CN114998975A - Foreign language teaching method and device based on big data - Google Patents
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Abstract
The invention relates to a foreign language teaching method and a foreign language teaching device based on big data, wherein the method comprises the following steps: acquiring an emotional state picture of a user in a learning process; performing data analysis on the emotional state picture; judging the real-time learning emotion state of the user according to data analysis; and reminding the user according to the real-time learning emotional state, and timely replacing the teaching mode. The invention not only can play a role in monitoring the current learning state of the user in real time, but also can remind the user in time according to the current learning state, thereby avoiding the problem that the learning quality is influenced because of the inattention, and simultaneously, the invention can also adjust the teaching mode according to the real-time learning state of the user, thereby leading the teaching mode to be more flexible.
Description
Technical Field
The invention relates to the field of big data, in particular to a remote teaching technology applied to online education, and specifically relates to a foreign language teaching method and device based on big data.
Background
At present, the internet is widely popularized, and under the influence, the operation modes of various industries are changed greatly. In the field of education, the combination of teaching and the internet promotes the generation of an online teaching mode.
The interactive online learning system established by utilizing the multimedia computer technology and the network technology becomes a new learning mode for realizing the communication and interaction between teachers and students and between students, is a brand new teaching mode which is explored out of the frame of the traditional education system and the teaching method, creates an ideal interactive learning environment through the network, realizes online teaching, management and interactive learning services, overcomes the resource allocation limitation in time and space, and leads learners to achieve the purpose of learning in the interactive relationship.
However, even so, it is still an interesting question of how to maintain a high quality teaching level in large-scale courses (hundreds of people-scale offline courses and thousands or even tens of thousands of people-scale online courses).
It is obvious that simply increasing the human resource allocation linearly is not the most advantageous way to solve the problem, and the traditional online education generally cannot ensure the learning quality of the user and cannot monitor and remind the user, so that the method has a great defect.
The present invention has been made in view of this point.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a foreign language teaching method and device based on big data, which can monitor the current learning state of a user in real time and remind the user in time according to the current learning state.
In order to solve the technical problems, the invention adopts the technical scheme that:
a big data based foreign language instruction method, the method comprising the steps of:
acquiring an emotional state picture of a user in a learning process;
performing data analysis on the emotional state picture;
judging the real-time learning emotion state of the user according to data analysis;
and reminding the user according to the real-time learning emotional state, and timely replacing the teaching mode.
In an embodiment of any of the foregoing schemes, before the acquiring an emotional state picture of the user in the learning process, the method further includes:
registering exclusive account numbers on a teaching platform, wherein each user corresponds to one exclusive account number;
logging in a teaching platform according to the exclusive account;
browsing and clicking the home page display information of the teaching platform, and searching course contents from a cloud database of the teaching platform;
and performing selection learning according to the course content.
In an embodiment of any of the foregoing solutions, searching for course content from the cloud database of the teaching platform includes:
acquiring ID information of a user corresponding to the exclusive account;
acquiring a browsing record corresponding to the ID information;
and distributing a priority to each course content according to the browsing record, wherein the distribution of the priority of the course content is determined according to the proportion of the course content in the browsing record, and all the course contents are arranged in sequence according to the proportion of the course content.
In an embodiment of any of the foregoing schemes, the data analysis of the emotional state image includes:
capturing an expression picture once within a preset time, and if no face image can be detected in N times of continuous shooting, judging that the expression picture is in an unmanned learning state;
if the human face is detected continuously, eye detection and mouth detection are carried out, and if the images detected continuously and repeatedly are all closed-eye state images or the images detected repeatedly and yawning state images of the mouth are detected in the detection process, the learner is judged to be in a fatigue doze state.
In an embodiment of any of the foregoing schemes, the determining, according to the data analysis, the real-time learning emotional state of the user includes:
continuously extracting the eye height, the eye width, the mouth height and the mouth width in the same face image shot in different time periods, and recording;
according to the shooting time, if the height of eyes shot in the next second is normal but tends to be larger, and the mouth is normally closed or slightly opened, the eyes are in the concentrated state;
if the eye height shot in the next second is normal or the eye height slightly changes but does not change much, and the mouth is in a normal closed or half-open state, the eye height is in a normal state;
if the eye height shot in the next second is small, normal or large, but the eye height tends to be significantly small, and the mouth is wide and open or yawned, the eye is in a fatigue state.
