CN114565486A - Method for constructing course learning system - Google Patents
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Abstract
A method for constructing a course learning system relates to a method for constructing a learning system. The construction steps are as follows: firstly, acquiring multi-source data; secondly, constructing a body in a mixed mode of top-down and bottom-up; thirdly, information extraction; fourthly, information fusion; fifthly, creating a course knowledge graph; sixthly, pushing courses; and seventhly, personalized learning path planning. The method can reduce the energy cost and the time cost consumption in the process of constructing the knowledge base, and enables a user to construct the knowledge map of the vertical field more quickly, conveniently and efficiently, and can update in real time to follow the era footsteps.
Description
Technical Field
The invention relates to a learning system construction method.
Background
The course has the characteristic of advancing with the times, so the course learning system is also required to be continuously enriched and improved in the current generation direction.
Disclosure of Invention
The invention aims to provide a method for constructing a course learning system.
The invention relates to a construction method of a course learning system, which is constructed according to the following steps:
firstly, acquiring multi-source data;
secondly, a mixed mode of top-down and bottom-up is adopted to construct the body: establishing a hierarchical coarse-grained course ontology information table, performing citation and keyword data element statistical ordering on a document database by using a CiteSpace visualization tool, and performing comparison, evaluation and data fusion on the course elements to establish a course field ontology model;
thirdly, information extraction: directly clustering structured data, extracting entities, relations and attributes of semi-structured and unstructured data by adopting a deep learning method of BilSTM + CRF and GRU-Attention, and establishing document paragraph hierarchical classification;
fourthly, information fusion: semantic similarity calculation and evaluation are carried out on the large-scale knowledge elements extracted by the information, and then entity disambiguation, entity alignment and attribute combination are carried out;
fifthly, creating a course knowledge graph: storing the extracted course knowledge data in a structured data table with the body as a header in a label mapping mode, and then realizing automatic batch transfer to a Neo4j database;
sixthly, pushing courses: extracting a plurality of knowledge points from the course knowledge graph by using a new word discovery algorithm, and clustering the knowledge points to obtain a plurality of knowledge clusters; identifying at least one course corresponding to each knowledge point from a course database; generating a knowledge graph according to the plurality of knowledge clusters and at least one course corresponding to the knowledge point in each knowledge cluster, wherein a course link is displayed on each node in the knowledge course directed graph;
acquiring knowledge points queried by a user, and judging whether the queried knowledge points are leaf nodes in a first knowledge cluster corresponding to the queried knowledge points; when the queried knowledge point is not a leaf node in the first knowledge cluster, pushing course links in all child nodes of the queried knowledge point in the first knowledge cluster to the user;
seventhly, personalized learning path planning:
acquiring current learning state information of the student, generating the recommended learning path according to the current learning state information and the course knowledge map knowledge points, wherein the learning path consists of the knowledge points which are not mastered, and simultaneously associating the preposed knowledge points and the subsequent knowledge points of the knowledge points;
the acquiring of the current learning state information of the student specifically comprises:
701) acquiring basic information of the student;
702) generating a corresponding course test question set according to the basic information and the subject, and pushing the corresponding course test question set;
703) acquiring behavior data of the student on the antecedent test question set;
704) obtaining current learning state information of the student according to the behavior data;
the course test question set is generated and pushed to be updated in real time, the generated and pushed first question is a preset middle difficulty question, if the questions are answered correctly, the question difficulty is improved, and the subsequent questions are pushed, and if the questions are answered incorrectly, the question difficulty is reduced or is not changed;
the behavior data comprises the correctness of the feedback answers of all the questions in the course test question set and the feedback time;
the method for acquiring the current learning state information of the student according to the behavior data specifically comprises the following steps:
and calculating a knowledge point capacity value, a knowledge point efficiency value and a knowledge point stability value of the student according to the behavior data, and taking a three-dimensional vector consisting of the knowledge point capacity value, the knowledge point efficiency value and the knowledge point stability value as current learning state information, wherein the knowledge point capacity value is in direct proportion to the correct error rate of the knowledge point, the knowledge point efficiency value is in inverse proportion to the feedback time of the knowledge point, and the knowledge point stability value is related to the stability of the knowledge point capacity value of the knowledge point.
