Intelligent teaching auxiliary system and method based on behavior data and knowledge graph

Abstract

The invention discloses an intelligent teaching assistance system and method based on behavior data and a knowledge graph, and relates to the technical field of teaching assistance, and the method comprises the steps: evaluating the mastering degree of a student for different learned knowledge points, and obtaining knowledge mastering data; obtaining defect knowledge points from the knowledge mastering data, obtaining a knowledge graph of the student, and evaluating the learning influence degree of the knowledge points in the knowledge graph on learning of the defect knowledge points by the student; acquiring historical learning behavior records and historical knowledge mastering data of other students, and analyzing learning reference values of the other students to the student in combination with the historical learning behavior records and defect learning data of the student to obtain a target reference student; and acquiring historical learning data of the target reference student, generating teaching reference data of the student in combination with the defect learning data of the student, and pushing the teaching reference data to a course teacher of the student to assist the course teacher in teaching the student.

Description

Technical Field

[0001] The present invention relates to the field of teaching assistance technology, and specifically to an intelligent teaching assistance system and method based on behavior data and knowledge graphs. Background Art

[0002] Behavioral data refers to the data of various behaviors generated by students during the learning process. Knowledge graph is a structured knowledge representation method that organizes knowledge in the form of a graph. Therefore, with the continuous development of intelligent teaching, the requirements for teaching assistance systems are getting higher and higher. The application of behavioral data and knowledge graphs can solve various problems generated in teaching assistance systems. The application of behavioral data and knowledge graphs in teaching assistance systems includes but is not limited to the following benefits: 1. Personalized learning. By analyzing students' behavioral data, intelligent teaching assistance systems can identify students' learning characteristics. Knowledge graphs are conducive to understanding students' learning characteristics and needs, so as to dynamically adjust learning content and achieve personalized learning; 2. Optimization of teaching strategies. Teachers can use behavioral data to understand the learning progress of all students and adjust teaching strategies in a timely manner. The use of knowledge graphs can provide teachers with a global perspective on teaching and help teachers optimize teaching strategies.

[0003] In the traditional teaching assistance system, students can use Internet devices to independently learn various information related to the course, and teachers can also publish learning content for students through the teaching platform. However, this has its shortcomings. First of all, the number of students taught by the teacher is large, and the learning situation of each student is different. Even if two students get the same question wrong, the error in the question is the result, which cannot mean that the two have the same degree of mastery of the knowledge points involved in the question, and students of different levels have different degrees of absorption of the same teaching content. This will not only make it impossible for teachers to grasp the students' situation and formulate teaching plans, but also lead to a mismatch between the content of students' learning and their learning ability, resulting in no obvious improvement in students' grades after using the teaching assistance system, and even a decline in students' enthusiasm for learning and reduced learning initiative. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent teaching assistance system and method based on behavior data and knowledge graphs to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: an intelligent teaching assistance method based on behavior data and knowledge graph, the method comprising: Step S100: Obtain the student's historical learning behavior records, obtain historical learning behavior data from the historical learning behavior records, evaluate the student's mastery of different knowledge points learned, and obtain knowledge mastery data; Step S200: Obtain defective knowledge points from the knowledge mastery data, obtain the student's knowledge graph, evaluate the influence of the knowledge points in the knowledge graph on the student's learning of the defective knowledge points, and obtain defective learning data; Step S300: Obtaining the historical learning behavior records and historical knowledge mastery data of other students, and combining the historical learning behavior records and defective learning data of the student, analyzing the learning reference value of other students to the student, and obtaining the target reference student; Step S400: Obtain the historical learning data of the target reference student, and generate the student's teaching reference data in combination with the student's defective learning data, and push the teaching reference data to the student's teacher to assist the teacher in teaching the student.

