User capability assessment method and device, digital intelligent courseware platform, equipment and medium

Abstract

The invention relates to the field of digital education, in particular to a user capability evaluation method and device, a digital intelligent courseware platform, equipment and a medium, and the method comprises the steps: obtaining original user data, and determining the capability feature data of a user based on the original user data; associating the capability characteristic data with a predefined knowledge entity to obtain a capability label of the user; constructing a capability knowledge graph of the user based on the relevance between the capability tags; and performing capability evaluation on the user based on the capability knowledge graph of the user. By adopting the scheme, the efficiency of user capability evaluation and the reliability of a user capability evaluation result are improved.

Description

Technical Field

[0001] The present invention relates to the field of digital education, and in particular to a user ability assessment method, device, digital intelligence courseware platform, equipment and medium. Background Art

[0002] With the continuous development of society, all walks of life, especially in the fields of online education, human resource management and recruitment, have an increasing demand for user ability assessment. Traditional user ability assessment methods often rely on limited data sources, such as resumes submitted by users, test scores, etc., which often cannot fully reflect the user's ability level. Different institutions or fields may use different standards and indicators when assessing user capabilities, resulting in poor comparability of assessment results.

[0003] In addition, traditional evaluation methods often rely on subjective judgments, such as the interviewer's impression and the personal preferences of the review experts, which may lead to unfair evaluation results. In addition, manual evaluation methods require relevant personnel to process a large amount of data, which is not only time-consuming and labor-intensive, but also prone to errors, thereby reducing the efficiency of user ability evaluation and the reliability of ability evaluation results. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a user capability assessment method, apparatus, digital courseware platform, equipment and medium to improve the efficiency of user capability assessment and the reliability of user capability assessment results.

[0005] In a first aspect, an embodiment of the present application provides a method for evaluating user capabilities, the method comprising:

[0006] Acquire original user data, and determine user capability characteristic data based on the original user data;

[0007] Associating the capability characteristic data with a predefined knowledge entity to obtain a capability tag of the user;

[0008] Constructing a capability knowledge graph of the user based on the associations between the capability tags;

[0009] The user's capabilities are evaluated based on the capability knowledge graph.

[0010] Optionally, determining the capability characteristic data of the user based on the original user data includes:

[0011] Preprocessing the original user data to obtain target user data, wherein the preprocessing includes abnormal information correction and duplicate data cleaning;

[0012] Extracting features from the target user data to obtain user feature data, wherein the user feature data includes keywords, entities, named entities, topics, and discourse;

[0013] The user characteristic data is expanded to obtain the capability characteristic data.

[0014] Optionally, the performing capability assessment on the user based on the capability knowledge graph of the user includes:

[0015] Determining the sub-scores of the user in each capability dimension according to the capability knowledge graph;

[0016] The total ability score of the user is determined according to the sub-scores of the user in each ability dimension to implement user ability assessment.

[0017] Optionally, determining the sub-scores of the user in each capability dimension based on the capability knowledge graph includes:

[0018] Determine the original score of the user in each capability dimension according to the capability knowledge graph;

[0019] Configure dimension weights for each capability dimension;

[0020] Calculate the product of the raw score of each ability dimension and the dimension weight of each ability dimension;

[0021] The product corresponding to each capability dimension is determined as the sub-score possessed by the user in each capability dimension.

[0022] Optionally, determining the total ability score of the user according to the sub-scores of the user in each ability dimension includes:

[0023] Calculate the sum of the sub-scores of each ability dimension;

[0024] The sum of the sub-scores of each capability dimension is determined as the total capability score of the user.

[0025] Optionally, the method further comprises:

[0026] Obtaining the total ability scores of the user at different time points, and evaluating the change in the ability of the user based on the total ability scores of the user at each time point;

[0027] Alternatively, feedback suggestions are generated based on the total ability scores of the user at each time point, and the feedback suggestions are displayed to the user.

[0028] In a second aspect, an embodiment of the present application provides a user capability assessment device, the device comprising:

[0029] A feature data determination module, used to obtain original user data and determine the user's capability feature data based on the original user data;

[0030] A capability label determination module, used to associate the capability characteristic data with a predefined knowledge entity to obtain the capability label of the user;

[0031] A capability knowledge graph construction module, used to construct the capability knowledge graph of the user based on the association between each capability label and each capability label;

[0032] A capability assessment module is used to assess the capability of the user based on the capability knowledge graph.

