CN116416097B - Teaching method, system and equipment based on multidimensional teaching model - Google Patents
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Abstract
The invention discloses a teaching method, a system and a device based on a multidimensional teaching model, which relate to intelligent teaching and solve the problem that the teaching style of the existing intelligent teaching method is not adjustable, and the technical scheme is as follows: acquiring student data, including initial academic data, target academic data and style configuration data; inputting student data into a multi-dimensional teaching model to obtain a teaching plan, wherein the multi-dimensional teaching model comprises a teaching content generation model for obtaining teaching contents according to initial learning data and target learning data, and a teaching style generation model for configuring the teaching contents according to style configuration data and outputting the teaching contents through a 3D virtual person to form the teaching plan; acquiring physiological data of students under the teaching plan, inputting the physiological data into a pre-trained concentration model to acquire concentration indexes, adjusting the teaching plan according to the concentration indexes, and generating a prediction state report; the teaching content containing the teaching style is provided for students, and meanwhile, the teaching style is adaptively adjusted according to the states of the students.
Description
Technical Field
The invention relates to the field of intelligent teaching, in particular to a teaching method, a system and equipment based on a multidimensional teaching model.
Background
The intelligent teaching refers to an entity teaching environment provided for students by means of the Internet and big data, and an educational informatization teaching mode of leading edge technologies such as cloud computing, big data and interactive cloud is fused, so that along with the rapid development of new generation artificial intelligence, the innovation of the artificial intelligence will lead to the further development of intelligent teaching.
At present, the study of intelligent teaching is mainly focused on individuation of teaching contents, and students do not pay attention to diversified demands of teaching styles. For example, the disclosed patent document CN111754370a is an artificial intelligence based online education course management method and system, when the matching degree between the first time planning information and the standard course planning image information meets the first preset condition, the standard course planning image information is used as the first course management standard of the first user, so as to solve the problem of customizing the personalized course for the user; CN112435515a is an artificial intelligence educational robot, which analyzes learning efficiency of students based on learning curves and learning records, and performs personalized pushing of learning materials.
The existing intelligent teaching method mainly focuses on personalized pushing of teaching contents, has the defects of directly outputting the video of a course recorded in advance as the teaching contents, has fixed and uncontrollable teaching styles, and is difficult to meet the demands of students on diversified teaching styles; and secondly, the teaching content is directly output, and the teaching content cannot be adaptively adjusted according to the class listening situation of students.
In view of the above, the present invention provides a teaching method, system and device based on a multidimensional teaching model, which solves the above problems.
Disclosure of Invention
The invention aims to provide a teaching method, a system and a device based on a multidimensional teaching model, which provide teaching contents containing teaching styles for students, monitor the class listening state of the students and adaptively adjust the teaching styles to solve the problems.
The first aspect of the invention provides a teaching method based on a multidimensional teaching model, comprising the following steps:
s1, student data are acquired, wherein the student data comprise initial academic data, target academic data and style configuration data;
s2, inputting the student data into a pre-trained multi-dimensional teaching model to obtain a teaching plan, wherein the multi-dimensional teaching model comprises a teaching content generation model and a teaching style generation model, the teaching content generation model is used for obtaining teaching contents according to the initial learning data and the target learning data, and the teaching style generation model is used for configuring the teaching contents according to the style configuration data and outputting the teaching contents through a 3D virtual person to form the teaching plan;
S3, acquiring physiological data of the student under the teaching plan, wherein the physiological data comprise but are not limited to facial features, posture features and eye-rest features of the student during class, inputting the physiological data into a pre-trained concentration model to obtain a concentration index, adjusting the teaching plan according to the concentration index, and generating a prediction state report.
By adopting the technical scheme, the teaching content and the teaching style are combined and output, and the teaching style is flexibly configured according to the style configuration data so as to meet the demands of students on diversified teaching styles; in addition, the teaching style of the example is adjusted according to the concentration index of the student, and when the concentration index of the student is higher or lower than the average concentration index, the teaching style is adjusted correspondingly so as to adapt to the class listening state of the student.
In some possible embodiments, the teaching content generation model is configured to obtain teaching content according to the initial learning data and the target learning data, including:
extracting knowledge points to be learned and corresponding historical grasping degrees from the target learning data, and extracting learned knowledge points and corresponding grasping degrees from the initial learning data;
if the learned knowledge points exist in the knowledge points to be learned, calculating a review difficulty value of the learned knowledge points according to the grasping degree of the learned knowledge points, arranging a learning sequence from low to high according to the review difficulty value, and removing the learned knowledge points from the knowledge points to be learned, if the learned knowledge points do not exist in the knowledge points to be learned, calculating a history difficulty value of each knowledge point according to the history grasping degree of the knowledge points to be learned, and arranging the learning sequence from low to high according to the history difficulty value;
Extracting teaching contents corresponding to knowledge points to be learned from a teaching resource library, and pushing the teaching contents one by one according to the learning sequence, wherein the teaching contents comprise: matched text content and courseware content.
In some possible embodiments, the teaching style generation model is configured to configure the teaching content according to the style configuration data and output through a 3D virtual person to form a teaching plan, including:
converting the text content in the teaching content into voice content, and configuring voice characteristics of the voice content according to style configuration data: speech speed and intonation;
converting courseware content in the teaching content into 3D courseware;
and outputting the voice content and the 3D courseware through a 3D virtual person.