[0006] Furthermore, step S100 includes: Step S101: monitoring and recording the learning behavior of students on the teaching platform to obtain historical learning behavior records of students, and extracting historical learning behavior data from the historical learning behavior records, wherein the historical learning behavior data includes several knowledge points involved in the learning behavior of students; Step S102: according to different knowledge points involved, a plurality of historical learning behavior records of the same knowledge point are collected in chronological order to obtain a historical record set of the knowledge point; Step S103: Evaluate the students' mastery of the learned knowledge points. The specific evaluation process is as follows: Obtain a historical record set of knowledge points, and form a time step of the time point according to each historical learning behavior record in the historical record set, wherein the total number of time steps t of the knowledge points in the current cycle is the same as the total number T of each historical learning behavior record, t=1, 2, ..., T; Step S104: setting the characteristic value a, setting the student's knowledge mastery variable of the knowledge point to L, and the result variable to Q, where a>0, L∈{0,a}, Q∈{0,a}; Get the preset initial probability b, initialize the probability of the mastery of the knowledge point p(L0=a)=b, p(L0=0)=1-b respectively, and in the prediction step, calculate the prior probability p(L0=a)=b of the student's mastery of the knowledge point in time step t. t =a|Q 1:t-1 ); Step S105: When the student's answer to the knowledge point in the historical learning behavior record corresponding to a certain time step is correct, the result variable Q=a, otherwise Q=0. In the update step, the result variable Q of the knowledge point in each time step is obtained, and the posterior probability p(L) of the student's mastery of the knowledge point in time step t is calculated. t =a|Q 1:t), use the posterior probability as the prior probability of the next time step; The prior probability of knowledge points is continuously updated to obtain the probability p(L T =1|Q 1:T ); Step S106: When the characteristic mastery probability of a knowledge point is greater than a preset mastery probability threshold, it is determined that the student has mastered the knowledge point; when the characteristic mastery probability is less than or equal to the mastery probability threshold, it is determined that the student has not mastered the knowledge point, and the knowledge point is recorded as a defective knowledge point of the student; The students' several deficient knowledge points and the characteristic mastery probabilities of several knowledge points in the current cycle are collected to obtain the students' knowledge mastery data.

[0007] Further, step S200 includes: Step S201: obtaining the student's knowledge mastery data, and obtaining a number of deficient knowledge points of the student from the knowledge mastery data; Step S202: Obtain the subject to which the student's deficient knowledge point belongs, obtain the knowledge graph of the subject, and evaluate the influence of the knowledge points in the knowledge graph on the student's learning of the deficient knowledge point. The specific process is as follows: Obtain upstream knowledge points that are directly or indirectly connected to the defect knowledge point from the knowledge graph and record them as the predecessor knowledge points of the defect knowledge point; When the characteristic mastering probability of the predecessor knowledge point is less than or equal to the mastering probability threshold, it is determined that the predecessor knowledge point has a learning influence on the students' mastering of the defective knowledge point, and the predecessor knowledge point is recorded as a defective predecessor knowledge point; Step S203: obtaining several defect precursor knowledge points of the defect knowledge point, sorting them from the most upstream defect precursor knowledge point according to their positions in the knowledge graph and their connection relationships, and aggregating the several defect precursor knowledge points, taking the defect knowledge point as the last element, to obtain a defect knowledge set of the defect knowledge point; Step S204: acquiring and aggregating defect knowledge sets of several defect knowledge points of the students to obtain defect learning data of the students; In the above steps, by analyzing the students' mastery of different knowledge points in the learning process, the defective knowledge points with mastery defects are obtained. Taking into account that the mastery of knowledge is not isolated, the knowledge graph is used to find several defective knowledge points related to the defective knowledge points, and collect them to obtain the defective knowledge set of the defective knowledge points. In this way, in the process of tutoring students on defective knowledge points, only the defective knowledge set is needed to quickly and accurately obtain the students' tutoring materials, so that students can master the defective knowledge points as quickly as possible.

[0008] Furthermore, step S300 includes: Step S301: Acquire other users who are studying on the teaching platform, and when the knowledge points learned by a certain other student in the historical period contain knowledge points that the student has already learned, retain the certain other student; Step S302: Obtain the retained historical learning behavior records of other students, obtain the student's most recent historical learning behavior record from the current period, and record it as the student's characteristic historical learning behavior record; Extract several knowledge points involved in learning from the characteristic historical learning behavior records, obtain the knowledge graph in the teaching platform, obtain the knowledge points at the bottom of the knowledge graph from the several knowledge points, and record them as the characteristic knowledge points of the students; Step S303: Obtain the learning stage of the characteristic knowledge points that other students first learned, and record it as the characteristic learning stage, remove the historical learning behavior records of other students after the characteristic learning stage, obtain the learning time and test scores of other students from the historical learning behavior records, normalize the learning time and test scores respectively, divide the test scores by the learning time, and obtain the marked learning efficiency of other students; Obtain the average of the characteristic learning efficiencies of several retained historical learning behavior records of other students, and record it as the characteristic learning efficiency u´ of other students, and obtain the characteristic learning efficiency u of the student; Step S304: obtaining the historical knowledge mastery data of other students in the feature learning stage, and obtaining the feature mastery probability of the defective knowledge points of other students from the historical knowledge mastery data; Obtain the total number of defective knowledge points of other students n, obtain the total number of defective knowledge points of the student m from the defective learning data, calculate the defect ratio coefficient r=m / max{n,m} between the student and other students, and obtain the approximate defect value of the student and other students in each defective knowledge point; Among them, the approximate value of the defect between the student and other students on the i-th defective knowledge point of the student is D i =p i / max{p´ i ,p i}, where p i is the probability of students mastering the features of the i-th defect knowledge point in the defect learning data, p´ i is the probability of mastering the features of the i-th defective knowledge point in the historical knowledge mastery data of other students, and the average value e of the defect approximation of the student and other students on each defective knowledge point of the student is obtained; Step S305: Obtain the approximate characteristic learning efficiency of other students and the student f = u / max{u,u´}, and calculate the learning reference value of other students to the student H = f × r × e; When the learning reference value H is greater than a preset threshold, it is determined that other students have reference value to the student's learning, and the other students are recorded as the student's target reference students.

