CN111813889A

CN111813889A - Method, device, medium and electronic equipment for sorting question information

Info

Publication number: CN111813889A
Application number: CN202010587855.4A
Authority: CN
Inventors: 王珂晟; 黄劲; 黄钢; 许巧龄; 郝缘
Original assignee: Beijing Anbo Shengying Education Technology Co ltd
Current assignee: Beijing Anbo Shengying Education Technology Co ltd
Priority date: 2020-06-24
Filing date: 2020-06-24
Publication date: 2020-10-23

Abstract

The disclosure provides a questioning information sorting method, a questioning information sorting device, a questioning information sorting medium and electronic equipment. And acquiring a first keyword from the acquired questioning words, matching the first keyword with the course knowledge keywords of the nodes in the course knowledge structure tree, acquiring the function weight values of the matched nodes, and sorting in a questioning word sorting list based on the sum of the calculated function weight values. Namely, the questions posed by different students are automatically sequenced. Thereby avoided the screen brushing too fast, leaded to the phenomenon of the effective problem that the unable collection student provided. Meanwhile, the questions are subjected to score management, so that students can participate in answering activities in a targeted manner, and precious review time is avoided. The live broadcast teaching is more targeted, and the superiority different from the on-site teaching is reflected. A large number of repeated problems are filtered, the question answering efficiency is improved, and the inefficiency of manual question selection is avoided.

Description

Method, device, medium and electronic equipment for sorting question information

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a method, an apparatus, a medium, and an electronic device for ranking questioning information.

Background

Remote education is an education form which adopts various media ways to carry out system teaching and communication, and is education for transmitting courses to one or more student terminals outside a campus. Modern distance education refers to education in which a course is delivered to a remote student through audio, video, and computer technologies including real-time and non-real-time.

Live broadcast teaching is a form of remote education, and remote teaching is carried out in a real-time video form. The advantages are that: the teaching range is wide, is not limited by sites and the number of personnel, and can carry out direct teaching interaction and on-site question answering. Live teaching is a remote education mode closest to on-site teaching. Because the students who attend lessons facing live teaching can be thousands of, in the process of answering questions, the questions from different student terminals can rush into the data center in a short time, and are forwarded to the teacher terminal by the data center. In this case, it is generally arranged that the attendant is responsible for selecting the contents of the questions in the content field displayed by the teacher's terminal and notifying the lecturer of the selected questions, and the lecturer answers the questions of the students. Under the condition of high problem quantity density, a service person does not see a display content column clearly and is refreshed by newly-entered problem content, so that the problem of high quality is missed. The uneven levels of the service personnel also cause the selected problems to be disordered, and the situations of repeated problems or lost problems exist. The unordered answering mode also enables students of different levels to be forced to stay in front of the student terminals, and a large amount of precious time is consumed.

Disclosure of Invention

This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.

The present disclosure is directed to a method, an apparatus, a medium, and an electronic device for ranking questioning information, which are capable of solving at least one of the above-mentioned technical problems. The specific scheme is as follows:

according to a specific implementation manner of the disclosure, in a first aspect, the disclosure provides a method for sorting question information, including:

acquiring a first question word;

performing semantic analysis on the first question words to obtain first keywords and forming a first keyword set; the first keyword is a word which represents question core information in the first question text;

matching the first keyword of the first keyword set with the course knowledge keyword of a node in a preset course knowledge structure tree; each node of the course knowledge structure tree comprises a course knowledge keyword and a corresponding function weight value; the course knowledge key words refer to words representing course core information in teaching courses;

when each first keyword is successfully matched and each matching node has a direct incidence relation in the course knowledge structure tree, acquiring a function weight value of the matching node; the direct association relationship comprises the brother relationship and/or the parent-child relationship and/or the connection relationship existing between the matching nodes;

calculating the sum of the function weight values of the matching nodes to obtain a function ranking value of the first question words;

and adding the first question words into a question word ordered list for ordering based on the function ordering value.

