An artificial intelligence-based elderly education learning evaluation system and method thereof

Abstract

The application belongs to the field of evaluation and big data technology, and provides an old-age education learning evaluation system and method based on artificial intelligence. The method comprises the following steps: obtaining old-age education course content in a public state in a region to be evaluated, and extracting a plurality of course element features; an artificial intelligence model searches and deforms test questions based on the course element features, and obtains a plurality of characteristic test questions; the characteristic test questions and basic test questions are fused to obtain evaluation questions; receiving answer data fed back by a testee, completing evaluation of old-age education learning in the region to be evaluated according to the answer data, and outputting an evaluation result. The basic test questions and the characteristic test questions automatically generated through the artificial intelligence technology are fused, so that the evaluation result can better reflect the real effect of old-age education in the region to be evaluated.

Description

Technical Field

[0001] This invention relates to the fields of assessment and big data technology, and more specifically, to an artificial intelligence-based assessment system and method for elderly education and learning. Background Technology

[0002] Senior education is an important measure to improve the quality of life for the elderly and promote social harmony. To better advance senior education, it is necessary to assess those who have received it, thereby evaluating its effectiveness and providing a data foundation for future optimization and improvement. Existing senior education assessment systems are based on standardized test questions. However, the focus of senior education varies significantly across regions, and test results obtained using standardized questions often do not reflect the actual outcomes of local senior education, thus failing to guide subsequent optimization and improvement. Summary of the Invention

[0003] To address this issue, the present invention provides an artificial intelligence-based method, system, electronic device, storage medium, and computer program product for assessing learning in elderly education, thereby resolving the aforementioned technical problems.

[0004] This invention discloses an artificial intelligence-based learning assessment method for elderly education, the method comprising the following steps: Obtain publicly available elderly education course content from the region to be evaluated, and extract several course element characteristics from it; The artificial intelligence model searches for and modifies test questions based on the characteristics of each of the course elements, and obtains a number of distinctive test questions. The various featured test questions are combined with the basic test questions to obtain the assessment test questions; Receive the answer data from the testers, complete the assessment of elderly education and learning in the region to be assessed based on the answer data, and output the assessment results.

[0005] In some embodiments, the step of obtaining publicly available elderly education course content for the region to be evaluated, and extracting several course element features from it, includes: Based on the geographic identifier of the region to be evaluated, obtain several sets of publicly available elderly education course content, and extract a first number of first course element features and corresponding first characteristic values ​​from them; wherein, the first characteristic value is inversely proportional to the first number of the first course element features. The first course element feature whose first feature value is greater than the feature threshold is selected as the course element feature.

[0006] In some embodiments, the step of filtering the first course element feature whose first feature value is greater than the feature threshold as the course element feature includes: Obtain several second curriculum element features for each adjacent region of the region to be evaluated, perform similarity calculations on each first curriculum element feature and each second curriculum element feature to obtain several third curriculum element features that satisfy similarity relationships, and perform clustering and integration on the third curriculum element features to obtain several fourth curriculum element features. The adaptation weight of the first course element feature corresponding to the fourth course element feature is determined based on the second quantity of the fourth course element feature, and the second feature value is calculated based on the adaptation weight and the first feature value. The first course element features whose second feature value is greater than the feature threshold are selected as the course element features.

[0007] In some embodiments, the method further includes: Calculate the ratio of the second quantity of the fourth course element feature to the first quantity of the first course element feature; The outer expansion distance is determined based on the ratio and the geographical radius of the region to be evaluated, and other regions located within the outer expansion distance are identified as the adjacent regions.

[0008] In some embodiments, the step of merging the featured test questions with the basic test questions to obtain assessment questions includes: Determine the first ratio of the featured test items and the second ratio of the basic test items; In each question type, the third number of the featured test questions and the fourth number of the basic test questions are determined according to the first ratio and the second ratio, respectively. The third number of questions are randomly selected from the featured test questions, and the fourth number of questions are randomly selected from the basic test questions. The two sets of questions are then combined to obtain the assessment questions.

