A method and system for selecting evaluation indicators of a computer-assisted language evaluation system

Abstract

The present invention relates to the field of evaluation index screening technology, and in particular to an evaluation index screening method and system for a computer-assisted language evaluation system. The method comprises the following steps: obtaining learner auxiliary language learning information data from a computer-assisted language evaluation system and performing evaluation index statistical analysis to obtain an initial evaluation index set for auxiliary language learning; performing evaluation standard index screening processing at the same time to obtain an auxiliary language evaluation primary index set; performing expert coordination evaluation analysis and cognitive impairment association evaluation calculation on the auxiliary language evaluation primary index set, and performing index evaluation screening processing to obtain an auxiliary language evaluation secondary index set; and integrating the auxiliary language evaluation secondary index set into a computer-assisted language evaluation system for evaluation analysis to obtain a computer-assisted language evaluation output result. The present invention can dynamically screen out suitable evaluation indicators, thereby improving the accuracy and practicality of the system evaluation results.

Description

Technical Field

[0001] The present invention relates to the technical field of evaluation index screening, and in particular to an evaluation index screening method and system for a computer-assisted language evaluation system. Background Art

[0002] In recent years, with the development of natural language processing technology, data mining and machine learning, some new computer-assisted language assessment systems have begun to emerge. These systems analyze learners' language expression, grammatical structure, vocabulary usage and other multi-dimensional data by combining natural language processing algorithms, data-driven assessment models and expert knowledge systems, and automatically select and optimize assessment indicators suitable for specific assessment tasks. These assessment indicators can cover multiple aspects such as grammatical accuracy, vocabulary richness, and discourse coherence, which can not only comprehensively reflect learners' language ability, but also improve the objectivity and consistency of the assessment. At present, computer-assisted language assessment systems play an important role in the field of language learning and educational assessment, but the existing assessment indicator screening methods still face some challenges, such as improper selection of assessment indicators and low assessment efficiency, which limits their widespread promotion in practical applications. Summary of the invention

[0003] Based on this, it is necessary for the present invention to provide an evaluation indicator screening method and system for a computer-assisted language evaluation system to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for selecting evaluation indicators of a computer-assisted language evaluation system comprises the following steps:

[0005] Step S1: Obtain learner auxiliary language learning information data from a computer-assisted language assessment system, and perform evaluation index statistical analysis on the learner auxiliary language learning information data to obtain an initial evaluation index set for auxiliary language learning; obtain learner auxiliary language learning target data, and perform learning task application scope and difficulty evaluation analysis on the initial evaluation index set for auxiliary language learning based on the learner auxiliary language learning target data to obtain the learning task application scope and learning task difficulty level of each auxiliary language evaluation index;

[0006] Step S2: Based on the applicable scope of each auxiliary language evaluation indicator and the difficulty level of the learning task, the auxiliary language learning initial evaluation indicator set is screened for evaluation standard indicators to obtain an auxiliary language evaluation primary indicator set;

[0007] Step S3: Perform expert coordination evaluation analysis on the auxiliary language assessment first-level indicator set to obtain the expert coordination evaluation degree coefficient of each auxiliary language assessment first-level indicator; perform cognitive impairment association evaluation calculation on the auxiliary language assessment first-level indicator set based on the learner's auxiliary language learning information data to obtain the cognitive impairment association validity value of each auxiliary language assessment first-level indicator;

[0008] Step S4: Based on the expert coordination evaluation degree coefficient of each auxiliary language assessment first-level indicator and the cognitive impairment-related validity value, the auxiliary language assessment first-level indicator set is screened to obtain the auxiliary language assessment second-level indicator set; the auxiliary language assessment second-level indicator set is integrated into the computer-assisted language assessment system for evaluation and analysis to obtain the computer-assisted language assessment output result.

[0009] The present invention first obtains auxiliary language learning information data of learners' relevant learning process from a computer-assisted language assessment system to collect learners' auxiliary language learning stage activity records, interactive information, learning goals and other learning process related data from the system. These data can include learners' language performance data, online learning behavior, learning process and other information. Through systematic recording and storage, it can provide necessary data basis guarantee for subsequent evaluation and analysis. By statistically analyzing the evaluation indicators of learners' auxiliary language learning information data, it aims to establish an initial evaluation indicator set for auxiliary language learning. The detailed analysis of this step helps to identify various evaluation indicators of learners' learning performance in the auxiliary language learning process, such as indicators in terms of language ability test, learning process record, learning achievement display, etc., so as to provide data support for subsequent processing. By mining and analyzing learners' auxiliary language learning target data from learners' auxiliary language learning information data, this analysis process not only covers learners' own learning expectations and goal setting, but also takes into account the teaching goals and standards of educational institutions or systems, so as to ensure the effective connection and achievement of learners' personal goals and overall course goals. At the same time, by analyzing the scope of application and difficulty of the learning task of the initial evaluation indicator set of auxiliary language learning based on the learner's auxiliary language learning goal data, the detailed analysis of this step helps to determine the scope of application and difficulty level of each evaluation indicator in the actual learning task, and then provide a scientific basis for the subsequent evaluation indicator screening process. Secondly, by screening the evaluation standard indicators of the initial evaluation indicator set of auxiliary language learning based on the scope of application of learning tasks and the difficulty level of learning tasks of each auxiliary language evaluation indicator, the key of this step is to ensure that the final selected evaluation indicator set can have high predictive ability and effectiveness in practical applications, and can accurately reflect the learner's language ability and learning progress. Through screening, unnecessary or inappropriate evaluation indicators can be excluded, making the evaluation tool more streamlined and practical, and providing more effective support and guidance for subsequent language learning evaluation. Then, by conducting expert coordination evaluation analysis on the auxiliary language evaluation first-level indicator set, the key of this step is to comprehensively consider the consistency and difference of expert opinions and evaluate the expert coordination evaluation degree coefficient of each auxiliary language evaluation first-level indicator. Through expert-coordinated evaluation analysis, it is possible to determine which evaluation indicators have a high degree of consensus and recognition among the expert group and which may require further discussion or modification. This analysis helps to improve the credibility and validity of the evaluation indicators, thereby ensuring that the evaluation tools can be widely applied in different contexts and learning environments.By calculating the cognitive impairment association evaluation of the auxiliary language assessment primary indicator set based on the learners' auxiliary language learning information data, the aim is to quantify the association validity value between the auxiliary language assessment primary indicator and cognitive impairment. This calculation can help determine which evaluation indicators have higher predictive ability and applicability in learners with cognitive impairment, so as to adjust the design and application strategy of the evaluation tool and improve its effectiveness and practicality in a diverse group of learners. Through these evaluations, the adaptability of the evaluation tool to learners with cognitive impairment can be effectively improved, which can improve the effectiveness and progress in the language learning process, thereby improving the accuracy and applicability of the subsequent evaluation indicator selection. Finally, the corresponding auxiliary language assessment primary indicators in the auxiliary language assessment primary indicator set are evaluated and screened based on the expert coordination evaluation degree coefficient of each auxiliary language assessment primary indicator and the cognitive impairment association validity value. The purpose of this step is to select the most representative and discriminative secondary indicators from the comprehensive primary indicator set to further optimize the accuracy and practicality of the evaluation system. Through indicator evaluation and screening, it can be ensured that the final evaluation tool is not only comprehensive and scientific, but also can effectively identify and distinguish the language learning characteristics of learners with different cognitive impairments. In addition, by integrating the secondary indicator set of assisted language assessment into the computer-assisted language assessment system for evaluation and analysis, this step transforms the previous theoretical framework and indicator screening work into practical application. With the support and analysis capabilities of the computer system, the language learning performance of learners with cognitive impairment can be evaluated more quickly and accurately. This not only improves the efficiency of the evaluation, but also enhances the objectivity and scientific nature of the evaluation results, thereby providing strong decision-making support for education and clinical practice.

[0010] Preferably, the present invention further provides an evaluation indicator screening system for a computer-assisted language evaluation system, which is used to execute the evaluation indicator screening method for a computer-assisted language evaluation system as described above. The evaluation indicator screening system for a computer-assisted language evaluation system comprises:

[0011] The auxiliary language assessment system evaluation index analysis module is used to obtain learners' auxiliary language learning information data from the computer-assisted language assessment system, and perform evaluation index statistical analysis on the learners' auxiliary language learning information data to obtain an initial evaluation index set for auxiliary language learning; obtain learners' auxiliary language learning target data, and perform learning task application scope and difficulty evaluation analysis on the initial evaluation index set for auxiliary language learning based on the learners' auxiliary language learning target data, so as to obtain the learning task application scope and learning task difficulty level of each auxiliary language evaluation index;

[0012] The auxiliary language evaluation primary indicator screening module is used to screen the auxiliary language learning initial evaluation indicator set based on the learning task application scope and learning task difficulty level of each auxiliary language evaluation indicator, so as to obtain the auxiliary language evaluation primary indicator set;

[0013] The auxiliary language assessment first-level indicator evaluation and analysis module is used to perform expert coordination evaluation and analysis on the auxiliary language assessment first-level indicator set to obtain the expert coordination evaluation degree coefficient of each auxiliary language assessment first-level indicator; based on the learner's auxiliary language learning information data, the auxiliary language assessment first-level indicator set is evaluated and calculated for cognitive impairment association to obtain the cognitive impairment association validity value of each auxiliary language assessment first-level indicator;

[0014] The auxiliary language assessment indicator optimization screening and evaluation module is used to perform indicator evaluation screening processing on the auxiliary language assessment first-level indicator set based on the expert coordination evaluation degree coefficient of each auxiliary language assessment first-level indicator and the cognitive impairment-related validity value to obtain the auxiliary language assessment second-level indicator set; by integrating the auxiliary language assessment second-level indicator set into the computer-assisted language assessment system for evaluation and analysis, the computer-assisted language assessment output result is obtained.

[0015] In summary, the present invention provides an evaluation index screening system for a computer-assisted language evaluation system. The evaluation index screening system for the computer-assisted language evaluation system consists of an auxiliary language evaluation system evaluation index analysis module, an auxiliary language evaluation first-level index screening module, an auxiliary language evaluation first-level index evaluation analysis module, and an auxiliary language evaluation index optimization screening and evaluation module. It can implement any evaluation index screening method for a computer-assisted language evaluation system described in the present invention, and is used to combine the operations between computer programs running on various modules to implement an evaluation index screening method for a computer-assisted language evaluation system. The internal structures of the system cooperate with each other, which can greatly reduce duplication of work and manpower investment, and can quickly and effectively provide a more accurate and efficient evaluation index screening process, thereby simplifying the operation flow of the evaluation index screening system for the computer-assisted language evaluation system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings:

[0017] Figure 1 A schematic diagram of the steps of the method for selecting evaluation indicators of the computer-assisted language evaluation system of the present invention;

[0018] Figure 2 for Figure 1 Detailed step flow diagram of step S1;

[0019] Figure 3 for Figure 2 Detailed step flow chart of step S13 in FIG. DETAILED DESCRIPTION

[0020] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0021] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0022] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0023] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for selecting evaluation indicators of a computer-assisted language evaluation system, the method comprising the following steps:

[0024] Step S1: Obtain learner auxiliary language learning information data from a computer-assisted language assessment system, and perform evaluation index statistical analysis on the learner auxiliary language learning information data to obtain an initial evaluation index set for auxiliary language learning; obtain learner auxiliary language learning target data, and perform learning task application scope and difficulty evaluation analysis on the initial evaluation index set for auxiliary language learning based on the learner auxiliary language learning target data to obtain the learning task application scope and learning task difficulty level of each auxiliary language evaluation index;

[0025] Step S2: Based on the applicable scope of each auxiliary language evaluation indicator and the difficulty level of the learning task, the auxiliary language learning initial evaluation indicator set is screened for evaluation standard indicators to obtain an auxiliary language evaluation primary indicator set;

[0026] Step S3: Perform expert coordination evaluation analysis on the auxiliary language assessment first-level indicator set to obtain the expert coordination evaluation degree coefficient of each auxiliary language assessment first-level indicator; perform cognitive impairment association evaluation calculation on the auxiliary language assessment first-level indicator set based on the learner's auxiliary language learning information data to obtain the cognitive impairment association validity value of each auxiliary language assessment first-level indicator;

[0027] Step S4: Based on the expert coordination evaluation degree coefficient of each auxiliary language assessment first-level indicator and the cognitive impairment-related validity value, the auxiliary language assessment first-level indicator set is screened to obtain the auxiliary language assessment second-level indicator set; the auxiliary language assessment second-level indicator set is integrated into the computer-assisted language assessment system for evaluation and analysis to obtain the computer-assisted language assessment output result.

