CN112990848A - Classroom teaching analysis and evaluation system and method based on big data - Google Patents
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Abstract
The invention discloses a classroom teaching analysis and evaluation system based on big data, which comprises a management machine, a student tablet computer, an attendance device, a touch interaction all-in-one machine, an environment detection device, a video monitoring device and a recording and broadcasting device, wherein a data storage module, a big data analysis module and a data statistics module are arranged in the management machine, the analysis of the teaching big data is carried out through an analysis model of the big data analysis module, and the data statistics module carries out data statistics and visual display. By adopting the classroom teaching analysis and evaluation system based on big data and the evaluation method based on the system, the data acquisition in the classroom teaching process is realized, and students can evaluate and teach on line after class and check the evaluation and teaching result; a teacher checks the acquired data through a terminal to know the class condition, and meanwhile, accurate supervision is realized to help the teacher to improve teaching; a manager visually knows the teaching condition through visual display of data, and provides support for scientific evaluation of teaching quality and objective and fair treatment of teaching accidents.
Description
Technical Field
The invention relates to the technical field of educational data analysis, in particular to a classroom teaching analysis evaluation system and method based on big data.
Background
In the traditional education environment, the main methods for learning about the classroom situation are questionnaire survey, classroom behavior observation, examination, homework analysis and the like. The methods have the defects of long time consumption, inaccurate data, missing or unavailable acquisition of process data and the like, and the analysis result obtained by establishing the incomplete data can only reveal certain specific problems and is lack of comprehensiveness. In addition, data from different sources are difficult to integrate, and due to the collection cost and other reasons, the obtained data lack continuity, so that the information connection hidden in the data is split. Such as mining of existing relationships between student examination levels and student classroom learning activities; the analysis of the influence of the reading ability of students on the performance of the mathematical disciplines is difficult to realize. Therefore, teaching problems can be only processed according to experience, which causes adverse effects on scientifically and accurately understanding students, making teaching decisions and even making education policies. A large amount of effective information is wasted in the processing process, and an educator cannot accurately obtain an evaluation result, so that the adjustment of the teaching content cannot completely adapt to the requirements of students, the teaching cannot be improved in real time, and a scientific basis cannot be provided for improving the teaching level. Education evaluation refers to a process of judging the value of education on the basis of systematically, scientifically and comprehensively collecting, collating, processing and analyzing education information. Educational evaluation is to let us know better about students, examine our classroom and teaching process. The subjectivity and the randomness in the classroom teaching evaluation are generated, so that a classroom teaching analysis evaluation system based on big data, which has the advantages of simple structure, higher accuracy and superior performance, is urgently needed.
Disclosure of Invention
The invention aims to provide a classroom teaching analysis and evaluation system based on big data and an evaluation method based on the system, which realize data acquisition in the classroom teaching process, visually know the teaching condition and provide support for scientifically evaluating the teaching quality and objectively and fairly handling teaching accidents.
In order to achieve the purpose, the invention provides a classroom teaching analysis and evaluation system based on big data, which comprises a management machine, a student tablet computer, an attendance device, a touch interaction all-in-one machine, an environment detection device, a video monitoring device and a recording and broadcasting device, wherein a data storage module, a big data analysis module and a data statistics module are arranged in the management machine, the student tablet computer, the attendance device, the touch interaction all-in-one machine, the environment detection device, the video monitoring device and the recording and broadcasting device upload collected data to the data storage module in the management machine, the stored data are analyzed through an analysis model of the big data analysis module, and the analyzed data are subjected to data statistics and visual display through the data statistics module.
Preferably, the attendance device comprises a wireless radio frequency attendance machine and a pressure sensor arranged on the seat, the wireless radio frequency attendance machine is identified by a radio frequency card of a student, the pressure sensor is used for detecting whether the corresponding student is seated within a specified time, and the pressure sensor is communicated with the wireless radio frequency attendance machine through a wireless module.
Preferably, environment detection device includes controller, temperature and humidity sensor, carbon dioxide sensor and PM2.5 sensor, temperature and humidity sensor the carbon dioxide sensor with the PM2.5 sensor all is connected with the controller, the controller communicates with air conditioner, exhaust fan and air purifier in the classroom mutually.
A classroom teaching analysis and evaluation method based on big data comprises the following steps,
step S1: the management machine acquires data of each device in a classroom and stores the data in a data storage module;
step S2: analyzing the stored data by the aid of an analysis model of the big data analysis module;
step S3: and the analyzed data is subjected to data statistics and visual display through a data statistics module.
Furthermore, the acquired data comprises student tablet computer data, attendance data, teaching data, student achievement data, environment data and video data, and the student tablet computer data comprises assessment and education information, browsing information, answer information and classroom interaction information; teaching data are obtained through the touch interactive all-in-one machine and the recording and playing device and comprise teaching contents and teaching videos, and student score data teachers enter the teaching data through terminals.
