Student inertia activatability measuring method and system based on large concept

Abstract

The invention relates to the technical field of teaching evaluation, and discloses a student inertia activatability measuring method and system based on a large concept, and the method comprises the steps: defining a concept with a migration value in a large concept knowledge graph as an idea node, and defining a teaching practical problem based on the large concept as a problem node; obtaining all associated problem nodes and corresponding paths of the target idea node by using a node search method; obtaining a set about points and paths after traversal is finished; feature definitions are given to elements in the set respectively, and multi-dimensional vectors are generated; based on a scalar function of the multi-dimensional vector, the multi-dimensional vector is mapped into a real number representing the inertia activatability, and the larger the real number is, the higher the knowledge migration value of the concept corresponding to the target idea node for students is. According to the invention, the method achieves the quantitative evaluation of the value of the large concept for knowledge migration in the prior art through the construction of a measurement mode of the inertia knowledge activation degree of the large concept for students in large concept teaching.

Description

Technical Field

[0001] The present invention relates to the technical field of teaching evaluation, and in particular to a method and system for measuring the activatability of student inertia based on big concepts. Background Art

[0002] If students learn something in class but it's not activated and applied effectively in practice, becoming a fresh concept in their minds, then this knowledge is considered inert knowledge. Therefore, in actual teaching, we aim to transform students' inert knowledge into fresh, non-inert knowledge as much as possible, and define this transformation process as the degree of inert knowledge activation.

[0003] Currently, the lack of an assessment of the inertial activation of big concepts makes it difficult to provide targeted instruction. With a reference indicator for inertial activation, teachers can increase the intensity of instruction for big concepts with high inertial activation, helping students better understand the underlying content and ultimately transfer these concepts learned in class to real-life situations. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a method and system for measuring the activatability of students' inertia based on big concepts.

[0005] In a first aspect, the present invention provides a method for measuring the activatability of student inertia based on big concepts, including the following technical solutions:

[0006] Obtain a big concept knowledge graph of the target subject, define big concepts and small concepts with transfer value in the big concept knowledge graph as idea nodes, and define practical teaching problems based on big concepts as problem nodes; each idea node has a corresponding associated problem node;

[0007] Using the node search method, get all the associated problem nodes e of the target idea node i And the corresponding path l i , recorded as (e i ,l i ); After the traversal, we get the set of points and paths E = {(e0,l0),(e1,l1),(e2,l2),…};

[0008] Each element in the set E is given a feature definition to generate a multidimensional vector X = (x0, x1, ..., x n ), where x n represents the eigenvalue of the characteristic term n;

[0009] Based on the scalar function H(X) corresponding to the multidimensional vector X, the multidimensional vector X is mapped to a real number representing the degree of inertia activatability, wherein a larger real number indicates a higher value of the concept corresponding to the target idea node for students' knowledge transfer.

[0010] Furthermore, the feature items include at least: the number of associated problem nodes x0, the complexity value of the associated problem node x1, the path depth of the associated problem node x2, the abstraction level of the associated idea node x3, the interdisciplinary level of the associated idea node x4, the abstraction level of the target idea node x5 and the isCoreldea value x6 of the target idea node.

[0011] Furthermore, the step of obtaining the number x0 of the associated problem nodes includes:

[0012] Obtain the number of elements N of the set e and establish a mapping function g0(E); the mapping function g0(E) is in a monotonically increasing relationship with the number of elements N, and define an upper bound value η0:

[0013] Then x0∈[0,η0].

[0014] Furthermore, the step of obtaining the complexity x1 of the associated problem node includes:

[0015] Obtain the complexity value of each element in the set E and aggregate them to obtain a vector C = {c0, c1, ...}; wherein the complexity value is the complexity of the problem node, and its value range is 1-100;

[0016] Calculate the metric of vector C to get ||C||1, and normalize ||C||1 according to the number of elements to get μ1∈[0,100] represents the comprehensive complexity score of all associated problem nodes of the target idea node;

[0017] A mapping function g1(E) is established, wherein the mapping function g1(E) is in a monotonically increasing relationship with μ1, and the upper bound is defined as η1:

[0018] g1(E)=μ1, η1=100; then x1∈[0, η1].

