WO2019169795A1

WO2019169795A1 - Attention degree evaluation method and apparatus for network teaching

Info

Publication number: WO2019169795A1
Application number: PCT/CN2018/092773
Authority: WO
Inventors: 刘善果; 陈鹏宇; 刘胜强
Original assignee: 深圳市鹰硕技术有限公司
Priority date: 2018-03-08
Filing date: 2018-06-26
Publication date: 2019-09-12
Also published as: CN108564495A

Abstract

The present disclosure relates to an attention degree evaluation method and apparatus for network teaching, an electronic device, and a storage medium. Said method comprises: acquiring, by means of a user terminal, user's attention degree characteristics including behavior characteristics and state characteristics; predetermining attention degree characteristic levels and weight coefficients; in accordance with a predetermined evaluation algorithm, obtaining, according to the attention degree characteristics and the weight coefficients, level evaluation information corresponding to the attention degree characteristic level; determining attention degree information according to the level evaluation information of the attention degree characteristics; and acquiring current teaching content, generating a correlation between the attention degree information and the teaching content, and determining, according to the attention degree information, an attention degree of the user with respect to the teaching content. The present disclosure can achieve a comprehensive and accurate attention degree evaluation for network teaching.

Description

Network teaching attention evaluation method and device

Technical field

The present disclosure relates to the field of computer technologies, and in particular, to a network teaching attention evaluation method, apparatus, electronic device, and computer readable storage medium.

Background technique

In teaching, the rapid and accurate detection of students' attention to teaching content can not only remind teachers of the key teaching of high-interest teaching content, but also promote students' emphasis on different knowledge points of knowledge, and do more with less. effect.

However, in actual teaching, it is usually through the teacher's personal experience to actually observe the learning state of each student to achieve the student's attention to the teaching content. This method not only occupies the teacher's teaching energy, but also is difficult to Suitable for occasions such as online teaching.

In the prior art, around the subject attention detection theme, there are also some patent applications for beneficial attempts, such as:

The patent application with the application number CN201110166693.8 discloses a method for quantifying the regional attention of an object, comprising: acquiring a line of sight direction of a human eye; recording a stay time of the line of sight direction in each area of the object; and staying time Long areas give high attention weights, and areas with short stay times give low attention weights. Since the application mainly evaluates the degree of attention through the analysis of the duration of the human eye, and does not comprehensively reflect various attention indicators in the teaching content, it is impossible to comprehensively evaluate the attention of the teaching content.

The patent application with the application number CN201110166693.8 discloses a method and device for evaluating user attention. The method for evaluating user attention includes: detecting a line of sight direction of the user; and determining an area on the screen corresponding to the detected line of sight direction; Obtaining a metric of the user's expression on the determined area for each of the predetermined emotions; and generating a user's attention to the determined area based on the obtained metric. Since the application mainly realizes the evaluation of the degree of attention through the user's measure of the direction of the emotion, it cannot comprehensively reflect various attention indicators in the teaching content, and cannot comprehensively evaluate the attention of the teaching content.

The patent application with the application number CN201110166693.8 discloses a method and system for counting attention degree of display screen, which transmits a wireless access broadcast signal through a wireless access point device; and a user who enters a signal strength distance range of the wireless access point device The terminal receives the radio access broadcast signal and accesses the wireless access point device, and obtains information of the accessed user terminal, such as a unique identifier and access time information, through the wireless access point device; the device accesses the device through the wireless access point device. The information of the user terminal is provided to the server; and the degree of attention of the target display screen is counted by the server according to the information of the accessed user terminal. Since the application mainly collects statistics on the degree of connection of the display by counting the number of connected devices in the wireless access point, the method can only be used in a situation where the user is concentrated, and is not suitable for the situation in which the network teaching user is more dispersed. .

In the prior art, the following problems exist in the processing of student attention detection in online teaching:

1. In the existing method, the evaluation of attention is mainly achieved through the unilateral analysis of the duration of the human eye or the emotion, and the various attention indicators in the teaching content cannot be comprehensively integrated, and the accuracy of the attention assessment is insufficient;

2, the degree of attention can not be assessed by the file, the corresponding can not provide users with accurate tips or services according to the degree of attention after the file;

3. Since the correlation between the degree of attention and the teaching content cannot be realized in the prior art, when the evaluation is low, only the single prompt function for the user can be realized, and the teaching content corresponding to the attention degree cannot be analyzed in a targeted manner, and Optimize the teaching content with low attention and learn from or reward the teaching content with high attention.

