CN113065986B - Educational resource generation method based on intelligent interaction - Google Patents

Educational resource generation method based on intelligent interaction Download PDF

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CN113065986B
CN113065986B CN202110304244.9A CN202110304244A CN113065986B CN 113065986 B CN113065986 B CN 113065986B CN 202110304244 A CN202110304244 A CN 202110304244A CN 113065986 B CN113065986 B CN 113065986B
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knowledge
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CN113065986A (en
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洪美双
李文健
张兰港
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Beijing Visitor Interactive Technology Co ltd
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Beijing Visitor Interactive Technology Co ltd
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Abstract

The invention provides an educational resource generating method based on intelligent interaction, which comprises the following steps: acquiring user identity information and checking the user identity information; based on the verification result, determining a mapping relation between the user and a preset interaction function item in the education platform, and determining a target interaction option of the user according to the mapping relation; based on a preset interaction rule, teaching education contents of target interaction options to a user, and capturing learning data of the user in real time; analyzing and processing the learning data of the user, and determining knowledge points corresponding to the knowledge blind areas of the user; and searching a corresponding solution from a preset database according to the knowledge points corresponding to the knowledge blind areas, and analyzing and explaining the solution to the user. Through intelligent interaction mode, arouses child's enthusiasm and enthusiasm to study, lets child independently think in intelligent interaction, and the knowledge blind area of child in the learning process is explained and is saved in the explanation of parsing simultaneously to confirm corresponding educational resources.

Description

Educational resource generation method based on intelligent interaction
Technical Field
The invention relates to the technical field of cloud information interaction and data processing, in particular to an educational resource generation method based on intelligent interaction.
Background
At present, the traditional education system only merges communication between teachers and students, and more communication is realized by means of a third party platform, so that the operation process is not real-time, direct and flexible; and more important points are that the traditional education system can not arouse the learning interest of students, and the teaching process is single; furthermore, conventional educational systems are not capable of processing information, and have limited processing power and reliability;
Therefore, the invention provides an educational resource generating method based on intelligent interaction, which is used for exploring knowledge through interaction modes such as science popularization animation, chinese-English bilingual explanation, knowledge competition and the like, stimulating enthusiasm and enthusiasm of children for learning, enabling the children to independently think in intelligent interaction, self-help learning and enjoying the pleasure of exploring the world.
Disclosure of Invention
The invention provides an educational resource generation method based on intelligent interaction, which is used for stimulating enthusiasm and enthusiasm of children for learning through an intelligent interaction mode, enabling the children to independently think in the intelligent interaction, and simultaneously analyzing and explaining knowledge blind areas of the children in the learning process and storing the knowledge blind areas so as to determine corresponding educational resources.
The invention provides an educational resource generating method based on intelligent interaction, which comprises the following steps:
step 1: acquiring user identity information and checking the user identity information;
Step 2: based on the verification result, determining a mapping relation between the user and a preset interaction function item in the education platform, and determining a target interaction option of the user according to the mapping relation;
Step 3: based on a preset interaction rule, teaching the educational content of the target interaction option to a user, and capturing learning data of the user in real time;
Step 4: analyzing and processing the learning data of the user, and determining knowledge points corresponding to the knowledge blind areas of the user;
Step 5: searching a corresponding solution from a preset database according to the knowledge points corresponding to the knowledge blind areas, and analyzing and explaining the solution to a user in a preset explanation mode.
Preferably, in step 1, user identity information is obtained and verified, including:
acquiring login information of a user terminal, wherein the login information comprises an account number of a user and ID information of the user terminal;
judging whether the login information is in a preset user database or not;
if not, judging that the user is a new user, and carrying out registration operation;
otherwise, comparing the login information with the data root when the user registers to obtain the matching degree of the login information and the data root when the user registers;
And comparing the matching degree with a preset matching degree, completing verification of user login information when the matching degree is greater than or equal to the preset matching degree, and determining user identity information corresponding to the login information based on a preset user database.
Preferably, in step 2, a mapping relationship between a user and a preset interaction function item in an education platform is determined, and a target interaction option of the user is determined according to the mapping relationship, which comprises the following steps:
Acquiring historical learning record data of a user, and acquiring a corresponding relation between the user and at least one preset interactive function item in preset interactive function items in an education platform according to the historical data;
determining the mapping direction of the user and the preset interactive function item according to the corresponding relation, and establishing a mapping relation tree between the user and the preset interactive function item according to the mapping direction;
Based on the mapping relation tree, determining a mapping relation between the user and a preset interactive function item in the education platform;
Based on the mapping relation, determining completed interactive function items, ongoing interactive function items and non-ongoing interactive function items in preset interactive function items in the user and education platform;
Wherein the ongoing interactive function item is then the target interactive option of the user.
