CN107665473B - Learning path planning method and device - Google Patents
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Abstract
The application provides a learning path planning method and a device, and the learning path planning method comprises the following steps: collecting question making records of students on each knowledge point; constructing a knowledge graph for the student to learn according to the question making records; acquiring the degree of mastery and the actual mastery degree of each knowledge point of each student according to the knowledge graph; and planning an individualized learning path which corresponds to each student and takes the knowledge point as a basic unit according to the degree to be mastered and the actual mastered degree. The method can plan the learning path by taking the knowledge points as the granularity, thereby ensuring the easy-to-difficult learning sequence of the students and more effectively improving the learning ability of the students. Furthermore, the individual requirements of students can be met.
Description
Technical Field
The application relates to the technical field of online education, in particular to a learning path planning method and device.
Background
With the popularization of the internet and the wide application of computer technology, the traditional education way is gradually changed to the online education way, so that a one-to-many education way is more deeply applied, wherein 'one' refers to an online education platform, and 'more' refers to users or students of the online education platform; such an online education model may result in a "one" corresponding to hundreds of thousands or even millions of users or students; due to the fact that the number of the learning people is large, how to plan the learning path by combining the learning conditions of the user is very important, and the improvement of the personal learning ability is very important. However, the existing online education platform generally recommends some subjects which are not mastered to strengthen the practice of the students according to the problem-making situation of the students, and the subjects which are not mastered are wrong, and the method generally uses the subjects as the recommendation granularity, and the recommendation granularity is coarse, which may cause the students not to make the recommended subjects all the time, not only wastes time, but also the learning ability of the students cannot be improved.
Disclosure of Invention
The present application is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, an object of the present application is to provide a learning path planning method, which can plan a learning path with knowledge points as granularity, thereby ensuring an easy-to-difficult learning sequence of students and more effectively improving the learning ability of students. Furthermore, different learning paths can be planned corresponding to different students, and the individual requirements of the students are met.
Another object of the present application is to provide a learned route planning apparatus.
In order to achieve the above object, an embodiment of the present application provides a learned path planning method, including: collecting question making records of students on each knowledge point; constructing a knowledge graph for the student to learn according to the question making records; acquiring the degree of mastery and the actual mastery degree of each knowledge point of each student according to the knowledge graph; and planning an individualized learning path which corresponds to each student and takes the knowledge point as a basic unit according to the degree to be mastered and the actual mastered degree.
According to the learning path planning method provided by the embodiment of the first aspect of the application, the learning path with the knowledge points as the basic units is planned, and the learning path can be planned by taking the knowledge points as the granularity, so that the learning sequence of students from easy to difficult is ensured, and the learning ability of the students is more effectively improved. Furthermore, the individual requirements of the students can be met by acquiring the degree of mastery and the actual mastery of each knowledge point by each student and planning the individual learning path.
In order to achieve the above object, a learned route planning apparatus according to an embodiment of the second aspect of the present application includes: the collection module is used for collecting question making records of students on each knowledge point; the construction module is used for constructing a knowledge graph learned by the student according to the question making records; the acquisition module is used for acquiring the degree of mastery and the actual mastery degree of each knowledge point of each student according to the knowledge graph; and the planning module is used for planning the personalized learning path which takes the knowledge point as the basic unit and corresponds to each student according to the degree to be mastered and the actual mastered degree.
The learning path planning device provided by the embodiment of the second aspect of the application can plan the learning path by planning the learning path with the knowledge points as the basic units and taking the knowledge points as the granularity, thereby ensuring the easy-to-difficult learning sequence of students and more effectively improving the learning ability of the students. Furthermore, the individual requirements of the students can be met by acquiring the degree of mastery and the actual mastery of each knowledge point by each student and planning the individual learning path.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
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The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart of a learned route planning method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of creating a knowledge graph for student learning according to the problem making records of students on each knowledge point in the embodiment of the present application;
FIG. 3 is a schematic flow chart of creating a knowledge graph for student learning according to a knowledge point mastery degree matrix in the embodiment of the present application;
FIG. 4 is a diagram of a knowledge graph of a specific example constructed according to a knowledge point mastery degree matrix of the specific example in the embodiment of the present application;
FIG. 5 is a schematic flow chart illustrating how each student should grasp each knowledge point and how much the student actually grasps each knowledge point according to the knowledge graph in the embodiment of the present application;
FIG. 6 is a schematic diagram of a knowledge-graph obtained by warping the knowledge-graph shown in FIG. 4;
fig. 7 is a schematic flow chart illustrating a process of planning an individualized learning path with knowledge points as basic units corresponding to each student according to the degree of mastery and the actual degree of mastery in the embodiment of the present application;
fig. 8 is a schematic structural diagram of a learned route planning apparatus according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of a learned route planning apparatus according to another embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar modules or modules having the same or similar functionality throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application. On the contrary, the embodiments of the application include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.
