A classroom achievement analysis method and system applied to critical thinking guided teaching
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NANCHANG MEDICAL COLLEGE
- Filing Date
- 2026-04-29
- Publication Date
- 2026-07-21
Smart Images
Figure CN122433993A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of critical thinking education, and in particular to a method and system for analyzing classroom outcomes in critical thinking-guided teaching. Background Technology
[0002] With the deepening of educational informatization, the analysis of educational outcomes is undergoing a transformation from traditional subjective experience-based judgment to data-driven objective quantitative analysis. Particularly in the field of higher-order thinking skills development, effectively capturing students' cognitive processes has become a research hotspot. Critical thinking ability, as a crucial component of core competencies, has implicit and dynamic characteristics in its teaching process. Relying solely on teacher observation and outcome-oriented assessments cannot fully reflect the trajectory of students' thinking development. How to construct a systematic framework for guiding critical thinking instruction and leverage multimodal data fusion technology to achieve precise quantitative analysis of the critical thinking process has become a key issue urgently needing breakthroughs in the field of educational technology.
[0003] Current analyses of critical thinking teaching outcomes largely rely on subjective observation and outcome-based assessments, lacking dynamic tracking and quantification methods for students' critical thinking processes. Traditional methods struggle to integrate classroom performance data with thinking outcomes, resulting in lagging and one-sided evaluations of teaching effectiveness. Furthermore, adjustments to guidance strategies depend heavily on teacher experience, lacking objective, data-driven evidence, and thus failing to achieve adaptive optimization of the teaching process. Summary of the Invention
[0004] The purpose of this application is to provide a method and system for analyzing classroom outcomes in critical thinking-guided teaching, which can enhance the quantitative level of classroom outcome analysis, achieve objective evaluation of teaching effectiveness and optimization of guidance strategies, and improve the quality of critical thinking teaching.
[0005] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for analyzing classroom outcomes in guided critical thinking instruction. The guided instruction comprises four stages: non-interference analysis, superficial guidance, free debate, and in-depth guidance. The method includes: selecting target critical thinking topics; recording students' classroom performance in real time to obtain classroom performance data; the classroom performance data includes video and physiological signals; the physiological signals include at least skin conductance and heart rate; in the non-interference analysis stage, students are asked to conduct critical thinking analysis on the target critical thinking topics and construct a critical thinking map for the non-interference analysis stage; the critical thinking analysis process is as follows: following the critical thinking logic of argumentation-debate-dialectics... The process involves analyzing and discussing the target argumentative proposition. The proposed argumentative map is a tree-like structure built around the target argumentative proposition as the central node, with supporting arguments, opposing arguments, evidence, and logical connections added outwards. In the initial guidance phase, the teacher provides basic guidance to students, followed by argumentative analysis and the creation of the argumentative map for this phase. In the free debate phase, the teacher guides students in a free debate on the target argumentative proposition within a pre-set timeframe, followed by argumentative analysis and the creation of the argumentative map for this phase. Finally, in the in-depth guidance phase, the teacher provides further in-depth guidance to students. The process involves guiding students to engage in critical thinking and analysis after in-depth guidance, and constructing a critical thinking map for the in-depth guidance stage. For each stage, explicit and implicit features of critical thinking cognition are extracted based on students' classroom performance data, resulting in explicit and implicit feature tensors. Critical thinking ability features are extracted from the critical thinking map, resulting in a critical thinking ability feature tensor. The explicit and implicit feature tensors include: explicit feature tensors and implicit feature tensors. The explicit feature tensors include: micro-expression features, pupillary response features, turn-taking features, verbal interaction features, and body movement amplitude features. The implicit feature tensors include: skin conductance response features and heart rate change features. Canonical correlation analysis is then used to analyze these features. The maximum correlation between explicit and implicit feature tensors and critical thinking ability feature tensors is analyzed to perform feature fusion, resulting in a multimodal fused feature tensor. Using a state-space model, critical thinking ability is treated as a latent state, and the multimodal fused feature tensor during the alternation of each stage is used as observation data. The evolution trend of the latent state is estimated through the observation data, yielding a critical thinking ability change curve. Linear fitting is performed on the critical thinking ability change curves of all students in the classroom to obtain a critical thinking-guided classroom outcome fitting curve. Based on the trend of students' critical thinking ability change in the critical thinking-guided classroom outcome fitting curve, classroom outcome analysis is conducted, and the corresponding stages in subsequent classroom teaching are optimized.