[0019] Furthermore, the step of obtaining the path depth x2 of the associated problem node includes:

[0020] Get the path length l of each element in the set E i, get vector L = {l0,l1,l2,…}; determine the longest path length is L max , normalize the vector L to obtain

[0021] Normalize the ||L||1 according to the number of elements to obtain μ2∈[0,1] represents the comprehensive path length of all associated problem nodes of the target idea node;

[0022] A mapping function g2(E) is established, wherein the mapping function g2(E) is in a monotonically increasing relationship with μ2, and the upper bound is defined as η2:

[0023] g2(E)=μ2, then x2∈[0, η2].

[0024] Furthermore, the step of obtaining the abstraction level x3 of the associated idea includes:

[0025] The preset maximum search distance is D max , get the max The idea nodes associated with the scope are denoted as vector I = {i0, i1, i2, ...};

[0026] Obtain the attribute abstractionDegree value for each element of the vector I and aggregate them to obtain a vector D = {d0, d1, d2, ...}, where the abstractionDegree value is the abstraction degree of the idea node;

[0027] The metric of vector D is calculated based on the L1 norm to obtain ||D||1, and ||D||1 is normalized according to the number of elements to obtain μ3∈[0,1] represents the D of the target idea node max The comprehensive abstractness of all associated idea nodes within the distance;

[0028] A mapping function g3(I) is established, wherein the mapping function g3(I) is in a monotonically increasing relationship with μ3, and the upper bound is defined as η3:

[0029] g3(I)=μ3, then x3∈[0, η3].

[0030] Furthermore, the step of obtaining the interdisciplinary degree x4 of the associated idea node includes:

[0031] Obtain the attribute isCrosscuttingldea value for each element of the vector I to obtain a vector F = {f0, f1, f2, ...}, wherein the isCrosscuttingldea value indicates whether the idea node is an interdisciplinary idea node, and takes a value of 0 or 1;

[0032] The metric of the vector F is calculated based on the L1 norm to obtain ||F||1, and ||F||1 is normalized according to the number of elements to obtain μ4∈[0,1] represents the comprehensive interdisciplinary degree of the target idea node;

[0033] A mapping function g4(I) is established, wherein the mapping function g4(I) is in a monotonically increasing relationship with μ4, and the upper bound is defined as η4:

[0034] g4(I)=μ4, then x4∈[0, η4].

[0035] Furthermore, the step of obtaining the abstraction level x5 of the target idea node includes:

[0036] Obtain the abstractionDegree value of the target idea node and use it as the value of the abstraction degree x5 of the target idea node, x5∈[0,100];

[0037] The steps of obtaining the isCoreldea value x6 of the target idea node include:

[0038] Get the isCoreldea value of the target idea node and use it as the isCoreldea value x6 of the target idea node, where x6∈[0,1] indicates whether the current idea is a big concept. If the isCoreldea value is close to 0, it is a small concept; if it is close to 1, it is a big concept.

[0039] Furthermore, the expression of the scalar function H(X) is:

[0040] in, w0, w1, w2, w3, w4 and w6 are weight parameters, w0, w1, w2, w3, w4 and w6∈(0,1); for The upper bound value of Then H(X)∈[0,100].

[0041] In a second aspect, the present invention provides a system for measuring the activatability of student inertia based on big concepts, including the following technical solutions:

[0042] 10. A system for measuring the activatability of student inertia based on big concepts, comprising:

[0043] A determination module is used to obtain a big concept knowledge graph of the target subject, define big concepts and small concepts with transfer value in the big concept knowledge graph as idea nodes, and define practical teaching problems based on the big concepts as problem nodes; wherein each idea node has a corresponding associated problem node;

[0044] The search module is used to obtain all the associated problem nodes of the target idea node using the node search method. i And the corresponding path l i , recorded as (e i ,l i ); After the traversal, we get the set of points and paths E = {(e0,l0),(e1,l1),(e2,l2),…};

[0045] A generation module is used to assign feature definitions to the elements in the set E and generate a multidimensional vector X = (x0, x1, ..., x n ), where x n represents the eigenvalue of the characteristic term n;

[0046] The measurement module is used to map the multidimensional vector X into a real number representing the degree of inertia activatability based on the scalar function H(X) corresponding to the multidimensional vector X, wherein a larger real number indicates a higher value of the concept corresponding to the target idea node for the student's knowledge transfer.