Fig. 1 is a schematic flowchart of a learned route planning method according to an embodiment of the present application.
As shown in fig. 1, the present embodiment includes the following steps:
s11: and collecting the problem making records of students on each knowledge point.
The question making records refer to records of questions related to knowledge points made by students in order to master the related knowledge points, or records of questions related to knowledge points made by students in examinations; for example, the records of exercise questions made by students during online learning, of course, the records of other questions related to knowledge points made by students may also be used, and the specific questions are not limited in this application.
S12: constructing a knowledge graph for the student to learn according to the question making records;
the knowledge map is used for indicating knowledge points learned by students, the sequence of the students when learning the knowledge points and the probability of the sequence.
Specifically, the knowledge graph may be composed of points and edges, where each point corresponds to a knowledge point, an edge is used to connect two points, and the direction of existence points indicate the precedence of the student when learning the knowledge point, and each edge has a corresponding weight to indicate the probability of the precedence.
The specific process of constructing the knowledge graph can be seen in the following examples.
S13: and acquiring the degree of mastery and the actual mastery degree of each knowledge point of each student according to the knowledge graph.
The specific process can be seen in the following examples.
S14: and planning an individualized learning path which corresponds to each student and takes the knowledge point as a basic unit according to the degree to be mastered and the actual mastered degree.
The learning path taking the knowledge points as the basic unit is used for indicating the knowledge points needing to be learned and the sequence of learning of the knowledge points.
In addition, different learning paths can be planned corresponding to different students, and therefore the learning path can be called an individualized learning path.
The specific process of planning the personalized learning path can be seen in the following embodiments.
In the embodiment, the learning path with the knowledge points as the basic units is planned, and the learning path can be planned by taking the knowledge points as the granularity, so that the easy-to-difficult learning sequence of students is ensured, and the learning ability of the students is more effectively improved. And planning an individualized learning path which corresponds to each student and takes the knowledge point as a basic unit according to the degree to be mastered and the actual mastered degree.
Fig. 2 is a schematic flow chart of creating a knowledge graph learned by students according to the question making records of the students on each knowledge point in the embodiment of the present application.
As shown in fig. 2, the construction process includes:
s21: and constructing a knowledge point mastery degree matrix according to the question making records of the students on each knowledge point.
The knowledge point mastery degree matrix is used for indicating the mastery degree of each knowledge point of each student.
Specifically, the mastery degree of each knowledge point by each student is calculated according to the question making record of each knowledge point by each student, and the mastery degree of each knowledge point by each student forms a knowledge point mastery degree matrix.
For example, the knowledge pointsEach element in the mastery degree matrix represents the mastery degree a of each knowledge point by each studentijAssuming that there are n students and m knowledge points, the knowledge point mastery degree matrix a can be expressed as:
wherein, aijRepresenting student SiFor knowledge point KjThe degree of mastery of (c).
When the mastery degree of each student on each knowledge point is calculated, corresponding to each student and each knowledge point, if the total number of the student on the question making records of the knowledge points is smaller than a first threshold value, the mastery degree of the student on the knowledge points is determined to be 0, otherwise, if the total number is larger than or equal to the first threshold value, the average score of the question making records is taken as the mastery degree of the student on the knowledge points.
Specifically, calculate aijTimely, judge student SiItem and knowledge point KjA related total, if said total is less than a first threshold, the student SiFor knowledge point KjThe mastery degree of (1) is 0; otherwise, the student SiWhat made and knowledge point KjThe average score of the related questions is used as student SiFor knowledge point KjDegree of mastery of aijWhen calculating the average score, the student S is calculated firstiWhat made and knowledge point KjScore of each question in question, student SiMaking a ratio of the score of each question to the standard score; recalculating student SiWhat made and knowledge point KjThe specific calculation method of the average of the score ratios of all relevant topics is shown as formula (1):
wherein, TijRepresenting student SiWhat made and knowledge point KjTotal number of all questions related, ScoretRepresenting student SiWhat made and knowledge point KjActual score, stdScore, for the relevant tth topictThe standard score of the tth topic is shown. Wherein the standard score of each topic is a known and definite value, such as 10 points or 15 points.
And after the mastery degree of each knowledge point by all students is calculated, a knowledge point mastery degree matrix can be obtained.
Further, it can be understood that, with the constant update of the data, if the student continuously learns to generate new question making records, the knowledge point mastering degree matrix can be updated according to the new question making records. And the specific updating time can be updated regularly or according to the occurrence of a preset event.
S22: and constructing a knowledge graph learned by the student according to the knowledge point mastering degree matrix.