In some possible embodiments, inputting the physiological data into a pre-trained concentration model to obtain a concentration index comprises:
inputting the facial features of students in class into a concentration model, acquiring the expression types of the students according to an expression classification model in the concentration model, and mapping the expression types into a first concentration index;
inputting the gesture characteristics of the students in class into a concentration model, acquiring the gesture types of the students according to a gesture classification model in the concentration model, and mapping the gesture types into a second concentration index;
Inputting the eye characteristics of the students in class into a concentration model, acquiring the eye types of the students according to the eye models in the concentration model, and mapping the eye types into a third concentration index;
and calculating the concentration index of the student according to the first concentration index, the second concentration index and the third concentration index.
In some possible embodiments, adjusting the teaching plan and generating a prediction status report according to the concentration index comprises:
comparing the concentration index of the student with the average concentration index of the student, accelerating the speech speed in the teaching style when the concentration index of the student is greater than or equal to the average concentration index of the student, reducing the speech speed in the teaching style when the concentration index of the student is lower than the average concentration index of the student, and improving the intonation in the teaching style.
In some possible embodiments, the speech rate and intonation are calculated by the following formula:
；
；
wherein ,speech rate at time t +.>Represents the average speech rate of the setting,/-, for example>Concentration index indicating time t, < ->Represents an average concentration index,/->Indicating the amount of change in speech rate- >Intonation indicating time t +.>Represents the average intonation of the setting +.>Representing the amount of intonation change.
A second aspect of the present invention provides a teaching system based on a multidimensional teaching model, comprising:
the data acquisition module is used for acquiring student data, wherein the student data comprises initial academic data, target academic data and style configuration data;
the teaching plan generation module is used for inputting the student data into a pre-trained multi-dimensional teaching model to obtain a teaching plan, the multi-dimensional teaching model comprises a teaching content generation model and a teaching style generation model, the teaching content generation model is used for obtaining teaching contents according to the initial learning data and the target learning data, and the teaching style generation model is used for configuring the teaching contents according to the style configuration data and outputting the teaching contents through a 3D virtual person to form the teaching plan;
the concentration optimization module is used for acquiring physiological data of students under the teaching plan, wherein the physiological data comprise but are not limited to facial features, posture features and eye-mind features of the students in class, inputting the physiological data into a pre-trained concentration model to obtain concentration indexes, adjusting the teaching plan according to the concentration indexes, and generating a prediction state report.
In some possible embodiments, the teaching plan generation module includes: the teaching content generation module and the teaching style generation module;
the teaching content generation module comprises:
the knowledge point extraction module is used for extracting knowledge points to be learned and corresponding historical mastery degrees from the target learning data, and extracting learned knowledge points and corresponding mastery degrees from the initial learning data;
the learning sequence generation module is used for calculating the review difficulty value of the learned knowledge points according to the grasping degree of the learned knowledge points when the learned knowledge points exist in the to-be-learned knowledge points, arranging the learning sequence from low to high according to the review difficulty value, removing the learned knowledge points from the to-be-learned knowledge points, and calculating the historical difficulty value of each knowledge point when the learned knowledge points do not exist in the to-be-learned knowledge points, and arranging the learning sequence from low to high according to the historical difficulty value;
the teaching content generation module is used for extracting teaching contents corresponding to knowledge points to be learned from a teaching resource library and pushing the teaching contents one by one according to the learning sequence, and the teaching contents comprise: matched text content and courseware content;
The teaching style generation module comprises:
the text content conversion module is used for converting text content in the teaching content into voice content, and configuring voice characteristics of the voice content according to style configuration data: speech speed and intonation;
the courseware conversion module is used for converting courseware content in the teaching content into 3D courseware;
and the teaching planning output module is used for outputting the voice content and the 3D courseware through a 3D virtual person.
In some possible embodiments, the concentration optimization module comprises: a concentration calculating module and a teaching style optimizing module,
the concentration calculating module comprises:
the first concentration index calculation module is used for inputting the facial features of the students in class into a concentration model, acquiring the expression types of the students according to the expression classification model in the concentration model, and mapping the expression types into a first concentration index;
the second concentration index calculation module is used for inputting the gesture characteristics of the students in class into a concentration model, acquiring gesture types of the students according to a gesture classification model in the concentration model, and mapping the gesture types into second concentration indexes;
The third concentration index calculation module is used for inputting the eye characteristics of the students in class into a concentration model, acquiring the eye types of the students according to the eye models in the concentration model, and mapping the eye types into the third concentration index;
the concentration index calculation module is used for calculating the concentration index of the student according to the first concentration index, the second concentration index and the third concentration index;
the teaching style optimization module is used for comparing the concentration index of the student with the average concentration index of the student, accelerating the speech rate in the teaching style when the concentration index of the student is greater than or equal to the average concentration index of the student, reducing the speech rate in the teaching style when the concentration index of the student is lower than the average concentration index of the student, and improving the intonation in the teaching style;
the speech speed and intonation are calculated by the following formula:
；
；
wherein ,speech rate at time t +.>Represents the average speech rate of the setting,/-, for example>Concentration index indicating time t, < ->Represents an average concentration index,/->Indicating the amount of change in speech rate->Intonation indicating time t +.>Represents the average intonation of the setting +. >Representing the amount of intonation change.
