Evaluation method for language behavior development of children

Abstract

The invention relates to a child language behavior development evaluation method, which comprises the following steps: collecting a child language instruction, and constructing an instruction vector; mapping the instruction vector to a preset virtual environment through a semantic vectorization method, and controlling an object in the virtual environment; tracking the position and state change of the object in the virtual environment, executing the corresponding operation in the action instruction sequence, and obtaining an execution result; comparing the child language instruction with the execution result, and calculating an instruction completion degree and an execution accuracy rate; and based on the instruction completion degree and the execution accuracy rate, evaluating the language behavior development of the children by using a preset evaluation index system. According to the method, through quantitative scoring and detailed data analysis, the accuracy and reliability of evaluation are improved, the error of subjective judgment is reduced, and key support is provided for early intervention and making of educational strategies.

Description

Technical Field

[0001] The present invention relates to the technical field of children's language behavior analysis, and in particular to a method for evaluating the development of children's language behavior. Background Art

[0002] The assessment of children's language and behavior development is a key area of research in children's cognitive and social abilities, crucial for early intervention and educational strategy development. Traditional assessment methods often rely on standardized tests and questionnaires, which fail to fully capture children's language and behavior in real-world interaction situations. These methods typically focus on static language knowledge and neglect dynamic language application skills, particularly children's ability to use language for operational control and problem-solving. Current assessment methods lack a multidimensional and contextualized analysis of children's language behavior, making it difficult to accurately reflect children's language organization and executive functioning abilities. Existing methods are particularly deficient in assessing children's ability to control external objects through verbal instructions to complete tasks. This ability involves the clarity, logic, sequential organization of language expression, and the coordination of language and behavior, and is a key indicator of children's cognitive development. The core challenge in assessing children's language and operational control abilities lies in designing an assessment system that can both simulate real-world interaction situations and objectively quantify assessment results. This involves technical challenges such as ensuring the ecological validity of task design, accurately measuring instruction execution, and comprehensively collecting language and behavior data. Key challenges remain in the assessment process, particularly in capturing the dynamic processes of language planning, adjustment, and feedback, as well as analyzing the coordinated development of language and executive function. Therefore, developing an innovative technical approach that can effectively assess children's ability to manipulate language through operational control, comprehensively evaluate their language organization and problem-solving strategies, and provide a scientific basis for early intervention for executive dysfunction has become a key issue in the field of evaluating children's language behavior development. This approach requires integrating task-oriented assessment systems, virtual or physical manipulation environments, and language behavior recording and analysis to comprehensively and dynamically assess children's language operational control abilities. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for evaluating the development of children's language behavior to solve the problems existing in the above-mentioned prior art and provide data support and development prediction for the design of targeted intervention and training programs.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for evaluating children's language behavior development, including:

[0006] Collect children's language instructions and construct instruction vectors;

[0007] Mapping the instruction vector to a preset virtual environment through a semantic vectorization method to control objects in the virtual environment;

[0008] Track the position and state changes of objects in the virtual environment, execute corresponding operations in the action instruction sequence, and obtain execution results;

[0009] Compare children's language instructions with their execution results, and calculate instruction completion and execution accuracy;

[0010] Based on the completion and execution accuracy of instructions, children's language behavior development is evaluated using a preset evaluation indicator system.

[0011] Optionally, collecting the child's language instructions and constructing the instruction vector includes:

[0012] Simulate life scenarios and use speech recognition technology to convert children's language instructions into text data;

[0013] Perform word segmentation, part-of-speech tagging, and semantic analysis on the obtained text data to extract key instruction information;

[0014] Construct an instruction vector based on key instruction information.

[0015] Optionally, mapping the instruction vector to a preset virtual environment by a semantic vectorization method includes:

[0016] Processing the instruction vector by a semantic vectorization method to generate mapped operation information;

[0017] Extract action instructions from the mapped operation information and construct action sequences;

[0018] A predefined virtual environment operation set is used to match the action sequence to determine the corresponding environment operation; if the environment operation matches the virtual environment state, the object control parameters are adjusted through the control logic to obtain the adjusted control instructions, and the real-time state of the object in the virtual environment is obtained based on the adjusted control instructions.

