CN112990687A - Teaching quality management method and system based on text analysis and face recognition - Google Patents
Teaching quality management method and system based on text analysis and face recognition Download PDFInfo
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Abstract
The invention provides a teaching quality management method and a system based on text analysis and face recognition, wherein the method comprises a text collection step, a text analysis step and a face recognition step, wherein the text collection step is used for collecting preset text information for classroom teaching and storing the preset text information into a database; a voice collection step, namely collecting voice data in the classroom teaching scene and storing the voice data in the database; and a first evaluation step of comparing the voice data with the preset text information and evaluating the classroom teaching based on a first preset standard according to the similarity of the comparison. The invention solves the problem of strong subjectivity when the classroom teaching evaluation is carried out by the prior art scheme.
Description
Technical Field
The invention belongs to the field of data analysis, and particularly relates to a teaching quality management method and system based on text analysis and face recognition.
Background
Currently, a classroom is the most basic place for implementing school education, and classroom teaching is the most basic teaching activity of school education. The classroom teaching quality level is a direct factor influencing the culture quality of talents. The existing classroom teaching work is mainly that any teacher subjectively decides a teaching progress plan and teaching contents, so that the classroom teaching quality of the teacher is reasonably and timely evaluated from the perspective of a third party and the classroom teaching work of the teacher is interfered difficultly.
Disclosure of Invention
The embodiment of the application provides a teaching quality management method and system based on text analysis and face recognition, and aims to at least solve the problem of strong subjectivity in classroom teaching evaluation in the prior art.
In a first aspect, an embodiment of the present application provides a teaching quality management method based on text analysis and face recognition, including: a text collection step, collecting preset text information for classroom teaching, and storing the preset text information into a database; a voice collection step, namely collecting voice data in the classroom teaching scene and storing the voice data in the database; and a first evaluation step of comparing the voice data with the preset text information and evaluating the classroom teaching based on a first preset standard according to the similarity of the comparison.
Preferably, the voice collecting step includes collecting the voice data by a wearable voice collecting device, and transmitting and storing the voice data to the database.
Preferably, the first evaluation step includes: a voice conversion step, using an ASR engine to convert the voice data into classroom text information; a text processing step of performing natural language processing on the preset text information and the classroom text information; and an information comparison step, namely comparing the classroom text information with the preset text information according to the result of the natural language processing.
Preferably, the text processing step includes performing word segmentation on the preset text information and the classroom text information by using a word segmentation algorithm to obtain word segmentation results of the preset text information and the classroom text information.
Preferably, the text processing step further includes obtaining word vectors of the preset text information word segmentation result and the classroom text information word segmentation result by using a word vector representation algorithm.
Preferably, the information comparison step includes performing text similarity calculation using a trained semantic matching model according to the preset text information and the word vectors of the classroom text information.
Preferably, the method further comprises a second evaluation step of evaluating the classroom teaching based on a second preset standard according to the time information of the classroom teaching.
Preferably, the method further comprises a third evaluation step of collecting face information in the classroom teaching and evaluating the classroom teaching based on a third preset standard according to the face information.
Preferably, the method further comprises an evaluation utilization step of sending the results of evaluating the classroom teaching obtained in the first evaluation step, the second evaluation step and the third evaluation step to a terminal for utilizing the evaluation results.
In a second aspect, an embodiment of the present application provides a teaching quality management system based on text analysis and face recognition, which is suitable for the teaching quality management method based on text analysis and face recognition, and includes: the text collection unit is used for collecting preset text information for classroom teaching and storing the preset text information into a database; the voice collecting unit is used for collecting voice data in the classroom teaching scene and storing the voice data into the database; the first evaluation unit is used for comparing the voice data with the preset text information and evaluating the classroom teaching based on a first preset standard according to the similarity of the comparison; the second evaluation unit is used for evaluating the classroom teaching based on a second preset standard according to the time information of the classroom teaching; the third evaluation unit is used for collecting the face information in the classroom teaching and evaluating the classroom teaching based on a third preset standard according to the face information; and the evaluation utilization unit is used for sending the results of evaluating the classroom teaching obtained in the first evaluation step, the second evaluation step and the third evaluation step to a terminal for utilizing the evaluation results.