[0047] The present invention adopts the node attribute values of big concepts in the big concept knowledge graph and the node attribute values of actual teaching problems, performs mathematical processing based on graph theory and matrix-related theories, and constructs a method to measure the degree of activation of students' inert knowledge by big concepts in big concept teaching, thereby solving the problem of quantitative evaluation of the value of big concepts for knowledge transfer in the existing technology.

[0048] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.

[0050] Figure 1 This is a flowchart of the method for measuring the activatability of students' inertia based on big concepts;

[0051] Figure 2 This is a structural diagram of the system for measuring the activatability of student inertia based on big concepts. DETAILED DESCRIPTION

[0052] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0053] Figure 1 FIG. 1 shows a flow chart of an embodiment of a method for measuring the degree of activation of student inertia based on a large concept provided by the present invention. Figure 1 As shown, the method includes the following steps:

[0054] S1. Obtain the big concept knowledge graph of the target subject, define the big concepts and small concepts with transfer value in the big concept knowledge graph as idea nodes, and define the actual teaching problems based on the big concepts as problem nodes.

[0055] Each idea node has a corresponding problem node. The target subject is set according to the actual situation, such as mathematics.

[0056] It's important to note that the transfer value of a concept primarily refers to its ability to be applied in different contexts. A concept with high transfer value means that students can transfer what they've learned from textbooks to various situations in their daily lives. This means they can not only apply what they've learned in a specific subject area, but also transfer their knowledge to new environments and solve problems in different fields. The difference in transferability between large and small concepts lies in their applicability in different contexts. For example, in mathematics, "reduction thinking" is a large concept, while "methods for simplifying quadratic radicals" is a small concept. In actual teaching, teachers often prioritize teaching the specific knowledge concept of "methods for simplifying quadratic radicals," but this concept can only be applied to specific algebraic equations and is difficult to transfer to other subjects or scenarios. This problem-solving method actually embodies the larger concept of "reduction thinking," which involves transforming complex problems into simpler ones. The idea of reduction can be applied to different scenarios. For example, in mathematical modeling, complex objective problems can be abstracted and mathematically reduced to a single mathematical problem, which can then be reduced to an appropriate mathematical model for solution. In algorithm design, complex algorithmic problems can be reduced to simpler subproblems to design more effective solutions. In physics, complex mechanical systems can be reduced to basic Newton's laws for solution. Therefore, large concepts are highly transferable, allowing students to transfer them to various disciplines and real life. In contrast, small concepts may have limited applicability in other disciplines or contexts.

[0057] S2. Use the node search method to obtain all the associated problem nodes e of the target idea node i And the corresponding path l i , recorded as (e i ,l i ); After the traversal is completed, we get the set of points and paths E = {(e0,l0), (e1,l1), (e2,l2),…}.

[0058] The node search method is specifically a breadth-first search algorithm.

[0059] S3, assign feature definitions to the elements in the set E respectively, and generate a multidimensional vector X=(x0, x1, ..., x n ), where x n represents the eigenvalue of the characteristic term n;

[0060] Based on the scalar function H(X) corresponding to the multidimensional vector X, the multidimensional vector X is mapped to a real number representing the degree of inertia activatability, wherein a larger real number indicates a higher value of the concept corresponding to the target idea node for students' knowledge transfer.

[0061] It should be noted that teachers should pay more attention to knowledge with higher inertia activation levels in teaching. For the learning of knowledge with higher inertia activation levels, students should be given more help and support, and more explanations and training on the transfer and application of this knowledge should be provided to help students better complete the learning of this knowledge, so as to activate and transfer it in future real life.

[0062] In an optional manner, the feature items include at least: the number of associated problem nodes x0, the complexity value of the associated problem node x1, the path depth of the associated problem node x2, the abstraction level of the associated idea node x3, the interdisciplinary level of the associated idea node x4, the abstraction level of the target idea node x5 and the isCoreldea value x6 of the target idea node.