[0019] Optionally, tracking the position and state changes of objects in the virtual environment, executing corresponding operations in the action instruction sequence, and obtaining execution results include:

[0020] Based on the acquired position and state changes of objects in the virtual environment, a preset threshold is used to judge the significance of the state change and obtain the change trend;

[0021] According to the change trend, the corresponding action instructions are extracted from the operation sequence to determine the execution order;

[0022] The actions of objects in the virtual environment are controlled by executing the sequence and the status data during the execution is recorded.

[0023] Optionally, comparing the child's language instructions with the execution results and calculating the instruction completion degree and execution accuracy includes:

[0024] Compare the semantic content of the language instruction with the execution result data to obtain the compared result data;

[0025] The values of instruction completion and execution accuracy are extracted from the comparison results; among them, the completion is calculated by the ratio of the actual completion amount to the expected completion amount, and the accuracy is calculated by the degree of match between the actual execution result and the expected result.

[0026] Optionally, based on the degree of instruction completion and execution accuracy, a pre-set evaluation indicator system is used to evaluate the child's language behavior development, including:

[0027] Use language ability assessment indicators, language planning indicators and executive function indicators to quantitatively score children's language behavior and generate multi-dimensional ability score data;

[0028] Based on the multi-dimensional ability scoring data combined with instruction completion and execution accuracy, a child language behavior development assessment report is generated.

[0029] Optionally, quantitative scoring of children's language behavior using language ability assessment indicators includes:

[0030] Calculate vocabulary richness:

[0031] Count the number of different words children used during the task;

[0032] Compare the statistical results with the average vocabulary size or vocabulary size standards of children of the same age in similar tasks, calculate the scores, and obtain the vocabulary richness score;

[0033] Calculate sentence complexity:

[0034] Using natural language processing technology, perform syntactic analysis on children's language instructions to obtain sentence complexity indicators; wherein the sentence complexity indicators include: average length, number of clause nesting levels, and sentence diversity;

[0035] Based on the sentence complexity index, converting the sentence complexity into a score according to a preset scoring rule;

[0036] Calculate semantic clarity:

[0037] Perform semantic role labeling on language instructions to identify the semantic components in the sentence; semantic components include: agent, patient, action, and location;

[0038] A semantic clarity score is performed based on the semantic components.

[0039] Optionally, quantitative scoring of children's language behaviors using language planning indicators includes:

[0040] Calculate the rationality of the planning sequence:

[0041] Compare the child's instruction sequence with the standard operating procedure or optimal planning path of the task to check whether there are repeated steps or missing key steps in the instruction sequence, and deduct points based on the degree of impact on task completion;

[0042] Reasonable allocation of computing resources:

[0043] Identify the preset resources involved in the task; wherein the preset resources include: tools, materials, and time in the virtual environment;

[0044] Statistics were collected on how children allocated preset resources in the plan, and the rationality of resource allocation was scored.

[0045] Optionally, quantitative scoring of children's language behaviors using indicators of executive function may include:

[0046] Evaluate reaction time:

[0047] recording the time interval between when the child issues an instruction and when the system starts to execute the instruction, and evaluating the time interval;

[0048] Assessing error correction ability:

[0049] Compare the task execution results with the expected goals, identify the errors made by children during task execution, observe the correction strategies adopted by children after discovering the errors, and evaluate their error correction ability.

