Method, device, medium and electronic equipment for generating language teaching guidance information

Abstract

The embodiments of the present application provide a method and apparatus for generating language teaching guidance information. The method for generating language teaching guidance information includes: obtaining evaluation keywords by quantifying and encoding questionnaire information on second language learning of a target subject, then determining the probability distribution of the evaluation keywords corresponding to the attribute dimension based on the correlation between the evaluation keywords and a preset attribute dimension, determining the target attribute corresponding to the target subject based on the comparative relationship between the probability distribution of the evaluation keywords corresponding to the attribute dimension and the preset probability distribution corresponding to the preset attribute, and finally determining the target teaching guidance information corresponding to the target attribute based on the teaching guidance method set for each attribute, so that the target subject can obtain learning guidance consistent with its attribute, thereby improving learning efficiency, and the teacher can obtain teaching guidance information consistent with the learning subject's preferred attribute, thereby improving teaching efficiency.

Description

Technical Field

[0001] The present application relates to the field of computer and communication technology, and in particular to a method and device for generating language teaching guidance information. Background Art

[0002] Second language learning and teaching typically involves online or offline tutoring, or through courses delivered via apps. This approach targets a broad range of students and teachers, but each student's situation is unique, and the focus and learning areas may vary. Consequently, this approach may not meet the individual student's circumstances and learning needs, reducing learning efficiency and enthusiasm, and leading to lower teaching effectiveness and outcomes. Summary of the Invention

[0003] The embodiments of the present application provide a method and apparatus for generating language teaching guidance information, thereby improving learning efficiency and teaching efficiency at least to a certain extent.

[0004] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the present application.

[0005] According to one aspect of an embodiment of the present application, a method for generating language teaching guidance information is provided, comprising: obtaining questionnaire information for a target object learning a second language; wherein the questionnaire information includes the target object's place of origin, place of learning the second language, major studied, motivation for learning the second language, and degree of self-efficacy in learning the second language; quantizing and encoding the questionnaire information to obtain a quantized sequence, and extracting evaluation keywords from the quantized sequence; determining a probability distribution of the evaluation keywords corresponding to a preset attribute dimension based on a correlation between the evaluation keywords and the preset attribute dimension; determining a target attribute corresponding to the target object based on the probability distribution of the evaluation keywords corresponding to the attribute dimension and a preset probability distribution corresponding to a preset attribute; and determining target teaching guidance information corresponding to the target attribute based on a teaching guidance method set for each attribute.

[0006] According to one aspect of an embodiment of the present application, a device for generating language teaching guidance information is provided, including:

[0007] An acquisition unit is used to obtain questionnaire information for a target object learning a second language; wherein the questionnaire information includes the target object's place of origin, the place of learning the second language, the major studied, the motivation for learning the second language, and the degree of self-efficacy in learning the second language; a coding unit is used to quantize and encode the questionnaire information to obtain a quantized sequence, and to extract evaluation keywords from the quantized sequence; a probability unit is used to determine the probability distribution of the evaluation keywords corresponding to the attribute dimensions based on the correlation between the evaluation keywords and the preset attribute dimensions; an attribute unit is used to determine the target attribute corresponding to the target object based on the probability distribution of the evaluation keywords corresponding to the attribute dimensions and the preset probability distribution corresponding to the preset attributes; a guidance unit is used to determine the target teaching guidance information corresponding to the target attribute based on the teaching guidance method set for each attribute.

[0008] In some embodiments of the present application, based on the aforementioned scheme, the encoding unit includes: an identification unit for identifying the place of origin, place of study, major, motivation and degree of self-efficacy in the questionnaire information; a sequence unit for encoding the place of origin, place of study, major, motivation and degree of self-efficacy in the questionnaire information based on set quantitative labels to obtain a quantitative sequence containing the quantitative labels; a detection unit for detecting keywords in the quantitative sequence to obtain the evaluation keywords.