[0028] In the embodiment of the present invention, please refer to Figure 1 FIG. 1 is a schematic diagram of the steps of the evaluation index screening method of the computer-assisted language evaluation system of the present invention. In this example, the evaluation index screening method of the computer-assisted language evaluation system includes the following steps:

[0029] Step S1: Obtain learner auxiliary language learning information data from a computer-assisted language assessment system, and perform evaluation index statistical analysis on the learner auxiliary language learning information data to obtain an initial evaluation index set for auxiliary language learning; obtain learner auxiliary language learning target data, and perform learning task application scope and difficulty evaluation analysis on the initial evaluation index set for auxiliary language learning based on the learner auxiliary language learning target data to obtain the learning task application scope and learning task difficulty level of each auxiliary language evaluation index;

[0030] The embodiment of the present invention effectively collects and records various data generated by learners in the language learning process from a computer-assisted language assessment system to track learners' learning activities, including but not limited to course progress, online practice scores, learning time and frequency, etc., and should also include dynamic learning feedback and interaction data, such as interactive records and feedback information between learners and the system, so as to obtain learners' auxiliary language learning information data. At the same time, by deeply studying the performance data and system feedback information of each learner at different learning stages, the corresponding learners' auxiliary language learning information data is statistically analyzed for evaluation indicators, so as to comprehensively evaluate the learners' language learning situation from multiple dimensions (including auxiliary language ability, auxiliary language learning process and auxiliary language learning results) to extract appropriate evaluation indicators, including learners' language ability, learning progress, effectiveness of learning strategies, etc., so as to obtain an initial evaluation indicator set for auxiliary language learning. By using a data mining analysis method to analyze learners' auxiliary language learning information data, the specific learning goals set by learners in the language learning process are mined and analyzed, so as to obtain learners' auxiliary language learning goal data. Then, by converting the corresponding learning goal information data into specific learning tasks, and analyzing and determining the role and performance of each auxiliary language evaluation indicator in the learning task, the applicability boundary of each evaluation indicator is accurately defined, so as to obtain the learning task applicability scope of each auxiliary language evaluation indicator. Finally, by systematically analyzing the learning task, the structure and requirements of the learning task are analyzed to decompose and screen out the more complex task components in the learning task, and the relevance and influence of each evaluation indicator in the complex task component are comprehensively considered to quantify the specific difficulty level of each evaluation indicator in the learning process, and the difficulty level of the overall learning task is determined according to the quantified difficulty level (for example, the difficulty level of 0-40 is determined as the primary difficulty, the difficulty level of 41-70 is determined as the intermediate difficulty, and the difficulty level of 71-100 is determined as the advanced difficulty), so as to realize the mapping of the learning difficulty level of each evaluation indicator to the difficulty level of the overall task, and finally obtain the learning task difficulty level of each auxiliary language evaluation indicator.

[0031] Step S2: Based on the applicable scope of each auxiliary language evaluation indicator and the difficulty level of the learning task, the auxiliary language learning initial evaluation indicator set is screened for evaluation standard indicators to obtain an auxiliary language evaluation primary indicator set;

[0032] The embodiment of the present invention explicitly designs corresponding evaluation indicator screening criteria by combining the scope of application of learning tasks and the difficulty level of learning tasks (including elementary, intermediate and advanced) of each auxiliary language evaluation indicator obtained by analysis, including three aspects of consideration: applicability, implementation complexity and relevance to language learning effect. The corresponding auxiliary language evaluation indicators in the initial evaluation indicator set of auxiliary language learning are screened by combining the designed evaluation indicator screening criteria, so as to evaluate and screen each evaluation indicator in the initial evaluation indicator set one by one, and comprehensively consider the applicability, implementation complexity and relevance to language learning effect of each indicator to screen out the evaluation indicators that are considered to be the most representative, predictive and feasible to implement, and finally obtain the auxiliary language evaluation primary indicator set.

[0033] Step S3: Perform expert coordination evaluation analysis on the auxiliary language assessment first-level indicator set to obtain the expert coordination evaluation degree coefficient of each auxiliary language assessment first-level indicator; perform cognitive impairment association evaluation calculation on the auxiliary language assessment first-level indicator set based on the learner's auxiliary language learning information data to obtain the cognitive impairment association validity value of each auxiliary language assessment first-level indicator;

[0034] The embodiment of the present invention selects experts with authority and experience in the field of auxiliary language assessment (including linguists, educational psychologists, language assessment researchers, and practitioners who are actually engaged in auxiliary language learning assessment, etc.), and designs a well-structured questionnaire to collect and record the experts' views, opinions and suggestions on the corresponding auxiliary language assessment first-level indicators in the auxiliary language assessment first-level indicator set. At the same time, the expert inquiry recovery rate and the expert opinion proposal rate of each evaluation first-level indicator are statistically calculated from the expert inquiry survey results. Secondly, the corresponding auxiliary language assessment first-level indicators in the auxiliary language assessment first-level indicator set are evaluated and analyzed using a consistency evaluation method by combining the expert inquiry recovery rate and the expert opinion proposal rate obtained by quantitative calculation, so as to compare the consistency of expert opinions to analyze whether there is obvious convergence or heterogeneity between experts, and the degree of consensus of experts on key indicators, and to determine which evaluation indicators have a higher consensus and recognition in the expert group by using an expert coordination statistical analysis method, and at the same time, the expert coordination degree of each auxiliary language assessment first-level indicator is evaluated and analyzed, so as to obtain the expert coordination evaluation degree coefficient of each auxiliary language assessment first-level indicator. Then, by using data analysis methods, the learners' auxiliary language learning information data is analyzed for cognitive impairment identification, so as to identify the cognitive impairments faced by learners in the language learning process, and by combining the cognitive impairment information, the Spearman rank correlation coefficient calculation method is used to quantify the correlation coefficient between each auxiliary language assessment first-level indicator and cognitive impairment, and the correlation coefficient obtained by combining the analysis is used to evaluate the corresponding auxiliary language assessment first-level indicators in the auxiliary language assessment first-level indicator set using the association validity evaluation algorithm, so as to comprehensively consider the size of the correlation coefficient and the statistical significance to evaluate the association validity value of each evaluation indicator in cognitively impaired learners, and finally obtain the cognitive impairment association validity value of each auxiliary language assessment first-level indicator.

[0035] Step S4: Based on the expert coordination evaluation degree coefficient of each auxiliary language assessment first-level indicator and the cognitive impairment-related validity value, the auxiliary language assessment first-level indicator set is screened to obtain the auxiliary language assessment second-level indicator set; the auxiliary language assessment second-level indicator set is integrated into the computer-assisted language assessment system for evaluation and analysis to obtain the computer-assisted language assessment output result.

[0036] The embodiment of the present invention allocates corresponding weights to the expert coordination evaluation degree coefficients of each auxiliary language evaluation first-level indicator obtained by quantitative calculation by combining the subjective opinions of experts and objective conditions, so as to allocate corresponding weight ratios to the expert coordination parameters of each evaluation first-level indicator. At the same time, the corresponding weight ratios are allocated by determining the influence of cognitive impairment on different evaluation first-level indicators and the importance of such influence in the evaluation process according to the cognitive impairment association validity value obtained by quantitative calculation, and by combining the previously determined weight ratios and applying them to the evaluation results of each auxiliary language evaluation first-level indicator, so as to multiply the evaluation parameters of each indicator by the corresponding weight ratio to reflect its importance and contribution in the overall evaluation, and at the same time, the product results of the two evaluation parameters are added together to calculate the comprehensive score value of each evaluation first-level indicator, and secondly, the evaluation first-level indicators corresponding to the comprehensive score values ​​greater than or equal to the preset evaluation threshold are screened out, and the evaluation first-level indicators corresponding to the evaluation threshold values ​​less than the preset evaluation threshold values ​​are eliminated, so as to screen out the most representative and discriminative second-level indicators, thereby obtaining the auxiliary language evaluation second-level indicator set. Then, by integrating the screened auxiliary language assessment secondary indicator set into the computer-assisted language assessment system and optimizing the system's evaluation model and algorithm, the auxiliary language ability of the corresponding assessment object is analyzed and evaluated in detail, so as to accurately evaluate the language learning performance and language learning ability of learners with auxiliary language cognitive impairment, and finally obtain the computer-assisted language assessment output results.

[0037] The present invention first obtains auxiliary language learning information data of learners' relevant learning process from a computer-assisted language assessment system to collect learners' auxiliary language learning stage activity records, interactive information, learning goals and other learning process related data from the system. These data can include learners' language performance data, online learning behavior, learning process and other information. Through systematic recording and storage, it can provide necessary data basis guarantee for subsequent evaluation and analysis. By statistically analyzing the evaluation indicators of learners' auxiliary language learning information data, it aims to establish an initial evaluation indicator set for auxiliary language learning. The detailed analysis of this step helps to identify various evaluation indicators of learners' learning performance in the auxiliary language learning process, such as indicators in terms of language ability test, learning process record, learning achievement display, etc., so as to provide data support for subsequent processing. By mining and analyzing learners' auxiliary language learning target data from learners' auxiliary language learning information data, this analysis process not only covers learners' own learning expectations and goal setting, but also takes into account the teaching goals and standards of educational institutions or systems, so as to ensure the effective connection and achievement of learners' personal goals and overall course goals. At the same time, by analyzing the scope of application and difficulty of the learning task of the initial evaluation indicator set of auxiliary language learning based on the learner's auxiliary language learning goal data, the detailed analysis of this step helps to determine the scope of application and difficulty level of each evaluation indicator in the actual learning task, and then provide a scientific basis for the subsequent evaluation indicator screening process. Secondly, by screening the evaluation standard indicators of the initial evaluation indicator set of auxiliary language learning based on the scope of application of learning tasks and the difficulty level of learning tasks of each auxiliary language evaluation indicator, the key of this step is to ensure that the final selected evaluation indicator set can have high predictive ability and effectiveness in practical applications, and can accurately reflect the learner's language ability and learning progress. Through screening, unnecessary or inappropriate evaluation indicators can be excluded, making the evaluation tool more streamlined and practical, and providing more effective support and guidance for subsequent language learning evaluation. Then, by conducting expert coordination evaluation analysis on the auxiliary language evaluation first-level indicator set, the key of this step is to comprehensively consider the consistency and difference of expert opinions and evaluate the expert coordination evaluation degree coefficient of each auxiliary language evaluation first-level indicator. Through expert-coordinated evaluation analysis, it is possible to determine which evaluation indicators have a high degree of consensus and recognition among the expert group and which may require further discussion or modification. This analysis helps to improve the credibility and validity of the evaluation indicators, thereby ensuring that the evaluation tools can be widely applied in different contexts and learning environments.By calculating the cognitive impairment association evaluation of the auxiliary language assessment primary indicator set based on the learners' auxiliary language learning information data, the aim is to quantify the association validity value between the auxiliary language assessment primary indicator and cognitive impairment. This calculation can help determine which evaluation indicators have higher predictive ability and applicability in learners with cognitive impairment, so as to adjust the design and application strategy of the evaluation tool and improve its effectiveness and practicality in a diverse group of learners. Through these evaluations, the adaptability of the evaluation tool to learners with cognitive impairment can be effectively improved, which can improve the effectiveness and progress in the language learning process, thereby improving the accuracy and applicability of the subsequent evaluation indicator selection. Finally, the corresponding auxiliary language assessment primary indicators in the auxiliary language assessment primary indicator set are evaluated and screened based on the expert coordination evaluation degree coefficient of each auxiliary language assessment primary indicator and the cognitive impairment association validity value. The purpose of this step is to select the most representative and discriminative secondary indicators from the comprehensive primary indicator set to further optimize the accuracy and practicality of the evaluation system. Through indicator evaluation and screening, it can be ensured that the final evaluation tool is not only comprehensive and scientific, but also can effectively identify and distinguish the language learning characteristics of learners with different cognitive impairments. In addition, by integrating the secondary indicator set of assisted language assessment into the computer-assisted language assessment system for evaluation and analysis, this step transforms the previous theoretical framework and indicator screening work into practical application. With the support and analysis capabilities of the computer system, the language learning performance of learners with cognitive impairment can be evaluated more quickly and accurately. This not only improves the efficiency of the evaluation, but also enhances the objectivity and scientific nature of the evaluation results, thereby providing strong decision-making support for education and clinical practice.