[0033] Optionally, determining the capability characteristic data of the user based on the original user data includes:

[0034] Preprocessing the original user data to obtain target user data, wherein the preprocessing includes abnormal information correction and duplicate data cleaning;

[0035] Extracting features from the target user data to obtain user feature data, wherein the user feature data includes keywords, entities, named entities, topics, and discourse;

[0036] The user characteristic data is expanded to obtain the capability characteristic data.

[0037] Optionally, the performing capability assessment on the user based on the capability knowledge graph of the user includes:

[0038] Determining the sub-scores of the user in each capability dimension according to the capability knowledge graph;

[0039] The total ability score of the user is determined according to the sub-scores of the user in each ability dimension to implement user ability assessment.

[0040] Optionally, determining the sub-scores of the user in each capability dimension based on the capability knowledge graph includes:

[0041] Determine the original score of the user in each capability dimension according to the capability knowledge graph;

[0042] Configure dimension weights for each capability dimension;

[0043] Calculate the product of the raw score of each ability dimension and the dimension weight of each ability dimension;

[0044] The product corresponding to each capability dimension is determined as the sub-score possessed by the user in each capability dimension.

[0045] Optionally, determining the total ability score of the user according to the sub-scores of the user in each ability dimension includes:

[0046] Calculate the sum of the sub-scores of each ability dimension;

[0047] The sum of the sub-scores of each capability dimension is determined as the total capability score of the user.

[0048] Optionally, the device further comprises:

[0049] A capability change evaluation module is used to obtain the total capability scores of the user at different time points, and evaluate the capability change of the user based on the total capability scores of the user at each time point;

[0050] The feedback suggestion display module is used to generate feedback suggestions based on the total ability score of the user at each time point and display the feedback suggestions to the user.

[0051] In a third aspect, an embodiment of the present application provides a digital courseware platform, which, when running, executes the steps of the user capability assessment method described in any optional implementation manner of the first aspect above.

[0052] In a fourth aspect, an embodiment of the present application provides a computer device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps of the user capability assessment method described in any optional implementation manner of the first aspect are performed.

[0053] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the user capability assessment method described in any optional implementation manner in the above-mentioned first aspect are executed.

[0054] The technical solution provided by this application includes but is not limited to the following beneficial effects:

[0055] This application first collects original user data extensively to ensure the comprehensiveness of the evaluation and avoid missing key information, thereby improving the accuracy of the evaluation. Then, based on the original user data, the user's ability feature data is determined, and the user's ability features, such as learning ability, communication ability, innovation ability, etc., can be deeply excavated to provide a reliable basis for subsequent evaluation. Next, the extracted ability feature data is associated with the predefined knowledge entity, and a specific label is given to each ability feature, which can clearly show the user's ability status and provide a standardized basis for subsequent evaluation. Then, based on the correlation between each ability label and each ability label, the user's ability knowledge map is constructed, and the relationship between nodes and edges can intuitively show the intrinsic connection and development trend between abilities. Finally, based on the user's ability knowledge map, the user is evaluated for ability, which can comprehensively consider the user's various abilities, achieve accurate evaluation, and avoid the one-sidedness of a single indicator.

[0056] This application achieves a comprehensive, in-depth, intuitive and accurate assessment of user capabilities through four steps: determining capability feature data, determining capability labels, building a knowledge graph and conducting an assessment. This not only improves the accuracy and comprehensiveness of the assessment, but also provides strong support for personalized assessment and guidance, thereby improving the efficiency of user capability assessment and the reliability of user capability assessment results.

[0057] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0059] Figure 1 A flowchart of a user capability assessment method provided by Embodiment 1 of the present invention is shown;

[0060] Figure 2 A flow chart of a method for determining capability characteristic data provided by the first embodiment of the present invention is shown;

[0061] Figure 3 A flowchart of a method for constructing a capability knowledge graph provided by the first embodiment of the present invention is shown;

[0062] Figure 4 A schematic diagram of an education field knowledge entity provided by the first embodiment of the present invention is shown;

[0063] Figure 5 A flowchart of a method for determining a total score of user capabilities provided by the first embodiment of the present invention is shown;

[0064] Figure 6 A flow chart of a method for determining a sub-score of an ability dimension provided by the first embodiment of the present invention is shown;

[0065] Figure 7 A flowchart of a specific method for determining a total score of user capabilities provided by the first embodiment of the present invention is shown;

[0066] Figure 8 A schematic diagram showing the structure of a user capability assessment device provided by a second embodiment of the present invention is shown;

[0067] Fig. 9 A schematic diagram of the structure of a computer device provided in Embodiment 4 of the present invention is shown. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

[0069] Embodiment 1

[0070] To facilitate understanding of this application, Figure 1 The flowchart of a method for evaluating user capabilities provided in the first embodiment of the present invention is shown to describe the contents of the first embodiment of the present application in detail.