[0009] In some embodiments of the present application, based on the aforementioned scheme, the sequence unit is used to determine the quantitative labels corresponding to the place of origin, place of study, major, motivation and self-efficacy level in the questionnaire information based on the set quantitative labels; the quantitative labels corresponding to the place of origin, place of study, major, motivation and self-efficacy level in the questionnaire information are added to the corresponding positions respectively to obtain a quantitative sequence containing the quantitative labels.

[0010] In some embodiments of the present application, based on the aforementioned scheme, the attribute unit is used to weight the elements in the probability distribution based on the preset attribute weights corresponding to each probability dimension to obtain a weighted probability distribution corresponding to the probability distribution; based on the element values ​​in the weighted probability distribution and the element values ​​in the preset probability distribution, determine the similarity between the weighted probability distribution and the preset probability distribution; and use the attribute corresponding to the preset probability distribution when the similarity is the highest as the target attribute corresponding to the target object.

[0011] In some embodiments of the present application, based on the aforementioned scheme, the device for generating language teaching guidance information is also used to classify the evaluation keywords of the sample objects based on a classification model constructed by a neural network, and determine the preference attributes corresponding to the sample objects; based on the comparison results between the preference attributes and the attribute labels of the sample objects, update the parameters of the classification model to obtain an updated classification model; input the questionnaire information of the target object into the classification model for classification, and determine the target attributes corresponding to the target object.

[0012] In some embodiments of the present application, based on the aforementioned scheme, the preset attributes include self-efficacy type and learning motivation type; wherein the self-efficacy type represents the type of students who need to improve their learning confidence; and the learning motivation type represents the type of students who need to adjust their learning motivation.

[0013] In some embodiments of the present application, based on the aforementioned scheme, the second language includes English, and the guidance unit is used to determine the target teaching guidance information corresponding to the self-efficacy type based on the set teaching guidance method if the target attribute corresponding to the target object is the self-efficacy type; wherein the target teaching guidance information includes teaching method, teaching process, teaching time representation, and teaching suggestions.

[0014] According to one aspect of an embodiment of the present application, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method for generating language teaching guidance information as described in the above embodiment is implemented.

[0015] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for generating language teaching guidance information as described in the above embodiment.

[0016] According to one aspect of an embodiment of the present application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the method for generating language teaching guidance information provided in the various optional implementations described above.

[0017] In the technical solutions provided in some embodiments of the present application, evaluation keywords are obtained by quantitatively encoding questionnaire information on second language learning based on the target object, and then the probability distribution of the evaluation keywords corresponding to the attribute dimension is determined based on the correlation between the evaluation keywords and the preset attribute dimension. The target attributes corresponding to the target object are determined based on the comparative relationship between the probability distribution of the evaluation keywords corresponding to the attribute dimension and the preset probability distribution corresponding to the preset attribute. Finally, the target teaching guidance information corresponding to the target attribute is determined based on the teaching guidance method set for each attribute, so that the target object can obtain learning guidance consistent with its attributes, thereby improving learning efficiency, and the teacher can obtain teaching guidance information consistent with the preferred attributes of the learning object, thereby improving teaching efficiency.

[0018] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings are incorporated into and constitute a part of the specification, illustrating embodiments consistent with the present application and, together with the specification, explaining the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort. In the drawings:

[0020] Figure 1 A schematic diagram showing an exemplary system architecture to which the technical solutions of the embodiments of the present application can be applied;

[0021] Figure 2 A flowchart schematically illustrates a method for generating language teaching guidance information according to an embodiment of the present application;

[0022] Figure 3 Schematically shows a flow chart for determining evaluation keywords according to one embodiment of the present application;

[0023] Figure 4 A flowchart for determining a target attribute corresponding to a target object according to an embodiment of the present application is schematically shown;

[0024] Figure 5 A block diagram schematically illustrates an apparatus for generating language teaching guidance information according to an embodiment of the present application;

[0025] Figure 6 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0026] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0027] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner.In the following description, many specific details are provided so as to provide a full understanding of the embodiments of the present application. However, it will be appreciated by those skilled in the art that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps etc. can be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0028] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0029] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0030] Figure 1 A schematic diagram shows an exemplary system architecture to which the technical solutions of the embodiments of the present application can be applied.