Preferably, in step 3, the educational content of the target interaction option is taught to the user, and learning data of the user is captured in real time, including:
the method comprises the steps that an interactive execution instruction sent by a user is obtained, and after the interactive execution instruction is received, an interactive interface of the user and the education platform is divided into a presenter information release interface and a student information release interface by the education platform;
the education platform explains the education content to the user based on the main speaker information release interface;
Wherein, the explanation comprises Chinese-English bilingual explanation and animation explanation;
Meanwhile, learning state data of students when listening to and speaking education contents and interaction data with the education platform are collected in real time based on the student information release interface, and learning data of users are captured.
Preferably, in step 4, analysis processing is performed on learning data of the user, and a knowledge point corresponding to a knowledge blind area of the user is determined, which includes:
placing the learning data of the user in a preset coordinate system, and obtaining coordinate points of the learning data of the user based on the preset coordinate system;
According to the coordinate points of the learning data of the user, performing data fitting in the preset coordinate system, drawing a learning data fitting curve of the user, and simultaneously, determining outlier data according to the learning data fitting curve of the user;
Deleting the outlier data to obtain standard learning data of the user;
dividing the standard data into M data segments with fixed lengths, and extracting an offset value of each data segment;
Classifying the data segments to obtain data segment type values corresponding to the data segments and the offset values, and extracting characteristic data in the data segments based on the data segment type values;
Constructing a feature data classification model, classifying standard learning data of the user based on the feature data, and obtaining a target classification result;
determining the knowledge field to which the learning data of each type of user belongs based on the target classification result, and determining the achievement information of the user on the knowledge point corresponding to the target interaction option based on the knowledge field;
Correlating the achievement information of the knowledge field and the knowledge points to a preset achievement database, wherein the preset achievement database stores average achievement information of the knowledge points corresponding to the target interaction options;
Comparing the score information of the knowledge points corresponding to the target interaction options with the average score information of the knowledge points corresponding to the target interaction options, and finishing the assessment of the current learning effect of the user;
determining knowledge point areas which are not mastered by the user in the target interaction options based on the evaluation result, and obtaining knowledge blind areas of the user;
acquiring element knowledge points in the knowledge blind area and constructing a knowledge graph;
And determining a basic knowledge point combination with the dependency relationship of the element knowledge points based on the knowledge graph to obtain knowledge points corresponding to the knowledge blind areas of the users.
Preferably, in step 5, a corresponding solution is searched from a preset database according to a knowledge point corresponding to the knowledge blind area, and the solution is parsed and explained to a user in a preset explanation mode, which includes:
acquiring knowledge points corresponding to the knowledge blind areas, and performing name vectorization processing on the knowledge points based on a pre-trained knowledge point name matching model to obtain name matching vectors corresponding to the knowledge points;
determining field correspondence between a name matching vector corresponding to the knowledge point and a preset solution knowledge point standard name vector table based on a preset solution knowledge point standard name vector table, and determining similarity between corresponding fields;
When the similarity is greater than or equal to the preset similarity, determining a solution of the knowledge point corresponding to the knowledge blind area;
wherein the solution comprises a plurality of explanation steps;
and determining a weight value of each explanation step based on a preset algorithm, and pushing the solutions to the user according to the descending order of the weight values.
Preferably, the method for generating educational resources based on intelligent interaction determines a solution of a knowledge point corresponding to the knowledge blind area, and further includes:
Acquiring a solution of the knowledge point corresponding to the knowledge blind area and a configuration parameter of the solution;
The configuration parameters comprise storage mode types corresponding to the solutions;
Determining a target storage area based on the storage mode type, and determining a capacity value of the target storage area;
determining a byte value of the solution based on a preset algorithm, and judging the sizes of the byte value of the solution and the capacity value of the target storage area;
And storing the solution to the target storage area based on the storage mode type when the byte value of the solution is less than or equal to the capacity value of the target storage area.
