US20210097264A1 - Electronic device and method for analyzing responses to questionnaires - Google Patents
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- US20210097264A1 US20210097264A1 US16/668,051 US201916668051A US2021097264A1 US 20210097264 A1 US20210097264 A1 US 20210097264A1 US 201916668051 A US201916668051 A US 201916668051A US 2021097264 A1 US2021097264 A1 US 2021097264A1
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- 238000000034 method Methods 0.000 title claims abstract description 32
- 230000004044 response Effects 0.000 title abstract description 14
- 230000006399 behavior Effects 0.000 claims abstract description 68
- 238000012216 screening Methods 0.000 description 9
- 238000004590 computer program Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- G06K9/00288—
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/20—Education
- G06Q50/205—Education administration or guidance
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- G—PHYSICS
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
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- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
Definitions
- the subject matter herein generally relates to information-gathering and psychology.
- Some responses to questions in a questionnaire may not be trustworthy. For example, for the question “Do you like the teacher of the XXX course”, the student may respond favorably because of public psychology, even if the student may be bored by the course. If the responses to such questionnaire are used as a sample, accurate results of such survey may not be achieved.
- FIG. 1 is a block diagram of an embodiment of an electronic device.
- FIG. 2 is a block diagram of an embodiment of a system for analyzing responses to questionnaires.
- FIG. 3 is flowchart of an embodiment of a method for analyzing responses to questionnaires.
- Coupled is defined as connected, whether directly or indirectly through intervening components, and is not necessarily limited to physical connections.
- the connection can be such that the objects are permanently connected or releasably connected.
- comprising means “including, but not necessarily limited to”; it specifically indicates open-ended inclusion or membership in a so-described combination, group, series, and the like.
- FIG. 1 illustrates an electronic device 10 in accordance with an embodiment of the present disclosure.
- the electronic device 10 can communicate with a camera device 20 .
- the electronic device 10 receives a classroom image of a student acquired by the camera device 20 .
- the electronic device 10 can further include, but is not limited to, at least one processor 12 , a storage device 14 , and a program segment 16 stored in the storage device 14 .
- the processor 12 may execute the program code of program segment 16 to implement steps 301 - 308 in method shown in FIG. 3 .
- the processor 12 may execute the program code of program segment 16 to implement the functions of a system 30 for analyzing responses to questionnaires shown in FIG. 2 .
- the electronic device 10 may be a computing device, such as a personal computer or a server.
- the server may be a single server, a server cluster, or a cloud server.
- the block diagram merely shows an example of the electronic device 10 and does not constitute a limitation to the electronic device 10 . More or less components than those illustrated may be included, or some components may be combined, or different components used.
- the electronic device 10 may also include input and output devices, a network access devices, a bus, and the like.
- the processor 12 may be a central processing unit (CPU), or may be another general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-Programmable gate array (FPGA) or other programmable logic device, a transistor logic device, a discrete hardware component.
- the general purpose processor may be a microprocessor.
- the processor 12 may also be any conventional processor.
- the processor 12 is a control center of the electronic device 10 .
- the processor 12 connects the parts of electronic device 10 by using various interfaces and lines.
- the storage device 14 can be used to store the program segment 16 .
- the processor 12 operates or executes the program segment stored in the storage device 14 and recalls data stored in the storage device 14 , and implements various functions of the electronic device 10 .
- the storage device 14 may mainly include a storage program area and a storage data area, the storage program area may store an operating system, an application (such as image processing program) required for at least one function.
- the storage data area may store data created (such as image of face of each student and student name and screening rules).
- the storage device 14 may include a RAM, and may also include non-volatile memory such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMC), and a Secure Digital (SD) card, a flash card, at least one disk storage device, flash device, or other volatile or non-volatile solid-state storage device.
- non-volatile memory such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMC), and a Secure Digital (SD) card, a flash card, at least one disk storage device, flash device, or other volatile or non-volatile solid-state storage device.
- FIG. 2 illustrates a questionnaires analyzing system 30 in accordance with an embodiment of the present disclosure.
- the questionnaires analyzing system 30 operates in the electronic device 10 .
- the teach questionnaire analysis system 30 may include functional modules consisting of program code.
- the functional modules can include an identification module 31 , a behavior analysis module 32 , a determination module 33 , a screening module 34 , and a questionnaire analysis module 35 .