[0006] Optionally, this includes: selecting multiple initial critical thinking propositions from a critical thinking proposition bank or historical critical thinking course propositions; extracting subject characteristics from the initial critical thinking propositions and using a natural language processing model to screen out critical thinking propositions that match the subject of the students in the classroom, thus obtaining preliminary screening critical thinking propositions; analyzing the critical thinking guidance anchors of the preliminary screening critical thinking propositions through simulated critical thinking analysis; the critical thinking guidance anchors include: shallow guidance anchors and deep guidance anchors; selecting preliminary screening critical thinking propositions that simultaneously contain both shallow and deep guidance anchors to obtain secondary screening critical thinking propositions; performing weighted analysis on the shallow and deep guidance anchors of the secondary screening critical thinking propositions, and selecting the secondary screening critical thinking proposition with the highest total weight as the target critical thinking proposition.
[0007] Optionally, in the non-interference analysis stage, students are asked to conduct critical analysis of the target proposition and construct a critical analysis map for the non-interference analysis stage. Specifically, in the non-interference analysis stage, the teacher does not intervene or guide the students to conduct critical analysis of the target proposition independently. After the students have completed their critical analysis, the knowledge graph is used to decompose and summarize the students' critical analysis process and draw a critical analysis map for the non-interference analysis stage.
[0008] Optionally, in the preliminary guidance stage, the teacher provides preliminary guidance to students. After the preliminary guidance, students are asked to conduct critical analysis and construct a critical thinking map for the preliminary guidance stage. Specifically, this includes: In the preliminary guidance stage, the teacher guides students to find the entry point for critical thinking by clarifying the concepts of key words in the target critical thinking proposition, analyzing the relationship between key words, and expanding their thinking with examples, but does not intervene in the analysis of the arguments. After the preliminary guidance, students are asked to conduct critical analysis. After the students' critical analysis, a knowledge graph is used to decompose and summarize the students' critical analysis process and draw a critical thinking map for the preliminary guidance stage.
[0009] Optionally, during the free debate phase, the teacher guides students to engage in a free debate on the target argumentative proposition within a pre-set time. After the free debate, students are asked to conduct analytical analysis and construct a analytical framework for the free debate phase. Specifically, this includes: during the free debate phase, the teacher divides students into groups with differing viewpoints and guides them to engage in one-on-one or one-to-many free debates on the target argumentative proposition within a pre-set time. During the debate, the teacher guides students who are stuck in an argumentative deadlock to break free from it. After the free debate, students are guided to summarize the changes in their viewpoints before and after the debate and conduct analytical analysis. After the students' analytical analysis, a knowledge graph is used to decompose and summarize the students' analytical process and draw an analytical framework for the free debate phase.
[0010] Optionally, in the in-depth guidance stage, the teacher provides in-depth guidance to students. After the in-depth guidance, students are asked to conduct critical analysis and construct a critical thinking map for the in-depth guidance stage. Specifically, this includes: In the in-depth guidance stage, the teacher guides students to develop their arguments in depth by asking follow-up questions about logical flaws, comparing opposing viewpoints, and inspiring value reflection. After the in-depth guidance, students are asked to conduct critical analysis. After the students' critical analysis, a knowledge graph is used to decompose and summarize the students' critical analysis process, and a critical thinking map for the in-depth guidance stage is drawn.
[0011] Optionally, for each stage, explicit and implicit features of critical thinking cognition are extracted from students' classroom performance data to obtain explicit and implicit feature tensors; critical thinking ability features are extracted from the critical thinking graph to obtain a critical thinking ability feature tensor. Specifically, this includes: chronologically aligning the classroom performance data for each stage and updating the classroom performance data; using OpenPose and conversation analysis algorithms to extract students' micro-expression features, pupillary response features, turn-taking features, verbal interaction features, and body movement amplitude features from the videos of classroom performance data for each stage; using continuous decomposition analysis to extract skin conductance response features from the skin conductance response of classroom performance data for each stage; using heart rate variability analysis to extract heart rate change features from the heart rate of classroom performance data for each stage; and based on the critical thinking graph for each stage, using graph theory algorithms to extract core argument influence features and logical depth features respectively, and constructing a critical thinking ability feature tensor.