Further, when the pressure detected by the pressure sensor is smaller than a set value within a set time, timing is started, when the pressure is not detected again within an overtime period and is larger than the set value, the wireless radio frequency attendance machine is recorded as normal, when the timing time exceeds the set time, the wireless radio frequency attendance machine is recorded as abnormal and is communicated with the management machine, and the management machine records videos of abnormal positions by controlling the video monitoring device.
Further, step S2 is specifically to establish a student achievement factor analysis model, a student achievement prediction model and a learning condition analysis model according to the structured teaching data, and establish a student classification model according to the structured feature data;
and analyzing the big teaching data according to the student achievement factor analysis model, the student achievement prediction model, the learning condition analysis model and the student classification model.
Further, the visual display includes a graph analysis and a trend curve analysis.
Therefore, the classroom teaching analysis and evaluation system based on the big data and the evaluation method based on the system with the structure are adopted, so that data acquisition in the classroom teaching process is realized, students can evaluate and teach on line after class, and evaluation and teaching results are checked; a teacher checks the acquired data through a terminal to know the class condition, and meanwhile, accurate supervision is realized to help the teacher to improve teaching; a manager visually knows the teaching condition through visual display of data, and provides support for scientific evaluation of teaching quality and objective and fair treatment of teaching accidents.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
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Fig. 1 is a schematic structural diagram of a classroom teaching analysis and evaluation system based on big data.
Detailed Description
Examples
FIG. 1 is a schematic structural diagram of a classroom teaching analysis and evaluation system based on big data according to the present invention, as shown in the figure, the classroom teaching analysis and evaluation system based on big data comprises a management machine, a student tablet computer, an attendance device, a touch interaction all-in-one machine, an environment detection device, a video monitoring device and a recording and broadcasting device, the management machine is internally provided with a data storage module, a big data analysis module and a data statistics module, the student tablet personal computer, the attendance checking device, the touch interaction all-in-one machine, the environment detection device, the video monitoring device and the recording and playing device upload the acquired data to a data storage module in a management machine, the stored data is analyzed through an analysis model of the big data analysis module, and the analyzed data is subjected to data statistics and visual display through the data statistics module. The attendance checking device comprises a wireless radio frequency attendance checking machine and a pressure sensor arranged on a seat, the wireless radio frequency attendance checking machine is identified with a radio frequency card of a student, the pressure sensor is used for detecting whether the corresponding student is seated in a specified time, the pressure sensor is communicated with the wireless radio frequency attendance checking machine through a wireless module, timing is started when the pressure detected by the pressure sensor in the specified time is smaller than a set value, the wireless radio frequency attendance checking machine is recorded as normal when the pressure is detected again when the pressure is not overtime and is larger than the set value, the wireless radio frequency attendance checking machine is recorded as abnormal when the timing time exceeds the set time and is communicated with a management machine, the management machine records abnormal position video by controlling a video monitoring device, and the attendance checking accuracy is greatly improved. The environment detection device comprises a controller, a temperature and humidity sensor, a carbon dioxide sensor and a PM2.5 sensor, wherein the temperature and humidity sensor is connected with the carbon dioxide sensor and the PM2.5 sensor, and the controller is communicated with an air conditioner, an exhaust fan and an air purifier in the classroom to ensure that the environment in the intelligent classroom keeps suitable.
A classroom teaching analysis and evaluation method based on big data comprises the following steps,
step S1: the management machine acquires data of each device in the classroom and stores the data in the data storage module. The acquired data comprises student tablet computer data, attendance data, teaching data, student score data, environment data and video screen data, and the student tablet computer data comprises assessment and education information, browsing information, answer information and classroom interaction information; teaching data are obtained through the touch interactive all-in-one machine and the recording and playing device and comprise teaching contents and teaching videos, and student score data teachers enter the teaching data through terminals.
Step S2: and analyzing the stored data according to the analysis model of the big data analysis module. Establishing a student achievement factor analysis model, a student achievement prediction model and a learning condition analysis model according to the structured teaching data, and establishing a student classification model according to the structured feature data;
and analyzing the big teaching data according to the student achievement factor analysis model, the student achievement prediction model, the learning condition analysis model and the student classification model.
The student achievement factor analysis model has data characteristics including test achievement, answer duration, checking and analyzing data, classroom question/answer times, browsing/browsed times, evaluation and education times and the like. And (3) mining strong association rules by adopting a big data analysis algorithm to obtain the relation between data characteristics, such as the direct proportion of classroom question/answer times to test results.
The student achievement prediction model has the same data characteristics as the student achievement factor analysis model, firstly, all the characteristics of data are standardized and normalized, and as students learn in a progressive process, the achievement at the time t can be considered to be influenced by the achievement time sequence before the time t.
The learning condition analysis model can perform cluster analysis on scores and data characteristics in the data characteristics, classifies students and facilitates the later-period specific student group to perform the education according to the factors.