The knowledge graph is a directed acyclic graph and is represented by G ═ V, E, wherein V represents a node in the graph and E represents a directed edge in the graph. The nodes in the graph represent knowledge points, two knowledge points with precedence order are connected by using a directed edge, the direction of the directed edge is that the prior knowledge point in the order points to the subsequent knowledge point in the order, for example, E (s, r) represents the prior knowledge point KsPoint to the posterior knowledge point KrOne directed edge of; the weight of each directed edge represents the probability that two knowledge points connected by the edge have precedence, namely the probability that a student learns a later knowledge point needs to learn the earlier knowledge point, and W is used for representing, for example W (s, r) represents a knowledge point KsAnd knowledge point KrThere is a sequential probability, i.e., the weight of the edge E (s, r).
Specifically, referring to fig. 3, the process of constructing the knowledge graph learned by the student according to the knowledge point mastery degree matrix includes:
s31: and determining nodes in the knowledge graph according to the knowledge points corresponding to the knowledge point mastery degree matrix.
The number of nodes in the knowledge graph is the same as the number of knowledge points corresponding to the knowledge point mastery degree matrix, for example, if the knowledge point mastery degree matrix has m knowledge points, the knowledge graph has m nodes, and each node corresponds to one knowledge point.
S32: and taking each node in the knowledge graph as a current node in sequence, and calculating the probability of the current node pointing to each other node in sequence according to the knowledge point mastering degree matrix.
When calculating the probability, calculating the credibility that the current node corresponding to each student points to each other node according to the knowledge point mastery degree matrix; and then taking the mean value of the credibility of the current nodes corresponding to all students pointing to each other node as the probability of the current nodes pointing to each other node.
Specifically, assuming that the current node is referred to as a first node and one other node is referred to as a second node, when calculating the probability that the first node points to the second node, the reliability that the first node corresponding to each student points to the second node is calculated first, and then the average of the reliabilities corresponding to all students is determined as the probability.
When the credibility that the first node corresponding to each student points to the second node is calculated, the credibility is determined according to the mastery degree of each student on the first node (knowledge point) and the mastery degree of each student on the second node (knowledge point). Specifically, corresponding to each student, a first mastery degree and a second mastery degree are obtained from the knowledge point mastery degree matrix, wherein the first mastery degree is the mastery degree of the student on the knowledge point corresponding to the current node, and the second mastery degree is the mastery degree on the knowledge point corresponding to each other node; calculating a difference value between the first mastery degree and the second mastery degree; and if the difference is larger than or equal to a second threshold value, setting the reliability to be 1, otherwise, setting the reliability to be 0.
For example: the current node (knowledge point) is KsOne other node is KrThen calculate the corresponding K of a certain studentsPoint of direction KrThe reliability is calculated by calculating the knowledge point K of the studentsDegree of mastery and knowledge point KrWhether the difference is greater than or equal to a second threshold value is judged, if so, the reliability is 1, otherwise, the difference is judged to be greater than or equal to the second threshold valueThe reliability is 0.
After the confidence degrees corresponding to each student are calculated, taking the mean value of the confidence degrees corresponding to all students as the probability, namely the weight of the directed edge between two knowledge points, wherein the weight calculation method of the directed edge between the two knowledge points is shown as the formula (2):
wherein W (s, r) represents a knowledge point KsAnd KrIn the order of "Ks、Kr"(i.e., K)sPoint of direction Kr) I.e. the weight of the edge E (s, r), n is the total number of students, f (i, s, r) is the knowledge point K according to each student pairsAnd KrThe confidence function of the current two knowledge point sequences is calculated, and the calculation method of f (i, s, r) is shown as a formula (3):
wherein, aisRepresenting student SiFor knowledge point KsDegree of mastery of, airRepresenting student SiFor knowledge point KrDegree of mastery of, TH2Representing a knowledge point KsAnd KrIn the order of "Ks、Kr"the value of the probability threshold, i.e. the second threshold, can be determined according to specific application or experimental result, for example, the value is 0.4, when the student SiFor knowledge point KsDegree of mastery and knowledge point KrWhen the difference between the mastery degrees of (1) is greater than the threshold value, the value of the reliability function is 1, otherwise, the value is 0.
S33: and judging whether a directed edge pointing to each other node by the current node needs to be constructed or not according to the probability, constructing the directed edge when needed, and taking the probability as the weight of the directed edge.
When the probability is greater than the third threshold, it may be determined that the above directed edge needs to be constructed.
The value of the third threshold is determined according to application requirements or experimental results, for example, the value is 0.7.
For example, the probability that the first node points to the second node is 0.8, and since the probability is greater than the third threshold value 0.7, a directed edge is constructed in which the first node points to the third node, and the weight of the directed edge is 0.8.
Since the nodes can be determined according to S31, the directed edges and the corresponding weights can be constructed according to S32-S33, and the knowledge graph is composed of the nodes and the directed edges with the weights, so that the knowledge graph can be constructed through S31-S33.