The method can reduce the energy cost and the time cost consumption in the process of constructing the knowledge base, enables a user to construct the vertical domain knowledge graph more quickly, conveniently and efficiently, and can update in real time to follow the era footsteps.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The first embodiment is as follows: the construction method of the course learning system in the embodiment is constructed according to the following steps:
firstly, acquiring multi-source data;
secondly, a body is constructed in a mixed mode of top-down and bottom-up: establishing a hierarchical coarse-grained course ontology information table, performing citation and keyword data element statistical ordering on a document database by using a CiteSpace visualization tool, and performing comparison, evaluation and data fusion on the course elements to establish a course field ontology model;
thirdly, information extraction: directly clustering structured data, extracting entities, relations and attributes of semi-structured and unstructured data by adopting a deep learning method of BilSTM + CRF and GRU-Attention, and establishing document paragraph hierarchical classification;
wherein, the document paragraph level classification rule is as follows: firstly, performing a first-level hierarchical structure on extracted information according to article titles and paragraphs, then clustering according to similarity evaluation to cluster abstract information, taking the information as a previous-level information node, and finally constructing an optimized information structure;
fourthly, information fusion: calculating and evaluating semantic similarity of large-scale knowledge elements extracted from information by a method based on combination of terms and structure mapping, and then performing entity disambiguation, entity alignment and attribute merging;
string similarity definition:
the two strings contain the similarity definition of the same part:
structural similarity:
and oc (a, b) is the depth of the ontology hierarchy.
Fifthly, creating a course knowledge graph: storing the extracted course knowledge data in a structured data table with the body as a header in a label mapping mode, and then realizing automatic batch transfer to a Neo4j database;
sixthly, pushing courses: extracting a plurality of knowledge points from the course knowledge graph by using a new word discovery algorithm, and clustering the knowledge points to obtain a plurality of knowledge clusters; identifying at least one course corresponding to each knowledge point from a course database; generating a knowledge graph according to the plurality of knowledge clusters and at least one course corresponding to the knowledge point in each knowledge cluster, wherein a course link is displayed on each node in the knowledge course directed graph;
acquiring knowledge points queried by a user, and judging whether the queried knowledge points are leaf nodes in a first knowledge cluster corresponding to the queried knowledge points; when the queried knowledge point is not a leaf node in the first knowledge cluster, pushing course links in all child nodes of the queried knowledge point in the first knowledge cluster to the user;
seventhly, personalized learning path planning:
acquiring current learning state information of the student, generating the recommended learning path according to the current learning state information and the course knowledge map knowledge points, wherein the learning path consists of the knowledge points which are not mastered, and simultaneously associating the preposed knowledge points and the subsequent knowledge points of the knowledge points;
the acquiring of the current learning state information of the student specifically comprises:
701) acquiring basic information of the student;
702) generating knowledge graph data information of corresponding courses according to the basic information and the disciplines, and pushing the knowledge graph data information;
703) acquiring behavior data of the student on the antecedent test question set;
704) obtaining current learning state information of the student according to the behavior data;
the knowledge map data information of the course is generated and pushed to be updated in real time, the generated and pushed first question is a preset middle difficulty question, if the question is answered correctly, the question difficulty is increased to push the subsequent question, and if the question is answered incorrectly, the question difficulty is reduced or is not changed to push the subsequent question;
the behavior data comprises the correction and the feedback time of the feedback answers of all the questions in the knowledge graph data information of the curriculum;
the obtaining of the current learning state information of the student according to the behavior data specifically includes:
and calculating a knowledge point capacity value, a knowledge point efficiency value and a knowledge point stability value of the student according to the behavior data, and taking a three-dimensional vector consisting of the knowledge point capacity value, the knowledge point efficiency value and the knowledge point stability value as current learning state information, wherein the knowledge point capacity value is in direct proportion to the correct error rate of the knowledge point, the knowledge point efficiency value is in inverse proportion to the feedback time of the knowledge point, and the knowledge point stability value is related to the stability of the knowledge point capacity value of the knowledge point.
In the third step of the embodiment, the document paragraph hierarchical classification is established, so that the accuracy of data mapping can be enhanced.
In the fourth step of the embodiment, entity disambiguation, entity alignment and attribute combination are performed to achieve complete and perfect course knowledge data.
The document database in the present embodiment includes a known network database, a national institution official information platform, official websites of various colleges and universities, and the like.
The second embodiment is as follows: the present embodiment is different from the first embodiment in that: the multi-source data is obtained by adopting a crawler technology and a subscription or purchase mode. Other steps and parameters are the same as in the first embodiment.
The data content acquired by the embodiment includes audio, image, professional and culture plan corpus of education department, management departments of colleges and universities and internet forums.