Step S3: and the analyzed data is subjected to data statistics and visual display through a data statistics module. The visual display comprises chart analysis and change trend curve analysis, so that teachers can conveniently check teaching effects, attendance conditions, differences with other teachers and the like, and classrooms can conveniently improve teaching. The supervision expert can collect electronic evidences in real time, so that accurate supervision is realized, and a teacher is helped to improve teaching; leaders and management departments of schools and colleges can know teaching conditions through data statistics and chart analysis, scientifically and objectively evaluate teaching quality, analyze variation trend of the teaching conditions, and provide support for advanced exterior recognition, backward whip, unqualified elimination and objective fair treatment of teaching accidents.
Therefore, the classroom teaching analysis and evaluation system based on the big data and the evaluation method based on the system with the structure are adopted, so that data acquisition in the classroom teaching process is realized, students can evaluate and teach on line after class, and evaluation and teaching results are checked; a teacher checks the acquired data through a terminal to know the class condition, and meanwhile, accurate supervision is realized to help the teacher to improve teaching; a manager visually knows the teaching condition through visual display of data, and provides support for scientific evaluation of teaching quality and objective and fair treatment of teaching accidents.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the invention without departing from the spirit and scope of the invention.
Claims (8)
1. The classroom teaching analysis and evaluation system based on big data is characterized in that: including supervisor, student's panel computer, attendance device, the interactive all-in-one of touch, environment detection device, video monitoring device and recorded broadcast device, be provided with data storage module, big data analysis module and data statistics module in the supervisor, student's panel computer the attendance device the interactive all-in-one of touch the environment detection device video monitoring device and the recorded broadcast device is uploaded the data of gathering to the data storage module in the supervisor in, and the analysis of big data of teaching is carried out through big data analysis module's analysis model to the stored data, and the data after the analysis carry out data statistics and visual display through data statistics module.
2. The big-data-based classroom teaching analysis and evaluation system according to claim 1, wherein: the attendance checking device comprises a wireless radio frequency attendance checking machine and a pressure sensor arranged on a seat, the wireless radio frequency attendance checking machine is identified by a radio frequency card of a student, the pressure sensor is used for detecting whether the corresponding student is seated within a specified time, and the pressure sensor is communicated with the wireless radio frequency attendance checking machine through a wireless module.
3. The big-data-based classroom teaching analysis and evaluation system according to claim 1, wherein: the environment detection device comprises a controller, a temperature and humidity sensor, a carbon dioxide sensor and a PM2.5 sensor, wherein the temperature and humidity sensor, the carbon dioxide sensor and the PM2.5 sensor are connected with the controller, and the controller is in communication with an air conditioner, an exhaust fan and an air purifier in a classroom.
4. A big data-based classroom teaching analysis and evaluation method based on any one of claims 1-3, characterized in that: the specific steps are as follows,
step S1: the management machine acquires data of each device in a classroom and stores the data in a data storage module;
step S2: analyzing the stored data by the aid of an analysis model of the big data analysis module;
step S3: and the analyzed data is subjected to data statistics and visual display through a data statistics module.
5. The big-data-based classroom teaching analysis and evaluation method according to claim 4, wherein the big-data-based classroom teaching analysis and evaluation method comprises the following steps: the acquired data comprises student tablet computer data, attendance data, teaching data, student score data, environment data and video screen data, and the student tablet computer data comprises assessment and education information, browsing information, answer information and classroom interaction information; teaching data are obtained through the touch interactive all-in-one machine and the recording and playing device and comprise teaching contents and teaching videos, and student score data teachers enter the teaching data through terminals.
6. The big-data-based classroom teaching analysis and evaluation method according to claim 5, wherein: and when the timing time exceeds the set time, the wireless radio frequency attendance machine is recorded as abnormal and is communicated with the management machine, and the management machine records the video of the abnormal position by controlling the video monitoring device.
7. The big-data-based classroom teaching analysis and evaluation method according to claim 6, wherein: step S2 is specifically that a student achievement factor analysis model, a student achievement prediction model and a learning condition analysis model are established according to the structured teaching data, and a student classification model is established according to the structured feature data;
and analyzing the big teaching data according to the student achievement factor analysis model, the student achievement prediction model, the learning condition analysis model and the student classification model.
8. The big-data-based classroom teaching analysis and evaluation method according to claim 7, wherein: the visual display comprises chart analysis and variation trend curve analysis.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113487213A (en) * | 2021-07-20 | 2021-10-08 | 贵州大学 | Vocational education teaching evaluation method based on big data |
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| CN111242515A (en) * | 2020-03-05 | 2020-06-05 | 长沙师范学院 | Classroom teaching quality evaluation system and method based on education big data |
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| CN107316261A (en) * | 2017-07-10 | 2017-11-03 | 湖北科技学院 | A kind of Evaluation System for Teaching Quality based on human face analysis |
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