CN113407733A - Course knowledge graph automatic generation method and system based on culture scheme - Google Patents
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Abstract
The invention discloses a course knowledge graph automatic generation method based on a culture scheme, which comprises the following steps: prefabricating a knowledge point dendrogram according to the course outline and the post-class learning data, setting elements under the knowledge points, establishing a plurality of nodes under the elements, corresponding each node to one post-class learning data, establishing a relation between the knowledge points by setting weight values for the knowledge points, establishing a relation between the nodes by setting weight values for the nodes, and generating a preset knowledge map; acquiring teaching content knowledge points and post-class learning data in the teaching process, matching the acquired teaching content according to the knowledge content in the preset knowledge map, and analyzing the occupation ratio of the teaching content knowledge points; and summarizing and analyzing the collected teaching contents, and matching and analyzing the collected teaching content knowledge points with the knowledge contents of the preset knowledge graph. The system comprises classroom learning and post-class learning data, can more comprehensively understand knowledge point mastering conditions of students, and generates more comprehensive and accurate evaluation reports.
Description
Technical Field
The invention belongs to the technical field of knowledge maps of knowledge contents, and particularly relates to a method and a system for automatically generating a course knowledge map based on a culture scheme.
Background
The knowledge map is also called scientific knowledge map, is called knowledge domain visualization or knowledge domain mapping map in the book information field, is a series of different graphs for displaying the relation between the knowledge development process and the structure, describes knowledge resources and carriers thereof by using visualization technology, and excavates, analyzes, constructs, draws and displays knowledge and the mutual relation between the knowledge resources and the carriers. The modern theory of the multidisciplinary fusion purpose is achieved by combining the theory and method of applying mathematics, graphics, information visualization technology, information science and other disciplines with the method of metrology introduction analysis, co-occurrence analysis and the like and utilizing a visual map to vividly display the core structure, development history, frontier field and overall knowledge framework of the disciplines. Provides a practical and valuable reference for subject research.
The conventional student study culture scheme is that a teacher gives lessons, students, and manages students, and the teacher gives lessons, asks for answers, and works to modify to culture students. The traditional learning method consumes a great deal of energy of teachers, can not clearly analyze the weight of knowledge, and can not digitally display the learning condition of students in time.
Chinese patent No. CN 107644062 a discloses a knowledge content weight analysis system and method based on knowledge graph, which includes: the benchmark presetting module is used for prefabricating a knowledge point tree graph according to the rundown requirements, establishing the relation between knowledge points by setting weight values for the knowledge points and generating a preset knowledge graph; the data acquisition module is connected with the reference presetting module and is used for acquiring teaching content knowledge points in the classroom teaching process and matching the acquired teaching content; and the calculation analysis module is connected with the data acquisition module and is used for summarizing and analyzing the acquired teaching contents and matching and analyzing the acquired teaching content knowledge points with the knowledge contents of the preset knowledge graph. The method is generated only according to the knowledge points of the classroom books, and only comprises the learning in the classroom. The mastering conditions of the knowledge points of the students cannot be mastered, and systematic evaluation reports have limitations and cannot help the students find weak links. The invention is achieved accordingly.
Disclosure of Invention
1. Objects of the invention
Aiming at the technical problems, the automatic curriculum knowledge graph generation method and system based on the culture scheme are provided, and the method and system not only comprise learning in a classroom, but also comprise post-class learning videos, exercises, examinations, practical exercises and the like, can more comprehensively know the knowledge point mastering conditions of students, can generate more comprehensive and accurate evaluation reports, can quickly analyze the advantages and disadvantages of the learning scores of the students through big data analysis, and can more quickly master the learning method.
2. The technical scheme adopted by the invention
A course knowledge graph automatic generation method based on a culture scheme comprises the following steps:
s01: prefabricating a knowledge point dendrogram according to the course outline and the post-class learning data, setting elements under the knowledge points, establishing a plurality of nodes under the elements, corresponding each node to one post-class learning data, establishing a relation between the knowledge points by setting weight values for the knowledge points, establishing a relation between the nodes by setting weight values for the nodes, and generating a preset knowledge map;
s02: acquiring teaching content knowledge points and post-class learning data in the teaching process, matching the acquired teaching content according to the knowledge content in the preset knowledge map, and analyzing the occupation ratio of the teaching content knowledge points;
s03: and summarizing and analyzing the collected teaching contents, and matching and analyzing the collected teaching content knowledge points with the knowledge contents of the preset knowledge graph.
