A cognitive assessment method based on attribute-oriented concept reduction

Abstract

A cognitive assessment method based on attribute-oriented concept reduction, comprising: constructing a formal background based on knowledge point mapping τ; generating an object intuitive graph H about the problem based on the formal background Q ; According to the object intuitive diagram H Q All object concepts of the attribute-oriented concept lattice are obtained to obtain an attribute-oriented concept coordination set, and redundant concepts in the attribute-oriented concept coordination set are deleted to obtain an attribute-oriented concept reduction F; a cognitive assessment of the student is performed based on the student answer data set and the attribute-oriented concept reduction to obtain an assessment result. The present invention obtains the attribute-oriented concept reduction through an object intuitive diagram based on the binary relationship between the question and the knowledge point, extracts the core structure from the relationship between the complex question and the knowledge point, simplifies the cognitive assessment process, reduces redundant information, retains key educational information, significantly reduces the amount of computer operation, and improves the operation speed.

Description

Technical Field

[0001] The present invention belongs to the technical field of educational information analysis, and in particular relates to a cognitive assessment method based on attribute-oriented concept reduction. Background Art

[0002] Over the past few decades, with the rapid development of educational informatization and internet technology, data collection and analysis capabilities in education have greatly improved. The widespread adoption of various smart devices and online learning platforms has enabled the real-time collection and processing of student learning behavior data, test response records, and other information. This data not only reflects students' learning progress and performance, but also provides insights into their mastery of knowledge points and cognitive characteristics. This massive amount of student behavior data provides favorable conditions for cognitive assessment research.

[0003] The Journal of Nanjing University (Natural Science), Vol. 59, No. 4, published a paper titled "Knowledge Assessment and Learning Path Selection in a Knowledge Point Network." This method utilizes formal concept analysis to reveal the intrinsic connections between problem sets and knowledge point sets. Using an attribute-oriented concept lattice, the corresponding relationships between problems and knowledge points are identified, thereby revealing the underlying cognitive structure. However, this method has some shortcomings when dealing with large-scale data for cognitive assessment. First, with the increasing number of exercises and knowledge point data, the scale and complexity of the formal context also increase. The attribute concept lattice requires comprehensive modeling of all attribute combinations, increasing the computational complexity of constructing the attribute concept lattice and placing a significant computational burden on the computer. Second, the attribute-oriented concept lattice of this method is prone to containing redundant information. Because the attribute-oriented concept lattice describes data relationships through all possible attribute combinations, it often generates irrelevant or redundant attributes. This redundant information can interfere with the accuracy of the assessment results, increase the complexity of the analysis, and reduce the efficiency of cognitive assessment. Furthermore, the attribute-oriented concept lattice of this method is typically static and lacks the ability to adapt to dynamic changes. When the data or attributes change, the concept lattice needs to be reconstructed, resulting in poor real-time performance and flexibility. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the shortcomings of the existing technology and provide a cognitive assessment method based on attribute concept reduction that is rationally designed and can achieve more accurate teaching intervention and personalized learning support.

[0005] The technical solution adopted to solve the above technical problems is: a cognitive evaluation method based on attribute-oriented concept reduction, including the following steps:

[0006] Step 1. Construct the formal context (Q, D, R) based on the knowledge point mapping τ, where Q is the question set, D is the set of knowledge points associated with the question, and R is the binary relationship between the question and the knowledge point;

[0007] Step 2. Formal background (Q, D, R) Generate the labeled question class set G of the question set Q according to the formal concept analysis method, construct the corresponding hierarchical structure according to the partial order relationship between the labeled question classes in the labeled question class set G, and obtain the object intuitive graph H of the question Q , object intuitive diagram H Q Each node in represents a labeled question class, and the edges between nodes represent partial order relations;

[0008] Step 3. According to the object intuitive diagram H Q Obtain all object concepts of the attribute-oriented concept lattice to obtain the attribute-oriented concept coordination set, delete the redundant concepts in the attribute-oriented concept coordination set, and obtain the attribute-oriented concept reduction F;

[0009] The redundant concepts are object concepts that do not affect the integrity of the complementary binary relationship between the attribute-oriented concept coordination set reconstruction problem and the knowledge points;

[0010] Step 4. Test students with question set Q, collect their test results, remove the results of "careless wrong answers" and "guessing the right answers", and obtain the student answer data set. Conduct cognitive assessment of students based on the student answer data set and attribute-oriented concept reduction to obtain the assessment results.

