CN117252461B - Online training multimode teaching method and system based on big data - Google Patents
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Abstract
The invention relates to an on-line training multi-mode teaching method and system based on big data, which belong to the technical field of teaching. According to the invention, the learning data information of the user is fully considered, so that the learning ability condition of the user within the preset time is obtained according to the learning data information of the user, the learning training mode is dynamically adjusted according to the learning ability condition of the user, and the learning efficiency of the user is further improved.
Description
Technical Field
The invention relates to the technical field of teaching, in particular to an online training multi-mode teaching method and system based on big data.
Background
With the continuous development of artificial intelligence technology and big data analysis technology, various online evaluation platforms and systems have been developed. The result of classroom teaching mode leads to the evaluation, gradually turns to the procedural evaluation on the line, and the teacher can monitor student's study duration, online homework completion condition etc. through platforms such as intelligent learning net, examination cloud to in time master student's daily online study condition, and carry out individualized evaluation and instruction to the student with the help of intelligent technology and big data analysis technique. The learning ability is based on all the ability, and six indexes for evaluating the learning ability of students generally include learning concentration, learning achievement sense, self-confidence, thinking flexibility, independence and thinking dislike. However, under the influence of multiple aspects, the learning ability of the user is easy to change in stages, and in the prior art, the learning training mode cannot be dynamically adjusted according to the learning ability of the user, so that the learning efficiency of the user is low.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides an on-line training multi-mode teaching method and system based on big data.
In order to achieve the above purpose, the invention adopts the following technical scheme:
The invention provides an on-line training multi-mode teaching method based on big data, which comprises the following steps:
acquiring multiple learning data information of a user within a preset time, and acquiring learning ability evaluation score information of the user by evaluating the multiple learning data information;
constructing a user learning ability change prediction model, and acquiring learning ability preference data of the current user based on the learning ability evaluation score information of the user and the user learning ability change prediction model;
Acquiring knowledge item data information learned by a current user, and acquiring a related knowledge training mode according to the knowledge item data information learned by the current user;
And acquiring corresponding adaptation degree based on the learning ability preference data of the current user and the related knowledge training mode, and adjusting the related knowledge training mode according to the corresponding adaptation degree.
Further, in a preferred embodiment of the present invention, learning ability evaluation score information of a user is obtained by evaluating the multivariate learning data information, which specifically includes the following steps:
Setting keyword information according to learning ability, constructing a search tag according to the keyword information, and searching through big data based on the search tag to obtain related evaluation index data of the learning ability;
Constructing an evaluation index system according to the related evaluation index data of the learning ability by a hierarchical analysis method, determining a scheme layer, a target layer and a criterion layer of the evaluation index system, and inputting the related evaluation index data of the learning ability into the criterion layer;
inputting the multiple learning data information into a scheme layer, inputting a preset learning ability effect into a target layer, and calculating the multiple learning data information based on an evaluation index system to obtain weight vector information corresponding to each multiple learning data information;
And inputting weight vector information corresponding to the multiple learning data information into a gray correlation analysis method, comprehensively evaluating the weight vector information corresponding to each multiple learning data information, and acquiring learning ability evaluation score information of the user.
Further, in a preferred embodiment of the present invention, a learning ability change prediction model of the user is constructed, and learning ability change data of the current user is obtained based on learning ability evaluation score information of the user and the learning ability change prediction model of the user, which specifically includes:
acquiring learning ability evaluation score information of a user within a preset time, inputting the learning ability evaluation score information of the user within the preset time into a random forest algorithm, calculating a base index, and acquiring feature information with highest correlation according to the base index;
Constructing a user learning capacity feature matrix according to the feature information with highest correlation, constructing a user learning capacity change prediction model based on LSTM, and introducing a singular value feature decomposition algorithm to decompose the feature value of the user learning capacity feature matrix;
Sorting the obtained characteristic values, selecting the characteristic values with the frequency value higher than the preset frequency value to construct a low-dimensional characteristic vector projection matrix, and inputting the low-dimensional characteristic vector projection matrix into a user learning capacity change prediction model to perform coding learning;
And outputting the user learning ability change prediction model after the user learning ability change prediction model meets the preset training requirement, and acquiring the learning ability preference data of the current user within the preset time according to the user learning ability change prediction model.