Preferably, the method for generating educational resources based on intelligent interaction analyzes and processes learning data of the user, determines knowledge points corresponding to a knowledge blind area of the user, searches a corresponding solution from a preset database according to the knowledge points corresponding to the knowledge blind area, and further comprises:
the method comprises the specific steps of obtaining the number of knowledge points corresponding to a knowledge blind area, calculating and determining the interaction accuracy of a user aiming at the knowledge blind area according to the number of knowledge points corresponding to the knowledge blind area, and calculating and finding the effective value of a corresponding solution according to the interaction accuracy, wherein the specific steps comprise:
and calculating and determining the interaction accuracy of the user aiming at the knowledge blind area according to the following formula:
Wherein alpha represents the interaction accuracy rate of the user aiming at the knowledge blind area, and the value range is (0, 1); gamma represents the number of knowledge points not mastered by the user in the target interaction option; delta represents all knowledge point magnitudes involved in the target interaction option; sigma represents the area value occupied by the knowledge blind area in the target interaction option knowledge graph; τ represents the total area value of the knowledge graph in the target interaction option; mu represents the average difficulty coefficient of knowledge points of the knowledge blind area, and the value range is (0.4,0.6); p 1 represents the number of associated knowledge points between the knowledge points not mastered by the user in the target interaction option and the remaining knowledge points excluding the knowledge points not mastered among all the knowledge points involved in the target interaction option; p 2 represents the number of irrelevant knowledge points between the knowledge points not mastered by the user in the target interaction option and the rest of knowledge points except for the knowledge points not mastered in all the knowledge points involved in the target interaction option; p 1+p2 = δ;
according to the interaction accuracy, searching a corresponding solution, and simultaneously, calculating the effective value of the searched solution according to the following formula:
wherein, beta represents the effective value of the corresponding solution found; A capability factor representing the user's receiving solution, and a range of values is (0.6,0.8); xi represents the matching degree value of the knowledge points corresponding to the found solution and the knowledge blind area; θ represents the feasibility of the solution and the range of values is (0, 1); The applicability of the solution to knowledge points of the knowledge blind area is represented, and the value range is (0, 1); omega represents the knowledge point number value in the solution-capable blind area corresponding to the solution;
Comparing the calculated effective value with a preset effective value;
If the calculated effective value is smaller than the preset effective value, judging that the searched corresponding solution is unqualified, marking a final blind area from the knowledge blind area, and carrying out listed output display on knowledge points corresponding to the final blind area;
Otherwise, judging that the searched corresponding solution is qualified, and outputting and displaying the solution and the solution process of the solved unknown knowledge point.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of an educational resource generating method based on intelligent interaction in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1:
the embodiment provides an educational resource generating method based on intelligent interaction, as shown in fig. 1, comprising the following steps:
step 1: acquiring user identity information and checking the user identity information;
Step 2: based on the verification result, determining a mapping relation between the user and a preset interaction function item in the education platform, and determining a target interaction option of the user according to the mapping relation;
Step 3: based on a preset interaction rule, teaching the educational content of the target interaction option to a user, and capturing learning data of the user in real time;
Step 4: analyzing and processing the learning data of the user, and determining knowledge points corresponding to the knowledge blind areas of the user;
Step 5: searching a corresponding solution from a preset database according to the knowledge points corresponding to the knowledge blind areas, and analyzing and explaining the solution to a user in a preset explanation mode.
In this embodiment, the preset interactive function item is a learning item that the education platform allows the user to learn, for example, may be exploring the world on the ocean floor, exploring the world on the universe, and so on.
In this embodiment, the mapping relationship refers to an association relationship between a user and a preset interactive function item, and one preset interactive function item may correspond to one user, or a plurality of preset interactive function items may correspond to one user.
In this embodiment, the target interaction option refers to determining from a plurality of preset interaction function items that the user is learning.
In this embodiment, the preset interaction rules are set in advance, and may be a plurality of rules such as science popularization animation, online human knowledge competition and Chinese-English bilingual explanation.
In this embodiment, the preset database is obtained through multiple sets of training, and solutions corresponding to multiple knowledge points are stored inside the preset database.
The beneficial effects of the technical scheme are as follows: through intelligent interaction mode, arouses child's enthusiasm and enthusiasm to study, lets child independently think in intelligent interaction, and the knowledge blind area of child in the learning process is explained and is saved in the explanation of parsing simultaneously to confirm corresponding educational resources.
Example 2:
On the basis of the above embodiment 1, the present embodiment provides an educational resource generating method based on intelligent interaction, in step 1, user identity information is obtained, and verification is performed on the user identity information, including:
acquiring login information of a user terminal, wherein the login information comprises an account number of a user and ID information of the user terminal;
judging whether the login information is in a preset user database or not;
if not, judging that the user is a new user, and carrying out registration operation;
otherwise, comparing the login information with the data root when the user registers to obtain the matching degree of the login information and the data root when the user registers;
And comparing the matching degree with a preset matching degree, completing verification of user login information when the matching degree is greater than or equal to the preset matching degree, and determining user identity information corresponding to the login information based on a preset user database.
In this embodiment, the data root refers to basic information filled in by the user at the time of registration, including name, gender, age, and the like.
In this embodiment, the preset matching degree is set in advance, and is used to measure the matching degree of the login information and the data root when the user registers, so as to facilitate the determination of the identity information of the user.
The beneficial effects of the technical scheme are as follows: by acquiring the identity information of the user and checking the identity of the user, the learning data corresponding to the user can be ensured to be accurately grasped according to the interactive content of the user, so that a solution suitable for the user when encountering problems in the learning process is conveniently provided for the user.
