CN114579760A - Student wrong question knowledge point self-adaptive stage learning system - Google Patents
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Abstract
The invention provides a learning system based on student wrong question knowledge point self-adaption stage, which comprises: the collection module is used for acquiring the learning data of each student, identifying the learning data and determining wrong knowledge points of each student; the pushing module is used for filtering the teaching contents by taking the wrong knowledge points as constraint conditions to obtain target learning contents of each student and pushing the target learning contents to the corresponding students based on a target pushing frame; and the evaluation module is used for acquiring response data of the student to the target learning content, evaluating the learning effect of the student based on the response data, and adjusting the target learning content based on an evaluation result. Be convenient for in time carry out corresponding adjustment to every student's study content, realize carrying out the pertinence teaching to every student, improve student's learning efficiency, the teaching square of being convenient for simultaneously in time adjusts the teaching scheme according to student's learning effect, promotes teaching efficiency.
Description
Technical Field
The invention relates to the technical field of online education, in particular to a learning system based on student wrong-question knowledge points self-adaption stages.
Background
At present, because online education environments are more and more complex to compile, the backgrounds of students are complex and diverse, the learning requirements are different, the learning progress and the knowledge mastering degree are different, and it is very difficult to know the learning purpose aiming at different students;
therefore, the invention provides a student wrong-question knowledge point self-adaptive stage learning system, which is used for gathering and counting the wrong-question knowledge points of students, facilitating timely corresponding adjustment of the learning content of each student, realizing targeted teaching of each student, improving the learning efficiency of the students, facilitating timely adjustment of a teaching scheme or a teaching strategy by a teaching party according to the learning effect of the students and improving the teaching efficiency.
Disclosure of Invention
The invention provides a student wrong-question knowledge point self-adaptive stage learning system which is used for gathering and counting wrong-question knowledge points of students, facilitating corresponding adjustment of learning contents of each student in time, realizing targeted teaching of each student, improving the learning efficiency of the students, facilitating a teaching party to adjust a teaching scheme or a teaching strategy in time according to the learning effect of the students and improving the teaching efficiency.
The invention provides a learning system based on student wrong question knowledge point self-adaption stage, which comprises:
the collection module is used for acquiring learning data of each student, identifying the learning data and determining wrong knowledge points of each student;
the pushing module is used for filtering the teaching contents by taking the wrong-question knowledge points as constraint conditions to obtain target learning contents of each student, and pushing the target learning contents to the corresponding students based on a target pushing frame;
and the evaluation module is used for acquiring response data of the student to the target learning content, evaluating the learning effect of the student based on the response data, and adjusting the target learning content based on an evaluation result.
Preferably, a learning system based on student wrong-question knowledge point self-adaptation stage, the summarizing module includes:
the student identification unit is used for acquiring identity information of students and determining the target grade of the students based on the identity information;
the subject determining unit is used for determining a target subject learned by the student based on the target grade and calling a historical learning condition of the target subject of the student from a preset database, wherein the target subject is at least one subject;
and the learning data acquisition unit is used for analyzing the historical learning condition of the student based on a preset analysis method to obtain the learning data of the objective subject of the student.
Preferably, a learning system based on student wrong-question knowledge point self-adaptation stage, the module that gathers still includes:
the data identification unit is used for acquiring learning data of each student, determining a target test paper made by the student in each target subject based on the learning data, and determining a target error test question of the student with wrong answer in the target test paper;
the knowledge point determining unit is used for extracting the question stem of the target error test question and determining the knowledge point name of the target knowledge point corresponding to the question stem;
the knowledge point determining unit is further used for determining a knowledge point label set associated with the target knowledge point based on the knowledge point name, and acquiring an associated knowledge point from a preset knowledge point database based on the knowledge point label set;
and the knowledge point integration unit is used for integrating the target knowledge points and the associated knowledge points to obtain wrong-question knowledge points of each student.
Preferably, a learning system based on student wrong-question knowledge point self-adaptive stage, the knowledge point integration unit includes:
the identity acquisition subunit is used for determining the personal basic information of the student when a sorting instruction sent by the management terminal is received, and determining the identity label of the student based on the personal basic information, wherein the student is not unique;
the sorting subunit is used for determining the corresponding relation between the student and the target knowledge point and the associated knowledge point based on the identity label and creating a target mapping table based on the corresponding relation;
and the arrangement subunit is used for filling the personal basic information of the student, the corresponding target knowledge point and the associated knowledge point into a target preset area in the target mapping table to finish the arrangement of the student wrong-question knowledge points.
Preferably, the learning system based on the student wrong question knowledge point self-adaptive stage comprises a knowledge point determining unit and a learning unit, wherein the knowledge point determining unit comprises:
the analysis subunit is used for acquiring target knowledge points and associated knowledge points corresponding to the target error test questions and extracting test question contents of the target error test questions;
the analysis subunit is further configured to analyze the target knowledge point and the associated knowledge point based on the test question content to obtain a first analysis parameter, and extract test question difficulty level evaluation data corresponding to the target wrong test question from a preset test question evaluation database;
the difficulty coefficient determining subunit is configured to determine, based on the test question difficulty degree evaluation data, weight values of the target knowledge point and the associated knowledge point in the target error test question, and obtain a second analysis parameter of the target knowledge point and the associated knowledge point based on the weight values;
and the difficulty coefficient determining subunit is further configured to obtain difficulty coefficients of the target knowledge point and the associated knowledge point based on the first analysis parameter and the second analysis parameter.