[0009] Furthermore, step S400 includes: Step S401: Obtain each target reference student of the student, and from each target reference student, obtain and retain the target reference students whose learning ranking in the current cycle is in the top v% of their grade, where v is a preset value; Step S402: Acquire historical learning data of the target reference student, where the historical learning data includes learning resources of the target reference student in a historical period; Obtaining a defective knowledge set of defective knowledge points in the defective learning data of the student, obtaining learning materials involving defective knowledge points in the defective knowledge set from several target reference students' historical learning data, and using them as teaching reference data for the defective knowledge points in the defective learning data; The students' defective learning data and teaching reference data are sent to the students' teachers on the teaching platform to assist the teachers in teaching the students.

[0010] In order to better implement the above method, an intelligent teaching assistance system based on behavioral data and knowledge graph is also proposed. The system includes a knowledge mastery evaluation module, a defect learning data module, a reference value analysis module, and a teaching assistance module; The knowledge mastery assessment module is used to obtain students' historical learning behavior records, assess students' mastery of different knowledge points they have learned, and obtain knowledge mastery data; The defective learning data module is used to obtain the students' knowledge graph, evaluate the influence of the knowledge points in the knowledge graph on the students' learning of defective knowledge points, and obtain defective learning data; The reference value analysis module is used to analyze the learning reference value of other students to students and obtain the target reference students; The teaching assistance module is used to generate teaching reference data for students, push the teaching reference data to the students' teachers, and assist the teachers in teaching the students.

[0011] Further, the knowledge mastery assessment module includes a feature mastery probability unit and a knowledge mastery assessment unit; The feature mastery probability unit is used to obtain the probability of students mastering the features of knowledge points in the current cycle; The knowledge mastery evaluation unit is used to evaluate the students' mastery of different knowledge points they have learned based on the feature mastery probability and obtain the students' knowledge mastery data.

[0012] Further, the defect learning data module includes a defect knowledge set unit and a defect learning data unit; The defect knowledge set unit is used to obtain several defect precursor knowledge points of the defect knowledge point, and according to the positions of the several defect precursor knowledge points in the knowledge graph and the connection relationship, the several defect precursor knowledge points are collected to obtain a defect knowledge set of the defect knowledge point; The defect learning data unit is used to acquire and aggregate the defect knowledge sets of several defect knowledge points of the students to obtain the students' defect learning data.

[0013] Further, the reference value analysis module includes a learning reference value unit and a reference value analysis unit; Learning reference value unit, used to calculate the learning reference value of other students to students; The reference value analysis unit is used to analyze the learning reference value of other students to students based on the learning reference value, and obtain the target reference students.

[0014] Further, the teaching auxiliary module includes a teaching reference data unit and a teaching auxiliary unit; The teaching reference data unit is used to obtain a defect knowledge set of defect knowledge points in the defect learning data of the student, obtain learning materials involving defect knowledge points in the defect knowledge set from the historical learning data of several target reference students, and use them as teaching reference data for the defect knowledge points in the defect learning data; The teaching auxiliary unit is used to send students' defective learning data and teaching reference data to the students' teachers on the teaching platform to assist the teachers in teaching the students.