- the modules 31 - 34 include computer instructions or codes in form of one or more programs that may be stored in the storage device 14 , and which are executed by the at least one processor 12 .
- the modules 31 - 34 may also be a program instruction or firmware that is embedded in the processor 12 .
- the identification module 31 is configured to identify the identity of each student in the classroom image transmitted by the camera device 20 .
- the identity includes, but is not limited to, the student's name and number.
- the identification module 31 identifies the identity of each student in the classroom image according to a pre-stored face image of each student. In other embodiment, the identification module 31 identifies the identity of each student according to the name or student number in a database, or on a student uniform.
- the behavior analysis module 32 analyzes the classroom image corresponding to each student in a preset time period, to obtain the classroom behavior of each student in the class.
- the classroom behavior may include behavior in a learning state and behavior in a non-learning state. Behaviors in the learning state include, but are not limited to, looking at the blackboard and taking own notes. Behaviors in the non-learning state include, but are not limited to, dozing, using mobile phones, and whispering with other students.
- the preset time period may be one month, one semester, or one academic year.
- the determination module 33 is configured to determine whether the behavior of each student in the non-learning state meets a preset condition.
- the preset condition may be that the number of non-learning behaviors is less than a preset value of the condition.
- the determination module 33 determines whether the number of times that each student shows non-learning behaviors is less or not less than the preset value.
- the preset condition may be that a duration of the behaviors in the non-learning state is less than a preset duration.
- the determination module 33 determines whether the duration of the behavior of each student in the non-learning state is less than the preset duration.
- the screening module 34 is configured to apply a filter to the questionnaire responses given by each student according to the determination result and a screening rule.
- the screening rule determines whether the questionnaire responses are available or not available. When the behaviors of the student in the non-learning state meet (do not exceed) the preset conditions his responses to questionnaires are available, and when the behaviors of the student in the non-learning state do not meet (that is to say, exceed) the preset conditions, his responses to questionnaire are not available.
- the questionnaire analysis module 35 is configured to perform a questionnaire analysis on the selected questionnaires after screening, to obtain a conclusion such as the quality of classroom teaching.
- the modules and units integrated by the electronic device 10 may be stored in a computer readable storage medium. Based on such understanding, the present disclosure implements all or part of the processes in the foregoing embodiments, and the purposes of the disclosure may also be implemented and achieved by a computer program instructing related hardware.
- the computer program may be stored in a computer readable storage medium.
- the steps of the various method embodiments described above may be implemented by a computer program when executed by a processor.
- the computer program includes a computer program code, which may be in the form of source code, object code form, executable file, or some intermediate form.
- the computer readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), electrical carrier signals, telecommunications signals, and software distribution media.
- FIG. 3 is flowchart depicting an embodiment of a method for analyzing questionnaires.
- the method is provided by way of example, as there are a variety of ways to carry out the method. The method described below can be carried out using the configurations illustrated in FIGS. 1 and 2 for example, and various elements of these figures are referenced in explaining the example method.
- Each block shown in FIG. 3 represents one or more processes, methods, or subroutines, carried out in the example method.
- the illustrated order of blocks is illustrative only and the order of the blocks can change. Additional blocks can be added or fewer blocks may be utilized, without departing from the present disclosure.
- the example method can begin at block 31 .
- the identification module 31 receives the classroom image of students.
- the classroom image may be from the camera device 20 or a relay device (not shown in figure).
- the identification module 31 identifies the identity of each student in the classroom image transmitted by the camera device 20 .
- the identity includes, but is not limited to, the student's name and number.
- the identification module 31 identifies the identity of each student in the class image according to the pre-stored face image of each student. In other embodiment, the identification module 31 identifies each student according to the name or student number in a database, or on a student uniform.
- the behavior analysis module 32 analyzes the classroom image corresponding to each student in a preset time period, to obtain the classroom behavior of each student in the class.
- the classroom behavior may include behavior in a learning state and behavior in a non-learning state.
- Behaviors in the learning state include, but are not limited to, looking at the blackboard, and taking own notes.
- Behaviors in the non-learning state include, but are not limited to, dozing, using mobile phones, and whispering with other students.
- the preset time period may be one month, one semester, or one academic year.