Further, in a preferred embodiment of the present invention, knowledge item data information learned by a current user is obtained, and a related knowledge training mode is obtained according to the knowledge item data information learned by the current user, which specifically includes the following steps:
acquiring knowledge item data information learned by a current user, and acquiring a related training mode of the knowledge item data information and learning ability evaluation scores of historical users on the knowledge item data information through big data;
acquiring the correlation between the training mode and the learning ability of the user according to the related training mode of the knowledge item data information and the learning ability evaluation score of the historical user on the knowledge item data information;
Inputting the correlation between the training mode and the learning ability of the user into a topological space for related expression, obtaining a topological structure diagram, generating an adjacent matrix according to the topological structure diagram, constructing a database, and inputting the adjacent matrix into the database for storage;
The knowledge item data information learned by the current user is input into a database, euclidean distance between the knowledge item data information learned by the current user and each sample data in the adjacent matrix is calculated, and a related knowledge training mode with the Euclidean distance lower than a preset Euclidean distance is selected to be output as a related knowledge training mode.
Further, in a preferred embodiment of the present invention, the acquiring the corresponding adaptation degree based on the learning ability preference data of the current user and the related knowledge training mode, and adjusting the related knowledge training mode according to the corresponding adaptation degree specifically includes the following steps:
Calculating the adaptation degree between the related knowledge training mode and the learning ability preference data of the current user through the related information entropy measurement, and comparing the adaptation degree with a preset adaptation degree to obtain a deviation rate;
Judging whether the deviation rate is smaller than a preset deviation rate threshold value, and if the deviation rate is smaller than the preset deviation rate threshold value, sequencing relevant knowledge training modes corresponding to the adaptation degree;
After sorting, generating an adaptation degree priority knowledge training mode sorting table, and acquiring a training mode with the maximum adaptation degree according to the adaptation degree priority knowledge training mode sorting table;
generating corresponding recommendation information according to the training mode with the maximum adaptation degree, displaying the corresponding recommendation information according to a preset mode, and regularly adjusting the training mode according to the learning ability preference data of the current user.
Further, in a preferred embodiment of the present invention, the on-line training multi-mode teaching method based on big data further comprises the following steps:
acquiring learning ability preference data of a current user, constructing a preference curve of the learning ability of the user according to the learning ability preference data of the current user, and setting a related learning ability related threshold;
Acquiring course information with learning ability preference data lower than a relevant learning ability relevant threshold value from a preference curve of the learning ability of a user, and calculating knowledge association characteristics between the course information with the learning ability preference data lower than the relevant learning ability relevant threshold value;
Judging whether the association characteristic is larger than a preset association characteristic, and acquiring knowledge item data information corresponding to learning ability preference data lower than a relevant learning ability relevant threshold value when the association characteristic is larger than the preset association characteristic;
Acquiring learning data information of knowledge item data information corresponding to the learning ability preference data lower than the relevant learning ability relevant threshold value of the user, and generating relevant learning emphasis points and attention points according to the learning data information of the corresponding knowledge item data information.
The second aspect of the present invention provides an on-line training multimode teaching system based on big data, the system comprising a memory and a processor, the memory comprising an on-line training multimode teaching method program based on big data, the on-line training multimode teaching method program based on big data being executed by the processor, implementing the following steps:
acquiring multiple learning data information of a user within a preset time, and acquiring learning ability evaluation score information of the user by evaluating the multiple learning data information;
constructing a user learning ability change prediction model, and acquiring learning ability preference data of the current user based on the learning ability evaluation score information of the user and the user learning ability change prediction model;
Acquiring knowledge item data information learned by a current user, and acquiring a related knowledge training mode according to the knowledge item data information learned by the current user;
And acquiring corresponding adaptation degree based on the learning ability preference data of the current user and the related knowledge training mode, and adjusting the related knowledge training mode according to the corresponding adaptation degree.
In the system, a user learning ability change prediction model is constructed, and learning ability change data of a current user is obtained based on learning ability evaluation score information of the user and the user learning ability change prediction model, and the method specifically comprises the following steps:
acquiring learning ability evaluation score information of a user within a preset time, inputting the learning ability evaluation score information of the user within the preset time into a random forest algorithm, calculating a base index, and acquiring feature information with highest correlation according to the base index;
Constructing a user learning capacity feature matrix according to the feature information with highest correlation, constructing a user learning capacity change prediction model based on LSTM, and introducing a singular value feature decomposition algorithm to decompose the feature value of the user learning capacity feature matrix;
Sorting the obtained characteristic values, selecting the characteristic values with the frequency value higher than the preset frequency value to construct a low-dimensional characteristic vector projection matrix, and inputting the low-dimensional characteristic vector projection matrix into a user learning capacity change prediction model to perform coding learning;
And outputting the user learning ability change prediction model after the user learning ability change prediction model meets the preset training requirement, and acquiring the learning ability preference data of the current user within the preset time according to the user learning ability change prediction model.