[0015] Compared with the prior art, the beneficial effects of the present invention are: the present invention realizes intelligent teaching assistance for teachers. First, a defective knowledge set of defective knowledge points is obtained according to students' historical learning behavior and knowledge graph. In the teaching process, students only need to study the defective knowledge set to quickly master the defective knowledge points. In addition, other students considered in the historical learning process have reference value for current students. To obtain the target reference students, it is only necessary to generate the teaching reference data of the students based on the historical learning materials of the target students, thereby supporting teachers to teach, improve students' enthusiasm for learning, and effectively improve students' grades. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1It is a method flow chart of the intelligent teaching assistance method based on behavior data and knowledge graph of the present invention; Figure 2 It is a module schematic diagram of the intelligent teaching assistance system based on behavior data and knowledge graph of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution, an intelligent teaching assistance method based on behavior data and knowledge graph, the method comprising: Step S100: Obtain the student's historical learning behavior records, obtain historical learning behavior data from the historical learning behavior records, evaluate the student's mastery of different knowledge points learned, and obtain knowledge mastery data; Wherein, step S100 includes: Step S101: monitoring and recording the learning behavior of students on the teaching platform to obtain historical learning behavior records of students, and extracting historical learning behavior data from the historical learning behavior records, wherein the historical learning behavior data includes several knowledge points involved in the learning behavior of students; Step S102: according to different knowledge points involved, a plurality of historical learning behavior records of the same knowledge point are collected in chronological order to obtain a historical record set of the knowledge point; Step S103: Evaluate the students' mastery of the learned knowledge points. The specific evaluation process is as follows: Obtain a historical record set of knowledge points, and form a time step of the time point according to each historical learning behavior record in the historical record set, wherein the total number of time steps t of the knowledge points in the current cycle is the same as the total number T of each historical learning behavior record, t=1, 2, ..., T; For example, time steps are arranged in order to form a sequence, and the update of each time step depends on the state of the previous time step and the current answer; Step S104: setting the characteristic value a, setting the student's knowledge mastery variable of the knowledge point to L, and the result variable to Q, where a>0, L∈{0,a}, Q∈{0,a}; For example, in actual calculations, a=1. t= 1, indicating that the student has mastered the knowledge point at time step t. t =0, it means that the student has not mastered the knowledge point at time step t; When Q t =1, it means that the student’s answer to the question corresponding to the knowledge point at time step t is correct. t =0, it means that the student’s answer to the question corresponding to the knowledge point at time step t is wrong; Get the preset initial probability b, initialize the probability of the mastery of the knowledge point p(L0=a)=b, p(L0=0)=1-b respectively, and in the prediction step, calculate the prior probability p(L0=a)=b of the student's mastery of the knowledge point in time step t. t =a|Q 1:t-1 ); For example, the prediction step is to predict the current state based on the previous day. In the prediction step, the transition probability needs to be substituted to calculate the prior probability p(L t =a|Q 1:t-1 ), therefore, the prior probability p(L t =a|Q 1:t-1 )=p(L t-1 =a|Q 1:t-1 )+p(T)·p(L t-1 =0|Q 1:t-1 ), where p(T) is the learning rate preset by the teaching platform according to the students, and p(L t-1 =a|Q 1:t-1 ) is the posterior probability of the student’s mastery of the knowledge point in time step t-1, p(L t-1 =0|Q 1:t-1 ) is the posterior probability that the student has not mastered the knowledge point in time step t-1; Step S105: When the student's answer to the knowledge point in the historical learning behavior record corresponding to a certain time step is correct, the result variable Q=a, otherwise Q=0. In the update step, the result variable Q of the knowledge point in each time step is obtained, and the posterior probability p(L) of the student's mastery of the knowledge point in time step t is calculated. t =a|Q 1:t ), use the posterior probability as the prior probability of the next time step; For example, in the update step, p(L t =a|Q 1:t ): , Among them, p(Q t |Q 1:t-1 )=p(Q t |L t =a)·p(L t=a|Q 1:t-1 )+p(Q t |L t =0)·p(L t =0|Q 1:t-1 ); p(Q t |L t =a) is the posterior probability of correctly answering the question when the student has mastered the knowledge point at time step t; p(Q t |L t =0) is the posterior probability of correctly answering the question when the student has not mastered the knowledge point at time step t; For example, when actually calculating the posterior probability through recursion, it is necessary to make corrections based on the current answer results; The prior probability of knowledge points is continuously updated to obtain the probability p(L T =1|Q 1:T ); Step S106: When the characteristic mastery probability of a knowledge point is greater than a preset mastery probability threshold, it is determined that the student has mastered the knowledge point; when the characteristic mastery probability is less than or equal to the mastery probability threshold, it is determined that the student has not mastered the knowledge point, and the knowledge point is recorded as a defective knowledge point of the student; The students' knowledge mastery data is obtained by collecting the probabilities of mastering several deficient knowledge points and characteristic mastery of several knowledge points in the current cycle; Step S200: Obtain defective knowledge points from the knowledge mastery data, obtain the student's knowledge graph, evaluate the influence of the knowledge points in the knowledge graph on the student's learning of the defective knowledge points, and obtain defective learning data; For example, a student's knowledge map refers to a structured representation of the student's mastery of a certain subject. It displays the knowledge points that the student has mastered, the knowledge points that the student has not mastered, and the relationship between the knowledge points in the form of a graph. Taking junior high school mathematics as an example, the student's knowledge map may include the following: Nodes (knowledge points): Numbers and algebra include integers, fractions, decimals, algebraic expressions, etc.; geometry includes points, lines, surfaces, angles, triangles, etc.; Edge (connection between knowledge points): "Algebraic expression" is the upstream knowledge point of "equation". They are connected by edges, and "algebraic expression" is the basis for learning "equation"; Wherein, step S200 includes: Step S201: obtaining the student's knowledge mastery data, and obtaining a number of deficient knowledge points of the student from the knowledge mastery data; Step S202: Obtain the subject to which the student's deficient knowledge point belongs, obtain the knowledge graph of the subject, and evaluate the influence of the knowledge points in the knowledge graph on the student's learning of the deficient knowledge point. The specific process is as follows: For example, subjects include mathematics, English, biology, etc.; Obtain upstream knowledge points that are directly or indirectly connected to the defect knowledge point from the knowledge graph and record them as the predecessor knowledge points of the defect knowledge point; For example, in the knowledge graph, "multiplication" is the upstream knowledge point of "factorization"; When the characteristic mastering probability of the predecessor knowledge point is less than or equal to the mastering probability threshold, it is determined that the predecessor knowledge point has a learning influence on the students' mastering of the defective knowledge point, and the predecessor knowledge point is recorded as a defective predecessor knowledge point; Step S203: obtaining several defect precursor knowledge points of the defect knowledge point, sorting them from the most upstream defect precursor knowledge point according to their positions in the knowledge graph and their connection relationships, and aggregating the