[0011] As a preferred technical solution, the method for generating the labeled question set G of the question set Q based on the formal concept analysis method from the formal background (Q, D, R) is as follows:

[0012] Step 2.1. For each question q∈Q, find all d that satisfy (q,d)∈R, where d is the knowledge point related to question q, d∈D;

[0013] Step 2.2. Build a labeled question class

[0014] Each labeled question class is a pair (A, B), where A is a subset of the question set Q and B is a subset of the knowledge point set D, satisfying the following conditions:

[0015] Condition 1: The set of knowledge points common to all questions in A is B;

[0016] Condition 2: All problem sets with knowledge point B are A;

[0017] Step 2.3. Systematically traverse the formal background and use the Galois connection "*" operation in the formal concept analysis method to generate labeled question classes;

[0018] The Galois connection "*" operation is: for any problem set Calculate its knowledge point closure X′; for any knowledge point set Calculate its problem closure Y′;

[0019] The method for generating a labeled question class is as follows: by traversing all question sets X and knowledge point sets Y, and applying the "*" operation, all pairs (A, B) that satisfy X'=B and Y'=A are generated, and (A, B) is a labeled question class;

[0020] Step 2.4. Collect all generated labeled question classes (A, B) to form a labeled question class set G.

[0021] As a preferred technical solution, the object intuitive map H Q The method to obtain all object concepts of the attribute-oriented concept lattice and obtain the attribute-oriented concept coordination set is:

[0022] Step 3.1. From the object intuitive graph H Q In , for each labeled question class, all the labeled question classes related to it are found downward according to the partial order structure to form a related set;

[0023] Step 3.2. For each question set in the labeled question class, combine it with all question sets in the related set that have the labeled question class as the question set of the new question class. For each knowledge point set in the labeled question class, directly use it as the knowledge point set of the new question class. The new question class is obtained as the object concept in the attribute-oriented concept lattice.

[0024] Step 3.3. Collect all new problem classes to form a set F Q , the set F Q That is, the attribute-oriented concept coordination set.

[0025] As a preferred technical solution, the method of cognitively evaluating students based on student answer data sets and attribute-oriented concept simplification is: matching the student answer data with the object concepts obtained in the attribute-oriented concept simplification, analyzing the student's performance on each knowledge point, and determining which knowledge points the student performs well on and which knowledge points he has difficulties on.

[0026] The beneficial effects of the present invention are as follows:

[0027] Based on the binary relationship between questions and knowledge points, the present invention uses an object-oriented intuitive diagram to obtain attribute-oriented concept simplification, extracting the core structure from the relationship between complex questions and knowledge points. This simplifies the cognitive assessment process, reduces redundant information, retains key educational information, significantly reduces computer operation volume, and improves operation speed. This improves data processing efficiency, especially for large-scale educational scenarios. At the same time, removing redundant information can make the assessment process more focused, reduce information interference, and thus improve the accuracy and precision of the assessment results. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of the cognitive evaluation method based on attribute concept reduction of the present invention.

[0029] Figure 2 Schematic diagram of a formal background constructed based on knowledge point mapping τ in an embodiment of the present invention.

[0030] Figure 3 This is an intuitive diagram of the object H in the embodiment of the present invention. Q .

[0031] Figure 4 It is a schematic diagram of the attribute-oriented concept reduction process in an embodiment of the present invention.

[0032] Figure 5 This is a schematic diagram of cognitive diagnosis performed by the present invention through concept simplification and student answer data sets. DETAILED DESCRIPTION

[0033] The present invention will be further described in detail below with reference to the accompanying drawings and examples, but the present invention is not limited to the following embodiments.

