CN116433427A - Personalized learning career data image drawing method and system - Google Patents
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Abstract
The invention relates to a personalized learning career data image drawing method, which comprises the following steps: s1: collecting student learning career data through a learning condition acquisition system; s2: analyzing the learning condition information through an intelligent analysis system to obtain a learning career data portrait; s3: the personalized learning system provides personalized learning resources for students according to the learning career data portraits. A personalized learning career data portrait system realizes a personalized learning career data portrait method, which comprises a learning condition acquisition system, an intelligent analysis system and a personalized learning system; the learning condition acquisition system is used for collecting learning career data of students; the intelligent analysis system analyzes the learning information to obtain a learning career data portrait; the personalized learning system provides personalized learning resources for students according to the learning career data representation. The invention can complete learning career data image, and accordingly provides individualized learning resources for students, and helps the students to learn weak links of learning.
Description
Technical Field
The invention relates to the technical field of educational Internet, in particular to a personalized learning career data image drawing method and system.
Background
In the field of discipline education, weak knowledge points of students must be mastered in order to effectively improve the student performance, and the student is helped to tamper with a foundation to quickly improve the performance by supplementing the weak knowledge points. Because the textbook content is formed by combining small knowledge points, the knowledge points are not isolated, and are mutually related to form a complex network structure, so that causal relationship is formed. The root cause of the poor knowledge point mastery may be that the pre-knowledge point mastery of the knowledge point is poor. All analyses of student achievements cannot be performed only on the surface of the current exercise or the current test paper, and only through the means of big data, the learning process of learning careers of students is analyzed throughout. The knowledge points of students are mastered, the whole portrait is carried out on the knowledge points of the students, the learning career process (elementary school, junior middle school and high school) is fully known, the knowledge points of the link are not mastered well, the subsequent learning difficulty is caused, the knowledge points are started from the most root source, effective learning resources are provided for the students, and the students are helped to improve the performance rapidly.
The traditional teaching mode consists of links of learning, examination and training. Through classroom teaching, practice, examination paper and re-practice, a teacher only can know the mastering condition of the current knowledge points of students and cannot know the mastering condition of the related knowledge points, and the teaching method also comprises a plurality of subjective teaching experiences, so that teaching deviation is easily caused. Various achievement analysis systems are also proposed in the market aiming at the situation, but only the current test paper is analyzed, so that subjective teaching experience is more objective, and teaching deviation is avoided. However, the method can only stay on the surface of the current exercise or the current test paper for analysis, and cannot mine the back logic and association relation of the knowledge points, so that the essential cause of the learning difficulty of students cannot be really known. Meanwhile, the teaching can be performed only in a class mode, and the teaching can not be performed according to the material because the mastering conditions of the same knowledge points of each student are different. The learning scheme cannot be formulated according to different students, so that a great deal of precious learning time is wasted.
Thus, there is a need for a method or system for completing a learning career data representation and thereby providing a personalized learning resource for students.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a personalized learning career data image method and a personalized learning career data image system, which finish learning career data images and provide personalized learning resources for students.
The aim of the invention is realized by the following technical scheme:
a personalized learning career data image drawing method comprises the following steps:
s1: collecting student learning career data through a learning condition acquisition system;
s2: analyzing the learning condition information through an intelligent analysis system to obtain a learning career data portrait;
s3: the personalized learning system provides personalized learning resources for students according to the learning career data portraits.
Further, the step S1 includes the following substeps:
s101: collecting original learning condition data;
s102: carrying out data identification on the original learning information to obtain learning information;
s103: and saving the learning information.
Further, the collection modes of the original emotion data include but are not limited to the following three modes:
(1) Collecting by an image collecting method;
(2) Acquiring through an own learning system;
(3) Obtained by interfacing with a third party system.
Further, the step S2 includes the following sub-steps:
s201: extracting the learning information from the learning information acquisition system;
s202: and (3) combining the knowledge information of each school of the student with the knowledge point knowledge graph by adopting a big data analysis engine, carrying out iteration and association analysis, analyzing the weak condition of the knowledge point of the student and the condition influenced by other knowledge points, and forming a learning career data portrait according to the level of the student.
Further, the big data analysis engine is Spark or Flink.
Further, the step S3 includes the following substeps:
s301: extracting a learning career data portrait;
s302: analyzing the learning career data portrait by adopting a score lifting model to generate a score lifting path diagram;
s303: acquiring personalized learning resources from a teaching resource library by lifting a path diagram;
s304: and storing personalized learning resources to a learning career personalized recommendation information base.
A personalized learning career data portrait system realizes a personalized learning career data portrait method, which comprises a learning condition acquisition system, an intelligent analysis system and a personalized learning system; the learning condition acquisition system is used for collecting learning career data of students; the intelligent analysis system analyzes the learning information to obtain a learning career data portrait; the personalized learning system provides personalized learning resources for students according to the learning career data representation.