several defect precursor knowledge points, taking the defect knowledge point as the last element, to obtain a defect knowledge set of the defect knowledge point; Step S204: acquiring and aggregating defect knowledge sets of several defect knowledge points of the students to obtain defect learning data of the students; Step S300: Obtaining the historical learning behavior records and historical knowledge mastery data of other students, and combining the historical learning behavior records and defective learning data of the student, analyzing the learning reference value of other students to the student, and obtaining the target reference student; Wherein, step S300 includes: Step S301: Acquire other users who are studying on the teaching platform, and when the knowledge points learned by a certain other student in the historical period contain knowledge points that the student has already learned, retain the certain other student; Step S302: Obtain the retained historical learning behavior records of other students, obtain the student's most recent historical learning behavior record from the current period, and record it as the student's characteristic historical learning behavior record; Extract several knowledge points involved in learning from the characteristic historical learning behavior records, obtain the knowledge graph in the teaching platform, obtain the knowledge points at the bottom of the knowledge graph from the several knowledge points, and record them as the characteristic knowledge points of the students; Step S303: Obtain the learning stage of the characteristic knowledge points that other students first learned, and record it as the characteristic learning stage, remove the historical learning behavior records of other students after the characteristic learning stage, obtain the learning time and test scores of other students from the historical learning behavior records, normalize the learning time and test scores respectively, divide the test scores by the learning time, and obtain the marked learning efficiency of other students; For example, this learning stage includes the first semester of the first year of junior high school and the second semester of the second year of junior high school; Obtain the average of the characteristic learning efficiencies of several retained historical learning behavior records of other students, and record it as the characteristic learning efficiency u´ of other students, and obtain the characteristic learning efficiency u of the student; Step S304: obtaining the historical knowledge mastery data of other students in the feature learning stage, and obtaining the feature mastery probability of the defective knowledge points of other students from the historical knowledge mastery data; Obtain the total number of defective knowledge points of other students n, obtain the total number of defective knowledge points of the student m from the defective learning data, calculate the defect ratio coefficient r=m / max{n,m} between the student and other students, and obtain the approximate defect value of the student and other students in each defective knowledge point; Among them, the approximate value of the defect between the student and other students on the i-th defective knowledge point of the student is D i =p i / max{p´ i ,p i}, where p i is the probability of students mastering the features of the i-th defect knowledge point in the defect learning data, p´ i is the probability of mastering the features of the i-th defective knowledge point in the historical knowledge mastery data of other students, and the average value e of the defect approximation of the student and other students on each defective knowledge point of the student is obtained; Step S305: Obtain the approximate characteristic learning efficiency of other students and the student f = u / max{u,u´}, and calculate the learning reference value of other students to the student H = f × r × e; When the learning reference value H is greater than the preset threshold, it is determined that other students have reference value to the student's learning, and the other students are recorded as the student's target reference students; Step S400: Obtain the historical learning data of the target reference student, and generate the student's teaching reference data in combination with the student's defective learning data, and push the teaching reference data to the student's teacher to assist the teacher in teaching the student; Wherein, step S400 includes: Step S401: Obtain each target reference student of the student, and from each target reference student, obtain and retain the target reference students whose learning ranking in the current cycle is in the top v% of their grade, where v is a preset value; Step S402: Acquire historical learning data of the target reference student, where the historical learning data includes learning resources of the target reference student in a historical period; Obtaining a defective knowledge set of defective knowledge points in the defective learning data of the student, obtaining learning materials involving defective knowledge points in the defective knowledge set from several target reference students' historical learning data, and using them as teaching reference data for the defective knowledge points in the defective learning data; Send students’ defective learning data and teaching reference data to their teachers on the teaching platform to assist them in teaching students; In order to better implement the above method, an intelligent teaching assistance system based on behavioral data and knowledge graph is also proposed. The system includes a knowledge mastery evaluation module, a defect learning data module, a reference value analysis module, and a teaching assistance module; The knowledge mastery assessment module is used to obtain students' historical learning behavior records, assess students' mastery of different knowledge points they have learned, and obtain knowledge mastery data; The defective learning data module is used to obtain the students' knowledge graph, evaluate the influence of the knowledge points in the knowledge graph on the students' learning of defective knowledge points, and obtain defective learning data; The reference value analysis module is used to analyze the learning reference value of other students to students and obtain the target reference students; The teaching assistance module is used to generate teaching reference data for students, push the teaching reference data to the students' teachers, and assist the teachers in teaching the students; Among them, the knowledge mastery assessment module includes a feature mastery probability unit and a knowledge mastery assessment unit; The feature mastery probability unit is used to obtain the probability of students mastering the features of knowledge points in the current cycle; The knowledge mastery evaluation unit is used to evaluate the students' mastery of different knowledge points learned based on the feature mastery probability and obtain the students' knowledge mastery data; Among them, the defect learning data module includes a defect knowledge set unit and a defect learning data unit; The defect knowledge set unit is used to obtain several defect precursor knowledge points of the defect knowledge point, and according to the positions of the several defect precursor knowledge points in the knowledge graph and the connection relationship, the several defect precursor knowledge points are collected to obtain a defect knowledge set of the defect knowledge point; The defect learning data unit is used to obtain the defect knowledge sets of several defect knowledge points of the students and collect them to obtain the defect learning data of the students; Among them, the reference value analysis module includes a learning reference value unit and a reference value analysis unit; Learning reference value unit, used to calculate the learning reference value of other students to students; The reference value analysis unit is used to analyze the learning reference value of other students to the student based on the learning reference value, and obtain the target reference student; Among them, the teaching auxiliary module includes a teaching reference data unit and a teaching auxiliary unit; The teaching reference data unit is used to obtain a defect knowledge set of defect knowledge points in the defect learning data of the student, obtain learning materials involving defect knowledge points in the defect knowledge set from the historical learning data of several target reference students, and use them as teaching reference data for the defect knowledge points in the defect learning data; The teaching auxiliary unit is used to send students' defective learning data and teaching reference data to the students' teachers on the teaching platform to assist the teachers in teaching the students.