[0012] Optionally, classroom outcomes can be analyzed based on the changing trends of students' critical thinking abilities in the fitted curve of critical thinking-guided classroom outcomes, and the corresponding stages of subsequent classroom teaching can be optimized. Specifically, this includes: when the critical thinking ability in the fitted curve of critical thinking-guided classroom outcomes shows a continuous upward trend, then in the corresponding stage of subsequent classroom teaching, basic concepts should be reviewed or the guidance time should be shortened; when the critical thinking ability in the fitted curve of critical thinking-guided classroom outcomes shows a continuous downward trend, then in the corresponding stage of subsequent classroom teaching, low-level critical thinking guidance should be introduced and the guidance time should be extended; when the critical thinking ability in the fitted curve of critical thinking-guided classroom outcomes shows stagnation, then in the corresponding stage of subsequent classroom teaching, the questioning method should be adjusted or cognitive conflict should be stimulated by providing more diverse perspectives; when the critical thinking ability in the fitted curve of critical thinking-guided classroom outcomes shows drastic fluctuations, then logical contradictions should be addressed in the initial stage of the corresponding stage of subsequent classroom teaching.
[0013] Optionally, the linear fit is a least-squares fit.
[0014] Secondly, this application also provides a computer system, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the classroom outcome analysis method for critical thinking-guided teaching described in the first aspect.
[0015] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application improves the quantitative level of classroom outcome analysis by dividing teaching into four stages: non-interference analysis, superficial guidance, free debate, and in-depth guidance. First, target critical thinking topics are selected, and students' videos and physiological signals such as skin conductance and heart rate are collected in real time at each stage as classroom performance data. Students are guided to construct a tree-like critical thinking map according to the logic of argumentation-debate-dialectics after each stage. For each stage, explicit and implicit feature tensors are extracted from the performance data, and critical thinking ability feature tensors are extracted from the critical thinking map. Canonical correlation analysis is used to deeply fuse the two types of feature tensors to obtain a multimodal fused feature tensor, which improves the representational ability of subsequent outcome analysis. Then, using a state-space model, critical thinking ability is treated as a latent state, and the fused features during the alternation of each stage are used as observation data to estimate the dynamic evolution trend of critical thinking ability, generating individual critical thinking ability change curves. Finally, linear fitting is performed on the change curves of all students to obtain classroom outcome fitting curves, and the subsequent teaching stages are optimized based on the trend of the curves. This application enhances the quantitative level of classroom outcome analysis, enables objective evaluation of teaching effectiveness and optimization of guidance strategies, and improves the quality of critical thinking teaching. Attached Figure Description
[0016] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a flowchart illustrating a classroom outcome analysis method for critical thinking-guided teaching, provided as an embodiment of this application.
[0018] Figure 2 This is a flowchart illustrating the various stages of guided instruction provided in the embodiments of this application.
[0019] Figure 3 The internal structure diagram of the computer system provided in this application embodiment is shown. Detailed Implementation
[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0021] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0022] Example 1, as Figures 1-2 As shown, this embodiment provides a method for analyzing classroom outcomes in critical thinking-guided teaching. The guided teaching process includes four stages: non-interference analysis, superficial guidance, free debate, and in-depth guidance. The method includes: S1. Select target-oriented speculative propositions.
[0023] Furthermore, step S1 specifically includes: S11. Select several initial speculative propositions from the speculative proposition bank or the propositions in the historical speculative course.
[0024] S12. Extract subject characteristics from the initial critical thinking propositions and use a natural language processing model to screen out critical thinking propositions that match the subject of the students in the classroom, thus obtaining the initial screening of critical thinking propositions.
[0025] S13. Analyze the reasoning guidance anchors of the initial screening reasoning propositions through simulated reasoning analysis; the reasoning guidance anchors include: shallow guidance anchors and deep guidance anchors.
[0026] S14. Select the initial screening speculative propositions that simultaneously contain both shallow and deep guiding anchor points to obtain the secondary screening speculative propositions.
