CN112184511A - Online vocational education personalized course content pushing algorithm based on big data - Google Patents
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Abstract
The invention discloses an online vocational education personalized course content pushing algorithm based on big data, which can provide refined and exquisite course resources, can automatically identify learning requirements according to the characteristic information of learners, dynamically and adaptively present a personalized learning activity sequence, and implement content accurate pushing, thereby improving the learning efficiency of students and saving the learning time. It comprises the following steps: based on the personalized learning of students receiving vocational education, a vocational education personalized teaching service system framework is constructed by a learning situation model, a professional model, a self-adaptive engine and a presentation model; self-adaptive recommendation is carried out according to the learning situation model and the professional model, learning content, a learning activity sequence and a knowledge tree structure learning navigation which are suitable for a learner are presented in a page; meanwhile, the learning behavior historical record of the learner can be modified, the learning condition model is maintained, and the accuracy of the learning condition model is improved.
Description
Technical Field
The invention relates to the technical field of online education, in particular to an online vocational education personalized course content pushing algorithm based on big data.
Background
The talents become strategic resources which promote the development of economic society all the more so throughout the comprehensive national competition in the world at present, the basic precedent global status and action of education are more prominent, and the personalized teaching service is the most important content in the theoretical research and practice of higher education. Meanwhile, developing personalized teaching is the fundamental requirement of returning nurses, and the internal and external rules of higher education determine that higher education is an organic integration for cultivating natural people and social people, and is a process for promoting individual personalization and individual socialization. The internal and external laws of higher education determine that higher education is provided by promoting the organic integration of individual personalization and socialization, and the process of promoting the organic integration of individual personalization and socialization is a complex process which needs to continuously influence elements of the higher education to carry out deepened teaching reform. In addition, the development of personalized teaching is the development direction of vocational education. But the method is different from local high education greatly, can not borrow directly and needs precise construction. There are mainly the following differences: (1) the level difference of the same professional level is large. Because different objects aiming at the same specialty in the vocational education have the characteristics of more layers, larger difference of competency level and the like, the integral level difference of the objects in the network teaching developed by the local education is not particularly obvious. (2) The application conditions are more different. Because the influence of factors in confidentiality, safety and the like needs to be considered, the difference between a network environment and a place is large, the expression form of professional education content is different from the high-grade education construction of the place, and the personalized service method also needs to be changed.
In summary, there is a lack of an online vocational education personalized course content push algorithm based on big data, which can provide refined and exquisite course resources, automatically identify learning needs according to the characteristic information of learners, dynamically and adaptively present personalized learning activity sequences, and implement content accurate push, thereby improving the learning efficiency of students and saving learning time.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a personalized course content pushing algorithm based on big data for online vocational education, which can provide refined and exquisite course resources, automatically identify learning requirements according to the characteristic information of learners, dynamically and adaptively present personalized learning activity sequences, and implement content accurate pushing, thereby improving the learning efficiency of students and saving the learning time.
In order to solve the technical problems, the technical scheme provided by the invention is as follows: an online vocational education personalized course content pushing algorithm based on big data comprises the following steps:
firstly, constructing a professional education personalized teaching service system framework by a learning condition model, a professional model, a self-adaptive engine and a presentation model on the basis of personalized learning of students receiving professional education;
step two, self-adaptive recommendation is carried out on learning content, a learning activity sequence and a knowledge tree structure learning navigation which are suitable for a learner according to the learning situation model and the professional model, and the learning navigation is presented in a page; meanwhile, according to the learning process of the learner, the adaptive rule is executed, the learning behavior historical record of the learner is modified, the learning situation model is maintained, and the accuracy of the learning situation model is improved;
thirdly, designing user behavior indexes contained in different learning styles, determining a threshold range corresponding to each user behavior index, and preliminarily determining the learning style of the user through the threshold range of a single behavior index;
and fourthly, determining a learning path and a content object according to the user learning style classification.
Preferably, the third step comprises the following specific sub-steps:
step 1), establishing a quadruple theta0The learning style of the user is represented by { Processing, duration, Input, and uncertainty }, where four-tuple elements Processing, duration, Input, and uncertainty represent the four learning style dimensions of the user, i.e., Processing, Perception, Input, and Understanding, respectively, and each element of a four-tuple is a two-dimensional variable defined as follows:
in the formula, variables Active, contextual, sentent, Intuitive, Visual, Verbal, Sequential and Comprehensive respectively represent the membership degrees of 8 learning styles, namely the membership degrees of Active, sincere, comprehension, intuition, vision, speech, sequence and Comprehensive 8 learning styles;
step 2) calculating the learning style quadruplet theta of different learning users0And respectively calculating the membership degrees of the 8 learning styles of the user. And analyzing the behavior indexes by adopting a Nearest Neighbor algorithm KNN (K-Nearest Neighbor) according to the plurality of behavior indexes, wherein the process comprises three processes of data preprocessing, data analysis model establishment and big data parameter fitting.