According to a second aspect, the present disclosure provides a device for sorting quiz information, including:

the method comprises the steps of obtaining a first question word unit, wherein the first question word unit is used for obtaining first question words;

a first keyword obtaining unit, configured to perform semantic analysis on the first question text, obtain a first keyword, and form a first keyword set; the first keyword is a word which represents question core information in the first question text;

the matching unit is used for matching the first keyword of the first keyword set with the course knowledge keyword of the node in the preset course knowledge structure tree; each node of the course knowledge structure tree comprises a course knowledge keyword and a corresponding function weight value; the course knowledge key words refer to words representing course core information in teaching courses;

the function weight value obtaining unit is used for obtaining the function weight value of each matching node when each first keyword is successfully matched and each matching node has a direct incidence relation in the course knowledge structure tree; the direct association relationship comprises the brother relationship and/or the parent-child relationship and/or the connection relationship existing between the matching nodes;

the function ordering value obtaining unit is used for calculating the sum of the function weight values of the matching nodes and obtaining a function ordering value of the first question words;

and the sorting unit is used for adding the first question words into a question word sorting list for sorting based on the function sorting value.

According to a third aspect, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method of ranking question information according to any one of the first aspect.

According to a fourth aspect thereof, the present disclosure provides an electronic device, comprising: one or more processors; storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a method of ranking question information according to any one of the first aspects.

Compared with the prior art, the scheme of the embodiment of the disclosure at least has the following beneficial effects:

Drawings

The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale. In the drawings:

FIG. 1 shows a flow diagram of a method of ranking questioning information according to an embodiment of the present disclosure;

FIG. 2 illustrates a course knowledge structure tree for a method of ranking questioning information according to an embodiment of the present disclosure;

FIG. 3 shows a block diagram of the elements of a questioning information ranking apparatus according to an embodiment of the present disclosure;

fig. 4 shows an electronic device connection structure schematic according to an embodiment of the present disclosure.

Detailed Description

Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.

It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.

The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.

It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.

It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.

The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.

Alternative embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

The first embodiment provided by the present disclosure, namely, an embodiment of a method for sorting quiz information.

The embodiments of the present disclosure are described in detail below with reference to fig. 1.

Step S101, a first question word is obtained.

The embodiment of the disclosure mainly takes questions and answers between teachers and students in live broadcast teaching as an application scene.

The first question words are questions that students ask to a teaching teacher in live teaching, and the questions are presented in the form of words. For example, students input the content of the question through an interactive window provided by live software and submit the content to the teacher giving lessons.

In order to improve the question efficiency, the embodiment of the disclosure provides a more convenient question mode. Optionally, the obtaining the first question text includes the following steps:

step S101-1, receiving a first question voice, and converting the first question voice into a first question word.

The voice input avoids the complexity of character input, and the voice input is obviously faster than the input method input, thereby improving the efficiency of character input.

Step S102, carrying out semantic analysis on the first question words, obtaining first keywords, and forming a first keyword set.

The first keyword is a word representing question core information in the first question text.

Optionally, the first keyword includes a first key noun and a first key relation word between the first key nouns. For example, live tutoring teaches newton's second law: the magnitude of the acceleration of the object is in direct proportion to the acting force, in inverse proportion to the mass of the object and in direct proportion to the reciprocal of the mass of the object, and the direction of the acceleration is the same as the direction of the acting force; for newton's second law, the student's first question is: in newton's second law why is the direction of acceleration the same as the direction of the force? Wherein, the first key noun includes: "Newton's second law", "acceleration" and "force"; the acceleration and the acting force are respectively subordinate to Newton's second law, and the first key relation word between the acceleration and the acting force is the same as the direction.

Because the expression habits of each person are different, the expression modes adopted by the same first key noun or first key relation word are different, for example, the second questions of the students are: why is the direction of a the same as that of F in newton's second law?

In newton's second law, a represents "acceleration" and F represents "force", so that the first question and the second question of the student in the above two examples have the same meaning. If a and "acceleration" and F and "force" are used as the first keywords, the complexity of the ranking method is increased. The disclosed embodiments provide a method for solving the above problems. Optionally, the semantic analysis of the first question text to obtain the first keyword includes the following steps:

and S102-1, performing semantic analysis on the first question words to obtain second keywords.

And S102-2, retrieving a standardized word bank based on the second keyword, and acquiring the standardized first keyword.

The standardized thesaurus stores synonyms or synonyms of the first keywords related to the teaching content, step S102-2 normalizes the second keywords, and converts all synonyms or synonyms into unified and normalized first keywords by searching the standardized thesaurus, for example, continuing the above example, a in the second question is converted into "acceleration" and F is converted into "acting force".