In a preferred embodiment of any of the foregoing solutions, the open-eye classifier and the closed-eye classifier store a face sample library, and the face sample library stores face photographs in open-eye and closed-eye states.
In an embodiment of any of the foregoing schemes, the reminding a user according to the real-time learning emotional state includes:
if the learning state is judged to be unmanned, the teaching content is automatically suspended or a window or audio prompt is given;
if the learner is judged to be in a fatigue doze state, window audio reminding is carried out.
In an embodiment of any of the foregoing solutions, the timely changing teaching mode includes:
acquiring a real-time concentration state, a fatigue state or a normal state of a user;
matching different corresponding teaching modes in a teaching platform according to the concentration state, the fatigue state or the normal state, wherein the teaching platform stores a plurality of teaching modes;
if the state is the concentration state or the normal state, normal teaching is carried out;
and if the user is in a fatigue state, adjusting the teaching mode into an interaction mode, and carrying out real-time interaction on the user line.
A big data based foreign language teaching device, the teaching device comprising:
the system comprises an acquisition module, a learning module and a learning module, wherein the acquisition module is used for acquiring emotional state pictures of a user in the learning process, and specifically comprises the steps of capturing expression pictures within preset time, and judging that the emotional state pictures are in an unmanned learning state at the moment if facial images cannot be detected in N times of continuous shooting; if the human face is continuously detected, carrying out human eye detection and mouth detection, and if the images which are continuously detected for multiple times are all eye-closing state images or the images of the mouth which are continuously detected for multiple times are detected, judging that the learner is in a fatigue doze state;
the analysis module is used for carrying out data analysis on the emotional state picture;
the judging module is used for judging the real-time learning emotion state of the user according to data analysis;
and the processing module is used for reminding the user according to the real-time learning emotional state and timely replacing the teaching mode.
In an embodiment preferred in any of the above solutions, further comprising:
the system comprises a registration module, a display module and a display module, wherein the registration module is used for registering exclusive account numbers on a teaching platform, and each user corresponds to one exclusive account number;
the login module is used for logging in a teaching platform according to the exclusive account;
the browsing module is used for browsing and clicking the home page display information of the teaching platform and searching course contents from a cloud database of the teaching platform, and specifically comprises the steps of acquiring ID information of a user corresponding to the exclusive account; acquiring a browsing record corresponding to the ID information; distributing a priority to each course content according to the browsing records, wherein the distribution of the priority of the course content is determined by the proportion of the course content in the browsing records, and all the course contents are arranged in sequence according to the proportion of the course content;
and the selection module is used for selecting learning according to the course content.
After the technical scheme is adopted, compared with the prior art, the invention has the following beneficial effects.
By collecting the emotional state pictures and analyzing the data of the emotional state pictures, the real-time learning emotional state of the user can be judged, the user can be reminded according to the real-time learning emotional state, and the teaching mode can be changed in time, so that the effect of monitoring the learning state of the current user in real time can be achieved, the user can be reminded in time according to the current learning state, the problem that the learning quality is influenced due to the fact that attention is not focused is avoided, meanwhile, the teaching mode can be adjusted according to the real-time learning state of the user, and the teaching mode can be more flexible.
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
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. Some specific embodiments of the present application will be described in detail hereinafter by way of illustration and not limitation with reference to the accompanying drawings. The same reference numbers will be used throughout the drawings to refer to the same or like parts or portions, and it will be understood by those skilled in the art that the drawings are not necessarily drawn to scale, in which:
fig. 1 is a flow chart of a big data-based foreign language teaching method according to the present invention.
Fig. 2 is a schematic flow chart of the big data-based foreign language teaching method before the emotional state picture of the user in the learning process is acquired.
Fig. 3 is a schematic diagram of a big data-based foreign language instruction device according to the present invention.