Preferably, a learning system based on student wrong-question knowledge point self-adaptation stage, the pushing module includes:
the framework construction unit is used for acquiring the wrong-question knowledge points and inputting the wrong-question knowledge points into a Bayesian network model for structure training to obtain a wrong-question knowledge point system structure;
the frame construction unit is also used for constructing a first pushing frame through a preset encoder according to a collaborative filtering algorithm, inputting the wrong question knowledge point system structure as a constraint condition into the first pushing frame for training, and obtaining a second pushing frame;
the frame construction unit is further used for acquiring the identity label of the student, inputting the identity label of the student as a pushing main body into the second pushing frame for training, and obtaining a target pushing frame;
the system comprises a preparation unit, a learning unit and a learning unit, wherein the preparation unit is used for extracting a knowledge point set corresponding to the current learning stage of a student from a preset knowledge base and determining at least one meta knowledge point in the knowledge point set based on a courseware learning sequence of the student;
the analysis unit is used for extracting word vectors of the meta-knowledge points and determining the content characteristics of the meta-knowledge points based on the word vectors;
the analysis unit is further used for determining a basic knowledge point which has a dependency relationship with the meta knowledge point from the knowledge point set based on the content characteristics of the meta knowledge point, and determining the dependency degree and the path length of the basic knowledge point and the meta knowledge point;
the map construction unit is used for constructing a knowledge map between the meta knowledge point and the basic knowledge point based on the dependency and the path length, and determining a preorder associated knowledge point and a postorder associated knowledge point of the meta knowledge point based on the knowledge map;
the screening unit is used for screening the preorder associated knowledge points and the subsequent associated knowledge points of the meta knowledge points based on the wrong-question knowledge point system structure in the target pushing frame to obtain target learning content of each student;
a pushing unit, configured to push the target learning content to a student corresponding to an identity tag of the student based on the target pushing framework.
Preferably, the system for learning based on student wrong-question knowledge point self-adaptive stage comprises an evaluation module and a learning module, wherein the evaluation module comprises:
the data acquisition unit is used for acquiring historical learning data and expected learning effect of the students, taking the historical learning data as training data, and inputting the expected learning effect as a training target into a neural network model for training to obtain a corresponding relation between the historical learning data and the expected learning effect;
the model building unit is used for building a learning effect evaluation model based on the corresponding relation and simultaneously acquiring operation data of the target learning content learned by the students, wherein the operation data comprises time management data, correct rate holding data and stable analysis data of the target learning content;
the evaluation unit is used for analyzing the operation data of the target learning content learned by the students based on the learning effect evaluation model to obtain the learning reference value of the target teaching content learned by the students, and meanwhile, counting the target knowledge points related to the target teaching content to obtain the target knowledge point list;
the evaluation unit is further used for determining an average progress index and a skewness index of the student to a target knowledge point based on the learning reference value and a target knowledge point list, and determining the mastery degree of the student to the target knowledge point based on the average progress index and the skewness index;
the evaluation unit is used for obtaining the learning effect of the student on the target teaching content based on the mastery degree and comparing the learning effect with the expected learning effect;
if the learning effect meets the requirement of the preset learning effect, judging that the target teaching content is qualified to be pushed, and converting the target teaching content into a target teaching material for storage;
otherwise, judging that the target teaching content is unqualified to be pushed, acquiring classroom video data of the target teaching content learned by the student and the learning score of the student, and determining the learning ability of the student based on the classroom video data and the learning score of the student;
the adjusting unit is used for planning a learning path for the student to learn the target teaching content based on the learning ability of the student and pushing the planned learning path to the student;
the adjusting unit is further used for analyzing the learning effect of the students in the planned learning path in real time until the learning effect meets the requirement of the preset learning effect.
Preferably, the adjusting unit of the learning system based on the student wrong-question knowledge point self-adaptive stage comprises:
the data acquisition subunit is used for acquiring all knowledge points corresponding to the current learning stage of the student, acquiring a historical test paper of the student in the current learning stage, and determining a question making record of the student based on the historical test paper;
the learning path planning subunit is used for determining the distribution situation of learned knowledge points related to the students in the historical test paper in all the knowledge points based on the question making records, and determining the unlearned knowledge points in all the knowledge points based on the distribution situation;
the learning path planning subunit is further configured to determine, based on the theoretical mastery degree and the actual mastery degree of the learned knowledge points by the students, receptivity of different students to the knowledge points with different difficulty coefficients, and plan learning paths of the unlearned knowledge points by the different students based on the receptivity to obtain personalized learning paths corresponding to the different students.
Preferably, the adjusting unit of the learning system based on the student wrong-question knowledge point self-adaptive stage comprises:
the data analysis subunit is used for acquiring the application frequency of the students to different knowledge points in the current learning stage and determining the personal learning characteristics of the students based on the application frequency;
the association subunit is used for acquiring the learning effect of the student in the current learning stage and establishing an association model between the learning effect and the personal learning characteristics;
the prediction subunit is used for determining the trajectory information of the to-be-selected course of the next stage of the student and predicting the learning effect of the student on the to-be-selected course in the next stage based on the association model;
the prediction subunit is further configured to push the target to-be-selected course with the best learning effect in the to-be-selected courses in the next stage to the corresponding student.