[0027] S15. Perform weighted analysis on the shallow and deep guiding anchor points of the re-screened speculative propositions, and select the re-screened speculative proposition with the highest total weight as the target speculative proposition.
[0028] In practical application, step S1 uses subject matching and natural language processing to screen topics to ensure they match students' cognitive background; it uses simulated critical thinking analysis to identify deep and shallow guidance anchors and assigns weights to quantitatively optimize the selection, ensuring that the topics have explorability and depth of critical thinking, thereby improving guidance efficiency and teaching relevance.
[0029] S2. Record students' classroom performance in real time to obtain classroom performance data; the classroom performance data includes: video and physiological signals; the physiological signals include at least: skin conductance response and heart rate.
[0030] In practical applications, video is captured via camera recording, while physiological signals are collected by having students wear wristbands. These physiological signals also include electroencephalography (EEG) and eye movements (eye tracking and pupillary responses). Eye tracking records eye movement trajectories, fixation points, and pupil diameter to analyze students' attention allocation and points of interest. Pupil responses correlate changes in pupil size with cognitive load and emotional arousal.
[0031] S3. In the non-interference analysis stage, students are asked to conduct a critical analysis of the target proposition and construct a critical analysis diagram for the non-interference analysis stage. The critical analysis process is as follows: the target proposition is analyzed and discussed according to the critical logic of argumentation-debate-dialectics. The critical analysis diagram is a tree-like thinking structure diagram constructed with the target proposition as the central node and supporting arguments, opposing arguments, evidence, and logical connecting lines added outward.
[0032] Furthermore, step S3 specifically includes: S31. In the non-interference analysis stage, the teacher does not intervene or guide the students, who are asked to independently conduct critical analysis of the target proposition.
[0033] S32. After the students finish their critical thinking analysis, use a knowledge graph to decompose and summarize the students' critical thinking analysis process, and draw a critical thinking graph for the non-interference analysis stage.
[0034] Optionally, establishing an argument involves putting forward a clear viewpoint and setting the direction of the argument; argumentation involves using facts, logic, or theory to support the viewpoint and constructing a chain of reasoning; dialectics involves examining opposing viewpoints, clarifying one's own limitations, and deepening understanding through refutation and absorption.
[0035] In practical applications, the non-interference analysis stage collects students' original reasoning state without teacher intervention and visualizes the thinking process by constructing a reasoning map. This map can realistically reflect students' initial logical structure and cognitive starting point, providing a quantitative benchmark for comparing the effectiveness of subsequent guidance stages and identifying individual students' weaknesses in reasoning.
[0036] S4. In the initial guidance stage, the teacher provides initial guidance to the students. After the initial guidance, the students are asked to conduct critical thinking and analysis and construct a critical thinking map for the initial guidance stage.
[0037] Furthermore, step S4 specifically includes: S41. In the initial guidance stage, the teacher guides students to find the entry point for critical thinking by clarifying the concepts of key words in the target argument, analyzing the relationship between key words, and expanding their thinking with examples, but does not intervene in the analysis of the argument. After the initial guidance, the teacher asks students to conduct critical analysis.
[0038] S42. After the students finish their critical thinking and analysis, use a knowledge graph to decompose and summarize the students' critical thinking and analysis process, and draw a critical thinking graph for the shallow guidance stage.
[0039] In practical applications, superficial guidance is merely a way for teachers to clarify concepts and expand students' thinking in the classroom, helping them break through the initial thinking bottleneck and enabling their reasoning map to move from vague and scattered to clear and structured.
[0040] S5. During the free debate stage, the teacher guides students to engage in a free debate on the target argument within a pre-set time. After the free debate, students are asked to conduct a critical analysis and construct a critical thinking map for the free debate stage.
[0041] Furthermore, step S5 specifically includes: S51. During the free debate phase, the teacher divides students into groups with differing viewpoints and guides them to engage in free debate on the target argumentative proposition in a one-on-one or one-to-many manner within a pre-set time. During the debate, the teacher also guides students who are stuck in an argumentative deadlock to break out of it.
[0042] S52. After the free debate, guide students to summarize the changes in their own viewpoints before and after the debate, and ask students to conduct critical thinking and analysis.
[0043] S53. After the students finish their critical thinking and analysis, use a knowledge graph to break down and summarize the students' critical thinking and analysis process, and draw a critical thinking graph for the free debate stage.