It should be noted that the information disclosed in the Background section above is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.

Summary of the invention

The purpose of the present disclosure is to provide a network teaching attention evaluation method, apparatus, electronic device, and computer readable storage medium, which aim to solve the problem that the accuracy of the attention assessment in the prior art is insufficient, especially in the teaching process. The issue of attention. Improvements of the invention include the following:

After the user terminal acquires the attention feature of the user including the behavior feature and the state feature, the preset attention feature gear and the weight coefficient are obtained, and the gear of the attention feature is obtained according to the attention feature and the weight coefficient. Evaluating the information, determining the degree of interest information according to the score evaluation information of all the attention features, obtaining the current teaching content, generating a correspondence between the attention information and the teaching content, and determining the user according to the attention information The degree of attention to the teaching content. Since the user terminal can obtain more comprehensive attention features, such as face orientation, binocular focus, teaching content display state of the user terminal, volume status, operation information, and the like, the comprehensiveness of attention can be obtained after analysis and calculation. Evaluation, greatly improving the accuracy of attention assessment.

By appraising the degree of attention, it is also possible to provide users with accurate prompts or services according to the degree of attention after the binning; and after associating the degree of attention with the teaching content, when the evaluation is low, the pair is passed. The comparison of the attention of different teachers and different teaching contents, the use of attention to achieve teaching attention reminders, teaching method early warning and other functions, greatly enhance the user experience.

Using the attention degree information to indicate the degree of attention of the teaching content, the attention degree of the teaching content may be sorted according to the attention degree information, and the content whose attention degree is higher than the preset attention degree value is recommended to the user Focus on learning. At the same time, a timer can also be set to start timing before the preset time for the user to watch the teaching video, thereby realizing a reminding function for the user's high attention content or interest content.

According to an aspect of the present disclosure, a method for evaluating a network teaching degree of interest includes: a degree of interest feature obtaining step of acquiring a user's attention degree feature by a user terminal, the attention degree feature including a behavior feature and a state feature;

Step and coefficient preset steps: preset attention feature position and weight coefficient;

An evaluation information obtaining step: obtaining, according to the preset evaluation algorithm, the score evaluation information of the gear position of the attention degree feature according to the attention degree feature and the weight coefficient;

The degree of interest information determining step: determining the degree of interest information according to the piecewise evaluation information of all attention features;

Corresponding relationship generating step: acquiring current teaching content, generating a correspondence relationship between the attention degree information and the teaching content, and determining, according to the attention degree information, the degree of attention of the user to the teaching content.

In an exemplary embodiment of the present disclosure, the attention feature acquisition step includes one or more of the following sub-steps:

a first feature acquisition sub-step: acquiring, by the auxiliary device of the user terminal, a state feature of the user, the state feature including a facial feature of the user and a binocular focus feature;

a second feature acquisition sub-step: acquiring, by the auxiliary device of the user terminal, a behavior characteristic of the user;

The third feature acquisition sub-step: acquiring terminal operation information of the user terminal, and determining a behavior characteristic of the user according to the terminal operation information.

In an exemplary embodiment of the present disclosure, in the evaluating information obtaining step, the preset evaluation algorithm includes:

Wherein, for the sub-file evaluation information, Zib is the attention degree feature, Zis and Ziy are respectively the minimum and maximum values of the binning information, A is the preset weight coefficient, and B is the attention feature gear shift. value;

In the attention degree information determining step, the manner of calculating the attention degree information includes:

Attention information

In an exemplary embodiment of the present disclosure, the preset evaluation method is a power efficiency coefficient method or a multi-objective genetic evaluation algorithm.

In an exemplary embodiment of the present disclosure, after the corresponding relationship generating step, the method further includes:

Statistics of all users' attention to the teaching content;

The overall evaluation information of the teaching content is determined according to the attention information of all the users.

In an exemplary embodiment of the present disclosure, after the overall evaluation information determining step, the method further includes:

The teaching content that satisfies the preset condition as the interest content;

Setting a timer, after the timing of reaching the timer, outputting prompt information for prompting to play the interest content; wherein the timing duration set for the timer is before playing the interest content.

In an exemplary embodiment of the present disclosure, after determining the overall evaluation information of the teaching content according to the attention information of all the users, the method further includes:

Rate the classroom teacher corresponding to the teaching content according to the overall evaluation information; and/or

The teaching content is scored according to the overall evaluation information, and the teaching content is adjusted.