In the system, knowledge item data information learned by a current user is acquired, and a related knowledge training mode is acquired according to the knowledge item data information learned by the current user, and the system specifically comprises the following steps:
acquiring knowledge item data information learned by a current user, and acquiring a related training mode of the knowledge item data information and learning ability evaluation scores of historical users on the knowledge item data information through big data;
acquiring the correlation between the training mode and the learning ability of the user according to the related training mode of the knowledge item data information and the learning ability evaluation score of the historical user on the knowledge item data information;
Inputting the correlation between the training mode and the learning ability of the user into a topological space for related expression, obtaining a topological structure diagram, generating an adjacent matrix according to the topological structure diagram, constructing a database, and inputting the adjacent matrix into the database for storage;
The knowledge item data information learned by the current user is input into a database, euclidean distance between the knowledge item data information learned by the current user and each sample data in the adjacent matrix is calculated, and a related knowledge training mode with the Euclidean distance lower than a preset Euclidean distance is selected to be output as a related knowledge training mode.
In the system, corresponding adaptation degree is obtained based on learning ability preference data of a current user and related knowledge training modes, and the related knowledge training modes are adjusted according to the corresponding adaptation degree, and specifically the system comprises the following steps:
Calculating the adaptation degree between the related knowledge training mode and the learning ability preference data of the current user through the related information entropy measurement, and comparing the adaptation degree with a preset adaptation degree to obtain a deviation rate;
Judging whether the deviation rate is smaller than a preset deviation rate threshold value, and if the deviation rate is smaller than the preset deviation rate threshold value, sequencing relevant knowledge training modes corresponding to the adaptation degree;
After sorting, generating an adaptation degree priority knowledge training mode sorting table, and acquiring a training mode with the maximum adaptation degree according to the adaptation degree priority knowledge training mode sorting table;
generating corresponding recommendation information according to the training mode with the maximum adaptation degree, displaying the corresponding recommendation information according to a preset mode, and regularly adjusting the training mode according to the learning ability preference data of the current user.
The invention solves the defects existing in the background technology and has the following beneficial effects:
According to the invention, the multiple learning data information of the user within the preset time is obtained, the multiple learning data information is evaluated to obtain learning ability evaluation score information of the user, a user learning ability change prediction model is further constructed, the learning ability preference data of the current user is obtained based on the learning ability evaluation score information of the user and the user learning ability change prediction model, the knowledge item data information of the current user is obtained, the relevant knowledge training mode is obtained according to the knowledge item data information of the current user, the corresponding adaptation degree is obtained based on the learning ability preference data of the current user and the relevant knowledge training mode, and the relevant knowledge training mode is adjusted according to the corresponding adaptation degree. According to the invention, the learning data information of the user is fully considered, so that the learning ability condition of the user within the preset time is obtained according to the learning data information of the user, the learning training mode is dynamically adjusted according to the learning ability condition of the user, and the learning efficiency of the user is further improved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows an overall method flow diagram of an on-line training multi-mode teaching method based on big data;
FIG. 2 shows a first method flow diagram of an on-line training multi-mode teaching method based on big data;
FIG. 3 shows a second method flow diagram of an on-line training multi-mode teaching method based on big data;
fig. 4 shows a system block diagram of an on-line training multi-mode teaching system based on big data.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
As shown in fig. 1, a first aspect of the present invention provides an on-line training multi-mode teaching method based on big data, comprising the steps of:
s102, acquiring multiple learning data information of a user within a preset time, and acquiring learning ability evaluation score information of the user by evaluating the multiple learning data information;
s104, constructing a user learning ability change prediction model, and acquiring learning ability preference data of the current user based on learning ability evaluation score information of the user and the user learning ability change prediction model;
S106, acquiring knowledge item data information learned by the current user, and acquiring a related knowledge training mode according to the knowledge item data information learned by the current user;
S108, acquiring corresponding adaptation degree based on learning ability preference data of the current user and the related knowledge training mode, and adjusting the related knowledge training mode according to the corresponding adaptation degree.
The invention fully considers the learning data information of the user, thereby obtaining the learning ability condition of the user within the preset time according to the learning data information of the user, dynamically adjusting the learning training mode according to the learning ability condition of the user, and further improving the learning efficiency of the user. The multi-element learning data information comprises learning concentration conditions, thinking flexibility, thinking contrast, test data in the learning process and the like of the user.
In step S102, learning ability evaluation score information of the user is obtained by evaluating the multivariate learning data information, and specifically includes the following steps:
Setting keyword information according to learning ability, constructing a search tag according to the keyword information, and searching through big data based on the search tag to obtain related evaluation index data of the learning ability;
Constructing an evaluation index system according to the related evaluation index data of the learning ability by a hierarchical analysis method, determining a scheme layer, a target layer and a criterion layer of the evaluation index system, and inputting the related evaluation index data of the learning ability into the criterion layer;
inputting the multiple learning data information into a scheme layer, inputting a preset learning ability effect into a target layer, and calculating the multiple learning data information based on an evaluation index system to obtain weight vector information corresponding to each multiple learning data information;
And inputting weight vector information corresponding to the multiple learning data information into a gray correlation analysis method, comprehensively evaluating the weight vector information corresponding to each multiple learning data information, and acquiring learning ability evaluation score information of the user.