[0031] like Figure 1 As shown, the system architecture may include terminal devices (such as Figure 1 101, tablet computer 102, and portable computer 103, which may also be a desktop computer, etc.), network 104, and server 105. Network 104 is a medium for providing a communication link between the terminal device and server 105. Network 104 can include various connection types, such as wired communication links, wireless communication links, etc.

[0032] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as needed. For example, the server 105 may be a server cluster consisting of multiple servers.

[0033] The user can use the terminal device to interact with the server 105 through the network 104 to receive or send messages, such as sending questionnaire information to the server. The server 105 can be a server that provides various services. For example, the user uses the terminal device to upload questionnaire information for learning a second language to the server 105; wherein the questionnaire information includes the target object's place of origin, the place of learning the second language, the major studied, the motivation for learning the second language, and the degree of self-efficacy for learning the second language; the questionnaire information is quantified and encoded to obtain a quantitative sequence, and evaluation keywords are extracted from the quantitative sequence; based on the correlation between the evaluation keywords and the preset attribute dimensions, the probability distribution of the evaluation keywords corresponding to the attribute dimensions is determined; based on the probability distribution of the evaluation keywords corresponding to the attribute dimensions, and the preset probability distribution corresponding to the preset attributes, the target attributes corresponding to the target object are determined; based on the teaching guidance method set for each attribute, the target teaching guidance information corresponding to the target attribute is determined.

[0034] The above scheme obtains the evaluation keywords by quantitatively encoding the questionnaire information on second language learning based on the target object, and then determines the probability distribution of the evaluation keywords corresponding to the attribute dimension based on the correlation between the evaluation keywords and the preset attribute dimension. The target attribute corresponding to the target object is determined based on the comparative relationship between the probability distribution of the evaluation keywords corresponding to the attribute dimension and the preset probability distribution corresponding to the preset attribute. Finally, the target teaching guidance information corresponding to the target attribute is determined based on the teaching guidance method set for each attribute, so that the target object can obtain learning guidance consistent with its attribute, thereby improving learning efficiency, and the teacher can obtain teaching guidance information consistent with the preferred attributes of the learning object, thereby improving teaching efficiency.

[0035] It should be noted that the method for generating language teaching guidance information provided in the embodiments of the present application is generally executed by the server 105, and accordingly, the device for generating language teaching guidance information is generally provided in the server 105. However, in other embodiments of the present application, the terminal device may also have similar functions as the server, thereby executing the method for generating language teaching guidance information provided in the embodiments of the present application.

[0036] The following is a detailed description of the implementation details of the technical solution of the embodiment of the present application:

[0037] Figure 2 A flowchart of a method for generating language teaching guidance information according to an embodiment of the present application is shown. The method for generating language teaching guidance information can be executed by a server, which can be Figure 1 Refer to the server shown in . Figure 2 As shown, the method for generating language teaching guidance information includes at least steps S210 to S250, which are described in detail as follows:

[0038] In step S210, questionnaire information on the target subject's second language learning is obtained; wherein the questionnaire information includes the target subject's place of origin, place of second language learning, major studied, motivation for learning the second language, and self-efficacy level for learning the second language.

[0039] In one embodiment of the present application, questionnaire information can be collected from the target object. The collection method in this embodiment can include obtaining it through email, interviews, etc., or collecting and summarizing the information after the questionnaire to obtain questionnaire information for the target object to learn a second language.

[0040] In one embodiment of the present application, the target objects are students learning a second language, people in society, etc.; the second language is a language other than the mother tongue. For example, for objects whose mother tongue is Chinese, the second language can be English, French, etc.