In an exemplary embodiment of the present disclosure, after the teacher corresponding to the teaching content is scored according to the overall evaluation information, the method further includes:

If there are multiple classroom teachers in the teaching content, the teaching scores of each classroom teacher are separately counted;

Sorting the teaching scores of the various classroom teachers in descending order;

Generating a scoring list of the teaching scores of the respective classroom teachers in descending order and the URLs of the teaching contents corresponding to the classroom teachers;

Outputting the rating list;

After detecting that the user triggers the URL of the corresponding teaching content according to the rating list, the user is provided with the teaching content of the corresponding classroom teacher.

In an exemplary embodiment of the present disclosure, the method further includes:

And generating a teaching test question for the user according to the attention degree information and the teaching content.

In an aspect of the disclosure, a network teaching attention evaluation apparatus is provided, including:

a feature acquiring module, configured to acquire a user's attention feature by using a user terminal, where the attention feature includes a behavior feature and a state feature;

a setting module, configured to preset a attention feature file position and a weight coefficient;

An information evaluation module, configured to obtain, according to the preset evaluation algorithm, the score evaluation information of the gear position of the attention feature according to the attention feature and the weight coefficient;

An information determining module, configured to determine the attention information according to the split evaluation information of all attention features;

The information generating module is configured to acquire the current teaching content, generate a correspondence between the attention information and the teaching content, and determine the user's attention to the teaching content according to the attention information.

In an aspect of the disclosure, an electronic device is provided, comprising:

Processor;

A memory having stored thereon computer readable instructions that, when executed by the processor, implement the method of any of the above.

In an aspect of the present disclosure, a computer readable storage medium having stored thereon a computer program, the computer program being executed by a processor, implements the method of any of the above.

The network teaching attention degree evaluation method in the exemplary embodiment of the present disclosure, the user terminal acquires the attention degree feature of the user including the behavior feature and the state feature, the preset attention degree feature gear position and the weight coefficient, according to a preset evaluation algorithm, Obtaining the score evaluation information of the gear position of the attention feature according to the attention feature and the weight coefficient, and determining the attention information according to the score evaluation information of all the attention features, acquiring the current teaching content, and generating the attention The correspondence between the degree information and the teaching content determines the degree of attention of the user to the teaching content according to the attention degree information. On the one hand, through the user terminal to obtain a more comprehensive attention feature, and after the analysis and calculation, get a comprehensive evaluation of the degree of attention, greatly improve the accuracy of the attention assessment; on the other hand, through the evaluation of the degree of attention, you can According to the degree of attention after the file, the user is provided with accurate tips or services; on the other hand, by correlating the degree of attention with the teaching content, when the evaluation is low, the attention of different teachers and different teaching contents is adopted. Comparing, using attention to achieve teaching attention reminders, teaching methods early warning and other functions, greatly enhance the user experience.

The above general description and the following detailed description are intended to be illustrative and not restrictive.

DRAWINGS

The above and other features and advantages of the present disclosure will become more apparent from the detailed description.

FIG. 1 illustrates a flowchart of a method of network teaching attention evaluation according to an exemplary embodiment of the present disclosure;

2A-2B illustrate schematic diagrams of a web-based instructional video including high-attention reminder information, in accordance with an exemplary embodiment of the present disclosure;

3A-3C are diagrams showing a network teaching attention degree information list according to an exemplary embodiment of the present disclosure;

FIG. 4 illustrates a schematic block diagram of a network teaching attention degree evaluation apparatus according to an exemplary embodiment of the present disclosure;

FIG. 5 schematically illustrates a block diagram of an electronic device in accordance with an exemplary embodiment of the present disclosure;

FIG. 6 schematically illustrates a schematic diagram of a computer readable storage medium in accordance with an exemplary embodiment of the present disclosure.

Detailed ways

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in a variety of forms and should not be construed as being limited to the embodiments set forth herein. To those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and the repeated description thereof will be omitted.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are set forth However, one skilled in the art will appreciate that the technical solution of the present disclosure may be practiced without one or more of the specific details, or other methods, components, materials, devices, steps, etc. may be employed. In other instances, well-known structures, methods, devices, implementations, materials or operations are not shown in detail to avoid obscuring aspects of the present disclosure.

The block diagrams shown in the figures are merely functional entities and do not necessarily have to correspond to physically separate entities. That is, these functional entities may be implemented in software, or implemented in one or more software-hardened modules, or in different network and/or processor devices and/or microcontroller devices. Implement these functional entities.