The third concrete implementation mode: the present embodiment is different from the first or second embodiment in that: extracting a plurality of knowledge points from the course knowledge graph, and clustering the knowledge points to obtain a plurality of knowledge clusters, wherein the step six comprises the following steps:
the extracting a plurality of knowledge points from the course knowledge graph and clustering the knowledge points to obtain a plurality of knowledge clusters comprises:
identifying a plurality of words in the domain knowledge graph by using a new word discovery algorithm;
calculating the word frequency of each word and the word frequency value of the inverse file;
identifying a plurality of knowledge points from the plurality of words according to the word frequency-inverse file word frequency value;
carrying out embedded coding on the plurality of knowledge points to obtain a plurality of first coding vectors;
and clustering the first encoding vectors to obtain a plurality of knowledge clusters. Other steps and parameters are the same as in the first or second embodiment.
The fourth concrete implementation mode is as follows: the present embodiment is different from one of the first to third embodiments in that: the step of identifying at least one course corresponding to each knowledge point from the course database comprises:
extracting the course title and the course brief introduction of each course in the course database;
carrying out embedded coding on the course title of each course to obtain a second coding vector;
carrying out embedded coding on the course introduction of each course to obtain a third coding vector;
calculating the course coding vector of each course according to the second coding vector and the third coding vector corresponding to each course;
calculating the similarity between corresponding knowledge points and courses according to the first encoding vector and the course encoding vector;
and identifying at least one course corresponding to each knowledge point according to the similarity. Other steps and parameters are the same as in one of the first to third embodiments.
The fifth concrete implementation mode: the present embodiment is different from one of the first to fourth embodiments in that: the generating a knowledge course directed graph according to the plurality of knowledge clusters and at least one course corresponding to the knowledge point in each knowledge cluster comprises:
defining a cluster name of each knowledge cluster, and taking each cluster name as a root node of the knowledge course directed graph;
determining the root level of the corresponding root node in the knowledge course directed graph according to the number of the knowledge points in each knowledge cluster;
determining the level of the corresponding knowledge point in the knowledge course directed graph according to the word frequency value of the word frequency-inverse file;
calculating the similarity between any two knowledge points;
generating a first directed line segment between any two knowledge points with different root levels according to the similarity;
and generating a second directed line segment between any two knowledge points of the same root level and different levels according to the similarity. Other steps and parameters are the same as in one of the first to fourth embodiments.
The sixth specific implementation mode: the present embodiment is different from one of the first to fifth embodiments in that: the determining the level of the corresponding knowledge point in the knowledge course directed graph according to the word frequency value of the word frequency-inverse file comprises the following steps:
matching the word frequency-inverse file word frequency value corresponding to each knowledge point with a plurality of preset value range ranges;
determining a preset value range successfully matched with the word frequency value of the word frequency-inverse file as a target value range;
and determining the level of the knowledge point in the knowledge course directed graph according to the label identification corresponding to the target value range. Other steps and parameters are the same as in one of the first to fifth embodiments.
Claims (6)
1. A construction method of a course learning system is constructed according to the following steps:
firstly, acquiring multi-source data;
secondly, a mixed mode of top-down and bottom-up is adopted to construct the body: establishing a hierarchical coarse-grained course ontology information table, performing citation and keyword data element statistical ordering on a document database by using a CiteSpace visualization tool, and performing comparison, evaluation and data fusion on the course elements to establish a course field ontology model;
thirdly, information extraction: directly clustering structured data, extracting entities, relations and attributes of semi-structured and unstructured data by adopting a deep learning method of BilSTM + CRF and GRU-Attention, and establishing document paragraph hierarchical classification;
fourthly, information fusion: semantic similarity calculation and evaluation are carried out on the large-scale knowledge elements extracted by the information, and then entity disambiguation, entity alignment and attribute combination are carried out;
fifthly, establishing a course knowledge graph: storing the extracted course knowledge data in a structured data table with the body as a header in a label mapping mode, and then realizing automatic batch transfer to a Neo4j database;
sixthly, pushing courses: extracting a plurality of knowledge points from the course knowledge graph by using a new word discovery algorithm, and clustering the knowledge points to obtain a plurality of knowledge clusters; identifying at least one course corresponding to each knowledge point from a course database; generating a knowledge graph according to the plurality of knowledge clusters and at least one course corresponding to the knowledge point in each knowledge cluster, wherein a course link is displayed on each node in the knowledge course directed graph;