[0041] Specifically, the questionnaire information in this embodiment includes the target subject's place of origin, place of second language learning, major studied, motivation for learning the second language, and degree of self-efficacy in learning the second language. Among them, the target subject's place of origin can be the target subject's hometown, the region of their hometown, etc.; the place of study is the place where the second language is learned. For example, if the target subject is a Chinese student studying French in the UK, the place of origin is China, the place of study is the UK, and the second language is French. Learning motivation includes intrinsic motivation, extrinsic motivation, and amotivation. Among them, intrinsic motivation is based on internal interest in the activity, extrinsic motivation depends on external rewards, and amotivation refers to the lack of any regulation. The degree of self-efficacy is used to express the evaluation of a person's ability, including general beliefs about a person's ability, such as the target subject's belief in second language learning.

[0042] In step S220, the questionnaire information is quantized and encoded to obtain a quantized sequence, and evaluation keywords are extracted from the quantized sequence.

[0043] In one embodiment of the present application, after obtaining questionnaire information, the length of the character strings included therein is uncertain. Therefore, in this embodiment, the questionnaire information is quantized and encoded to obtain a corresponding quantized sequence to ensure the accuracy and consistency of the questionnaire information. Subsequently, evaluation keywords are extracted from the quantized sequence to obtain concise evaluation keywords from the overly long quantized sequence, thereby improving the efficiency and accuracy of the target object evaluation.

[0044] In one embodiment of the present application, step S220 quantizes and encodes the questionnaire information to obtain a quantized sequence, and extracts evaluation keywords from the quantized sequence, including steps S221 to S223, which are described in detail as follows:

[0045] S221: Identify the place of origin, place of study, major, motivation, and level of self-efficacy in the questionnaire information.

[0046] In one embodiment of the present application, the place of origin, place of study, major, motivation and self-efficacy level in the questionnaire information are identified, wherein the specific identification method can be determined by text recognition or a recognition model obtained by pre-training based on a neural network.

[0047] S222: Based on the set quantitative labels, the place of origin, place of study, major, motivation, and self-efficacy level in the questionnaire information are encoded to obtain a quantitative sequence including the quantitative labels.

[0048] In one embodiment of the present application, based on the set quantitative labels, the place of origin, place of study, major, motivation and self-efficacy level in the questionnaire information are encoded to obtain a quantitative sequence containing quantitative labels, including: based on the set quantitative labels, determining the quantitative labels corresponding to the encoding of the place of origin, place of study, major, motivation and self-efficacy level in the questionnaire information; adding the quantitative labels corresponding to the place of origin, place of study, major, motivation and self-efficacy level in the questionnaire information to the corresponding positions respectively, to obtain a quantitative sequence containing quantitative labels.

[0049] Based on the set quantitative labels, the identified place of origin, place of study, major, motivation, and degree of self-efficacy are encoded. For example, before adding the quantitative labels to their corresponding character string positions, the obtained character strings are merged to finally obtain a quantitative sequence containing the quantitative labels.

[0050] S223: Detect keywords in the quantization sequence to obtain evaluation keywords.

[0051] After obtaining the quantization sequence, the keywords in the quantization sequence are detected to obtain the evaluation keywords. The keyword detection method can be determined by text recognition.

[0052] Optionally, the evaluation keywords in this embodiment may be at least two, or keywords obtained by detecting the place of origin, place of study, major, motivation, and self-efficacy in the questionnaire information, so as to represent these information one by one through these evaluation keywords.

[0053] In this embodiment, by detecting keywords in the quantization sequence, evaluation keywords can be extracted based on the text in the entire quantization sequence, thereby improving the accuracy of the evaluation keywords.

[0054] In step S230 , based on the correlation between the evaluation keywords and the preset attribute dimensions, the probability distribution of the evaluation keywords corresponding to the attribute dimensions is determined.

