Learner argumentation process effect evaluation method in critical thinking training mode

Abstract

The invention discloses a learner argumentation process effect evaluation method in a critical thinking training mode, relates to the technical field of natural languages and knowledge maps, and aims to solve the key problem that the argumentation process is difficult to quantify and track in traditional thinking evaluation. According to the method, knowledge triples in a demonstration text are extracted to construct a knowledge graph, a graph database is adopted to store and visualize the knowledge graph, and a demonstration knowledge graph is obtained; generating a generated argument path, and obtaining a reference argument path set; determining a maximum intersection path of the observation argument path and the reference argument path; the evaluation value of the maximum intersection path is calculated; and the evaluation value of the critical thinking demonstration effect is calculated. According to the method, a bidirectional characterization mechanism of the observation argument path and the reference argument path is established, and path intersection discovery and path evaluation calculation are utilized, so that refined tracking and quantitative evaluation of the argument process of the learner are finally realized, data support is provided for teaching intervention, and systematic development of critical thinking ability is promoted.

Description

Technical Field

[0001] The present invention relates to the field of natural language and knowledge graph technology, and specifically to a method for evaluating the effectiveness of a learner's argumentation process under a critical thinking training model. Background Art

[0002] Because thinking development is implicit and complex, traditional assessment methods struggle to objectively and systematically measure the argumentation process of critical thinking. For example, existing technologies (such as keyword extraction and simple scoring) can only perform surface text analysis and are unable to transform argument content into structured entity-relationship triples, making it difficult to accurately capture the logical structure. Traditional tools (such as the CCTDI scale) only provide general dimensional scores and cannot identify specific logical flaws in the argumentation process (such as insufficient premises and broken reasoning), making it difficult to accurately guide improvement. Moreover, existing methods rely on static rules or fixed scoring criteria, making it difficult to adapt to the dynamic changes in different disciplines or argumentation styles and lack the ability to automatically match standard reference paths.

[0003] Therefore, the present invention provides a method for evaluating the effectiveness of learners' argumentation process in a critical thinking training mode. Summary of the Invention

[0004] In order to solve the key problem that the thinking process in the current teaching evaluation system is difficult to objectively represent and quantitatively evaluate, the present invention proposes a method for evaluating the effectiveness of learners' argumentation process under the critical thinking training model.

[0005] The evaluation method of learners' argumentation process effectiveness in the critical thinking training model is implemented by the following steps:

[0006] Step 1: Extract knowledge triples from the argument text to construct a knowledge graph, use a graph database to store and visualize the knowledge graph, and obtain the argument knowledge graph;

[0007] Step 2: Obtain the argumentation knowledge graph based on step 1, generate the argumentation paths that have occurred, and obtain a reference argumentation path set;

[0008] Step 3: Determine the maximum intersection path of the observation demonstration path and the reference demonstration path;

[0009] Step 4: Calculate the evaluation value of the maximum intersection path, and calculate the evaluation value of the critical thinking argumentation effect based on the evaluation value.

[0010] Furthermore, in step 1, natural language processing tools are used to extract knowledge triples from the learner's argument text and output a set of triples. , where h represents the head entity, r represents the relationship, and t represents the tail entity; the entities in the triples are used as nodes and the relationships as edges to construct a directed graph. Based on the directed graph, a graph database is used to store and visualize the knowledge graph, and the argument knowledge graph is output. ,in, is a collection of nodes, is the edge set.

[0011] Furthermore, the specific process of step 2 is:

[0012] Step 2.1: Initialize the observation and demonstration path set and a list of temporary paths ;

[0013] Step 22: Determine the temporary path list Is it not empty? If so, Extract a path , set the path The first node is , the tail node is ; The remaining path set is ,renew , execute steps 2 and 3; otherwise, execute step 3;

[0014] Step 2 and 3: Judgment Is it not empty? If so, Take out a triple , set the triple The first node is , the tail node is , the remaining triples are ,renew , execute step 24; otherwise, return to execute step 22;

[0015] Step 24: If or , execute step 23; otherwise, execute step 25;