In the present exemplary embodiment, a network teaching attention evaluation method is first provided, which can be applied to an electronic device such as a computer; as shown in FIG. 1, the network teaching attention evaluation method may include the following steps:

The attention feature acquiring step S110 is to acquire the user's attention degree feature through the user terminal, and the attention degree feature includes a behavior feature and a state feature;

The gear position and coefficient preset step S120, the preset attention degree feature gear position and the weight coefficient;

An evaluation information obtaining step S130, according to the preset evaluation algorithm, obtaining the score evaluation information of the gear position of the attention degree feature according to the attention degree feature and the weight coefficient;

The degree of interest information determining step S140, determining the degree of interest information according to the piecewise evaluation information of all the attention features;

Corresponding relationship generating step S150, acquiring the current teaching content, generating a correspondence between the attention degree information and the teaching content, and determining the degree of attention of the user to the teaching content according to the attention degree information.

According to the network teaching attention degree evaluation method in the exemplary embodiment, on the one hand, a more comprehensive attention degree feature is obtained through the user terminal, and a comprehensive evaluation of the attention degree is obtained after the analysis and calculation, thereby greatly improving the accuracy of the attention degree evaluation; On the one hand, through the evaluation of the degree of attention, it is possible to provide users with accurate tips or services according to the degree of attention after the file is divided; on the other hand, by comparing the degree of attention with the content of the teaching, the evaluation is more focused. When the time is low, through the comparison of the attention of different teachers and different teaching contents, the use of attention to realize the teaching attention reminder, the teaching method early warning and other functions greatly enhance the user experience.

Hereinafter, the network teaching attention evaluation method in the present exemplary embodiment will be further described.

In the attention feature acquisition step S110, the user's attention degree feature may be acquired by the user terminal, and the attention degree feature includes a behavior feature and a state feature.

In this exemplary embodiment, the user terminal may be a dedicated network teaching device, such as a PC, a network television, or the like, or a portable device such as a mobile phone or a pad for the user to view the network teaching content. When the user terminal user is used to view the online or offline teaching device, the user's attention feature can be collected by the camera, the positioning device, and the like of the user terminal, and used as a basis for the attention evaluation. The attention feature may be a behavior characteristic of the user, such as information such as a teaching content display state and a play time of the user terminal, or may be a state feature, such as a facial feature of the user.

In this example embodiment, the acquiring the user's attention feature by the user terminal in the attention feature acquiring step S110 may include one of the first feature acquiring sub-step S1101, the second feature acquiring sub-step S1102, and the third feature acquiring sub-step S1103. Or multiple substeps:

a first feature acquisition sub-step S1101: acquiring, by the auxiliary device of the user terminal, a state feature of the user, the state feature including a facial feature of the user and a binocular focus feature;

a second feature acquisition sub-step S1102: acquiring, by the auxiliary device of the user terminal, a behavior characteristic of the user;

The third feature acquisition sub-step S1103: acquiring terminal operation information of the user terminal, and determining a behavior characteristic of the user according to the terminal operation information.

The facial features of the user may be acquired by a camera provided by the user terminal, or may be acquired by using an auxiliary image capturing device that communicates with the user terminal. Further, in order to improve the accuracy of the acquired facial features of the user. Sex, can be used with multiple image acquisition devices or with distance sensors. The facial features include face orientation, binocular focus features, and the like. The behavior of the user acquired by the user terminal may be the teaching content display state of the user terminal, and may be whether the user switches the teaching page to the current teaching content detail page in the user terminal, or whether the current volume of the user terminal is obvious. Greater than the ambient volume, etc. The user attention feature further includes terminal operation information of the user terminal, such as whether the user views other online non-teaching related content while viewing the network teaching content on the user terminal. According to the above feature information, the user's attention feature can be analyzed.

In the gear position and coefficient presetting step S120, the attention degree feature gear position and the weight coefficient may be preset.

In this example embodiment, different gear positions and weight coefficients may be defined for the attention feature according to the attribute, content, and severity of the behavior of the attention feature, such as the user's eyes temporarily focusing on the non-teaching content and leaving the user. Compared with the teaching terminal, the former has a lower level of attention and a lighter weight system.

In the evaluation information obtaining step S130, the index evaluation information of the gear position of the attention degree feature may be obtained according to the attention degree feature and the weight coefficient according to a preset evaluation algorithm.

In the example embodiment, the weighting evaluation information of the attention degree is calculated according to the weighting system of each attention degree feature and the gear position, and the indexing evaluation information represents the attention degree of the attention degree feature in the respective gear positions. Value size and corresponding sorting information.