A third aspect of the present invention provides a teaching apparatus based on a multidimensional teaching model, comprising: the device comprises a holographic projection device, a central processing unit, a collecting device, a communication interface and an inkless printer, wherein the central processing unit is connected with the holographic projection device, the collecting device, the communication interface and the inkless printer;
the holographic projection device is used for presenting the teaching plan in a 3D form, the acquisition device is used for acquiring physiological data of students under the teaching plan, the communication interface is used for communicating with the server, transmitting the physiological data and receiving the teaching plan, and the inkless printer is used for printing courseware content.
Compared with the prior art, the invention has the following beneficial effects: the invention provides a teaching method, a system and a device based on a multidimensional teaching model, which combine teaching contents with teaching styles through the multidimensional teaching model to carry out 3D virtual output, thereby meeting the demands of students on diversified teaching styles and realizing 3D virtual teaching; the concentration degree model is used for monitoring the concentration degree of students in class, and the teaching style is further adjusted to adapt to the class listening state of the students, so that the concentration degree of the students is improved, missing knowledge points are reduced, and the learning efficiency is improved; in addition, the content generation model of the embodiment adopts a mode of first reviewing and then learning new knowledge and first and last difficulty to arrange the learning sequence, so that students can adapt to teaching contents step by step, and better learning effect is obtained step by step.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:
fig. 1 is a flow chart of a teaching method based on a multidimensional teaching model according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a target learning data structure according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a teaching system based on a pre-training multidimensional teaching model according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a teaching device based on a pre-training multidimensional teaching model according to an embodiment of the present application.
In the drawings, the reference numerals and corresponding part names:
1. a holographic projection device; 2. a collection device; 3. a communication interface; 4. an inkless printer.
Detailed Description
Hereinafter, the terms "comprises" or "comprising" as may be used in various embodiments of the present application indicate the presence of the claimed function, operation or element, and are not limiting of the increase of one or more functions, operations or elements. Furthermore, as used in various embodiments of the application, the terms "comprises," "comprising," and their cognate terms are intended to refer to a particular feature, number, step, operation, element, component, or combination of the foregoing, and should not be interpreted as first excluding the existence of or increasing likelihood of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing.
In various embodiments of the application, the expression "or" at least one of B or/and C "includes any or all combinations of the words listed simultaneously. For example, the expression "B or C" or "at least one of B or/and C" may include B, may include C or may include both B and C.
Expressions (such as "first", "second", etc.) used in the various embodiments of the application may modify various constituent elements in the various embodiments, but the respective constituent elements may not be limited. For example, the above description does not limit the order and/or importance of the elements. The above description is only intended to distinguish one element from another element. For example, the first user device and the second user device indicate different user devices, although both are user devices. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of various embodiments of the present application.
For the purpose of making apparent the objects, technical solutions and advantages of the present application, the present application will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present application and the descriptions thereof are for illustrating the present application only and are not to be construed as limiting the present application.
Based on the defects of the existing intelligent teaching method, the invention provides a teaching method, a system and equipment based on a multidimensional teaching model, which are used for combining teaching contents with teaching styles and outputting the teaching styles, and adjusting the teaching styles according to style configuration data so as to meet the demands of students on diversification of the teaching styles; based on the concentration index of students on class, the class listening state of students is determined, the teaching style is flexibly adjusted to adapt to the state of students, and further development of intelligent teaching is facilitated. The teaching method, system and equipment based on the multidimensional teaching model provided by the invention are further described below with reference to the embodiment and the attached drawings.
Referring to fig. 1, an embodiment of the invention discloses a teaching method based on a multidimensional teaching model, which comprises the following steps:
s1, student data are acquired, wherein the student data comprise initial academic data, target academic data and style configuration data;
s2, inputting the student data into a pre-trained multi-dimensional teaching model to obtain a teaching plan, wherein the multi-dimensional teaching model comprises a teaching content generation model and a teaching style generation model, the teaching content generation model is used for obtaining teaching contents according to the initial learning data and the target learning data, and the teaching style generation model is used for configuring the teaching contents according to the style configuration data and outputting the teaching contents through a 3D virtual person to form the teaching plan;
S3, acquiring physiological data of the student under the teaching plan, wherein the physiological data comprise but are not limited to facial features, behavioral features and eye-rest features of the student during class, inputting the physiological data into a pre-trained concentration model to obtain a concentration index, adjusting the teaching plan according to the concentration index, and generating a prediction state report.
It can be understood that the teaching method of the example combines the teaching content with the teaching style for output, and the teaching style is flexibly configured according to the style configuration data so as to meet the demands of students on diversified teaching styles; in addition, the teaching style of the example is adjusted according to the concentration index of the student, and when the concentration index of the student is higher or lower than the average concentration index, the teaching style is adjusted correspondingly so as to adapt to the class listening state of the student.
The teaching method of this example is divided into three steps, which can be summarized as: step S1, student data are collected, step S2, teaching planning is generated, and step S3 concentration degree optimizing teaching planning is performed; these three steps are described below.
Step S1, student data is collected, wherein the student data comprises initial academic data, target academic data and style configuration data. Student data can be obtained directly through human input or indirectly through questionnaires and test evaluations; student data includes initial academic data, target academic data, and style configuration data. The initial learning data refers to learned knowledge points of students and the mastery degree of the learned knowledge points, and the mastery degree can be quantified through scores; the target academic data refers to knowledge points to be learned and the history mastery degree of the knowledge points to be learned, namely the history average score; style configuration data refers to the quantitative data of a teaching style, generally to some specific features of a teacher, such as: behavioral characteristics, speech characteristics, facial characteristics, and the like.