Preferably, the learning system based on student wrong-question knowledge point self-adaptation stage further includes:
the learning efficiency analysis unit is used for acquiring the learning effect of different students on the target learning content in the same time period and comparing the learning effect with a preset learning effect, wherein the learning effect is represented based on examination scores, and the target learning content is not uniform;
the learning efficiency analysis unit is further used for determining the learning efficiency of different students on different target learning contents based on the comparison result, and classifying the learning efficiency of the students corresponding to the same target learning contents;
and the recording unit is used for recording the learning efficiency of different students in the same type of target learning content and adjusting the teaching scheme of the student of which the learning efficiency does not meet the preset learning efficiency based on the recording result.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a block diagram of a learning system based on a student wrong-question knowledge point adaptive stage according to an embodiment of the present invention;
FIG. 2 is a block diagram of a summarization module in a learning system based on student wrong-question knowledge points adaptive stage according to an embodiment of the present invention;
fig. 3 is a block diagram of a push module in a learning system based on student wrong knowledge points adaptive stage according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Example 1:
the embodiment provides a learning system based on student wrong-question knowledge point self-adaption stage, as shown in fig. 1, including:
the collection module is used for acquiring learning data of each student, identifying the learning data and determining wrong knowledge points of each student;
the pushing module is used for filtering the teaching contents by taking the wrong-question knowledge points as constraint conditions to obtain target learning contents of each student, and pushing the target learning contents to the corresponding students based on a target pushing frame;
and the evaluation module is used for acquiring response data of the student to the target learning content, evaluating the learning effect of the student based on the response data, and adjusting the target learning content based on an evaluation result.
In this embodiment, the learning data refers to the degree of mastery of different teaching contents and the degree of understanding of different teaching knowledge points by each student in the learning process.
In this embodiment, the wrong-subject knowledge points refer to knowledge points that the student cannot or cannot firmly grasp the teaching contents.
In this embodiment, the constraint condition refers to a requirement for screening the teaching content, and the teaching content corresponding to the constraint condition is screened out to filter out teaching content irrelevant to the wrong knowledge point.
In this embodiment, the target pushing framework is set in advance, and may be a pushing framework generated by an automatic encoder, and is used for pushing personalized learning content and a learning path to each student.
In this embodiment, the target learning content refers to teaching content corresponding to the wrong-question knowledge point of each student.
In this embodiment, the response data refers to a learning condition in the process of relearning the target learning content by each student, and may be, for example, a tandem ability, a confluent-through ability, or the like of the target learning content.
In this embodiment, evaluating the learning effect of the student based on the response data refers to timely knowing the relearning condition of the student on the target learning content, so as to facilitate mastering the relearning condition of the student on the teaching content corresponding to the wrong-subject knowledge point, thereby facilitating timely adjustment of the teaching scheme or plan.
The beneficial effects of the above technical scheme are: through gathering statistics to student's wrong question knowledge point, be convenient for in time carry out corresponding adjustment to every student's study content, realize carrying out the teaching of pertinence to every student, improve student's learning efficiency, the teaching scheme or the teaching strategy are in time adjusted according to student's learning effect to the teaching party of being convenient for simultaneously, promote teaching efficiency.
Example 2:
on the basis of the foregoing embodiment 1, this embodiment provides an adaptive stage learning system based on student wrong-question knowledge points, as shown in fig. 2, the summarizing module includes:
the student identification unit is used for acquiring identity information of students and determining the target grade of the students based on the identity information;
the subject determining unit is used for determining a target subject learned by the student based on the target grade and calling a historical learning condition of the target subject of the student from a preset database, wherein the target subject is at least one subject;
and the learning data acquisition unit is used for analyzing the historical learning condition of the student based on a preset analysis method to obtain the learning data of the objective subject of the student.
In this embodiment, the identity information refers to the name, age, etc. of the student.
In this embodiment, the target subject refers to a cultural subject that the student learns, and may be, for example, a language, a mathematics, an english language, a science, and the like.
In this embodiment, the preset database is set in advance and is used for storing the learning conditions of the students on the knowledge points of different subjects.
In this embodiment, the history learning situation refers to the degree of grasp of knowledge points involved in different subjects at the current learning stage.
In this embodiment, the preset analysis method is set in advance, and is used for determining the learning data of the student according to the learning condition of the student.
The beneficial effects of the above technical scheme are: by determining the identity information of the student, the subject of the student can be accurately locked, and the learning data of the student in different subjects can be conveniently determined according to the subject, so that convenience is provided for determining wrong knowledge points of the student, and a guarantee is provided for determining the learning content of the student.
Example 3:
on the basis of the foregoing embodiment 1, this embodiment provides a learning system based on student wrong-question knowledge points self-adaptive stage, and the summarizing module further includes:
the data identification unit is used for acquiring learning data of each student, determining a target test paper made by the student in each target subject based on the learning data, and determining a target error test question of the student with wrong answer in the target test paper;
the knowledge point determining unit is used for extracting the question stem of the target error test question and determining the knowledge point name of the target knowledge point corresponding to the question stem;
the knowledge point determining unit is further configured to determine a knowledge point label set associated with the target knowledge point based on the knowledge point name, and acquire an associated knowledge point from a preset knowledge point database based on the knowledge point label set;
and the knowledge point integration unit is used for integrating the target knowledge points and the associated knowledge points to obtain wrong-question knowledge points of each student.
In this embodiment, the target test paper refers to examination questions that the student makes when the student evaluates the learning effect of the target subject in the current learning stage.
In this embodiment, the target wrong test question refers to a test question that the student answers wrong in the target test paper, and is at least one.
In this embodiment, the target knowledge points refer to knowledge points that can be intuitively associated with the target error test questions, and the target error test questions at least include one or more different knowledge points.
In this embodiment, the knowledge point label set refers to the names of other knowledge points associated with the target knowledge point.