One is data preprocessing. Recording the behavior index of the user in the learning process by xiAnd (4) showing. Firstly, according to the upper and lower limits of each index threshold value, the index data x is processediAnd (3) performing compression and normalization processing, wherein the calculation process is as follows:
in the formula, variable
The index value after the compression and normalization processing of the ith index,
and
the lower limit and the upper limit of the ith index threshold value are respectively;
and 3) establishing a data analysis model. And analyzing the behavior indexes by adopting a KNN algorithm to obtain the membership degrees of 8 learning styles. And calculating the Euclidean distance Dl from the ith user to the index center x s of different learning styles, namely:
wherein alpha isiThe contribution coefficient of the ith index to the style membership is a parameter needing to be optimized based on tagged user big data. After the distance Dl to the center of each index is calculated, the distance Dl is subjected to reciprocal calculation, and then the probability Pl corresponding to the first learning style is calculated by adopting a Softmax function:
the obtained probability Pl is the membership degree of each learning style, alpha0The distance conversion coefficient is a parameter needing to be optimized based on tagged user big data;
step 4), big data parameter fitting, setting relevant parameters, alpha0And alphaiUser big data optimization of labeling is required to be carried out according to data;
in the formula (I), the compound is shown in the specification,
ytbig data for tagged users.
Preferably, the fourth step comprises the following substeps:
step 1: firstly, dividing a learning style of a user into four dimensions of information processing, perception, input and understanding according to a Felder-Silverman learning style model, and then decomposing the learning style into different learning types from different dimensions, wherein the specific types comprise an active type, an immersed type, an apprehension type, an intuition type, a visual type, a speech type, a sequence type and a comprehensive type;
and 2, confirming a corresponding recommended learning path and a corresponding content object for each learning type, wherein the specific corresponding relation is as follows: active type: participation in discussion (optional) → reading learning materials (recommended) → case study (recommended) → doing practice (optional) → completing testing (optional); sinking to the Si: reading learning materials (optional) → case study (optional) → participation in discussion (recommendation) → doing exercise (optional) → completing testing (optional); and (3) comprehension: multiple recommended facts and data learning resources; intuitive type: multiple recommended theoretical learning resources; visual type: content of media types such as multi-recommendation pictures, charts, flow charts, videos and the like; the language type: multi-recommendation text information content; sequence type: multi-recommendation linear learning content; comprehensive type: and recommending nonlinear learning content, and simultaneously connecting all the points through a system to form a knowledge tree.
After adopting the structure, the invention has the following beneficial effects: the method can not only finish the learning objective, stimulate the learning interest and ensure the education quality, but also provide refined and exquisite course resources, provide personalized teaching service according to the characteristic information (such as learning preference, knowledge level and the like) of learners, automatically identify the learning requirement, dynamically and adaptively present a personalized learning activity sequence (including learning objects), change the conventional 'flood irrigation' method, implement 'precise drip irrigation', and further finish the knowledge construction more quickly and better, improve the personalized service level of online learning, improve the learning efficiency of learners and save the learning time.
Drawings
FIG. 1 is a system framework of the personalized teaching service for vocational education in the invention.
Fig. 2 is the adaptive engine operation process of the present invention.
FIG. 3 is a B/S architecture of a personalized instructional service system.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
With reference to fig. 1 to fig. 3, an online vocational education personalized course content push algorithm based on big data comprises the following steps:
firstly, constructing a professional education personalized teaching service system framework by a learning condition model, a professional model, a self-adaptive engine and a presentation model on the basis of personalized learning of students receiving professional education;
step two, self-adaptive recommendation is carried out on learning content, a learning activity sequence and a knowledge tree structure learning navigation which are suitable for a learner according to the learning situation model and the professional model, and the learning navigation is presented in a page; meanwhile, according to the learning process of the learner, the adaptive rule is executed, the learning behavior historical record of the learner is modified, the learning situation model is maintained, and the accuracy of the learning situation model is improved;
thirdly, designing user behavior indexes contained in different learning styles, determining a threshold range corresponding to each user behavior index, and preliminarily determining the learning style of the user through the threshold range of a single behavior index;
and fourthly, determining a learning path and a content object according to the user learning style classification.