Step S2, generating a teaching plan, generating teaching contents and generating a teaching style, wherein the teaching plan and the teaching style are combined to generate the teaching plan, and the teaching plan refers to the teaching contents containing the teaching style. Teaching styles refer to certain specific features of 3D virtual humans, such as: behavior characteristics, voice characteristics, facial characteristics and the like, teaching contents are configured according to style configuration data, and then the 3D virtual person outputs the teaching contents to form a teaching plan.
And S3, concentration optimization teaching planning, namely calculating concentration indexes through physiological data of students under the teaching planning, adjusting teaching styles in the teaching planning based on the concentration indexes, and optimizing the teaching planning. Physiological data of students under the teaching plan are periodically collected through sensors, the physiological data comprise but are not limited to facial features, behavioral features and eye-concentration features of the students during class, the physiological data are input into a pre-trained concentration model to calculate concentration indexes, the concentration indexes are compared with average concentration indexes of the students, a comparison result is input into a style generation model to adjust teaching styles, and when the concentration indexes are lower than the average concentration indexes, a prediction state report can be generated to give an alarm.
In order to further explain the teaching method provided in this embodiment, first, a teaching content generating model and a teaching style generating model in the multidimensional teaching model in step S2 are described.
In this embodiment, the teaching content generating model is configured to obtain teaching content according to the initial learning data and the target learning data, and includes:
extracting knowledge points to be learned and corresponding historical grasping degrees from the target learning data, and extracting learned knowledge points and corresponding grasping degrees from the initial learning data;
if the learned knowledge points exist in the knowledge points to be learned, calculating a review difficulty value of the learned knowledge points according to the grasping degree of the learned knowledge points, arranging a learning sequence from low to high according to the review difficulty value, removing the learned knowledge points from the knowledge points to be learned, and if the learned knowledge points do not exist in the knowledge points to be learned, calculating a history difficulty value of each knowledge point, and arranging the learning sequence from low to high according to the history difficulty value;
extracting teaching contents corresponding to knowledge points to be learned from a teaching resource library, and pushing the teaching contents one by one according to the learning sequence, wherein the teaching contents comprise: matched text content and courseware content.
Specifically, the first step: extracting knowledge points to be learned and corresponding historical mastery degrees from target learning data, wherein the target learning data consists of a plurality of knowledge points to be learned, as shown in fig. 2, the knowledge points to be learned can be divided into discrete knowledge points and continuous knowledge points, the discrete knowledge points are single knowledge points, such as K1 in the figure, the continuous knowledge points are composed of a series of associated sub-knowledge points, such as K2 in the figure, the intron knowledge points K21, K22 and K23, and the historical mastery degrees of the knowledge points to be learned are the historical average of the knowledge points to be learned; the learned knowledge points and the mastery degree of the learned knowledge points are extracted from the initial learning data, wherein the mastery degree of the learned knowledge points refers to the test results of the learned knowledge points.
And a second step of: if the learned knowledge points exist in the knowledge points to be learned, judging whether review is needed and determining the learning sequence. And artificially setting the qualified mastering degree of the learned knowledge points, comparing the mastering degree of the learned knowledge points with the qualified mastering degree of the learned knowledge points, determining the review difficulty value of the learned knowledge points, learning from low to high according to the review difficulty value of the learned knowledge points, and removing the learned knowledge points from the knowledge points to be learned after learning. When the mastering degree of the student is not lower than the qualified mastering degree, the student is regarded as mastering the knowledge point, and the student does not need to learn again; when the mastering degree of the student is lower than the qualified mastering degree, the student is regarded as not mastering the knowledge point and needs to learn again; the re-learning is performed from low to high according to the review difficulty value, so that the knowledge points with the priority learning mastery degree close to the qualified mastery degree are ensured, students can adapt to a new learning mode, and the situation that the difficulty emotion is hard to happen prematurely is avoided. The specific calculation process of the review difficulty value is as follows:
；
wherein ,for knowledge points->Is a review difficulty value of->For knowledge points->Mastery degree of->For knowledge points->Qualified mastery degree of (c).
It should be noted that, in this example, the qualified mastering degree has multiple setting modes, and the average historical score, the passing score or other manually set scores of the knowledge points can be taken, for example, the test score of a certain knowledge point is fully divided into 100 scores, and the student can set the qualified mastering degree to 90, 80 or 60, so as to be convenient for adjusting the learning content according to the demands of the student. Knowledge points in this example refer to complete knowledge points such as K1, K2, K3 in fig. 2, and the knowledge points may include sub-knowledge points, for example, K21, K22, etc.; the learning is performed by taking the knowledge points as units, so that the continuity of the sub-knowledge points in the knowledge points is ensured.
When the learned knowledge points do not exist in the knowledge points to be learned, learning is performed through the low-to-high ranking of the historical difficulty values of the knowledge points to be learned. The historical difficulty value is calculated by the following formula:
；
wherein ,for knowledge points->Historical difficulty value of->For knowledge points->History of mastery of (2)>For knowledge points->Is the total score of (2).
And a third step of: extracting teaching contents corresponding to knowledge points to be learned from a teaching resource library, pushing the teaching contents one by one according to a learning sequence, wherein the teaching contents comprise: matched text content and courseware content.