[0009] In some embodiments, determining the first ratio of the featured test items to the second ratio of the basic test items includes: In the formula, The first proportion of the featured test questions. This is the second proportion of the basic test questions; The first quantity is the first feature of the first course element. The second quantity is the fourth course element feature; The outward expansion distance is... This is a reference value for the outward expansion distance; for The reference value for the weights.

[0010] The present invention also discloses an artificial intelligence-based learning assessment system for elderly education, comprising a communication module, a processing module, and a storage module, wherein the processing module is electrically connected to the storage module and the communication module respectively; The communication module is used to acquire publicly available elderly education course content from the region to be evaluated, send the generated test questions to the test takers, and receive the test takers' answer data. The storage module is used to store computer programs; The processing module is used to retrieve and execute the computer program in the storage module to perform the method described in the preceding item, to generate test questions, and to generate and output evaluation results based on the answer data provided by the testers.

[0011] The present invention also discloses an electronic device comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, the processor executing the computer program to implement the method as described in any of the preceding claims.

[0012] The present invention also discloses a computer storage medium storing a computer program that is executed by a processor to implement the methods described in any of the preceding methods.

[0013] The present invention also discloses a computer program product, characterized in that: when the computer program product is run on a terminal, the terminal executes the method as described in the preceding one.

[0014] The beneficial effects of this invention are as follows: The solution of this invention not only considers universally applicable basic test questions, but also automatically generates special test questions related to the characteristics of elderly education in the region to be evaluated through artificial intelligence technology. By combining the test questions of both types, the effectiveness of elderly education in the region to be evaluated is assessed, so that the evaluation results can better reflect the true effect of elderly education in the region to be evaluated. This is conducive to managers making more accurate optimization and improvement measures for the elderly education work in the region to be evaluated. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating an artificial intelligence-based learning assessment method for elderly education disclosed in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of an artificial intelligence-based learning assessment system for elderly education disclosed in an embodiment of the present invention. Detailed Implementation

[0017] The following specific embodiments illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] Furthermore, the technical features involved in the different embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0019] like Figure 1 As shown in the figure, this invention discloses an artificial intelligence-based learning assessment method for elderly education, the method comprising the following steps: Obtain publicly available elderly education course content from the region to be evaluated, and extract several course element characteristics from it; The artificial intelligence model searches for and modifies test questions based on the characteristics of each of the course elements, and obtains a number of distinctive test questions. The various featured test questions are combined with the basic test questions to obtain the assessment test questions; Receive the answer data from the testers, complete the assessment of elderly education and learning in the region to be assessed based on the answer data, and output the assessment results.

[0020] In this embodiment of the invention, as described in the background section, existing methods for assessing elderly education are based on standardized test questions. These standardized questions lack consideration for the specific characteristics of elderly education in particular regions, and the resulting test results naturally fail to reflect the true effectiveness of local elderly education. Therefore, this invention acquires and analyzes publicly available elderly education course content for the region to be assessed, extracting multiple course element features, including the subjects covered and the level of difficulty. An artificial intelligence model is then used to search for test questions online based on these identified course element features. To avoid assessors searching for questions beforehand, the questions are modified to a certain extent, thus obtaining specialized test questions adapted to the characteristics of elderly education in the region to be assessed. These specialized test questions are then integrated with basic test questions, which refer to general questions related to elderly education applicable in multiple regions, resulting in the final assessment test questions. Finally, the assessment test questions are output to assessors for testing, and the assessment of elderly education in the region is completed based on the answers. The assessment results are then output to relevant parties.

[0021] The solution of this invention not only considers universally applicable basic test questions, but also automatically generates special test questions related to the characteristics of elderly education in the region to be evaluated through artificial intelligence technology. By combining the test questions of both types, the effectiveness of elderly education in the region to be evaluated is assessed, so that the evaluation results can better reflect the true effect of elderly education in the region to be evaluated. This is conducive to managers making more accurate optimization and improvement measures for the elderly education work in the region to be evaluated.