[0044] In practical applications, the free debate stage, through the confrontation of viewpoints and the clash of arguments, inspires students to reflect on the shortcomings of their own arguments and absorb opposing viewpoints, which can promote the evolution of the reasoning framework from linear argumentation to a dialectical structure.
[0045] S6. In the in-depth guidance stage, the teacher provides in-depth guidance to the students. After the in-depth guidance, the students are asked to conduct critical analysis and construct a critical thinking map for the in-depth guidance stage.
[0046] Furthermore, step S6 specifically includes: S61. In the in-depth guidance stage, the teacher guides students to develop their arguments in depth by asking follow-up questions about logical loopholes, comparing opposing viewpoints, and inspiring value reflection. After the in-depth guidance, students are asked to conduct critical analysis.
[0047] S62. After the students finish their critical thinking and analysis, use a knowledge graph to decompose and summarize the students' critical thinking and analysis process, and draw a critical thinking graph for the in-depth guidance stage.
[0048] In practical application, the in-depth guidance stage guides students to see beyond the surface and grasp the essence through critical questioning and value reflection, which can promote critical thinking from the level of argumentation skills to the level of value judgment and meaning construction.
[0049] S7. For each stage, extract explicit and implicit features of critical thinking cognition based on students' classroom performance data to obtain explicit and implicit feature tensors; extract critical thinking ability features from the critical thinking graph to obtain critical thinking ability feature tensors; the explicit and implicit feature tensors include: explicit feature tensors and implicit feature tensors; the explicit feature tensors include: micro-expression features, pupillary response features, turn-taking features, verbal interaction features, and body movement amplitude features; the implicit feature tensors include: skin conductance response features and heart rate change features.
[0050] Furthermore, step S7 specifically includes: S71. Align classroom performance data for each stage according to time sequence and update the classroom performance data.
[0051] S72. Using OpenPose and conversation analysis algorithms, extract students' micro-expression features, pupil response features, turn-taking features, verbal interaction features, and body movement amplitude features from videos of classroom performance data at each stage.
[0052] S73. The continuous decomposition analysis method was used to extract the characteristics of the skin conductance response from the skin conductance response data of each stage of classroom performance.
[0053] S74. Heart rate variability analysis was used to extract heart rate variation characteristics from the heart rate data of classroom performance at each stage.
[0054] S75. Based on the reasoning graphs of each stage, extract the core argument influence features and logical depth features using graph theory algorithms, and construct a reasoning ability feature tensor.
[0055] In practical applications, step S7 extracts explicit behaviors and implicit physiological characteristics from multiple dimensions, overcoming the limitations of a single dimension and improving the ability to represent the state of reasoning and cognition.
[0056] S8. The maximum correlation between the explicit and implicit feature tensors and the speculative ability feature tensor is analyzed by canonical correlation analysis to perform feature fusion and obtain the multimodal fused feature tensor.
[0057] Optionally, canonical correlation analysis involves solving the covariance matrix of the explicit and implicit feature tensors and the speculative ability feature tensor, and then using eigenvalue decomposition to find the projection vector that maximizes the correlation between the linear combinations of the two sets of features.
[0058] In practical applications, canonical correlation analysis (CCI) fuses two heterogeneous features by finding the maximum correlation between them, thus eliminating redundancy and differences between modalities and unifying students' external behaviors, physiological signals, and cognitive outcomes into a single representation space. Furthermore, the fused feature tensor possesses multi-dimensional information, enhancing its representational ability of thought processes.
[0059] S9. Using a state-space model, reasoning ability is taken as a potential state, and the multimodal fusion feature tensor in the alternation of each stage is taken as the observation data. The evolution trend of the potential state is estimated through the observation data to obtain the change curve of reasoning ability.
[0060] In practical applications, the state-space model of this application defines critical thinking ability as an unobservable latent state variable that evolves over time, while the multimodal fusion feature tensor obtained from each teaching stage is used as observable data. The state equation describes the dynamic evolution of critical thinking ability from the previous stage to the current stage, typically assumed to be a first-order Markov process; the observation equation establishes the mapping relationship between critical thinking ability at the current stage and the multimodal fusion feature. Through Kalman filtering, the model can recursively estimate the posterior probability distribution of the latent state based on historical observation data, achieving dynamic inference of the critical thinking ability value at each stage. Connecting the critical thinking abilities analyzed from the four stages in chronological order yields a critical thinking ability change curve describing the trajectory of students' critical thinking ability as the teaching process progresses.