The third step comprises the following specific sub-steps:
1 st) Step, establish a tetrad theta0The learning style of the user is represented by { Processing, duration, Input, and uncertainty }, where four-tuple elements Processing, duration, Input, and uncertainty represent the four learning style dimensions of the user, i.e., Processing, Perception, Input, and Understanding, respectively, and each element of a four-tuple is a two-dimensional variable defined as follows:
in the formula, variables Active, contextual, sentent, Intuitive, Visual, Verbal, Sequential and Comprehensive respectively represent the membership degrees of 8 learning styles, namely the membership degrees of Active, sincere, comprehension, intuition, vision, speech, sequence and Comprehensive 8 learning styles;
step 2) calculating the learning style quadruplet theta of different learning users0And respectively calculating the membership degrees of the 8 learning styles of the user. And analyzing the behavior indexes by adopting a Nearest Neighbor algorithm KNN (K-Nearest Neighbor) according to the plurality of behavior indexes, wherein the process comprises three processes of data preprocessing, data analysis model establishment and big data parameter fitting.
One is data preprocessing. Recording the behavior index of the user in the learning process by xiAnd (4) showing. Firstly, according to the upper and lower limits of each index threshold value, the index data x is processediAnd (3) performing compression and normalization processing, wherein the calculation process is as follows:
in the formula, variable
The index value after the compression and normalization processing of the ith index,
and
the lower limit and the upper limit of the ith index threshold value are respectively;
and 3) establishing a data analysis model. And analyzing the behavior indexes by adopting a KNN algorithm to obtain the membership degrees of 8 learning styles. And calculating the Euclidean distance Dl from the ith user to the index center x s of different learning styles, namely:
wherein alpha isiThe contribution coefficient of the ith index to the style membership is a parameter needing to be optimized based on tagged user big data. After the distance Dl to the center of each index is calculated, the distance Dl is subjected to reciprocal calculation, and then the probability Pl corresponding to the first learning style is calculated by adopting a Softmax function:
the obtained probability Pl is the membership degree of each learning style, alpha0The distance conversion coefficient is a parameter needing to be optimized based on tagged user big data;
step 4), big data parameter fitting, setting relevant parameters, alpha0And alphaiUser big data optimization of labeling is required to be carried out according to data;
in the formula (I), the compound is shown in the specification,
ytbig data for tagged users.
The fourth step comprises the following substeps:
step 1: firstly, dividing a learning style of a user into four dimensions of information processing, perception, input and understanding according to a Felder-Silverman learning style model, and then decomposing the learning style into different learning types from different dimensions, wherein the specific types comprise an active type, an immersed type, an apprehension type, an intuition type, a visual type, a speech type, a sequence type and a comprehensive type;
and 2, confirming a corresponding recommended learning path and a corresponding content object for each learning type, wherein the specific corresponding relation is as follows: active type: participation in discussion (optional) → reading learning materials (recommended) → case study (recommended) → doing practice (optional) → completing testing (optional); sinking to the Si: reading learning materials (optional) → case study (optional) → participation in discussion (recommendation) → doing exercise (optional) → completing testing (optional); and (3) comprehension: multiple recommended facts and data learning resources; intuitive type: multiple recommended theoretical learning resources; visual type: content of media types such as multi-recommendation pictures, charts, flow charts, videos and the like; the language type: multi-recommendation text information content; sequence type: multi-recommendation linear learning content; comprehensive type: and recommending nonlinear learning content, and simultaneously connecting all the points through a system to form a knowledge tree.
In order to realize the personalized teaching service for each user, the learning style and the cognitive level are accurately grasped by a big data analysis method. Accurate analysis of learning style of user through big data
Classifying and determining learning path and content object according to user learning style
Firstly, according to a Felder-Silverman learning style model, a learning style of a user is divided into four dimensions of information processing, perception, input and understanding, then the four dimensions are decomposed into different learning types from different dimensions, and finally a learning path and a learning content object are designed.
Secondly, constructing user behavior indexes of four learning style types
User behavior indexes contained in different learning styles are designed, a threshold range corresponding to each user behavior index is determined, and the learning style of a user can be preliminarily determined through the threshold range of a single behavior index.
The personalized teaching service system adopts a B/S system architecture, and learners access the server through a browser. The server has two JAVA objects, Servlet object and JAVA application object. The Servlet object calls the JAVA application to handle the JavaScript request of the learner and then sends the processing result to the browser page of the learner. The JAVA application object is first initialized, and then by using the API of JDBC and the SQL session layer interface, the JAVA application component reads the database related data and updates the system model data, and then generates an adaptive HTML page. And at the end of a session, calling the database access object to store the initialized and updated data in the database. According to the early-stage investigation requirement, the system development is preliminarily completed.