[0022] In some embodiments, the step of obtaining publicly available elderly education course content for the region to be evaluated, and extracting several course element features from it, includes: Based on the geographic identifier of the region to be evaluated, obtain several sets of publicly available elderly education course content, and extract a first number of first course element features and corresponding first characteristic values ​​from them; wherein, the first characteristic value is inversely proportional to the first number of the first course element features. The first course element feature whose first feature value is greater than the feature threshold is selected as the course element feature.

[0023] In this embodiment of the invention, relevant elderly education course content can be obtained by accessing the official websites, public accounts, WeChat video accounts, and other online channels of various elderly education institutions. From this content, a first number of first course element features can be extracted, such as calligraphy courses, dance courses, foreign language courses, embroidery courses, and volunteer service courses. Simultaneously, a first characteristic value is calculated for each first course element feature based on its first number. The higher the first number of first course element features, the greater the probability that it belongs to a regular course; conversely, the lower the number, the greater the probability that it belongs to a featured course. That is, the first characteristic value is inversely proportional to the first number of first course element features. Therefore, first course element features with a first characteristic value greater than the characteristic threshold are selected, thus obtaining several course element features for the region to be evaluated.

[0024] In some embodiments, the step of filtering the first course element feature whose first feature value is greater than the feature threshold as the course element feature includes: Obtain several second curriculum element features for each adjacent region of the region to be evaluated, perform similarity calculations on each first curriculum element feature and each second curriculum element feature to obtain several third curriculum element features that satisfy similarity relationships, and perform clustering and integration on the third curriculum element features to obtain several fourth curriculum element features. The adaptation weight of the first course element feature corresponding to the fourth course element feature is determined based on the second quantity of the fourth course element feature, and the second feature value is calculated based on the adaptation weight and the first feature value. The first course element features whose second feature value is greater than the feature threshold are selected as the course element features.

[0025] In this embodiment of the invention, simply considering the content of elderly education courses in the region to be evaluated is insufficient to accurately identify the region's distinctive courses. Furthermore, the industrial relationships between the region and its neighboring regions must also be considered. Specifically, the first course element characteristics of the region to be evaluated are compared with the second course element characteristics of each neighboring region using similarity calculations. This yields several third course element characteristics that satisfy a similarity relationship (i.e., similarity exceeding a similarity threshold). Then, clustering is used to merge identical course element characteristics to obtain several fourth course element characteristics. The second quantity of the fourth course element characteristics reflects the degree of similarity between the region to be evaluated and its neighboring regions in terms of distinctive course element characteristics. For example, if region A and its neighboring regions B, C, and D all have embroidery and silkworm industries, the second quantity of the fourth course element characteristics corresponding to these industries will be relatively large. In this case, the adaptation weight of the first course element characteristic corresponding to the fourth course element characteristic can be determined based on this second quantity. The adaptation weight is negatively correlated with the second quantity. The second characteristic value is then obtained by multiplying the adaptation weight by the first characteristic value. Thus, we can obtain the first number of first curriculum element characteristics and the corresponding second characteristic values. We can then filter out the first curriculum element characteristics whose second characteristic values ​​are higher than the characteristic threshold, thereby obtaining several curriculum element characteristics of the region to be evaluated. These first curriculum element characteristics are the characteristics of the characteristic elderly education of the region to be evaluated.

[0026] Among them, the adaptation weight and the second quantity have a negative correlation function relationship: the larger the second quantity of the fourth curriculum element characteristics, the higher the similarity of the first curriculum element characteristics of the elderly education curriculum in the region to be evaluated with the surrounding regions, the weaker the distinctiveness in this local area (the region to be evaluated and its adjacent regions), and the smaller the adaptation weight is set, that is, the first characteristic value is lowered; conversely, the larger the adaptation weight is set, that is, the first characteristic value is appropriately increased.

[0027] In some embodiments, the method further includes: Calculate the ratio of the second quantity of the fourth course element feature to the first quantity of the first course element feature; The outer expansion distance is determined based on the ratio and the geographical radius of the region to be evaluated, and other regions located within the outer expansion distance are identified as the adjacent regions.