In this example embodiment, the preset evaluation method is a power coefficient method or a multi-objective genetic evaluation algorithm. The preset evaluation method may be a power efficiency coefficient method based on the leveling information comparison, and the power coefficient method is a special multi-objective evaluation algorithm based on the weight evaluation, and the power coefficient method is compared with the ordinary weight evaluation algorithm. According to the multi-objective planning principle, a satisfactory value and an impermissible value are determined for each evaluation index, and the satisfaction value is the upper limit, and the lower limit is not allowed, and the degree of achievement of each indicator is calculated, and the indicators are determined. The scores are then combined by a weighted average to evaluate the overall status of the subjects. The efficiency coefficient method is based on the principle of multi-objective planning. It can calculate and score the evaluation objects from different aspects according to the complexity of the evaluation object, which satisfies the requirements of comprehensive evaluation of multiple indicators.

The preset evaluation method may also be a multi-objective genetic evaluation algorithm.

The multi-objective genetic evaluation algorithm is based on the multi-objective optimization discussed by the pareto optimal solution, including NSGA (non-dominated sorting genetic algorithm) and NSGA-II (fast non-dominated sorting genetic algorithm with elite strategy).

In the example implementation manner, in the evaluating information obtaining step, the preset evaluation algorithm includes:

Wherein, for the sub-file evaluation information, Zib is the attention degree feature, Zis and Ziy are respectively the minimum and maximum values of the binning information, A is the preset weight coefficient, and B is the attention feature gear shift. value.

The sub-file evaluation information is the attention degree information corresponding to the attention degree feature, and reflects the attention degree information of the user on the attention degree feature dimension, and presets the minimum value and the maximum value of the bin file where the attention degree feature is located, Calculating a ratio of the attention feature to a minimum value and a maximum value, multiplying the weight coefficient of the attention feature, and adding the attention feature shift value to obtain a score corresponding to the attention feature File evaluation information.

In the attention degree information determining step S140, the degree of interest information may be determined based on the piece index evaluation information of all the attention feature.

In this example embodiment, the binning evaluation information of all the attention features is integrated to obtain the user's attention information. The integrated calculation method may be one of summation, average calculation, and geometric mean value.

In this example embodiment, the manner of calculating the attention information includes:

Attention information

In the network teaching, each degree of attention feature often has a large amplitude change, which results in the calculation of the binning evaluation information, which produces an extreme value that is significantly different from the other sub-level evaluation information. In order to evaluate the above information to the attention information The influence of the information is minimized, and the method of calculating the geometric mean of the information by using each of the bins may be used when calculating the degree of interest information through the evaluation information of each bin, or may be obtained by obtaining the logarithm of the geometric mean value of interest information. The arithmetic mean of the attention information. Further, a weighted geometric mean calculation method may be used to minimize the influence of the abnormal attention feature extreme value on the attention information.

In the correspondence relationship generating step S150, the current teaching content may be acquired, the correspondence relationship between the attention degree information and the teaching content is generated, and the user's attention degree to the teaching content is determined according to the attention degree information.

In the example embodiment, the attention degree information is used to indicate the degree of attention of the teaching content, and the attention degree of different parts of the teaching content may be sorted according to the attention degree information, and the attention degree is higher than a preset The content of the attention value is recommended to the user for key learning.

In this example, after determining the degree of attention of the user to the teaching content according to the attention degree information, the method further includes: collecting attention information of all the users on the teaching content; according to the all users The attention information determines the overall evaluation information of the teaching content. The attention degree information determines that the user's attention to the teaching content may be an evaluation of the attention of different content, such as different chapters and different teaching forms in the same network teaching content, or may be different teachers or different teaching institutions. The same teaching content is evaluated, and the overall evaluation result of the attention of the teaching content is fed back to the corresponding related user, and the reference information is provided for the user to improve the teaching level.

In this example embodiment, after determining the overall evaluation information of the teaching content according to the attention information of all the users, the method further includes: using the teaching content that meets the preset condition as the interest content as the interest content; setting a timer After the timing of the timer is reached, the prompt information for prompting to play the interest content is output; wherein the timing duration set for the timer is before the content of interest is played. As shown in FIG. 2A, for the online teaching video of a postgraduate advanced mathematics tutoring, according to the historical attention information of the user who learned the web teaching video, the Lobita rule that begins to be explained in the 26th minute of the video is high concern. The content can be marked as interest content and displayed in the video playback information. At the same time, a timer can be set to start timing before the user presets the teaching video, thereby achieving high attention to the user. The reminder function of the content or interest content, as shown in Fig. 2B, is two minutes after the start of the countdown of the high-interest content Lobida rule, reminding the user that the teaching content is 93% of the user's high-interest content after two minutes.