[0061] S10. Perform linear fitting on the curves of changes in critical thinking ability of all students in the classroom to obtain the fitting curve of the critical thinking-guided classroom outcome.
[0062] Furthermore, the linear fit is a least-squares fit.
[0063] In practical applications, least squares fitting transforms the curve of individual critical thinking ability changes into a general classroom curve, enabling cognitive analysis from the micro-level of individuals to the macro-level of the group. The fitted curve can visually present the overall evolution trend of the class's critical thinking ability and quantitatively evaluate the guidance effect at each teaching stage.
[0064] S11. Based on the trend of students' critical thinking ability changes in the fitting curve of critical thinking-guided classroom outcomes, classroom outcomes are analyzed, and the corresponding stages in subsequent classroom teaching are optimized.
[0065] Furthermore, step S11 specifically includes: S111. When the critical thinking ability in the fitted curve of the critical thinking guided classroom results shows a continuous upward trend, the basic concepts should be reviewed or the guidance time should be shortened in the corresponding stage of subsequent classroom teaching.
[0066] S112. When the critical thinking ability in the fitted curve of the critical thinking guidance classroom results shows a continuous downward trend, then low-level critical thinking guidance should be introduced in the corresponding stage of subsequent classroom teaching and the guidance time should be extended.
[0067] S113. When critical thinking skills stagnate in the fitting curve of the classroom outcome, the questioning methods should be adjusted at the corresponding stage in the subsequent classroom teaching process, or more diverse perspectives should be provided to stimulate cognitive conflict.
[0068] S114. When critical thinking ability fluctuates drastically in the fitting curve of the classroom results, the logical contradictions in the corresponding stage should be sorted out in the initial stage of subsequent classroom teaching.
[0069] In practical applications, the optimization process enables dynamic adjustments to the pace, depth, and methods of teaching, thereby improving the adaptability and effectiveness of guidance strategies.
[0070] The technical effects of this application are as follows: This application improves the quantitative level of classroom outcome analysis by dividing teaching into four stages: non-interference analysis, superficial guidance, free debate, and in-depth guidance. First, target critical thinking topics are selected, and students' videos and physiological signals such as skin conductance and heart rate are collected in real time at each stage as classroom performance data. Students are guided to construct a tree-like critical thinking map according to the logic of argumentation-debate-dialectics after each stage. For each stage, explicit and implicit feature tensors are extracted from the performance data, and critical thinking ability feature tensors are extracted from the critical thinking map. Canonical correlation analysis is used to deeply fuse the two types of feature tensors to obtain a multimodal fused feature tensor, which improves the representational ability of subsequent outcome analysis. Then, using a state-space model, critical thinking ability is treated as a latent state, and the fused features during the alternation of each stage are used as observation data to estimate the dynamic evolution trend of critical thinking ability, generating individual critical thinking ability change curves. Finally, linear fitting is performed on the change curves of all students to obtain classroom outcome fitting curves, and the subsequent teaching stages are optimized based on the trend of the curves. This application enhances the quantitative level of classroom outcome analysis, enables objective evaluation of teaching effectiveness and optimization of guidance strategies, and improves the quality of critical thinking teaching.
[0071] Example 2: This example provides a computer system, which can be a server or a terminal, and its internal structure diagram can be as follows. Figure 3As shown, the computer system includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores forced oscillation samples and sub / supersynchronous oscillation samples. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the aforementioned method for rapid prediction and identification of the dominant frequency of sub / supersynchronous oscillations in new energy power systems based on transfer learning.
[0072] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer system to which the present application is applied. A specific computer system may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0073] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0074] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0075] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.