[0028] In this embodiment of the invention, when there are many similarities between the potential distinctive elderly education courses in the region to be evaluated and the surrounding adjacent regions, it indicates that the degree of similarity between the industrial characteristics of the region to be evaluated and the surrounding adjacent regions is higher. At this time, the scope of the adjacent regions can be appropriately expanded, that is, those indirect "adjacent" regions that are not directly adjacent can also be included. The above-mentioned adaptation weight is calculated based on all the characteristics of the second course elements in the directly adjacent and indirectly adjacent regions, thereby achieving accurate identification of distinctive elderly education courses in the region to be evaluated.

[0029] The outward expansion distance is determined by the ratio of the second quantity of the fourth curriculum element characteristics to the first quantity of the first curriculum element characteristics. This ratio reflects the proportion of the same potential characteristic courses in the region under evaluation and its neighboring regions out of all potential characteristic courses in the region under evaluation. The larger the ratio, the greater the outward expansion distance will be adjusted, so as to calculate a more accurate adaptation weight based on the second curriculum element characteristics of neighboring areas over a larger range. That is, the outward expansion distance is positively correlated with this ratio. For example, outward expansion distance = (1 + ratio) * geographic radius.

[0030] In some embodiments, the step of merging the featured test questions with the basic test questions to obtain assessment questions includes: Determine the first ratio of the featured test items and the second ratio of the basic test items; In each question type, the third number of the featured test questions and the fourth number of the basic test questions are determined according to the first ratio and the second ratio, respectively. The third number of questions are randomly selected from the featured test questions, and the fourth number of questions are randomly selected from the basic test questions. The two sets of questions are then combined to obtain the assessment questions.

[0031] In this embodiment of the invention, after obtaining the two types of test questions, a corresponding number of questions can be randomly selected based on their proportions. For example, both multiple-choice and subjective questions may include 4 special test questions and 6 basic test questions. This achieves the fusion of the two types of test questions, thus obtaining the final assessment questions.

[0032] In some embodiments, determining the first ratio of the featured test items to the second ratio of the basic test items includes: In the formula, The first proportion of the featured test questions. This is the second proportion of the basic test questions; The first quantity is the first feature of the first course element. The second quantity is the fourth course element feature; The outward expansion distance is... This is a reference value for the outward expansion distance; for The reference value for the weights.

[0033] In this embodiment of the invention, the invention sets up a method to comprehensively calculate the first proportion of the featured test items based on the aforementioned ratio, the ratio between the outward expansion distance and its reference value. Based on the former in the above formula, it can be seen that the aforementioned ratio... The larger, The larger the value, the lower the degree of distinctiveness of the elderly education curriculum in the region being evaluated; correspondingly, the smaller the proportion of distinctive test items in the first section, and vice versa. After calculating the proportion of distinctive test items, the proportion of basic test items can naturally be calculated.

[0034] in, It is greater than Yes, obviously The settings can ensure It is a value less than 1.

[0035] like Figure 2 As shown in the figure, this embodiment of the invention also discloses an artificial intelligence-based elderly education learning assessment system, including a communication module, a processing module, and a storage module, wherein the processing module is electrically connected to the storage module and the communication module respectively; The communication module is used to acquire publicly available elderly education course content from the region to be evaluated, send the generated test questions to the test takers, and receive the test takers' answer data. The storage module is used to store computer programs; The processing module is used to retrieve and execute the computer program in the storage module to perform the method described in the foregoing embodiments, so as to generate test questions and generate and output evaluation results based on the answer data fed back by the testers.

[0036] This invention also discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, characterized in that: the processor executes the computer program to implement the method described in the foregoing embodiments.

[0037] This invention also discloses a computer storage medium storing a computer program, characterized in that the computer program is executed by a processor to implement the method described in the foregoing embodiments.

[0038] This invention also discloses a computer program product that, when run on a terminal, causes the terminal to execute the method described in the foregoing embodiments.