[0055] In one embodiment of the present application, various attribute dimensions are preset, wherein the attribute dimensions correspond to place of origin, place of study, major, motivation, and degree of self-efficacy, respectively, and are used to measure the probability distribution of evaluation keywords corresponding to these attribute dimensions.

[0056] Specifically, this embodiment calculates the similarities between the evaluation keywords and the attribute dimensions corresponding to place of origin, place of study, major, motivation, and self-efficacy. Based on these similarities, a probability matrix is ​​generated, which is used as the probability distribution of the attribute dimensions. This probability distribution represents the corresponding situation of the evaluation keywords of the target object.

[0057] In step S240, the target attribute corresponding to the target object is determined based on the probability distribution of the evaluation keywords corresponding to the attribute dimension and the preset probability distribution corresponding to the preset attribute.

[0058] In one embodiment of the present application, the preset attributes include self-efficacy type and learning motivation type; the self-efficacy type represents the type of students who need to improve their learning confidence; the learning motivation type represents the type of students who need to adjust their learning motivation.

[0059] In one embodiment of the present application, each preset attribute has a corresponding preset probability distribution, wherein the preset probability distribution can be in the form of an interval probability distribution. For example, if the elements in the probability matrix corresponding to the probability distribution belong to a certain interval, it means that the probability distribution corresponds to the attribute corresponding to the preset probability distribution.

[0060] In one embodiment of the present application, the process of determining the target attribute corresponding to the target object based on the probability distribution of the evaluation keyword corresponding to the attribute dimension and the preset probability distribution corresponding to the preset attribute in step S240 includes:

[0061] Step S241 : weighting the elements in the probability distribution based on the preset attribute weights corresponding to the probability dimensions to obtain a weighted probability distribution corresponding to the probability distribution.

[0062] In one embodiment of the present application, the preset attribute weights corresponding to each probability dimension are: α 11 , α 12 ···α nm ; The weight matrix composed of the preset attribute weights corresponding to each probability dimension is:

[0063]

[0064] Among them, 1~m and 1~n represent the row and column identifiers of probability distribution and weight distribution respectively.

[0065] The probability distribution in this embodiment is:

[0066]

[0067] Based on the preset attribute weights corresponding to each probability dimension, the elements in the probability distribution are weighted, and the weighted probability distribution corresponding to the probability distribution is obtained as follows:

[0068]

[0069] In step S242 , based on the element values ​​in the weighted probability distribution and the element values ​​in the preset probability distribution, a similarity between the weighted probability distribution and the preset probability distribution is determined.

[0070] In one embodiment of the present application, the weighted probability distribution is X and the preset probability distribution is Y. In one embodiment of the present application, in the process of determining the similarity between the weighted probability distribution and each preset probability distribution, the similarity between each element in the weighted probability distribution and each element in the preset probability distribution is calculated using the following formula:

[0071]

[0072] Among them, s represents the similarity coefficient, X nm 、Y nm They represent the element values ​​in the weighted probability distribution and the preset probability distribution respectively.

[0073] The element similarity S corresponding to each element in the weighted probability distribution is obtained nm Afterwards, based on the similarity S of these elements nm The combined similarity matrix is:

[0074]

[0075] Afterwards, the eigenvalue corresponding to the similarity matrix S is obtained, and the eigenvalue is used as the similarity between the weighted probability distribution and the preset probability distribution.

[0076] Step S243 : Using the attribute corresponding to the preset probability distribution when the similarity is the highest as the target attribute corresponding to the target object.

[0077] Optionally, in addition to using the attribute corresponding to the preset probability distribution when the similarity is highest as the target attribute corresponding to the target object, the attributes ranked in the top few positions can also be used as backup attributes or reference attributes to improve the comprehensiveness of the target object evaluation.

[0078] In one embodiment of the present application, the method for generating language teaching guidance information further includes:

[0079] Based on the classification model built by the neural network, the evaluation keywords of the sample objects are classified to determine the preference attributes corresponding to the sample objects;

[0080] Based on the comparison results between the preferred attributes and the attribute labels of the sample objects, the parameters of the classification model are updated to obtain an updated classification model;

[0081] The questionnaire information of the target object is input into the classification model for classification to determine the target attributes corresponding to the target object.