In a preferred technical solution, the entities of the knowledge point tree diagram prefabricated in step S01 include books, chapters, nodes, points and elements.
In a preferred technical solution, the post-lesson learning data in step S01 includes learning videos, courseware, operations, and test questions, and a unique identification code is generated for each piece of post-lesson learning data, where the identification code is further used to record the call duration or number of times, calculate the completion degree, and associate each identification code with each node.
In a preferred embodiment, the step S01 further includes assigning weights to the chapters, the nodes, the points, and the elements according to a preset rule, wherein the sum of the weights of all the chapters is 1, the sum of the weights of all the nodes is 1, the sum of the weights of all the points is 1, and the sum of the weights of all the elements is 1.
In a preferred embodiment, step S01 further includes assigning different color values to different chapters, and the color values of the nodes and the points inherit the chapters.
In a preferred technical solution, the method for setting a weight value for a node in step S01 includes:
acquiring the number N of learning videos, courseware, operations and test questions related under elements;
and calculating the weight of each node according to the obtained number N, wherein the weight value of each node is 1/N.
In a preferred technical solution, the summarizing and analyzing in step S03 includes acquiring identification codes of learning videos, courseware, operations, and test questions, calculating the completion degree of learning data after the lesson according to the calling duration or times of the identification codes, summarizing the completion degree of learning data after the lesson, and generating an evaluation report of a corresponding grasping condition.
The invention also discloses a course knowledge graph automatic generation system based on the culture scheme, which comprises the following steps:
the preset knowledge map generation module is used for prefabricating a knowledge point dendrogram according to the course outline and the post-class learning data, setting elements under the knowledge points, establishing a plurality of nodes under the elements, corresponding each node to one post-class learning data, establishing a relation between the knowledge points by setting weight values for the knowledge points, establishing a relation between the nodes by setting weight values for the nodes, and generating a preset knowledge map;
the data acquisition module is used for acquiring teaching content knowledge points and post-class learning data in the teaching process, matching the acquired teaching content according to the knowledge content in the preset knowledge map and analyzing the proportion of the teaching content knowledge points;
and the calculation analysis module is used for summarizing and analyzing the acquired teaching contents and matching and analyzing the acquired teaching content knowledge points and the knowledge contents of the preset knowledge map.
In a preferred technical scheme, the post-lesson learning data in the preset knowledge map generation module comprises learning videos, courseware, operations and test questions, a unique identification code is generated for each piece of post-lesson learning data, the identification codes are further used for recording calling time or times, calculating completion degree and associating each identification code with each node.
In a preferred technical scheme, the summarizing and analyzing in the calculation and analysis module includes acquiring identification codes of learning videos, courseware, operation and test questions, calculating the completion degree of learning data after the class according to the calling time or times of the identification codes, summarizing the completion degree of learning data after the class, and generating an evaluation report of corresponding grasping conditions.
3. Advantageous effects adopted by the present invention
The invention not only comprises the learning in class, but also comprises the learning videos, exercises, exams, actual exercises and the like after class, can more comprehensively understand the knowledge point mastering conditions of students, can generate more comprehensive and accurate evaluation reports, can quickly analyze the merits of the learning scores of the students through big data analysis, and can more quickly master the learning method. The knowledge point mastering conditions of the students can be automatically generated according to the learning conditions of the students, and the students can be helped to learn related knowledge more pertinently aiming at the evaluation report of the system.
Drawings
FIG. 1 is a flow chart of a method for automatic generation of a course knowledge graph based on an incubation protocol according to the present invention;
FIG. 2 is a schematic block diagram of an automatic curriculum knowledge base generation system based on an incubation protocol according to the present invention;
FIG. 3 is a schematic view of a curriculum knowledge graph of the present invention.