In this example embodiment, after determining the overall evaluation information of the teaching content according to the attention information of all the users, the method further includes: rating the classroom teacher corresponding to the teaching content according to the overall evaluation information; and And or scoring the teaching content according to the overall evaluation information, and adjusting the teaching content. Evaluating the attention degree information of the same teaching content of different teachers, and alerting the whole teaching content, or the teacher whose attention degree information of a specific part of the teaching content is significantly lower than the average level, prompting the teacher to pay attention to the teaching method or adjusting Teaching content also provides teaching management reference information for online teaching organizers.

In this example, after the teacher corresponding to the teaching content is scored according to the overall evaluation information, the method further includes: if the teaching content of the teaching content is multiple, respectively, the teaching scores of each classroom teacher are separately counted; Performing a descending order of the teaching scores of the respective classroom teachers; generating a scoring list of the teaching scores of the classroom teachers and the URLs of the teaching contents corresponding to the classroom teachers in descending order; outputting the scoring list; detecting the user basis After the rating list triggers the URL of the corresponding teaching content, the user is provided with the teaching content of the corresponding classroom teacher. As shown in FIG. 3A, the attention degree information display content of the network tutoring teaching video of a certain network teaching algebra subject is taught by different teachers; as shown in FIG. 3B, the attention of different teachers teaching a network teaching geometric subject network tutoring teaching video The information ranking display content, the display content can help the student to select the teacher of the network teaching; as shown in FIG. 3C, the content of the attention information ranking display of the network tutoring teaching video for different subjects of the same teacher teaching a certain network teaching teaching, The display content can help the student select the teacher's specialty course of the online teaching.

In this example embodiment, the method further includes: generating a teaching test question for the user according to the attention degree information and the teaching content. According to the different content of different users, different personalized teaching test questions are customized for the user, which is more conducive to improving the level of network teaching.

It should be noted that, although the various steps of the method of the present disclosure are described in a particular order in the drawings, this does not require or imply that the steps must be performed in the specific order, or that all the steps shown must be performed. Achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps being combined into one step execution, and/or one step being decomposed into multiple step executions and the like.

Further, in the present exemplary embodiment, a network teaching attention evaluation device is also provided. Referring to FIG. 4, the network teaching attention evaluation device 400 may include a feature acquisition module 410, a setting module 420, an information evaluation module 430, an information determination module 440, and an information generation module 450. among them:

The feature acquiring module 410 is configured to acquire, by the user terminal, the attention feature of the user, where the attention feature includes a behavior feature and a state feature;

a setting module 420, configured to preset a attention feature file position and a weight coefficient;

The information evaluation module 430 is configured to obtain, according to the preset evaluation algorithm, the score evaluation information of the gear position of the attention feature according to the attention feature and the weight coefficient;

The information determining module 440 is configured to determine the degree of interest information according to the binning evaluation information of all the attention features;

The information generating module 450 is configured to acquire the current teaching content, generate a correspondence between the attention information and the teaching content, and determine, according to the attention information, the degree of attention of the user to the teaching content.

The specific details of each network teaching attention evaluation device module in the above have been described in detail in the corresponding audio segment identification method, and therefore will not be described herein.

It should be noted that although several modules or units of the network teaching attention evaluation device 400 are mentioned in the above detailed description, such division is not mandatory. Indeed, in accordance with embodiments of the present disclosure, the features and functions of two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of one of the modules or units described above may be further divided into multiple modules or units.

Further, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

Those skilled in the art will appreciate that various aspects of the present invention can be implemented as a system, method, or program product. Accordingly, aspects of the present invention may be embodied in the form of a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or a combination of hardware and software aspects, which may be collectively referred to herein. "Circuit," "module," or "system."

An electronic device 500 in accordance with such an embodiment of the present invention is described below with reference to FIG. The electronic device 500 shown in FIG. 5 is merely an example and should not impose any limitation on the function and scope of use of the embodiments of the present invention.

As shown in FIG. 5, electronic device 500 is embodied in the form of a general purpose computing device. The components of the electronic device 500 may include, but are not limited to, the at least one processing unit 510, the at least one storage unit 520, the bus 530 connecting the different system components (including the storage unit 520 and the processing unit 510), and the display unit 540.