Compared with the related technology, the teaching quality management method based on text analysis and face recognition provided by the embodiment of the application develops PDCA (P (Plan), D (Do, implementation and execution), C (Check and evaluation) and A (Action, treatment and improvement)) processes for classroom teaching work based on image recognition and text analysis technologies, and helps school managers and teachers evaluate classroom teaching quality levels by continuously circulating PDCA processes, and accordingly, classroom teaching quality can be optimized and improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart of a teaching quality management method based on text analysis and face recognition according to the present invention;
FIG. 2 is a flowchart illustrating the substeps of step S3 in FIG. 1;
FIG. 3 is a block diagram of a teaching quality management system based on text analysis and face recognition in accordance with the present invention;
FIG. 4 is a block diagram of an electronic device of the present invention;
in the above figures:
1. a text collection unit; 2. a voice collection unit; 3. a first evaluation unit; 4. a second evaluation unit; 5. a third evaluation unit; 6. an evaluation utilization unit; 31. a voice conversion module; 32. a text processing module; 33. an information comparison module; 60. a bus; 61. a processor; 62. a memory; 63. a communication interface.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application.
It is obvious that the drawings in the following description are only examples or embodiments of the present application, and that it is also possible for a person skilled in the art to apply the present application to other similar contexts on the basis of these drawings without inventive effort. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Embodiments of the invention are described in detail below with reference to the accompanying drawings:
fig. 1 is a flowchart of a teaching quality management method based on text analysis and face recognition, please refer to fig. 1, and the teaching quality management method based on text analysis and face recognition of the present invention includes the following steps:
s1: the method comprises the steps of collecting preset text information for classroom teaching, and storing the preset text information to a database.
In specific implementation, the preset text information mentioned in this embodiment is in the form of a teaching plan; based on the historical teaching plan text information of the classroom teaching work, an electronic version standard teaching plan of the new classroom teaching work is manually formulated, particularly the classroom arrangement, the teaching knowledge points and the knowledge point testing practice of each classroom are carried out, and the electronic version standard teaching plan is stored in a server database.
S2: and collecting voice data in the classroom teaching scene, and storing the voice data in the database.
Optionally, the voice data is collected by a wearable voice collecting device, and the voice data is transmitted to and stored in the database.
In specific implementation, the wearable voice collecting equipment is used for collecting actual classroom teaching voice data and transmitting the voice data to the server database through the wearable voice collecting equipment; optionally, the transmission mode of the voice data includes but is not limited to bluetooth transmission, WiFi transmission and wired connection transmission.
S3: and comparing the voice data with the preset text information, and evaluating the classroom teaching based on a first preset standard according to the similarity of the comparison.
Optionally, fig. 2 is a flowchart illustrating a sub-step of step S3 in fig. 1, please refer to fig. 2:
s31: the speech data is converted into classroom text information using an ASR engine.
In a particular implementation, the server converts speech data collected in the database into text data using a speech/text conversion module based on an ASR engine.
S32: performing natural language processing on the preset text information and the classroom text information; optionally, a word segmentation algorithm is used for carrying out word segmentation on the preset text information and the classroom text information to obtain a word segmentation result of the preset text information and a word segmentation result of the classroom text information; and obtaining word vectors of the preset text information word segmentation result and the classroom text information word segmentation result by using a word vector representation algorithm.
In the specific implementation, the text information of the electronic standard teaching plan in the step S1 is subjected to word segmentation processing by using a word segmentation algorithm, and after the word segmentation is completed, a word vector result of each segmented word in the electronic standard teaching plan is obtained by using a word vector representation algorithm and is stored in the server database. In specific implementation, the word segmentation algorithm used in the embodiment of the present application includes, but is not limited to, a jieba word segmentation algorithm; in specific implementation, the word vector identification algorithm used in the embodiments of the present application includes, but is not limited to, word2vec algorithm.