The analytic hierarchy process is a decision making method of decomposing elements always related to decision making into layers of targets, criteria, schemes and the like, and performing qualitative and quantitative analysis on the basis of the layers. The learning ability evaluation score information of the user can be calculated through the combined action of the analytic hierarchy process and the gray correlation analysis process, for example, the evaluation score is between the scores of 90 and 100, the learning ability is excellent, the learning ability of the user is high, and the like. As another example, the learning concentration does not necessarily represent the knowledge grasping degree of the user, and the learning ability evaluation score information of the user is high because the user may touch the related knowledge for which the concentration is poor even when learning the knowledge, but the score of the related test data is still high, and the learning ability and the receiving ability for the knowledge are still strong. The learning ability evaluation score information of the user is evaluation data integrating all factors, and when the learning ability evaluation score information of the user is higher, the learning ability of the user is strong and the knowledge receiving condition of the user is good within the period. By the aid of the method, accurate recognition of the learning condition of the user can be further provided.
As shown in fig. 2, further, in step S104, specifically includes:
S202, acquiring learning ability evaluation score information of a user within preset time, inputting the learning ability evaluation score information of the user within the preset time into a random forest algorithm, calculating a radix index, and acquiring feature information with highest correlation according to the radix index;
In this embodiment, if the evaluation scores of two time points of the user are greatly different, but due to concentration factors, the evaluation score of the early stage is very low, the evaluation score of the later stage is very high (even if the user ignores concentration), the user still grasps the knowledge, at this time, the random forest can select the evaluation score of the later stage as the feature information, and the matrix is often used for evaluating the importance of the feature, and the matrix is constructed by selecting the matrix higher than the feature information corresponding to the preset matrix, thereby facilitating the selection of the sample and improving the prediction accuracy of the learning ability of the user.
S204, constructing a user learning capacity feature matrix according to the feature information with highest correlation, constructing a user learning capacity change prediction model based on LSTM, and introducing a singular value feature decomposition algorithm to decompose the feature value of the user learning capacity feature matrix;
S206, sorting the obtained characteristic values, selecting the characteristic values with the frequency value higher than the preset frequency value to construct a low-dimensional characteristic vector projection matrix, and inputting the low-dimensional characteristic vector projection matrix into a user learning capacity change prediction model to perform coding learning;
And a singular value feature decomposition algorithm is introduced to decompose the feature value of the feature matrix with the learning ability of the user, so that the calculation complexity of the LSTM is reduced, and the running speed of the algorithm is further improved.
And S208, outputting the user learning ability change prediction model after the user learning ability change prediction model meets the preset training requirement, and acquiring learning ability preference data of the current user within the preset time according to the user learning ability change prediction model.
As shown in fig. 3, in step S106, the following steps are specifically included:
S302, acquiring knowledge item data information learned by a current user, and acquiring a relevant training mode of the knowledge item data information and learning ability evaluation scores of historical users on the knowledge item data information through big data;
S304, acquiring the correlation between the training mode and the learning ability of the user according to the related training mode of the knowledge item data information and the learning ability evaluation score of the historical user on the knowledge item data information;
s306, inputting the correlation between the training mode and the learning ability of the user into a topological space for related expression, obtaining a topological structure diagram, generating an adjacent matrix according to the topological structure diagram, constructing a database, and inputting the adjacent matrix into the database for storage;
S308, inputting knowledge item data information learned by the current user into a database, calculating Euclidean distance between the knowledge item data information learned by the current user and each sample data in the adjacent matrix, and selecting a related knowledge training mode with the Euclidean distance lower than a preset Euclidean distance as a related knowledge training mode to output.
For example, training patterns may be categorized by the progress of learning, such as low-speed training patterns, medium-speed training patterns, high-speed training patterns, or by teaching, such as teaching, presentation, discussion, audiovisual, role-playing and case-learning, simulation and game, etc. In general, for training patterns, it is noted that different learning abilities represent the ability of a user to receive the knowledge item within a certain period of time, and are related to various factors. Wherein, the correlation of the training mode and the learning ability of the user, such as under the learning ability condition, the learning ability of the user is in direct proportion to the training mode; or under the learning ability condition, the learning ability of the user is inversely proportional to the training pattern. In fact, the learning ability of the user is high within a certain time, and the training mode of faster learning progress should be matched; the learning ability of the user is low within a certain time, and the learning progress should be appropriately reduced or the teaching manner should be adjusted. The training mode more suitable for the user can be selected through the method. And inputting the correlation between the training mode and the learning ability of the user into a topological space for related expression, and when the Euclidean distance is lower than the preset Euclidean distance, the matching degree of the knowledge item data information learned by the current user and the adjacent matrix is high, so that the corresponding training mode is selected.