Specifically, if the value of the second threshold is 0.4 and the value of the third threshold is 0.7, the knowledge graph learned by the student on the right side of fig. 4 can be constructed according to the knowledge point mastery degree matrix on the left side of fig. 4. The specific construction process is as follows:
knowledge graph G ═ (V, E), and node set V ═ K in the graph1,K2,K3,K4And sequentially taking out nodes in the node set as a current node, and sequentially calculating the probability that the current node and other nodes have a previous sequence, wherein the probability is specifically as follows:
first, node K is taken out1As the current node, K is sequentially calculated1And K2,K3,K4The sequential probability is calculated to obtain that W (1,2) is 0.75, W (1,3) is 0, and W (1,4) is 0.75, and since the value of the third threshold is 0.7, the node K is1And K2And K4There is a directed edge in between, and the edge E (1,2) and the edge E (1,4) are added to the edge set, respectively, i.e., E ═ { E (1,2), E (1,4) };
node K is taken out again2As the current node, sequentially calculating the nodes K2And K1,K3,K4Calculating the probability of the order, wherein the probability of W (2,1) is 0, W (2,3) is 0, W (2,4) is 0, the weight of the third threshold is not exceeded, and the edges which do not meet the condition are added into the edge set;
node K is taken out again3As the current node, sequentially calculating the nodes K3And K1,K2,K4When there is a sequential probability, W (3,1) is 0, W (3,2) is 0.75, and W (3,4) is 1, the node K is calculated3And K2And K4There are directed edges in between, and the edge E (3,2) and the edge E (3,4) are added to the set of edges, respectively, i.e., E ═ { E (1,2), E (1,4), E (3,2), E (3,4) };
finally, take out node K4As the current node, sequentially calculating the nodes K4And K1,K2,K3Calculating the probability of the order, wherein the probability of W (4,1) is 0, W (4,2) is 0, W (4,3) is 0, the weight of the third threshold is not exceeded, and the edges which do not meet the condition are added into the edge set;
and finishing the probability calculation of the nodes with the sequence to obtain an edge set E ═ { E (1,2), E (1,4), E (3,2) and E (3,4) }, and connecting edges among the nodes according to the edge set to obtain the constructed knowledge graph.
Fig. 5 is a schematic flow chart illustrating how each student should grasp each knowledge point and how much each student actually grasps each knowledge point according to the knowledge graph in the embodiment of the present application.
As shown in fig. 5, includes:
s51: and regulating the knowledge graph to obtain a regulated knowledge graph, and calculating the mastery degree of each student to each knowledge point according to the regulated knowledge graph.
The method for regulating the knowledge graph comprises the following steps:
and taking the nodes with a plurality of precursor nodes in the knowledge graph as current nodes, normalizing the weight of the directed edges between the current nodes and each precursor node, and taking the knowledge graph after weight normalization as a normalized knowledge graph.
Specifically, normalization refers to dividing the weight before normalization by the sum of the weights of the directed edges between the current node and all the predecessor nodes of the current node. The specific rule method is shown as formula (4):
wherein N isorm (o, r) is the weight of the nth node to the nth node in the predecessor nodes of the nth node after warping, and W (o, r) represents the weight of the mth node to the nth node before warping; r isoRepresents the total number of the precursor nodes of the r-th node, and W (j, s) represents the weight before the regulation from the j-th node to the r-th node in the precursor nodes of the r-th node.
Taking a specific example as an example, the knowledge-graph shown in fig. 6 can be obtained by warping the knowledge-graph shown in fig. 4.
After the knowledge graph is normalized, the degree to be mastered can be calculated by adopting the following steps:
and corresponding to each node in the knowledge graph, if the node has precursor nodes, calculating the degree of mastery of each student on the knowledge point corresponding to the node according to the actual mastery degree of each student on the knowledge point corresponding to each precursor node of the node and the normalized weight of the directed edge between the node and each precursor node. In addition, if there is no predecessor node in a node, the degree of knowledge of the knowledge point corresponding to the node to be grasped can be set to a fixed value, for example, to 1.
During specific calculation, the mastery degree of the knowledge point corresponding to each node by the student to the precursor node of each node in the knowledge graph needs to be considered, so that the mastery degree of the knowledge point corresponding to each node is determined by the actual mastery degree of the knowledge point corresponding to the precursor node of the node; the degree of mastery of the knowledge point corresponding to each node is the product of the actual mastery degree of the knowledge points corresponding to all predecessor nodes of the node and the weight of the edge between each predecessor node and the node, and the specific calculation is as shown in formula (5):
wherein, Ability (i, r) represents student SiFor knowledge point KrThe degree of understanding of (a); r isoRepresents the total number of predecessor nodes of the r-th node, aioRepresentation studyRaw SiAnd the actual mastery degree of the knowledge point corresponding to the o-th node in the precursor nodes of the r-th node.
S52: and determining the mastering degree of each student on the knowledge point corresponding to each node in the knowledge map as the actual mastering degree of each student on each knowledge point.
