WO2019149264A1

WO2019149264A1 - Auxiliary method and system for learning knowledge structure

Info

Publication number: WO2019149264A1
Application number: PCT/CN2019/074345
Authority: WO
Inventors: 马正方
Original assignee: 上海颐为网络科技有限公司
Priority date: 2018-02-02
Filing date: 2019-02-01
Publication date: 2019-08-08
Also published as: CN110110036A

Abstract

Disclosed is an auxiliary method and system for a learning knowledge structure, which are capable of improving the efficiency with which learners use a learning knowledge structure of a knowledge sharing platform system, thus increasing the utilization rate of knowledge sharing. The technical solution thereof comprises: recording learning sequences of different learners for each knowledge point within a knowledge structure; acquiring a statistical value for the learning sequences recorded for each of the knowledge points when the different learners are learning; and sequencing the statistical values of each of the knowledge points to act as optimal learning sequences of a knowledge structure system to recommend to other learners.

Description

Auxiliary method and system for learning knowledge structure

Field of invention

The present invention relates to a technical solution on a knowledge sharing platform, and more particularly to a method and system for providing intelligent assistance for learning a knowledge structure.

Background technique

With the rapid development of the Internet and mobile Internet, people can use the network to systematically learn on some knowledge sharing platforms. A knowledge system can be built based on a tree-like knowledge structure, splitting the complete knowledge system into multiple knowledge points, and then using the logical relationship between the knowledge points to build a tree structure, such as the upper level knowledge point and the next The relationship between the level of knowledge points, the parallel relationship between the knowledge points belonging to a superior knowledge point, and so on.

There are some authoritative knowledge structure systems in the knowledge sharing platform, usually set by the administrator in the platform or by authoritative experts. In order to learn the knowledge content, the users on the platform usually use the corresponding authority. Sexual knowledge structure system for systematic learning.

However, for beginners, this knowledge structure system is first presented to the learner as a complete knowledge structure system. The learner can only see the position of each node in the system in the system, but the node ( The position of the knowledge point can only indicate its superiority in the whole system, but it cannot provide the learner with the best order suitable for the learner to learn the system. This gives the learner the efficiency of learning the knowledge structure by using the knowledge sharing platform system. There are certain obstacles.

Summary of invention

A brief overview of one or more aspects is provided below to provide a basic understanding of these aspects. This summary is not an extensive overview of all aspects that are conceived, and is not intended to identify key or critical elements in all aspects. Its sole purpose is to present some concepts of one or more aspects

The object of the present invention is to solve the above problems, and to provide an auxiliary method and system for learning a knowledge structure, which can improve the efficiency of a learner using a knowledge sharing platform system to learn a knowledge structure, thereby improving the utilization of knowledge sharing.

The technical solution of the present invention is as follows: The present invention discloses an auxiliary method for learning a knowledge structure, including:

Step 1: Record the learning order of different knowledge points of different learners in the knowledge structure;

Step 2: Obtain the statistical value of the learning order recorded by each knowledge point when learning by different learners;

Step 3: Sort the statistical values of each knowledge point and recommend it to other learners as the best learning order of the knowledge structure system.

According to an embodiment of the method for assisting the learning knowledge structure of the present invention, the learning order is that the knowledge point is learned by learning the first of all knowledge points in the entire knowledge structure, that is, the number of learning steps.

According to an embodiment of the method for assisting the learning knowledge structure of the present invention, the statistical value is to average all the learning steps of each knowledge point and use the average value as the standard learning step number of the knowledge point; or a statistical value. According to the learner's user level on the knowledge sharing platform, different weights are set, and the standard learning steps of the knowledge point are obtained by weighted averaging.

According to an embodiment of the method for assisting the learning knowledge structure of the present invention, the step 3 further includes:

The standard learning steps of each knowledge point are sorted in ascending order, so that the learners can sequentially learn the various knowledge points in the knowledge structure system according to the ascending order.

According to an embodiment of the method for assisting the learning knowledge structure of the present invention, the knowledge structure includes the same knowledge structure recommended by the system or a plurality of similar knowledge structures queried by the system.

The invention also discloses an auxiliary system for learning a knowledge structure, comprising:

a learning sequence recording module for recording the learning order of different knowledge points of different learners in the knowledge structure;

a learning order statistics module, configured to record the learning order of each knowledge point when learning by different learners, and obtain the statistical value thereof;

The knowledge point learning sorting module is used for sorting the statistical values of each knowledge point, and is recommended to the learner as the best learning order of the knowledge structure system.

According to an embodiment of the auxiliary system for learning knowledge structure of the present invention, the learning order is that the knowledge point is learned by learning the first of all knowledge points in the entire knowledge structure, that is, the number of learning steps.