In this embodiment, the integration refers to counting the target knowledge points and the associated knowledge points, so as to ensure accurate counting of the knowledge points related to the target error test questions.
The beneficial effects of the above technical scheme are: through making statistics on wrong questions of different learning subjects of the student according to the learning data of the student, effective association is carried out on knowledge points related to the wrong questions according to a statistical result, accurate and effective locking of the wrong question knowledge points of the student is guaranteed, and convenience is provided for targeted teaching of each student.
Example 4:
on the basis of the above embodiment 3, this embodiment provides a learning system based on student wrong-question knowledge point adaptive stage, and the knowledge point integrating unit includes:
the identity acquisition subunit is used for determining the personal basic information of the student when a sorting instruction sent by the management terminal is received, and determining the identity label of the student based on the personal basic information, wherein the student is not unique;
the sorting subunit is used for determining the corresponding relation between the student and the target knowledge point and the associated knowledge point based on the identity label and creating a target mapping table based on the corresponding relation;
and the arrangement subunit is used for filling the personal basic information of the student, the corresponding target knowledge point and the associated knowledge point into a target preset area in the target mapping table to finish the arrangement of the student wrong-question knowledge points.
In this embodiment, the personal basic information refers to the name, facial features, the grade of the student, and the like.
In this embodiment, the identity tag refers to a kind of mark symbol used to distinguish the identity of different students.
In this embodiment, the target mapping table is a data table in which the basic information of the student, the target knowledge point and the associated knowledge point are recorded in order according to the correspondence.
In this embodiment, the target preset area refers to a storage area set in the target mapping table in advance for the student identity information, the target knowledge point and the associated knowledge point, and may be, for example, a column in the target mapping table corresponding to the student identity information, the target knowledge point and the associated knowledge point respectively.
The beneficial effects of the above technical scheme are: through confirming student's individual basic information, be convenient for accurately judge the corresponding relation of different students and corresponding target knowledge point and associated knowledge point to for accurately generating the relation record table provide convenience, also be convenient for simultaneously the teaching party teaches different student's factors according to the mastery condition of different students to the knowledge point, improved student and teaching party's work efficiency.
Example 5:
on the basis of the above embodiment 1, the present embodiment provides a learning system based on student wrong-question knowledge point adaptive stage, the knowledge point determining unit includes:
the analysis subunit is used for acquiring target knowledge points and associated knowledge points corresponding to the target error test questions and extracting test question contents of the target error test questions;
the analysis subunit is further configured to analyze the target knowledge point and the associated knowledge points based on the test question content to obtain a first analysis parameter, and meanwhile, extract test question difficulty level evaluation data corresponding to the target wrong test question from a preset test question evaluation database;
the difficulty coefficient determining subunit is configured to determine, based on the test question difficulty degree evaluation data, weight values of the target knowledge point and the associated knowledge point in the target error test question, and obtain a second analysis parameter of the target knowledge point and the associated knowledge point based on the weight values;
and the difficulty coefficient determining subunit is further configured to obtain difficulty coefficients of the target knowledge point and the associated knowledge point based on the first analysis parameter and the second analysis parameter.
In this embodiment, the first resolution parameter refers to the difficulty level represented by the target knowledge point and the associated knowledge point itself.
In this embodiment, the preset test question evaluation database is set in advance and is used for determining the difficulty level of different test questions in the test paper.
In this embodiment, the test question difficulty level evaluation data refers to a criterion for evaluating the difficulty level of the test question, such as the number of the series knowledge points in the test question and the common frequency of different knowledge points.
In this embodiment, the second analysis parameter refers to the importance degree of the target knowledge point and the associated knowledge point in answering the target error question, the difficulty degree of the student in thinking about the target knowledge point and the associated knowledge point when answering the target error question, and the like.
In this embodiment, the difficulty coefficient of the target knowledge point and the associated knowledge point obtained based on the first analysis parameter and the second analysis parameter refers to the difficulty of the student in mastering the target knowledge point and the associated knowledge point, which is obtained by comprehensively calculating the difficulty of the target knowledge point and the associated knowledge point at different angles.
The beneficial effects of the above technical scheme are: by determining the difficulty degree of the independent target knowledge points and the independent associated knowledge points and the difficulty degree of the associated knowledge points which can be conjectured by students after the students combine with test questions, the difficulty coefficients of the target knowledge points and the associated knowledge points are accurately judged, a teaching party can adjust a teaching scheme timely according to the mastering conditions of the students, and meanwhile, the learning efficiency of the students is improved.
Example 6:
on the basis of the foregoing embodiment 1, this embodiment provides an adaptive stage learning system based on student wrong-question knowledge points, as shown in fig. 3, the pushing module includes:
the framework construction unit is used for acquiring the wrong question knowledge points and inputting the wrong question knowledge points into a Bayesian network model for structure training to obtain a wrong question knowledge point system structure;
the frame construction unit is further used for constructing a first pushing frame through a preset encoder according to a collaborative filtering algorithm, inputting the wrong knowledge point system structure as a constraint condition into the first pushing frame for training, and obtaining a second pushing frame;
the frame construction unit is further used for acquiring the identity label of the student, inputting the identity label of the student as a pushing main body into the second pushing frame for training, and obtaining a target pushing frame;
the system comprises a preparation unit, a learning unit and a learning unit, wherein the preparation unit is used for extracting a knowledge point set corresponding to the current learning stage of a student from a preset knowledge base and determining at least one meta knowledge point in the knowledge point set based on a courseware learning sequence of the student;
the analysis unit is used for extracting word vectors of the meta-knowledge points and determining the content characteristics of the meta-knowledge points based on the word vectors;
the analysis unit is further used for determining a basic knowledge point which has a dependency relationship with the meta knowledge point from the knowledge point set based on the content characteristics of the meta knowledge point, and determining the dependency degree and the path length of the basic knowledge point and the meta knowledge point;
the map construction unit is used for constructing a knowledge map between the meta knowledge point and the basic knowledge point based on the dependency and the path length, and determining a preorder associated knowledge point and a postorder associated knowledge point of the meta knowledge point based on the knowledge map;
the screening unit is used for screening the preorder associated knowledge points and the subsequent associated knowledge points of the meta knowledge points based on the wrong-question knowledge point system structure in the target pushing frame to obtain target learning content of each student;
a pushing unit, configured to push the target learning content to a student corresponding to an identity tag of the student based on the target pushing framework.