For example, the elements in the first row of the knowledge point mastery degree matrix represent the mastery degree of the first student on each knowledge point, the elements in the second row of the knowledge point mastery degree matrix represent the mastery degree of the second student on each knowledge point, and so on.
Fig. 7 is a schematic flow chart illustrating a process of planning an individualized learning path with knowledge points as basic units corresponding to each student according to the degree of mastery and the actual degree of mastery in the embodiment of the present application.
As shown in fig. 7, the specific learned path planning method includes:
s71: and calculating the difference between the to-be-mastered degree and the actual mastered degree corresponding to each knowledge point corresponding to each student.
S72: and sequencing the knowledge points according to the difference value to obtain an individualized learning path which corresponds to the student and takes the knowledge points as a basic unit.
The knowledge points can be sorted according to the sequence of the difference values from large to small during sorting. While the order of knowledge points with the same difference may be random.
And (3) individually planning paths of knowledge points learned by students subsequently according to the degree of each knowledge point mastered by the students and the degree of each knowledge point mastered by the students, wherein if the difference value between the degree of each knowledge point mastered by a knowledge point and the actual degree of the knowledge point mastered by the students is larger, the knowledge point should be learned earlier, and when the learning path is planned, the probability calculation mode that each knowledge point should be learned earlier is as shown in formula (6):
Prec(i,r)＝Ability(i,r)-air (6)
wherein, Prec(i, r) represents student SiThe probability that the r-th node corresponds to a knowledge point should be learned first, and Ability (i, r) represents student SiDegree of knowledge point to be grasped, a, for the r-th nodeirRepresenting student SiThe degree of mastery of the corresponding knowledge point of the r-th node;
and after the probabilities of learning each knowledge point by the students are ranked from large to small, taking the knowledge point path corresponding to each probability as an individualized learning path planned for the students.
For example:
student SiFor knowledge point K1、K2、K3、K4Successively has an actual grasping degree ofi1＝0.8，ai2＝0.6，ai3＝0.5，ai40.3; (the value may be obtained from a knowledge point mastery level matrix, with the elements of each row in the knowledge point mastery level as the actual mastery level of each knowledge point for the corresponding student;
the student S is obtained by calculating the regularized knowledge graph in the formula (5) and the figure 6iThe mastery degree of each knowledge point is that the learning degree is 1 (i,1), 0.65 (i,2), 1 (i,3) and 0.629 (i, 4);
the student S can be obtained by calculation according to the formula (7)iThe probability that each knowledge point should be learned first is Prec(i,1)＝0.2，Prec(i,2)＝0.05，Prec(i,3)＝0.5，Prec(i,4)＝0.329；
Thus, for student SiThe planned personalized learning path is K3→K4→K1→K2。
Furthermore, after the personalized learning path is determined, the related questions of the corresponding knowledge points can be recommended to the corresponding students according to each knowledge point on the personalized learning path.
Fig. 8 is a schematic structural diagram of a learned route planning apparatus according to an embodiment of the present application.
As shown in fig. 8, the apparatus 80 includes: a collection module 81, a construction module 82, an acquisition module 83, and a planning module 84.
The collection module 81 is used for collecting question making records of students on each knowledge point;
the building module 82 is used for building a knowledge graph learned by the student according to the question making records;
the acquisition module 83 is configured to acquire the degree of mastery and the actual degree of mastery of each knowledge point by each student according to the knowledge graph;
and the planning module 84 is configured to plan an individualized learning path corresponding to each student and using the knowledge point as a basic unit according to the degree of mastery and the actual degree of mastery.
In some embodiments, referring to fig. 9, the obtaining module 83 includes:
a regularization submodule 831, configured to regularize the knowledge graph to obtain a regularized knowledge graph, and calculate a degree of mastery of each knowledge point by each student according to the regularized knowledge graph;
the determining submodule 832 is configured to determine a degree of mastery of each student on the knowledge point corresponding to each node in the knowledge graph as an actual degree of mastery of each knowledge point by each student.
In some embodiments, referring to fig. 9, the planning module 84 includes:
the calculation submodule 841 is used for calculating a difference value between the degree of mastery to be achieved and the actual degree of mastery corresponding to each knowledge point, corresponding to each student;
the sorting submodule 842 is configured to sort the knowledge points according to the difference, and obtain an individualized learning path corresponding to the student and using the knowledge points as a basic unit.
In some embodiments, the regularizing submodule is configured to regularize the knowledge-graph to obtain a regularized knowledge-graph, and includes:
and taking the nodes with a plurality of precursor nodes in the knowledge graph as current nodes, normalizing the weight of the directed edges between the current nodes and each precursor node, and taking the knowledge graph after weight normalization as a normalized knowledge graph.