[0016] Step 25: If , then for the path To expand, that is: , and in Update path , execute step 26; otherwise, execute step 23;

[0017] Step 26: If , then for the path To expand, that is: , and in Update path ; Execute step 27, otherwise execute step 23;

[0018] Step 27: If or ,but , execute step 3; otherwise, execute step 22;

[0019] Step 3: Obtain a set of reference argumentation paths ;

[0020] Furthermore, the specific process of step three is:

[0021] Step 3.1. Initialize the intersection path set

[0022] Step 32: If Not empty, from Take out an observational proof path , the remaining path set is ,renew , execute step 33; if If it is empty, go to step 4;

[0023] Step 3. If Not empty, from Take out an observational proof path , the remaining path set is ,renew ; Execute steps 3 and 4; if If it is empty, go to step 32;

[0024] Step 34: Obtaining the Observational Demonstration Path and reference argumentation paths The maximum intersection path ,

[0025] Furthermore, in step 4, the evaluation value of the maximum intersection path is calculated , and return to step 32; evaluation value It can be expressed as follows: ;

[0026] Where, is the number of nodes of the maximum intersection path, is the number of nodes in the observation demonstration path, The number of nodes in the reference argument path.

[0027] Furthermore, the number of nodes of the maximum intersection path ;

[0028] The number of nodes in the observation demonstration path ;

[0029] The number of nodes in the reference argument path .

[0030] Furthermore, the evaluation value of critical thinking argumentation effect is calculated , which can be expressed as follows: .

[0031] The present invention also provides a learner argumentation process effectiveness evaluation system, which includes a memory, a processor, and a learner argumentation process effectiveness evaluation program under a critical thinking training mode stored in the memory and running on the processor. When the effectiveness evaluation program of the critical thinking learning model is executed by the processor, the steps of the learner argumentation process effectiveness evaluation method under the critical thinking training mode are implemented.

[0032] Beneficial effects of the present invention: The evaluation method described in the present invention, combined with knowledge graph modeling and dynamic path matching methods, has achieved a breakthrough in the evaluation of critical thinking. By converting traditional subjective scoring into quantifiable calculations, it can not only objectively evaluate the quality of thinking, but also accurately diagnose specific defects, such as common problems such as logical breaks or incorrect premises. With the help of natural language processing and automated path matching technology, the evaluation efficiency is greatly improved. At the same time, the evaluation process is presented through an intuitive visual knowledge graph, allowing users to clearly understand the logical basis behind the scoring. This intelligent evaluation method not only ensures the accuracy of the results, but also improves the transparency of the process, providing a new technical support for the cultivation of critical thinking.

[0033] The method described in the present invention establishes a two-way representation mechanism of observed argumentation paths and reference argumentation paths, adopts path intersection discovery and path evaluation calculation, and realizes refined tracking and quantitative evaluation of learners' argumentation process.

[0034] This method uses natural language processing to transform textual arguments into entity-relationship triplets and constructs a visual knowledge graph, achieving a structured digital mapping of the thought process (existing technologies often rely on keyword extraction or simple scoring). Its innovative dynamic path matching algorithm progressively compares learners' argument paths with standard reference paths, employing a critical thinking argument effectiveness assessment method to achieve objective evaluation (traditional methods rely on manual qualitative evaluation). Compared to existing technologies, this method offers a breakthrough in process diagnostic capabilities: it not only identifies the correctness of conclusions but also pinpoints specific flaws in the logical chain. Existing tools (such as the CCTDI scale) only provide general dimensional scores and fail to provide a detailed analysis of the argument process. This assessment method not only effectively captures the logical coherence and depth of thought in the argument process, but also provides data support for educational interventions, thereby promoting the systematic development of critical thinking skills. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A brief flow chart of the method for evaluating the effectiveness of learners' argumentation process in the critical thinking training model of the present invention;

[0036] Figure 2 A detailed flow chart of the method for evaluating the effectiveness of learners' argumentation process in the critical thinking training model of the present invention;