- the determination module 33 determines whether the behavior of each student in the non-learning state meets a preset condition. If the behavior of each student in the non-learning state meets a preset condition, block 305 is implemented, otherwise block 306 is implemented.
- the preset condition may be that the number of non-learning behaviors is less than a preset value of the condition.
- the determination module 33 determines whether the number of times that each student shows non-learning behaviors is less than the preset value at block 304 .
- the preset value may be 3, 4, or 5 times.
- the preset condition may be that a duration of the behavior in the non-learning state is less than a preset duration.
- the determination module 33 determines whether the duration of the behavior of each student in the non-learning state is less than the preset duration at block 304 .
- the duration of the behavior in the non-learning state is proportional to the time of the behavior in the non-learning state, such as 0.9, 0.8, or 0.7.
- the screening module 34 determines the questionnaire responses are available.
- the screening module 34 determines the questionnaire responses are not available.
- the questionnaire analysis module 35 performs a questionnaire analysis on the selected questionnaires after screening, to obtain a conclusion such as the quality of classroom teaching.
- the questionnaire analysis module 35 deletes unavailable questionnaires.
- the questionnaires analyzing method may not include block 301 , and the classroom image is stored in a local device.
- the questionnaires analyzing method may not include block 307 , and the analysis of the teach questionnaires are completed by manually analyzing available questionnaires.
- the questionnaires analyzing method may not include block 308 , and the unavailable questionnaires are not used as an analysis sample, but is reserved for later use.
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Abstract
Description
- The subject matter herein generally relates to information-gathering and psychology.
- Some responses to questions in a questionnaire may not be trustworthy. For example, for the question “Do you like the teacher of the XXX course”, the student may respond favorably because of public psychology, even if the student may be bored by the course. If the responses to such questionnaire are used as a sample, accurate results of such survey may not be achieved.
- Therefore, there is a room for improvement.
- Implementations of the present disclosure will now be described, by way of embodiments, with reference to the attached figures.
-
FIG. 1 is a block diagram of an embodiment of an electronic device. -
FIG. 2 is a block diagram of an embodiment of a system for analyzing responses to questionnaires. -
FIG. 3 is flowchart of an embodiment of a method for analyzing responses to questionnaires. - It will be appreciated that for simplicity and clarity of illustration, where appropriate, reference numerals have been repeated among the different figures to indicate corresponding or analogous elements. Additionally, numerous specific details are set forth in order to provide a thorough understanding of the embodiments described herein. However, it will be understood by those of ordinary skill in the art that the embodiments described herein can be practiced without these specific details. In other instances, methods, procedures, and components have not been described in detail so as not to obscure the related relevant feature being described. The drawings are not necessarily to scale and the proportions of certain parts may be exaggerated to better illustrate details and features. The description is not to be considered as limiting the scope of the embodiments described herein.
- Several definitions that apply throughout this disclosure will now be presented.
- The term “coupled” is defined as connected, whether directly or indirectly through intervening components, and is not necessarily limited to physical connections. The connection can be such that the objects are permanently connected or releasably connected. The term “comprising” means “including, but not necessarily limited to”; it specifically indicates open-ended inclusion or membership in a so-described combination, group, series, and the like.