[0019] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An intelligent teaching assistance method based on behavioral data and knowledge graph, characterized in that: The method comprises: Step S100: obtaining a student's historical learning behavior record, obtaining historical learning behavior data from the historical learning behavior record, evaluating the student's mastery of different knowledge points learned, and obtaining knowledge mastery data; Step S200: Obtaining defective knowledge points from the knowledge mastery data, obtaining the student's knowledge graph, evaluating the degree of influence of the knowledge points in the knowledge graph on the student's learning of the defective knowledge points, and obtaining defective learning data; Step S300: Obtaining the historical learning behavior records and historical knowledge mastery data of other students, and combining the historical learning behavior records and defective learning data of the student, analyzing the learning reference value of the other students to the student, and obtaining the target reference student; Step S400: Acquire the historical learning data of the target reference student, and generate the teaching reference data of the student in combination with the defective learning data of the student, and push the teaching reference data to the teacher of the student to assist the teacher in teaching the student.

2. The intelligent teaching assistance method based on behavior data and knowledge graph according to claim 1 is characterized in that: The step S100 includes: Step S101: monitoring and recording the learning behavior of the student on the teaching platform to obtain the historical learning behavior record of the student, and extracting historical learning behavior data from the historical learning behavior record, wherein the historical learning behavior data includes several knowledge points involved in the learning behavior of the student; Step S102: according to different knowledge points involved, a plurality of historical learning behavior records of the same knowledge point are collected in chronological order to obtain a historical record set of the knowledge point; Step S103: Evaluate the student's mastery of the learned knowledge points. The specific evaluation process is as follows: Obtain a historical record set of the knowledge point, and form a time step of the time point according to each historical learning behavior record in the historical record set, wherein the total number of time steps t of the knowledge point in the current cycle is the same as the total number T of each historical learning behavior record, t=1, 2, ..., T; Step S104: setting a characteristic value a, setting the student's knowledge mastery variable of the knowledge point to L, and the result variable to Q, where a>0, L∈{0,a}, Q∈{0,a}; Obtain the preset initial probability b, initialize the probability of the mastery of the knowledge point p(L0=a)=b, p(L0=0)=1-b respectively, and in the prediction step, calculate the prior probability p(L0=a) of the student's mastery of the knowledge point within time step t. t =a|Q 1:t-1 ); Step S105: When the student's answer to the knowledge point in the historical learning behavior record corresponding to a certain time step is correct, the result variable Q=a, otherwise Q=0. In the update step, the result variable Q of the knowledge point in each time step is obtained, and the posterior probability p(L) of the student's mastery of the knowledge point in the time step t is calculated. t =a|Q 1:t ), taking the posterior probability as the prior probability of the next time step; The prior probability of the knowledge point is continuously updated to obtain the probability p(L T =1|Q 1:T ); Step S106: when the characteristic mastery probability of the knowledge point is greater than a preset mastery probability threshold, it is determined that the student has mastered the knowledge point; when the characteristic mastery probability is less than or equal to the mastery probability threshold, it is determined that the student has not mastered the knowledge point, and the knowledge point is recorded as a defective knowledge point of the student; The student's several deficient knowledge points and the characteristic mastering probabilities of several knowledge points in the current cycle are collected to obtain the student's knowledge mastering data.