[0038] Preferably, step S1 comprises the following steps:

[0039] Step S11: acquiring learner-assisted language learning information data from a computer-assisted language assessment system;

[0040] Step S12: performing learning stage performance and feedback evaluation analysis on the learner's auxiliary language learning information data to obtain the learner's auxiliary language learning performance information data and auxiliary language learning feedback information data at each learning stage;

[0041] Step S13: performing evaluation index statistical analysis on the learner's auxiliary language learning information data based on the learner's auxiliary language learning performance information data and auxiliary language learning feedback information data at each learning stage to obtain an initial evaluation index set for auxiliary language learning;

[0042] Step S14: performing learning goal mining analysis on the learner's auxiliary language learning information data to obtain the learner's auxiliary language learning goal data;

[0043] Step S15: Based on the learner's auxiliary language learning goal data, the initial evaluation indicator set for auxiliary language learning is analyzed for the scope of application and difficulty of the learning task, and the scope of application and difficulty level of the learning task of each auxiliary language evaluation indicator are obtained.

[0044] As an embodiment of the present invention, refer to Figure 2 As shown, Figure 1 Detailed step flow diagram of step S1 in FIG. 1 , in this embodiment, step S1 includes the following steps:

[0045] Step S11: acquiring learner-assisted language learning information data from a computer-assisted language assessment system;

[0046] The embodiments of the present invention effectively collect and record various data generated by learners in the language learning process from a computer-assisted language assessment system to track learners' learning activities, including but not limited to course progress, online practice scores, learning time and frequency, etc., and should also include dynamic learning feedback and interaction data, such as interaction records and feedback information between learners and the system, and finally obtain learners' assisted language learning information data.

[0047] Step S12: performing learning stage performance and feedback evaluation analysis on the learner's auxiliary language learning information data to obtain the learner's auxiliary language learning performance information data and auxiliary language learning feedback information data at each learning stage;

[0048] The embodiment of the present invention uses a statistical analysis method of learning performance and learning feedback to perform statistical analysis on the learning performance and feedback of learners at different learning stages in the learner auxiliary language learning information data, so as to deeply study the performance data and system feedback information of each learner at different learning stages, thereby reflecting the learner's learning progress and achievements in the course, and revealing the learner's strengths and weaknesses in specific tasks or topics, and finally obtaining the learner's auxiliary language learning performance information data and auxiliary language learning feedback information data at each learning stage.

[0049] Step S13: performing evaluation index statistical analysis on the learner's auxiliary language learning information data based on the learner's auxiliary language learning performance information data and auxiliary language learning feedback information data at each learning stage to obtain an initial evaluation index set for auxiliary language learning;

[0050] The embodiment of the present invention performs statistical analysis of evaluation indicators on the corresponding learner auxiliary language learning information data by combining the auxiliary language learning performance information data and auxiliary language learning feedback information data of the learners at each learning stage, so as to comprehensively evaluate the learner's language learning situation from multiple dimensions (including auxiliary language ability, auxiliary language learning process and auxiliary language learning results, etc.) to extract appropriate evaluation indicators, including the learner's language ability, learning progress, effectiveness of learning strategies and other aspects, and finally obtain an initial evaluation indicator set for auxiliary language learning.

[0051] Step S14: performing learning goal mining analysis on the learner's auxiliary language learning information data to obtain the learner's auxiliary language learning goal data;

[0052] The embodiment of the present invention analyzes learner-assisted language learning information data by using a data mining analysis method to mine and analyze the specific learning goals set by learners in the language learning process, and extract the learners' personal goals and expectations, such as the language level they want to achieve, application scenarios, time limits, etc., to ultimately obtain learners' assisted language learning goal data.

[0053] Step S15: Based on the learner's auxiliary language learning goal data, the initial evaluation indicator set for auxiliary language learning is analyzed for the scope of application and difficulty of the learning task, and the scope of application and difficulty level of the learning task of each auxiliary language evaluation indicator are obtained.

[0054] The embodiment of the present invention analyzes in detail the language learning goals set for learners, including the development of language skills, application scenarios and actual needs, and converts these macro learning goals into specific learning tasks. At the same time, the role position and performance function of each auxiliary language evaluation indicator in the learning task are analyzed to quantitatively analyze the importance and learning depth of each auxiliary language evaluation indicator in the learning task. Secondly, the learning scenarios and conditions applicable to each auxiliary language evaluation indicator are evaluated by using an analysis method of the task applicability scope to accurately define the applicability boundary of each evaluation indicator, thereby obtaining the learning task applicability scope of each auxiliary language evaluation indicator. Then, by systematically analyzing the learning tasks, the structure and requirements of the learning tasks are analyzed to decompose and screen out the more complex task components in the learning tasks, and by analyzing the performance of the corresponding auxiliary language evaluation indicators in the initial evaluation indicator set of auxiliary language learning under different task complexity components, the specific difficulty level of each evaluation indicator in the learning process is quantified by comprehensively considering the relevance and influence of each evaluation indicator in the complexity factor. At the same time, the difficulty level of the overall learning task is determined according to the quantified difficulty level (for example, the difficulty level of 0-40 is determined as elementary difficulty, the difficulty level of 41-70 is determined as intermediate difficulty, and the difficulty level of 71-100 is determined as advanced difficulty), so as to map the learning difficulty level of each evaluation indicator to the difficulty level of the overall task, and finally obtain the learning task difficulty level of each auxiliary language evaluation indicator.

[0055] The present invention first obtains auxiliary language learning information data of learners' relevant learning process from the computer-assisted language assessment system to collect learners' auxiliary language learning stage activity records, interactive information, learning goals and other learning process related data from the system. These data can include learners' language performance data, online learning behavior, learning process and other information. Through systematic recording and storage, it can provide necessary data basis guarantee for subsequent evaluation and analysis. Secondly, by conducting learning stage performance and feedback evaluation and analysis on learners' auxiliary language learning information data, it is possible to deeply study the performance data and system feedback information of each learner at different learning stages. These data not only reflect the learners' learning progress and achievements in the course, but also reveal the learners' strengths and weaknesses in specific tasks or topics, thereby providing a basis for the subsequent evaluation index statistical analysis process. Then, by conducting statistical analysis of the evaluation indicators of learners' auxiliary language learning information data based on the learners' auxiliary language learning performance information data and auxiliary language learning feedback information data at each learning stage, the aim is to establish an initial evaluation indicator set for auxiliary language learning. The detailed analysis of this step helps to identify various evaluation indicators of learners' learning performance in the auxiliary language learning process, such as language proficiency tests, learning process records, and learning results display indicators, thereby providing data support for subsequent processing. Next, by conducting learning goal mining analysis on learners' auxiliary language learning information data, the learners' auxiliary language learning goal data is explored. This analysis process not only covers the learners' own learning expectations and goal setting, but also takes into account the teaching goals and standards of educational institutions or systems, thereby ensuring that learners' personal goals are effectively connected and achieved with the overall course goals. Finally, by conducting an evaluation analysis of the scope of application and difficulty of the initial evaluation indicator set of auxiliary language learning based on the learners' auxiliary language learning goal data, the detailed analysis of this step helps to determine the scope of application and difficulty level of each evaluation indicator in the actual learning task, thereby providing a scientific basis for the subsequent evaluation indicator screening process.

[0056] Preferably, step S13 comprises the following steps:

[0057] Step S131: performing language learning dimension division processing on the learner's auxiliary language learning information data to obtain auxiliary language ability dimension learning information data, auxiliary language learning process dimension learning information data and auxiliary language learning achievement dimension learning information data;

[0058] Step S132: performing language ability evaluation index analysis on the auxiliary language ability dimension learning information data based on the learner's auxiliary language learning performance information data at each learning stage to obtain an auxiliary language ability evaluation index set;

[0059] Step S133: performing learning process evaluation index analysis on the auxiliary language learning process dimension learning information data based on the learner's auxiliary language learning feedback information data at each learning stage to obtain an auxiliary language learning process evaluation index set;

[0060] Step S134: performing learning achievement evaluation index analysis on the learning information data of the auxiliary language learning achievement dimension based on the auxiliary language learning feedback information data of the learners at each learning stage to obtain an auxiliary language learning achievement evaluation index set;

[0061] Step S135: merging the auxiliary language ability evaluation indicator set, the auxiliary language learning process evaluation indicator set and the auxiliary language learning achievement evaluation indicator set to obtain an auxiliary language learning initial evaluation indicator set.

[0062] As an embodiment of the present invention, refer to Figure 3 As shown, Figure 2 FIG. 1 is a schematic diagram of a detailed step flow chart of step S13 in FIG. 1 . In this embodiment, step S13 includes the following steps:

[0063] Step S131: performing language learning dimension division processing on the learner's auxiliary language learning information data to obtain auxiliary language ability dimension learning information data, auxiliary language learning process dimension learning information data and auxiliary language learning achievement dimension learning information data;

[0064] The embodiment of the present invention classifies and organizes the learner's language learning process in the learner's auxiliary language learning information data according to different language learning dimensions, and the language learning dimensions include auxiliary language ability, auxiliary language learning process and auxiliary language learning results. Each dimension represents a different perspective and measurement indicator, wherein the auxiliary language ability dimension focuses on the learner's performance in language skills, grammar, vocabulary and other aspects; the auxiliary language learning process dimension focuses on the learner's strategy, feedback acceptance and processing, autonomous learning ability and other aspects in the learning process; and the auxiliary language learning results dimension focuses on the learner's performance after completing the language learning task, and finally divides it into auxiliary language ability dimension learning information data, auxiliary language learning process dimension learning information data and auxiliary language learning results dimension learning information data.

[0065] Step S132: performing language ability evaluation index analysis on the auxiliary language ability dimension learning information data based on the learner's auxiliary language learning performance information data at each learning stage to obtain an auxiliary language ability evaluation index set;

[0066] The embodiment of the present invention uses a data statistical analysis method to evaluate and analyze the language ability of the corresponding auxiliary language ability dimension learning information data by combining the auxiliary language learning performance information data of the learners at each learning stage obtained by analysis, so as to determine and extract the corresponding speech ability evaluation indicators according to the learners' performance in different language ability dimensions, including the development level of various language skills, the ability to apply professional terms, the mastery of grammatical rules, etc., and finally obtain a set of auxiliary language ability evaluation indicators.