[0063] In an optional manner, the step of obtaining the number x0 of the associated problem nodes includes:

[0064] Obtain the number of elements N of the set E and establish a mapping function g0(E); the mapping function g0(E) is in a monotonically increasing relationship with the number of elements N, and define an upper bound value η0:

[0065] Then x0∈[0,η0].

[0066] In an optional manner, the step of obtaining the complexity x1 of the associated problem node includes:

[0067] Obtain the complexity value of each element in the set E and aggregate them to obtain a vector C = {c0, c1, ...}; wherein the complexity value is the complexity of the problem node, and its value range is 1-100;

[0068] Calculate the metric of vector C to get ||C||1, and normalize ||C||1 according to the number of elements to get μ1∈[0,100] represents the comprehensive complexity score of all associated problem nodes of the target idea node;

[0069] A mapping function g1(E) is established, wherein the mapping function g1(E) is in a monotonically increasing relationship with μ1, and the upper bound is defined as η1:

[0070] g1(E)=μ1, η1=100; then x1∈[0, η1].

[0071] In an optional manner, the step of obtaining the path depth x2 of the associated problem node includes:

[0072] Get the path length l of each element in the set E i , get vector L = {l0,l1,l2,…}; determine the longest path length is L max , normalize the vector L to obtain

[0073] Normalize the ||L||1 according to the number of elements to obtain μ2∈[0,1] represents the comprehensive path length of all associated problem nodes of the target idea node;

[0074] A mapping function g2(E) is established, wherein the mapping function g2(E) is in a monotonically increasing relationship with μ2, and the upper bound is defined as η2:

[0075] g2(E)=μ2, then x2∈[0, η2].

[0076] It should be noted that L max It is the maximum value of all path lengths in the big concept knowledge graph.

[0077] In an optional manner, the step of obtaining x3 of the abstraction level of the associated idea includes:

[0078] The preset maximum search distance is D max , get the max The idea nodes associated with the scope are denoted as vector I = {i0, i1, i2, ...};

[0079] Obtain the attribute abstractionDegree value for each element of the vector I and aggregate them to obtain a vector D = {d0, d1, d2, ...}, where the abstractionDegree value is the abstraction degree of the idea node;

[0080] The metric of vector D is calculated based on the L1 norm to obtain ||D||1, and ||D||1 is normalized according to the number of elements to obtain μ3∈[0,1] represents the D of the target idea node max The comprehensive abstractness of all associated idea nodes within the distance;

[0081] A mapping function g3(I) is established, wherein the mapping function g3(I) is in a monotonically increasing relationship with μ3, and the upper bound is defined as η3:

[0082] g3(I)=μ3, then x3∈[0, η3].

[0083] In an optional manner, the step of obtaining the interdisciplinary degree x4 of the associated idea node includes:

[0084] Obtain the attribute isCrosscuttingldea value for each element of the vector I to obtain a vector F = {f0, f1, f2, ...}, wherein the isCrosscuttingldea value indicates whether the idea node is an interdisciplinary idea node, and takes a value of 0 or 1;

[0085] The metric of the vector F is calculated based on the L1 norm to obtain ‖F‖1, and ‖F‖1 is normalized according to the number of elements to obtain μ4∈[0,1] represents the comprehensive interdisciplinary degree of the target idea node;

[0086] A mapping function g4(I) is established, wherein the mapping function g4(I) is in a monotonically increasing relationship with μ4, and the upper bound is defined as η4:

[0087] g4(I)=μ4, then x4∈[0, η4].

[0088] In an optional manner, the step of obtaining the abstraction level x5 of the target idea node includes:

[0089] Obtain the abstractionDegree value of the target idea node and use it as the value of the abstraction degree x5 of the target idea node, x5∈[0,100];

[0090] The steps of obtaining the isCoreldea value x6 of the target idea node include:

[0091] Get the isCoreldea value of the target idea node and use it as the isCoreldea value x6 of the target idea node, where x6∈[0,1] indicates whether the current idea is a big concept. If the isCoreldea value is close to 0, it is a small concept; if it is close to 1, it is a big concept.