Therefore, in this embodiment, the learning sequence is to review old knowledge points that are not mastered before learning new knowledge points; when the old knowledge points which are not mastered are reviewed, the knowledge points which are relatively better are learned and mastered, then the knowledge points which are relatively worse are learned, and when the new knowledge points are learned, the knowledge points which are easier to master are learned, and then the knowledge points which are difficult to master are learned; the learning principle of first-easy last-difficult first-review and then learning is integrally followed, so that students can adapt to teaching planning step by step.
In this embodiment, the teaching style generating model is configured to configure the teaching content according to the style configuration data and output the teaching content through a 3D virtual person to form a teaching plan, and includes:
converting the text content in the teaching content into voice content, and configuring voice characteristics of the voice content according to style configuration data: speech speed and intonation;
converting courseware content in the teaching content into 3D courseware;
and outputting the voice content and the 3D courseware through a 3D virtual person.
Specifically, the teaching style is initialized by style configuration data in the student data, the style configuration data reflects the teaching style of student tendency, and the teaching style is quantized into voice characteristics in this example: speech speed and intonation; converting text content in the teaching content into voice content, and configuring the speed and intonation of the voice content according to style configuration data; converting courseware content in the teaching content into 3D courseware; and then outputting the voice content and the 3D courseware together through a 3D virtual person, so as to realize the combined output of the teaching content and the teaching style.
In this example, the teaching style is quantized into the voice feature of the 3D virtual person, and in other possible embodiments, the teaching style may be quantized into the expression feature, the gesture feature, and the like of the 3D virtual person. The 3D virtual person can adopt the holographic image technology to carry out naked eye 3D display, and the teaching is separated from a common electronic screen, so that stronger interactivity is realized.
Therefore, the combined output of teaching contents and teaching styles is realized on the one hand, 3D virtual persons are realized on the other hand, naked eye 3D virtual teaching is realized, and the teaching mode of traditional professor or screen teaching is changed.
In order to further explain the teaching method provided in this example, the concentration model and concentration index adjustment teaching plan of step S3 are described next.
In this embodiment, inputting the physiological data into a pre-trained concentration model to obtain a concentration index includes:
inputting the facial features of students in class into a concentration model, acquiring the expression types of the students according to an expression classification model in the concentration model, and mapping the expression types into a first concentration index;
inputting the gesture characteristics of the students in class into a concentration model, acquiring the gesture types of the students according to a gesture classification model in the concentration model, and mapping the gesture types into a second concentration index;
Inputting the eye characteristics of the students in class into a concentration model, acquiring the eye types of the students according to the eye models in the concentration model, and mapping the eye types into a third concentration index;
and calculating the concentration index of the student according to the first concentration index, the second concentration index and the third concentration index.
Specifically, a camera and other acquisition devices acquire videos of students in class according to set frequency, and physiological data are extracted from the videos according to the existing extraction algorithm, wherein the physiological data comprise facial features, posture features and eye-mind features of the students in the example; the three types of characteristics are input into a concentration degree model for classification, the classification model can directly adopt the existing model, a mapping table is prepared by an expert evaluation method, the classification result is mapped into specific first, second and third concentration degree indexes, and finally the average value of the first, second and third concentration degree indexes is used as the concentration degree index of the student at the current moment.
In this embodiment, adjusting the teaching plan and generating a prediction status report according to the concentration index includes:
comparing the concentration index of the student with the average concentration index of the student, accelerating the speech speed in the teaching style when the concentration index of the student is greater than or equal to the average concentration index of the student, reducing the speech speed in the teaching style when the concentration index of the student is lower than the average concentration index of the student, and improving the intonation in the teaching style.
Specifically, taking the average value of the concentration indexes calculated in a period of time as the average concentration index of the students, and comparing the concentration index of the students at the current moment with the average concentration index of the students; when the concentration index at the current moment is lower than the average concentration index, the student class listening state is indicated to be quite usual and is sliding downwards, on the one hand, a prediction state report is generated and sent to a mobile phone end of a parent for early warning, on the other hand, the teaching style in the teaching plan is adjusted to adapt to the current student class listening state, and meanwhile, the attention of the student is promoted as much as possible; in this example, through reducing the speech rate, avoid the student to miss the teaching content, through improving the intonation simultaneously, attract student's attention through the high frequency. When the concentration index at the current moment is higher than the average concentration index, the class listening state of the students is improved more commonly, the course progress can be properly accelerated at the moment, and the learning effect is improved. It should be noted that, in this example, the average concentration index of the student is adopted to display the class state of the student, and the calculated concentration index of different students is in different intervals due to different habits, so that the average concentration index of the individual is adopted to evaluate the class state of the individual, and the method is more suitable for individual differences than the method adopting the quantitative concentration index.
In this embodiment, the speech speed and intonation are calculated by the following formula:
；
；
wherein ,speech rate at time t +.>Represents the average speech rate of the setting,/-, for example>Concentration index indicating time t, < ->Represents an average concentration index,/->Indicating the amount of change in speech rate->Intonation indicating time t +.>Represents the average intonation of the setting +.>Representing the amount of intonation change.