In some embodiments, the warping submodule is configured to calculate a degree of mastery each student should have for each knowledge point according to the warped knowledge graph, and includes:
and corresponding to each node in the knowledge graph, if the node has precursor nodes, calculating the degree of mastery of each student on the knowledge point corresponding to the node according to the actual mastery degree of each student on the knowledge point corresponding to each precursor node of the node and the normalized weight of the directed edge between the node and each precursor node.
In some embodiments, referring to fig. 9, the building module 82 includes:
the first construction submodule 821 is used for constructing a knowledge point mastery degree matrix according to the question making records;
and the second construction submodule 822 is used for constructing a knowledge graph learned by the student according to the knowledge point mastery degree matrix.
In some embodiments, the first building submodule is specifically configured to: and calculating the mastery degree of each knowledge point by each student according to the question making record of each knowledge point by each student, and forming a knowledge point mastery degree matrix by the mastery degree of each knowledge point by each student.
In some embodiments, the first constructing submodule is configured to calculate a degree of mastery of each knowledge point by each student according to a question making record of each knowledge point by each student, and includes: corresponding to each student and each knowledge point, if the total number of the student to the question making records of the knowledge point is smaller than a first threshold value, determining that the mastery degree of the student to the knowledge point is 0, otherwise, if the total number is larger than or equal to the first threshold value, taking the average score of the question making records as the mastery degree of the student to the knowledge point.
In some embodiments, the second building submodule is specifically configured to:
determining nodes in the knowledge graph according to knowledge points corresponding to the knowledge point mastery degree matrix;
taking each node in the knowledge graph as a current node in sequence, and calculating the probability of the current node pointing to each other node in sequence according to the knowledge point mastering degree matrix;
and judging whether a directed edge pointing to each other node by the current node needs to be constructed or not according to the probability, constructing the directed edge when needed, and taking the probability as the weight of the directed edge.
In some embodiments, the second constructing sub-module is configured to sequentially calculate, according to the knowledge point mastery degree matrix, a probability that the current node points to each of the other nodes in the sequence, and includes:
calculating the credibility of the current node corresponding to each student pointing to each other node according to the knowledge point mastery degree matrix;
and taking the mean value of the credibility of the current node corresponding to all students pointing to each other node as the probability of the current node pointing to each other node.
In some embodiments, the second constructing submodule is configured to calculate, according to the knowledge point mastery degree matrix, a reliability that the current node corresponding to each student points to each other node, and includes:
corresponding to each student, acquiring a first mastery degree and a second mastery degree from a knowledge point mastery degree matrix, wherein the first mastery degree is the mastery degree of the student on the knowledge point corresponding to the current node, and the second mastery degree is the mastery degree on the knowledge point corresponding to each other node;
calculating a difference value between the first mastery degree and the second mastery degree;
and if the difference is larger than or equal to a second threshold value, setting the reliability to be 1, otherwise, setting the reliability to be 0.
In some embodiments, the second constructing sub-module is configured to determine whether it is necessary to construct a directed edge of the current node pointing to each of the other nodes according to the probability, and includes:
and if the probability is larger than a third threshold value, determining that a directed edge pointing to each other node by the current node needs to be constructed.
It is understood that the apparatus of the present embodiment corresponds to the method embodiment described above, and specific contents may be referred to the related description of the method embodiment, and are not described in detail herein.
In the embodiment, the learning path with the knowledge points as the basic units is planned, and the learning path can be planned by taking the knowledge points as the granularity, so that the easy-to-difficult learning sequence of students is ensured, and the learning ability of the students is more effectively improved.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present application, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (13)
1. A method of learning path planning, comprising:
collecting question making records of students on each knowledge point;
constructing a knowledge point mastery degree matrix according to the question making records;
determining nodes in the knowledge graph according to knowledge points corresponding to the knowledge point mastery degree matrix;
taking each node in the knowledge graph as a current node in sequence, and calculating the probability of the current node pointing to each other node in sequence according to the knowledge point mastering degree matrix;
judging whether a directed edge pointing to each other node by the current node needs to be constructed or not according to the probability, constructing the directed edge when needed, and taking the probability as the weight of the directed edge to construct a knowledge graph learned by students;
acquiring the degree of mastery and the actual mastery degree of each knowledge point of each student according to the knowledge graph;
and planning an individualized learning path which corresponds to each student and takes the knowledge point as a basic unit according to the degree to be mastered and the actual mastered degree.
2. The method according to claim 1, wherein the obtaining of the degree of mastery and the actual mastery of each knowledge point of each student according to the knowledge graph comprises:
the knowledge graph is normalized to obtain a normalized knowledge graph, and the mastery degree of each student to each knowledge point is calculated according to the normalized knowledge graph;
and determining the mastering degree of each student on the knowledge point corresponding to each node in the knowledge map as the actual mastering degree of each student on each knowledge point.