The present invention and its embodiments have been described above, and the description is not intended to be limiting, and the drawings are only one embodiment of the present invention, and the actual configuration is not limited thereto. In summary, those skilled in the art should appreciate that they can readily use the disclosed conception and specific embodiments as a basis for designing or modifying other structures for carrying out the same purposes of the present invention without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (3)
1. An online vocational education personalized course content pushing algorithm based on big data is characterized in that: which comprises the following steps of,
firstly, constructing a professional education personalized teaching service system framework by a learning condition model, a professional model, a self-adaptive engine and a presentation model on the basis of personalized learning of students receiving professional education;
step two, self-adaptive recommendation is carried out on learning content, a learning activity sequence and a knowledge tree structure learning navigation which are suitable for a learner according to the learning situation model and the professional model, and the learning navigation is presented in a page; meanwhile, according to the learning process of the learner, the adaptive rule is executed, the learning behavior historical record of the learner is modified, the learning situation model is maintained, and the accuracy of the learning situation model is improved;
thirdly, designing user behavior indexes contained in different learning styles, determining a threshold range corresponding to each user behavior index, and preliminarily determining the learning style of the user through the threshold range of a single behavior index;
and fourthly, determining a learning path and a content object according to the user learning style classification.
2. The big-data based on-line vocational education personalized course content push algorithm of claim 1, wherein: the third step comprises the following specific sub-steps:
step 1), establishing a quadruple theta0The learning style of the user is represented by { Processing, duration, Input, and uncertainty }, where four-tuple elements Processing, duration, Input, and uncertainty represent the four learning style dimensions of the user, i.e., Processing, Perception, Input, and Understanding, respectively, and each element of a four-tuple is a two-dimensional variable defined as follows:
in the formula, variables Active, contextual, sentent, Intuitive, Visual, Verbal, Sequential and Comprehensive respectively represent the membership degrees of 8 learning styles, namely the membership degrees of Active, sincere, comprehension, intuition, vision, speech, sequence and Comprehensive 8 learning styles;
step 2) calculating the learning style quadruplet theta of different learning users0Respectively calculating the membership degrees of 8 learning styles of the user; analyzing the behavior indexes by adopting a Nearest Neighbor algorithm KNN (K-Nearest Neighbor) according to the behavior indexes, wherein the process comprises three processes of data preprocessing, data analysis model building and big data parameter fitting;
firstly, data preprocessing; recording the behavior index of the user in the learning process by xiRepresents; firstly, according to the upper and lower limits of each index threshold value, the index data x is processediAnd (3) performing compression and normalization processing, wherein the calculation process is as follows:
in the formula, variable
The index value after the compression and normalization processing of the ith index,
and
the lower limit and the upper limit of the ith index threshold value are respectively;
step 3), establishing a data analysis model; analyzing the behavior indexes by adopting a KNN algorithm to obtain membership degrees of 8 learning styles; and calculating the Euclidean distance Dl from the ith user to the index center x s of different learning styles, namely:
wherein alpha isiThe contribution coefficient of the ith index to the style membership is a parameter needing to be optimized based on tagged user big data; after the distance Dl to the center of each index is calculated, the distance Dl is subjected to reciprocal calculation, and then the probability Pl corresponding to the first learning style is calculated by adopting a Softmax function:
the obtained probability Pl is the membership degree of each learning style, alpha0The distance conversion coefficient is a parameter needing to be optimized based on tagged user big data;
step 4), big data parameter fitting, setting relevant parameters, alpha0And alphaiUser big data optimization of labeling is required to be carried out according to data;
in the formula (I), the compound is shown in the specification,
ytbig data for tagged users.
3. The big-data based on-line vocational education personalized course content push algorithm of claim 1, wherein: the fourth step comprises the following substeps:
step 1: firstly, dividing a learning style of a user into four dimensions of information processing, perception, input and understanding according to a Felder-Silverman learning style model, and then decomposing the learning style into different learning types from different dimensions, wherein the specific types comprise an active type, an immersed type, an apprehension type, an intuition type, a visual type, a speech type, a sequence type and a comprehensive type;
and 2, confirming a corresponding recommended learning path and a corresponding content object for each learning type, wherein the specific corresponding relation is as follows: active type: participation in discussion (optional) → reading learning materials (recommended) → case study (recommended) → doing practice (optional) → completing testing (optional); sinking to the Si: reading learning materials (optional) → case study (optional) → participation in discussion (recommendation) → doing exercise (optional) → completing testing (optional); and (3) comprehension: multiple recommended facts and data learning resources; intuitive type: multiple recommended theoretical learning resources; visual type: content of media types such as multi-recommendation pictures, charts, flow charts, videos and the like; the language type: multi-recommendation text information content; sequence type: multi-recommendation linear learning content; comprehensive type: and recommending nonlinear learning content, and simultaneously connecting all the points through a system to form a knowledge tree.
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