Specifically, the average speed and the average intonation of the voice content are configured according to the style configuration data, when the concentration index of the student is monitored to be lower than the average concentration index, the speed is reduced on the basis of the average speed, and the intonation is improved on the basis of the average intonation; when the concentration index of the student is monitored to be higher than the average concentration index, the speech speed is improved on the basis of the average speech speed, and the average intonation is kept unchanged. Through the above, the lower the concentration index, the slower the speech speed, the higher the intonation, and the higher the concentration index, the faster the speech speed.
Therefore, according to the teaching method based on the multidimensional teaching model, the teaching content and the teaching style are combined through the multidimensional teaching model to carry out naked eye 3D virtual output, so that the demands of students on various teaching styles can be met, and naked eye 3D virtual teaching is realized; the concentration degree model is used for monitoring the concentration degree of students in class, and the teaching style is further adjusted to adapt to the class listening state of the students, so that the concentration degree of the students is improved, missing knowledge points are reduced, and the learning efficiency is improved; in addition, the content generation model of the embodiment adopts a mode of first review and then learning, and is characterized in that the learning sequence is arranged in a first-easy-last-difficult mode, so that students can adapt to teaching contents step by step, and a better learning effect is obtained step by step.
Referring to fig. 3, the embodiment of the invention also discloses a teaching system based on the pre-training multi-dimensional teaching model, corresponding to a teaching method based on the pre-training multi-dimensional teaching model, the system comprises:
the data acquisition module is used for acquiring student data, wherein the student data comprises initial academic data, target academic data and style configuration data;
the teaching plan generation module is used for inputting the student data into a pre-trained multi-dimensional teaching model to obtain a teaching plan, the multi-dimensional teaching model comprises a teaching content generation model and a teaching style generation model, the teaching content generation model is used for obtaining teaching contents according to the initial learning data and the target learning data, and the teaching style generation model is used for configuring the teaching contents according to the style configuration data and outputting the teaching contents through a 3D virtual person to form the teaching plan;
the concentration optimization module is used for acquiring physiological data of students under the teaching plan, wherein the physiological data comprise but are not limited to facial features, posture features and eye-mind features of the students in class, inputting the physiological data into a pre-trained concentration model to obtain concentration indexes, adjusting the teaching plan according to the concentration indexes, and generating a prediction state report.
It can be understood that the teaching planning generation module in the teaching system of the example combines and outputs teaching content and teaching styles, and the teaching styles are flexibly configured according to style configuration data so as to meet the demands of students on diversified teaching styles; in addition, the concentration optimization module of the embodiment adjusts the teaching style according to the concentration index of the student so as to adapt to the class listening state of the student.
In this embodiment, the teaching plan generating module includes: the teaching content generation module and the teaching style generation module;
the teaching content generation module comprises:
the knowledge point extraction module is used for extracting knowledge points to be learned and corresponding historical mastery degrees from the target learning data, and extracting learned knowledge points and corresponding mastery degrees from the initial learning data;
the learning sequence generation module is used for calculating the review difficulty value of the learned knowledge points according to the grasping degree of the learned knowledge points when the learned knowledge points exist in the to-be-learned knowledge points, arranging the learning sequence from low to high according to the review difficulty value, removing the learned knowledge points from the to-be-learned knowledge points, and calculating the historical difficulty value of each knowledge point when the learned knowledge points do not exist in the to-be-learned knowledge points, and arranging the learning sequence from low to high according to the historical difficulty value;
The teaching content generation module is used for extracting teaching contents corresponding to knowledge points to be learned from a teaching resource library and pushing the teaching contents one by one according to the learning sequence, and the teaching contents comprise: matched text content and courseware content;
the teaching style generation module comprises:
the text content conversion module is used for converting text content in the teaching content into voice content, and configuring voice characteristics of the voice content according to style configuration data: speech speed and intonation;
the courseware conversion module is used for converting courseware content in the teaching content into 3D courseware;
and the teaching planning output module is used for outputting the voice content and the 3D courseware through a 3D virtual person.
Therefore, the teaching content generating module in this embodiment learns new knowledge points after reviewing old knowledge points that are not mastered; when the old knowledge points which are not mastered are reviewed, the knowledge points which are relatively better are learned and mastered, then the knowledge points which are relatively worse are learned, and when the new knowledge points are learned, the knowledge points which are easier to master are learned, and then the knowledge points which are difficult to master are learned; the learning principle of first-easy-last-difficult, first-review-later-learning is integrally followed, so that students can adapt to teaching planning step by step; and the teaching style generation module combines and outputs the teaching content and the teaching style, so as to realize 3D virtual teaching and change the teaching form of the traditional professor or screen teaching.
In this embodiment, the concentration optimization module includes: a concentration calculating module and a teaching style optimizing module,
the concentration calculating module comprises:
the first concentration index calculation module is used for inputting the facial features of the students in class into a concentration model, acquiring the expression types of the students according to the expression classification model in the concentration model, and mapping the expression types into a first concentration index;
the second concentration index calculation module is used for inputting the gesture characteristics of the students in class into a concentration model, acquiring gesture types of the students according to a gesture classification model in the concentration model, and mapping the gesture types into second concentration indexes;
the third concentration index calculation module is used for inputting the eye characteristics of the students in class into a concentration model, acquiring the eye types of the students according to the eye models in the concentration model, and mapping the eye types into the third concentration index;
the concentration index calculation module is used for calculating the concentration index of the student according to the first concentration index, the second concentration index and the third concentration index;
the teaching style optimization module is used for comparing the concentration index of the student with the average concentration index of the student, accelerating the speech rate in the teaching style when the concentration index of the student is greater than or equal to the average concentration index of the student, reducing the speech rate in the teaching style when the concentration index of the student is lower than the average concentration index of the student, and improving the intonation in the teaching style;
The speech speed and intonation are calculated by the following formula:
；/>
；
wherein ,speech rate at time t +.>Represents the average speech rate of the setting,/-, for example>Concentration index indicating time t, < ->Represents an average concentration index,/->Indicating the amount of change in speech rate->Intonation indicating time t +.>Represents the average intonation of the setting +.>Representing the amount of intonation change.