[0076] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for analyzing classroom outcomes in critical thinking-guided teaching, characterized in that, The guided instruction comprises four stages: non-interference analysis, superficial guidance, free debate, and in-depth guidance. The methods include: Targeted critical thinking questions; The system records students' classroom performance in real time to obtain classroom performance data; the classroom performance data includes: video and physiological signals; the physiological signals include at least: skin conductance response and heart rate. In the non-interference analysis stage, students are asked to conduct a critical analysis of the target proposition and construct a critical analysis diagram for the non-interference analysis stage. The critical analysis process is as follows: the target proposition is analyzed and discussed according to the critical logic of argumentation-debate-dialectics. The critical analysis diagram is a tree-like thinking structure diagram with the target proposition as the central node, and supporting arguments, opposing arguments, evidence, and logical connecting lines are added outward. In the initial guidance stage, the teacher provides basic guidance to the students. After the initial guidance, the students are asked to conduct critical thinking and analysis, and construct a critical thinking map for the initial guidance stage. During the free debate phase, the teacher guides students to engage in a free debate on the target argument within a pre-set time. After the free debate, students are asked to conduct a critical analysis and construct a critical thinking map of the free debate phase. In the in-depth guidance stage, the teacher provides in-depth guidance to the students. After the in-depth guidance, the students are asked to conduct critical thinking and analysis, and construct a critical thinking map for the in-depth guidance stage. For each stage, explicit and implicit features of critical thinking cognition are extracted based on students' classroom performance data to obtain explicit and implicit feature tensors; critical thinking ability features are extracted from the critical thinking graph to obtain critical thinking ability feature tensors; the explicit and implicit feature tensors include: explicit feature tensors and implicit feature tensors; the explicit feature tensors include: micro-expression features, pupillary response features, turn-taking features, verbal interaction features, and body movement amplitude features; the implicit feature tensors include: skin conductance response features and heart rate change features; By analyzing the maximum correlation between the explicit and implicit feature tensors and the speculative ability feature tensor using canonical correlation analysis, feature fusion is performed to obtain a multimodal fused feature tensor. Using a state-space model, reasoning ability is taken as a latent state, and the multimodal fusion feature tensor during the alternation of each stage is taken as the observation data. The evolution trend of the latent state is estimated through the observation data, and the change curve of reasoning ability is obtained. Linear fitting was performed on the change curves of critical thinking ability of all students in the classroom to obtain the fitting curve of critical thinking guidance classroom outcomes; Based on the trend of students' critical thinking ability changes in the fitted curve of critical thinking-guided classroom outcomes, classroom outcomes were analyzed, and corresponding stages in subsequent classroom teaching were optimized.
2. The classroom outcome analysis method for critical thinking-guided teaching according to claim 1, characterized in that, The selection of target critical thinking topics specifically includes: Select several initial speculative propositions from a speculative proposition bank or from historical speculative course propositions; Subject-specific features are extracted from the initial critical thinking propositions, and natural language processing models are used to screen out critical thinking propositions that match the subject of the students in the classroom, thus obtaining the initial screening of critical thinking propositions. The analytical methods used in the simulation of critical thinking analysis are employed to identify the critical thinking guidance anchors for the initial screening of critical thinking propositions. These critical thinking guidance anchors include both shallow and deep guidance anchors. Select the initial screening speculative propositions that simultaneously contain both shallow and deep guiding anchor points to obtain the secondary screening speculative propositions; We conduct a weighted analysis on the shallow and deep guiding anchor points of the re-screened speculative propositions and select the re-screened speculative proposition with the highest total weight as the target speculative proposition.
3. The classroom outcome analysis method for critical thinking-guided teaching according to claim 1, characterized in that, In the non-interference analysis phase, students are asked to conduct speculative analysis on the target proposition and construct a speculative map for the non-interference analysis phase, specifically including: In the non-interventional analysis phase, the teacher does not intervene or guide the students, who are asked to independently conduct critical analysis of the target proposition. After the students' critical thinking and analysis are completed, the critical thinking and analysis process is decomposed and summarized using knowledge graphs, and a critical thinking graph of the non-interference analysis stage is drawn.
4. The classroom outcome analysis method for critical thinking-guided teaching according to claim 1, characterized in that, In the initial guidance phase, the teacher provides basic guidance to students. After this phase, students are asked to engage in critical thinking and analysis, and construct a critical thinking map for the initial guidance phase. This includes: In the initial guidance phase, the teacher guides students to find the entry point for critical thinking by clarifying the concepts of key words in the target argument, analyzing the relationship between key words, and expanding their thinking with examples, but does not intervene in the analysis of the arguments. After the initial guidance phase, the teacher asks students to conduct critical analysis. After the students' critical thinking and analysis are completed, a knowledge graph is used to decompose and summarize the students' critical thinking and analysis process, and a critical thinking graph of the shallow guidance stage is drawn.