Detailed Description
The technical solution in the embodiment of the present invention is clearly and completely described below with reference to the accompanying drawings in the embodiment of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without inventive step, are within the scope of the present invention.
The present invention will be described in further detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, a method for automatically generating a course knowledge graph based on a culture scheme includes the following steps:
s01: prefabricating a knowledge point dendrogram according to the course outline and the post-class learning data, setting elements under the knowledge points, establishing a plurality of nodes under the elements, corresponding each node to one post-class learning data, establishing a relation between the knowledge points by setting weight values for the knowledge points, establishing a relation between the nodes by setting weight values for the nodes, and generating a preset knowledge map according to a visual chart rule;
s02: acquiring teaching content knowledge points and post-class learning data in the teaching process, matching the acquired teaching content according to the knowledge content in the preset knowledge map, and analyzing the occupation ratio of the teaching content knowledge points;
s03: and summarizing and analyzing the collected teaching contents, and matching and analyzing the collected teaching content knowledge points with the knowledge contents of the preset knowledge graph.
In a preferred embodiment, the entities of the prefabricated knowledge point tree in step S01 include books, chapters, nodes, points and elements, as shown in fig. 3, where a point is a knowledge point and a book is a body of a knowledge graph. Of course, the present invention may be divided into other entities, which are not limited in this respect. The books, the chapters, the nodes, the points, the elements and the knowledge points are respectively stored in a database in a corresponding table mode and can be established through interface input.
Establishing entity-relation-entity triples, establishing relations among entities, establishing entity-attribute values, wherein each attribute-attribute value pair is used for describing the inherent characteristics of the entities.
The method is different from the traditional method which only generates according to the knowledge points of books in the classroom, and not only comprises courseware, but also comprises practice, examination and video watching (learning video). In a preferred embodiment, the post-lesson learning data in step S01 includes learning videos, courseware, operations, and test questions, and a unique identifier is generated for each piece of post-lesson learning data, and the identifier is further used to record the calling duration or number of times, calculate the completion degree, and associate each identifier with each node. The specific identification code can be composed of numbers and letters or pure numbers, and the identification codes correspond to the nodes one by one and are used for identifying the learning data after class.
In a preferred embodiment, step S01 further includes assigning weights to the chapters, nodes, points and elements according to a preset rule, wherein the sum of the weights of all the chapters is 1, the sum of the weights of all the nodes is 1, the sum of the weights of all the points is 1, and the sum of the weights of all the elements is 1. The preset rule is a preset rule, and can be set manually or set as a function, and the specific function can adopt the existing weight function value.
In a preferred embodiment, step S01 further includes assigning different color values to different chapters, and the color values of the nodes inherit the chapters. The chapter color value is added to clearly distinguish how many chapters are and the lower nodes related to the chapters when the knowledge graph is generated.
In a preferred embodiment, the method for setting the weight value for the node in step S01 includes:
acquiring the number N of learning videos, courseware, operations and test questions related under elements;
and calculating the weight of each node according to the obtained number N, wherein the weight value of each node is 1/N.
The above method for setting the weight can reduce the calculation amount, and certainly, other weight functions can be used for distribution, which is not limited in the present invention.
According to the incidence relation of the tree structures at the upper level and the lower level among the classes, the knowledge graph of the input course can be directly generated in the form of a relational graph in a visual graph chart.
In a preferred embodiment, the summarizing and analyzing in step S03 includes acquiring identification codes of learning videos, courseware, operations, and test questions, calculating the completion degree of learning data after the lesson according to the calling duration or times of the identification codes, summarizing the completion degree of learning data after the lesson, and generating an evaluation report of corresponding grasping conditions.
In another embodiment, as shown in fig. 2, the present invention further discloses an automatic generation system of course knowledge graph based on cultivation recipes, comprising:
a preset knowledge map generation module 10, which pre-manufactures a knowledge point dendrogram according to the course outline and the post-class learning data, sets elements under the knowledge points, establishes a plurality of nodes under the elements, corresponds each node to one post-class learning data, establishes a relationship between the knowledge points by setting weight values for the knowledge points, establishes a relationship between the nodes by setting weight values for the nodes, and generates a preset knowledge map;
the data acquisition module 20 is used for acquiring teaching content knowledge points and post-class learning data in the teaching process, matching the acquired teaching content according to the knowledge content in the preset knowledge map, and analyzing the proportion of the teaching content knowledge points;
and the calculation analysis module 30 is used for summarizing and analyzing the acquired teaching contents and matching and analyzing the acquired teaching content knowledge points with the knowledge contents of the preset knowledge map.