Therefore, the teaching system based on the multidimensional teaching model provided by the embodiment combines the teaching content with the teaching style to perform 3D virtual output through the teaching planning generation module, so that the demands of students on various teaching styles can be met, and 3D virtual teaching is realized; the concentration degree optimization module is used for monitoring the concentration degree of students in class, and the teaching style is further adjusted to adapt to the class listening state of the students, so that the concentration degree of the students is improved, missing knowledge points are reduced, and the learning efficiency is improved; in addition, the learning sequence generation module of the embodiment adopts a mode of first review and then learning, and is easy to learn and difficult to learn, so that students can adapt to teaching contents step by step, and better learning effect is obtained step by step.
Referring to fig. 4, the embodiment of the invention also discloses a teaching device based on the pre-training multi-dimensional teaching model, which is used for realizing the teaching method based on the multi-dimensional teaching model. The device comprises: the device comprises a holographic projection device 1, a central processing unit, a collecting device 2, a communication interface 3 and an inkless printer 4, wherein the central processing unit is connected with the holographic projection device 1, the collecting device 2, the communication interface 3 and the inkless printer 4;
The holographic projection device 1 is used for presenting the teaching plan in a 3D form, the acquisition device 2 is used for acquiring physiological data of students under the teaching plan, the communication interface 3 is used for communicating with a server, transmitting the physiological data and receiving the teaching plan, and the inkless printer 4 is used for printing courseware content.
Therefore, the device provided by the embodiment can realize the functions of teaching planning presentation and data acquisition.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (9)
1. A teaching method based on a multidimensional teaching model is characterized in that: comprising
S1, student data are acquired, wherein the student data comprise initial academic data, target academic data and style configuration data;
s2, inputting the student data into a pre-trained multi-dimensional teaching model to obtain a teaching plan, wherein the multi-dimensional teaching model comprises a teaching content generation model and a teaching style generation model, the teaching content generation model is used for obtaining teaching contents according to the initial learning data and the target learning data, and the teaching style generation model is used for configuring the teaching contents according to the style configuration data and outputting the teaching contents through a 3D virtual person to form the teaching plan;
S3, acquiring physiological data of students under the teaching plan, wherein the physiological data comprise but are not limited to facial features, posture features and eye-rest features of the students in class, inputting the physiological data into a pre-trained concentration model to obtain concentration indexes, adjusting the teaching plan according to the concentration indexes, and generating a prediction state report;
wherein adjusting the teaching plan and generating a prediction status report according to the concentration index comprises: comparing the concentration index of the student with the average concentration index of the student, accelerating the speech speed in the teaching style when the concentration index of the student is greater than or equal to the average concentration index of the student, reducing the speech speed in the teaching style when the concentration index of the student is lower than the average concentration index of the student, and improving the intonation in the teaching style.
2. The teaching method based on the multidimensional teaching model according to claim 1, characterized in that: the teaching content generation model is used for obtaining teaching content according to the initial academic data and the target academic data, and comprises the following steps:
extracting knowledge points to be learned and corresponding historical grasping degrees from the target learning data, and extracting learned knowledge points and corresponding grasping degrees from the initial learning data;
If the learned knowledge points exist in the knowledge points to be learned, calculating a review difficulty value of the learned knowledge points according to the grasping degree of the learned knowledge points, arranging a learning sequence from low to high according to the review difficulty value, and removing the learned knowledge points from the knowledge points to be learned, if the learned knowledge points do not exist in the knowledge points to be learned, calculating a history difficulty value of each knowledge point according to the history grasping degree of the knowledge points to be learned, and arranging the learning sequence from low to high according to the history difficulty value;
extracting teaching contents corresponding to knowledge points to be learned from a teaching resource library, and pushing the teaching contents one by one according to the learning sequence, wherein the teaching contents comprise: matched text content and courseware content.
3. The teaching method based on the multidimensional teaching model according to claim 2, characterized in that: the teaching style generation model is used for configuring the teaching content according to the style configuration data and outputting the teaching content through a 3D virtual person to form a teaching plan, and comprises the following steps:
converting the text content in the teaching content into voice content, and configuring voice characteristics of the voice content according to style configuration data: speech speed and intonation;
Converting courseware content in the teaching content into 3D courseware;
and outputting the voice content and the 3D courseware through a 3D virtual person.
4. The teaching method based on the multidimensional teaching model according to claim 1, characterized in that: inputting the physiological data into a pre-trained concentration model to obtain a concentration index, comprising:
inputting the facial features of students in class into a concentration model, acquiring the expression types of the students according to an expression classification model in the concentration model, and mapping the expression types into a first concentration index;
inputting the gesture characteristics of the students in class into a concentration model, acquiring the gesture types of the students according to a gesture classification model in the concentration model, and mapping the gesture types into a second concentration index;
inputting the eye characteristics of the students in class into a concentration model, acquiring the eye types of the students according to the eye models in the concentration model, and mapping the eye types into a third concentration index;
and calculating the concentration index of the student according to the first concentration index, the second concentration index and the third concentration index.