It should be noted that the drawings and the description are not intended to limit the scope of the inventive concept in any way, but rather to illustrate it for those skilled in the art by reference to specific embodiments.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It will be understood that when an element is referred to as being "secured to" or "disposed on" another element, it can be directly on the other element or be indirectly on the other element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or be indirectly connected to the other element.
In the description of the present application, it is to be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like, as used herein, refer to an orientation or positional relationship indicated in the drawings, which is solely for the purpose of facilitating the description and simplifying the description, and does not indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and thus, is not to be construed as limiting the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
The following examples of the present application illustrate the details of the present application with the examples of the method and apparatus for teaching foreign languages based on big data, but the present application is not limited by the examples.
Examples
As shown in fig. 1 and 2, the present invention provides a big data based foreign language teaching method, which includes the steps of:
step 1: registering exclusive account numbers on a teaching platform, wherein each user corresponds to one exclusive account number;
and 2, step: logging in a teaching platform according to the exclusive account;
and step 3: browsing and clicking the display information of the home page of the teaching platform, and searching course contents from a cloud database of the teaching platform;
and 4, step 4: according to the course content, carrying out selection learning;
in the foreign language teaching method based on big data according to the embodiment of the invention, the object roles of the teaching platform are divided into three types: the method comprises the steps of accessing learners, teaching editing uploaders and background resource managers, logging in a teaching platform, accessing learners to browse on-demand teaching platform courses, downloading learning materials such as courseware and exercises, carrying out self-evaluation, or interacting with the problems encountered in face-to-face communication learning through multimedia videos and an online live broadcast platform, and the like. The teaching editor uploader guides the learner to perform course teaching through the teaching platform, releases the online homework, interacts with the student through the live broadcast platform and the like. And the background resource manager is responsible for the related work of user information management, course information management, multimedia acquisition, multimedia on-demand and live broadcast, interactive communication management and the like in the platform.
The teaching platform comprises a user management module, a personal user management module and a user management module, wherein the user management module is responsible for providing a teaching platform administrator permission account for the teaching platform and managing the permission of all levels of accounts, and the main operations comprise specific operations of adding a user account, deleting the user account, modifying a user password, freezing the user account and the like; the personal user management module manages personal information, and related management operations comprise browsing, adding and modifying.
The user management module ensures real-time legality of teaching platform access and operation aiming at the teaching platform, and comprises registered user application, user permission setting, auditing management and user permission setting. And the user registers by filling in the form, and needs to wait for the identity verification of an administrator after submitting the specific information to the teaching platform. After the teaching platform user is registered, the administrator allocates matched roles to the teaching platform user, the teaching platform user can obtain the authority to obtain the actual service of the roles corresponding to the teaching platform after obtaining the roles, the registered user can determine a user name and a password through the teaching platform to carry out teaching platform login operation, and the identity is judged according to the user login information, so that different user operation interfaces are provided.
In the embodiment of the invention, the cloud database is stored in the resource manager, the resource manager can provide historical trace browsing functions of downloading and learning resources for students, and can store specific information fed back to teachers by the students through the teaching platform in the teaching platform database, when the students enter a downloading interface of the learning resources through the teaching platform interface, the students can select the specific resources to be learned and enter the teaching platform browsing interface by downloading the corresponding resources.
And 5: acquiring an emotional state picture of a user in a learning process;
step 6: performing data analysis on the emotional state picture;
and 7: judging the real-time learning emotion state of the user according to data analysis;
and 8: and reminding the user according to the real-time learning emotional state, and timely replacing the teaching mode.
In the foreign language teaching method based on big data, by collecting the emotional state picture and performing data analysis on the emotional state picture, the real-time learning emotional state of the user can be judged, the user can be reminded according to the real-time learning emotional state, and the teaching mode can be changed in time, so that the effect of monitoring the learning state of the current user in real time can be achieved, the user can be reminded in time according to the current learning state, the problem that the learning quality is influenced due to the fact that attention is not concentrated can be avoided, meanwhile, the teaching mode can be adjusted according to the real-time learning state of the user, the teaching mode can be more flexible, wherein the emotional state picture is collected by a camera, and the camera is arranged on a computer.