[0034] Example

[0035] exist Figure 1 In this embodiment, the cognitive evaluation method based on attribute concept reduction includes the following steps:

[0036] Step 1. Construct the formal background (Q, D, R) based on the knowledge point mapping τ, where Q is the problem set, D is the set of knowledge points associated with the problem, and R is the relationship between the problem and the knowledge points. Q = {problem 1, problem 2, problem 3, problem 4, problem 5}, referred to as Q = {1, 2, 3, 4, 5}, D = {knowledge point a, knowledge point b, knowledge point c, knowledge point d, knowledge point e}, referred to as D = {a, b, c, d, e}. In the formal background, "×" indicates that the problem and the knowledge point are related, and a blank indicates that the problem and the knowledge point are not related, such as Figure 2 ;

[0037] Step 2. Formal background (Q, D, R) Generate the labeled question class set G of the question set Q according to the formal concept analysis method, construct the corresponding hierarchical structure according to the partial order relationship between the labeled question classes in the labeled question class set G, and obtain the object intuitive graph H of the question Q , object intuitive diagram H Q Each node in represents a labeled question class, and the edges between nodes represent partial order relations, such as Figure 3 ;

[0038] The formal context (Q, D, R) generates the labeled question set G of the question set Q according to the formal concept analysis method as follows:

[0039] Step 2.1. For each question q∈Q, find all d that satisfy (q,d)∈R, where d is the knowledge point related to question q, d∈D;

[0040] Step 2.2. Build a labeled question class

[0041] Each labeled question class is a pair (A, B), where A is a subset of the question set Q and B is a subset of the knowledge point set D, satisfying the following conditions:

[0042] Condition 1: The set of knowledge points common to all questions in A is B;

[0043] Condition 2: All problem sets with knowledge point B are A;

[0044] Step 2.3. Systematically traverse the formal background and use the Galois connection "*" operation in the formal concept analysis method to generate labeled question classes;

[0045] The Galois connection "*" operation is: for any problem set Calculate its knowledge point closure X′; for any knowledge point set Calculate its problem closure Y′;

[0046] By traversing all question sets X and knowledge point sets Y and applying the "*" operation, all pairs (A, B) that satisfy X'=B and Y'=A are generated, where (A, B) is a labeled question class;

[0047] Step 2.4. Collect all generated labeled question classes (A, B) to form a labeled question class set G. The labeled question class set G in this embodiment = {({1},{a,c,d,e}),({2},{a,c}),({3},{b,e}),({4},{a}),({5},{a,b,e})}.

[0048] Step 3. Figure 4 , according to the object intuitive diagram H Q Obtain all object concepts of the attribute-oriented concept lattice to obtain an attribute-oriented concept coordination set, delete redundant concepts in the attribute-oriented concept coordination set, and obtain an attribute-oriented concept reduction, wherein the redundant concepts are object concepts that do not affect the integrity of the complementary binary relationship between the attribute-oriented concept coordination set reconstruction problem and the knowledge points;

[0049] Among them, according to the object intuitive diagram H Q The method to obtain all object concepts of the attribute-oriented concept lattice and obtain the attribute-oriented concept coordination set is:

[0050] Step 3.1. From the object intuitive graph H Q In , for each labeled question class (A, B), all the labeled question classes related to it are found downward according to the partial order structure to form a related set;

[0051] In this embodiment, the relevant set of the labeled question class ({1},{a,c,d,e}) is {({2},{a,c}),({4},{a})};

[0052] The relevant set of the labeled question class ({2},{a,c}) is {({4},{a})};

[0053] The relevant set of the labeled question class ({3},{b,e}) is an empty set;

[0054] The relevant set of the labeled question class ({4},{a}) is an empty set;

[0055] The relevant set for the labeled question class ({5},{a,b,e}) is {({3},{b,e}),({4},{a})}.

[0056] Step 3.2. For each question set in the labeled question class, combine it with all question sets in the related set that have the labeled question class as the question set of the new question class. For each knowledge point set in the labeled question class, directly use it as the knowledge point set of the new question class. The new question class is obtained as the object concept in the attribute-oriented concept lattice.

[0057] The new problem classes obtained in this embodiment are ({1,2,4},{a,c,d,e}), ({2,4},{a,c}), ({3},{b,e}), ({4},{a}), and ({3,4,5},{a,b,e}).

[0058] Step 3.3. Collect all new problem classes to form a set F Q ={({1,2,4},{a,c,d,e}),({2,4},{a,c}),({3},{b,e}),({4},{a}),({3,4,5},{a,b,e})}, the set F Q That is, the attribute-oriented concept coordination set.