5. The teaching method based on the multidimensional teaching model according to claim 1, characterized in that: the speech speed and intonation are calculated by the following formula:
wherein ,speech rate at time t +.>Represents the average speech rate of the setting,/-, for example>Concentration index indicating time t, < ->Represents an average concentration index,/->Indicating the amount of change in speech rate->Intonation indicating time t +.>Represents the average intonation of the setting +.>Representing the amount of intonation change.
6. A teaching system based on a multidimensional teaching model is characterized in that: comprising
The data acquisition module is used for acquiring student data, wherein the student data comprises initial academic data, target academic data and style configuration data;
the teaching plan generation module is used for inputting the student data into a pre-trained multi-dimensional teaching model to obtain a teaching plan, the multi-dimensional teaching model comprises a teaching content generation model and a teaching style generation model, the teaching content generation model is used for obtaining teaching contents according to the initial learning data and the target learning data, and the teaching style generation model is used for configuring the teaching contents according to the style configuration data and outputting the teaching contents through a 3D virtual person to form the teaching plan;
the concentration optimization module is used for acquiring physiological data of students under the teaching plan, wherein the physiological data comprise but are not limited to facial features, posture features and eye-mind features of the students in class, inputting the physiological data into a pre-trained concentration model to acquire concentration indexes, adjusting the teaching plan according to the concentration indexes and generating a prediction state report;
Wherein, the concentration optimization module includes: the teaching style optimization module is used for comparing the concentration index of the student with the average concentration index of the student, accelerating the speech speed in the teaching style when the concentration index of the student is greater than or equal to the average concentration index of the student, reducing the speech speed in the teaching style when the concentration index of the student is lower than the average concentration index of the student, and improving the intonation in the teaching style.
7. The teaching system based on the multidimensional teaching model according to claim 6, wherein the teaching system is characterized in that: the teaching plan generating module comprises: the teaching content generation module and the teaching style generation module;
the teaching content generation module comprises:
the knowledge point extraction module is used for extracting knowledge points to be learned and corresponding historical mastery degrees from the target learning data, and extracting learned knowledge points and corresponding mastery degrees from the initial learning data;
the learning sequence generation module is used for calculating the review difficulty value of the learned knowledge points according to the grasping degree of the learned knowledge points when the learned knowledge points exist in the to-be-learned knowledge points, arranging the learning sequence from low to high according to the review difficulty value, removing the learned knowledge points from the to-be-learned knowledge points, and calculating the historical difficulty value of each knowledge point when the learned knowledge points do not exist in the to-be-learned knowledge points, and arranging the learning sequence from low to high according to the historical difficulty value;
The teaching content generation module is used for extracting teaching contents corresponding to knowledge points to be learned from a teaching resource library and pushing the teaching contents one by one according to the learning sequence, and the teaching contents comprise: matched text content and courseware content;
the teaching style generation module comprises:
the text content conversion module is used for converting text content in the teaching content into voice content, and configuring voice characteristics of the voice content according to style configuration data: speech speed and intonation;
the courseware conversion module is used for converting courseware content in the teaching content into 3D courseware;
and the teaching planning output module is used for outputting the voice content and the 3D courseware through a 3D virtual person.
8. The teaching system based on the multidimensional teaching model according to claim 6, wherein the teaching system is characterized in that: the concentration optimization module comprises: an concentration calculation module, the concentration calculation module comprising:
the first concentration index calculation module is used for inputting the facial features of the students in class into a concentration model, acquiring the expression types of the students according to the expression classification model in the concentration model, and mapping the expression types into a first concentration index;
The second concentration index calculation module is used for inputting the gesture characteristics of the students in class into a concentration model, acquiring gesture types of the students according to a gesture classification model in the concentration model, and mapping the gesture types into second concentration indexes;
the third concentration index calculation module is used for inputting the eye characteristics of the students in class into a concentration model, acquiring the eye types of the students according to the eye models in the concentration model, and mapping the eye types into the third concentration index;
the concentration index calculation module is used for calculating the concentration index of the student according to the first concentration index, the second concentration index and the third concentration index;
the speech speed and intonation are calculated by the following formula:
wherein ,speech rate at time t +.>Represents the average speech rate of the setting,/-, for example>Concentration index indicating time t, < ->Represents an average concentration index,/->Indicating the amount of change in speech rate->Intonation indicating time t +.>Represents the average intonation of the setting +.>Representing the amount of intonation change.
9. A teaching device based on a multidimensional teaching model, for implementing a teaching method based on a multidimensional teaching model as claimed in any one of claims 1-5, characterized in that: the system comprises a holographic projection device, a central processing unit, a collecting device, a communication interface and an inkless printer, wherein the central processing unit is connected with the holographic projection device, the collecting device, the communication interface and the inkless printer;
The holographic projection device is used for presenting the teaching plan in a 3D form, the acquisition device is used for acquiring physiological data of students under the teaching plan, the communication interface is used for communicating with the server, transmitting the physiological data and receiving the teaching plan, and the inkless printer is used for printing courseware content.
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