As shown in fig. 1, searching the course content from the cloud database of the teaching platform includes:
step 31: acquiring ID information of a user corresponding to the exclusive account;
step 32: acquiring a browsing record corresponding to the ID information;
step 33: and distributing a priority to each course content according to the browsing record, wherein the distribution of the priority of the course content is determined according to the proportion of the course content in the browsing record, and all the course contents are arranged in sequence according to the proportion of the course content.
In the foreign language teaching method based on big data according to the embodiment of the present invention, in order to ensure the real-time performance of the characteristics of the course contents, such as downloading from resources in the cloud database and online access, a priority is assigned to each course content, and the assignment of the priority of the course contents has a certain relationship with the frequency of accessing the course contents. Who will get the highest priority, which has the highest access frequency, and so on, to get a lower priority than this event in the next period.
When a plurality of users access the teaching platform, in order to ensure the effectiveness of the course content characteristics in the database, each user is also assigned with a priority, and generally, the assignment of the user priority has a certain relationship with the frequency of using the teaching platform and the level of the dedicated account. The higher the access frequency of the teaching platform is, the higher the priority of the teaching platform is, the next user can obtain the priority which is lower than the access frequency by one level, and the operation of a plurality of users accessing the database at the same time is correctly coordinated so as to ensure that the data operation of the database is correct and consistent.
As shown in fig. 1, performing data analysis on the emotional state image includes:
step 61: capturing an expression picture once within a preset time, if no human face image is detected in N times of continuous shooting, judging that the current state is an unmanned learning state, and automatically suspending teaching content or giving window or audio reminding;
step 62: if the human face is continuously detected, eye detection and mouth detection are carried out, if the images detected for a plurality of times are all eye-closing state images (for example, the images are detected once per second within 1 minute, and the like) in the detection process, or if the images detected for a plurality of times of yawning state images of the mouth are detected, the learner is judged to be in a fatigue doze state, and window audio prompting is carried out.
In the foreign language teaching method based on big data according to the embodiment of the present invention, after the picture is taken, the picture needs to be preprocessed, and after the preprocessing, the subsequent processing and feature extraction can be facilitated, and the accuracy of matching image features can be improved, wherein the preprocessing includes:
step 611: the collected pictures are grayed, generally, face images collected by various image collection devices are color images, and in the color images, color backgrounds of the images may interfere with extraction or identification of face features, so that before the face is detected, the face is grayed, and the color pictures are uniformly converted into negative films with consistent grays. All colors in a color image may be combined from the three primary colors R, G, B. Any color can be represented by a quadrant in the first quadrant in three-dimensional space, and the colors are random. When all three primary colors are 0, the color is black; and appears white when the maximum value is reached. The gray scale map has no color information, and is an image between white and black to represent the luminance value of a pixel. An eight-bit binary is often used to represent a gray level, so there are 256 gray levels, so all 0's represent black and all 1's represent white, where the formula for the image to be gray converted is:
Step 612: histogram equalization, specifically comprising the steps of: the variable r represents the gray level of a pixel in a detection sample, in the identification process, the gray level number and the value of the pixel are processed and graded, the general gray level range is that r is more than or equal to 0 and less than or equal to 1, wherein r =0 represents that the gray level tends to pure black, and r =1 represents that the gray level tends to pure white, for any test sample, the gray level of the pixel value contained in the test sample obeys the random distribution between 0 and 1, and because the human face is collected and seriously influenced by the illumination change, the obtained human face model presents the condition of uneven brightness, the accuracy of human face identification of the images can be reduced when the characteristics are extracted, and therefore, after the histogram equalization processing is adopted, the extraction can be more accurate.
Step 613: the image after histogram equalization is subjected to Gaussian filtering, so that the image is smoother;
step 614: after the image after Gaussian filtering is subjected to geometric normalization processing, the influence of hairs and backgrounds on the face model is reduced, the face recognition accuracy is further improved, and the translation invariance of the face model in the image is guaranteed.