Further, in a preferred embodiment of the present invention, the acquiring the corresponding adaptation degree based on the learning ability preference data of the current user and the related knowledge training mode, and adjusting the related knowledge training mode according to the corresponding adaptation degree specifically includes the following steps:
Calculating the adaptation degree between the related knowledge training mode and the learning ability preference data of the current user through the related information entropy measurement, and comparing the adaptation degree with a preset adaptation degree to obtain a deviation rate;
Judging whether the deviation rate is smaller than a preset deviation rate threshold value, and if the deviation rate is smaller than the preset deviation rate threshold value, sequencing relevant knowledge training modes corresponding to the adaptation degree;
After sorting, generating an adaptation degree priority knowledge training mode sorting table, and acquiring a training mode with the maximum adaptation degree according to the adaptation degree priority knowledge training mode sorting table;
generating corresponding recommendation information according to the training mode with the maximum adaptation degree, displaying the corresponding recommendation information according to a preset mode, and regularly adjusting the training mode according to the learning ability preference data of the current user.
It should be noted that, the adaptation degree between the related knowledge training mode and the learning ability preference data of the current user is calculated through the associated information entropy measurement, and when the deviation rate is smaller than the preset deviation rate threshold value, the adaptation degree between the related knowledge training mode and the learning ability preference data of the current user is high, and the training mode is more suitable for the user.
Further, in a preferred embodiment of the present invention, the on-line training multi-mode teaching method based on big data further comprises the following steps:
acquiring learning ability preference data of a current user, constructing a preference curve of the learning ability of the user according to the learning ability preference data of the current user, and setting a related learning ability related threshold;
Acquiring course information with learning ability preference data lower than a relevant learning ability relevant threshold value from a preference curve of the learning ability of a user, and calculating knowledge association characteristics between the course information with the learning ability preference data lower than the relevant learning ability relevant threshold value;
Judging whether the association characteristic is larger than a preset association characteristic, and acquiring knowledge item data information corresponding to learning ability preference data lower than a relevant learning ability relevant threshold value when the association characteristic is larger than the preset association characteristic;
Acquiring learning data information of knowledge item data information corresponding to the learning ability preference data lower than the relevant learning ability relevant threshold value of the user, and generating relevant learning emphasis points and attention points according to the learning data information of the corresponding knowledge item data information.
In this embodiment, for example, the learning ability of the user is reduced due to a certain factor during the learning process, for example, the learning ability of the user is reduced due to insufficient concentration of the user after tracking. The knowledge is related with the subsequent knowledge (the knowledge related characteristic between course information with learning ability preference data lower than the related learning ability related threshold value), so that the overall learning ability is rapidly reduced, and the review emphasis point and the attention point of the user can be further recommended by the method.
The method specifically includes the steps of generating relevant learning emphasis points and attention points according to learning data information of corresponding knowledge item data information, wherein the learning emphasis points and the attention points specifically include:
acquiring evaluation index data information of the learning ability of the user according to learning data information of corresponding knowledge item data information; when the abnormal item of the evaluation index data information of the learning ability of the user is the learning concentration degree of the user, acquiring learning image data information of the user in the learning process;
The learning image data information of the user in the learning process is subjected to user learning concentration recognition, image fragments with the user learning concentration lower than the preset learning concentration are obtained, and learning knowledge item data information with the user learning concentration lower than the preset learning concentration is obtained according to the image fragments with the user learning concentration lower than the preset learning concentration;
Calculating the association characteristic of the learned knowledge item data information, and acquiring a learning route of the historical user through big data retrieval according to the learned knowledge item data information with the user learning concentration lower than the preset learning concentration when the association characteristic is higher than the preset association characteristic;
and sequentially combining and sorting the image fragments with the learning concentration degree lower than the preset learning concentration degree according to the learning route of the historical user, and generating relevant learning side points and attention points.
When the abnormal item of the evaluation index data information of the learning ability of the user is the learning concentration degree of the user, the learning ability of the user is closely related to the learning concentration degree, and the learning route arrangement for the knowledge which the user does not grasp can be accurately provided by the method, so that the method is beneficial to reminding the user of later knowledge arrangement, emphasis and attention points.