[0082] In one embodiment of the present application, the target attributes corresponding to the questionnaire information are identified by means of a neural network, thereby improving the efficiency and accuracy of the target attribute judgment.

[0083] In step S250 , target teaching guidance information corresponding to the target attribute is determined based on the teaching guidance method set for each attribute.

[0084] In one embodiment of the present application, the second language includes English. Based on the teaching guidance method set for each attribute, target teaching guidance information corresponding to the target attribute is determined, including:

[0085] If the target attribute corresponding to the target object is the self-efficacy type, then based on the set teaching guidance method, the target teaching guidance information corresponding to the self-efficacy type is determined;

[0086] Among them, the target teaching guidance information includes teaching methods, teaching processes, teaching time representation, teaching suggestions, etc.

[0087] In this embodiment, after determining the teaching guidance information corresponding to the target attribute, it can be set in the teaching application based on the teaching guidance information, and then a learning plan consistent with the teaching guidance information can be obtained, so that the target object can learn a second language based on the learning plan and improve learning efficiency.

[0088] The teaching guidance information can also be sent to teachers who teach second languages, so that teachers can provide targeted guidance and teaching to target students based on the teaching guidance information, thereby improving teaching and learning efficiency.

[0089] The following describes an embodiment of the device of the present application, which can be used to execute the method for generating language teaching guidance information in the above-mentioned embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method for generating language teaching guidance information in the above-mentioned embodiment of the present application.

[0090] Figure 5 A block diagram of an apparatus for generating language teaching guidance information according to an embodiment of the present application is shown.

[0091] Reference Figure 5As shown, according to an embodiment of the present application, the device 500 for generating language teaching guidance information includes: an acquisition unit 510, which is used to obtain questionnaire information for a target object to learn a second language; wherein the questionnaire information includes the target object's place of origin, the place of learning the second language, the major studied, the motivation for learning the second language, and the degree of self-efficacy for learning the second language; an encoding unit 520, which is used to quantize and encode the questionnaire information to obtain a quantized sequence, and extract evaluation keywords from the quantized sequence; a probability unit 530, which is used to determine the probability distribution of the evaluation keyword corresponding to the attribute dimension based on the correlation between the evaluation keyword and the preset attribute dimension; an attribute unit 540, which is used to determine the target attribute corresponding to the target object based on the probability distribution of the evaluation keyword corresponding to the attribute dimension and the preset probability distribution corresponding to the preset attribute; a guidance unit 550, which is used to determine the target teaching guidance information corresponding to the target attribute based on the teaching guidance method set for each attribute.

[0092] In some embodiments of the present application, based on the aforementioned scheme, the encoding unit 520 includes: an identification unit for identifying the place of origin, place of study, major, motivation and degree of self-efficacy in the questionnaire information; a sequence unit for encoding the place of origin, place of study, major, motivation and degree of self-efficacy in the questionnaire information based on set quantitative labels to obtain a quantitative sequence containing the quantitative labels; a detection unit for detecting keywords in the quantitative sequence to obtain the evaluation keywords.

[0093] In some embodiments of the present application, based on the aforementioned scheme, the sequence unit is used to determine the quantitative labels corresponding to the place of origin, place of study, major, motivation and self-efficacy level in the questionnaire information based on the set quantitative labels; the quantitative labels corresponding to the place of origin, place of study, major, motivation and self-efficacy level in the questionnaire information are added to the corresponding positions respectively to obtain a quantitative sequence containing the quantitative labels.

[0094] In some embodiments of the present application, based on the aforementioned scheme, the attribute unit 540 is used to weight the elements in the probability distribution based on the preset attribute weights corresponding to each probability dimension to obtain a weighted probability distribution corresponding to the probability distribution; based on the element values ​​in the weighted probability distribution and the element values ​​in the preset probability distribution, determine the similarity between the weighted probability distribution and the preset probability distribution; and use the attribute corresponding to the preset probability distribution when the similarity is the highest as the target attribute corresponding to the target object.

