Exercise error cognition dimension analysis system and analysis method thereof

Abstract

The invention relates to the technical field of learning education, and particularly discloses an exercise error cognition dimension analysis system and method, and the system comprises an error analysis module, a data analysis module, an exercise library management module, a learning resource module, and a user management module. The error recognition module is used for recognizing the source and type of error recognition, the data analysis module is used for summarizing and analyzing learning data of a user and providing decision support for teachers, and the exercise library management module is responsible for managing exercises in the system and the answering training process of the user; according to the method, plane geometry knowledge is decomposed into a plurality of stage targets, a user is gradually guided to master basic knowledge and skills, the cognitive load of students is reduced, the feeling of contusion caused by too large difficulty is reduced, the user is driven to think and master a knowledge point solving method through targeted exercise training and instant feedback, the logical reasoning ability of the students is cultivated, and the learning efficiency of the students is improved. And geometric proving can be independently carried out.

Description

Technical Field

[0001] The present invention belongs to the field of learning and education technology, and specifically relates to an exercise error cognitive dimension analysis system and an analysis method thereof. Background Art

[0002] Teachers’ understanding of students’ mistakes and misunderstandings is an important part of the knowledge they should have when engaging in teaching activities. Evaluating and correcting the mistakes made by students during the learning process can help teachers adjust teaching priorities and plans in a timely manner, and ultimately improve students’ academic performance. However, in actual teaching, many mathematics teachers regard learning errors as obstacles to teaching. Once students make mistakes, the most common method used by teachers is to repeat the standard answers constantly, let students practice more, or simply attribute the learning errors to superficial reasons such as students’ carelessness and not listening carefully in class. This superficial and arbitrary behavior is harmful to students’ understanding of knowledge and development of abilities, and will also have a negative impact on students’ learning interest and self-confidence.

[0003] Plane geometry teaching is the focus of junior high school mathematics teaching and is also the key to cultivating students' logical reasoning ability. However, as the grade increases, the difficulty of geometry learning gradually increases, and students obviously feel the difficulty of geometry learning, especially when they move from the stage of reasoning geometry in the first year of junior high school to the stage of argumentation geometry in the second year of junior high school. Most of the existing research on plane geometry learning is the study of geometric figures, the discussion of geometric proof methods, and the comparison of geometric cognitive differences among students at all levels, but there is less analysis of the specific reasons for students' geometry difficulties, and there is no teaching response plan and strategy for students' geometry learning difficulties.

[0004] Therefore, it is necessary to propose an exercise error cognitive dimension analysis system and an analysis method thereof to at least partially solve the problems existing in the prior art.

[0005] The above information disclosed in this background technology is only used to increase the understanding of the background technology of the present invention and therefore, it may include information that does not constitute the prior art known to ordinary technicians in this field. Summary of the invention

[0006] The purpose of the present invention is to provide a system and method for analyzing the dimensionality of exercise errors and to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A system for analyzing the dimensionality of error cognition in exercises, comprising: an error analysis module, a data analysis module, an exercise database management module, a learning resource module, and a user management module. The error analysis module is used to classify and analyze the user's errors and identify the source and type of the error cognition. The data analysis module is used to summarize and analyze the user's learning data and provide decision support for teachers. The exercise database management module is responsible for managing the exercises in the system and the process of users performing question-answering training. The learning resource module is used to provide learning resources related to the exercises to help users make up for knowledge gaps. The user management module is responsible for authenticating and using the user's identity to ensure user data security and personalized settings.

[0009] Preferably, the error analysis module uses the IRT model to analyze the user's performance on exercises, evaluates the user's mastery of specific knowledge points, and identifies the user's cognitive status in multiple dimensions through a multi-dimensional cognitive diagnosis model, and then uses the K-means algorithm to identify groups of users who show similar error patterns in the process of answering questions, and introduces an adaptive testing strategy based on IRT, so that the difficulty and type of exercises can be dynamically adjusted according to the user's ability to achieve a personalized learning experience. At the same time, the module provides personalized learning feedback and suggestions based on the user's performance to help the user improve.

[0010] In the IRT model analysis process, Van Hier's geometric thinking theory is applied to divide the development of users' geometric thinking into five development levels: ① Level 0: Visual, ② Level 1: Analysis, ③ Level 2: Informal deduction, ④ Level 3: Formal deduction, ⑤ Level 4: Rigor. It is not difficult to see from Van Hier's geometric thinking theory that the appropriate expression of mathematical terms is an important stage of users' geometric learning, and geometric learning covers the expression and transformation of text language, symbolic language and graphic language. When users have obstacles in using these three languages, highlighting the ability to use the three languages ​​in teaching strategies will help students develop geometric thinking.