Further, the intelligent analysis system is electrically connected with a learning career report system; the learning career reporting system learns career data representation to generate a learning career report.
The beneficial effects of the invention are as follows:
the method and the system for individuating the learning lifetime data portrait finish the learning lifetime data portrait, provide individuation learning resources for students according to the method and the system, help the students to learn weak links of learning, provide a targeted classification scheme, avoid blind learning and achieve the aim of rapidly improving the performance.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
Embodiment one:
as shown in fig. 1, a personalized learning career data image method includes the following steps:
s1: collecting student learning career data through a learning condition acquisition system;
s101: collecting original learning condition data;
the collection modes of the original emotion data include but are not limited to the following three modes:
(1) Collecting by an image collecting method;
the method is mainly aimed at daily operations, exercises and exams uploaded by the independent photographing mode of students.
Students submit original study condition data (such as homework images and examination images) to the study condition acquisition system in a photographing and uploading mode
(2) Acquiring through an own learning system;
the self learning system is a learning career individualization system, and the learning career individualization system recommends practice questions to students and receives practice results of the students (namely, learning career data of the students).
(3) The method comprises the steps of obtaining by docking with a third party system;
third party systems include, but are not limited to, web-based systems, performance analysis systems, and examination systems.
And interfacing with examination systems of other manufacturers according to a custom protocol to obtain original learning condition data such as response conditions and analysis of students.
S102: carrying out data identification on the original learning information to obtain learning information;
the method comprises the steps of carrying out pattern recognition on a student test paper by an image acquired by an image acquisition method through a test question pattern analysis module and an artificial intelligent module for pattern recognition, automatically recognizing a response area and test question contents of the student, and acquiring learning information
And (3) reading the data acquired by the self-learning system (2) in a mode of being acquired by being docked with a third-party system through a custom protocol and a docking protocol.
S103: saving the learning information;
and saving the student emotion information to the Hadoop big data system, and providing analysis resources for subsequent operations.
S2: analyzing the learning condition information through an intelligent analysis system to obtain a learning career data portrait;
the intelligent analysis system is used for comprehensively analyzing the collected learning information, and analyzing the mastering conditions of all knowledge points of the students by combining with artificial intelligent algorithms such as big data technology, knowledge maps and the like, so as to form learning career data portraits for the students from elementary school, junior middle school to high school.
S201: extracting the learning information from the learning information acquisition system;
s202: adopting a big data analysis engine to combine the knowledge information of each school of the students with the knowledge point knowledge graph, carrying out iteration and association analysis to analyze the weak condition of the knowledge points of the students and the condition influenced by other knowledge points, and then forming a learning career data portrait according to the level of the students;
the big data analysis engine is Spark or Flink.
The knowledge point knowledge graph is built in a teaching resource system.
The teaching resource system stores question bank resources and teaching video resources, classifies the question bank resources and the teaching video resources according to various dimensions such as difficulty, knowledge points, specific gravity of the knowledge points, questions and the like, provides a multi-dimensional query mode, and ensures that accurate resource recommendation can be performed according to different student levels to ensure learning quality.
The teaching resource system is an open platform and is a docking protocol for a third partner. The resources of the partner may be accessed. The richness of the teaching resource library is perfected, and the high-quality teaching resources are improved for students to the greatest extent.
The construction of the teaching resource system comprises the following steps:
1) Storing the question bank resources of each subject by adopting an ES database mode, classifying the questions in the question bank resources according to the difficulty, the knowledge points, the specific gravity of the knowledge points and the various dimensions of the questions, and providing a multi-dimensional query mode;
2) Establishing knowledge point knowledge graph according to textbooks (elementary school, junior middle school, high school) by using graph database, and labeling the front-back relationship of knowledge points in the knowledge point knowledge graph
The teaching resource system provides an equal-classification statistical function, can carry out classification statistics according to knowledge points, questions, difficulty and other multidimensional degrees, is convenient for monitoring a resource system library, and improves recommendation precision.
The teaching resource system provides a proofreading function, and the problematic teaching resources can be manually modified and supplemented to improve the recommendation quality of the system.
S3: the personalized learning system provides personalized learning resources for students according to the learning career data portraits;
s301: extracting a learning career data portrait;
s302: analyzing the learning career data portrait by adopting a score lifting model to generate a score lifting path diagram;
the achievement promoting model comprises intelligent recommendation models such as collaborative filtering, matrix decomposition and clustering, and machine learning schemes such as deep learning are combined.
The score-improving model forms a score-improving path diagram according to the causal relation of the knowledge points.