[0067] Step S133: performing learning process evaluation index analysis on the auxiliary language learning process dimension learning information data based on the learner's auxiliary language learning feedback information data at each learning stage to obtain an auxiliary language learning process evaluation index set;

[0068] The embodiment of the present invention evaluates and analyzes the learning information data of the corresponding auxiliary language learning process dimension by combining the auxiliary language learning feedback information data of the learners at each learning stage obtained by analysis, so as to analyze the learners' feedback data from the perspective of the learning process, and identify the key factors and behavior patterns that affect the learning process to evaluate and analyze the learners' reactions in the learning process, the use of learning strategies, the ability to overcome difficulties and other evaluation indicators, and finally obtain a set of auxiliary language learning process evaluation indicators.

[0069] Step S134: performing learning achievement evaluation index analysis on the learning information data of the auxiliary language learning achievement dimension based on the auxiliary language learning feedback information data of the learners at each learning stage to obtain an auxiliary language learning achievement evaluation index set;

[0070] The embodiment of the present invention evaluates and analyzes the corresponding auxiliary language learning outcome dimension learning information data by combining and analyzing the learner's auxiliary language learning feedback information data at each learning stage, so as to identify and extract relevant evaluation indicators based on the learner's actual outcome data in the language learning process, including the quality of task completion, the actual application of language ability, performance in actual communication, etc., so as to reflect the learner's progress in language mastery, application and understanding, and finally obtain a set of auxiliary language learning outcome evaluation indicators.

[0071] Step S135: merging the auxiliary language ability evaluation indicator set, the auxiliary language learning process evaluation indicator set and the auxiliary language learning achievement evaluation indicator set to obtain an auxiliary language learning initial evaluation indicator set.

[0072] The embodiment of the present invention combines and integrates the auxiliary language ability evaluation indicator set, the auxiliary language learning process evaluation indicator set and the auxiliary language learning outcome evaluation indicator set obtained by previous evaluation and analysis to provide learners with a comprehensive and accurate evaluation system, including indicators of multiple aspects such as language ability, learning process and learning outcomes, and finally obtains an initial evaluation indicator set for auxiliary language learning.

[0073] The present invention first divides the learner's auxiliary language learning information data into language learning dimensions, and can classify and organize the learning data according to different language learning dimensions. These dimensions include auxiliary language ability, auxiliary language learning process and auxiliary language learning results. Each dimension covers specific learning performance and result data. Among them, the auxiliary language ability dimension learning information data focuses on the learner's performance in language skills, grammar, vocabulary, etc.; the auxiliary language learning process dimension learning information data focuses on the learner's strategy, feedback acceptance and processing, autonomous learning ability, etc. in the learning process; and the auxiliary language learning result dimension learning information data focuses on the learner's performance after completing the language learning task, such as ability improvement, task completion quality, etc. Through this detailed dimensional division and data organization, a detailed data basis and a multi-dimensional learning information perspective can be provided for subsequent evaluation and analysis. Secondly, the language ability evaluation index analysis is conducted on the auxiliary language ability dimension learning information data based on the auxiliary language learning performance information data of the learners at each learning stage. The main purpose of this step is to identify the strengths and weaknesses of learners in different language ability dimensions through detailed data analysis and statistics, and form a specific set of evaluation indicators. The evaluation indicators can cover the development level of various language skills, the application ability of professional terms, the mastery of grammatical rules, etc., thereby providing data support for the subsequent processing process. Then, the learning process evaluation index analysis is conducted on the auxiliary language learning process dimension learning information data based on the auxiliary language learning feedback information data of the learners at each learning stage. This step focuses on analyzing the learners' reactions in the learning process, the use of learning strategies, the ability to overcome difficulties, etc. These evaluation indicators not only help evaluate the learners' adaptability to different learning tasks and situations, but also reveal the learners' coping strategies and learning attitudes when facing challenges, providing a basis for personalized support and feedback in the subsequent evaluation indicator screening process. Next, we analyze the learning outcome evaluation indicators of the auxiliary language learning outcome dimension based on the learners’ auxiliary language learning feedback information data at each learning stage, aiming to evaluate the results and effects achieved by learners after completing specific learning tasks or projects. These evaluation indicators include the quality of task completion, the actual application of language skills, and performance in actual communication. Through detailed data analysis and comparison, we can provide an objective basis for the evaluation of learning outcomes, and also help adjust and optimize teaching design and evaluation strategies.Finally, by merging the auxiliary language ability assessment indicator set, the auxiliary language learning process assessment indicator set and the auxiliary language learning outcome assessment indicator set, an initial assessment indicator set for auxiliary language learning was formed. This comprehensive indicator set not only integrates multi-dimensional learning data and evaluation results, but also provides a scientific basis for the subsequent assessment indicator screening process. The establishment of a comprehensive assessment indicator set not only helps to fully understand the learners' learning process and results, but also provides important support for the refined management and personalized support of the subsequent screening and processing process.

[0074] Preferably, step S15 comprises the following steps:

[0075] Step S151: performing learning task conversion analysis on the learner's auxiliary language learning target data to obtain the learner's auxiliary language learning task;

[0076] The embodiment of the present invention analyzes in detail the language learning goals set for learners, including the development of language skills, application scenarios and actual needs, and converts these macro learning goals into specific learning tasks, and determines the specific content, learning methods and time frame of each learning task, and finally obtains learners' auxiliary language learning tasks.

[0077] Step S152: performing task position and performance role analysis on the auxiliary language learning initial evaluation indicator set based on the learner's auxiliary language learning task to obtain the specific position of each auxiliary language evaluation indicator in the auxiliary language learning task and learning performance role data;

[0078] The embodiment of the present invention uses a statistical analysis method to analyze the corresponding auxiliary language evaluation indicators in the initial evaluation indicator set of auxiliary language learning by combining the learner's auxiliary language learning task determined by transformation, so as to analyze and determine the role position and performance effect of each evaluation indicator in the learning task, that is, to determine how the corresponding evaluation indicator can help evaluate the performance and effectiveness of the learner in a specific task, and finally obtain the specific position of each auxiliary language evaluation indicator in the auxiliary language learning task and the learning performance effect data.

[0079] Step S153: performing a statistical analysis of the importance and learning depth of the auxiliary language learning initial evaluation indicator set according to the specific position of each auxiliary language evaluation indicator in the auxiliary language learning task and the learning performance effect data, and obtaining the importance and learning depth of each auxiliary language evaluation indicator in the auxiliary language learning task;

[0080] The embodiment of the present invention uses an importance evaluation algorithm and an impact depth analysis method to perform statistical analysis on the corresponding auxiliary language evaluation indicators in the initial evaluation indicator set for auxiliary language learning by combining the specific position of each auxiliary language evaluation indicator in the auxiliary language learning task and the learning performance role data obtained by analysis, so as to quantitatively analyze the importance and learning depth of each auxiliary language evaluation indicator in the learning task, so as to better understand the role and influence of the evaluation indicator in the actual learning process, and finally obtain the importance and learning depth of each auxiliary language evaluation indicator in the auxiliary language learning task.

[0081] Step S154: Based on the importance and learning depth of each auxiliary language evaluation indicator in the auxiliary language learning task, the auxiliary language learning initial evaluation indicator set is evaluated and analyzed for the learning task application scope to obtain the learning task application scope of each auxiliary language evaluation indicator;

[0082] The embodiment of the present invention uses a task applicability analysis method to evaluate and analyze the corresponding auxiliary language evaluation indicators in the initial evaluation indicator set for auxiliary language learning by combining the importance and learning depth of each auxiliary language evaluation indicator in the auxiliary language learning task obtained by analysis, so as to evaluate and determine the learning scenarios and conditions applicable to each evaluation indicator to accurately define the applicability boundary of each evaluation indicator, and ensure that the best teaching effect and learning results can be achieved in actual teaching implementation, and finally obtain the learning task applicability scope of each auxiliary language evaluation indicator.

[0083] Step S155: performing difficulty assessment analysis on the auxiliary language learning initial assessment indicator set based on the learner's auxiliary language learning task to obtain the learning task difficulty level of each auxiliary language assessment indicator.

[0084] The embodiment of the present invention systematically analyzes the learner's auxiliary language learning task to analyze the structure and requirements of the learner's auxiliary language learning task to decompose and screen out the more complex task components in the learner's auxiliary language learning task, and analyzes the performance of the corresponding auxiliary language evaluation indicators in the initial evaluation indicator set of auxiliary language learning under different task complexity components, so as to analyze and compare the performance of each evaluation indicator under different task complexity factors in detail to identify those key challenge points that need special attention or adjustment. At the same time, by comprehensively considering the relevance and influence of each evaluation indicator in the complexity factor, the specific difficulty level of each evaluation indicator in the learning process is quantified, and the difficulty level of the overall learning task is determined according to the quantified difficulty level (for example, the difficulty level of 0-40 is determined as the primary difficulty, the difficulty level of 41-70 is determined as the intermediate difficulty, and the difficulty level of 71-100 is determined as the advanced difficulty), so as to achieve the mapping of the learning difficulty level of each evaluation indicator to the difficulty level of the overall task, and finally obtain the learning task difficulty level of each auxiliary language evaluation indicator.

[0085] The present invention firstly analyzes the learning task conversion of learners' auxiliary language learning target data, and can convert learners' learning goals and intentions into specific learning tasks. This process not only involves the clear definition of the goal, but also includes determining the specific content, learning methods and time frame of the learning task. Through detailed analysis and discussion, it is ensured that each learning task can clearly reflect the learner's language learning needs and expectations, thereby providing a basis for subsequent evaluation and adjustment. Secondly, by analyzing the task position and performance role of the initial evaluation indicator set of auxiliary language learning based on the learner's auxiliary language learning task, the key to this step is to determine the role and function of each evaluation indicator in the learning task, that is, how the evaluation indicator can help evaluate the performance and effectiveness of the learner in a specific task. By analyzing the performance data of learners in different tasks, the actual application scenario and effect of each evaluation indicator can be clarified, providing a theoretical basis for the selection and optimization of the evaluation indicator. Then, by performing a statistical analysis of the importance and learning depth of the initial evaluation indicator set of auxiliary language learning according to the specific position of each auxiliary language evaluation indicator in the auxiliary language learning task and the learning performance role data, this step aims to quantify the importance and depth of each evaluation indicator in the learning task, so as to better understand the role and influence of the evaluation indicator in the actual learning process. Through statistical analysis, we can determine which evaluation indicators play a key role in task completion, as well as their specific performance in terms of learning depth and complexity, providing data support for the establishment and optimization of the evaluation system. Next, we conduct an evaluation analysis of the scope of application of the initial evaluation indicator set for auxiliary language learning based on the importance and learning depth of each auxiliary language evaluation indicator in the auxiliary language learning task. This step aims to determine the applicability and effect of each evaluation indicator in different types of learning tasks, so as to help educators and evaluators better select and adjust evaluation indicators to meet the needs of different learners and learning environments. The scope of application evaluation analysis not only helps to accurately design evaluation tools and methods, but also improves the practicality and accuracy of the evaluation. Finally, we conduct a difficulty evaluation analysis of the initial evaluation indicator set for auxiliary language learning based on learners' auxiliary language learning tasks. The core of this step is to evaluate the difficulty level of each evaluation indicator in the learning task, that is, the degree of challenge of the evaluation task to the learner's ability and skills. Through difficulty evaluation analysis, appropriate learning goals and challenges can be set for learners, and educators can be provided with a basis for formulating personalized teaching plans and evaluation strategies. The evaluation difficulty level not only helps to improve learners' learning motivation and effect, but also optimizes the design and implementation process of evaluation tools.