[0050] The beneficial effects of the present invention are:

[0051] The present invention discloses a method for evaluating the development of children's language behavior. First, the child's language instructions are collected and an instruction vector is constructed. Second, the instruction vector is mapped to a preset virtual environment through a semantic vectorization method to control objects in the virtual environment. Then, the position and state changes of the objects in the virtual environment are tracked, and the corresponding operations in the action instruction sequence are executed to obtain the execution results. Then, the child's language instructions are compared with the execution results to calculate the instruction completion degree and execution accuracy. Finally, based on the instruction completion degree and execution accuracy, the child's language behavior development is evaluated using a preset evaluation index system. By combining the semantic content of the language instructions with the execution result data, the present invention can conduct a multi-dimensional quantitative evaluation of the child's language organization ability, problem-solving strategy and executive function level, providing a more comprehensive and in-depth analysis than traditional evaluation methods; improving the accuracy and reliability of the evaluation, reducing the error of subjective judgment; and being able to identify possible problems in children's language and executive function at an early stage, providing key support for the formulation of early intervention and education strategies, and contributing to the early development and potential development of children. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 This is a flow chart of a method for evaluating children's language behavior development according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0055] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0056] like Figure 1 As shown, this embodiment proposes a method for evaluating children's language behavior development, including:

[0057] Collect children's language instructions and construct instruction vectors;

[0058] Mapping the instruction vector to a preset virtual environment through a semantic vectorization method to control objects in the virtual environment;

[0059] Track the position and state changes of objects in the virtual environment, execute corresponding operations in the action instruction sequence, and obtain execution results;

[0060] Compare children's language instructions with their execution results, and calculate instruction completion and execution accuracy;

[0061] Based on the completion and execution accuracy of instructions, children's language behavior development is evaluated using a preset evaluation indicator system.

[0062] Furthermore, collecting children's language instructions and constructing instruction vectors include:

[0063] Simulate life scenarios and use speech recognition technology to convert children's language instructions into text data;

[0064] Perform word segmentation, part-of-speech tagging, and semantic analysis on the obtained text data to extract key instruction information;

[0065] Construct an instruction vector based on key instruction information.

[0066] Specifically, in this embodiment, collecting children's language instructions and constructing instruction vectors specifically include the following:

[0067] According to preset task matching rules, the cognitive level is used to determine a matching virtual task type, wherein the virtual task type includes at least a jigsaw puzzle task, a building block construction task, and a life simulation task. When the child is operating the scene, the language instructions issued by the child are obtained and converted into text data through speech recognition technology.

[0068] Use word segmentation to break down text data and obtain word sequences. Part-of-speech tagging is performed on these word sequences to obtain part-of-speech tags. Semantic analysis is used to process the part-of-speech tags and extract instruction information. The language structure of the instruction information is analyzed to construct an instruction vector.

[0069] Furthermore, mapping the instruction vector to a preset virtual environment includes:

[0070] Processing the instruction vector by a semantic vectorization method to generate mapped operation information;

[0071] Extract action instructions from the mapped operation information and construct action sequences;

[0072] A predefined set of virtual environment operations is used to match the action sequence and determine the corresponding environment operation. If the environment operation matches the virtual environment state, the object control parameters are adjusted through the control logic to obtain the adjusted control instructions. Based on the adjusted control instructions, the real-time state of the object in the virtual environment is obtained.

[0073] Specifically, in this embodiment, the instruction vector is processed by semantic vectorization technology to generate mapped operation information. Action instructions are extracted from the mapped operation information to construct an action sequence. A predefined operation set is used to match the action sequence to determine the corresponding environment operation. If the environment operation matches the virtual environment state, the object control parameters are adjusted through the control logic to obtain the adjusted control instruction. Based on the adjusted control instruction, the real-time state of the object in the virtual environment is obtained to determine the result of the operation execution. The instruction sequence is updated according to the result of the operation execution to determine a new action instruction. The optimized instruction vector is obtained by iterative vector processing of the new action instruction.

[0074] Furthermore, tracking the position and state changes of objects in the virtual environment, executing corresponding operations in the action instruction sequence, and obtaining execution results include:

[0075] Based on the acquired position and state changes of objects in the virtual environment, a preset threshold is used to judge the significance of the state change and obtain the change trend;

[0076] According to the change trend, the corresponding action instructions are extracted from the operation sequence to determine the execution order;

[0077] The actions of objects in the virtual environment are controlled by executing the sequence and the status data during the execution is recorded.