In the specific implementation, the classroom teaching text data in the step S31 is subjected to Word segmentation processing by using a Jieba Word segmentation algorithm, and after Word segmentation is completed, a Word vector result of each Word segmentation in the teaching classroom text data is obtained by using a Word2vec algorithm and stored in a server database.
S33: comparing the classroom text information with the preset text information according to the natural language processing result; optionally, according to the preset text information and the word vector of the classroom text information, a trained semantic matching model is used for performing text similarity calculation.
In specific implementation, after actual classroom teaching is finished every time, performing text similarity calculation on word vector results of classroom teaching text data and word vector results of corresponding electronic standard teaching plans by using a semantic matching model trained in advance; optionally, the semantic matching model used in the embodiments of the present application includes, but is not limited to, the LSTM-DSSM model.
In specific implementation, an evaluation standard can be formulated, optionally, when the text similarity result is lower than 70%, the system judges that a teacher obviously departs from a teaching plan to give lessons in the classroom teaching process, too much content irrelevant to the teaching content exists, and the teaching efficiency is low; when the text similarity result is higher than 95%, the system judges that the teacher excessively looks after the propaganda department in the classroom teaching process and the teaching attitude and method are improper; when the text similarity result is between 70% and 95%, the system judges that the classroom teaching process of the teacher is normal.
Please continue to refer to fig. 1:
s4: and evaluating the classroom teaching based on a second preset standard according to the time information of the classroom teaching.
In specific implementation, the actual time length data of the classroom teaching process can be calculated through the timestamp information of the classroom teaching text data. The system compares the actual classroom teaching time length data with the planned time length data in advance, optionally, when the actual classroom teaching time length exceeds the planned time length in advance by 10%, the system judges that the teaching rhythm of the teacher is wadded in the classroom teaching process, and the teaching efficiency is low; when the actual classroom teaching time length is lower than the planned classroom time length by 80 percent, the system judges that the teaching rhythm jumps during the classroom teaching process of the teacher and the teaching method is improper; when the actual classroom teaching time length is 80% -110% of the planned classroom time length in advance, the system judges that the teaching rhythm of the teacher in the classroom teaching process is normal.
S5: and collecting the face information in the classroom teaching, and evaluating the classroom teaching based on a third preset standard according to the face information.
In specific implementation, a camera fixed in a classroom can be arranged, the camera can record the classroom teaching process and compare with student facial information recorded in advance in a server database through a face recognition technology so as to record the interaction condition of knowledge point test practice between a teacher and students in the classroom teaching process, and the system can judge whether the interaction number result reaches a set target in an electronic version standard teaching plan.
S6: and sending the results of the classroom teaching evaluation obtained in the first evaluation step, the second evaluation step and the third evaluation step to a terminal for utilizing the evaluation results.
In the specific implementation, after the classroom teaching work in the appointed time period is finished, the knowledge point coverage rate, the classroom teaching time standard reaching rate and the classroom interaction rate for evaluating the classroom teaching quality of a teacher are sent to a school manager and the teacher, the manager and the teacher carry out discussion and communication aiming at the defects, electronic standard teaching plans are optimized, the knowledge point teaching skill in the classroom teaching process is improved, the classroom teaching rhythm is improved, and the knowledge point testing and training interaction in the classroom teaching process is ensured.
It should be noted that the steps illustrated in the above-described flow diagrams or in the flow diagrams of the figures may be performed in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flow diagrams, in some cases, the steps illustrated or described may be performed in an order different than here.
The embodiment of the application provides a teaching quality management system based on text analysis and face recognition, which is suitable for the teaching quality management method based on text analysis and face recognition. As used below, the terms "unit," "module," and the like may implement a combination of software and/or hardware of predetermined functions. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware or a combination of software and hardware is also possible and contemplated.