Example 3:
On the basis of the above embodiment 1, the present embodiment provides an educational resource generating method based on intelligent interaction, in step 2, a mapping relationship between a user and a preset interactive function item in an educational platform is determined, and a target interactive option of the user is determined according to the mapping relationship, including:
Acquiring historical learning record data of a user, and acquiring a corresponding relation between the user and at least one preset interactive function item in preset interactive function items in an education platform according to the historical data;
determining the mapping direction of the user and the preset interactive function item according to the corresponding relation, and establishing a mapping relation tree between the user and the preset interactive function item according to the mapping direction;
Based on the mapping relation tree, determining a mapping relation between the user and a preset interactive function item in the education platform;
Based on the mapping relation, determining completed interactive function items, ongoing interactive function items and non-ongoing interactive function items in preset interactive function items in the user and education platform;
Wherein the ongoing interactive function item is then the target interactive option of the user.
In this embodiment, the mapping direction is used to determine the mapping direction of the mapping party and the mapped party, e.g. the result of mapping a is different from mapping B to mapping B.
In this embodiment, the mapping relationship tree is used to record the association relationship between the user and the interactive function items, and the interaction relationship between a plurality of interactive function items and the same user is stored in the mapping relationship tree.
In this embodiment, the mapping relationship refers to an association relationship between a user and a preset interactive function item, and one preset interactive function item may correspond to one user, or a plurality of preset interactive function items may correspond to one user.
The beneficial effects of the technical scheme are as follows: the mapping relation between the user and the preset interactive function item is determined, the target interactive function type of the user is accurately determined according to the mapping relation, the education platform can conveniently and accurately grasp the current learning data of the user according to the target interactive function item, and the corresponding knowledge blind area is determined according to the learning data, so that the corresponding education resource is determined.
Example 4:
On the basis of the above embodiment 1, the present embodiment provides an educational resource generating method based on intelligent interaction, in step 3, the teaching of the educational content of the target interaction option to the user, and capturing the learning data of the user in real time, including:
the method comprises the steps that an interactive execution instruction sent by a user is obtained, and after the interactive execution instruction is received, an interactive interface of the user and the education platform is divided into a presenter information release interface and a student information release interface by the education platform;
the education platform explains the education content to the user based on the main speaker information release interface;
Wherein, the explanation comprises Chinese-English bilingual explanation and animation explanation;
Meanwhile, learning state data of students when listening to and speaking education contents and interaction data with the education platform are collected in real time based on the student information release interface, and learning data of users are captured.
In this embodiment, the interactive execution instruction refers to an instruction sent by the user to the education platform, and the education platform starts pushing the education content after receiving the instruction.
In this embodiment, the presenter information distribution interface refers to an interface used by the education platform terminal to push the education, for example, in a net lesson, where a teacher is the subject.
In this embodiment, the student information distribution interface refers to an interface for students to listen to the information transmitted from the education platform, for displaying their own learning status, and the like.
The beneficial effects of the technical scheme are as follows: through pushing the education content to the user, and acquiring the learning data of the user in real time, the learning data of the user is convenient to analyze, and the place where the user is not firm in mastering the knowledge points is accurately determined, so that the education platform is convenient to determine the corresponding solution according to the knowledge blind areas, the corresponding education resources are generated, and the pertinence of the education resources is improved.
Example 5:
On the basis of the above embodiment 1, the present embodiment provides an educational resource generating method based on intelligent interaction, in step4, analysis processing is performed on learning data of the user, and a knowledge point corresponding to a knowledge blind area of the user is determined, including:
placing the learning data of the user in a preset coordinate system, and obtaining coordinate points of the learning data of the user based on the preset coordinate system;
According to the coordinate points of the learning data of the user, performing data fitting in the preset coordinate system, drawing a learning data fitting curve of the user, and simultaneously, determining outlier data according to the learning data fitting curve of the user;
Deleting the outlier data to obtain standard learning data of the user;
dividing the standard data into M data segments with fixed lengths, and extracting an offset value of each data segment;
Classifying the data segments to obtain data segment type values corresponding to the data segments and the offset values, and extracting characteristic data in the data segments based on the data segment type values;
Constructing a feature data classification model, classifying standard learning data of the user based on the feature data, and obtaining a target classification result;
determining the knowledge field to which the learning data of each type of user belongs based on the target classification result, and determining the achievement information of the user on the knowledge point corresponding to the target interaction option based on the knowledge field;
Correlating the achievement information of the knowledge field and the knowledge points to a preset achievement database, wherein the preset achievement database stores average achievement information of the knowledge points corresponding to the target interaction options;
Comparing the score information of the knowledge points corresponding to the target interaction options with the average score information of the knowledge points corresponding to the target interaction options, and finishing the assessment of the current learning effect of the user;
determining knowledge point areas which are not mastered by the user in the target interaction options based on the evaluation result, and obtaining knowledge blind areas of the user;
acquiring element knowledge points in the knowledge blind area and constructing a knowledge graph;
And determining a basic knowledge point combination with the dependency relationship of the element knowledge points based on the knowledge graph to obtain knowledge points corresponding to the knowledge blind areas of the users.