After the standardization processing, the word amount is reduced, and the matching efficiency is improved.

Step S103, matching the first keyword of the first keyword set with the course knowledge keyword of the node in the preset course knowledge structure tree.

The course knowledge structure tree stores words representing course core information in teaching courses in a tree structure. Before the first question words are obtained, the method further comprises the following steps:

and step S100-1, acquiring pre-written teaching contents.

The pre-written teaching contents comprise teaching outlines or lesson preparation notes. The teaching outline or the lesson preparation notes contain course knowledge keywords and corresponding function weight values so as to automatically generate a course knowledge structure tree.

And S100-2, performing semantic analysis on the teaching content to obtain second keywords and form a second keyword set.

And S100-3, generating the course knowledge structure tree based on the second keyword set.

Each node of the course knowledge structure tree comprises a course knowledge keyword and a corresponding function weight value. The course knowledge key words refer to words representing course core information in teaching courses and include course knowledge key nouns and course knowledge key relation words between the course knowledge key nouns. The course knowledge structure tree comprises tree nodes of the course knowledge key nouns and relation nodes of the course knowledge key relation words, the tree nodes form a tree structure of the course knowledge structure tree, and the relation nodes are connected with the tree nodes. For example, continuing the above example of newton's second law, as shown in fig. 2, a curriculum knowledge structure tree is shown, wherein the arc boxes are tree nodes of key nouns of curriculum knowledge, including: "newton's second law", "acceleration", "force" and "mass"; the thick line part connects tree nodes to form a tree structure of the course knowledge structure tree, a father node of the tree structure is Newton's second law, and child nodes of the father node are acceleration, acting force and mass; the key relation words of the course knowledge between the acceleration and the acting force are in direct proportion and in the same direction, and the key relation words of the course knowledge between the acceleration and the mass are in inverse proportion; the box shown in fig. 2 is a relationship node of the key relationship words of the course knowledge.

And step S104, when each first keyword is successfully matched and each matching node has a direct incidence relation in the course knowledge structure tree, acquiring a function weight value of the matching node.

The direct association relationship comprises the existence of the brother relationship and/or the father-child relationship and/or the connection relationship between the matching nodes.

Tree nodes having the same parent node are mutually referred to as sibling nodes. The relationships between sibling nodes become sibling relationships.

If a tree node contains a child node, the tree node is called the parent node of its child node. The relationship of a parent node to a child node is referred to as a parent-child relationship.

The nodes connecting the sibling nodes become relationship nodes, and the relationship between the relationship nodes and the connected tree nodes becomes a connection relationship.

For example, continuing with the above example, tree node "newton's second law" is in a parent-child relationship with "acceleration", "effort", and "mass", the tree nodes "acceleration", "effort", and "mass" being in a sibling relationship with one another; the relation node is in a connection relation with the tree node acceleration or acting force in a direct proportion or same direction; the relation node 'inverse proportion' and the tree node 'acceleration' or 'quality' are in a connection relation.

And each first keyword is successfully matched, namely each first keyword has a corresponding node in the course knowledge structure tree. For example, continuing with the student's first question in the above example, a first key noun, includes: "Newton's second law", "acceleration" and "force"; the acceleration and the acting force are respectively subordinate to Newton's second law, and the first key relation word between the acceleration and the acting force is in the same direction; in the course knowledge structure tree, Newton's second law, acceleration and acting force are in parent-child relationship, acceleration and acting force are in brother relationship, and the same direction, acceleration and acting force are in connection relationship; therefore, the first keywords are successfully matched, and the corresponding nodes in the course knowledge structure tree have direct association relation; the tree nodes Newton's second law, acceleration, acting force and relation nodes are all matched nodes with the same direction.

Each node in the course knowledge structure tree has a preset functional weight value. The function weight value may be set manually or generated automatically.

Step S105, calculating the sum of the function weight values of the matching nodes, and obtaining the function ranking value of the first question words.

And S106, adding the first question words into a question word ranking list for ranking based on the function ranking value.