Fig. 3 is a block diagram of a teaching quality management system based on text analysis and face recognition according to the present invention, please refer to fig. 3, which includes:
the text collection unit 1: the method comprises the steps of collecting preset text information for classroom teaching, and storing the preset text information to a database.
In specific implementation, the preset text information mentioned in this embodiment is in the form of a teaching plan; based on the historical teaching plan text information of the classroom teaching work, an electronic version standard teaching plan of the new classroom teaching work is manually formulated, particularly the classroom arrangement, the teaching knowledge points and the knowledge point testing practice of each classroom are carried out, and the electronic version standard teaching plan is stored in a server database.
The voice collecting unit 2: and collecting voice data in the classroom teaching scene, and storing the voice data in the database.
Optionally, the voice data is collected by a wearable voice collecting device, and the voice data is transmitted to and stored in the database.
In specific implementation, the wearable voice collecting equipment is used for collecting actual classroom teaching voice data and transmitting the voice data to the server database through the wearable voice collecting equipment; optionally, the transmission mode of the voice data may be bluetooth transmission, WiFi transmission, and wired connection transmission, and in a specific implementation, any operable transmission mode is considered in this embodiment of the present application.
First evaluation unit 3: and comparing the voice data with the preset text information, and evaluating the classroom teaching based on a first preset standard according to the similarity of the comparison.
The voice conversion module 31: the speech data is converted into classroom text information using an ASR engine.
In a particular implementation, the server converts speech data collected in the database into text data using a speech/text conversion module based on an ASR engine.
The text processing module 32: performing natural language processing on the preset text information and the classroom text information; optionally, a word segmentation algorithm is used for carrying out word segmentation on the preset text information and the classroom text information to obtain a word segmentation result of the preset text information and a word segmentation result of the classroom text information; and obtaining word vectors of the preset text information word segmentation result and the classroom text information word segmentation result by using a word vector representation algorithm.
In the specific implementation, the text information of the electronic standard teaching plan of the text collection unit 1 is subjected to word segmentation processing by using a word segmentation algorithm, and after word segmentation is completed, a word vector result of each word segmentation in the electronic standard teaching plan is obtained by using a word vector representation algorithm and is stored in a server database. In specific implementation, the word segmentation algorithm used in the embodiment of the present application includes, but is not limited to, a jieba word segmentation algorithm; in specific implementation, the word vector identification algorithm used in the embodiments of the present application includes, but is not limited to, word2vec algorithm.
In the specific implementation, the classroom teaching text data of the voice conversion module 31 is subjected to Word segmentation processing by using a Jieba Word segmentation algorithm, and after Word segmentation is completed, a Word vector result of each Word segmentation in the teaching classroom text data is obtained by using a Word2vec algorithm and is stored in the server database.
The information comparison module 33: comparing the classroom text information with the preset text information according to the natural language processing result; optionally, according to the preset text information and the word vector of the classroom text information, a trained semantic matching model is used for performing text similarity calculation.
In specific implementation, after actual classroom teaching is finished every time, performing text similarity calculation on word vector results of classroom teaching text data and word vector results of corresponding electronic standard teaching plans by using a semantic matching model trained in advance; optionally, the semantic matching model used in the embodiments of the present application includes, but is not limited to, the LSTM-DSSM model.
In specific implementation, an evaluation standard can be formulated, optionally, when the text similarity result is lower than 70%, the system judges that a teacher obviously departs from a teaching plan to give lessons in the classroom teaching process, too much content irrelevant to the teaching content exists, and the teaching efficiency is low; when the text similarity result is higher than 95%, the system judges that the teacher excessively looks after the propaganda department in the classroom teaching process and the teaching attitude and method are improper; when the text similarity result is between 70% and 95%, the system judges that the classroom teaching process of the teacher is normal.
Second evaluation unit 4: and evaluating the classroom teaching based on a second preset standard according to the time information of the classroom teaching.