S303: acquiring personalized learning resources from a teaching resource library of a teaching resource system by lifting a path diagram;
s304: storing personalized learning resources to a learning career personalized recommendation information base;
the personalized learning system combines the knowledge point knowledge graph and the intelligent recommendation model algorithm, and provides personalized learning resources (such as video explanation, wrong question explanation, consolidation exercise and the like) for students according to the grades of the students. Students can quickly improve the performance by looking at video explanation, wrong question explanation, consolidation exercise and other modes.
A personalized learning career data portrait system realizes a personalized learning career data portrait method, which comprises a learning condition acquisition system, an intelligent analysis system and a personalized learning system; the learning condition acquisition system is used for collecting learning career data of students; the intelligent analysis system analyzes the learning information to obtain a learning career data portrait; the personalized learning system provides personalized learning resources for students according to the learning career data representation.
The intelligent analysis system is electrically connected with a learning career report system; the learning career reporting system learns career data representation to generate a learning career report.
The learning career report system displays the learning career report to the students in the form of mobile phone APP according to the learning career portrait data and the personalized promotion scheme of the students, and enables the students to fully know the mastery degree of each knowledge point (including the front knowledge point and the rear knowledge point). The student can clearly know which basic knowledge points influence the learning score (for example, the knowledge points in the middle and high school are not mastered and possibly caused by the knowledge points in the junior middle school), meanwhile, good personalized learning resources are provided for the student, and the student can efficiently use the learning time to obtain the maximized improvement score.
The personalized learning career data image drawing method and system are based on learning data (which can penetrate through elementary school, junior middle school and high school) of the learning career of the students, help the students to learn weak links of learning, provide a targeted classification scheme, avoid blind learning and achieve the aim of rapidly improving the performance. Through collecting the exercise and examination data of each stage (primary school, junior middle school, high school) of student, combine analysis means such as big data to find student's weak knowledge point and weak knowledge point's relevant knowledge point fast, from the most basic knowledge point, provide effectual study resource to the student through intelligent recommendation system to reach the purpose of quick promotion achievement.
The method and the system for individuating the learning lifetime data portrait finish the learning lifetime data portrait, provide individuation learning resources for students according to the method and the system, help the students to learn weak links of learning, provide a targeted classification scheme, avoid blind learning and achieve the aim of rapidly improving the performance.
The foregoing examples merely illustrate specific embodiments of the invention, which are described in greater detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention.
Claims (8)
1. A personalized learning career data image drawing method is characterized in that: the method comprises the following steps:
s1: collecting student learning career data through a learning condition acquisition system;
s2: analyzing the learning condition information through an intelligent analysis system to obtain a learning career data portrait;
s3: the personalized learning system provides personalized learning resources for students according to the learning career data portraits.
2. The method for personalizing learning career data imagery according to claim 1, wherein:
said step S1 comprises the sub-steps of:
s101: collecting original learning condition data;
s102: carrying out data identification on the original learning information to obtain learning information;
s103: and saving the learning information.
3. The method for personalizing learning career data imagery according to claim 2, wherein:
the collection modes of the original emotion data include but are not limited to the following three modes:
(1) Collecting by an image collecting method;
(2) Acquiring through an own learning system;
(3) Obtained by interfacing with a third party system.
4. The method for personalizing learning career data imagery according to claim 1, wherein:
said step S2 comprises the sub-steps of:
s201: extracting the learning information from the learning information acquisition system;
s202: and (3) combining the knowledge information of each school of the student with the knowledge point knowledge graph by adopting a big data analysis engine, carrying out iteration and association analysis, analyzing the weak condition of the knowledge point of the student and the condition influenced by other knowledge points, and forming a learning career data portrait according to the level of the student.
5. The method for personalizing learning career data imagery as defined in claim 4, wherein:
the big data analysis engine is Spark or Flink.
6. The method for personalizing learning career data imagery according to claim 1, wherein:
said step S3 comprises the sub-steps of:
s301: extracting a learning career data portrait;
s302: analyzing the learning career data portrait by adopting a score lifting model to generate a score lifting path diagram;
s303: acquiring personalized learning resources from a teaching resource library by lifting a path diagram;
s304: and storing personalized learning resources to a learning career personalized recommendation information base.
7. A personalized learning career data portrayal system implementing a personalized learning career data portrayal method as defined in any one of claims 1-6, characterized in that: the intelligent learning system comprises a learning condition acquisition system, an intelligent analysis system and a personalized learning system; the learning condition acquisition system is used for collecting learning career data of students; the intelligent analysis system analyzes the learning information to obtain a learning career data portrait; the personalized learning system provides personalized learning resources for students according to the learning career data representation.
8. The personalized learning career data representation system of claim 7, wherein:
the intelligent analysis system is electrically connected with a learning career report system; the learning career reporting system learns career data representation to generate a learning career report.
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