In this embodiment, the wrong-question knowledge point architecture is used to represent the association relationship between wrong-question knowledge points.
In this embodiment, the collaborative filtering algorithm is a computer technology that discovers the preferences of the user by mining historical behavior data of the user, groups the user based on different preferences, and recommends corresponding preferred content.
In this embodiment, the constraint condition refers to that all knowledge points are screened by using the wrong-question knowledge points as the screening condition.
In this embodiment, the first push framework refers to a basic framework constructed by a collaborative filtering algorithm, and has no constraint condition and no push subject.
In this embodiment, the second push frame refers to a push frame obtained by training and adding constraint conditions to the basic frame, and may be used to filter the knowledge points.
In this embodiment, identity tags refer to a type of token used to tag the identity of different students.
In this embodiment, the preset knowledge base is set in advance, and all knowledge points of different stages of the student are stored in the preset knowledge base.
In this embodiment, the courseware learning sequence refers to the teaching courseware to which the student is involved in the current learning stage.
In this embodiment of the present invention,
in this embodiment, the meta knowledge point refers to the most basic knowledge point in the current learning stage, i.e. the teaching content that needs to be known first in the current stage.
In this embodiment, the word vector refers to the position and weight of the meta-knowledge point in the current learning stage.
In this embodiment, the content feature refers to the specific content of the meta knowledge point.
In this embodiment, the preamble associated knowledge points refer to teaching contents capable of deriving question knowledge points, for example, a right triangle has three sides, that is, an available pythagorean theorem can be derived.
In this embodiment, the subsequent associated knowledge points refer to teaching contents that can be derived through wrong knowledge points, for example, the right triangle knows values of two sides, and a specific value of the other side can be obtained through the pythagorean theorem.
The beneficial effects of the above technical scheme are: through the structural system for determining wrong-question knowledge points, the pushing frame is accurately and orderly built, and then, the knowledge map is constructed for all knowledge points related to the current learning stage of the student, so that all knowledge points related to the wrong-question knowledge points are accurately screened through the knowledge map, the accuracy of determining the target teaching content is improved, and the improvement of the learning efficiency of the student is guaranteed.
Example 7:
on the basis of the foregoing embodiment 1, this embodiment provides an adaptive stage learning system based on student wrong-question knowledge points, and the evaluation module includes:
the data acquisition unit is used for acquiring historical learning data and expected learning effect of the students, taking the historical learning data as training data, and inputting the expected learning effect as a training target into a neural network model for training to obtain a corresponding relation between the historical learning data and the expected learning effect;
the model building unit is used for building a learning effect evaluation model based on the corresponding relation and simultaneously acquiring operation data of the target learning content learned by the students, wherein the operation data comprises time management data, correct rate holding data and stable analysis data of the target learning content;
the evaluation unit is used for analyzing the operation data of the target learning content learned by the student based on the learning effect evaluation model to obtain a learning reference value of the student to the target teaching content, and meanwhile, counting target knowledge points related to the target teaching content to obtain a target knowledge point list;
the evaluation unit is further used for determining an average progress index and a skewness index of the student to a target knowledge point based on the learning reference value and a target knowledge point list, and determining the mastery degree of the student to the target knowledge point based on the average progress index and the skewness index;
the evaluation unit is used for obtaining the learning effect of the student on the target teaching content based on the mastery degree and comparing the learning effect with the expected learning effect;
if the learning effect meets the requirement of the preset learning effect, judging that the target teaching content is qualified to be pushed, and converting the target teaching content into a target teaching material for storage;
otherwise, judging that the target teaching content is unqualified to be pushed, acquiring classroom video data of the target teaching content learned by the student and the learning score of the student, and determining the learning ability of the student based on the classroom video data and the learning score of the student;
the adjusting unit is used for planning a learning path for the student to learn the target teaching content based on the learning ability of the student and pushing the planned learning path to the student;
the adjusting unit is further used for analyzing the learning effect of the students in the planned learning path in real time until the learning effect meets the requirement of the preset learning effect.
In this embodiment, the historical learning data refers to classroom learning data and question making data of a student in a past period of time.
In this embodiment, the expected learning effect means the application proficiency that the student is expected to achieve after relearning the target teaching content, and is set in advance.
In this embodiment, the operation data refers to the time taken by the student to learn the target teaching content, the degree of stability of the associated target teaching content when making questions, the grasp of the accuracy, and the like.
In this embodiment, the learning reference value is to measure how well the student grasps and uses the target teaching content.
In this embodiment, the target knowledge point refers to a knowledge point involved in the teaching content, that is, a knowledge point corresponding to a wrong question.
In this embodiment, the average progress index refers to a case where the student never has mastered the teaching to the present extent.