[0071] See also Figure 1 As shown, Figure 1 A flowchart of a user capability assessment method provided in the first embodiment of the present invention is shown, wherein the method comprises steps S101 to S104:

[0072] S101: Acquire original user data, and determine user capability feature data based on the original user data.

[0073] Specifically, the original user data in the digital learning environment is obtained. The source of the original user data is not limited to traditional academic performance or questionnaire results, but also covers the user's interactive behavior in the digital courseware, learning progress, content and quality of participation in discussions, and efficiency and creativity in completing tasks through tools such as VR libraries and exercise banks. The original user data includes interaction data between users, teachers, and users and teachers (such as chat boxes, forums, navigation behaviors), management data (for example, institutions, courses, teachers), demographic data (for example, age, nationality, gender), user activity data (for example, evaluation, questions, feedback) and user attitude and emotional data (for example, attitude and motivation). These data are automatically collected through the digital learning platform, ensuring the immediacy and objectivity of the data, and providing a solid foundation for subsequent ability feature analysis.

[0074] After collecting the original user data, the user's ability characteristic data is extracted through data analysis technology to identify key indicators directly related to the user's "three innovations" ability. In the digital learning courseware, this may include the quantity and quality of ideas proposed by users in the innovative thinking training module, the market analysis ability and teamwork ability demonstrated in the entrepreneurial simulation game, and the problem-solving ability and knowledge application ability reflected in the creative practice. These ability characteristic data are quantified through the intelligent algorithms of the digital platform, such as automatically scoring the works submitted by users through machine learning models, or evaluating the user's learning attitude and motivation level through sentiment analysis technology.

[0075] S102: Associating the capability characteristic data with predefined knowledge entities to obtain a capability label of the user.

[0076] Before step S102, it is necessary to configure predefined knowledge entities for each capability dimension according to the digital intelligence learning courseware. Specifically, the digital intelligence courseware teaching structure consists of a series of concepts in the educational taxonomy, instances of each concept, and the relationships between them, which contain knowledge entities related to each capability dimension. For example, in a fragment of the digital intelligence courseware framework concept focusing on the concept of creativity, general cognitive thinking skills are included, which are the psychological processes for acquiring knowledge and understanding. These cognitive thinking processes include the skills of generating ideas, memorizing, using a wide range of categories, and discovering problems; they also include skills and concepts related to the field, that is, the extent to which a person's product or response can surpass previous responses in the field depends on his or her use of creativity-related abilities, including expertise, knowledge, technical skills, intelligence, and talent in a specific field; they also include emotions, tendencies, and motivations, because emotions and tendencies include the way students emotionally deal with external and internal phenomena, such as self-efficacy, independence, curiosity, and commitment. In addition, motivation also includes internal and external factors, such as passion, challenge, interest, fun, and satisfaction. Then, a theoretical framework on innovation, creation and entrepreneurship is established in the digital learning courseware. This framework clarifies the seven key teaching steps of the "three creations" module. This framework will serve as the basis for the development of courseware content and teaching methods. The key teaching steps are as follows:

[0077] Step 1: Determine the curriculum system. Taking the textbook unit as the starting point, design the "three innovations" module. Under the "three innovations" module, there are 7 sub-steps, which are: Sub-step 1: Based on the innovation case, compare the task list completed before and after the innovation, that is, what is the new problem, what is the new? What is the new thinking, what is the new? What is the new method, what is the new? What is the new knowledge, what is the new? What is the new value, what is the new? What is the required spiritual quality? Sub-step 2: In-depth analysis of the actual situation, new needs, new problems, and be able to clearly define the problem analysis problem. Sub-step 3: Analyze the problem, find the key nodes, identify the inherent concepts and traditional thinking patterns. Sub-step 4: Break the old model and be different. Sub-step 5: Focus on being different, clarify which parts can be realized in reality and which parts cannot be realized in reality. Sub-step 6: Collect information, search literature, examine the research results of predecessors, use various resources and knowledge bases, open up multi-dimensional perspectives, and make breakthroughs through divergent, reverse, critical, imaginative, and associative thinking and methods to turn the unrealizable into the achievable. Sub-step 7: Organize and complete the innovation plan and verify it. Each step is divided into 7 cognitive micro-steps, namely: scenario, task, problem, path, method, evaluation, and expression. The 10 teaching scaffolds are: case, famous quotes, database, video library, VR library, question library, exercise library, tool library, community, and AI assistant.