In addition, the method can further comprise the following steps:
Acquiring evaluation score data of the learning ability of the user, which influences the learning ability of the user within a preset time, and acquiring main influence factor data of the user according to the evaluation score data of the learning ability of the user, which influences the learning ability of the user within the preset time;
judging whether the main influence factor data of the user is preset influence factor data or not, and acquiring learning knowledge item information of the user when the main influence factor data of the user is the preset influence factor data;
Calculating influence weight information of main influence factor data of the user on learning knowledge item information of the user through an analytic hierarchy process,
When the influence weight information is larger than the preset influence weight information, acquiring training items related to the influence weight information through big data retrieval according to main influence factor data of the user, and recommending the training items to the user according to a preset mode.
It should be noted that, the preset influence factor data includes an observation force, a memory and an abstract summarization capability, and since the user is not the influence of concentration, the memory, the abstract summarization capability and the observation force of important influence factors of some knowledge need to be trained by other ways to improve the observation force, the memory and the abstract summarization capability so as to improve the learning efficiency of the user.
The second aspect of the present invention provides an on-line training multimode teaching system 4 based on big data, the system includes a memory 41 and a processor 62, the memory 41 includes an on-line training multimode teaching method program based on big data, and when the on-line training multimode teaching method program based on big data is executed by the processor 62, the following steps are implemented:
acquiring multiple learning data information of a user within a preset time, and acquiring learning ability evaluation score information of the user by evaluating the multiple learning data information;
constructing a user learning ability change prediction model, and acquiring learning ability preference data of the current user based on the learning ability evaluation score information of the user and the user learning ability change prediction model;
Acquiring knowledge item data information learned by a current user, and acquiring a related knowledge training mode according to the knowledge item data information learned by the current user;
And acquiring corresponding adaptation degree based on the learning ability preference data of the current user and the related knowledge training mode, and adjusting the related knowledge training mode according to the corresponding adaptation degree.
In the system, a user learning ability change prediction model is constructed, and learning ability change data of a current user is obtained based on learning ability evaluation score information of the user and the user learning ability change prediction model, and the method specifically comprises the following steps:
acquiring learning ability evaluation score information of a user within a preset time, inputting the learning ability evaluation score information of the user within the preset time into a random forest algorithm, calculating a base index, and acquiring feature information with highest correlation according to the base index;
Constructing a user learning capacity feature matrix according to the feature information with highest correlation, constructing a user learning capacity change prediction model based on LSTM, and introducing a singular value feature decomposition algorithm to decompose the feature value of the user learning capacity feature matrix;
Sorting the obtained characteristic values, selecting the characteristic values with the frequency value higher than the preset frequency value to construct a low-dimensional characteristic vector projection matrix, and inputting the low-dimensional characteristic vector projection matrix into a user learning capacity change prediction model to perform coding learning;
And outputting the user learning ability change prediction model after the user learning ability change prediction model meets the preset training requirement, and acquiring the learning ability preference data of the current user within the preset time according to the user learning ability change prediction model.
In the system, knowledge item data information learned by a current user is acquired, and a related knowledge training mode is acquired according to the knowledge item data information learned by the current user, and the system specifically comprises the following steps:
acquiring knowledge item data information learned by a current user, and acquiring a related training mode of the knowledge item data information and learning ability evaluation scores of historical users on the knowledge item data information through big data;
acquiring the correlation between the training mode and the learning ability of the user according to the related training mode of the knowledge item data information and the learning ability evaluation score of the historical user on the knowledge item data information;
Inputting the correlation between the training mode and the learning ability of the user into a topological space for related expression, obtaining a topological structure diagram, generating an adjacent matrix according to the topological structure diagram, constructing a database, and inputting the adjacent matrix into the database for storage;
The knowledge item data information learned by the current user is input into a database, euclidean distance between the knowledge item data information learned by the current user and each sample data in the adjacent matrix is calculated, and a related knowledge training mode with the Euclidean distance lower than a preset Euclidean distance is selected to be output as a related knowledge training mode.
In the system, corresponding adaptation degree is obtained based on learning ability preference data of a current user and related knowledge training modes, and the related knowledge training modes are adjusted according to the corresponding adaptation degree, and specifically the system comprises the following steps:
Calculating the adaptation degree between the related knowledge training mode and the learning ability preference data of the current user through the related information entropy measurement, and comparing the adaptation degree with a preset adaptation degree to obtain a deviation rate;
Judging whether the deviation rate is smaller than a preset deviation rate threshold value, and if the deviation rate is smaller than the preset deviation rate threshold value, sequencing relevant knowledge training modes corresponding to the adaptation degree;
After sorting, generating an adaptation degree priority knowledge training mode sorting table, and acquiring a training mode with the maximum adaptation degree according to the adaptation degree priority knowledge training mode sorting table;
generating corresponding recommendation information according to the training mode with the maximum adaptation degree, displaying the corresponding recommendation information according to a preset mode, and regularly adjusting the training mode according to the learning ability preference data of the current user.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or optical disk, or the like, which can store program codes.