In this embodiment, the preset coordinate system is used to determine the learning data of the user to a specific point, so as to facilitate clearing of abnormal data in the learning data of the user.
In this embodiment, the coordinate point of the learning data refers to a specific coordinate value of each data field obtained after the learning data is placed in the coordinate system.
In this embodiment, the learning data fitting curve refers to connecting data points with high concentration in the learning data to obtain a data curve, which is used to represent the fitting degree of the data.
In this embodiment, the outlier data refers to data points that deviate from the fitting curve of the learning data, and have an abnormal influence on the learning data of the user.
In this embodiment, the standard learning data refers to data obtained by removing abnormal data in the user learning data, and the data can be directly used to analyze the learning effect of the user.
In this embodiment, the offset value refers to the degree to which each data segment is offset by a preset standard, and can be used to determine the type of the data.
In this embodiment, the feature data refers to a representative key data segment in the learning data of the user, and the category of the learning data can be accurately determined according to the feature data.
In this embodiment, the preset score database is set in advance, and is obtained by training the learning scores of a plurality of users, and average score information corresponding to the target interaction options is stored therein.
In this embodiment, the meta-knowledge points refer to knowledge points with critical problems in knowledge blind areas, for example, when calculating triangle angles, the trigonometric function is a meta-knowledge point.
In this embodiment, the basic knowledge points refer to other knowledge points derived according to the meta-knowledge points, and have blood-related relationships with the meta-knowledge points, for example, trigonometric functions are meta-knowledge points, and the rest of derived trigonometric functions are combined to form the basic knowledge points.
The beneficial effects of the technical scheme are as follows: the learning score of the user is cleaned, abnormal data in the learning score are removed, the current learning effect of the user is evaluated according to the learning data of the user, so that a knowledge blind area of the user is accurately determined, knowledge points corresponding to the knowledge blind area of the user are obtained according to element knowledge points in the knowledge blind area, the knowledge points corresponding to the knowledge blind area of the user are accurately obtained, the corresponding solution is determined according to the knowledge blind area by the education platform, the education resources of the education platform are perfected, and convenience is provided for determining the corresponding education resources.
Example 6:
on the basis of the above embodiment 1, the present embodiment provides an educational resource generating method based on intelligent interaction, in step 5, a corresponding solution is searched from a preset database according to a knowledge point corresponding to the knowledge blind area, and the solution is parsed and explained to a user in a preset explanation manner, including:
acquiring knowledge points corresponding to the knowledge blind areas, and performing name vectorization processing on the knowledge points based on a pre-trained knowledge point name matching model to obtain name matching vectors corresponding to the knowledge points;
determining field correspondence between a name matching vector corresponding to the knowledge point and a preset solution knowledge point standard name vector table based on a preset solution knowledge point standard name vector table, and determining similarity between corresponding fields;
When the similarity is greater than or equal to the preset similarity, determining a solution of the knowledge point corresponding to the knowledge blind area;
wherein the solution comprises a plurality of explanation steps;
and determining a weight value of each explanation step based on a preset algorithm, and pushing the solutions to the user according to the descending order of the weight values.
In this embodiment, the name vectorization processing refers to performing transformation calculation on data to obtain a vector mode corresponding to the data.
In this embodiment, the name matching vector refers to a vector obtained by vectorizing the knowledge point names, and the similarity to the standard name vector is calculated from this vector.
In this embodiment, the field correspondence refers to a correspondence of data of the same position in the knowledge roll name vector and the standard name vector, for example: name length correspondence, data content correspondence, etc.
In this embodiment, the weight value refers to the specific weight of each step in the solution, and is used to represent the importance of the step.
The beneficial effects of the technical scheme are as follows: through looking for corresponding solution according to user's knowledge blind area, be convenient for carry out the explanation of parsing to the knowledge point that the user does not learn, improved user's learning efficiency, be convenient for arouse the enthusiasm of user to study simultaneously, let the user study independently.
Example 7:
On the basis of the above embodiment 6, this embodiment provides an educational resource generating method based on intelligent interaction, which is characterized in that determining a solution of a knowledge point corresponding to the knowledge blind area further includes:
Acquiring a solution of the knowledge point corresponding to the knowledge blind area and a configuration parameter of the solution;
The configuration parameters comprise storage mode types corresponding to the solutions;
Determining a target storage area based on the storage mode type, and determining a capacity value of the target storage area;
determining a byte value of the solution based on a preset algorithm, and judging the sizes of the byte value of the solution and the capacity value of the target storage area;
And storing the solution to the target storage area based on the storage mode type when the byte value of the solution is less than or equal to the capacity value of the target storage area.