In specific implementation, the actual time length data of the classroom teaching process can be calculated through the timestamp information of the classroom teaching text data. The system compares the actual classroom teaching time length data with the planned time length data in advance, optionally, when the actual classroom teaching time length exceeds the planned time length in advance by 10%, the system judges that the teaching rhythm of the teacher is wadded in the classroom teaching process, and the teaching efficiency is low; when the actual classroom teaching time length is lower than the planned classroom time length by 80 percent, the system judges that the teaching rhythm jumps during the classroom teaching process of the teacher and the teaching method is improper; when the actual classroom teaching time length is 80% -110% of the planned classroom time length in advance, the system judges that the teaching rhythm of the teacher in the classroom teaching process is normal.
Third evaluation unit 5: and collecting the face information in the classroom teaching, and evaluating the classroom teaching based on a third preset standard according to the face information.
In specific implementation, a camera fixed in a classroom can be arranged, the camera can record the classroom teaching process and compare with student facial information recorded in advance in a server database through a face recognition technology so as to record the interaction condition of knowledge point test practice between a teacher and students in the classroom teaching process, and the system can judge whether the interaction number result reaches a set target in an electronic version standard teaching plan.
The evaluation utilization unit 6: and sending the results of the classroom teaching evaluation obtained in the first evaluation step, the second evaluation step and the third evaluation step to a terminal for utilizing the evaluation results.
In the specific implementation, after the classroom teaching work in the appointed time period is finished, the knowledge point coverage rate, the classroom teaching time standard reaching rate and the classroom interaction rate for evaluating the classroom teaching quality of a teacher are sent to a school manager and the teacher, the manager and the teacher carry out discussion and communication aiming at the defects, electronic standard teaching plans are optimized, the knowledge point teaching skill in the classroom teaching process is improved, the classroom teaching rhythm is improved, and the knowledge point testing and training interaction in the classroom teaching process is ensured.
In addition, a teaching quality management method based on text analysis and face recognition described in conjunction with fig. 1 can be implemented by an electronic device. Fig. 4 is a block diagram of an electronic device of the present invention.
The electronic device may comprise a processor 61 and a memory 62 in which computer program instructions are stored.
Specifically, the processor 61 may include a Central Processing Unit (CPU), or A Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.
The memory 62 may be used to store or cache various data files that need to be processed and/or used for communication, as well as possible computer program instructions executed by the processor 61.
The processor 61 implements any one of the teaching quality management methods based on text analysis and face recognition in the above embodiments by reading and executing computer program instructions stored in the memory 62.
In some of these embodiments, the electronic device may also include a communication interface 63 and a bus 60. As shown in fig. 4, the processor 61, the memory 62, and the communication interface 63 are connected via a bus 60 to complete communication therebetween.
The communication port 63 may be implemented with other components such as: the data communication is carried out among external equipment, image/data acquisition equipment, a database, external storage, an image/data processing workstation and the like.
The bus 60 includes hardware, software, or both to couple the components of the electronic device to one another. Bus 60 includes, but is not limited to, at least one of the following: data Bus (Data Bus), Address Bus (Address Bus), Control Bus (Control Bus), expansion Bus (expansion Bus), and Local Bus (Local Bus). By way of example, and not limitation, Bus 60 may include an Accelerated Graphics Port (AGP) or other Graphics Bus, an Enhanced Industry Standard Architecture (EISA) Bus, a Front-Side Bus (Front Side Bus), an FSB (FSB), a Hyper Transport (HT) Interconnect, an ISA (ISA) Bus, an InfiniBand (InfiniBand) Interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a microchannel Architecture (MCA) Bus, a PCI (Peripheral Component Interconnect) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, an electronic Video Standard Architecture (Video) Bus, abbreviated VLB) bus or other suitable bus or a combination of two or more of these. Bus 60 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.
The electronic equipment can execute the teaching quality management method based on text analysis and face recognition in the embodiment of the application.
In addition, in combination with the teaching quality management method based on text analysis and face recognition in the foregoing embodiments, the embodiments of the present application may provide a computer-readable storage medium to implement. The computer readable storage medium having stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement any of the above-described embodiments of a teaching quality management method based on text analysis and face recognition.