[0078] Furthermore, the children's language instructions and execution results are compared, and the instruction completion and execution accuracy are calculated, including:

[0079] Compare the semantic content of the language instruction with the execution result data to obtain the compared result data;

[0080] The values of instruction completion and execution accuracy are extracted from the comparison results; among them, the completion is calculated by the ratio of the actual completion amount to the expected completion amount, and the accuracy is calculated by the degree of match between the actual execution result and the expected result.

[0081] Furthermore, based on the degree of instruction completion and execution accuracy, a preset evaluation indicator system is used to evaluate the development of children's language behavior, including:

[0082] Use language ability assessment indicators, language planning indicators and executive function indicators to quantitatively score children's language behavior and generate multi-dimensional ability score data;

[0083] Based on the multi-dimensional ability scoring data combined with instruction completion and execution accuracy, a child language behavior development assessment report is generated.

[0084] Specifically, in this embodiment, using language ability assessment indicators to quantitatively score children's language behavior includes:

[0085] Calculate vocabulary richness:

[0086] Vocabulary statistics: Count the number of different words used by children in the task, including nouns, verbs, adjectives, and other types of vocabulary;

[0087] Comparison with the standard: The statistical results are compared with the average vocabulary size of children of the same age in similar tasks or the vocabulary size standard to calculate the score. For example, if the standard vocabulary size is 50, and the child actually uses 60 different words, the vocabulary richness score is 120 points (based on the standard of 100 points);

[0088] Calculate sentence complexity:

[0089] Syntactic analysis: Using natural language processing technology, we conduct syntactic analysis on children's language instructions to obtain indicators such as the average length of sentences, the number of nested clauses, and sentence diversity;

[0090] Complexity quantification: Based on the results of syntactic analysis, sentence complexity is converted into a score according to certain scoring rules. For example, sentences with a longer average length, frequent use of clauses, and diverse structures will be given a higher score;

[0091] Calculate semantic clarity:

[0092] Semantic role labeling: perform semantic role labeling on language instructions to identify semantic components such as agent, patient, action, and location in the sentence;

[0093] Clarity Assessment: Evaluate whether the semantic components are complete and clear, and whether the semantic relationships are reasonable. High marks will be awarded for clarity and logical coherence; points will be deducted for ambiguity, ambiguity, or logical confusion.

[0094] Specifically, language planning indicators are used to quantitatively score children's language behavior, including:

[0095] Calculate the rationality of the planning sequence:

[0096] Logical sequence analysis: Compare the child's instruction sequence with the standard operating procedures or optimal planning path for the task to analyze whether the logical order of the steps is reasonable. For example, in a virtual kitchen scenario, the correct cooking steps should be "heat the pan first, then add oil, and then add vegetables." If the child's instruction sequence is consistent with this, the planning sequence rationality score is 100%; if the order is reversed, the corresponding score is deducted according to the severity of the error;

[0097] Redundancy and omission detection: Check whether there are unnecessary repeated steps or omissions of key steps in the instruction sequence. If there are redundant steps, points will be deducted according to the degree of redundancy; if there are omissions of key steps, points will be deducted according to the degree of impact of the omission on task completion;

[0098] Reasonable allocation of computing resources:

[0099] Resource identification and statistics: Identify the various resources involved in the task, such as tools, materials, and time in the virtual environment, and count how children allocate these resources in their planning;

[0100] Allocation effectiveness evaluation: Analyze whether resource allocation contributes to efficient and accurate task completion. For example, in a virtual building task, if a child arranges the order and quantity of different building blocks appropriately, resulting in a stable and aesthetically pleasing building, the resource allocation rationality score is 90 points (out of 100). If unreasonable resource allocation makes the task difficult or inefficient, points are deducted accordingly.