According to an embodiment of the auxiliary system for learning knowledge structure according to the present invention, the statistical value is to average all the learning steps of each knowledge point and use the average value as the standard learning step number of the knowledge point; or a statistical value. According to the learner's user level on the knowledge sharing platform, different weights are set, and the standard learning steps of the knowledge point are obtained by weighted averaging.

According to an embodiment of the auxiliary system for learning knowledge structure of the present invention, the knowledge point learning sorting module sorts the standard learning steps of each knowledge point in ascending order, so that the learner sequentially learns the knowledge structure system according to the ascending order. Various knowledge points.

According to an embodiment of the auxiliary system for learning knowledge structure according to the present invention, the knowledge structure includes the same knowledge structure recommended by the system or a plurality of similar knowledge structures queried by the system.

Compared with the prior art, the present invention has the following beneficial effects: the present invention obtains an optimal learning order by providing statistics on the existing learning order of a plurality of learners of a certain knowledge structure, and provides the learner for the knowledge structure to Improve learning efficiency. Compared with some existing knowledge sharing platforms, they do not provide learners with a learning order for the knowledge structure to be learned, so that beginners will have a feeling of not having to start with the complicated knowledge system.

DRAWINGS

The above features and advantages of the present invention will be better understood from the following description of the appended claims. In the figures, components are not necessarily drawn to scale, and components having similar related features or features may have the same or similar reference numerals.

1 is a flow chart showing an embodiment of an auxiliary method of learning a knowledge structure of the present invention.

2 is a schematic diagram showing an embodiment of an auxiliary system of the learning knowledge structure of the present invention.

Detailed description of the invention

The invention is described in detail below with reference to the drawings and specific embodiments. It is to be noted that the aspects described below in conjunction with the drawings and the specific embodiments are merely exemplary and are not to be construed as limiting the scope of the invention.

1 shows the flow of an embodiment of an auxiliary method of learning a knowledge structure of the present invention. Referring to FIG. 1, each implementation step of the method of this embodiment is described in detail below.

Step S1: Record the learning order of different knowledge points of different learners in the knowledge structure.

This learning order may be that the knowledge point is learned by learning the first of all the knowledge points in the entire knowledge structure, that is, the number of learning steps.

Step S2: Obtain the statistical value of the learning order recorded by each knowledge point when learning by different learners.

One way to statistic is to average all the learning steps for each knowledge point, using this average as the standard number of learning steps for that knowledge point. In addition, different weights may be set according to the learner's user level on the knowledge sharing platform, and the standard learning steps of the knowledge point are obtained by weighted averaging.

This standard learning step is also dynamic, and this value is updated as the learner continues to join.

Step S3: Sorting the statistical values of the various knowledge points, and recommending them to other learners as the best learning order of the knowledge structure system.

For example, the standard learning steps of each knowledge point are sorted in ascending order, and the learner sequentially learns each knowledge point in the knowledge structure system according to the ranking.

In addition to the examples of different learners described in this embodiment for the same authoritative knowledge structure system, appropriate extensions can be made. For example, different learners are not learning the same structural system, but a similar knowledge structure system. The system will first judge the knowledge structure system with similarity on the platform, and the order of each knowledge point in the user learning knowledge structure depends on the statistics of the learning order of the knowledge points in these similar knowledge structures. The specific means of finding a similar knowledge structure is to convert the structure format into a text format and store it in real time based on the root term of the knowledge structure being created. Then compare the new root terms converted to text format with the existing root terms converted to text format for pairwise cosine similarity. Finally, the text format of the existing root term whose cosine similarity exceeds the preset threshold is converted into a structural format and presented to the user, otherwise it is not presented to the user.

Figure 2 illustrates the principles of an embodiment of an auxiliary system for learning knowledge structures of the present invention. Referring to FIG. 2, the system of this embodiment includes: a learning sequence recording module, a learning order statistics module, and a knowledge point learning sorting module.

The learning sequence recording module is used to record the learning order of different learners' knowledge points in the knowledge structure. This learning order may be that the knowledge point is learned by learning the first of all the knowledge points in the entire knowledge structure, that is, the number of learning steps.

The learning order statistics module is used to obtain the statistical values of the learning order recorded for each knowledge point when learning by different learners. One way to statistic is to average all the learning steps for each knowledge point, using this average as the standard number of learning steps for that knowledge point. In addition, different weights may be set according to the learner's user level on the knowledge sharing platform, and the standard learning steps of the knowledge point are obtained by weighted averaging. This standard learning step is also dynamic, and this value is updated as the learner continues to join.