5. The classroom outcome analysis method for critical thinking-guided teaching according to claim 1, characterized in that, During the free debate phase, the teacher guides students to engage in a free debate on the target argument within a pre-set time. After the free debate, students are asked to conduct analytical analysis and construct a reasoning map for the free debate phase, which specifically includes: During the free debate phase, the teacher divides students into groups with differing viewpoints and guides them to engage in one-on-one or one-to-many debates on the target argumentative proposition within a pre-set time. During the debate, the teacher also guides students who are stuck in an argumentative deadlock to break free from it. After the free debate, guide students to summarize the changes in their own viewpoints before and after the debate, and ask students to conduct critical analysis. After the students' critical thinking and analysis, a knowledge graph is used to break down and summarize the students' critical thinking and analysis process, and to draw a critical thinking graph of the free debate stage.
6. The classroom outcome analysis method for critical thinking-guided teaching according to claim 1, characterized in that, In the in-depth guidance phase, the teacher provides in-depth guidance to students. After the in-depth guidance, students are asked to engage in critical thinking and analysis, and construct a critical thinking map for the in-depth guidance phase. This specifically includes: In the in-depth guidance stage, the teacher guides students to develop their arguments in greater depth by asking follow-up questions about logical flaws, comparing opposing viewpoints, and inspiring value reflection. After the in-depth guidance, students are asked to conduct critical analysis. After the students' critical thinking and analysis are completed, knowledge graphs are used to decompose and summarize the students' critical thinking and analysis process, and to draw a critical thinking graph for the in-depth guidance stage.
7. The classroom outcome analysis method for critical thinking-guided teaching according to claim 1, characterized in that, For each stage, explicit and implicit features of critical thinking cognition are extracted based on students' classroom performance data to obtain explicit and implicit feature tensors. The reasoning ability feature is extracted from the reasoning graph to obtain the reasoning ability feature tensor, which specifically includes: The classroom performance data at each stage is aligned chronologically and updated accordingly. OpenPose and conversation analysis algorithms were used to extract students’ micro-expression features, pupil response features, turn-taking features, verbal interaction features, and body movement amplitude features from videos of classroom performance data at each stage. The continuous decomposition analysis method was used to extract skin conductance characteristics from classroom performance data at each stage. Heart rate variability analysis was used to extract heart rate variation characteristics from classroom performance data at each stage; Based on the reasoning graphs at each stage, the core argument influence features and logical depth features are extracted using graph theory algorithms to construct a reasoning ability feature tensor.
8. The classroom outcome analysis method for critical thinking-guided teaching according to claim 1, characterized in that, Based on the trend of students' critical thinking ability changes in the fitted curve of critical thinking-guided classroom outcomes, classroom outcomes were analyzed, and corresponding stages in subsequent classroom teaching were optimized, specifically including: When the critical thinking ability in the fitted curve of the critical thinking guided classroom results shows a continuous upward trend, the basic concepts should be reviewed or the guidance time should be shortened in the corresponding stage of subsequent classroom teaching. When the critical thinking ability in the fitted curve of critical thinking guidance classroom outcomes shows a continuous downward trend, then low-level critical thinking guidance should be introduced in the corresponding stage of subsequent classroom teaching and the guidance time should be extended. When critical thinking skills stagnate in the fitting curve of the results of critical thinking-guided classes, the questioning methods should be adjusted at the corresponding stage in subsequent classroom teaching, or more diverse perspectives should be provided to stimulate cognitive conflict. When critical thinking skills fluctuate drastically in the fitted curve of classroom outcomes, logical contradictions should be addressed in the initial stages of the corresponding phases during subsequent classroom teaching.
9. The classroom outcome analysis method for critical thinking-guided teaching according to claim 1, characterized in that, The linear fit is a least-squares fit.
10. A computer system, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the classroom outcome analysis method for critical thinking-guided teaching as described in any one of claims 1-9.