[0092] In an optional manner, the expression of the scalar function H(X) is:

[0093] in, w0, w1, w2, w3, w4 and w6 are weight parameters, w0, w1, w2, w3, w4 and w6∈(0,1); for The upper bound value of Then H(X)∈[0,100].

[0094] It should be noted that w0+w1+w2+w3+w4+w6=1.

[0095] To better illustrate the technical solution of this embodiment, the following example is used to illustrate the target subject of mathematics:

[0096] 1) Definition Nodes: ① Major concept nodes (idea nodes) include: "Linear Equations," "Polynomial Operations," and "Function Domain." ② Minor concept nodes (idea nodes) include: "Solving Linear Equations," and "Factoring Techniques." ③ Practical Teaching Problems (problem nodes) include: "How to Solve Linear Equations with Absolute Values?" and "How to Determine the Domain of a Quadratic Function?"

[0097] 2) The association relationships are: ① "Linear equations" is associated with the problem "Solving absolute value equations"; ② "Polynomial operations" is associated with the problem "Factoring high-order polynomials"; ③ "Function domain" is associated with the problem "Finding the domain of a fractional function".

[0098] 3) Select "Function Domain" as the target idea node and use a breadth-first search algorithm with a maximum search depth of 3. The traversal results are: e0 is "Find the domain of the fraction function", l0 = 1; e1 is "Find the domain of the radical function", l1 = 2; e2 is "Find the domain of the composite function", l2 = 3; the set E = {(e0, l0), (e1, l1), (e2, l2)}.

[0099] 4) Generate a multidimensional vector X.

[0100] ① The process of obtaining the number of associated problem nodes x0:

[0101] N=3, N max =5, then g0(E)=0.6, if η0=1, then x0=0.6 (the normalized number of associated problem nodes).

[0102] ② The process of obtaining the complexity x1 of the associated problem node:

[0103] C={70,85,60}; μ1=71.67, s1=71.67.

[0104] ③ The process of obtaining the x2 path depth of the associated problem node:

[0105] L={1,2,3},L max =5, μ2=0.4, η2=1, then x2=0.4.

[0106] ④ The process of obtaining the abstraction level x3 of the associated idea:

[0107] In D max =2, D = {0.8, 0.6, 0.5}; μ3 = 0.63, η3 = 1, then x3 = 0.63.

[0108] ⑤ The process of obtaining the interdisciplinary degree x4 of the associated idea node:

[0109] μ4=0.33, eta4=1, then x4=0.33.

[0110] ⑥ The process of obtaining the abstraction degree x5 of the target idea node:

[0111] The abstractionDegree value of the "function domain" is 75, so x5=75.

[0112] ⑦The process of obtaining the isCoreldea value x6 of the target idea node:

[0113] “Function domain” is a broad concept. If isCoreldea=0.9, then x6=0.9.

[0114] The final eigenvector is X = (0.6, 71.67, 0.4, 0.63, 0.33, 75, 0.9).

[0115] 5) Calculate the scalar function H(X).

[0116] Assume w0=0.2, w1=0.15, w2=0.1, w3=0.15, w4=0.1, w6=0.3. Then H(X)=74.6.

[0117] 6) Interpretation of results.

[0118] The inertia activatability of the target idea node “function definition domain” is 74.6 (out of 100), indicating that it has high value in students’ knowledge transfer. It is necessary to prioritize the design of teaching activities to activate students’ deep understanding of this concept.

[0119] The technical solution of this embodiment adopts the node attribute values of big concepts in the big concept knowledge graph and the node attribute values of actual teaching problems, performs mathematical processing based on graph theory and matrix-related theories, and constructs a method to measure the degree of activation of students' inert knowledge by big concepts in big concept teaching, which solves the problem of quantitative evaluation of the value of big concepts for knowledge transfer in the existing technology.