3. The method of claim 2, wherein the regularizing the knowledge-graph to obtain a regularized knowledge-graph comprises:
and taking the nodes with a plurality of precursor nodes in the knowledge graph as current nodes, normalizing the weight of the directed edges between the current nodes and each precursor node, and taking the knowledge graph after weight normalization as a normalized knowledge graph.
4. The method according to claim 3, wherein the calculating the mastery degree of each knowledge point of each student according to the regularized knowledge graph comprises:
and corresponding to each node in the knowledge graph, if the node has precursor nodes, calculating the degree of mastery of each student on the knowledge point corresponding to the node according to the actual mastery degree of each student on the knowledge point corresponding to each precursor node of the node and the normalized weight of the directed edge between the node and each precursor node.
5. The method according to claim 1, wherein the planning of the personalized learning path with the knowledge point as the basic unit corresponding to each student according to the degree of mastery and the actual degree of mastery comprises:
corresponding to each student, calculating a difference value between the degree of mastery to be achieved and the actual degree of mastery corresponding to each knowledge point;
and sequencing the knowledge points according to the difference value to obtain an individualized learning path which corresponds to the student and takes the knowledge points as a basic unit.
6. The method according to claim 1, wherein constructing a knowledge point mastery degree matrix according to the question making records comprises:
and calculating the mastery degree of each knowledge point by each student according to the question making record of each knowledge point by each student, and forming a knowledge point mastery degree matrix by the mastery degree of each knowledge point by each student.
7. The method according to claim 6, wherein calculating the mastery degree of each knowledge point by each student according to the question making records of each knowledge point by each student comprises:
corresponding to each student and each knowledge point, if the total number of the student to the question making records of the knowledge point is smaller than a first threshold value, determining that the actual mastery degree of the student to the knowledge point is 0, otherwise, if the total number is larger than or equal to the first threshold value, taking the average score of the question making records as the actual mastery degree of the student to the knowledge point.
8. The method according to claim 1, wherein the sequentially calculating the probability that the current node points to each other node in the sequence according to the knowledge point mastery degree matrix comprises:
calculating the credibility of the current node corresponding to each student pointing to each other node according to the knowledge point mastery degree matrix;
and taking the mean value of the credibility of the current node corresponding to all students pointing to each other node as the probability of the current node pointing to each other node.
9. The method according to claim 8, wherein the calculating the reliability of the current node pointing to each other node corresponding to each student according to the knowledge point mastery degree matrix comprises:
corresponding to each student, acquiring a first mastery degree and a second mastery degree from a knowledge point mastery degree matrix, wherein the first mastery degree is the mastery degree of the student on the knowledge point corresponding to the current node, and the second mastery degree is the mastery degree on the knowledge point corresponding to each other node;
calculating a difference value between the first mastery degree and the second mastery degree;
and if the difference is larger than or equal to a second threshold value, setting the reliability to be 1, otherwise, setting the reliability to be 0.
10. The method of claim 1, wherein the determining whether a directed edge pointing to each other node from the current node needs to be constructed according to the probability comprises:
and if the probability is larger than a third threshold value, determining that a directed edge pointing to each other node by the current node needs to be constructed.
11. A learned path planning apparatus, comprising:
the collection module is used for collecting question making records of students on each knowledge point;
a build module, the build module comprising:
the first construction submodule is used for constructing a knowledge point mastery degree matrix according to the question making records;
the second construction submodule is used for determining nodes in the knowledge graph according to the knowledge points corresponding to the knowledge point mastery degree matrix; taking each node in the knowledge graph as a current node in sequence, and calculating the probability of the current node pointing to each other node in sequence according to the knowledge point mastering degree matrix; judging whether a directed edge pointing to each other node by the current node needs to be constructed or not according to the probability, constructing the directed edge when needed, and taking the probability as the weight of the directed edge to construct a knowledge graph learned by students;
the acquisition module is used for acquiring the degree of mastery and the actual mastery degree of each knowledge point of each student according to the knowledge graph;
and the planning module is used for planning the personalized learning path which takes the knowledge point as the basic unit and corresponds to each student according to the degree to be mastered and the actual mastered degree.
12. The apparatus of claim 11, wherein the obtaining module comprises:
the regulating submodule is used for regulating the knowledge graph to obtain a regulated knowledge graph and calculating the mastery degree of each student on each knowledge point according to the regulated knowledge graph;
and the determining submodule is used for determining the mastering degree of each student on the knowledge point corresponding to each node in the knowledge map as the actual mastering degree of each student on each knowledge point.
13. The apparatus of claim 11, wherein the planning module comprises:
the calculation submodule is used for corresponding to each student and calculating the difference value between the degree of mastery to be achieved and the actual degree of mastery corresponding to each knowledge point;
and the sequencing submodule is used for sequencing the knowledge points according to the difference value to obtain an individualized learning path which corresponds to the student and takes the knowledge points as a basic unit.