-
FIG. 1 illustrates anelectronic device 10 in accordance with an embodiment of the present disclosure. - The
electronic device 10 can communicate with acamera device 20. Theelectronic device 10 receives a classroom image of a student acquired by thecamera device 20. - The
electronic device 10 can further include, but is not limited to, at least oneprocessor 12, astorage device 14, and aprogram segment 16 stored in thestorage device 14. Theprocessor 12 may execute the program code ofprogram segment 16 to implement steps 301-308 in method shown inFIG. 3 . Theprocessor 12 may execute the program code ofprogram segment 16 to implement the functions of asystem 30 for analyzing responses to questionnaires shown inFIG. 2 . - In one embodiment, the
electronic device 10 may be a computing device, such as a personal computer or a server. In one embodiment, the server may be a single server, a server cluster, or a cloud server. The block diagram merely shows an example of theelectronic device 10 and does not constitute a limitation to theelectronic device 10. More or less components than those illustrated may be included, or some components may be combined, or different components used. For example, theelectronic device 10 may also include input and output devices, a network access devices, a bus, and the like. - The
processor 12 may be a central processing unit (CPU), or may be another general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-Programmable gate array (FPGA) or other programmable logic device, a transistor logic device, a discrete hardware component. The general purpose processor may be a microprocessor. Theprocessor 12 may also be any conventional processor. Theprocessor 12 is a control center of theelectronic device 10. Theprocessor 12 connects the parts ofelectronic device 10 by using various interfaces and lines. - The
storage device 14 can be used to store theprogram segment 16. Theprocessor 12 operates or executes the program segment stored in thestorage device 14 and recalls data stored in thestorage device 14, and implements various functions of theelectronic device 10. Thestorage device 14 may mainly include a storage program area and a storage data area, the storage program area may store an operating system, an application (such as image processing program) required for at least one function. The storage data area may store data created (such as image of face of each student and student name and screening rules). - The
storage device 14 may include a RAM, and may also include non-volatile memory such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMC), and a Secure Digital (SD) card, a flash card, at least one disk storage device, flash device, or other volatile or non-volatile solid-state storage device. -
FIG. 2 illustrates aquestionnaires analyzing system 30 in accordance with an embodiment of the present disclosure. Thequestionnaires analyzing system 30 operates in theelectronic device 10. - The teach
questionnaire analysis system 30 may include functional modules consisting of program code. The functional modules can include anidentification module 31, abehavior analysis module 32, adetermination module 33, ascreening module 34, and aquestionnaire analysis module 35. - The modules 31-34 include computer instructions or codes in form of one or more programs that may be stored in the
storage device 14, and which are executed by the at least oneprocessor 12. In other embodiment, the modules 31-34 may also be a program instruction or firmware that is embedded in theprocessor 12. - The
identification module 31 is configured to identify the identity of each student in the classroom image transmitted by thecamera device 20. The identity includes, but is not limited to, the student's name and number. In one embodiment, theidentification module 31 identifies the identity of each student in the classroom image according to a pre-stored face image of each student. In other embodiment, theidentification module 31 identifies the identity of each student according to the name or student number in a database, or on a student uniform. - The
behavior analysis module 32 analyzes the classroom image corresponding to each student in a preset time period, to obtain the classroom behavior of each student in the class. The classroom behavior may include behavior in a learning state and behavior in a non-learning state. Behaviors in the learning state include, but are not limited to, looking at the blackboard and taking own notes. Behaviors in the non-learning state include, but are not limited to, dozing, using mobile phones, and whispering with other students. The preset time period may be one month, one semester, or one academic year. - The
determination module 33 is configured to determine whether the behavior of each student in the non-learning state meets a preset condition. In one embodiment, the preset condition may be that the number of non-learning behaviors is less than a preset value of the condition. Thedetermination module 33 determines whether the number of times that each student shows non-learning behaviors is less or not less than the preset value. - In another embodiment, the preset condition may be that a duration of the behaviors in the non-learning state is less than a preset duration. The
determination module 33 determines whether the duration of the behavior of each student in the non-learning state is less than the preset duration. - The
screening module 34 is configured to apply a filter to the questionnaire responses given by each student according to the determination result and a screening rule. The screening rule determines whether the questionnaire responses are available or not available. When the behaviors of the student in the non-learning state meet (do not exceed) the preset conditions his responses to questionnaires are available, and when the behaviors of the student in the non-learning state do not meet (that is to say, exceed) the preset conditions, his responses to questionnaire are not available. - The
questionnaire analysis module 35 is configured to perform a questionnaire analysis on the selected questionnaires after screening, to obtain a conclusion such as the quality of classroom teaching. - The modules and units integrated by the
electronic device 10, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, the present disclosure implements all or part of the processes in the foregoing embodiments, and the purposes of the disclosure may also be implemented and achieved by a computer program instructing related hardware. The computer program may be stored in a computer readable storage medium. The steps of the various method embodiments described above may be implemented by a computer program when executed by a processor. The computer program includes a computer program code, which may be in the form of source code, object code form, executable file, or some intermediate form. The computer readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), electrical carrier signals, telecommunications signals, and software distribution media. -
FIG. 3 is flowchart depicting an embodiment of a method for analyzing questionnaires. The method is provided by way of example, as there are a variety of ways to carry out the method. The method described below can be carried out using the configurations illustrated inFIGS. 1 and 2 for example, and various elements of these figures are referenced in explaining the example method. Each block shown inFIG. 3 represents one or more processes, methods, or subroutines, carried out in the example method. Furthermore, the illustrated order of blocks is illustrative only and the order of the blocks can change. Additional blocks can be added or fewer blocks may be utilized, without departing from the present disclosure. The example method can begin atblock 31. - At
block 301, theidentification module 31 receives the classroom image of students. The classroom image may be from thecamera device 20 or a relay device (not shown in figure). - At
block 302, theidentification module 31 identifies the identity of each student in the classroom image transmitted by thecamera device 20. - In the embodiment, the identity includes, but is not limited to, the student's name and number. The
identification module 31 identifies the identity of each student in the class image according to the pre-stored face image of each student. In other embodiment, theidentification module 31 identifies each student according to the name or student number in a database, or on a student uniform. - At
block 303, thebehavior analysis module 32 analyzes the classroom image corresponding to each student in a preset time period, to obtain the classroom behavior of each student in the class. - In the embodiment, the classroom behavior may include behavior in a learning state and behavior in a non-learning state.