[0039] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0040] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0041] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0042] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. An artificial intelligence-based learning assessment method for elderly education, characterized in that: The method includes the following steps: Obtain publicly available elderly education course content from the region to be evaluated, and extract several course element characteristics from it; The artificial intelligence model searches for and modifies test questions based on the characteristics of each of the course elements, and obtains a number of distinctive test questions. The various featured test questions are combined with the basic test questions to obtain the assessment test questions; Receive the answer data from the testers, complete the assessment of elderly education and learning in the region to be assessed based on the answer data, and output the assessment results.

2. The method for assessing learning in elderly education based on artificial intelligence according to claim 1, characterized in that: Obtain publicly available senior education course content from the region to be evaluated, and extract several key course features, including: Based on the geographic identifier of the region to be evaluated, obtain several sets of publicly available elderly education course content, and extract a first number of first course element features and corresponding first characteristic values ​​from them; wherein, the first characteristic value is inversely proportional to the first number of the first course element features. The first course element feature whose first feature value is greater than the feature threshold is selected as the course element feature.

3. The method for assessing learning in elderly education based on artificial intelligence according to claim 2, characterized in that: Selecting the first course element features whose first feature value is greater than the feature threshold as the course element features includes: Obtain several second curriculum element features of each adjacent region of the region to be evaluated, perform similarity calculation on each first curriculum element feature and each second curriculum element feature to obtain several third curriculum element features that satisfy similarity relationship, and perform clustering and integration on the third curriculum element features to obtain several fourth curriculum element features. The adaptation weight of the first course element feature corresponding to the fourth course element feature is determined based on the second quantity of the fourth course element feature, and the second feature value is calculated based on the adaptation weight and the first feature value. The first course element features whose second feature value is greater than the feature threshold are selected as the course element features.

4. The method for assessing learning in elderly education based on artificial intelligence according to claim 3, characterized in that: The method further includes: Calculate the ratio of the second quantity of the fourth course element feature to the first quantity of the first course element feature; The outer expansion distance is determined based on the ratio and the geographical radius of the area to be evaluated, and other areas located within the outer expansion distance are identified as the adjacent areas.

5. The method for assessing learning in elderly education based on artificial intelligence according to claim 4, characterized in that: The aforementioned featured test questions are integrated with the basic test questions to obtain assessment test questions, including: Determine the first ratio of the featured test items and the second ratio of the basic test items; In each question type, the third number of the featured test questions and the fourth number of the basic test questions are determined according to the first ratio and the second ratio, respectively. The third number of questions are randomly selected from the featured test questions, and the fourth number of questions are randomly selected from the basic test questions. The two sets of questions are then combined to obtain the assessment questions.

6. The method for assessing learning in elderly education based on artificial intelligence according to claim 5, characterized in that: Determining the first ratio of the featured test items to the second ratio of the basic test items includes: In the formula, The first proportion of the featured test questions. This is the second proportion of the basic test questions; The first quantity is the first feature of the first course element. The second quantity is the fourth course element feature; The outward expansion distance, This is a reference value for the outward expansion distance; for The reference value for the weights.

7. An artificial intelligence-based learning assessment system for elderly education, comprising a communication module, a processing module, and a storage module, wherein the processing module is electrically connected to the storage module and the communication module respectively; The communication module is used to acquire publicly available elderly education course content from the region to be evaluated, send the generated test questions to the test takers, and receive the test takers' answer data. The storage module is used to store computer programs; The feature is that the processing module is used to retrieve and execute the computer program in the storage module to perform the method as described in any one of claims 1-6, to generate test questions, and to generate and output evaluation results based on the answer data fed back by the testers.

8. An electronic device, comprising: At least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, characterized in that: the processor executes the computer program to implement the method as claimed in any one of claims 1-6.

9. A storage medium storing a computer program, characterized in that: The computer program is executed by a processor to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that: When a computer program product is run on a terminal, the terminal executes to implement the method as described in any one of claims 1-6.

Images