The post-lesson learning data in the preset knowledge map generation module 10 includes learning videos, courseware, operations and test questions, and a unique identification code is generated for each piece of post-lesson learning data, and the identification code is further used for recording calling duration or times, calculating completion degree, and associating each identification code with each node.
The summary analysis in the calculation and analysis module 30 includes acquiring identification codes of learning videos, courseware, operation, and test questions, calculating the completion degree of learning data after class according to the calling duration or times of the identification codes, summarizing the completion degree of learning data after class, and generating an evaluation report of corresponding grasping conditions.
The following specific implementation steps of the course knowledge graph automatic generation system based on the culture scheme are described by taking the basic nursing science as an example:
1. firstly, the finally used resources corresponding to the learning and practice of students are recorded: inputting course videos and courseware resources related to basic nursing in series of courses; for example, a virtual simulation scoring table, a peer-to-peer mutual scoring table and a practice-while-watching scoring table can be established, each scoring table has a corresponding table stored in a database, and when skills corresponding to relevant practices of basic nursing are respectively recorded in each scoring table, step operation is required; inputting test questions in the examination system; when the videos, the courseware, the operation and the test questions are input, a unique system identification code consisting of letters and numbers is generated, and then when the knowledge points are associated and corresponding video watching, courseware learning, operation and exercise and test question examination are carried out, the identification code is used, and the following parts 6 and 8 can be seen in detail;
2. establishing a basic nursing book in a system, and then establishing corresponding chapter content, chapter weight and chapter color value according to chapters of the basic nursing book; it is noted here that the assignment of weights requires that the weights of all chapters are added together to equal 1. In addition, the added chapter color value is used for clearly distinguishing how many chapters are and the lower nodes related to the chapters when the knowledge graph is generated, as shown in FIG. 3;
3. under each chapter, establishing section content of the chapter and establishing a corresponding weight, color value inherits the chapter, wherein the weight proportion is similar to the chapter and requires that all weights are added to be equal to 1;
4. under each section, establishing the point content of the section and establishing corresponding weight and color value inheritance, wherein the weight proportion is similar to the chapter, and the sum of all weights is required to be equal to 1;
5. under each point, establishing the element content of the point and establishing corresponding weight and color value inheritance, wherein the weight proportion is similar to the chapter, and all weights are required to be added to be equal to 1;
6. below each element, the videos, courseware, operations, test questions entered during the first step are to be associated: establishing nodes under the elements, wherein each node corresponds to an identification code of an input video or courseware or operation or test question, and thus an incidence relation is established with the resources input in the step 1; in this step, the weight distribution is not needed, and the weight division is automatically performed according to the number of the associated videos, courseware, operation and test questions, and the division rule is as follows: if the number of the associated videos, courseware, operations and test questions under the element is N, each associated video, courseware, operation and test question accounts for 1/N of the number;
7. according to the incidence relation of the tree structures at the upper level and the lower level among the entities, the knowledge graph of the input course can be directly generated in the form of a relational graph in a visual graph;
8. then, when the student learns videos, courseware, operations and test questions, the system can automatically record the learning record of the student according to the identification codes of the videos, the courseware, the operations and the test questions; after a large number of learning processes are finished, the system automatically generates a summary of the mastering conditions of the student on the book according to the learning of the corresponding videos, courseware, operations and test questions of the student on the book and the operation records of the corresponding related operations, and generates a corresponding evaluation report; specifically, the summarizing analysis includes acquiring identification codes of learning videos, courseware, operation and test questions, calculating the completion degree of learning data after class according to the calling time or times of the identification codes, and summarizing the completion degree of learning data after class.