[0011] Preferably, the data analysis module uses a deep learning model to analyze complex learning data, combined with an analysis of the user's online learning behavior to help teachers understand the user's comprehensive learning style and potential problems, and combines the error analysis module to analyze the user's performance on exercises to provide targeted support and suggestions.

[0012] Based on the analysis results, we designed teaching models for solving problems at different stages: Aiming at the cognitive characteristics and problem-solving features of low-end users, we proposed a teaching model of "layer-by-layer questioning mode" to help users sort out their ideas, check and reflect, and summarize their gains; Aiming at the cognitive characteristics and problem-solving features of mid-end users, we proposed a teaching model of "independent explanation mode" to encourage users to think, explain solutions independently, enhance users' understanding, and summarize methods; Aiming at the cognitive characteristics and problem-solving features of high-end users, we proposed a teaching model of "screening the optimal mode" to let users think and explore to propose methods, analyze methods and optimize method strategies.

[0013] Preferably, the question bank management module utilizes NLP technology to analyze teaching material content, uses predefined question templates, automatically generates questions based on input variables, combines random number generation technology, randomly selects parameters in questions to create diverse exercises, integrates a text editor to support formatted input of question descriptions, answer examples, and answers, implements data communication between the server and the examination machine through the IoT platform and the MQTT protocol, introduces teachers or subject experts to review the generated exercises to ensure the validity and accuracy of the questions, records the use of exercises, and analyzes which exercises are the most popular or the most challenging in order to adjust the generation strategy.

[0014] Preferably, the learning resource module allows users to filter exercise-related resources according to subject, type, and knowledge point conditions, and provides an open API to allow seamless integration of third-party services, simplify user operations in resource retrieval, achieve instant data collection and feedback, and allow users to timely view their own performance and improvement suggestions, thereby promoting active learning.

[0015] Preferably, the user management module introduces multi-factor authentication to enhance the security of user accounts and prevent unauthorized access, integrates instant messaging functions to facilitate real-time communication between users and teachers or other users, quickly solve problems, and collects satisfaction and suggestions on analysis results in real time to continuously optimize the analysis process and user experience.

[0016] A method for analyzing the cognitive dimension of exercise errors includes the following steps:

[0017] Step 1: Collect the user's answers when completing the exercises, record the user's behavior data during the problem-solving process, and use the model to analyze the user's performance;

[0018] Step 2: Use support vector machines to classify user errors, identify different types of erroneous cognition, and classify them into corresponding cognitive dimensions;

[0019] Step 3: Create an error mapping table to link different types of errors with corresponding cognitive dimensions to help analyze the causes of user errors;

[0020] Step 4: Provide feedback and suggestions to users based on the analysis results, recommend relevant learning resources and exercises, and help users improve their weak points in a targeted manner;

[0021] Step 5: Track the user’s learning outcomes after receiving feedback, analyze whether their misconceptions have improved, and continuously optimize the analysis system.

[0022] Preferably, in step 2, each cognitive dimension is refined to define specific error types and manifestations; in step 3, scoring criteria are set for each cognitive dimension to quantify the user's performance in each dimension; in step 5, the method can also be piloted on a small scale to collect feedback and evaluate the effect based on the set indicators, and the analysis method can be adjusted to obtain better analysis results.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The present invention decomposes plane geometry knowledge into multiple phased goals, gradually guides users to master basic knowledge and skills, reduces students' cognitive load, and reduces the frustration caused by excessive difficulty. Through targeted exercise training and instant feedback, users are encouraged to think, master methods of answering knowledge points, cultivate students' logical reasoning ability, enable them to independently perform geometric proofs, explain the proof process through demonstrative questions, and provide diversified teaching resources. Through the collision of thinking, the user's understanding is deepened, the geometric understanding and application ability are improved, and the user's interest in geometry learning is cultivated.

[0025] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A framework diagram of a problem error cognitive dimension analysis system of the present invention;

[0027] Figure 2 The present invention is a flow chart of a method for analyzing the cognitive dimensions of exercise errors. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] It should be further understood that the term “and / or” used in the specification of this application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0030] Embodiment 1:

[0031] See also Figure 1 As shown, a system for analyzing the cognitive dimensions of exercise errors includes: an error analysis module for classifying and analyzing user errors, identifying the sources and types of cognitive errors, using an IRT model to analyze user performance on exercises, evaluating their mastery of specific knowledge points, and identifying the user's cognitive status in multiple dimensions through a multi-dimensional cognitive diagnosis model, and then using a K-means algorithm to identify groups of users who exhibit similar error patterns in the process of answering questions.