[0086] Preferably, step S155 includes the following steps:

[0087] Decomposing the learners' auxiliary language learning tasks into task complexity components, and obtaining the complexity components of each auxiliary language learning task;

[0088] The embodiment of the present invention systematically analyzes the structure and requirements of learners' auxiliary language learning tasks, and subdivides each auxiliary language learning task into its components. At the same time, the complexity value of each part in the overall learning task is determined based on, for example, the cognitive requirements of the task, the complexity of the language input, the abstractness or concreteness of the task, etc., so as to decompose and filter out the more complex task components, and finally obtain the complexity components of each auxiliary language learning task.

[0089] Preferably, based on the complexity components of each auxiliary language learning task, the initial evaluation indicator set for auxiliary language learning is analyzed for complex component challenge points, so as to obtain the key challenge points of each auxiliary language evaluation indicator in the complexity components of the learning task;

[0090] The embodiment of the present invention analyzes the performance of the corresponding auxiliary language evaluation indicators in the initial evaluation indicator set of auxiliary language learning under different task complexity components by combining the complexity components of each auxiliary language learning task obtained by analysis, so as to analyze and compare in detail the performance of each evaluation indicator under different task complexity factors, so as to identify those key challenge points that need special attention or adjustment, that is, those factors with significant influence or difficulty in the complexity components, and finally obtain the key challenge points of each auxiliary language evaluation indicator in the complexity component of the learning task.

[0091] Preferably, based on the key challenge points of each auxiliary language evaluation indicator in the learning task complexity component, the learning difficulty level of the learner's auxiliary language learning task is evaluated and analyzed to obtain the learning difficulty level of each auxiliary language evaluation indicator in the auxiliary language learning task;

[0092] The embodiment of the present invention uses a difficulty level assessment algorithm to evaluate and analyze the corresponding learner's auxiliary language learning task by combining the key challenge points of each auxiliary language assessment indicator in the complexity component of the learning task obtained by analysis, so as to quantify the specific difficulty level of each assessment indicator in the learning process by comprehensively considering the relevance and influence of each assessment indicator in the complexity factor, and finally obtain the learning difficulty level of each auxiliary language assessment indicator in the auxiliary language learning task.

[0093] Preferably, the learning task difficulty level of the auxiliary language learning initial evaluation indicator set is determined according to the learning difficulty level of each auxiliary language evaluation indicator in the auxiliary language learning task, so as to obtain the learning task difficulty level of each auxiliary language evaluation indicator.

[0094] The embodiment of the present invention determines the difficulty level of the overall learning task by clarifying the contribution and influence of each evaluation indicator in the learning task by combining the learning difficulty level of each auxiliary language evaluation indicator obtained by quantitative calculation in the auxiliary language learning task (for example, the difficulty level of 0-40 is determined as elementary difficulty, the difficulty level of 41-70 is determined as intermediate difficulty, and the difficulty level of 71-100 is determined as advanced difficulty), thereby mapping the learning difficulty level of each evaluation indicator to the difficulty level of the overall task, and finally obtaining the learning task difficulty level of each auxiliary language evaluation indicator.

[0095] The present invention firstly decomposes the learner's auxiliary language learning task into task complexity components, and can systematically analyze and understand the complexity source and components of each learning task. The key to this step is to identify the various factors that constitute the complexity of the learning task, such as the cognitive requirements of the task, the complexity of the language input, the abstractness or concreteness of the task, etc. By decomposing the complexity components, it is possible to deeply understand the specific challenges involved in each task and the difficulties faced by learners, thereby providing an in-depth theoretical basis and analysis basis for subsequent evaluation and teaching design. Secondly, by performing complex component challenge point identification and analysis on the initial evaluation indicator set of auxiliary language learning based on the complexity components of each auxiliary language learning task, the goal of this process is to determine the key challenge points of each auxiliary language evaluation indicator in the learning task complexity component. These key challenge points represent the learner's difficulties or challenges caused by the evaluation indicator when facing specific tasks, such as understanding grammatical complexity, processing language voice difficulty, etc. By accurately locating the key challenge points, the evaluation indicators can be adjusted and optimized in a targeted manner to improve the sensitivity and effectiveness of the evaluation, and ensure that the evaluation can fully cover the complexity requirements of the learning task and the actual performance of the learner. Then, the learning difficulty level of learners' auxiliary language learning tasks is evaluated and analyzed based on the key challenge points of each auxiliary language evaluation indicator in the learning task complexity component. The key to this step is to evaluate the difficulty level of each evaluation indicator in a specific learning task, that is, the cognitive load and skill requirements of the evaluation indicator on the learner. Through the learning difficulty level evaluation, the degree of challenge of each evaluation indicator in the task can be determined, thereby providing a basis for designing personalized learning paths and teaching strategies. This analysis helps to optimize the setting of learning tasks and the selection of evaluation tools, ensuring the fairness and effectiveness of the evaluation. Finally, the learning task difficulty level of the initial evaluation indicator set for auxiliary language learning is determined according to the learning difficulty level of each auxiliary language evaluation indicator in the auxiliary language learning task. The importance of this step lies in comprehensively considering the difficulty level of the evaluation indicator and setting the corresponding learning task difficulty level for each evaluation indicator. By determining the difficulty level, the matching degree of the evaluation tool and the learning task can be ensured, while providing educators and learners with a clear understanding of the challenge of the learning task. This grading helps to promote effective learning and progress of learners, and provides data-based evaluation tool optimization suggestions for educational decision makers.

[0096] Preferably, step S2 comprises the following steps:

[0097] Step S21: performing an evaluation indicator applicability evaluation analysis on the initial evaluation indicator set for auxiliary language learning based on the learning task applicability scope of each auxiliary language evaluation indicator, and obtaining the degree of applicability between each auxiliary language evaluation indicator and the learning task;

[0098] The embodiment of the present invention uses an applicability evaluation method to quantitatively analyze the corresponding auxiliary language evaluation indicators in the initial evaluation indicator set for auxiliary language learning by combining the applicable scope of learning tasks of each auxiliary language evaluation indicator obtained by analysis, so as to evaluate that the evaluation indicators have different effects and applicability in different language learning scenarios, and finally obtain the degree of applicability between each auxiliary language evaluation indicator and the learning task.

[0099] Step S22: performing an evaluation indicator complexity evaluation analysis on the auxiliary language learning initial evaluation indicator set based on the learning task difficulty level of each auxiliary language evaluation indicator to obtain the implementation complexity degree between each auxiliary language evaluation indicator and the learning task;

[0100] The embodiment of the present invention uses a complexity assessment method (such as expert scoring, task decomposition analysis or simulation test methods) to quantitatively analyze the corresponding auxiliary language assessment indicators in the initial assessment indicator set of auxiliary language learning by combining the learning task difficulty level (including elementary, intermediate and advanced) of each auxiliary language assessment indicator obtained by analysis, so as to analyze the coping ability and effect of each auxiliary language assessment indicator in learning tasks of different difficulty levels, including the implementation complexity of the indicator in solving complex problems, coping with challenges and providing accurate assessment capabilities, and finally obtains the implementation complexity degree between each auxiliary language assessment indicator and the learning task.

[0101] Step S23: Acquire auxiliary language learning effect data, and perform effect correlation evaluation analysis on the auxiliary language learning initial evaluation indicator set based on the auxiliary language learning effect data to obtain the degree of correlation between each auxiliary language evaluation indicator and the language learning effect;

[0102] The embodiment of the present invention first obtains auxiliary language learning effect data by collecting actual language learning effects from a computer-assisted language evaluation system, including performance data of learners in a specific time period, such as test scores, task completion, learning progress, etc. At the same time, by combining the collected auxiliary language learning effect data, a correlation analysis method (such as correlation coefficient analysis, regression analysis, factor analysis, etc.) is used to evaluate and calculate the corresponding auxiliary language evaluation indicators in the auxiliary language learning initial evaluation indicator set, so as to evaluate and quantify the correlation between each evaluation indicator and the language learning effect, and finally obtain the degree of correlation between each auxiliary language evaluation indicator and the language learning effect.

[0103] Step S24: Designing evaluation indicator screening criteria based on the applicability of each auxiliary language evaluation indicator to the learning task, the implementation complexity, and the correlation between each auxiliary language evaluation indicator and the language learning effect, so as to generate auxiliary language evaluation indicator screening criteria;

[0104] The embodiment of the present invention clearly designs the screening criteria for evaluation indicators by combining the applicability degree between each auxiliary language evaluation indicator and the learning task, the implementation complexity degree, and the correlation degree between each auxiliary language evaluation indicator and the language learning effect obtained by analysis, including three considerations of applicability, implementation complexity and correlation with language learning effect. For example, the applicability criterion will be more suitable for screening out evaluation indicators under different learning tasks and difficulty levels; the complexity criterion can screen out evaluation indicators with relatively easy implementation and operation complexity; and the correlation criterion will screen out evaluation indicators corresponding to those exceeding the relevant threshold, thereby collectively generating a comprehensive screening criterion, and finally designing and generating the screening criteria for auxiliary language evaluation indicators.

[0105] Step S25: performing evaluation standard indicator screening processing on the auxiliary language learning initial evaluation indicator set according to the auxiliary language evaluation indicator screening standard to obtain an auxiliary language evaluation primary indicator set.

[0106] The embodiment of the present invention screens the corresponding auxiliary language evaluation indicators in the initial evaluation indicator set of auxiliary language learning by combining the designed auxiliary language evaluation indicator screening standard, so as to evaluate and screen each evaluation indicator in the initial evaluation indicator set one by one, and comprehensively consider the applicability, implementation complexity and relevance of each indicator to the language learning effect to screen out the evaluation indicators that are considered to be the most representative, predictive and feasible to implement, and finally obtain the auxiliary language evaluation primary indicator set.

[0107] The present invention firstly analyzes the applicability of the evaluation indicators for the initial evaluation indicator set for auxiliary language learning based on the scope of application of each auxiliary language evaluation indicator in the learning task, and can deeply understand the applicability of each evaluation indicator under different learning tasks. This analysis takes into account that the evaluation indicators have different effects and applicability in different language learning scenarios, such as some indicators are more suitable for the evaluation of oral ability, while others are more suitable for the evaluation of written grammar. By evaluating the degree of applicability, the selection and use of evaluation indicators can be optimized to ensure that the evaluation tools can effectively measure the ability and performance of learners under specific tasks, and provide accurate feedback and guidance for the teaching and learning process. Secondly, the evaluation indicator complexity evaluation analysis is performed on the initial evaluation indicator set for auxiliary language learning based on the difficulty level of the learning task of each auxiliary language evaluation indicator. The goal of this step is to evaluate the implementation complexity of each evaluation indicator in different learning tasks, that is, the specific requirements of the evaluation indicators for learners' abilities and skills. For example, some evaluation indicators require learners to have a high level of grammatical comprehension, while others focus more on vocabulary and pragmatic ability. This step can help educators and evaluators understand the difficulty of operating the evaluation tool, as well as its scope of application and effect in different learner groups by analyzing complexity, so as to more effectively select and apply the evaluation tool and improve the accuracy and reliability of the evaluation. Then, by obtaining auxiliary language learning effect data and conducting effect correlation evaluation analysis on the initial evaluation indicator set of auxiliary language learning based on the auxiliary language learning effect data, the key to this step is to evaluate the degree of correlation between each auxiliary language evaluation indicator and the language learning effect through actual learning effect data. Through this analysis, the actual contribution of the evaluation indicators to the learners' language learning progress can be verified and quantified, and which indicators can more effectively predict the improvement of learning outcomes and language ability can be determined. This correlation analysis can not only optimize the selection and use of evaluation indicators, but also provide important guidance and basis for subsequent processing. Next, the corresponding evaluation indicator screening criteria are designed by comprehensively considering the degree of applicability of each auxiliary language evaluation indicator to the learning task, the degree of implementation complexity, and the correlation with the language learning effect. This design process aims to establish clear evaluation criteria and screening basis to ensure that the final selected evaluation indicator set can fully reflect the complexity of the learning task and the correlation with the learning effect. Through the standardized screening process, the selection and application of evaluation tools can be simplified, the effectiveness and practicality of the evaluation can be improved, and reliable measurement tools can be provided for education and language learning research. Finally, the evaluation standard indicators of the initial evaluation indicator set for auxiliary language learning are screened according to the designed evaluation indicator screening criteria. The key to this step is to ensure that the final selected evaluation indicator set can have high predictive ability and effectiveness in practical applications and can accurately reflect the learner's language ability and learning progress.Through screening, unnecessary or inappropriate evaluation indicators can be excluded, making the evaluation tools more streamlined and practical, and providing more effective support and guidance for subsequent language learning evaluations.