[0037] Figure 3 A diagram showing the effects of the evaluation using the method of the present invention (basic demonstration);

[0038] Figure 4 This is a diagram showing the effects of the evaluation using the method of the present invention (multi-angle analysis);

[0039] Figure 5 This is a diagram showing the effects of using the method of the present invention for evaluation (systematic thinking). DETAILED DESCRIPTION

[0040] Specific implementation method 1. Combination Figures 1 to 5 This embodiment describes a method for evaluating the effectiveness of learners' argumentation process in a critical thinking training mode. The method is implemented by the following steps:

[0041] Step 1: Use natural language processing (NLP) tools to extract knowledge triples from learners’ argument texts and organize the extracted entities and relations into triples. , the output is a set of triples , where h represents the head entity, r represents the relationship, and t represents the tail entity;

[0042] Step 2: Construct the extracted triples into a knowledge graph. The entities in the triples are used as nodes and the relationships as edges to construct a directed graph. Use a graph database to store and visualize the knowledge graph, and output it as an argument knowledge graph. ,in is a collection of nodes, is the edge set;

[0043] Step 3: Generate the argument path that has occurred and obtain the reference argument path set ; The specific process is:

[0044] Step 31: Initialize the observation argument path set , initialize the temporary path list ;

[0045] Step 32: If Not empty, from Extract a path , the path The first node is , the tail node is , the remaining set is recorded as ,renew , go to step 33; if If it is empty, go to step 4;

[0046] Step 33: If Not empty, from Take out a triple , triples The first node is , the tail node is , the remaining set is recorded as ,renew , go to step 34; if If it is empty, go to step 32;

[0047] Step 34: If or , go to step 33; otherwise, go to step 35;

[0048] Step 35: If , then the extended path , and in Update path , go to step 36, otherwise go to step 33;

[0049] Step 36: If , then the extended path , and in Update path , go to step 37, otherwise go to step 33;

[0050] Step 37: If , , go to step 4, otherwise go to step 32;

[0051] Step 4: Process the reference answer argument text according to steps 1 to 3 to obtain a reference argument path set ; Execute step 5;

[0052] Step 5: For , , find the observational proof path and reference argumentation paths The maximum intersection path includes the following steps:

[0053] Step 51: Initialize the intersection path set ;

[0054] Step 52: If Not empty, from Take out an observational proof path , the remaining set is recorded as ,renew ; Execute step 53; if If it is empty, go to step 6;

[0055] Step 53: If Not empty, from Take out an observational proof path , the remaining path set is ,renew ; Execute step 54; if If it is empty, return to step 52;

[0056] Step 54: Find the Observational Argument Path and reference argumentation paths The maximum intersection path .

[0057] Step 55: Calculate the number of nodes of the maximum intersection path ;

[0058] Step 56: Calculate the number of nodes in the observation argument path ;

[0059] Step 57: Calculate the number of nodes in the reference argument path ;

[0060] Step 58: Calculate the evaluation value of the maximum intersection path , execute step 52; ;

[0061] Step 6: Calculate the evaluation value of critical thinking argument effectiveness ; .

[0062] like Figures 3 to 5 As shown, the evaluation method described in this embodiment is used to achieve a multi-dimensional comparison and analysis of the learner's argument path and the reference model through an improved path matching algorithm. Figure 3 、 Figure 4 and Figure 5 , and calculate the quantitative critical thinking argumentation effect evaluation value E, thereby providing an objective evaluation for learners' critical thinking development at different stages, and realizing an innovative transformation from traditional subjective experience judgment to scientific quantitative analysis.

[0063] Specific embodiment 2. This embodiment provides a system for evaluating the effectiveness of a learner's argumentation process. The system includes a memory, a processor, and a program for evaluating the effectiveness of a learner's argumentation process under a critical thinking training model stored in the memory and executable on the processor. When the program for evaluating the effectiveness of the critical thinking learning model is executed by the processor, the steps of the method for evaluating the effectiveness of a learner's argumentation process under a critical thinking training model as described in specific embodiment 1 are implemented.