In an embodiment of the present invention, the determining, according to the data analysis, a real-time learning emotional state of the user includes:
continuously extracting the eye height, the eye width, the mouth height and the mouth width in the same face image shot in different time periods, and recording;
according to the shooting time, if the height of eyes shot in the next second is normal but tends to be larger, and the mouth is normally closed or slightly opened, the eyes are in the concentrated state;
if the eye height shot in the next second is normal or the eye height slightly changes but does not change much, and the mouth is in a normal closed or half-open state, the eye height is in a normal state;
if the eye height shot in the next second is small or normal or large, but the eye height is obviously reduced and the mouth is wide or yawned, the state is fatigue, the eye opening classifier and the eye closing classifier store a face sample library, and the face sample library stores face pictures in the eye opening state and the eye closing state.
In the big data-based foreign language teaching method according to the embodiment of the invention, because the different degrees of mouth opening states also represent different emotional states of learners, the mouth opening degree can also be described by geometric characteristics when
If so, the state is normal;
when in use
When the paper is in a half-sheet state;
when in use
If so, the state is full, so if so, it is determined to be yawning.
In the embodiment of the present invention, the timely changing of the teaching mode includes:
acquiring a real-time concentration state, a fatigue state or a normal state of a user;
matching different corresponding teaching modes in a teaching platform according to the concentration state, the fatigue state or the normal state, wherein the teaching platform stores a plurality of teaching modes;
if the state is the concentration state or the normal state, normal teaching is carried out;
if the user is in a fatigue state, the teaching mode is adjusted to be an interactive mode, and real-time interaction is carried out on the user line, so that the user can be prevented from sleeping, and the class quality is improved.
As shown in fig. 3, a foreign language teaching apparatus based on big data, the teaching apparatus comprising:
the system comprises an acquisition module, a learning module and a learning module, wherein the acquisition module is used for acquiring emotional state pictures of a user in the learning process, and specifically comprises the steps of capturing expression pictures once within preset time, and judging that the current learning state is an unmanned learning state if no face image can be detected in N times of continuous shooting; if the human face is continuously detected, carrying out human eye detection and mouth detection, and if the continuous repeated detections are all eye-closing state images in the detection process, or detecting a mouth yawning state image for multiple times, judging that the learner is in a fatigue doze state;
the analysis module is used for carrying out data analysis on the emotional state picture;
the judging module is used for judging the real-time learning emotion state of the user according to data analysis;
and the processing module is used for reminding the user according to the real-time learning emotional state and timely replacing the teaching mode.
In an embodiment of the present invention, the big data-based foreign language teaching apparatus further includes:
the system comprises a registration module, a display module and a display module, wherein the registration module is used for registering exclusive account numbers on a teaching platform, and each user corresponds to one exclusive account number;
the login module is used for logging in a teaching platform according to the exclusive account;
the browsing module is used for browsing and clicking the home page display information of the teaching platform and searching course contents from a cloud database of the teaching platform, and specifically comprises the steps of acquiring ID information of a user corresponding to the exclusive account; acquiring a browsing record corresponding to the ID information; according to the browsing records, assigning priorities to each course content, wherein the assignment of the priorities of the course contents is determined according to the proportion of the course contents in the browsing records, and all the course contents are arranged in sequence according to the proportion of the course contents;
and the selection module is used for selecting learning according to the course content.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A method for teaching a foreign language based on big data, the method comprising the steps of:
acquiring an emotional state picture of a user in a learning process;
performing data analysis on the emotional state picture;
judging the real-time learning emotion state of the user according to data analysis;
and reminding the user according to the real-time learning emotional state, and timely replacing the teaching mode.
2. The big data based foreign language teaching method according to claim 1, wherein before the collecting the emotional state picture of the user in the learning process, further comprising:
registering exclusive account numbers on a teaching platform, wherein each user corresponds to one exclusive account number;
logging in a teaching platform according to the exclusive account;
browsing and clicking the display information of the home page of the teaching platform, and searching course contents from a cloud database of the teaching platform;
and performing selection learning according to the course content.
3. The big-data based foreign language teaching method according to claim 2, wherein the step of searching the course content from the cloud database of the teaching platform comprises:
acquiring ID information of a user corresponding to the exclusive account;
acquiring a browsing record corresponding to the ID information;
and according to the browsing records, assigning a priority to each course content, wherein the assignment of the priority to the course content is determined according to the proportion of the course content in the browsing records, and all the course contents are arranged in sequence according to the proportion of the course content.