[0032] The error analysis module introduces an adaptive testing strategy based on IRT, which allows the difficulty and type of exercises to be dynamically adjusted according to the user's ability to achieve a personalized learning experience. At the same time, the module provides personalized learning feedback and suggestions based on the user's performance to help users improve.

[0033] The application of meta-learning ideas combined with the importance-weighted active learning algorithm enhances the model's learning ability and sample utilization, thereby reducing sample requirements during deployment and improving the model's ability to quickly adapt. Importance-weighted active learning algorithm is a divergence-based active learning algorithm that uses a predefined bounded loss to directly evaluate sample value from the perspective of model training. In the active learning test scenario, the algorithm assumes the existence of labeled samples and can actively ask them for sample labeling features.

[0034] The data analysis module is used to summarize and analyze users' learning data, provide decision support for teachers, use deep learning models to analyze complex learning data, and combine the analysis of users' online learning behaviors to help teachers understand the comprehensive user learning styles and potential problems. At the same time, combined with the error analysis module, it analyzes users' performance on exercises and provides targeted support and suggestions.

[0035] The question bank management module is responsible for managing the questions in the system and the process of users' question-answering training. It uses NLP technology to analyze the textbook content and uses predefined question templates to automatically generate questions based on input variables. It combines random number generation technology to randomly select parameters in the questions to create a variety of exercises. It integrates a text editor to support formatted input of question descriptions, answer examples, and answers.

[0036] For responses to open-ended questions, NLP technology can be used to analyze users' answers, identify key concepts and common errors, analyze users' emotional tendencies in feedback, and understand their level of understanding of certain concepts. Exercises, concepts, and their relationships can also be visualized through knowledge graphs, and knowledge graphs can be used for reasoning to find the root causes of user errors.

[0037] The learning resource module is used to provide learning resources related to exercises to help users fill knowledge gaps. It realizes data communication between the server and the examination machine through the IoT platform and MQTT protocol, introduces teachers or subject experts to review the generated exercises to ensure the validity and accuracy of the questions, records the use of exercises, and analyzes which exercises are the most popular or challenging in order to adjust the generation strategy.

[0038] The learning resource module allows users to filter exercise-related resources based on subject, type, and knowledge point conditions, and provides an open API to allow seamless integration of third-party services, simplify user operations in resource retrieval, achieve instant data collection and feedback, and allow users to promptly view their own performance and improvement suggestions, thereby promoting active learning.

[0039] The user management module is responsible for the authentication and use of user identities, ensuring user data security and personalized settings, introducing multi-factor authentication, enhancing the security of user accounts, preventing unauthorized access, integrating instant messaging functions, facilitating real-time communication between users and teachers or other users, quickly solving problems, and collecting satisfaction and suggestions on analysis results in real time to continuously optimize the analysis process and user experience.

[0040] Embodiment 2:

[0041] See also Figure 2 As shown, a method for analyzing the cognitive dimension of exercise errors includes the following steps:

[0042] Step 1: Collect the user's answers when completing the exercises, record the user's behavior data during the problem-solving process, and use the model to analyze the user's performance;

[0043] Step 2: Refine each cognitive dimension, define specific error types and manifestations, use support vector machines to classify user errors, identify different types of erroneous cognition, and classify them into corresponding cognitive dimensions;

[0044] Step 3: Create an error mapping table to link different types of errors with corresponding cognitive dimensions, set scoring criteria for each cognitive dimension to quantify the user's performance in each dimension and help analyze the cause of the user's error;

[0045] Step 4: Provide feedback and suggestions to users based on the analysis results, recommend relevant learning resources and exercises, and help users improve their weak points in a targeted manner;

[0046] Step 5: Track the learning effect of users after receiving feedback, analyze whether their misconceptions have improved, and continuously optimize the analysis system. You can also pilot the method on a small scale, collect feedback, evaluate the effect based on the set indicators, and adjust the analysis method to obtain better analysis results.

[0047] From the above, it can be seen that the present invention decomposes plane geometry knowledge into multiple phased goals, gradually guides users to master basic knowledge and skills, reduces students' cognitive load, reduces the frustration caused by excessive difficulty, stimulates users to think through targeted exercise training and instant feedback, masters methods of answering knowledge points, cultivates students' logical reasoning ability, enables them to independently perform geometric proofs, explains the proof process through demonstrative questions, and provides diversified teaching resources, deepens understanding through the collision of thoughts, improves geometric understanding and application capabilities, and cultivates users' interest in geometry learning.

[0048] Embodiment 3:

[0049] The embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, each process of the above-mentioned exercise error cognitive dimension analysis system embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. Among them, the computer-readable storage medium, such as read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random ACGessMemory, referred to as RAM), a disk or an optical disk, etc.