In this embodiment, the skewness index refers to the degree of the student having a skewness in the teaching content or having a defect in the application of some knowledge points.
In this embodiment, the learning ability refers to how fast the student accepts the knowledge points and exercises and grasps the knowledge points.
In this embodiment, the learning path refers to teaching contents that the student is interested in during learning or to knowledge points that need to be learned heavily.
The beneficial effects of the above technical scheme are: through the response data of acquireing the student to the target teaching content, realize carrying out accurate aassessment to the learning effect of student to the target teaching content, the learning effect that will obtain simultaneously compares with anticipated learning effect, realizes when the unsatisfied requirement of learning effect, in time adjusts teaching content or study path, provides convenience for improving student's learning efficiency, also is convenient for the teaching side of being convenient for simultaneously in time to know different student's study condition.
Example 8:
on the basis of the foregoing embodiment 7, this embodiment provides an adaptive stage learning system based on student wrong-question knowledge points, and the adjusting unit includes:
the data acquisition subunit is used for acquiring all knowledge points corresponding to the current learning stage of the student, acquiring a historical test paper of the student in the current learning stage, and determining a question making record of the student based on the historical test paper;
the learning path planning subunit is used for determining the distribution situation of learned knowledge points related to the students in the historical test paper in all the knowledge points based on the question making records, and determining the unlearned knowledge points in all the knowledge points based on the distribution situation;
the learning path planning subunit is further configured to determine, based on the theoretical mastery degree and the actual mastery degree of the learned knowledge points by the students, receptivity of different students to the knowledge points with different difficulty coefficients, and plan learning paths of the unlearned knowledge points by the different students based on the receptivity to obtain personalized learning paths corresponding to the different students.
In this embodiment, the recording of questions refers to how well the student grasps knowledge points related to each question in the test paper made in the current learning stage.
In this embodiment, the theoretical mastery degree refers to the mastery condition of the knowledge point that should be mastered by the student, and is obtained by repeatedly training and observing the learning conditions of different students in different stages.
In this embodiment, the personalized learning path refers to planning the teaching content of the student at the next stage according to the condition that the student accepts knowledge points with different difficulty coefficients, so as to ensure that the teaching content matches with the ability of the student to accept the knowledge points.
The beneficial effects of the above technical scheme are: the ability of the students to accept the knowledge points with different difficulty coefficients is accurately evaluated by determining the mastering conditions of the learned knowledge points by the students, and meanwhile, the teaching knowledge points in the next stage are conveniently planned according to the accepting ability of the students to the knowledge points, so that the learning content suitable for the students is made for different students, the learning efficiency of the students is improved, and the purpose of teaching according to the factors is facilitated.
Example 9:
on the basis of the foregoing embodiment 7, this embodiment provides an adaptive stage learning system based on student wrong-question knowledge points, and the adjusting unit includes:
the data analysis subunit is used for acquiring the application frequency of the students to different knowledge points in the current learning stage and determining the personal learning characteristics of the students based on the application frequency;
the association subunit is used for acquiring the learning effect of the student in the current learning stage and establishing an association model between the learning effect and the personal learning characteristics;
the prediction subunit is used for determining the trajectory information of the to-be-selected course of the next stage of the student and predicting the learning effect of the student on the to-be-selected course in the next stage based on the association model;
the prediction subunit is further configured to push the target to-be-selected course with the best learning effect in the to-be-selected courses in the next stage to the corresponding student.
In this embodiment, the individual learning characteristics refer to learning habits of different students and thinking ways of different knowledge points in the understanding process.
In this embodiment, the association model is constructed according to the correspondence between the individual learning characteristics and the learning effect, and is intended to predict the learning effect on the next stage of the learning course according to the learning characteristics of the student.
In this embodiment, the course track information to be selected refers to teaching contents that the student can select at the next stage.
In this embodiment, the target candidate course refers to a course with the best learning effect for different students in the next stage of learning.
The beneficial effects of the above technical scheme are: through individual study characteristics and the study effect according to different students predict the course study effect in next stage, be convenient for choose the most suitable course from the optional course in next stage, be convenient for accomplish to carry out the education according to the nature, also be convenient for improve student's learning efficiency simultaneously.
Example 10:
on the basis of the foregoing embodiment 1, this embodiment provides a learning system based on student wrong-question knowledge points adaptive stage, and the evaluation module further includes:
the learning efficiency analysis unit is used for acquiring the learning effect of different students on the target learning content in the same time period and comparing the learning effect with a preset learning effect, wherein the learning effect is represented based on examination scores, and the target learning content is not uniform;
the learning efficiency analysis unit is further used for determining the learning efficiency of different students on different target learning contents based on the comparison result, and classifying the learning efficiency of the students corresponding to the same target learning contents;
and the recording unit is used for recording the learning efficiency of different students in the same type of target learning content and adjusting the teaching scheme of the student of which the learning efficiency does not meet the preset learning efficiency based on the recording result.
In this embodiment, the preset learning effect is set in advance and is used for measuring the theoretical mastery degree of the target teaching content by the student.