Namely, the questions posed by different students are automatically sequenced. Thereby avoided the screen brushing too fast, leaded to the phenomenon of the effective problem that the unable collection student provided. Meanwhile, the questions are subjected to score management, so that students can participate in answering activities in a targeted manner, and precious review time is avoided. The live broadcast teaching is more targeted, and the superiority different from the on-site teaching is reflected.

Because the number of students participating in live broadcast teaching is large, a large number of repeated questions must be asked. In order to reduce repeated questioning, the embodiment of the present disclosure provides that, after the first keyword is obtained and the first keyword set is formed, the method further includes the following steps:

and S102-1, matching the first keyword set with a historical keyword set in a historical keyword library.

The standardized historical keyword set is stored in the historical keyword library, and the historical question words corresponding to the historical keyword set are added into the question word ordered list for ordering.

And S102-2, when the matching is successful, continuously acquiring the first question words.

The matching is successful, which indicates that the first keyword set has been asked. The first question text is abandoned, the step S101 is returned to, and the next first question text is continuously acquired.

And S102-3, when the matching fails, storing the first keyword set into the historical keyword library.

And if the matching fails, indicating that the first keyword set is not asked, continuing to sequence the question information, and storing the first keyword set serving as a historical keyword set into a historical keyword library.

A large number of repeated problems are filtered through the steps, the question answering efficiency is improved, and the inefficiency of manual question selection is avoided.

Optionally, before adding the first question text into the question text ranking list based on the functional ranking value, the method includes the following steps:

and step S105-1, when the function sorting value is lower than a preset low difficulty threshold value, continuously acquiring the first question words.

In the step, a preset low difficulty threshold value is set, and the question difficulty is limited for filtering too simple questions. If the question is too simple, the question text is discarded, the step S101 is returned, and the next first question text is continuously obtained, so that the question answering efficiency is improved.

The method and the device for obtaining the questioning text sequence list have the advantages that the first keyword is obtained from the obtained questioning text, the first keyword is matched with the course knowledge keyword of the node in the course knowledge structure tree, the function weight value of the matched node is obtained, and the questioning text sequence list is ranked based on the sum of the calculated function weight values. Namely, the questions posed by different students are automatically sequenced. Thereby avoided the screen brushing too fast, leaded to the phenomenon of the effective problem that the unable collection student provided. Meanwhile, the questions are subjected to score management, so that students can participate in answering activities in a targeted manner, and precious review time is avoided. The live broadcast teaching is more targeted, and the superiority different from the on-site teaching is reflected. A large number of repeated problems are filtered, the question answering efficiency is improved, and the inefficiency of manual question selection is avoided.

Corresponding to the first embodiment provided by the present disclosure, the present disclosure also provides a second embodiment, that is, a device for sorting quiz information. Since the second embodiment is basically similar to the first embodiment, the description is simple, and the relevant portions should be referred to the corresponding description of the first embodiment. The device embodiments described below are merely illustrative.

Fig. 3 shows an embodiment of a device for sorting quiz information provided by the present disclosure.

As shown in fig. 3, the present disclosure provides a device for sorting quiz information, including:

a first question word acquiring unit 301 for acquiring a first question word;

a first keyword obtaining unit 302, configured to perform semantic analysis on the first question text, obtain a first keyword, and form a first keyword set; the first keyword is a word which represents question core information in the first question text;

a matching unit 303, configured to match a first keyword of the first keyword set with a course knowledge keyword of a node in a preset course knowledge structure tree; each node of the course knowledge structure tree comprises a course knowledge keyword and a corresponding function weight value; the course knowledge key words refer to words representing course core information in teaching courses;

a function weight value obtaining unit 304, configured to obtain a function weight value of each matching node when each first keyword is successfully matched and each matching node has a direct association relationship in the course knowledge structure tree; the direct association relationship comprises the brother relationship and/or the parent-child relationship and/or the connection relationship existing between the matching nodes;

an obtaining function ranking value unit 305, configured to calculate a sum of function weight values of the matching nodes, and obtain a function ranking value of the first question text;

a sorting unit 306, configured to add the first question text to a question text sorted list for sorting based on the function sorting value.

Optionally, the obtaining the first keyword unit 302 includes:

a second keyword sub-unit is obtained and used for carrying out semantic analysis on the first question words to obtain second keywords;

and the standardization processing subunit is used for retrieving a standardization word bank based on the second keyword and acquiring the standardized first keyword.