[0078] After clarifying the various dimensions covered by the "three creations" capabilities, such as innovation ability, problem-solving ability, knowledge application ability, and practical execution ability, through the above-mentioned digital learning courseware, corresponding knowledge entities are configured for each capability dimension. Knowledge entities are the concrete and operational manifestations of capability dimensions. They represent the core concepts, skills, or experiences that users need to master under this capability dimension. For example, under the dimension of innovation ability, knowledge entities may include innovative thinking methods, innovative case analysis, and the use of innovative tools; under the dimension of problem-solving ability, knowledge entities may include problem definition skills, problem-solving strategies, and logical reasoning ability. These knowledge entities come from the educational resources in the digital learning courseware, as well as the professional knowledge of educational experts and scholars in the field, ensuring their accuracy and authority.

[0079] See also Figure 2 As shown, Figure 2 A schematic diagram of an education field knowledge entity provided by the first embodiment of the present invention is shown, wherein the education field knowledge entity includes performance, knowledge, and skills; skills include cooperation, problem solving, and innovation; innovation includes cognitive thinking ability, field-related skills, and emotional personality motivation; cognitive thinking ability includes creative generation, strong memory, and broad thinking; field-related skills include talent, expertise, etc.; emotional personality includes concentration, liveliness, dreams, etc.; motivation includes intrinsic and extrinsic; and the intrinsic includes entities such as challenge and emotion.

[0080] After collecting the user's original data, the data is processed and analyzed to extract the user's ability feature data, which reflects the user's performance level in various ability dimensions. Then, based on data mining and machine learning technology, the ability feature data can also be associated with knowledge entities based on association rules, and the potential connection between the user's ability feature data and knowledge entities is analyzed through algorithms, so as to label the user with corresponding ability labels. For example, if the user performs well in innovative thinking methods, he may be labeled with the ability label of "strong innovative thinking ability". These ability labels not only reflect the user's strengths and weaknesses in various ability dimensions, but also provide an important basis for subsequent personalized learning and teaching.

[0081] Furthermore, the capability feature data can be associated with the knowledge entity based on the following rule to obtain the user's capability label:

[0082] <rule> ::＝ <dataset> . <feature>.feature(<string|integer|boolean> )

[0083] Dataset is a dataset, feature is feature data, string is a string, integer is an integer, and boolean is a Boolean type.

[0084] You can also use additional rules to connect and / or anti-connect, such as using IF...THEN rules to specify labels and link the extracted feature data to knowledge entities. For example, to identify the user's creativity pattern (such as using broad categories), you can define two rules and connect them to mark a label, as shown in the following equation:

[0085]

[0086] Specifically, define rule Rule1: the number of times the user (ID) has been discussed in the type of activity (Type) in which he / she has participated (int), define rule Rule2: the number of questions asked in the type of activity (Type) in which he / she has participated (int). If the conditions of Rule1 and Rule2 are met at the same time (that is, both are True), perform the following operations: tag the user with the TAG tag and classify him / her into the categories of Creativity, Cognitive, and Wide Categories.

[0087] S103: Constructing a capability knowledge graph of the user based on the associations between the capability tags.

[0088] Specifically, the capability knowledge graph is a structured knowledge representation method that shows the associations and connections between users in different capability dimensions. Before constructing the capability knowledge graph, the associations between the capability labels are pre-configured. For example, innovation ability may depend on problem-solving ability and knowledge application ability, while practical execution ability may be a comprehensive reflection of these abilities. When constructing the capability knowledge graph, the capability labels are used as nodes and the associations between them are used as edges to construct the knowledge graph. The knowledge graph can clearly show the strength and weakness distribution of users in different capability dimensions and the relationship between abilities. By constructing the capability knowledge graph, the overall performance and development trend of users in the "three innovations" capabilities can be more intuitively understood. At the same time, the capability knowledge graph can also provide strong support for the recommendation of personalized learning paths, the optimization of teaching resources, and the evaluation of teaching effectiveness.