Wherein the storage unit stores program code, which can be executed by the processing unit 510, such that the processing unit 510 performs various exemplary embodiments according to the present invention described in the "Exemplary Method" section of the present specification. The steps of the examples. For example, the processing unit 510 can perform the attention feature acquisition step S110 to the correspondence generation step S150 as shown in FIG. 1.

The storage unit 520 can include a readable medium in the form of a volatile storage unit, such as a random access storage unit (RAM) 5201 and/or a cache storage unit 5202, and can further include a read only storage unit (ROM) 5203.

The storage unit 520 can also include a program/utility 5204 having a set (at least one) of the program modules 5205, such as but not limited to: an operating system, one or more applications, other program modules, and program data, Implementations of the network environment may be included in each or some of these examples.

Bus 530 may be representative of one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local area using any of a variety of bus structures. bus.

The electronic device 500 can also communicate with one or more external devices 570 (eg, a keyboard, pointing device, Bluetooth device, etc.), and can also communicate with one or more devices that enable the user to interact with the electronic device 500, and/or with Any device (eg, router, modem, etc.) that enables the electronic device 500 to communicate with one or more other computing devices. This communication can take place via an input/output (I/O) interface 550. Also, electronic device 500 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet) via network adapter 560. As shown, network adapter 560 communicates with other modules of electronic device 500 via bus 530. It should be understood that although not shown in the figures, other hardware and/or software modules may be utilized in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives. And data backup storage systems, etc.

Through the description of the above embodiments, those skilled in the art can easily understand that the exemplary embodiments described herein may be implemented by software, or may be implemented by software in combination with necessary hardware. Therefore, the technical solution according to an embodiment of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network. A number of instructions are included to cause a computing device (which may be a personal computer, server, terminal device, or network device, etc.) to perform a method in accordance with an embodiment of the present disclosure.

In an exemplary embodiment of the present disclosure, there is also provided a computer readable storage medium having stored thereon a program product capable of implementing the above method of the present specification. In some possible embodiments, aspects of the present invention may also be embodied in the form of a program product comprising program code for causing said program product to run on a terminal device The terminal device performs the steps according to various exemplary embodiments of the present invention described in the "Exemplary Method" section of the present specification.

Referring to FIG. 6, a program product 600 for implementing the above method, which may employ a portable compact disk read only memory (CD-ROM) and includes program code, and may be in a terminal device, is illustrated in accordance with an embodiment of the present invention. For example running on a personal computer. However, the program product of the present invention is not limited thereto, and in the present document, the readable storage medium may be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus or device.

The program product can employ any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive lists) of readable storage media include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the foregoing.

The computer readable signal medium may include a data signal that is propagated in the baseband or as part of a carrier, carrying readable program code. Such propagated data signals can take a variety of forms including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the foregoing. The readable signal medium can also be any readable medium other than a readable storage medium that can transmit, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium can be transmitted using any suitable medium, including but not limited to wireless, wireline, optical cable, RF, etc., or any suitable combination of the foregoing.

Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++, etc., including conventional procedural Programming language—such as the "C" language or a similar programming language. The program code can execute entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the remote computing device on the user computing device, or entirely on the remote computing device or server. Execute on. In the case of a remote computing device, the remote computing device can be connected to the user computing device via any kind of network, including a local area network (LAN) or wide area network (WAN), or can be connected to an external computing device (eg, provided using an Internet service) Businesses are connected via the Internet).

Further, the above-described drawings are merely illustrative of the processes included in the method according to the exemplary embodiments of the present invention, and are not intended to be limiting. It is easy to understand that the processing shown in the above figures does not indicate or limit the chronological order of these processes. In addition, it is also easy to understand that these processes may be performed synchronously or asynchronously, for example, in a plurality of modules.

Other embodiments of the present disclosure will be apparent to those skilled in the <RTIgt; The present application is intended to cover any variations, uses, or adaptations of the present disclosure, which are in accordance with the general principles of the disclosure and include common general knowledge or common technical means in the art that are not disclosed in the present disclosure. . The specification and examples are to be regarded as illustrative only,

It is to be understood that the invention is not limited to the details of the details and The scope of the disclosure is to be limited only by the appended claims.

Industrial applicability

On the one hand, through the user terminal to obtain a more comprehensive attention feature, and after the analysis and calculation, get a comprehensive evaluation of the degree of attention, greatly improve the accuracy of the attention assessment; on the other hand, through the evaluation of the degree of attention, you can According to the degree of attention after the file, the user is provided with accurate tips or services; on the other hand, by correlating the degree of attention with the teaching content, when the evaluation is low, the attention of different teachers and different teaching contents is adopted. Comparing, using attention to achieve teaching attention reminders, teaching methods early warning and other functions, greatly enhance the user experience.