In this embodiment, the configuration parameters refer to the storage mode type corresponding to the solution, the number of steps included in the solution, the amount of information, and the like.
In this embodiment, the target storage area refers to a storage area for storing a solution, and is a place selected from a plurality of storage areas where a suitable solution is stored.
In this embodiment, the preset algorithm is set in advance, and is used to determine the byte value of the solution and the capacity value of the target storage area, which are obtained through multiple training.
The beneficial effects of the technical scheme are as follows: through saving the solution that user's knowledge blind area corresponds, the education platform of being convenient for carries out comprehensive training to user's knowledge blind area, increases education platform's education resource library, and the education platform of being convenient for generates the education resource that corresponds according to user's knowledge blind area, has improved and has generated education resource efficiency and pertinence.
Example 8:
On the basis of the above embodiment 1, the present embodiment provides an educational resource generating method based on intelligent interaction, which performs analysis processing on learning data of the user, determines knowledge points corresponding to a knowledge blind area of the user, searches for a corresponding solution from a preset database according to the knowledge points corresponding to the knowledge blind area, and further includes:
the method comprises the specific steps of obtaining the number of knowledge points corresponding to a knowledge blind area, calculating and determining the interaction accuracy of a user aiming at the knowledge blind area according to the number of knowledge points corresponding to the knowledge blind area, and calculating and finding the effective value of a corresponding solution according to the interaction accuracy, wherein the specific steps comprise:
and calculating and determining the interaction accuracy of the user aiming at the knowledge blind area according to the following formula:
Wherein alpha represents the interaction accuracy rate of the user aiming at the knowledge blind area, and the value range is (0, 1); gamma represents the number of knowledge points not mastered by the user in the target interaction option; delta represents all knowledge point magnitudes involved in the target interaction option; sigma represents the area value occupied by the knowledge blind area in the target interaction option knowledge graph; τ represents the total area value of the knowledge graph in the target interaction option; mu represents the average difficulty coefficient of knowledge points of the knowledge blind area, and the value range is (0.4,0.6); p 1 represents the number of associated knowledge points between the knowledge points not mastered by the user in the target interaction option and the remaining knowledge points excluding the knowledge points not mastered among all the knowledge points involved in the target interaction option; p 2 represents the number of irrelevant knowledge points between the knowledge points not mastered by the user in the target interaction option and the rest of knowledge points except for the knowledge points not mastered in all the knowledge points involved in the target interaction option; p 1+p2 = δ;
according to the interaction accuracy, searching a corresponding solution, and simultaneously, calculating the effective value of the searched solution according to the following formula:
wherein, beta represents the effective value of the corresponding solution found; A capability factor representing the user's receiving solution, and a range of values is (0.6,0.8); xi represents the matching degree value of the knowledge points corresponding to the found solution and the knowledge blind area; θ represents the feasibility of the solution and the range of values is (0, 1); The applicability of the solution to knowledge points of the knowledge blind area is represented, and the value range is (0, 1); omega represents the knowledge point number value in the solution-capable blind area corresponding to the solution;
Comparing the calculated effective value with a preset effective value;
If the calculated effective value is smaller than the preset effective value, judging that the searched corresponding solution is unqualified, marking a final blind area from the knowledge blind area, and carrying out listed output display on knowledge points corresponding to the final blind area;
Otherwise, judging that the searched corresponding solution is qualified, and outputting and displaying the solution and the solution process of the solved unknown knowledge point.
In this embodiment, the capability factor is used to represent the degree of capability of the user to receive the solution.
In this embodiment, the applicability refers to the pertinence of the solution to the knowledge points corresponding to the knowledge blind area, and is used to determine whether the solution can process the knowledge point problem of the knowledge blind area.
In this embodiment, the preset effective value is set in advance, and is used to determine whether the calculated effective value is qualified, which is an index for measuring the calculation result.