[0089] S104: Evaluate the user's capabilities based on the user's capability knowledge graph.

[0090] Specifically, by comprehensively analyzing the user's performance in different dimensions and capability nodes, a comprehensive and objective capability assessment result is given. In digital learning courseware, capability assessment may adopt a combination of multiple methods, such as rule-based assessment methods, prediction methods of machine learning models, and comprehensive evaluation methods of expert systems. These methods can make full use of the advantages of large amounts of data and intelligent algorithms collected by digital platforms to improve the accuracy and efficiency of assessments.

[0091] In an alternative embodiment, see Figure 3 As shown, Figure 3 A flowchart of a method for determining capability characteristic data provided by the first embodiment of the present invention is shown, wherein the method for determining the capability characteristic data of a user based on the original user data comprises steps S301 to S303:

[0092] S301: Preprocessing the original user data to obtain target user data, wherein the preprocessing includes abnormal information correction and duplicate data cleaning.

[0093] Specifically, correct inaccurate or unreasonable information in the original user data, such as missing values, erroneous values, or values ​​inconsistent with other data, for example, by filling missing values ​​(such as using the mean, median, or mode), correcting erroneous values ​​(such as changing the incorrect date format to the correct format), or deleting values ​​that are obviously inconsistent with other data to ensure data integrity and consistency. De-duplicate data in the original user data is processed, that is, duplicate records or information are deleted, and only unique and valuable data is retained to avoid duplicate data causing deviations or redundancy in analysis results.

[0094] S302: Extracting features from the target user data to obtain user feature data, wherein the user feature data includes keywords, entities, named entities, topics, and discourse.

[0095] Specifically, feature extraction is performed on the preprocessed target user data to obtain key information that can reflect the user's ability characteristics. When performing keyword extraction, natural language processing technology is used to extract keywords from the text information in the target user data. These keywords can be topics, concepts or terms that users discuss or pay attention to, which can reflect the user's interests and concerns. When performing entity extraction, noun phrases with clear meaning and independence are identified from the target user data, such as noun phrases of names of people, places, and institutions, and their relationship with user ability characteristics is further analyzed. When performing named entity recognition, entities with specific meanings and contexts are identified. For example, in academic literature, named entities may include author names, paper titles, journal names, etc. In addition to keyword and entity extraction, the target user data is also subjected to topic and discourse analysis. Topic analysis can help identify the main topics or fields discussed in the text; while discourse analysis can analyze the structure, logical relationships, and semantic features of the text, so as to have a deeper understanding of the user's thinking and expression.

[0096] S303: Perform data expansion on the user characteristic data to obtain the capability characteristic data.

[0097] Specifically, in order to increase the richness and coverage of user feature data, synonyms and near-synonyms can be used to expand the keywords in the user feature data, or association rule mining technology can be used to discover the potential associations and patterns between user feature data. Domain knowledge can also be used to expand user feature data. For example, in digital learning courseware, educational taxonomy and creativity taxonomy can be combined to build domain-specific knowledge structures, and user feature data can be associated and matched with these knowledge structures.

[0098] In an alternative embodiment, see Figure 4 As shown, Figure 4 A flowchart of a method for determining a total ability score of a user provided in the first embodiment of the present invention is shown, wherein the ability assessment of the user based on the ability knowledge graph of the user includes steps S401 to S402:

[0099] S401: Determine the sub-scores of the user in each capability dimension according to the capability knowledge graph.

[0100] Specifically, firstly, based on the constructed capability knowledge graph, the user's performance in each capability dimension is carefully analyzed. The capability knowledge graph not only presents the user's capability labels in each dimension, but also implies the weights and hierarchical relationships of these labels, which are crucial for accurately evaluating the user's capabilities. Next, a professional capability assessment model or algorithm is used to convert the user's performance in the capability knowledge graph into specific sub-scores. These sub-scores not only reflect the user's mastery of a single capability label, but also reflect the correlation and importance between different capability labels.

[0101] S402: Determine the total ability score of the user according to the sub-scores of the user in each ability dimension to implement user ability assessment.