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Families Citing this family (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108665394B (en) * | 2018-05-07 | 2021-11-30 | 北京文华在线教育科技股份有限公司 | Self-adaptive learning method and system |
| CN108647363A (en) * | 2018-05-21 | 2018-10-12 | 安徽知学科技有限公司 | Map construction, display methods, device, equipment and storage medium |
| CN109191345B (en) * | 2018-09-17 | 2021-06-29 | 合肥工业大学 | Cognitive diagnosis method for student cognitive process |
| CN109191929A (en) * | 2018-09-25 | 2019-01-11 | 上海优谦智能科技有限公司 | The intellectual education system of knowledge based map |
| CN109886848B (en) * | 2019-01-30 | 2021-08-13 | 网易有道信息技术(杭州)有限公司 | Data processing method, device, medium and electronic equipment |
| CN109816265B (en) * | 2019-02-01 | 2022-03-01 | 北京作业盒子科技有限公司 | Knowledge characteristic mastery degree evaluation method, question recommendation method and electronic equipment |
| CN110197450B (en) * | 2019-05-31 | 2024-03-08 | 上海松鼠课堂人工智能科技有限公司 | Method, device, equipment and medium for acquiring sequence relation between knowledge points |
| CN110263181B (en) * | 2019-06-17 | 2021-08-06 | 北京作业盒子科技有限公司 | Knowledge structure mining method and learning path planning method |
| CN112446009B (en) * | 2019-09-02 | 2022-06-21 | 北京新唐思创教育科技有限公司 | Learning path planning method and device, readable storage medium and server |
| CN112446523A (en) * | 2019-09-04 | 2021-03-05 | 北京新唐思创教育科技有限公司 | Learning path planning method, electronic device and computer storage medium |
| CN112765406B (en) * | 2019-11-04 | 2022-11-11 | 亿度慧达教育科技（北京）有限公司 | Learning resource planning method, electronic device, and computer storage medium |
| CN112825147B (en) * | 2019-11-21 | 2022-08-26 | 亿度慧达教育科技（北京）有限公司 | Learning path planning method, device, equipment and storage medium |
| CN112825071B (en) * | 2019-11-21 | 2022-08-30 | 亿度慧达教育科技（北京）有限公司 | Learning path planning method, device, equipment and storage medium |
| CN111798138A (en) * | 2020-07-07 | 2020-10-20 | 广州视源电子科技股份有限公司 | Data processing method, computer storage medium and related equipment |
| CN112052342B (en) * | 2020-09-04 | 2023-05-12 | 西南大学 | Learning path recommendation method and system based on online test result big data analysis |
| CN112632295A (en) * | 2020-12-31 | 2021-04-09 | 郑雁 | Learning resource dynamic planning method and device based on knowledge graph |
| CN113361926B (en) * | 2021-06-07 | 2024-02-02 | 江苏师范大学 | Teaching quality evaluation method based on teaching sequence |
| CN113610237A (en) * | 2021-08-19 | 2021-11-05 | 北京京东乾石科技有限公司 | Learning path planning method and device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8155081B1 (en) * | 2007-05-21 | 2012-04-10 | Marvell International Ltd. | Self learning roaming optimization |
| CN104966427A (en) * | 2015-05-27 | 2015-10-07 | 北京创数教育科技发展有限公司 | Self-adaptation teaching interaction system and method |
| CN105389622A (en) * | 2015-10-20 | 2016-03-09 | 西安交通大学 | Multi-constraint learning path recommendation method based on knowledge map |
| CN105632285A (en) * | 2016-03-14 | 2016-06-01 | 成都爱易佰网络科技有限公司 | Variant improvement system and method meeting repairing of learning process |
| CN105761567A (en) * | 2016-05-04 | 2016-07-13 | 北京新唐思创教育科技有限公司 | Method and device for dynamically recommending exercises |
-
2016
- 2016-07-27 CN CN201610600597.2A patent/CN107665473B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8155081B1 (en) * | 2007-05-21 | 2012-04-10 | Marvell International Ltd. | Self learning roaming optimization |
| CN104966427A (en) * | 2015-05-27 | 2015-10-07 | 北京创数教育科技发展有限公司 | Self-adaptation teaching interaction system and method |
| CN105389622A (en) * | 2015-10-20 | 2016-03-09 | 西安交通大学 | Multi-constraint learning path recommendation method based on knowledge map |
| CN105632285A (en) * | 2016-03-14 | 2016-06-01 | 成都爱易佰网络科技有限公司 | Variant improvement system and method meeting repairing of learning process |
| CN105761567A (en) * | 2016-05-04 | 2016-07-13 | 北京新唐思创教育科技有限公司 | Method and device for dynamically recommending exercises |
Non-Patent Citations (1)
| Title |
|---|
| 论微课内容之"微"与"不微";曹荣;《中国教育信息化》;20151231;第60-61页 * |
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