[0050] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0051] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a program, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A way to specify functions in one or more boxes.

[0052] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0053] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0054] In the drawings of the embodiments disclosed in the present invention, only the structures involved in the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0055] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0056] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A system for analyzing the cognitive dimension of exercise errors, comprising: The error analysis module, data analysis module, question bank management module, learning resource module and user management module are characterized in that the error analysis module is used to classify and analyze user errors and identify the sources and types of erroneous cognition; the data analysis module is used to summarize and analyze user learning data and provide decision support for teachers; the question bank management module is responsible for managing the exercises in the system and the process of user training in answering questions; the learning resource module is used to provide learning resources related to exercises to help users fill in knowledge gaps; the user management module is responsible for authenticating and using user identities to ensure user data security and personalized settings.

2. The system for analyzing the dimension of exercise error cognition according to claim 1, characterized in that: The error analysis module uses the IRT model to analyze the user's performance on the exercises, evaluates their mastery of specific knowledge points, and identifies the user's cognitive status in multiple dimensions through a multi-dimensional cognitive diagnosis model, and then uses the K-means algorithm to identify groups of users who show similar error patterns in the process of answering questions.

3. The system for analyzing the cognitive dimension of exercise errors according to claim 2, characterized in that: The error analysis module introduces an adaptive testing strategy based on IRT, so that the difficulty and type of exercises can be dynamically adjusted according to the user's ability to achieve a personalized learning experience. At the same time, the module provides personalized learning feedback and suggestions based on the user's performance to help the user improve.

4. The system for analyzing the dimension of exercise error cognition according to claim 3, characterized in that: The data analysis module uses deep learning models to analyze complex learning data, and combines the analysis of users' online learning behaviors to help teachers understand comprehensive user learning styles and potential problems. At the same time, the error analysis module analyzes users' performance on exercises and provides targeted support and suggestions.

5. The system for analyzing the dimension of exercise error cognition according to claim 4, characterized in that: The question bank management module uses NLP technology to analyze textbook content, uses predefined question templates, automatically generates questions based on input variables, combines random number generation technology, randomly selects parameters in questions to create diverse questions, and integrates a text editor to support formatted input of question descriptions, answer examples, and answers.

6. The system for analyzing the dimension of exercise error cognition according to claim 5, characterized in that: The question bank management module realizes data communication between the server and the examination machine through the IoT platform and the MQTT protocol, introduces teachers or subject experts to review the generated questions, ensures the validity and accuracy of the questions, records the use of the questions, and analyzes which questions are the most popular or the most challenging in order to adjust the generation strategy.

7. The system for analyzing the dimension of exercise error cognition according to claim 6, characterized in that: The learning resource module allows users to filter exercise-related resources based on subject, type, and knowledge point conditions, and provides an open API to allow seamless integration of third-party services, simplify user operations in resource retrieval, achieve instant data collection and feedback, and allow users to timely view their own performance and improvement suggestions, thereby promoting active learning.

8. The system for analyzing the dimension of cognitive errors in exercises according to claim 7, characterized in that: The user management module introduces multi-factor authentication to enhance the security of user accounts and prevent unauthorized access. It integrates instant messaging functions to facilitate real-time communication between users and teachers or other users to quickly solve problems, and collects satisfaction and suggestions on analysis results in real time to continuously optimize the analysis process and user experience.

9. A method for analyzing the cognitive dimension of exercise errors, based on the cognitive dimension analysis system of exercise errors described in claims 1-8, characterized in that: The following steps are involved: Step 1: Collect the user's answers when completing the exercises, record the user's behavior data during the problem-solving process, and use the model to analyze the user's performance; Step 2: Use support vector machines to classify user errors, identify different types of erroneous cognition, and classify them into corresponding cognitive dimensions; Step 3: Create an error mapping table to link different types of errors with corresponding cognitive dimensions to help analyze the causes of user errors; Step 4: Provide feedback and suggestions to users based on the analysis results, recommend relevant learning resources and exercises, and help users improve their weak points in a targeted manner; Step 5: Track the user’s learning outcomes after receiving feedback, analyze whether their misconceptions have improved, and continuously optimize the analysis system.

10. The method for analyzing the cognitive dimension of exercise errors according to claim 9, characterized in that: In step 2, each cognitive dimension is refined to define specific error types and manifestations. In step 3, scoring criteria are set for each cognitive dimension to quantify the user's performance in each dimension. In step 5, the method can also be piloted on a small scale to collect feedback and evaluate the effect based on the set indicators, and the analysis method can be adjusted to obtain better analysis results.

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