Claims

A method for evaluating a network teaching attention degree, characterized in that the method comprises the following steps:

The attention feature acquisition step: acquiring, by the user terminal, a user's attention feature, the attention feature includes a behavior feature and a state feature;

Step and coefficient preset steps: preset attention feature position and weight coefficient;

An evaluation information obtaining step: obtaining, according to the preset evaluation algorithm, the score evaluation information of the gear position of the attention degree feature according to the attention degree feature and the weight coefficient;

The degree of interest information determining step: determining the degree of interest information according to the piecewise evaluation information of all attention features;

Corresponding relationship generating step: acquiring current teaching content, generating a correspondence between the attention degree information and the teaching content, and determining, according to the attention degree information, the degree of attention of the user to the teaching content.
The method of claim 1 wherein said attention feature acquisition step comprises one or more of the following sub-steps:

a first feature acquisition sub-step: acquiring, by the auxiliary device of the user terminal, a state feature of the user, the state feature including a facial feature of the user and a binocular focus feature;

a second feature acquisition sub-step: acquiring, by the auxiliary device of the user terminal, a behavior characteristic of the user;

The third feature acquisition sub-step: acquiring terminal operation information of the user terminal, and determining a behavior characteristic of the user according to the terminal operation information.
The method according to claim 1, wherein in the evaluating information obtaining step, the preset evaluation algorithm comprises:

;

Wherein, for the sub-file evaluation information, Zib is the attention degree feature, Zis and Ziy are respectively the minimum and maximum values of the binning information, A is the preset weight coefficient, and B is the attention feature gear shift. value;

In the attention degree information determining step, the manner of calculating the attention degree information includes:

.
The method of claim 1 wherein

The preset evaluation algorithm is a power coefficient method or a multi-objective genetic evaluation algorithm.
The method of claim 1, wherein after the step of generating a correspondence, the method further comprises:

Statistics of all users' attention to the teaching content;

The overall evaluation information of the teaching content is determined according to the attention information of all the users.
The method of claim 5, wherein after the step of determining the overall evaluation information, the method further comprises:

The teaching content that satisfies the preset condition as the interest content;

Setting a timer, after the timing of reaching the timer, outputting prompt information for prompting to play the interest content; wherein the timing duration set for the timer is before playing the interest content.
The method according to claim 5, wherein after determining the overall evaluation information of the teaching content according to the information of the degree of interest of the users, the method further includes:

Rate the classroom teacher corresponding to the teaching content according to the overall evaluation information; and/or

The teaching content is scored according to the overall evaluation information, and the teaching content is adjusted.
The method of claim 7, wherein the method further comprises: after the teacher corresponding to the teaching content is scored according to the overall evaluation information, the method further comprises:

If there are multiple classroom teachers in the teaching content, the teaching scores of each classroom teacher are separately counted;

Sorting the teaching scores of the various classroom teachers in descending order;

Generating a scoring list of the teaching scores of the respective classroom teachers in descending order and the URLs of the teaching contents corresponding to the classroom teachers;

Outputting the rating list;

After detecting that the user triggers the URL of the corresponding teaching content according to the rating list, the user is provided with the teaching content of the corresponding classroom teacher.
The method of any of claims 1 or 5, wherein the method further comprises:

And generating a teaching test question for the user according to the attention degree information and the teaching content.
A network teaching attention evaluation device, characterized in that the device comprises:

a feature acquiring module, configured to acquire a user's attention feature by using a user terminal, where the attention feature includes a behavior feature and a state feature;

a setting module, configured to preset a attention feature file position and a weight coefficient;

An information evaluation module, configured to obtain, according to the preset evaluation algorithm, the score evaluation information of the gear position of the attention feature according to the attention feature and the weight coefficient;

An information determining module, configured to determine the attention information according to the split evaluation information of all attention features;

The information generating module is configured to acquire the current teaching content, generate a correspondence between the attention information and the teaching content, and determine the user's attention to the teaching content according to the attention information.
An electronic device characterized by comprising

Processor;

A memory having computer readable instructions stored thereon, the computer readable instructions being executed by the processor to implement the method of any one of claims 1 to 9.
A computer readable storage medium having stored thereon a computer program, the computer program being executed by a processor to implement the method of any one of claims 1 to 9.