acquiring knowledge points queried by a user, and judging whether the queried knowledge points are leaf nodes in a first knowledge cluster corresponding to the queried knowledge points; when the queried knowledge point is not a leaf node in the first knowledge cluster, pushing course links in all child nodes of the queried knowledge point in the first knowledge cluster to the user;
seventhly, personalized learning path planning:
acquiring current learning state information of the student, generating the recommended learning path according to the current learning state information and the course knowledge map knowledge points, wherein the learning path consists of the knowledge points which are not mastered, and simultaneously associating the preposed knowledge points and the subsequent knowledge points of the knowledge points;
the acquiring of the current learning state information of the student specifically comprises:
101) acquiring basic information of the student;
102) generating knowledge graph data information of corresponding courses according to the basic information and the disciplines, and pushing the knowledge graph data information;
103) acquiring behavior data of the student on the antecedent test question set;
104) obtaining current learning state information of the student according to the behavior data;
the knowledge map data information of the course is generated and pushed to be updated in real time, the generated and pushed first question is a preset middle difficulty question, if the question is answered correctly, the question difficulty is increased to push the subsequent question, and if the question is answered incorrectly, the question difficulty is reduced or is not changed to push the subsequent question;
the behavior data comprises the correction and the feedback time of the feedback answers of all the questions in the knowledge graph data information of the curriculum; the obtaining of the current learning state information of the student according to the behavior data specifically includes:
and calculating a knowledge point capacity value, a knowledge point efficiency value and a knowledge point stability value of the student according to the behavior data, and taking a three-dimensional vector consisting of the knowledge point capacity value, the knowledge point efficiency value and the knowledge point stability value as current learning state information, wherein the knowledge point capacity value is in direct proportion to the correct error rate of the knowledge point, the knowledge point efficiency value is in inverse proportion to the feedback time of the knowledge point, and the knowledge point stability value is related to the stability of the knowledge point capacity value of the knowledge point.
2. The method of claim 1, wherein the multi-source data is obtained by a crawler technique, subscription, or purchase.
3. The method as claimed in claim 1, wherein the step six of extracting a plurality of knowledge points from the curriculum knowledge graph and clustering the plurality of knowledge points to obtain a plurality of knowledge clusters comprises:
the extracting a plurality of knowledge points from the course knowledge graph and clustering the knowledge points to obtain a plurality of knowledge clusters comprises:
identifying a plurality of words in the domain knowledge graph by using a new word discovery algorithm;
calculating the word frequency of each word and the word frequency value of the inverse file;
identifying a plurality of knowledge points from the plurality of words according to the word frequency value of the word frequency-inverse file;
carrying out embedded coding on the plurality of knowledge points to obtain a plurality of first coding vectors;
and clustering the first encoding vectors to obtain a plurality of knowledge clusters.
4. The method for constructing a course learning system as claimed in claim 1, wherein said identifying at least one course corresponding to each knowledge point from the course database comprises:
extracting the course title and the course brief introduction of each course in the course database;
carrying out embedded coding on the course title of each course to obtain a second coding vector;
carrying out embedded coding on the course introduction of each course to obtain a third coding vector;
calculating the course coding vector of each course according to the second coding vector and the third coding vector corresponding to each course;
calculating the similarity between corresponding knowledge points and courses according to the first encoding vector and the course encoding vector;
and identifying at least one course corresponding to each knowledge point according to the similarity.
5. The method of claim 1, wherein the generating a directed curriculum of knowledge graph according to the plurality of knowledge clusters and at least one curriculum corresponding to a knowledge point in each knowledge cluster comprises:
defining a cluster name of each knowledge cluster, and taking each cluster name as a root node of the knowledge course directed graph;
determining the root level of the corresponding root node in the knowledge course directed graph according to the number of the knowledge points in each knowledge cluster;
determining the level of the corresponding knowledge point in the knowledge course directed graph according to the word frequency value and the inverse file word frequency value;
calculating the similarity between any two knowledge points;
generating a first directed line segment between any two knowledge points with different root levels according to the similarity;
and generating a second directed line segment between any two knowledge points of the same root level and different levels according to the similarity.
6. The method as claimed in claim 1, wherein the determining the level of the corresponding knowledge point in the knowledge course directed graph according to the word frequency-inverse document word frequency value includes:
matching the word frequency-inverse file word frequency value corresponding to each knowledge point with a plurality of preset value range ranges;
determining a preset value range successfully matched with the word frequency value of the word frequency-inverse file as a target value range;
and determining the level of the knowledge point in the knowledge course directed graph according to the label identification corresponding to the target value range.
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