Or the above-described integrated units of the invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (8)
1. The on-line training multi-mode teaching method based on big data is characterized by comprising the following steps of:
Acquiring multiple learning data information of a user within a preset time, and acquiring learning ability evaluation score information of the user by evaluating the multiple learning data information;
constructing a user learning ability change prediction model, and acquiring learning ability preference data of the current user based on the learning ability evaluation score information of the user and the user learning ability change prediction model;
acquiring knowledge item data information learned by a current user, and acquiring a related knowledge training mode according to the knowledge item data information learned by the current user;
acquiring corresponding adaptation degree based on the learning ability preference data of the current user and the related knowledge training mode, and adjusting the related knowledge training mode according to the corresponding adaptation degree;
the method specifically comprises the following steps of:
Acquiring knowledge item data information learned by a current user, and acquiring a relevant training mode of the knowledge item data information and learning ability evaluation scores of historical users on the knowledge item data information through big data;
acquiring the correlation between the training mode and the learning ability of the user according to the related training mode of the knowledge item data information and the learning ability evaluation score of the historical user on the knowledge item data information;
Inputting the correlation between the training mode and the learning ability of the user into a topological space for related expression, obtaining a topological structure diagram, generating an adjacent matrix according to the topological structure diagram, constructing a database, and inputting the adjacent matrix into the database for storage;
And inputting the knowledge item data information learned by the current user into the database, calculating the Euclidean distance between the knowledge item data information learned by the current user and each sample data in the adjacent matrix, and selecting a related knowledge training mode with the Euclidean distance lower than a preset Euclidean distance as a related knowledge training mode to output.
2. The on-line training multimode teaching method based on big data according to claim 1, wherein learning ability evaluation score information of a user is obtained by evaluating the multivariate learning data information, specifically comprising the steps of:
Setting keyword information according to learning ability, constructing a search tag according to the keyword information, and searching through big data based on the search tag to obtain related evaluation index data of the learning ability;
Constructing an evaluation index system according to the related evaluation index data of the learning ability by a hierarchical analysis method, determining a scheme layer, a target layer and a criterion layer of the evaluation index system, and inputting the related evaluation index data of the learning ability into the criterion layer;
Inputting the multiple learning data information into a scheme layer, inputting a preset learning ability effect into a target layer, and calculating the multiple learning data information based on the evaluation index system to obtain weight vector information corresponding to each multiple learning data information;
and inputting weight vector information corresponding to the multiple learning data information into a gray correlation analysis method, and comprehensively evaluating the weight vector information corresponding to each multiple learning data information to obtain learning ability evaluation score information of a user.
3. The big data based on-line training multimode teaching method of claim 1, wherein constructing a user learning ability change prediction model, obtaining learning ability change data of a current user based on learning ability evaluation score information of the user and the user learning ability change prediction model, specifically comprises:
Acquiring learning ability evaluation score information of a user within preset time, inputting the learning ability evaluation score information of the user within the preset time into a random forest algorithm, calculating a base index, and acquiring feature information with highest correlation according to the base index;
Constructing a user learning capacity feature matrix according to the feature information with the highest correlation, constructing a user learning capacity change prediction model based on LSTM, and introducing a singular value feature decomposition algorithm to decompose the feature value of the user learning capacity feature matrix;
Sorting the obtained characteristic values, selecting the characteristic values with the frequency value higher than a preset frequency value to construct a low-dimensional characteristic vector projection matrix, and inputting the low-dimensional characteristic vector projection matrix into the user learning capacity change prediction model to perform coding learning;
And outputting the user learning ability change prediction model after the user learning ability change prediction model meets the preset training requirement, and acquiring learning ability preference data of the current user within the preset time according to the user learning ability change prediction model.
4. The big data based on-line training multi-mode teaching method according to claim 1, wherein the steps of obtaining a corresponding fitness based on learning ability preference data of the current user and a related knowledge training mode, and adjusting the related knowledge training mode according to the corresponding fitness specifically comprise the following steps:
Calculating the adaptation degree between the related knowledge training mode and the learning ability preference data of the current user through the related information entropy measurement, and comparing the adaptation degree with a preset adaptation degree to obtain a deviation rate;
judging whether the deviation rate is smaller than a preset deviation rate threshold, and if the deviation rate is smaller than the preset deviation rate threshold, sequencing relevant knowledge training modes corresponding to the adaptation degree;
after sorting, generating an adaptation degree priority knowledge training mode sorting table, and acquiring a training mode with the maximum adaptation degree according to the adaptation degree priority knowledge training mode sorting table;
Generating corresponding recommendation information according to the training mode with the maximum adaptation degree, displaying the corresponding recommendation information according to a preset mode, and regularly adjusting the training mode according to the learning ability preference data of the current user.