3. The intelligent teaching assistance method based on behavior data and knowledge graph according to claim 2 is characterized in that: The step S200 includes: Step S201: obtaining the knowledge mastery data of the student, and obtaining a number of deficient knowledge points of the student from the knowledge mastery data; Step S202: Obtain the subject to which the student's deficient knowledge point belongs, obtain the knowledge graph of the subject, and evaluate the influence of the knowledge points in the knowledge graph on the student's learning of the deficient knowledge point. The specific process is as follows: Acquire upstream knowledge points directly or indirectly connected to the defect knowledge point from the knowledge graph, and record them as predecessor knowledge points of the defect knowledge point; When the characteristic mastering probability of the precursor knowledge point is less than or equal to the mastering probability threshold, it is determined that the precursor knowledge point has a learning influence on the student's mastering of the defective knowledge point, and the precursor knowledge point is recorded as a defective precursor knowledge point; Step S203: obtaining several defect precursor knowledge points of the defect knowledge point, sorting the several defect precursor knowledge points from the upstream defect precursor knowledge point according to their positions in the knowledge graph and their connection relationships, and aggregating the several defect precursor knowledge points, taking the defect knowledge point as the last element, to obtain a defect knowledge set of the defect knowledge point; Step S204: acquiring and aggregating defect knowledge sets of several defect knowledge points of the student to obtain defect learning data of the student.

4. The intelligent teaching assistance method based on behavior data and knowledge graph according to claim 3 is characterized in that: The step S300 includes: Step S301: Acquire other users who are studying on the teaching platform, and when the knowledge points studied by a certain other student in the historical period contain knowledge points that the student has already studied, retain the certain other student; Step S302: Obtain the retained historical learning behavior records of other students, obtain the historical learning behavior record of the student closest to the current period, and record it as the characteristic historical learning behavior record of the student; Extracting several knowledge points involved in learning from the characteristic historical learning behavior records, obtaining a knowledge graph in the teaching platform, and obtaining the knowledge point at the bottom of the knowledge graph from the several knowledge points, and recording them as the characteristic knowledge points of the student; Step S303: obtaining the learning stage when other students first learn the characteristic knowledge point, and recording it as the characteristic learning stage, removing the historical learning behavior records of the other students generated after the characteristic learning stage, obtaining the learning time and test scores of the other students from the historical learning behavior records, normalizing the learning time and test scores respectively, dividing the test scores by the learning time, and obtaining the marked learning efficiency of the other students; Obtain an average value of the characteristic learning efficiency of several retained historical learning behavior records of the other students, and record it as the characteristic learning efficiency u´ of the other students, and obtain the characteristic learning efficiency u of the student; Step S304: obtaining the historical knowledge mastery data of the other students in the feature learning stage, and obtaining the feature mastery probability of the defective knowledge points of the other students from the historical knowledge mastery data; Obtain the total number of defective knowledge points of the other students n, obtain the total number of defective knowledge points of the student m from the defective learning data, calculate the defect ratio coefficient r=m / max{n,m} between the student and the other students, and obtain the defect approximate value of the student and the other students on the defective knowledge points; Among them, the approximate value of the defect between the student and the other students on the i-th defective knowledge point of the student is D i =p i / max{p´ i ,p i }, where p i is the probability of the student mastering the features of the i-th defective knowledge point in the defective learning data, p´ i For the feature mastery probability of the i-th defective knowledge point in the historical knowledge mastery data of the other students, obtain the average value e of the defect approximation values ​​of the student and the other students on each defective knowledge point of the student; Step S305: obtaining an approximate characteristic learning efficiency of the other students and the student f=u / max{u,u´}, and calculating the learning reference value of the other students to the student H=f×r×e; When the learning reference value H is greater than a preset threshold, it is determined that the other students have reference value to the student's learning, and the other students are recorded as the student's target reference students.