[0120] The present invention provides a system 200 for measuring student inertia activatability based on a macro concept. The system 200 includes:

[0121] Determination module 210 is used to obtain a big concept knowledge graph of the target subject, define big concepts and small concepts with transfer value in the big concept knowledge graph as idea nodes, and define practical teaching problems based on the big concepts as problem nodes; wherein each idea node has a corresponding associated problem node;

[0122] Search module 220 is used to obtain all the problem nodes associated with the target idea node using a node search method. i And the corresponding path l i , recorded as (e i ,l i ); After the traversal, we get the set of points and paths E = {(e0,l0),(e1,l1),(e2,l2),…};

[0123] The generating module 230 is used to assign feature definitions to the elements in the set E and generate a multidimensional vector X = (x0, x1, ..., x n ), where x n represents the eigenvalue of the characteristic term n;

[0124] The measurement module 240 is used to map the multidimensional vector X into a real number representing the degree of inertia activatability based on the scalar function H(X) corresponding to the multidimensional vector X, wherein a larger real number indicates a higher value of the concept corresponding to the target idea node for the student's knowledge transfer.

[0125] In an optional manner, the feature items include at least: the number of associated problem nodes x0, the complexity value of the associated problem node x1, the path depth of the associated problem node x2, the abstraction level of the associated idea node x3, the interdisciplinary level of the associated idea node x4, the abstraction level of the target idea node x5 and the isCoreldea value x6 of the target idea node.

[0126] In an optional manner, the step of obtaining the number x0 of the associated problem nodes includes:

[0127] Obtain the number of elements N of the set E and establish a mapping function g0(E); the mapping function g0(E) is in a monotonically increasing relationship with the number of elements N, and define an upper bound value η0:

[0128] Then x0∈[0,η0].

[0129] In an optional manner, the step of obtaining the complexity x1 of the associated problem node includes:

[0130] Obtain the complexity value of each element in the set E and aggregate them to obtain a vector C = {c0, c1, ...}; wherein the complexity value is the complexity of the problem node, and its value range is 1-100;

[0131] Calculate the metric of vector C to get ||C||1, and normalize ||C||1 according to the number of elements to get μ1∈[0,100] represents the comprehensive complexity score of all associated problem nodes of the target idea node;

[0132] A mapping function g1(E) is established, wherein the mapping function g1(E) is in a monotonically increasing relationship with μ1, and the upper bound is defined as η1:

[0133] g1(E)=μ1, η1=100; then x1∈[0, η1].

[0134] In an optional manner, the step of obtaining the path depth x2 of the associated problem node includes:

[0135] Get the path length l of each element in the set E i , get vector L = {l0,l1,l2,…}; determine the longest path length is L max , normalize the vector L to obtain

[0136] Normalize the ||L||1 according to the number of elements to obtain μ2∈[0,1] represents the comprehensive path length of all associated problem nodes of the target idea node;

[0137] A mapping function g2(E) is established, wherein the mapping function g2(E) is in a monotonically increasing relationship with μ2, and the upper bound is defined as η2:

[0138] g2(E)=μ2, then x2∈[0, η2].

[0139] In an optional manner, the step of obtaining x3 of the abstraction level of the associated idea includes:

[0140] The preset maximum search distance is D max , get the max The idea nodes associated with the scope are denoted as vector I = {i0, i1, i2, ...};

[0141] Obtain the attribute abstractionDegree value for each element of the vector I and aggregate them to obtain a vector D = {d0, d1, d2, ...}, where the abstractionDegree value is the abstraction degree of the idea node;

[0142] The metric of vector D is calculated based on the L1 norm to obtain ||D||1, and ||D||1 is normalized according to the number of elements to obtain μ3∈[0,1] represents the D of the target idea node max The comprehensive abstractness of all associated idea nodes within the distance;

[0143] A mapping function g3(I) is established, wherein the mapping function g3(I) is in a monotonically increasing relationship with μ3, and the upper bound is defined as η3:

[0144] g3(I)=μ3, then x3∈[0, η3].

[0145] In an optional manner, the step of obtaining the interdisciplinary degree x4 of the associated idea node includes:

[0146] Obtain the attribute isCrosscuttingldea value for each element of the vector I to obtain a vector F = {f0, f1, f2, ...}, wherein the isCrosscuttingldea value indicates whether the idea node is an interdisciplinary idea node, and takes a value of 0 or 1;

[0147] The metric of the vector F is calculated based on the L1 norm to obtain ||F||1, and ||F||1 is normalized according to the number of elements to obtain μ4∈[0,1] represents the comprehensive interdisciplinary degree of the target idea node;

[0148] A mapping function g4(I) is established, wherein the mapping function g4(I) is in a monotonically increasing relationship with μ4, and the upper bound is defined as η4:

[0149] g4(I)=μ4, then x4∈[0, η4].