5. The big data based on-line training multi-mode teaching method of claim 1, further comprising the steps of:
acquiring learning ability preference data of a current user, constructing a preference curve of the learning ability of the user according to the learning ability preference data of the current user, and setting a related learning ability related threshold;
acquiring course information with learning ability preference data lower than a relevant learning ability relevant threshold value from a preference curve of the user learning ability, and calculating knowledge association characteristics between the course information with the learning ability preference data lower than the relevant learning ability relevant threshold value;
Judging whether the association characteristic is larger than a preset association characteristic, and acquiring knowledge item data information corresponding to learning ability preference data lower than a relevant learning ability relevant threshold value when the association characteristic is larger than the preset association characteristic;
acquiring learning data information of knowledge item data information corresponding to the learning ability preference data lower than the relevant learning ability relevant threshold value of the user, and generating relevant learning emphasis points and attention points according to the learning data information of the corresponding knowledge item data information.
6. The on-line training multimode teaching system based on big data is characterized by comprising a memory and a processor, wherein the memory comprises an on-line training multimode teaching method program based on big data, and when the on-line training multimode teaching method program based on big data is executed by the processor, the following steps are realized:
Acquiring multiple learning data information of a user within a preset time, and acquiring learning ability evaluation score information of the user by evaluating the multiple learning data information;
constructing a user learning ability change prediction model, and acquiring learning ability preference data of the current user based on the learning ability evaluation score information of the user and the user learning ability change prediction model;
acquiring knowledge item data information learned by a current user, and acquiring a related knowledge training mode according to the knowledge item data information learned by the current user;
acquiring corresponding adaptation degree based on the learning ability preference data of the current user and the related knowledge training mode, and adjusting the related knowledge training mode according to the corresponding adaptation degree;
the method specifically comprises the following steps of:
Acquiring knowledge item data information learned by a current user, and acquiring a relevant training mode of the knowledge item data information and learning ability evaluation scores of historical users on the knowledge item data information through big data;
acquiring the correlation between the training mode and the learning ability of the user according to the related training mode of the knowledge item data information and the learning ability evaluation score of the historical user on the knowledge item data information;
Inputting the correlation between the training mode and the learning ability of the user into a topological space for related expression, obtaining a topological structure diagram, generating an adjacent matrix according to the topological structure diagram, constructing a database, and inputting the adjacent matrix into the database for storage;
And inputting the knowledge item data information learned by the current user into the database, calculating the Euclidean distance between the knowledge item data information learned by the current user and each sample data in the adjacent matrix, and selecting a related knowledge training mode with the Euclidean distance lower than a preset Euclidean distance as a related knowledge training mode to output.
7. The big data based on-line training multi-mode teaching system of claim 6, wherein constructing a user learning ability change prediction model, obtaining learning ability change data of a current user based on learning ability evaluation score information of the user and the user learning ability change prediction model, specifically comprises:
Acquiring learning ability evaluation score information of a user within preset time, inputting the learning ability evaluation score information of the user within the preset time into a random forest algorithm, calculating a base index, and acquiring feature information with highest correlation according to the base index;
Constructing a user learning capacity feature matrix according to the feature information with the highest correlation, constructing a user learning capacity change prediction model based on LSTM, and introducing a singular value feature decomposition algorithm to decompose the feature value of the user learning capacity feature matrix;
Sorting the obtained characteristic values, selecting the characteristic values with the frequency value higher than a preset frequency value to construct a low-dimensional characteristic vector projection matrix, and inputting the low-dimensional characteristic vector projection matrix into the user learning capacity change prediction model to perform coding learning;
And outputting the user learning ability change prediction model after the user learning ability change prediction model meets the preset training requirement, and acquiring learning ability preference data of the current user within the preset time according to the user learning ability change prediction model.
8. The big data based on-line training multi-mode teaching system of claim 6, wherein obtaining a corresponding fitness based on the learning ability preference data of the current user and a related knowledge training mode, and adjusting the related knowledge training mode according to the corresponding fitness specifically comprises the steps of:
Calculating the adaptation degree between the related knowledge training mode and the learning ability preference data of the current user through the related information entropy measurement, and comparing the adaptation degree with a preset adaptation degree to obtain a deviation rate;
judging whether the deviation rate is smaller than a preset deviation rate threshold, and if the deviation rate is smaller than the preset deviation rate threshold, sequencing relevant knowledge training modes corresponding to the adaptation degree;
after sorting, generating an adaptation degree priority knowledge training mode sorting table, and acquiring a training mode with the maximum adaptation degree according to the adaptation degree priority knowledge training mode sorting table;
Generating corresponding recommendation information according to the training mode with the maximum adaptation degree, displaying the corresponding recommendation information according to a preset mode, and regularly adjusting the training mode according to the learning ability preference data of the current user.
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