9. According to the report made by the system, the weak part is trained in a targeted way, the weak link is eliminated, and the 8 steps are repeated until the system is satisfied.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A course knowledge graph automatic generation method based on a culture scheme is characterized by comprising the following steps:
s01: prefabricating a knowledge point dendrogram according to the course outline and the post-class learning data, setting elements under the knowledge points, establishing a plurality of nodes under the elements, corresponding each node to one post-class learning data, establishing a relation between the knowledge points by setting weight values for the knowledge points, establishing a relation between the nodes by setting weight values for the nodes, and generating a preset knowledge map;
s02: acquiring teaching content knowledge points and post-class learning data in the teaching process, matching the acquired teaching content according to the knowledge content in the preset knowledge map, and analyzing the occupation ratio of the teaching content knowledge points;
s03: and summarizing and analyzing the collected teaching contents, and matching and analyzing the collected teaching content knowledge points with the knowledge contents of the preset knowledge graph.
2. The method for automatically generating lesson knowledge-graph based on culture protocol as claimed in claim 1, wherein the entities of the prefabricated knowledge point tree-graph in step S01 include books, chapters, nodes, points and elements.
3. The method for automatically generating a lesson knowledge graph based on an incubation scheme as claimed in claim 1 or claim 2, wherein the post-lesson learning data in step S01 includes learning videos, courseware, operations and test questions, a unique identification code is generated for each piece of post-lesson learning data, the identification codes are further used for recording calling time or times, calculating completion degree, and associating each identification code with each node.
4. The method for automatically generating a curriculum knowledge base based on culture solution as claimed in claim 2, wherein the step S01 further comprises assigning weights to the chapters, nodes, points and elements according to preset rules, wherein the sum of the weights of all chapters is 1, the sum of the weights of all nodes is 1, the sum of the weights of all points is 1, and the sum of the weights of all elements is 1.
5. The method of claim 4, wherein the step S01 further comprises assigning different color values to different chapters, and the color values of the nodes inherit the chapters.
6. The method for automatically generating a course knowledge graph based on an incubation scheme as claimed in claim 3, wherein the method for setting weight values to nodes in step S01 comprises:
acquiring the number N of learning videos, courseware, operations and test questions related under elements;
and calculating the weight of each node according to the obtained number N, wherein the weight value of each node is 1/N.
7. The method as claimed in claim 3, wherein the step of gathering and analyzing in step S03 includes obtaining identification codes of learning videos, courseware, operation and test questions, calculating the completion degree of the post-lesson learning data according to the calling duration or times of the identification codes, gathering the completion degree of the post-lesson learning data, and generating an evaluation report of corresponding mastery condition.
8. An automatic curriculum knowledge graph generation system based on an incubation scheme, comprising:
the preset knowledge map generation module is used for prefabricating a knowledge point dendrogram according to the course outline and the post-class learning data, setting elements under the knowledge points, establishing a plurality of nodes under the elements, corresponding each node to one post-class learning data, establishing a relation between the knowledge points by setting weight values for the knowledge points, establishing a relation between the nodes by setting weight values for the nodes, and generating a preset knowledge map;
the data acquisition module is used for acquiring teaching content knowledge points and post-class learning data in the teaching process, matching the acquired teaching content according to the knowledge content in the preset knowledge map and analyzing the proportion of the teaching content knowledge points;
and the calculation analysis module is used for summarizing and analyzing the acquired teaching contents and matching and analyzing the acquired teaching content knowledge points and the knowledge contents of the preset knowledge map.
9. The system of claim 8, wherein the lesson knowledge graph is generated by the preset knowledge graph generation module, the post-lesson learning data includes learning videos, courseware, operations and test questions, a unique identification code is generated for each piece of post-lesson learning data, and the identification codes are further used for recording calling time or times, calculating completion degree, and associating each identification code with each node.
10. The system of claim 9, wherein the analysis module for summarizing lesson knowledge graphs comprises obtaining identification codes of learning videos, courseware, operation and test questions, calculating the completion degree of the learning data after lesson according to the calling time or times of the identification codes, summarizing the completion degree of the learning data after lesson, and generating an evaluation report of corresponding mastery.
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