[0102] Specifically, weight allocation refers to assigning corresponding weights to each dimension based on the importance of different capability dimensions to the overall capability assessment. These weights reflect the relative position of each dimension in the assessment system, ensuring the comprehensiveness and objectivity of the assessment results. Scoring rules refer to the method of calculating the total capability score based on the user's sub-scores and weights in each capability dimension. For example, the weighted average method can be used to multiply the sub-scores of each dimension by the corresponding weights, and then sum them up to get the total capability score. By calculating the total capability score, you can intuitively understand the user's overall capability performance level, providing strong support for subsequent learning guidance, resource recommendations, and teaching effectiveness evaluation.

[0103] In an alternative embodiment, see Figure 5 As shown, Figure 5 A flowchart of a method for determining sub-scores of capability dimensions provided by the first embodiment of the present invention is shown, wherein the method for determining the sub-scores of the user in each capability dimension based on the capability knowledge graph includes steps S501 to S504:

[0104] S501: Determine the original score of the user in each capability dimension according to the capability knowledge graph.

[0105] Specifically, the user's ability labels and associated information in the ability knowledge graph are used to score the user in each ability dimension. These raw scores reflect the user's specific performance in each ability dimension and are the basis for the subsequent calculation of sub-scores. The determination of raw scores may involve a variety of methods and standards. For example, the user can be scored based on the degree of mastery, frequency of use, or effect of the ability label.

[0106] For example, when calculating the final ability dimension score, it is necessary to take into account the importance of the dimension in the entire "three innovations" module. The ability dimensions include innovation ability (CI), problem solving ability (PS), knowledge application ability (KA) and practical execution ability (PE).

[0107] Taking innovation capability (CI) as an example, the original CI raw It can be calculated by the following expression:

[0108]

[0109] Among them, WC i represents the original weight of the ith case, CS i represents the score of the user in the i-th case, and n is the total number of cases.

[0110] S502: Configure dimension weights for each capability dimension.

[0111] Specifically, according to the importance of each capability dimension in the overall capability assessment, corresponding weights are assigned to them. These weights reflect the relative status of different capability dimensions in the assessment system and are essential to ensure the comprehensiveness and objectivity of the assessment results. The configuration of weights is usually based on expert evaluation, data analysis or domain standards. For example, in digital learning courseware, if innovation ability is regarded as the most important capability dimension, it may be given a higher weight. On the contrary, if certain capability dimensions are relatively unimportant in specific situations, their weights may be reduced accordingly.

[0112] S503: Calculate the product of the original score of each capability dimension and the dimension weight of each capability dimension.

[0113] Specifically, the raw scores of each capability dimension are multiplied by the corresponding dimension weights to obtain the user's weighted scores in each capability dimension. These weighted scores reflect the user's relative performance in each capability dimension and their importance in the overall capability assessment. The process of calculating weighted scores may involve simple multiplication operations or more complex algorithms. The key is to ensure the accuracy of the weights and the reliability of the raw scores to ensure the objectivity and accuracy of the weighted scores.

[0114] For example, when calculating the final ability dimension score, it is necessary to take into account the importance of the dimension in the entire "three innovations" module. Taking innovation ability as an example, its weight-adjusted score CI weighted for:

[0115] CI weighted =CI raw ×W CI

[0116] Among them, W CI It is the dimension weight of innovation capability.

[0117] S504: Determine the product corresponding to each capability dimension as the sub-score possessed by the user in each capability dimension.

[0118] Specifically, the weighted scores of each capability dimension are determined as the sub-scores of the user in each capability dimension. These sub-scores are the specific quantitative performance of the user's multidimensional capabilities, providing an important basis for the subsequent calculation of the total capability score and personalized learning guidance.

[0119] In an alternative embodiment, see Figure 6 As shown, Figure 6 A flowchart of a specific method for determining a total ability score of a user provided in the first embodiment of the present invention is shown, wherein the total ability score of the user is determined according to the sub-scores of the user in each ability dimension, including steps S601 to S602:

[0120] S601: Calculate the sum of the sub-scores of each capability dimension.

[0121] Specifically, the sub-scores obtained by the user in all ability dimensions are summed up. This summation process is to integrate the user's performance in different abilities into a single value, which is a pre-step to the total ability score.

[0122] S602: Determine the sum of the sub-scores of each capability dimension as the total capability score of the user.