- Behaviors in the learning state include, but are not limited to, looking at the blackboard, and taking own notes. Behaviors in the non-learning state include, but are not limited to, dozing, using mobile phones, and whispering with other students. The preset time period may be one month, one semester, or one academic year.
- At
block 304, thedetermination module 33 determines whether the behavior of each student in the non-learning state meets a preset condition. If the behavior of each student in the non-learning state meets a preset condition, block 305 is implemented, otherwise block 306 is implemented. - In one embodiment, the preset condition may be that the number of non-learning behaviors is less than a preset value of the condition. The
determination module 33 determines whether the number of times that each student shows non-learning behaviors is less than the preset value atblock 304. In one embodiment, the preset value may be 3, 4, or 5 times. - In another embodiment, the preset condition may be that a duration of the behavior in the non-learning state is less than a preset duration. The
determination module 33 determines whether the duration of the behavior of each student in the non-learning state is less than the preset duration atblock 304. The duration of the behavior in the non-learning state is proportional to the time of the behavior in the non-learning state, such as 0.9, 0.8, or 0.7. - At
block 305, thescreening module 34 determines the questionnaire responses are available. - At
block 306, thescreening module 34 determines the questionnaire responses are not available. - At
block 307, thequestionnaire analysis module 35 performs a questionnaire analysis on the selected questionnaires after screening, to obtain a conclusion such as the quality of classroom teaching. - At
block 308, thequestionnaire analysis module 35 deletes unavailable questionnaires. - In another embodiment, the questionnaires analyzing method may not include
block 301, and the classroom image is stored in a local device. - In another embodiment, the questionnaires analyzing method may not include
block 307, and the analysis of the teach questionnaires are completed by manually analyzing available questionnaires. - In another embodiment, the questionnaires analyzing method may not include
block 308, and the unavailable questionnaires are not used as an analysis sample, but is reserved for later use. - Even though numerous characteristics and advantages of the present technology have been set forth in the foregoing description, together with details of the structure and function of the present disclosure, the disclosure is illustrative only, and changes may be made in the detail, especially in matters of shape, size, and arrangement of the parts within the principles of the present disclosure, up to and including the full extent established by the broad general meaning of the terms used in the claims. It will therefore be appreciated that the exemplary embodiments described above may be modified within the scope of the claims.
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CN109284737A (en) * | 2018-10-22 | 2019-01-29 | 广东精标科技股份有限公司 | A kind of students ' behavior analysis and identifying system for wisdom classroom |
CN109461104A (en) * | 2018-10-22 | 2019-03-12 | 杭州闪宝科技有限公司 | Classroom monitoring method, device and electronic equipment |
CN109859078A (en) * | 2018-12-24 | 2019-06-07 | 山东大学 | A kind of student's Learning behavior analyzing interference method, apparatus and system |
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2019
- 2019-09-29 CN CN201910930335.6A patent/CN112580910A/en active Pending
- 2019-10-30 US US16/668,051 patent/US10984227B1/en active Active
- 2019-11-07 TW TW108140480A patent/TWI740264B/en active
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TWI740264B (en) | 2021-09-21 |
CN112580910A (en) | 2021-03-30 |
TW202115667A (en) | 2021-04-16 |
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