4. The big-data-based foreign language teaching method according to claim 3, wherein the data analysis of the emotional state picture comprises:
capturing an expression picture once within a preset time, and if no face image can be detected in N times of continuous shooting, judging that the expression picture is in an unmanned learning state;
if the human face is detected continuously, eye detection and mouth detection are carried out, and if the images detected continuously and repeatedly are all closed-eye state images or the images detected repeatedly and yawning state images of the mouth are detected in the detection process, the learner is judged to be in a fatigue doze state.
5. The big data based foreign language teaching method according to claim 4, wherein said determining the real-time learning emotion state of the user based on the data analysis comprises:
continuously extracting the eye height, the eye width, the mouth height and the mouth width in the same face image shot in different time periods, and recording;
according to the shooting time, if the height of eyes shot in the next second is normal but tends to be larger, and the mouth is normally closed or slightly opened, the eyes are in the concentrated state;
if the eye height shot in the next second is normal or the eye height slightly changes but does not change much, and the mouth is in a normal closed or half-open state, the eye height is in a normal state;
if the eye height shot in the next second is small, normal or large, but the eye height tends to be significantly small, and the mouth is wide and open or yawned, the eye is in a fatigue state.
6. The big-data based foreign language teaching method according to claim 5, comprising an open-eye classifier and a closed-eye classifier, wherein the open-eye classifier and the closed-eye classifier store therein a face sample library, and the face sample library stores therein open-eye and closed-eye state face photos.
7. The big data based foreign language teaching method according to claim 6, wherein the reminding the user according to the real-time learning emotional state comprises:
if the learning state is judged to be unmanned, the teaching content is automatically suspended or a window or audio prompt is given;
if the learner is judged to be in the fatigue doze state, the audio reminding is carried out in the window.
8. The big-data based foreign language teaching method according to claim 7, wherein the timely changing teaching mode comprises:
acquiring a real-time concentration state, a fatigue state or a normal state of a user;
matching different corresponding teaching modes in a teaching platform according to the concentration state, the fatigue state or the normal state, wherein the teaching platform stores a plurality of teaching modes;
if the state is the concentration state or the normal state, normal teaching is carried out;
and if the user is in a fatigue state, adjusting the teaching mode into an interaction mode, and carrying out real-time interaction on the user line.
9. A foreign language teaching device based on big data, characterized in that the teaching device includes:
the system comprises an acquisition module, a learning module and a learning module, wherein the acquisition module is used for acquiring emotional state pictures of a user in the learning process, and specifically comprises the steps of capturing expression pictures once within preset time, and judging that the current learning state is an unmanned learning state if no face image can be detected in N times of continuous shooting; if the human face is continuously detected, carrying out human eye detection and mouth detection, and if the continuous repeated detections are all eye-closing state images in the detection process, or detecting a mouth yawning state image for multiple times, judging that the learner is in a fatigue doze state;
the analysis module is used for carrying out data analysis on the emotional state picture;
the judging module is used for judging the real-time learning emotion state of the user according to data analysis;
and the processing module is used for reminding the user according to the real-time learning emotional state and timely replacing the teaching mode.
10. The big-data based foreign language teaching device according to claim 9, further comprising:
the system comprises a registration module, a display module and a display module, wherein the registration module is used for registering exclusive account numbers on a teaching platform, and each user corresponds to one exclusive account number;
the login module is used for logging in a teaching platform according to the exclusive account;
the browsing module is used for browsing and clicking the home page display information of the teaching platform and searching course contents from a cloud database of the teaching platform, and specifically comprises the steps of acquiring ID information of a user corresponding to the exclusive account; acquiring a browsing record corresponding to the ID information; distributing a priority to each course content according to the browsing records, wherein the distribution of the priority of the course content is determined by the proportion of the course content in the browsing records, and all the course contents are arranged in sequence according to the proportion of the course content;
and the selection module is used for selecting learning according to the course content.
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