5. The intelligent teaching assistance method based on behavior data and knowledge graph according to claim 4 is characterized in that: The step S400 includes: Step S401: Obtain each target reference student of the student, and from the target reference students, obtain and retain the target reference students whose learning ranking in the current cycle is in the top v% of their grade, where v is a preset value; Step S402: Acquire the historical learning data of the target reference student, wherein the historical learning data includes the learning resources of the target reference student in a historical period; Obtaining a defective knowledge set of defective knowledge points in the defective learning data of the student, and obtaining learning materials involving defective knowledge points in the defective knowledge set from historical learning data of several target reference students of the student, and using the learning materials as teaching reference data for the defective knowledge points in the defective learning data; The student's defective learning data and teaching reference data are sent to the student's teacher on the teaching platform to assist the teacher in teaching the student.

6. An intelligent teaching assistance system based on behavior data and knowledge graph, used to execute the intelligent teaching assistance method based on behavior data and knowledge graph as described in any one of claims 1 to 5, characterized in that: The system includes a knowledge mastery assessment module, a defect learning data module, a reference value analysis module, and a teaching assistance module; The knowledge mastery evaluation module is used to obtain the student's historical learning behavior records, evaluate the student's mastery of different knowledge points learned, and obtain knowledge mastery data; The defective learning data module is used to obtain the knowledge graph of the student, evaluate the influence of the knowledge points in the knowledge graph on the student's learning of the defective knowledge points, and obtain defective learning data; The reference value analysis module is used to analyze the learning reference value of the other students to the student to obtain the target reference student; The teaching auxiliary module is used to generate teaching reference data for the student, push the teaching reference data to the student's teacher, and assist the teacher in teaching the student.

7. The intelligent teaching assistance system based on behavior data and knowledge graph according to claim 6 is characterized in that: The knowledge mastery assessment module includes a feature mastery probability unit and a knowledge mastery assessment unit; The feature mastering probability unit is used to obtain the feature mastering probability of the student for the knowledge point in the current cycle; The knowledge mastery evaluation unit is used to evaluate the student's mastery of different knowledge points learned based on the feature mastery probability, and obtain the student's knowledge mastery data.

8. The intelligent teaching assistance system based on behavior data and knowledge graph according to claim 6, characterized in that: The defect learning data module includes a defect knowledge set unit and a defect learning data unit; The defect knowledge set unit is used to obtain several defect precursor knowledge points of the defect knowledge point, and according to the positions of the several defect precursor knowledge points in the knowledge graph and the connection relationship, the several defect precursor knowledge points are collected to obtain the defect knowledge set of the defect knowledge point; The defect learning data unit is used to acquire and aggregate defect knowledge sets of several defect knowledge points of the student to obtain the defect learning data of the student.

9. The intelligent teaching assistance system based on behavior data and knowledge graph according to claim 6, characterized in that: The reference value analysis module includes a learning reference value unit and a reference value analysis unit; The learning reference value unit is used to calculate the learning reference value of the other students to the student; The reference value analysis unit is used to analyze the learning reference values ​​of other students to the student based on the learning reference value to obtain a target reference student.

10. The intelligent teaching assistance system based on behavior data and knowledge graph according to claim 6, characterized in that: The teaching auxiliary module includes a teaching reference data unit and a teaching auxiliary unit; The teaching reference data unit is used to obtain a defect knowledge set of defect knowledge points in the defect learning data of the student, and to obtain learning materials involving defect knowledge points in the defect knowledge set from historical learning data of several target reference students of the student, and to use the learning materials as teaching reference data for the defect knowledge points in the defect learning data; The teaching auxiliary unit is used to send the student's defect learning data and teaching reference data to the student's teacher on the teaching platform to assist the teacher in teaching the student.

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