The knowledge point learning sorting module is used to sort the statistical values of each knowledge point, and is recommended to other learners as the best learning order of the knowledge structure system. For example, the standard learning steps of each knowledge point are sorted in ascending order, and the learner sequentially learns each knowledge point in the knowledge structure system according to the ranking.

Although the above method is illustrated and described as a series of acts for simplicity of the explanation, it should be understood and appreciated that these methods are not limited by the order of the acts, as some acts may occur in different orders in accordance with one or more embodiments. And/or concurrently with other acts from what is illustrated and described herein or that are not illustrated and described herein, but are understood by those skilled in the art.

Those skilled in the art will further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps are described above generally in the form of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. The skilled person will be able to implement the described functionality in a different manner for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the invention.

Various illustrative logic blocks, modules, and circuits described in connection with the embodiments disclosed herein may be general purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or others. Programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein are implemented or executed. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor to enable the processor to read and write information to/from the storage medium. In the alternative, the storage medium can be integrated into the processor. The processor and the storage medium can reside in an ASIC. The ASIC can reside in the user terminal. In the alternative, the processor and the storage medium may reside as a discrete component in the user terminal.

In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented as a computer program product in software, the functions may be stored on or transmitted as one or more instructions or code on a computer readable medium. Computer readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage medium may be any available media that can be accessed by a computer. By way of example and not limitation, such computer readable media may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or can be used to carry or store instructions or data structures. Any other medium that is desirable for program code and that can be accessed by a computer. Any connection is also properly referred to as a computer readable medium. For example, if the software is transmitted from a web site, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave. The coaxial cable, fiber optic cable, twisted pair cable, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of the medium. Disks and discs as used herein include compact discs (CDs), laser discs, optical discs, digital versatile discs (DVDs), floppy discs, and Blu-ray discs, in which disks are often reproduced magnetically. Data, and discs optically reproduce data with a laser. Combinations of the above should also be included within the scope of computer readable media.

The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the present disclosure will be obvious to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. The present disclosure is not intended to be limited to the examples and designs described herein, but rather the broadest scope of the principles and novel features disclosed herein.

Claims

An auxiliary method for learning a knowledge structure, comprising:

Step 1: Record the learning order of different knowledge points of different learners in the knowledge structure;

Step 2: Obtain the statistical value of the learning order recorded by each knowledge point when learning by different learners;

Step 3: Sort the statistical values of each knowledge point and recommend it to other learners as the best learning order of the knowledge structure system.
The method for assisting learning knowledge structure according to claim 1, wherein the learning order is that the knowledge point is learned by learning the first of all knowledge points in the entire knowledge structure, that is, the number of learning steps.
The auxiliary method for learning a knowledge structure according to claim 2, wherein the statistical value is obtained by averaging all the learning steps of each knowledge point and using the average value as the standard learning step number of the knowledge point; Or the statistical value is set according to the user's user level on the knowledge sharing platform, and the standard learning steps of the knowledge point are obtained by weighted averaging.
The method for assisting a learning knowledge structure according to claim 3, wherein the step 3 further comprises:

The standard learning steps of each knowledge point are sorted in ascending order, so that the learners can sequentially learn the various knowledge points in the knowledge structure system according to the ascending order.
The method for assisting learning knowledge structure according to any one of claims 1 to 4, wherein the knowledge structure comprises the same knowledge structure recommended by the system or a plurality of similar knowledge structures queried by the system.
An auxiliary system for learning a knowledge structure, comprising:

a learning sequence recording module for recording the learning order of different knowledge points of different learners in the knowledge structure;

The learning order statistics module is configured to obtain the statistical values of the learning order recorded by each learner when learning by different learners;

The knowledge point learning sorting module is used for sorting the statistical values of each knowledge point, and is recommended to the learner as the best learning order of the knowledge structure system.
The auxiliary system for learning a knowledge structure according to claim 6, wherein the learning order is that the knowledge point is learned by learning the first of all knowledge points in the entire knowledge structure, that is, the number of learning steps.
The auxiliary system for learning knowledge structure according to claim 7, wherein the statistical value is obtained by averaging all the learning steps of each knowledge point and using the average value as the standard learning step number of the knowledge point; Or the statistical value is set according to the user's user level on the knowledge sharing platform, and the standard learning steps of the knowledge point are obtained by weighted averaging.
The auxiliary system for learning knowledge structure according to claim 8, wherein the knowledge point learning sorting module sorts the standard learning steps of each knowledge point in ascending order, so that the learner sequentially learns the knowledge structure according to the ascending order. Various knowledge points in the system.
The auxiliary system for learning a knowledge structure according to any one of claims 6 to 9, wherein the knowledge structure comprises the same knowledge structure recommended by the system or a plurality of similar knowledge structures queried by the system.