[0095] In some embodiments of the present application, based on the aforementioned scheme, the device 500 for generating language teaching guidance information is further used to classify the evaluation keywords of the sample objects based on a classification model constructed by a neural network, and determine the preference attributes corresponding to the sample objects; based on the comparison results between the preference attributes and the attribute labels of the sample objects, update the parameters of the classification model to obtain an updated classification model; input the questionnaire information of the target object into the classification model for classification, and determine the target attributes corresponding to the target object.

[0096] In some embodiments of the present application, based on the aforementioned scheme, the preset attributes include self-efficacy type and learning motivation type; wherein the self-efficacy type represents the type of students who need to improve their learning confidence; and the learning motivation type represents the type of students who need to adjust their learning motivation.

[0097] In some embodiments of the present application, based on the aforementioned scheme, the second language includes English, and the guidance unit 550 is used to determine the target teaching guidance information corresponding to the self-efficacy type based on the set teaching guidance method if the target attribute corresponding to the target object is the self-efficacy type; wherein the target teaching guidance information includes teaching method, teaching process, teaching time representation, and teaching suggestions.

[0098] Figure 6 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown.

[0099] It should be noted that Figure 6 The computer system 600 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0100] like Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage part 608 into the random access memory (RAM) 603, such as executing the method described in the above embodiment. Various programs and data required for system operation are also stored in the RAM 603. The CPU 601, ROM 602 and RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0101] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, and the like; an output section 607 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 608 including a hard disk; and a communication section 609 including a network interface card such as a LAN (Local Area Network) card or a modem. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. Removable media 611, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 610 as needed, so that computer programs read therefrom can be installed into the storage section 608 as needed.

[0102] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from a removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the various functions defined in the system of the present application are executed.

[0103] It should be noted that the computer-readable medium shown in the embodiments of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable computer program. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-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. A computer program embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0104] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. Among them, each box in the flowchart or block diagram can represent a module, program segment, or part of the code, and the above-mentioned module, program segment, or part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0105] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0106] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described above.

[0107] As another aspect, the present application further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device implements the method described in the above embodiments.

[0108] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0109] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.

[0110] Those skilled in the art will readily conceive of other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

[0111] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for generating language teaching guidance information, characterized in that: include: Obtaining questionnaire information on a target subject's second language learning; wherein the questionnaire information includes the target subject's place of origin, place of learning the second language, major studied, motivation for learning the second language, and self-efficacy level in learning the second language; Quantitatively encoding the questionnaire information to obtain a quantitative sequence, and extracting evaluation keywords from the quantitative sequence; Based on the correlation between the evaluation keyword and the preset attribute dimension, determining the probability distribution of the evaluation keyword corresponding to the attribute dimension; Based on the probability distribution of the evaluation keywords corresponding to the attribute dimensions and the preset attribute weights corresponding to the probability dimensions, weight the elements in the probability distribution to obtain a weighted probability distribution X corresponding to the probability distribution; Based on the weighted probability distribution X and the preset probability distribution Y corresponding to the preset attribute, the similarity between each element in the weighted probability distribution and each element in the preset probability distribution is calculated: Among them, s represents the similarity coefficient, X nm 、Y nm Represent the element values ​​in the weighted probability distribution and the preset probability distribution respectively; The similarity S between the elements nm Combining to obtain a similarity matrix, obtaining an eigenvalue corresponding to the similarity matrix, and using the eigenvalue as the similarity between the weighted probability distribution and the preset probability distribution; The attribute corresponding to the preset probability distribution when the similarity is the highest is used as the target attribute corresponding to the target object; Based on the teaching guidance methods set for each attribute, target teaching guidance information corresponding to the target attribute is determined.