[0150] In an optional manner, the step of obtaining the abstraction level x5 of the target idea node includes:

[0151] Obtain the abstractionDegree value of the target idea node and use it as the value of the abstraction degree x5 of the target idea node, x5∈[0,100];

[0152] The steps of obtaining the isCoreldea value x6 of the target idea node include:

[0153] Get the isCoreldea value of the target idea node and use it as the isCoreldea value x6 of the target idea node, where x6∈[0,1] indicates whether the current idea is a big concept. If the isCoreldea value is close to 0, it is a small concept; if it is close to 1, it is a big concept.

[0154] In an optional manner, the expression of the scalar function H(X) is:

[0155] in, w0, w1, w2, w3, w4 and w6 are weight parameters, w0, w1, w2, w3, w4 and w6∈(0,1); for The upper bound value of Then H(X)∈[0,100].

[0156] The technical solution of this embodiment adopts the node attribute values of big concepts in the big concept knowledge graph and the node attribute values of actual teaching problems, performs mathematical processing based on graph theory and matrix-related theories, and constructs a method to measure the degree of activation of students' inert knowledge by big concepts in big concept teaching, thereby solving the problem of quantitative evaluation of the value of big concepts for knowledge transfer in the existing technology.

[0157] The above description is merely a preferred embodiment of the present invention and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present invention is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the present invention.

[0158] It should be noted that the terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects and to define a specific order or precedence. Where appropriate, the order used for similar objects may be interchanged, such that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0159] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for measuring the activatability of students' inertia based on big concepts, characterized by: include: Obtain a big concept knowledge graph of the target subject, define big concepts and small concepts with transfer value in the big concept knowledge graph as idea nodes, and define practical teaching problems based on big concepts as problem nodes; each idea node has a corresponding associated problem node; Using the node search method, get all the associated problem nodes e of the target idea node i And the corresponding path l i , recorded as (e i ,l i ); After the traversal, we get the set of points and paths E = {(e0,l0),(e1,l1),(e2,l2),…}; Each element in the set E is given a feature definition to generate a multidimensional vector X = (x0, x1, ..., x n ), where x n represents the eigenvalue of the characteristic term n; Based on the scalar function H(X) corresponding to the multidimensional vector X, the multidimensional vector X is mapped to a real number representing the degree of inertia activatability, wherein a larger real number indicates a higher value of the concept corresponding to the target idea node for students' knowledge transfer.

2. The method for measuring the activatability of students' inertia based on big concepts according to claim 1 is characterized in that: The feature items include at least: the number of associated problem nodes x0, the complexity value of the associated problem node x1, the path depth of the associated problem node x2, the abstraction level of the associated idea node x3, the interdisciplinary level of the associated idea node x4, the abstraction level of the target idea node x5 and the isCoreldea value x6 of the target idea node.

3. The method for measuring the degree of activation of student inertia based on big concepts according to claim 2 is characterized in that: The step of obtaining the number x0 of the associated problem nodes includes: Obtain the number of elements N of the set E and establish a mapping function g0(E); the mapping function g0(E) is in a monotonically increasing relationship with the number of elements N, and define an upper bound value η0: Then x0∈[0,η0].

4. The method for measuring the activatability of students' inertia based on big concepts according to claim 2 is characterized in that: The step of obtaining the complexity x1 of the associated problem node includes: Obtain the complexity value of each element in the set E and aggregate them to obtain a vector C = {c0, c1, ...}; wherein the complexity value is the complexity of the problem node, and its value range is 1-100; Calculate the metric of vector C to get ||C||1, and normalize ||C||1 according to the number of elements to get Represents the comprehensive complexity score of all associated problem nodes of the target idea node; A mapping function g1(E) is established, wherein the mapping function g1(E) is in a monotonically increasing relationship with μ1, and the upper bound is defined as η1: g1(E)=μ1, η1=100; then x1∈[0, η1].