The beneficial effects of the technical scheme are as follows: the accuracy of the knowledge blind area is determined through calculation according to the number of the knowledge points corresponding to the knowledge blind area, and the effective value of the corresponding solution is found through calculation according to the accuracy, when the accuracy is calculated, the ratio of the number of the knowledge points corresponding to the knowledge blind area to the number of all the knowledge points and the area value occupied by the knowledge blind area in the target interaction option knowledge map are related, so that the accuracy of calculation is dependable, the accuracy of calculation is improved, when the effective value is calculated, the matching degree value, the feasibility rate of the solution and the capability factor of a user for receiving the solution are related, the calculation result is more accurate and reliable, the solution is ensured to effectively solve the knowledge blind area of the user, the solution efficiency of the user knowledge blind area is improved, and meanwhile the accuracy of the educational platform for generating educational resources according to the knowledge blind area of the user is improved.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (6)

1. An educational resource generating method based on intelligent interaction is characterized by comprising the following steps:
step 1: acquiring user identity information and checking the user identity information;
Step 2: based on the verification result, determining a mapping relation between the user and a preset interaction function item in the education platform, and determining a target interaction option of the user according to the mapping relation;
Step 3: based on a preset interaction rule, teaching the educational content of the target interaction option to a user, and capturing learning data of the user in real time;
Step 4: analyzing and processing the learning data of the user, and determining knowledge points corresponding to the knowledge blind areas of the user;
Step 5: searching a corresponding solution from a preset database according to the knowledge points corresponding to the knowledge blind areas, and analyzing and explaining the solution to a user in a preset explanation mode;
wherein, still include:
the method comprises the specific steps of obtaining the number of knowledge points corresponding to a knowledge blind area, calculating and determining the interaction accuracy of a user aiming at the knowledge blind area according to the number of knowledge points corresponding to the knowledge blind area, and calculating and finding the effective value of a corresponding solution according to the interaction accuracy, wherein the specific steps comprise:
and calculating and determining the interaction accuracy of the user aiming at the knowledge blind area according to the following formula:
Wherein alpha represents the interaction accuracy rate of the user aiming at the knowledge blind area, and the value range is (0, 1); gamma represents the number of knowledge points not mastered by the user in the target interaction option; delta represents all knowledge point magnitudes involved in the target interaction option; sigma represents the area value occupied by the knowledge blind area in the target interaction option knowledge graph; τ represents the total area value of the knowledge graph in the target interaction option; p 1 represents the number of associated knowledge points between the knowledge points not mastered by the user in the target interaction option and the remaining knowledge points excluding the knowledge points not mastered among all the knowledge points involved in the target interaction option; p 2 represents the number of irrelevant knowledge points between the knowledge points not mastered by the user in the target interaction option and the rest of knowledge points except for the knowledge points not mastered in all the knowledge points involved in the target interaction option; p 1+p2 = δ;
according to the interaction accuracy, searching a corresponding solution, and simultaneously, calculating the effective value of the searched solution according to the following formula:
wherein, beta represents the effective value of the corresponding solution found; A capability factor representing the user's receiving solution, and a range of values is (0.6,0.8); xi represents the matching degree value of the knowledge points corresponding to the found solution and the knowledge blind area; θ represents the feasibility of the solution and the range of values is (0, 1); θ represents the applicability of the solution to knowledge points of the knowledge blind area, and the value range is (0, 1); omega represents the knowledge point number value in the solution-capable blind area corresponding to the solution;
Comparing the calculated effective value with a preset effective value;
If the calculated effective value is smaller than the preset effective value, judging that the searched corresponding solution is unqualified, marking a final blind area from the knowledge blind area, and carrying out listed output display on knowledge points corresponding to the final blind area;
otherwise, judging that the found corresponding solution is qualified, and outputting and displaying the solution and the solution process of the solved unknown knowledge point;
in step 4, analyzing and processing the learning data of the user to determine a knowledge point corresponding to the knowledge blind area of the user, including:
placing the learning data of the user in a preset coordinate system, and obtaining coordinate points of the learning data of the user based on the preset coordinate system;
According to the coordinate points of the learning data of the user, performing data fitting in the preset coordinate system, drawing a learning data fitting curve of the user, and simultaneously, determining outlier data according to the learning data fitting curve of the user;
Deleting the outlier data to obtain standard learning data of the user;
dividing the standard data into M data segments with fixed lengths, and extracting an offset value of each data segment;
Classifying the data segments to obtain data segment type values corresponding to the data segments and the offset values, and extracting characteristic data in the data segments based on the data segment type values;
Constructing a feature data classification model, classifying standard learning data of the user based on the feature data, and obtaining a target classification result;
determining the knowledge field to which the learning data of each type of user belongs based on the target classification result, and determining the achievement information of the user on the knowledge point corresponding to the target interaction option based on the knowledge field;
Correlating the achievement information of the knowledge field and the knowledge points to a preset achievement database, wherein the preset achievement database stores average achievement information of the knowledge points corresponding to the target interaction options;
Comparing the score information of the knowledge points corresponding to the target interaction options with the average score information of the knowledge points corresponding to the target interaction options, and finishing the assessment of the current learning effect of the user;
determining knowledge point areas which are not mastered by the user in the target interaction options based on the evaluation result, and obtaining knowledge blind areas of the user;
acquiring element knowledge points in the knowledge blind area and constructing a knowledge graph;
And determining a basic knowledge point combination with the dependency relationship of the element knowledge points based on the knowledge graph to obtain knowledge points corresponding to the knowledge blind areas of the users.