And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A teaching quality management method based on text analysis and face recognition is characterized by comprising the following steps:
a text collection step, collecting preset text information for classroom teaching, and storing the preset text information into a database;
a voice collection step, namely collecting voice data in the classroom teaching scene and storing the voice data in the database;
and a first evaluation step of comparing the voice data with the preset text information and evaluating the classroom teaching based on a first preset standard according to the similarity of the comparison.
2. The teaching quality management method based on text analysis and face recognition according to claim 1, wherein the voice collection step comprises collecting the voice data by a wearable voice collection device, and transmitting and storing the voice data to the database.
3. The teaching quality management method based on text analysis and face recognition according to claim 1, wherein the first evaluation step comprises:
a voice conversion step, using an ASR engine to convert the voice data into classroom text information;
a text processing step of performing natural language processing on the preset text information and the classroom text information;
and an information comparison step, namely comparing the classroom text information with the preset text information according to the result of the natural language processing.
4. The teaching quality management method based on text analysis and face recognition according to claim 3, wherein the text processing step includes performing word segmentation on the preset text information and the classroom text information by using a word segmentation algorithm to obtain the word segmentation result of the preset text information and the word segmentation result of the classroom text information.
5. The teaching quality management method based on text analysis and face recognition according to claim 4, wherein the text processing step further comprises obtaining word vectors of the preset text information word segmentation result and the classroom text information word segmentation result using a word vector representation algorithm.
6. The teaching quality management method based on text analysis and face recognition according to claim 5, wherein the information comparison step comprises performing text similarity calculation using a trained semantic matching model based on the word vectors of the preset text information and the classroom text information.
7. The teaching quality management method based on text analysis and face recognition according to claim 1, further comprising a second evaluation step of evaluating the classroom teaching based on a second preset criterion based on the time information of the classroom teaching.
8. The method of claim 7, wherein the method further comprises a third evaluation step of collecting face information during the classroom teaching and evaluating the classroom teaching based on a third predetermined criterion based on the face information.
9. The teaching quality management method based on text analysis and face recognition according to claim 8, further comprising an evaluation utilization step of sending the results of evaluation of the classroom teaching obtained in the first evaluation step, the second evaluation step and the third evaluation step to a terminal for utilization of the results of evaluation.
10. A teaching quality management system based on text analysis and face recognition is characterized by comprising:
the text collection unit is used for collecting preset text information for classroom teaching and storing the preset text information into a database;
the voice collecting unit is used for collecting voice data in the classroom teaching scene and storing the voice data into the database;
the first evaluation unit is used for comparing the voice data with the preset text information and evaluating the classroom teaching based on a first preset standard according to the similarity of the comparison;
the second evaluation unit is used for evaluating the classroom teaching based on a second preset standard according to the time information of the classroom teaching;
the third evaluation unit is used for collecting the face information in the classroom teaching and evaluating the classroom teaching based on a third preset standard according to the face information;
and the evaluation utilization unit is used for sending the results of evaluating the classroom teaching obtained in the first evaluation step, the second evaluation step and the third evaluation step to a terminal for utilizing the evaluation results.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117078094A (en) * | 2023-08-22 | 2023-11-17 | 云启智慧科技有限公司 | Teacher comprehensive ability assessment method based on artificial intelligence |
| CN117195892A (en) * | 2023-11-08 | 2023-12-08 | 山东十二学教育科技有限公司 | Classroom teaching evaluation method and system based on big data |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117078094A (en) * | 2023-08-22 | 2023-11-17 | 云启智慧科技有限公司 | Teacher comprehensive ability assessment method based on artificial intelligence |
| CN117195892A (en) * | 2023-11-08 | 2023-12-08 | 山东十二学教育科技有限公司 | Classroom teaching evaluation method and system based on big data |
| CN117195892B (en) * | 2023-11-08 | 2024-01-26 | 山东十二学教育科技有限公司 | Classroom teaching evaluation method and system based on big data |
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