In this embodiment, determining the learning efficiency of different students on different target learning contents includes:
acquiring the total number of wrong-question knowledge points contained in the target learning content, and determining the median of the total number of wrong-question knowledge points, wherein the number of wrong-question knowledge points is at least more than 2;
calculating the influence degree of the wrong question knowledge points on students based on the total number of the wrong question knowledge points and the median of the total number of the wrong question knowledge points, and calculating the attention degree of the students to the wrong question knowledge points based on the influence degree, wherein the method specifically comprises the following steps:
calculating the influence degree of the wrong question knowledge point on the student according to the following formula:
wherein alpha represents the influence degree of the error question knowledge point on students;
representing the difficulty coefficient of the wrong question knowledge point, wherein the value range is (0, 1); beta represents the total number of wrong question knowledge points; gamma represents the median of the total number of wrong-question knowledge points; a represents a constant, typically a value of 0.2; p represents the average application frequency of the students to the wrong question knowledge points; tau represents the median of the average application frequency of the students to the wrong-question knowledge points; b represents a constant, typically a value of 0.4;
calculating the attention of the student to the wrong knowledge points according to the following formula:
wherein eta represents the attention of the student to the wrong-question knowledge points; a represents the influence degree of the wrong question knowledge point on students; i represents the number of the current wrong question knowledge points in the wrong question knowledge points; beta represents the total number of wrong question knowledge points; omegaiExpressing the application frequency value of the student to the ith wrong question knowledge point; mu.siThe weight value of the ith wrong knowledge point is represented; gamma represents an error coefficient, and the value range is (0.02, 0.05);
comparing the calculated attention degree with a preset attention degree;
if the attention degree is larger than or equal to the preset attention degree, judging that the learning of the wrong-question knowledge points by the students is qualified;
otherwise, judging that the learning of the wrong question knowledge points by the students is unqualified, and extracting the attribute information of the wrong question knowledge points;
and adjusting the teaching scheme of the wrong-question knowledge points based on the attribute information, and calculating the attention of the students to the wrong-question knowledge points again based on the adjusted teaching scheme until the attention is greater than or equal to the preset attention.
The preset attention is set in advance and used for judging whether the attention of the students to wrong knowledge points meets the expected requirements or not.
The influence degree refers to the influence degree of wrong question knowledge points with different difficulty coefficients on the student in the mastering process.
The attention degree refers to the learning progress degree of the wrong knowledge points of the students
The beneficial effects of the above technical scheme are: by determining the learning efficiency of different students in the same time period and recording the learning efficiency of students with the same type of learning content, the students with low learning efficiency can be conveniently and timely checked, the teaching scheme and the learning path of the students with low learning efficiency can be timely adjusted, and the improvement of the learning efficiency of the students can be guaranteed.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A learning system based on student wrong-question knowledge points adaptive stage, comprising:
the collection module is used for acquiring learning data of each student, identifying the learning data and determining wrong knowledge points of each student;
the pushing module is used for filtering the teaching contents by taking the wrong-question knowledge points as constraint conditions to obtain target learning contents of each student, and pushing the target learning contents to the corresponding students based on a target pushing frame;
and the evaluation module is used for acquiring response data of the student to the target learning content, evaluating the learning effect of the student based on the response data, and adjusting the target learning content based on an evaluation result.
2. The system of claim 1, wherein the summarizing module comprises:
the student identification unit is used for acquiring identity information of students and determining the target grade of the students based on the identity information;
the subject determining unit is used for determining a target subject learned by the student based on the target grade and calling a historical learning condition of the target subject of the student from a preset database, wherein the target subject is at least one subject;
and the learning data acquisition unit is used for analyzing the historical learning condition of the student based on a preset analysis method to obtain the learning data of the objective subject of the student.
3. The system of claim 1, wherein the summarizing module further comprises:
the data identification unit is used for acquiring learning data of each student, determining a target test paper made by the student in each target subject based on the learning data, and determining a target error test question of the student with wrong answer in the target test paper;
the knowledge point determining unit is used for extracting the question stem of the target error test question and determining the knowledge point name of the target knowledge point corresponding to the question stem;
the knowledge point determining unit is further configured to determine a knowledge point label set associated with the target knowledge point based on the knowledge point name, and acquire an associated knowledge point from a preset knowledge point database based on the knowledge point label set;
and the knowledge point integration unit is used for integrating the target knowledge points and the associated knowledge points to obtain wrong-question knowledge points of each student.
4. The learning system of claim 3, wherein the knowledge point integration unit comprises:
the identity acquisition subunit is used for determining the personal basic information of the student when a sorting instruction sent by the management terminal is received, and determining the identity label of the student based on the personal basic information, wherein the student is not unique;
the sorting subunit is used for determining the corresponding relation between the student and the target knowledge point and the associated knowledge point based on the identity label and creating a target mapping table based on the corresponding relation;
and the arrangement subunit is used for filling the personal basic information of the student, the corresponding target knowledge point and the associated knowledge point into a target preset area in the target mapping table to finish the arrangement of the student wrong-question knowledge points.
5. The learning system of claim 3, wherein the knowledge point determination unit comprises:
the analysis subunit is used for acquiring target knowledge points and associated knowledge points corresponding to the target error test questions and extracting test question contents of the target error test questions;
the analysis subunit is further configured to analyze the target knowledge point and the associated knowledge point based on the test question content to obtain a first analysis parameter, and extract test question difficulty level evaluation data corresponding to the target wrong test question from a preset test question evaluation database;
the difficulty coefficient determining subunit is configured to determine, based on the test question difficulty degree evaluation data, weight values of the target knowledge point and the associated knowledge point in the target error test question, and obtain a second analysis parameter of the target knowledge point and the associated knowledge point based on the weight values;
and the difficulty coefficient determining subunit is further configured to obtain difficulty coefficients of the target knowledge point and the associated knowledge point based on the first analysis parameter and the second analysis parameter.