2. The educational resource generating method based on intelligent interaction according to claim 1, wherein in step 1, user identity information is obtained and verified, comprising:
acquiring login information of a user terminal, wherein the login information comprises an account number of a user and ID information of the user terminal;
judging whether the login information is in a preset user database or not;
if not, judging that the user is a new user, and carrying out registration operation;
otherwise, comparing the login information with the data root when the user registers to obtain the matching degree of the login information and the data root when the user registers;
And comparing the matching degree with a preset matching degree, completing verification of user login information when the matching degree is greater than or equal to the preset matching degree, and determining user identity information corresponding to the login information based on a preset user database.
3. The method for generating educational resources based on intelligent interaction according to claim 1, wherein in step 2, a mapping relation between a user and a preset interactive function item in an educational platform is determined, and a target interactive option of the user is determined according to the mapping relation, comprising:
Acquiring historical learning record data of a user, and acquiring a corresponding relation between the user and at least one preset interactive function item in preset interactive function items in an education platform according to the historical data;
determining the mapping direction of the user and the preset interactive function item according to the corresponding relation, and establishing a mapping relation tree between the user and the preset interactive function item according to the mapping direction;
Based on the mapping relation tree, determining a mapping relation between the user and a preset interactive function item in the education platform;
Based on the mapping relation, determining completed interactive function items, ongoing interactive function items and non-ongoing interactive function items in preset interactive function items in the user and education platform;
Wherein the ongoing interactive function item is then the target interactive option of the user.
4. The method for generating educational resources based on intelligent interaction according to claim 1, wherein in step 3, explaining the educational content of the target interaction option to the user, and capturing the learning data of the user in real time, comprises:
the method comprises the steps that an interactive execution instruction sent by a user is obtained, and after the interactive execution instruction is received, an interactive interface of the user and the education platform is divided into a presenter information release interface and a student information release interface by the education platform;
the education platform explains the education content to the user based on the main speaker information release interface;
Wherein, the explanation comprises Chinese-English bilingual explanation and animation explanation;
Meanwhile, learning state data of students when listening to and speaking education contents and interaction data with the education platform are collected in real time based on the student information release interface, and learning data of users are captured.
5. The method for generating educational resources based on intelligent interaction according to claim 1, wherein in step 5, a corresponding solution is searched from a preset database according to the knowledge points corresponding to the knowledge blind areas, and the solution is parsed and explained to a user in a preset explanation mode, comprising:
acquiring knowledge points corresponding to the knowledge blind areas, and performing name vectorization processing on the knowledge points based on a pre-trained knowledge point name matching model to obtain name matching vectors corresponding to the knowledge points;
determining field correspondence between a name matching vector corresponding to the knowledge point and a preset solution knowledge point standard name vector table based on a preset solution knowledge point standard name vector table, and determining similarity between corresponding fields;
When the similarity is greater than or equal to the preset similarity, determining a solution of the knowledge point corresponding to the knowledge blind area;
wherein the solution comprises a plurality of explanation steps;
and determining a weight value of each explanation step based on a preset algorithm, and pushing the solutions to the user according to the descending order of the weight values.
6. The method for generating educational resources based on intelligent interaction according to claim 5, wherein determining a solution of a knowledge point corresponding to the knowledge blind area further comprises:
Acquiring a solution of the knowledge point corresponding to the knowledge blind area and a configuration parameter of the solution;
The configuration parameters comprise storage mode types corresponding to the solutions;
Determining a target storage area based on the storage mode type, and determining a capacity value of the target storage area;
determining a byte value of the solution based on a preset algorithm, and judging the sizes of the byte value of the solution and the capacity value of the target storage area;
And storing the solution to the target storage area based on the storage mode type when the byte value of the solution is less than or equal to the capacity value of the target storage area.
CN202110304244.9A 2021-03-22 Educational resource generation method based on intelligent interaction Active CN113065986B (en)

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105282181A (en) * 2014-05-28 2016-01-27 中兴通讯股份有限公司 Education system and information interaction method thereof
CN109840867A (en) * 2017-11-28 2019-06-04 中国移动通信集团公司 A kind of method of intelligent tutoring, equipment and device
CN111339258A (en) * 2020-02-29 2020-06-26 西安理工大学 University computer basic exercise recommendation method based on knowledge graph

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105282181A (en) * 2014-05-28 2016-01-27 中兴通讯股份有限公司 Education system and information interaction method thereof
CN109840867A (en) * 2017-11-28 2019-06-04 中国移动通信集团公司 A kind of method of intelligent tutoring, equipment and device
CN111339258A (en) * 2020-02-29 2020-06-26 西安理工大学 University computer basic exercise recommendation method based on knowledge graph

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