[0108] Preferably, step S3 comprises the following steps:

[0109] Step S31: conducting expert inquiry survey and analysis on the auxiliary language evaluation first-level indicator set to obtain expert inquiry survey results for each auxiliary language evaluation first-level indicator;

[0110] The embodiment of the present invention selects experts with authority and experience in the field of auxiliary language assessment (including linguists, educational psychologists, language assessment researchers, and practitioners who are actually engaged in auxiliary language learning assessment, etc.), and designs a well-structured questionnaire to collect and record the experts' views, opinions and suggestions on the corresponding auxiliary language assessment first-level indicators in the auxiliary language assessment first-level indicator set, and finally obtains the expert letter inquiry survey results of each auxiliary language assessment first-level indicator.

[0111] Step S32: Statistically analyzing the expert inquiry survey results of each auxiliary language evaluation primary indicator in terms of the inquiry recovery rate and the expert opinion submission rate, and obtaining the expert inquiry recovery rate and the expert opinion submission rate of each auxiliary language evaluation primary indicator;

[0112] The embodiment of the present invention obtains the number of survey invitations issued and the actual number of replies from the expert inquiry survey results of each auxiliary language evaluation first-level indicator, and calculates the expert inquiry recovery rate by dividing the actual number of replies by the number of survey invitations issued, that is, the proportion of experts participating in the survey who actually provide opinions, thereby obtaining the expert inquiry recovery rate of each auxiliary language evaluation first-level indicator. At the same time, by obtaining the number of experts who put forward opinions and the number of experts who issued opinions from the expert inquiry survey results of each auxiliary language evaluation first-level indicator, and calculating the efficiency of the opinions of the participating experts by dividing the number of experts who put forward opinions by the number of experts who issued opinions, the attention of experts to the auxiliary language evaluation first-level indicator set and their willingness to participate are understood, and finally the expert opinion raising rate of each auxiliary language evaluation first-level indicator is obtained.

[0113] Step S33: performing expert coordination evaluation analysis on the auxiliary language evaluation first-level indicator set based on the expert inquiry recovery rate and expert opinion proposal rate of each auxiliary language evaluation first-level indicator, so as to obtain the expert coordination evaluation degree coefficient of each auxiliary language evaluation first-level indicator;

[0114] The embodiment of the present invention uses a consistency evaluation method to evaluate and analyze the corresponding auxiliary language evaluation first-level indicators in the auxiliary language evaluation first-level indicator set by combining the expert inquiry recovery rate and the expert opinion proposal rate of each auxiliary language evaluation first-level indicator obtained by quantitative calculation, so as to compare the consistency of expert opinions to analyze whether there is obvious convergence or heterogeneity among experts, and the degree of consensus of experts on key indicators, and to determine which evaluation indicators have a high consensus and recognition among the expert group by using an expert coordination statistical analysis method, and at the same time evaluate and analyze the expert coordination degree of each auxiliary language evaluation first-level indicator, and finally obtain the expert coordination evaluation degree coefficient of each auxiliary language evaluation first-level indicator.

[0115] Step S34: performing language cognitive disorder identification analysis on the learner's auxiliary language learning information data to obtain auxiliary language learning cognitive disorder information data;

[0116] The embodiment of the present invention uses a data analysis method to perform cognitive impairment identification analysis on learners' auxiliary language learning information data to identify cognitive impairments faced by learners in the language learning process, such as problems with comprehension ability, learning memory, language processing speed, etc., and ultimately obtains auxiliary language learning cognitive impairment information data.

[0117] Step S35: performing cognitive impairment correlation evaluation calculation on the auxiliary language assessment first-level indicator set based on the auxiliary language learning cognitive impairment information data to obtain the cognitive impairment correlation validity value of each auxiliary language assessment first-level indicator.

[0118] The embodiment of the present invention analyzes auxiliary language learning cognitive impairment information data by using a statistical analysis method to deeply analyze various cognitive impairment characteristics that learners may face in the language learning process, including but not limited to learners' performance in aspects such as attention concentration ability, working memory capacity, language comprehension ability, and information processing speed, and quantifies and calculates each auxiliary language assessment first-level indicator in the auxiliary language assessment first-level indicator set by using a Spearman rank correlation coefficient calculation method to quantify and calculate the correlation coefficient between each auxiliary language assessment first-level indicator and cognitive impairment. At the same time, the corresponding auxiliary language assessment first-level indicator in the auxiliary language assessment first-level indicator set is evaluated and calculated using an associated validity evaluation algorithm by combining the correlation coefficient obtained by the analysis, so as to comprehensively consider the size of the correlation coefficient and the statistical significance to evaluate the associated validity value of each evaluation indicator in cognitive impairment learners, and finally obtain the cognitive impairment associated validity value of each auxiliary language assessment first-level indicator.

[0119] The present invention firstly conducts expert inquiry survey and analysis on the auxiliary language evaluation primary indicator set, and can obtain authoritative and professional expert opinions. These experts have in-depth understanding and experience in the selection and design of evaluation indicators, and can provide valuable feedback from the perspective of subject expertise. The opinions of experts can help identify and optimize the deficiencies or improvement points in the evaluation indicator set, ensuring that the evaluation tool can achieve the best results in theory and practice. Through the inquiry survey, the theoretical basis of the evaluation indicator can be established, thereby providing a solid foundation for subsequent analysis and evaluation. Secondly, by conducting statistical analysis on the inquiry recovery rate and opinion proposal rate of the expert inquiry survey results of each auxiliary language evaluation primary indicator, it is intended to quantify the collection of expert opinions and the feedback rate. This statistical analysis can not only evaluate the enthusiasm of experts in participating in the survey and the quality of feedback, but also identify the deviations or differences that may exist in the entire survey process. By analyzing the recovery rate and opinion proposal rate, the comprehensiveness and representativeness of the survey can be evaluated, ensuring that all kinds of expert opinions are covered, thereby enhancing the comprehensiveness and applicability of the evaluation indicator set. Then, based on the auxiliary language learning cognitive impairment information data, the expert coordination evaluation analysis of the auxiliary language assessment first-level indicator set is carried out. The key to this step is to comprehensively consider the consistency and difference of expert opinions and evaluate the expert coordination evaluation degree coefficient of each auxiliary language assessment first-level indicator. Through expert coordination evaluation analysis, it can be determined which evaluation indicators have a high consensus and recognition among the expert group and which may need further discussion or modification. This analysis helps to improve the credibility and effectiveness of the evaluation indicators, thereby ensuring that the evaluation tools can be widely used in different backgrounds and learning environments. Next, by conducting language cognitive impairment identification analysis on learners' auxiliary language learning information data, it is possible to identify cognitive impairments faced by learners in the language learning process, such as problems in comprehension ability, learning memory, language processing speed, etc. These data provide basic data guarantee for the subsequent optimization and screening process of the evaluation indicator set, thereby helping the indicator screening process to better identify and understand the individual differences of learners and provide data support for the subsequent processing process. Finally, by calculating the correlation between the first-level indicators of auxiliary language assessment and cognitive impairment based on the information data of auxiliary language learning cognitive impairment, the aim is to quantify the correlation validity value between the first-level indicators of auxiliary language assessment and cognitive impairment. This calculation can help determine which assessment indicators have higher predictive power and applicability in learners with cognitive impairment, thereby adjusting the design and application strategies of the assessment tools and improving their effectiveness and practicality in diverse learner groups. Through these assessments, the adaptability of the assessment tools to learners with cognitive impairment can be effectively improved, which can improve the effectiveness and progress in the language learning process.

[0120] Preferably, step S35 includes the following steps:

[0121] Step S351: performing cognitive impairment feature analysis on the auxiliary language learning cognitive impairment information data to obtain auxiliary language learning cognitive impairment feature data;

[0122] The embodiment of the present invention analyzes the auxiliary language learning cognitive disorder information data by using a statistical analysis method to deeply analyze the various cognitive disorder characteristics that learners may face in the language learning process, including but not limited to the learners' attention concentration ability, working memory capacity, language comprehension ability, and information processing speed. It also reveals the individual differences and potential learning difficulties of different learners when facing language learning tasks, and finally obtains the auxiliary language learning cognitive disorder feature data.

[0123] Step S352: performing correlation calculation on the auxiliary language assessment primary indicator set based on the auxiliary language learning cognitive impairment feature data using the Spearman rank correlation coefficient calculation method to obtain the correlation coefficient between each auxiliary language assessment primary indicator and cognitive impairment;

[0124] The embodiment of the present invention uses the Spearman rank correlation coefficient calculation method to quantify the various auxiliary language assessment first-level indicators in the auxiliary language assessment first-level indicator set by combining the auxiliary language learning cognitive impairment feature data obtained through analysis, so as to quantify the correlation coefficient between each auxiliary language assessment first-level indicator and cognitive impairment, that is, the relevant changes of the assessment indicators under the influence of cognitive impairment, and finally obtain the correlation coefficient between each auxiliary language assessment first-level indicator and cognitive impairment.

[0125] Step S353: performing cognitive impairment association evaluation calculation on the auxiliary language assessment first-level indicator set according to the correlation coefficient between each auxiliary language assessment first-level indicator and cognitive impairment, so as to obtain the cognitive impairment association validity value of each auxiliary language assessment first-level indicator.

[0126] The embodiment of the present invention uses a correlation validity evaluation algorithm to evaluate and calculate the corresponding auxiliary language assessment first-level indicators in the auxiliary language assessment first-level indicator set by combining the correlation coefficients between each auxiliary language assessment first-level indicator and cognitive impairment, so as to comprehensively consider the size and statistical significance of the correlation coefficient to evaluate the correlation validity value of each evaluation indicator in cognitively impaired learners, and finally obtain the cognitive impairment correlation validity value of each auxiliary language assessment first-level indicator.