[0123] Specifically, the sub-scores and values ​​calculated in the previous step are used as the user's total ability score Total score This total score is a comprehensive indicator that reflects the user's overall performance in all ability dimensions.

[0124]

[0125] in, is the weight-adjusted score, i.e., sub-score, of the kth competency dimension, and m is the total number of competency dimensions assessed.

[0126] In an optional embodiment, the method further comprises:

[0127] The total ability scores of the user at different time points are obtained, and the ability changes of the user are evaluated based on the total ability scores of the user at each time point.

[0128] Specifically, by obtaining the user's total ability score at different time points (such as before, during, and after learning), we can evaluate the changes in the user's ability over a period of time. This evaluation helps to understand the user's learning progress, learning effect, and possible problems. The user's total ability score at different time points can be compared to observe its changing trend. If the total ability score shows an upward trend, it means that the user's learning is effective and the ability is gradually improving; if the total ability score remains unchanged or decreases, it may be necessary to improve and optimize the learning method.

[0129] Alternatively, feedback suggestions are generated based on the total ability scores of the user at each time point, and the feedback suggestions are displayed to the user.

[0130] Specifically, personalized feedback suggestions can be generated based on the user's total ability score at each time point and displayed to the user. These feedback suggestions are intended to help users better understand their learning status and clarify the next learning direction and goals.

[0131] Feedback suggestions may include the following aspects: Affirmation of learning achievements: Affirm and encourage the progress and achievements made by users in different ability dimensions to enhance users' learning motivation and self-confidence. Diagnosis of learning problems: Analyze the problems and shortcomings that users may have in different ability dimensions, such as weak grasp of knowledge points, unskilled application of skills, etc., and point out the impact that these problems may have on learning outcomes. Providing learning suggestions: Based on the user's total ability score and existing problems, provide personalized learning suggestions and resource recommendations, such as strengthening the review of specific knowledge points, practicing questions of specific skills, etc., to help users better improve their abilities. Future learning plans: Based on the user's learning progress and changes in abilities, formulate future learning plans for users, including setting learning goals, formulating learning plans, etc., to guide users to learn continuously and effectively.

[0132] By showing these feedback suggestions to users, we can help them understand their learning situation more comprehensively, clarify their learning direction and goals, and improve their abilities more effectively. At the same time, this also provides users with a channel to interact with the system and provide feedback, which helps to continuously optimize the learning experience and learning results.

[0133] In practical applications, by comparing the total ability scores of users at different time points, the user's ability improvement can be evaluated. Combined with the dynamic feedback of the AI ​​assistant, personalized learning suggestions can be made. The expression can be simplified as follows:

[0134]

[0135] Among them, t1 and t2 represent different evaluation time points, Indicates the total ability score of the user at time t1, Indicates the total ability score of the user at time t2.

[0136] In order to better explain a user capability assessment method provided by this application, see Figure 7 As shown, Figure 7 A flowchart of a specific user capability assessment method provided in the first embodiment of the present invention is shown, wherein the main process includes the steps of obtaining original education data, extracting feature data, and constructing a knowledge graph based on associating feature data with knowledge entities in the education field.

[0137] Specifically, after obtaining the original education data, data curation is performed on the original education data, including data cleaning of the original education data, i.e., the content of the students, eliminating errors and cleaning to ensure data quality; then extracting key information from the data, including keywords, topics, part-of-speech tags (POS), etc.; finally, enrichment processing is performed, i.e., enriching the data with synonyms and stems to increase the richness and diversity of the data. Feature extraction is then performed on the processed data to obtain feature data, and feature selection is performed through preset rules and structures. Then, the feature data is selected to be associated with the knowledge entities in the education field to obtain the user's ability label, and then a knowledge graph is constructed based on the user's ability label.

[0138] When selecting features based on rules and structures, the rules can be simple feature matching or complex logical combinations (such as AND, OR, NOT). Here is a possible rule definition method: <rule> ::＝ <dataset> . <feature>.feature(<string|integer|boolean> ),in <rule>It can be a combination of multiple rules, such as <rule> ::＝ <rule>[AND|OR|NOT <rule>], Dataset is a dataset, feature is feature data, string is a string, integer is an integer, and boolean is a Boolean. The knowledge entities in the education field are pre-configured in the intelligent courseware platform, including performance, knowledge, and skills; skills include cooperation, problem solving, and innovation; innovation includes cognitive thinking ability, domain-related skills, and emotional personality motivation; cognitive thinking ability includes creative generation, strong memory, and broad thinking; domain-related skills include talent and expertise; emotional personality includes concentration, liveliness, and dreams; motivation includes internal and external; internal includes entities such as challenge and emotion.

