a computer program read out therefrom is installed into the storage section 1208 as needed.
In particular, according to embodiments of the present application, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication portion 1209, and/or installed from the removable media 1211. When executed by a Central Processing Unit (CPU) 1201, performs the various functions defined in the system of the present application.
It should be noted that, the computer readable medium shown in the embodiments of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium may be, for example, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (ErasableProgrammableReadOnlyMemory, EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with a computer-readable computer program embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. A computer program embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Where each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented by means of software, or may be implemented by means of hardware, and the described units may also be provided in a processor. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.
Another aspect of the present application also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a classroom data processing method as described above. The computer-readable storage medium may be included in the electronic device described in the above embodiment or may exist alone without being incorporated in the electronic device.
Another aspect of the present application also provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the classroom data processing method provided in the above-described respective embodiments.
The foregoing is merely a preferred exemplary embodiment of the present application and is not intended to limit the embodiments of the present application, and those skilled in the art may make various changes and modifications according to the main concept and spirit of the present application, so that the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A classroom data processing method, comprising:
obtaining answer data of a plurality of students in a classroom, wherein the answer data are generated by the students according to self-adjustment and selection of test questions in the classroom;
analyzing the answering data of the students to obtain answering options of each student;
and comparing the answer options of each student with preset correct options to generate the classroom performance record and the corresponding learning data of each student according to the comparison result.
2. The classroom data processing method according to claim 1, wherein the answer data is a reversible two-dimensional code image; analyzing the answer data of the plurality of students to obtain answer options of each student, including:
acquiring two-dimensional code images generated by the students according to the questions presented in the class;
analyzing the two-dimensional code images corresponding to the students, and obtaining answer options of each student based on analysis results.
3. The classroom data processing method according to claim 2, wherein the analyzing the two-dimensional code images corresponding to the plurality of students, and obtaining answer options of each student based on the analysis result, includes:
Positioning the two-dimensional code image, and performing image recognition processing on the positioned two-dimensional code image to obtain answer options corresponding to the two-dimensional code image, wherein the image recognition processing comprises: at least one processing mode of image enhancement processing, line segment approximation processing, polygon fitting algorithm and bump detection algorithm.
4. The classroom data processing method as in claim 2 wherein prior to said obtaining answering data for a plurality of students in a classroom, said answering data being generated by said students in response to self-adjusting selections of questions posed in the classroom, said method further comprises:
establishing a teacher-student relationship corresponding to the class;
acquiring teaching contents uploaded by teachers in the class, detection test questions and correct options corresponding to the detection test questions;
and sending two-dimensional code images to each student in the class, wherein the two-dimensional code images can be expressed into different answer data according to different directions.
5. The classroom data processing method as in claim 4 wherein said method includes:
in the teaching process, starting the detection test questions and acquiring two-dimensional code images corresponding to each student in the class;
After the answer is finished, analyzing the two-dimensional code image corresponding to each student, and comparing the analysis result with a preset correct option to generate the classroom performance record and the corresponding learning data of each student according to the comparison result.
6. The classroom data processing method according to claim 5, wherein the analyzing the two-dimensional code image corresponding to each student and comparing the analysis result with a preset correct option comprises:
and if the number of the detection test questions is multiple, after the answer of each detection test question is finished, analyzing the two-dimensional code image corresponding to each student, comparing the analysis result with the correct options preset for the detection test questions, and determining the accuracy corresponding to the detection test questions and the learning condition of the students according to the comparison result.
7. The classroom data processing method as in claim 1 wherein the answer data includes an identification, and wherein the analyzing the answer data of the plurality of students to obtain answer options for each student includes:
analyzing identity marks corresponding to students in the answer data and answer options;
Binding the identity mark with the answer options to obtain the answer options corresponding to each student.
8. A classroom data processing device, the device comprising:
the system comprises an acquisition module, a test question generation module and a test question generation module, wherein the acquisition module is used for acquiring answer data of a plurality of students in a classroom, and the answer data is generated by the students according to self-adjustment and selection of test questions in the classroom;
the analysis module is used for analyzing the answer data of the students to obtain answer options of each student;
and the comparison module is used for comparing the answer options of each student with preset correct options so as to generate the classroom performance record and the corresponding learning data of each student according to the comparison result.
9. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs which when executed by the one or more processors cause the electronic device to implement the classroom data processing method of any one of claims 1-7.
10. A computer readable storage medium having stored thereon computer readable instructions which, when executed by a processor of a computer, cause the computer to perform the classroom data processing method of any one of claims 1-7.
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