[0127] The present invention first analyzes the cognitive impairment characteristics of auxiliary language learning cognitive impairment information data, and can deeply understand the various cognitive impairment characteristics that learners may face in the language learning process, including but not limited to learners' ability to concentrate, working memory capacity, language comprehension ability, and information processing speed. This step can reveal the individual differences and potential learning difficulties of different learners when facing language learning tasks by analyzing these characteristics, thereby providing data support for the design and optimization of subsequent evaluation indicators. Then, the auxiliary language evaluation primary indicator set is correlated by using the Spearman rank correlation coefficient calculation method based on the auxiliary language learning cognitive impairment feature data. This calculation method can effectively evaluate the degree of association between each evaluation indicator and cognitive impairment, is not affected by the normality and outliers of the data, and has strong robustness and universality. By calculating the correlation coefficient, the strength and direction of the relationship between the evaluation indicator and the cognitive impairment characteristics can be quantified, thereby helping to determine which evaluation indicators are more suitable for identifying and evaluating the language learning performance of cognitively impaired learners. Finally, the correlation coefficient between each auxiliary language assessment primary indicator and cognitive impairment is used to calculate the correlation between the auxiliary language assessment primary indicator set and cognitive impairment. The key to this step is to comprehensively consider the size and statistical significance of the correlation coefficient and evaluate the predictive ability and correlation validity of each assessment indicator in learners with cognitive impairment. Through this correlation evaluation calculation, the assessment indicators that have the most significant impact on the language learning performance of learners with cognitive impairment can be identified, and then the design and application strategy of the assessment tool can be optimized to improve its effectiveness and applicability in actual auxiliary language teaching.

[0128] Preferably, step S4 comprises the following steps:

[0129] Step S41: performing parameter weight allocation on the expert coordination evaluation degree coefficient and cognitive impairment-related validity value of each auxiliary language assessment first-level indicator to obtain the expert coordination evaluation weight ratio and cognitive impairment-related weight ratio of each auxiliary language assessment first-level indicator;

[0130] The embodiment of the present invention combines the subjective opinions of experts and objective conditions to quantitatively calculate the expert coordination evaluation degree coefficients of each auxiliary language evaluation first-level indicator to perform corresponding weight allocation, so as to reflect the consistency evaluation results of different experts on the importance of the evaluation first-level indicators, thereby obtaining the expert coordination evaluation weight ratio of each auxiliary language evaluation first-level indicator. At the same time, by determining the degree of influence of cognitive impairment on different evaluation first-level indicators and the importance of this degree of influence in the evaluation process according to the cognitive impairment correlation validity value obtained by quantitative calculation, the corresponding weight ratio is allocated, so as to accurately reflect the degree of association between each evaluation first-level indicator and cognitive impairment, and finally obtain the cognitive impairment association weight ratio of each auxiliary language evaluation first-level indicator.

[0131] Step S42: Based on the expert coordination evaluation weight ratio and cognitive impairment-related weight ratio of each auxiliary language evaluation first-level indicator, the expert coordination evaluation degree coefficient and cognitive impairment-related validity value of each auxiliary language evaluation first-level indicator are comprehensively scored and calculated to obtain the indicator evaluation system evaluation comprehensive score value of each auxiliary language evaluation first-level indicator;

[0132] The embodiment of the present invention combines the expert coordination evaluation weight ratio and cognitive impairment-related weight ratio of each auxiliary language evaluation first-level indicator previously determined and applied to the evaluation results of each auxiliary language evaluation first-level indicator, so as to multiply the evaluation parameter of each indicator by the corresponding weight ratio to reflect its importance and contribution in the overall evaluation, and at the same time, add the product results of the two evaluation parameters to obtain the comprehensive score value of each evaluation first-level indicator, and finally obtain the comprehensive score value of the indicator evaluation system evaluation of each auxiliary language evaluation first-level indicator.

[0133] Step S43: performing an indicator evaluation screening process on the corresponding auxiliary language evaluation first-level indicators in the auxiliary language evaluation first-level indicator set based on the indicator evaluation system evaluation comprehensive score value of each auxiliary language evaluation first-level indicator, so as to obtain an auxiliary language evaluation second-level indicator set;

[0134] The embodiment of the present invention compares and judges the index evaluation system evaluation comprehensive score values ​​of each auxiliary language evaluation first-level indicator obtained by quantitative calculation according to a preset index evaluation system evaluation threshold, so as to screen out the index evaluation system evaluation comprehensive score values ​​of the corresponding auxiliary language evaluation first-level indicators in the auxiliary language evaluation first-level indicator set that are greater than or equal to the preset index evaluation system evaluation threshold, and eliminate those that are less than the preset index evaluation system evaluation threshold, thereby screening out the most representative and discriminative second-level indicators, and finally obtaining the auxiliary language evaluation second-level indicator set.

[0135] Step S44: Integrate the auxiliary language assessment secondary indicator set into the computer-assisted language assessment system for assessment analysis to obtain a computer-assisted language assessment output result.

[0136] The embodiment of the present invention integrates the screened auxiliary language assessment secondary indicator set into the computer-assisted language assessment system and optimizes the system's assessment model and algorithm to perform detailed analysis and assessment on the auxiliary language ability of the corresponding assessment object, so as to accurately assess the language learning performance and language learning ability of learners with auxiliary language cognitive impairment, and finally obtain the computer-assisted language assessment output result.

[0137] The present invention firstly distributes the parameter weights of the expert coordination evaluation degree coefficient and the cognitive impairment associated validity value of each auxiliary language evaluation primary indicator, and can realize the scientific distribution and adjustment of the evaluation indicator weight, wherein the weight distribution of the expert coordination evaluation degree coefficient can reflect the consistency evaluation of the importance of the evaluation indicator by different experts, which is the key to ensure the objectivity and authority of the evaluation system. At the same time, the weight distribution of the cognitive impairment associated validity value can accurately reflect the degree of association between each evaluation indicator and cognitive impairment, thereby ensuring the effectiveness and practicality of the evaluation system for a specific learner group. Secondly, the expert coordination evaluation degree coefficient and the cognitive impairment associated validity value of each auxiliary language evaluation primary indicator are calculated by comprehensive scoring based on the expert coordination evaluation weight ratio and the cognitive impairment associated weight ratio. The key to this step is to combine the expert weight and the cognitive impairment associated weight with the corresponding evaluation indicator parameters, so as to comprehensively consider the importance of the evaluation indicator and its actual influence in cognitive impairment learners. Through comprehensive scoring, the contribution of the evaluation indicator in the overall evaluation system can be quantified, providing a scientific basis for subsequent indicator optimization and evaluation strategies. Then, the corresponding first-level auxiliary language assessment indicators in the first-level auxiliary language assessment indicator set are screened by the indicator evaluation system evaluation comprehensive score based on each first-level auxiliary language assessment indicator. The purpose of this step is to screen out the most representative and discriminative second-level indicators from the comprehensive first-level indicator set to further optimize the accuracy and practicality of the evaluation system. Through indicator evaluation and screening, it can be ensured that the final evaluation tool is not only comprehensive and scientific, but also can effectively identify and distinguish the language learning characteristics of learners with different cognitive impairments. Finally, by integrating the second-level auxiliary language assessment indicator set into the computer-assisted language assessment system for evaluation and analysis, this step transforms the previous theoretical framework and indicator screening work into practical application. Through the support and analysis capabilities of the computer system, the language learning performance of learners with cognitive impairment can be evaluated more quickly and accurately, which not only improves the efficiency of the evaluation, but also enhances the objectivity and scientificity of the evaluation results, providing strong decision-making support for education and clinical practice.

[0138] Preferably, the present invention further provides an evaluation indicator screening system for a computer-assisted language evaluation system, which is used to execute the evaluation indicator screening method for a computer-assisted language evaluation system as described above. The evaluation indicator screening system for a computer-assisted language evaluation system comprises:

[0139] The auxiliary language assessment system evaluation index analysis module is used to obtain learners' auxiliary language learning information data from the computer-assisted language assessment system, and perform evaluation index statistical analysis on the learners' auxiliary language learning information data to obtain an initial evaluation index set for auxiliary language learning; obtain learners' auxiliary language learning target data, and perform learning task application scope and difficulty evaluation analysis on the initial evaluation index set for auxiliary language learning based on the learners' auxiliary language learning target data, so as to obtain the learning task application scope and learning task difficulty level of each auxiliary language evaluation index;

[0140] The auxiliary language evaluation primary indicator screening module is used to screen the auxiliary language learning initial evaluation indicator set based on the learning task application scope and learning task difficulty level of each auxiliary language evaluation indicator, so as to obtain the auxiliary language evaluation primary indicator set;

[0141] The auxiliary language assessment first-level indicator evaluation and analysis module is used to perform expert coordination evaluation and analysis on the auxiliary language assessment first-level indicator set to obtain the expert coordination evaluation degree coefficient of each auxiliary language assessment first-level indicator; based on the learner's auxiliary language learning information data, the auxiliary language assessment first-level indicator set is evaluated and calculated for cognitive impairment association to obtain the cognitive impairment association validity value of each auxiliary language assessment first-level indicator;

[0142] The auxiliary language assessment indicator optimization screening and evaluation module is used to perform indicator evaluation screening processing on the auxiliary language assessment first-level indicator set based on the expert coordination evaluation degree coefficient of each auxiliary language assessment first-level indicator and the cognitive impairment-related validity value to obtain the auxiliary language assessment second-level indicator set; by integrating the auxiliary language assessment second-level indicator set into the computer-assisted language assessment system for evaluation and analysis, the computer-assisted language assessment output result is obtained.

[0143] In summary, the present invention provides an evaluation index screening system for a computer-assisted language evaluation system. The evaluation index screening system for the computer-assisted language evaluation system consists of an auxiliary language evaluation system evaluation index analysis module, an auxiliary language evaluation first-level index screening module, an auxiliary language evaluation first-level index evaluation analysis module, and an auxiliary language evaluation index optimization screening and evaluation module. It can implement any evaluation index screening method for a computer-assisted language evaluation system described in the present invention, and is used to combine the operations between computer programs running on various modules to implement an evaluation index screening method for a computer-assisted language evaluation system. The internal structures of the system cooperate with each other, which can greatly reduce duplication of work and manpower investment, and can quickly and effectively provide a more accurate and efficient evaluation index screening process, thereby simplifying the operation flow of the evaluation index screening system for the computer-assisted language evaluation system.

[0144] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0145] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for screening evaluation indicators of a computer-assisted language evaluation system, characterized in that: The following steps are involved: Step S1: Obtain learner auxiliary language learning information data from a computer-assisted language assessment system, and perform evaluation index statistical analysis on the learner auxiliary language learning information data to obtain an initial evaluation index set for auxiliary language learning; obtain learner auxiliary language learning target data, and perform learning task application scope and difficulty evaluation analysis on the initial evaluation index set for auxiliary language learning based on the learner auxiliary language learning target data to obtain the learning task application scope and learning task difficulty level of each auxiliary language evaluation index; Step S2: Based on the applicable scope of each auxiliary language evaluation indicator and the difficulty level of the learning task, the auxiliary language learning initial evaluation indicator set is screened for evaluation standard indicators to obtain an auxiliary language evaluation primary indicator set; Step S3: Perform expert coordination evaluation analysis on the auxiliary language assessment first-level indicator set to obtain the expert coordination evaluation degree coefficient of each auxiliary language assessment first-level indicator; perform cognitive impairment association evaluation calculation on the auxiliary language assessment first-level indicator set based on the learner's auxiliary language learning information data to obtain the cognitive impairment association validity value of each auxiliary language assessment first-level indicator; wherein step S3 includes the following steps: Step S31: conducting expert inquiry survey and analysis on the auxiliary language evaluation first-level indicator set to obtain expert inquiry survey results for each auxiliary language evaluation first-level indicator; Step S32: Statistically analyzing the expert inquiry survey results of each auxiliary language evaluation primary indicator in terms of the inquiry recovery rate and the expert opinion submission rate, and obtaining the expert inquiry recovery rate and the expert opinion submission rate of each auxiliary language evaluation primary indicator; Step S33: performing expert coordination evaluation analysis on the auxiliary language evaluation first-level indicator set based on the expert inquiry recovery rate and expert opinion proposal rate of each auxiliary language evaluation first-level indicator, so as to obtain the expert coordination evaluation degree coefficient of each auxiliary language evaluation first-level indicator; Step S34: performing language cognitive disorder identification analysis on the learner's auxiliary language learning information data to obtain auxiliary language learning cognitive disorder information data; Step S35: performing cognitive impairment correlation evaluation calculation on the auxiliary language assessment primary indicator set based on the auxiliary language learning cognitive impairment information data to obtain the cognitive impairment correlation validity value of each auxiliary language assessment primary indicator; wherein step S35 includes the following steps: Step S351: performing cognitive impairment feature analysis on the auxiliary language learning cognitive impairment information data to obtain auxiliary language learning cognitive impairment feature data; Step S352: performing correlation calculation on the auxiliary language assessment primary indicator set based on the auxiliary language learning cognitive impairment feature data using the Spearman rank correlation coefficient calculation method to obtain the correlation coefficient between each auxiliary language assessment primary indicator and cognitive impairment; Step S353: performing cognitive impairment correlation evaluation calculation on the auxiliary language assessment first-level indicator set according to the correlation coefficient between each auxiliary language assessment first-level indicator and cognitive impairment, so as to obtain the cognitive impairment correlation validity value of each auxiliary language assessment first-level indicator; Step S4: Based on the expert coordination evaluation degree coefficient of each auxiliary language assessment first-level indicator and the cognitive impairment-related validity value, the auxiliary language assessment first-level indicator set is screened to obtain the auxiliary language assessment second-level indicator set; the auxiliary language assessment second-level indicator set is integrated into the computer-assisted language assessment system for evaluation and analysis to obtain the computer-assisted language assessment output result.