[0101] Specifically, in this embodiment, using executive function indicators to quantitatively score children's language behavior includes:

[0102] Reaction time score

[0103] Time measurement: Accurately record the time interval between the child giving an instruction and the system starting to execute the instruction, that is, the reaction time;

[0104] Scoring rules: According to the child's age and task difficulty, set a reasonable reaction time standard, compare the actual reaction time with the standard, and calculate the score. For example, for a 4-year-old child in a simple task, the reaction time standard is 2 seconds. If the actual reaction time is 1.5 seconds, the reaction time score is 120 points; if it is 3 seconds, the score is 80 points.

[0105] Error Correction Ability Score:

[0106] Error identification: Identify errors children make during task performance by systematically monitoring and comparing task performance results with expected goals;

[0107] Correction strategies and effectiveness evaluation: Observe the correction strategies children adopt after discovering their mistakes, such as retrying, adjusting their approach, and seeking help, and evaluate the effectiveness of these strategies. High marks will be awarded for quick and effective corrections; points will be deducted for ineffective corrections or inability to correct errors on their own.

[0108] More specifically, the weights of language ability, language planning, and executive functioning are determined based on the importance and relevance of each ability dimension in the development of children's language behavior. For example, language ability is weighted 40%, language planning 30%, and executive functioning 30%.

[0109] Within each dimension, the weights of the various evaluation indicators are further refined. For example, in the language proficiency dimension, the weight of vocabulary richness is 40%, the weight of sentence complexity is 30%, and the weight of semantic clarity is 30%.

[0110] The overall score for each dimension is calculated by multiplying the scores of each evaluation indicator by the corresponding indicator weight and adding them together. For example, the language ability score = vocabulary richness score × 40% + sentence complexity score × 30% + semantic clarity score × 30%.

[0111] The comprehensive scores of the three dimensions are multiplied by their respective dimension weights and then added together to obtain the multidimensional comprehensive ability score of children's language behavior development.

[0112] By combining the semantic content of language instructions with execution result data, this solution can conduct a multi-dimensional quantitative assessment of children's language organization ability, problem-solving strategies, and executive function levels, providing a more comprehensive and in-depth analysis than traditional assessment methods. Real-time tracking of the position and state changes of objects in the virtual environment enables dynamic monitoring during task execution. This helps to promptly identify problems and make adjustments, providing immediate feedback to children and promoting their language and cognitive development. Based on the multi-dimensional assessment report and the child's age and cognitive characteristics, the solution can construct a personalized language ability development model. This provides data support for the design of targeted intervention and training programs that better meet the unique needs of each child. Through quantitative scoring and detailed data analysis, the solution provides educators and parents with a scientific basis for decision-making, facilitating the development of more effective educational strategies and intervention measures. By integrating multiple technologies and methods, such as speech recognition, semantic analysis, and computer vision, the solution improves the accuracy and reliability of assessments and reduces subjective judgment errors. The ability to identify potential language and executive function problems in children early on provides key support for the development of early intervention and educational strategies, contributing to children's early development and potential development. The design of virtual task scenarios makes the assessment process more interesting and engaging, increasing children's participation and willingness to cooperate, and making the assessment results more realistic and effective. By building a personalized developmental model, the program can also predict children's language development, helping parents and educators better plan their educational paths. Accurately assessing children's abilities allows for more rational allocation of educational resources, ensuring that every child receives the educational support best suited to their development.

[0113] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A method for evaluating children's language behavior development, characterized in that: include: Collect children's language instructions and construct instruction vectors; Mapping the instruction vector to a preset virtual environment through a semantic vectorization method to control objects in the virtual environment; Track the position and state changes of objects in the virtual environment, execute corresponding operations in the action instruction sequence, and obtain execution results; Compare children's language instructions with their execution results, and calculate instruction completion and execution accuracy; Based on the completion and execution accuracy of instructions, children's language behavior development is evaluated using a preset evaluation indicator system.

2. The method for evaluating children's language behavior development according to claim 1, characterized in that: Collecting children's language instructions and constructing instruction vectors include: Simulate life scenarios and use speech recognition technology to convert children's language instructions into text data; Perform word segmentation, part-of-speech tagging, and semantic analysis on the obtained text data to extract key instruction information; Construct an instruction vector based on key instruction information.