5. The method for measuring the activatability of students' inertia based on big concepts according to claim 2 is characterized in that: The step of obtaining the path depth x2 of the associated problem node includes: Get the path length l of each element in the set E i , get vector L = {l0,l1,l2,…}; determine the longest path length is L max , normalize the vector L to obtain Normalize the ||L||1 according to the number of elements to obtain Indicates the comprehensive path length of all problem nodes associated with the target idea node; A mapping function g2(E) is established, wherein the mapping function g2(E) is in a monotonically increasing relationship with μ2, and the upper bound is defined as η2: g2(E)=μ2, then x2∈[0, η2].

6. The method for measuring the degree of activation of student inertia based on big concepts according to claim 2 is characterized in that: The steps of obtaining the abstraction level x3 of the associated idea include: The preset maximum search distance is D max , get the max The idea nodes associated with the scope are denoted as vector I = {i0, i1, i2, ...}; Obtain the attribute abstractionDegree value for each element of the vector I and aggregate them to obtain a vector D = {d0, d1, d2, ...}, where the abstractionDegree value is the abstraction degree of the idea node; The metric of vector D is calculated based on the L1 norm to obtain ||D||1, and ||D||1 is normalized according to the number of elements to obtain D representing the target idea node max The comprehensive abstractness of all associated idea nodes within the distance; A mapping function g3(I) is established, wherein the mapping function g3(I) is in a monotonically increasing relationship with μ3, and the upper bound is defined as η3: g3(I)=μ3, then x3∈[0, η3].

7. The method for measuring the degree of activation of student inertia based on big concepts according to claim 6 is characterized in that: The steps of obtaining the x4 interdisciplinary degree of the associated idea node include: Obtain the attribute isCrosscuttingldea value for each element of the vector I to obtain a vector F = {f0, f1, f2, ...}, wherein the isCrosscuttingldea value indicates whether the idea node is an interdisciplinary idea node, and takes a value of 0 or 1; The metric of the vector F is calculated based on the L1 norm to obtain ||F||1, and ||F||1 is normalized according to the number of elements to obtain Indicates the comprehensive interdisciplinary degree of the target idea node; A mapping function g4(I) is established, wherein the mapping function g4(I) is in a monotonically increasing relationship with μ4, and the upper bound is defined as η4: g4(I)=μ4, then x4∈[0, η4].

8. The method for measuring the degree of activation of student inertia based on big concepts according to claim 2 is characterized in that: The step of obtaining the abstraction degree x5 of the target idea node includes: Obtain the abstractionDegree value of the target idea node and use it as the value of the abstraction degree x5 of the target idea node, x5∈[0,100]; The steps of obtaining the isCoreldea value x6 of the target idea node include: Get the isCoreldea value of the target idea node and use it as the isCoreldea value x6 of the target idea node, where x6∈[0,1] indicates whether the current idea is a big concept. If the isCoreldea value is close to 0, it is a small concept; if it is close to 1, it is a big concept.

9. The method for measuring the degree of activation of student inertia based on big concepts according to claim 1 is characterized in that: The expression of the scalar function H(X) is: in, w0, w1, w2, w3, w4 and w6 are weight parameters, w0, w1, w2, w3, w4 and w6∈(0,1); for The upper bound value of Then H(X)∈[0,100].

10. A system for measuring the degree of student inertia activatability based on big concepts, characterized by: include: A determination module is used to obtain a big concept knowledge graph of the target subject, define big concepts and small concepts with transfer value in the big concept knowledge graph as idea nodes, and define practical teaching problems based on the big concepts as problem nodes; wherein each idea node has a corresponding associated problem node; The search module is used to obtain all the associated problem nodes of the target idea node using the node search method. i And the corresponding path l i , recorded as (e i ,l i ); After the traversal, we get the set of points and paths E = {(e0,l0),(e1,l1),(e2,l2),…}; A generation module is used to assign feature definitions to the elements in the set E and generate a multidimensional vector X = (x0, x1, ..., x n ), where x n represents the eigenvalue of the characteristic term n; The measurement module is used to map the multidimensional vector X into a real number representing the degree of inertia activatability based on the scalar function H(X) corresponding to the multidimensional vector X, wherein a larger real number indicates a higher value of the concept corresponding to the target idea node for the student's knowledge transfer.

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