2. The evaluation index screening method of the computer-assisted language evaluation system according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: acquiring learner-assisted language learning information data from a computer-assisted language assessment system; Step S12: performing learning stage performance and feedback evaluation analysis on the learner's auxiliary language learning information data to obtain the learner's auxiliary language learning performance information data and auxiliary language learning feedback information data at each learning stage; Step S13: performing evaluation index statistical analysis on the learner's auxiliary language learning information data based on the learner's auxiliary language learning performance information data and auxiliary language learning feedback information data at each learning stage to obtain an initial evaluation index set for auxiliary language learning; Step S14: performing learning goal mining analysis on the learner's auxiliary language learning information data to obtain the learner's auxiliary language learning goal data; Step S15: Based on the learner's auxiliary language learning goal data, the initial evaluation indicator set for auxiliary language learning is analyzed for the scope of application and difficulty of the learning task, and the scope of application and difficulty level of the learning task of each auxiliary language evaluation indicator are obtained.

3. The evaluation index screening method of the computer-assisted language evaluation system according to claim 2, characterized in that: Step S13 includes the following steps: Step S131: performing language learning dimension division processing on the learner's auxiliary language learning information data to obtain auxiliary language ability dimension learning information data, auxiliary language learning process dimension learning information data and auxiliary language learning achievement dimension learning information data; Step S132: performing language ability evaluation index analysis on the auxiliary language ability dimension learning information data based on the learner's auxiliary language learning performance information data at each learning stage to obtain an auxiliary language ability evaluation index set; Step S133: performing learning process evaluation index analysis on the auxiliary language learning process dimension learning information data based on the learner's auxiliary language learning feedback information data at each learning stage to obtain an auxiliary language learning process evaluation index set; Step S134: performing learning achievement evaluation index analysis on the learning information data of the auxiliary language learning achievement dimension based on the auxiliary language learning feedback information data of the learners at each learning stage to obtain an auxiliary language learning achievement evaluation index set; Step S135: merging the auxiliary language ability evaluation indicator set, the auxiliary language learning process evaluation indicator set and the auxiliary language learning achievement evaluation indicator set to obtain an auxiliary language learning initial evaluation indicator set.

4. The evaluation index screening method of the computer-assisted language evaluation system according to claim 2, characterized in that: Step S15 includes the following steps: Step S151: performing learning task conversion analysis on the learner's auxiliary language learning target data to obtain the learner's auxiliary language learning task; Step S152: performing task position and performance role analysis on the auxiliary language learning initial evaluation indicator set based on the learner's auxiliary language learning task to obtain the specific position of each auxiliary language evaluation indicator in the auxiliary language learning task and learning performance role data; Step S153: performing a statistical analysis of the importance and learning depth of the auxiliary language learning initial evaluation indicator set according to the specific position of each auxiliary language evaluation indicator in the auxiliary language learning task and the learning performance effect data, and obtaining the importance and learning depth of each auxiliary language evaluation indicator in the auxiliary language learning task; Step S154: Based on the importance and learning depth of each auxiliary language evaluation indicator in the auxiliary language learning task, the auxiliary language learning initial evaluation indicator set is evaluated and analyzed for the learning task application scope to obtain the learning task application scope of each auxiliary language evaluation indicator; Step S155: performing difficulty assessment analysis on the auxiliary language learning initial assessment indicator set based on the learner's auxiliary language learning task to obtain the learning task difficulty level of each auxiliary language assessment indicator.

5. The evaluation index screening method of the computer-assisted language evaluation system according to claim 4, characterized in that: Step S155 includes the following steps: Decomposing the learners' auxiliary language learning tasks into task complexity components, and obtaining the complexity components of each auxiliary language learning task; Based on the complexity components of each auxiliary language learning task, the complex component challenge point identification analysis is performed on the initial evaluation indicator set for auxiliary language learning, and the key challenge points of each auxiliary language evaluation indicator in the complexity component of the learning task are obtained; Based on the key challenge points of each auxiliary language assessment indicator in the learning task complexity component, the learning difficulty level of learners' auxiliary language learning tasks is evaluated and analyzed to obtain the learning difficulty level of each auxiliary language assessment indicator in the auxiliary language learning task; The learning task difficulty level of the auxiliary language learning initial evaluation indicator set is determined according to the learning difficulty level of each auxiliary language evaluation indicator in the auxiliary language learning task, and the learning task difficulty level of each auxiliary language evaluation indicator is obtained.

6. The evaluation index screening method of the computer-assisted language evaluation system according to claim 1, characterized in that: Step S2 includes the following steps: Step S21: performing an evaluation indicator applicability evaluation analysis on the initial evaluation indicator set for auxiliary language learning based on the learning task applicability scope of each auxiliary language evaluation indicator, and obtaining the degree of applicability between each auxiliary language evaluation indicator and the learning task; Step S22: performing an evaluation indicator complexity evaluation analysis on the auxiliary language learning initial evaluation indicator set based on the learning task difficulty level of each auxiliary language evaluation indicator to obtain the implementation complexity degree between each auxiliary language evaluation indicator and the learning task; Step S23: Acquire auxiliary language learning effect data, and perform effect correlation evaluation analysis on the auxiliary language learning initial evaluation indicator set based on the auxiliary language learning effect data to obtain the degree of correlation between each auxiliary language evaluation indicator and the language learning effect; Step S24: Designing evaluation indicator screening criteria based on the applicability of each auxiliary language evaluation indicator to the learning task, the implementation complexity, and the correlation between each auxiliary language evaluation indicator and the language learning effect, so as to generate auxiliary language evaluation indicator screening criteria; Step S25: performing evaluation standard indicator screening processing on the auxiliary language learning initial evaluation indicator set according to the auxiliary language evaluation indicator screening standard to obtain an auxiliary language evaluation primary indicator set.

7. The evaluation index screening method of the computer-assisted language evaluation system according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: performing parameter weight allocation on the expert coordination evaluation degree coefficient and cognitive impairment-related validity value of each auxiliary language assessment first-level indicator to obtain the expert coordination evaluation weight ratio and cognitive impairment-related weight ratio of each auxiliary language assessment first-level indicator; Step S42: Based on the expert coordination evaluation weight ratio and cognitive impairment-related weight ratio of each auxiliary language evaluation first-level indicator, the expert coordination evaluation degree coefficient and cognitive impairment-related validity value of each auxiliary language evaluation first-level indicator are comprehensively scored and calculated to obtain the indicator evaluation system evaluation comprehensive score value of each auxiliary language evaluation first-level indicator; Step S43: performing an indicator evaluation screening process on the corresponding auxiliary language evaluation first-level indicators in the auxiliary language evaluation first-level indicator set based on the indicator evaluation system evaluation comprehensive score value of each auxiliary language evaluation first-level indicator, so as to obtain an auxiliary language evaluation second-level indicator set; Step S44: Integrate the auxiliary language assessment secondary indicator set into the computer-assisted language assessment system for assessment analysis to obtain a computer-assisted language assessment output result.

8. An evaluation index screening system for a computer-assisted language evaluation system, characterized in that: For executing the evaluation index screening method of the computer-assisted language evaluation system according to claim 1, the evaluation index screening system of the computer-assisted language evaluation system comprises: The auxiliary language assessment system evaluation index analysis module is used to obtain learners' auxiliary language learning information data from the computer-assisted language assessment system, and perform evaluation index statistical analysis on the learners' auxiliary language learning information data to obtain an initial evaluation index set for auxiliary language learning; obtain learners' auxiliary language learning target data, and perform learning task application scope and difficulty evaluation analysis on the initial evaluation index set for auxiliary language learning based on the learners' auxiliary language learning target data, so as to obtain the learning task application scope and learning task difficulty level of each auxiliary language evaluation index; The auxiliary language evaluation primary indicator screening module is used to screen the auxiliary language learning initial evaluation indicator set based on the learning task application scope and learning task difficulty level of each auxiliary language evaluation indicator, so as to obtain the auxiliary language evaluation primary indicator set; The auxiliary language assessment first-level indicator evaluation and analysis module is used to conduct expert letter inquiry survey and analysis on the auxiliary language assessment first-level indicator set to obtain the expert letter inquiry survey results of each auxiliary language assessment first-level indicator; conduct statistical analysis on the letter inquiry recovery rate and opinion proposal rate of the expert letter inquiry survey results of each auxiliary language assessment first-level indicator to obtain the expert letter inquiry recovery rate and expert opinion proposal rate of each auxiliary language assessment first-level indicator; conduct expert coordination evaluation and analysis on the auxiliary language assessment first-level indicator set based on the expert letter inquiry recovery rate and expert opinion proposal rate of each auxiliary language assessment first-level indicator to obtain the expert coordination evaluation degree coefficient of each auxiliary language assessment first-level indicator; and conduct statistical analysis on the learner's auxiliary language learning information data. According to the language cognitive disorder identification analysis, the auxiliary language learning cognitive disorder information data is obtained; the auxiliary language learning cognitive disorder information data is analyzed for cognitive disorder characteristics, and the auxiliary language learning cognitive disorder characteristic data is obtained; based on the auxiliary language learning cognitive disorder characteristic data, the Spearman rank correlation coefficient calculation method is used to perform correlation calculation on the auxiliary language assessment first-level indicator set to obtain the correlation coefficient between each auxiliary language assessment first-level indicator and cognitive disorder; according to the correlation coefficient between each auxiliary language assessment first-level indicator and cognitive disorder, the auxiliary language assessment first-level indicator set is evaluated for cognitive disorder association, so as to obtain the cognitive disorder association validity value of each auxiliary language assessment first-level indicator; The auxiliary language assessment indicator optimization screening and evaluation module is used to perform indicator evaluation screening processing on the auxiliary language assessment first-level indicator set based on the expert coordination evaluation degree coefficient of each auxiliary language assessment first-level indicator and the cognitive impairment-related validity value to obtain the auxiliary language assessment second-level indicator set; by integrating the auxiliary language assessment second-level indicator set into the computer-assisted language assessment system for evaluation and analysis, the computer-assisted language assessment output result is obtained.

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