3. The method for evaluating children's language behavior development according to claim 1, characterized in that: Mapping the instruction vector to a preset virtual environment using a semantic vectorization method includes: Processing the instruction vector by a semantic vectorization method to generate mapped operation information; Extract action instructions from the mapped operation information and construct action sequences; A predefined virtual environment operation set is used to match the action sequence to determine the corresponding environment operation; if the environment operation matches the virtual environment state, the object control parameters are adjusted through the control logic to obtain the adjusted control instructions, and the real-time state of the object in the virtual environment is obtained based on the adjusted control instructions.

4. The method for evaluating children's language behavior development according to claim 1, characterized in that: Track the position and state changes of objects in the virtual environment, execute the corresponding operations in the action instruction sequence, and obtain the execution results including: Based on the acquired position and state changes of objects in the virtual environment, a preset threshold is used to judge the significance of the state change and obtain the change trend; According to the change trend, the corresponding action instructions are extracted from the operation sequence to determine the execution order; The actions of objects in the virtual environment are controlled by executing the sequence and the status data during the execution is recorded.

5. The method for evaluating children's language behavior development according to claim 1, characterized in that: Comparing children's language instructions with execution results, and calculating instruction completion and execution accuracy include: Compare the semantic content of the language instruction with the execution result data to obtain the compared result data; The values of instruction completion and execution accuracy are extracted from the comparison results; among them, the completion is calculated by the ratio of the actual completion amount to the expected completion amount, and the accuracy is calculated by the degree of match between the actual execution result and the expected result.

6. The method for evaluating children's language behavior development according to claim 1, characterized in that: Based on the degree of instruction completion and execution accuracy, the children's language behavior development is evaluated using a preset evaluation indicator system, including: Use language ability assessment indicators, language planning indicators and executive function indicators to quantitatively score children's language behavior and generate multi-dimensional ability score data; Based on the multi-dimensional ability scoring data combined with instruction completion and execution accuracy, a child language behavior development assessment report is generated.

7. The method for evaluating children's language behavior development according to claim 6, characterized in that: Quantitative scoring of children's language behavior using language ability assessment indicators includes: Calculate vocabulary richness: Count the number of different words children used during the task; Compare the statistical results with the average vocabulary size or vocabulary size standard of children of the same age in the same task, calculate the score, and obtain the vocabulary richness score; Calculate sentence complexity: Using natural language processing technology, perform syntactic analysis on children's language instructions to obtain sentence complexity indicators; wherein the sentence complexity indicators include: average length, number of clause nesting levels, and sentence diversity; Based on the sentence complexity index, converting the sentence complexity into a score according to a preset scoring rule; Calculate semantic clarity: Perform semantic role labeling on language instructions to identify the semantic components in the sentence; semantic components include: agent, patient, action, and location; A semantic clarity score is performed based on the semantic components.

8. The method for evaluating children's language behavior development according to claim 6, characterized in that: Quantitative scoring of children's language behaviors using language planning indicators includes: Calculate the rationality of the planning sequence: Compare the child's instruction sequence with the standard operating procedure or optimal planning path of the task to check whether there are repeated steps or missing key steps in the instruction sequence, and deduct points based on the degree of impact on task completion; Reasonable allocation of computing resources: Identify the preset resources involved in the task; wherein the preset resources include: tools, materials, and time in the virtual environment; Statistics were collected on how children allocated preset resources in the plan, and the rationality of resource allocation was scored.

9. The method for evaluating children's language behavior development according to claim 6, characterized in that: Quantitative scoring of children's language behaviors using executive function indicators includes: Evaluate reaction time: recording the time interval between when the child issues an instruction and when the system starts to execute the instruction, and evaluating the time interval; Assessing error correction ability: Compare the task execution results with the expected goals, identify the errors made by children during task execution, observe the correction strategies adopted by children after discovering the errors, and evaluate their error correction ability.

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