2. The method according to claim 1, characterized in that The questionnaire information is quantized and encoded to obtain a quantized sequence, and evaluation keywords are extracted from the quantized sequence, including: Identify the place of origin, place of study, major, motivation, and self-efficacy level in the questionnaire information; Based on the set quantitative labels, encoding the place of origin, place of study, major, motivation, and self-efficacy in the questionnaire information to obtain a quantitative sequence containing the quantitative labels; Detecting keywords in the quantization sequence to obtain the evaluation keywords.

3. The method according to claim 2, characterized in that Based on the set quantitative labels, the place of origin, place of study, major, motivation, and self-efficacy in the questionnaire information are encoded to obtain a quantitative sequence containing the quantitative labels, including: Based on the set quantitative labels, determining the quantitative labels corresponding to the place of origin, place of study, major, motivation, and self-efficacy level in the questionnaire information; The quantitative labels corresponding to the place of origin, place of study, major, motivation and degree of self-efficacy in the questionnaire information are added to the corresponding positions respectively to obtain a quantitative sequence containing the quantitative labels.

4. The method according to claim 1, wherein The method further comprises: Classify the evaluation keywords of the sample objects based on the classification model constructed by the neural network to determine the preference attributes corresponding to the sample objects; Based on the comparison result between the preference attribute and the attribute label of the sample object, updating the parameters of the classification model to obtain an updated classification model; The questionnaire information of the target object is input into the classification model for classification to determine the target attribute corresponding to the target object.

5. The method according to claim 1, wherein The preset attributes include self-efficacy type and learning motivation type; wherein the self-efficacy type represents the type of students who need to improve their learning confidence; and the learning motivation type represents the type of students who need to adjust their learning motivation.

6. The method according to claim 5, characterized in that The second language includes English. Based on the teaching guidance method set for each attribute, target teaching guidance information corresponding to the target attribute is determined, including: If the target attribute corresponding to the target object is the self-efficacy type, then based on the set teaching guidance method, determining the target teaching guidance information corresponding to the self-efficacy type; The target teaching guidance information includes teaching methods, teaching processes, teaching time representation, and teaching suggestions.

7. A device for generating language teaching guidance information, characterized in that: include: an acquisition unit, configured to acquire questionnaire information on a target subject's second language learning; wherein the questionnaire information includes the target subject's place of origin, place of learning the second language, major studied, motivation for learning the second language, and degree of self-efficacy in learning the second language; An encoding unit, configured to quantize and encode the questionnaire information to obtain a quantized sequence, and extract evaluation keywords from the quantized sequence; A probability unit, configured to determine a probability distribution of the evaluation keyword corresponding to the attribute dimension based on the correlation between the evaluation keyword and the preset attribute dimension; The attribute unit is configured to weight the elements in the probability distribution based on the probability distribution of the evaluation keyword corresponding to the attribute dimension and the preset attribute weight corresponding to each probability dimension to obtain a weighted probability distribution X corresponding to the probability distribution; and calculate the similarity between each element in the weighted probability distribution and each element in the preset probability distribution based on the weighted probability distribution X and the preset probability distribution Y corresponding to the preset attribute: Among them, s represents the similarity coefficient, X nm 、Y nm Respectively represent the element values ​​in the weighted probability distribution and the preset probability distribution; the similarity S between the elements nm A similarity matrix is ​​obtained by combining, an eigenvalue corresponding to the similarity matrix is ​​obtained, and the eigenvalue is used as the similarity between the weighted probability distribution and the preset probability distribution; the attribute corresponding to the preset probability distribution when the similarity is the highest is used as the target attribute corresponding to the target object; The guidance unit is configured to determine target teaching guidance information corresponding to the target attribute based on the teaching guidance method set for each attribute.

8. A computer-readable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for generating language teaching guidance information according to any one of claims 1 to 6 is implemented.

9. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the method for generating language teaching guidance information according to any one of claims 1 to 6.

Images