6. The system of claim 1, wherein the pushing module comprises:
the framework construction unit is used for acquiring the wrong-question knowledge points and inputting the wrong-question knowledge points into a Bayesian network model for structure training to obtain a wrong-question knowledge point system structure;
the frame construction unit is further used for constructing a first pushing frame through a preset encoder according to a collaborative filtering algorithm, inputting the wrong knowledge point system structure as a constraint condition into the first pushing frame for training, and obtaining a second pushing frame;
the frame construction unit is further used for acquiring the identity labels of the students and inputting the identity labels of the students as pushing subjects into the second pushing frame for training to obtain a target pushing frame;
the system comprises a preparation unit, a learning unit and a learning unit, wherein the preparation unit is used for extracting a knowledge point set corresponding to the current learning stage of a student from a preset knowledge base and determining at least one meta knowledge point in the knowledge point set based on a courseware learning sequence of the student;
the analysis unit is used for extracting word vectors of the meta-knowledge points and determining the content characteristics of the meta-knowledge points based on the word vectors;
the analysis unit is further used for determining a basic knowledge point which has a dependency relationship with the meta knowledge point from the knowledge point set based on the content characteristics of the meta knowledge point, and determining the dependency degree and the path length of the basic knowledge point and the meta knowledge point;
the map construction unit is used for constructing a knowledge map between the meta knowledge point and the basic knowledge point based on the dependency and the path length, and determining a preorder associated knowledge point and a postorder associated knowledge point of the meta knowledge point based on the knowledge map;
the screening unit is used for screening the preorder associated knowledge points and the subsequent associated knowledge points of the meta knowledge points based on the wrong-question knowledge point system structure in the target pushing frame to obtain target learning content of each student;
a pushing unit, configured to push the target learning content to a student corresponding to an identity tag of the student based on the target pushing framework.
7. The system of claim 1, wherein the evaluation module comprises:
the data acquisition unit is used for acquiring historical learning data and expected learning effect of the students, taking the historical learning data as training data, and inputting the expected learning effect as a training target into a neural network model for training to obtain a corresponding relation between the historical learning data and the expected learning effect;
the model building unit is used for building a learning effect evaluation model based on the corresponding relation and acquiring operation data of the target learning content learned by the students, wherein the operation data comprises time management data, correct rate holding data and stable analysis data of the target learning content;
the evaluation unit is used for analyzing the operation data of the target learning content learned by the student based on the learning effect evaluation model to obtain a learning reference value of the student to the target teaching content, and meanwhile, counting target knowledge points related to the target teaching content to obtain a target knowledge point list;
the evaluation unit is further used for determining an average progress index and a skewness index of the student to a target knowledge point based on the learning reference value and a target knowledge point list, and determining the mastery degree of the student to the target knowledge point based on the average progress index and the skewness index;
the evaluation unit is used for obtaining the learning effect of the student on the target teaching content based on the mastery degree and comparing the learning effect with the expected learning effect;
if the learning effect meets the requirement of the preset learning effect, judging that the target teaching content is qualified to be pushed, and converting the target teaching content into a target teaching material for storage;
otherwise, judging that the target teaching content is unqualified to be pushed, acquiring classroom video data of the target teaching content learned by the student and the learning score of the student, and determining the learning ability of the student based on the classroom video data and the learning score of the student;
the adjusting unit is used for planning a learning path for the student to learn the target teaching content based on the learning ability of the student and pushing the planned learning path to the student;
the adjusting unit is further used for analyzing the learning effect of the students in the planned learning path in real time until the learning effect meets the requirement of the preset learning effect.
8. The system of claim 7, wherein the adjusting unit comprises:
the data acquisition subunit is used for acquiring all knowledge points corresponding to the current learning stage of the student, acquiring a historical test paper of the student in the current learning stage, and determining a question making record of the student based on the historical test paper;
the learning path planning subunit is used for determining the distribution situation of learned knowledge points related to the students in the historical test paper in all the knowledge points based on the question making records, and determining the unlearned knowledge points in all the knowledge points based on the distribution situation;
the learning path planning subunit is further configured to determine, based on the theoretical mastery degree and the actual mastery degree of the learned knowledge points by the students, receptivity of different students to the knowledge points with different difficulty coefficients, and plan learning paths of the unlearned knowledge points by the different students based on the receptivity to obtain personalized learning paths corresponding to the different students.
9. The system of claim 7, wherein the adjusting unit comprises:
the data analysis subunit is used for acquiring the application frequency of the students to different knowledge points in the current learning stage and determining the personal learning characteristics of the students based on the application frequency;
the association subunit is used for acquiring the learning effect of the student in the current learning stage and establishing an association model between the learning effect and the personal learning characteristics;
the prediction subunit is used for determining the trajectory information of the to-be-selected course of the next stage of the student and predicting the learning effect of the student on the to-be-selected course in the next stage based on the association model;
the prediction subunit is further configured to push the target to-be-selected course with the best learning effect in the to-be-selected courses in the next stage to the corresponding student.
10. The system of claim 1, wherein the evaluation module further comprises:
the learning efficiency analysis unit is used for acquiring the learning effect of different students on the target learning content in the same time period and comparing the learning effect with a preset learning effect, wherein the learning effect is represented based on examination scores, and the target learning content is not uniform;
the learning efficiency analysis unit is further used for determining the learning efficiency of different students on different target learning contents based on the comparison result, and classifying the learning efficiency of the students corresponding to the same target learning contents;
and the recording unit is used for recording the learning efficiency of different students in the same type of target learning content and adjusting the teaching scheme of the student of which the learning efficiency does not meet the preset learning efficiency based on the recording result.
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