[0064] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0065] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. The characteristics of the evaluation method of learners' argumentation process in the critical thinking training model are: The method is implemented by the following steps: Step 1: Extract knowledge triples from the argument text to construct a knowledge graph, use a graph database to store and visualize the knowledge graph, and obtain the argument knowledge graph; Step 2: Obtain the argumentation knowledge graph based on step 1, generate the argumentation paths that have occurred, and obtain a reference argumentation path set; Step 3: Determine the maximum intersection path of the observation demonstration path and the reference demonstration path; Step 4: Calculate the evaluation value of the maximum intersection path, and calculate the evaluation value of the critical thinking argumentation effect based on the evaluation value.

2. The method for evaluating the effectiveness of learners' argumentation process in a critical thinking training model according to claim 1 is characterized by: Use natural language processing tools to extract knowledge triples from learners' argument texts and output a set of triples , where h represents the head entity, r represents the relationship, and t represents the tail entity; the entities in the triples are used as nodes and the relationships as edges to construct a directed graph. Based on the directed graph, a graph database is used to store and visualize the knowledge graph, and the argument knowledge graph is output. ,in, is a collection of nodes, is the edge set.

3. The method for evaluating the effectiveness of learners' argumentation process in a critical thinking training model according to claim 2 is characterized by: The specific process of step 2 is: Step 2.1: Initialize the observation and demonstration path set and a list of temporary paths ; Step 22: Determine the temporary path list Is it not empty? If so, Extract a path , set the path The first node is , the tail node is ; The remaining path set is ,renew , execute steps 2 and 3; Otherwise, go to step 3; Step 2 and 3: Judgment Is it not empty? If so, Take out a triple , set the triple The first node is , the tail node is , the remaining triples are ,renew , execute step 24; otherwise, return to execute step 22; Step 24: If or , execute steps 2 and 3; Otherwise, execute step 25; Step 25: If , then for the path To expand, that is: , and in Update path , execute step 26; Otherwise, execute steps 2 and 3; Step 26: If , then for the path To expand, that is: , and in Update path ; Execute step 27, otherwise execute step 23; Step 27: If or ,but , execute step 3; Otherwise, execute step 22; Step 3: Obtain a set of reference argumentation paths .

4. The method for evaluating the effectiveness of learners' argumentation process in a critical thinking training model according to claim 3 is characterized by: The specific process of step three is: Step 3.

1. Initialize the intersection path set ; Step 32: If Not empty, from Take out an observational proof path , the remaining path set is ,renew , execute step 33; if If it is empty, go to step 4; Step 3. If Not empty, from Take out an observational proof path , the remaining path set is ,renew ; Execute steps 3 and 4; if If it is empty, go to step 32; Step 34: Obtaining the Observational Demonstration Path and reference argumentation paths The maximum intersection path .

5. The method for evaluating the effectiveness of learners' argumentation process in a critical thinking training model according to claim 1 is characterized by: In step 4, the evaluation value of the maximum intersection path is calculated , which can be expressed as follows: ； Where, is the number of nodes of the maximum intersection path, is the number of nodes in the observation demonstration path, The number of nodes in the reference argument path.

6. The method for evaluating the effectiveness of learners' argumentation process in a critical thinking training model according to claim 5 is characterized by: The number of nodes of the maximum intersection path ; The number of nodes in the observation demonstration path ; The number of nodes in the reference argument path .

7. The method for evaluating the effectiveness of learners' argumentation process in a critical thinking training model according to claim 6 is characterized by: Calculate the evaluation value of critical thinking argument effectiveness , which can be expressed as follows: 。 8. A system for evaluating the effectiveness of a learner's argumentation process, characterized by: The system includes a memory, a processor, and a program for evaluating the effectiveness of the learner's argumentation process under a critical thinking training model, which is stored in the memory and runs on the processor. When the program for evaluating the effectiveness of the critical thinking learning model is executed by the processor, the steps of the method for evaluating the effectiveness of the learner's argumentation process under a critical thinking training model as described in any one of claims 1 to 7 are implemented.

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