[0059] Among them, ({4},{a}) is deleted as a redundant concept, and the resulting attribute-oriented concept reduction F = {({1,2,4},{a,c,d,e}), ({2,4},{a,c}), ({3},{b,e}), ({3,4,5},{a,b,e})}.

[0060] Step 4. Test students with the question set Q, collect the students' test results, remove the results of "careless wrong answers" and "guessing the right answers", and obtain the student answer data set. Conduct a cognitive assessment of the students based on the student answer data set and attribute-oriented concept reduction to obtain the assessment results. Specifically, match the student answer data with the question set in the attribute-oriented concept reduction to determine the knowledge point set corresponding to the questions answered by the students; analyze the students' performance on each knowledge point to determine which knowledge points the students perform well on and which knowledge points they have difficulties on, such as Figure 5 .

Claims

1. A cognitive assessment method based on attribute-oriented concept reduction, characterized in that: The following steps are involved: Step 1. Construct the formal context (Q, D, R) based on the knowledge point mapping τ, where Q is the question set, D is the set of knowledge points associated with the question, and R is the binary relationship between the question and the knowledge point; Step 2. Formal context (Q, D, R) Generate the labeled question class set G of the question set Q based on the formal concept analysis method, construct the corresponding hierarchical structure based on the partial order relationship between the labeled question classes in the labeled question class set G, and obtain the object intuitive graph H of the question Q , object intuitive diagram H Q Each node in represents a labeled question class, and the edges between nodes represent partial order relations; Step 3. According to the object intuitive diagram H Q Obtain all object concepts of the attribute-oriented concept lattice to obtain the attribute-oriented concept coordination set, delete the redundant concepts in the attribute-oriented concept coordination set, and obtain the attribute-oriented concept reduction F; The object intuitive map H Q The method to obtain all object concepts of the attribute-oriented concept lattice and obtain the attribute-oriented concept coordination set is: Step 3.

1. From the object intuitive graph H Q In , for each labeled question class, all the labeled question classes related to it are found downward according to the partial order structure to form a related set; Step 3.

2. For each question set in the labeled question class, combine it with all question sets in the related set that also have labeled question classes to form the question set of the new question class. For each knowledge point set in the labeled question class, directly use it as the knowledge point set of the new question class. This new question class is then used as the object concept in the attribute-oriented concept lattice. Step 3.

3. Collect all new problem classes to form a set F Q , the set F Q That is, it is an attribute-oriented concept coordination set; The redundant concepts are object concepts that do not affect the integrity of the complementary binary relationship between the attribute-oriented concept coordination set reconstruction problem and the knowledge points; Step 4. Test students with the question set Q, collect their test results, remove the results of "careless wrong answers" and "guessing correct answers", and obtain the student answer data set. Based on the student answer data set and attribute-oriented concept reduction, conduct a cognitive assessment of the students and obtain the assessment results.

2. The cognitive assessment method based on attribute concept reduction according to claim 1 is characterized in that: The method for generating the labeled question class set G of the question set Q based on the formal concept analysis method is as follows: Step 2.

1. For each question q∈Q, find all d that satisfy (q,d)∈R, where d is the knowledge point related to question q, d∈D; Step 2.

2. Build a labeled question class Each labeled question class is a pair (A, B), where A is a subset of the question set Q and B is a subset of the knowledge point set D, satisfying the following conditions: Condition 1: The set of knowledge points common to all questions in A is B; Condition 2: All problem sets with knowledge point B are A; Step 2.

3. Systematically traverse the formal background using the Galois connection from the formal concept analysis method Operation, generating labeled question classes; The Galois connection The operation is: For any problem set , calculate its knowledge point closure X′; for any knowledge point set , calculate its problem closure Y′; The method for generating labeled question classes is: traversing all question sets X and knowledge point sets Y, and applying Operation, generate all pairs (A, B) that satisfy X′=B and Y′=A, (A, B) is a labeled question class; Step 2.

4. Collect all generated labeled question classes (A, B) to form a labeled question class set G.

3. The cognitive assessment method based on attribute concept reduction according to claim 1 is characterized in that: The method for cognitively evaluating students based on student answer data sets and attribute-oriented concept simplification is as follows: matching the student answer data with the object concepts obtained in the attribute-oriented concept simplification, analyzing the student's performance on each knowledge point, and determining which knowledge points the student performs well on and which knowledge points he or she has difficulties with.

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