Optionally, the apparatus further includes: a repeat processing unit;

in the repetitive processing unit, comprising:

the matching historical keyword set subunit is used for matching the first keyword set with the historical keyword set in the historical keyword library on the basis of the first keyword set after the first keyword set is obtained and formed;

a discarding subunit, configured to continue to obtain the first question text when matching is successful;

and the storage subunit is used for storing the first keyword set into the historical keyword library when the matching fails.

Optionally, the apparatus includes:

and the filtering unit is used for continuously acquiring the first question words when the function sorting value is lower than a preset low difficulty threshold value before the first question words are added into the question word sorting list and sorted based on the function sorting value.

Optionally, the obtaining a first question text unit 301 includes:

the voice question sub-unit is used for receiving a first question voice and converting the first question voice into a first question text.

Optionally, the apparatus further includes: establishing a course knowledge structure tree unit;

in the unit for establishing the course knowledge structure tree, the method includes:

the obtaining teaching content subunit is used for obtaining the pre-written teaching content before the first question words are obtained;

a second keyword sub-unit is obtained and used for performing semantic analysis on the teaching content, obtaining second keywords and forming a second keyword set;

and generating a course knowledge structure tree subunit, configured to generate the course knowledge structure tree based on the second keyword set.

Optionally, the first keyword includes a first key noun and a first key relation word between the first key nouns;

the course knowledge key words comprise course knowledge key nouns and course knowledge key relation words between the course knowledge key nouns;

the course knowledge structure tree comprises tree nodes of the course knowledge key nouns and relation nodes of the course knowledge key relation words, the tree nodes form a tree structure of the course knowledge structure tree, and the relation nodes are connected with the tree nodes.

The embodiment of the present disclosure provides a third embodiment, that is, an electronic device, where the electronic device is used in a method for sorting question information, and the electronic device includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the one processor to cause the at least one processor to perform a method of ranking quiz information as described in the first embodiment.

The fourth embodiment provides a computer storage medium for sorting the question information, where the computer storage medium stores computer-executable instructions that can execute the method for sorting the question information as described in the first embodiment.

Referring now to FIG. 4, shown is a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.

As shown in fig. 4, the electronic device may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 401 that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)402 or a program loaded from a storage means 408 into a Random Access Memory (RAM) 403. In the RAM 403, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.

Generally, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. While fig. 4 illustrates an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.

In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication device 409, or from the storage device 408, or from the ROM 402. The computer program performs the above-described functions defined in the methods of the embodiments of the present disclosure when executed by the processing device 401.

It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (Hyper text transfer protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a Local Area Network (LAN), a Wide Area Network (WAN), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.

Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the C language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (socs), Complex Programmable Logic Devices (CPLDs), and the like.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.

Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

1. A method for ordering question information is characterized by comprising the following steps:

acquiring a first question word;

2. The ranking method according to claim 1, wherein the semantic analyzing the first question word to obtain a first keyword includes:

performing semantic analysis on the first question words to obtain second keywords;

and retrieving a standardized word bank based on the second keyword to obtain the standardized first keyword.

3. The ranking method according to claim 2, further comprising, after said obtaining the first keyword and composing the first keyword set:

matching is carried out on the basis of the first keyword set and a historical keyword set in a historical keyword library;

when the matching is successful, the first question words are continuously acquired;

and when the matching fails, storing the first keyword set into the historical keyword library.

4. The method of claim 1, wherein prior to said adding the first question text to a ranking in a ranked list of question texts based on the functional ranking value, comprising:

and when the function sorting value is lower than a preset low difficulty threshold value, continuously acquiring the first question words.

5. The ranking method of claim 1, wherein the obtaining the first question text includes:

receiving a first question voice, and converting the first question voice into a first question text.

6. The ranking method according to claim 1, further comprising, before the obtaining the first question text:

acquiring pre-written teaching contents;

performing semantic analysis on the teaching content to obtain second keywords and form a second keyword set;

and generating the course knowledge structure tree based on the second keyword set.

7. The ranking method according to claim 1, wherein the first keyword includes a first key noun and a first key relation word between the first key nouns;

8. A device for ranking quiz information, comprising:

9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1 to 7.

10. An electronic device, comprising:

one or more processors;

storage means for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to carry out the method of any one of claims 1 to 7.