[0139] When constructing a knowledge graph based on the user's ability labels, the user's ability labels are used as nodes, and the correlations between the ability labels are used as edges to construct the graph. For example, for several user labels, including the level label "A-Subject: Data-Semester 1", the assessment label "Type: In-class test-Subject: Mathematics-Semester 1", the competition label "Type: Difficult problem-Subject: Mathematics-Semester 1", the material label "Type: Learning material test-Subject: Mathematics-Semester 1", the platform label "Digital courseware-AI tutoring", the user information label "Student 1-Male-13 years old", the user information label "Student 2-Female-13 years old" and the community label "Type: Student Community-Name: Mathematics Community". Based on the possible correlations between the above labels, such as "Level is", "Automatic scoring", "Discussion", "Ask a question", "Submit", "Help", "Voluntary participation", "View", etc., the correlation between each two nodes (user labels) and the time of occurrence are used as the edges of the two nodes, and then the obtained graph is constructed. Figure 7 Finally, the user's ability is evaluated based on the user's ability knowledge graph to improve the efficiency of user ability evaluation and the reliability of user ability evaluation results.

[0140] Embodiment 2

[0141] The embodiment of the present invention provides a user capability assessment device, see Figure 8 As shown, Figure 8 A schematic diagram of the structure of a user capability assessment device provided by Embodiment 2 of the present invention is shown, wherein the device comprises:

[0142] The feature data determination module 801 is used to obtain original user data and determine the user's capability feature data based on the original user data;

[0143] A capability label determination module 802 is used to associate the capability characteristic data with a predefined knowledge entity to obtain the capability label of the user;

[0144] A capability knowledge graph construction module 803 is used to construct the capability knowledge graph of the user based on the association between each capability label and each capability label;

[0145] The capability assessment module 804 is used to assess the capability of the user based on the capability knowledge graph of the user.

[0146] In an optional implementation manner, determining the user's capability characteristic data based on the original user data includes:

[0147] Preprocessing the original user data to obtain target user data, wherein the preprocessing includes abnormal information correction and duplicate data cleaning;

[0148] Extracting features from the target user data to obtain user feature data, wherein the user feature data includes keywords, entities, named entities, topics, and discourse;

[0149] The user characteristic data is expanded to obtain the capability characteristic data.

[0150] In an optional implementation scheme, constructing the user's capability knowledge graph based on the user's capability feature data includes:

[0151] Configure knowledge entities for each capability dimension based on digital learning courseware;

[0152] Associating the capability characteristic data with the knowledge entity according to a preset association rule to obtain the capability label of the user;

[0153] The capability knowledge graph is constructed by using the capability labels as nodes.

[0154] In an optional implementation, the performing capability assessment on the user based on the capability knowledge graph of the user includes:

[0155] Determining the sub-scores of the user in each capability dimension according to the capability knowledge graph;

[0156] The total ability score of the user is determined according to the sub-scores of the user in each ability dimension to implement user ability assessment.

[0157] In an optional implementation manner, determining the sub-scores of the user in each capability dimension based on the capability knowledge graph includes:

[0158] Determine the original score of the user in each capability dimension according to the capability knowledge graph;

[0159] Configure dimension weights for each capability dimension;

[0160] Calculate the product of the raw score of each ability dimension and the dimension weight of each ability dimension;

[0161] The product corresponding to each capability dimension is determined as the sub-score possessed by the user in each capability dimension.

[0162] In an optional implementation manner, determining the user's total ability score according to the sub-scores of the user in each ability dimension includes:

[0163] Calculate the sum of the sub-scores of each ability dimension;

[0164] The sum of the sub-scores of each capability dimension is determined as the total capability score of the user.

[0165] In an optional embodiment, the device further comprises:

[0166] A capability change evaluation module is used to obtain the total capability scores of the user at different time points, and evaluate the capability change of the user based on the total capability scores of the user at each time point;

[0167] The feedback suggestion display module is used to generate feedback suggestions based on the total ability score of the user at each time point and display the feedback suggestions to the user.

[0168] Embodiment 3

[0169] Based on the same application concept, an embodiment of the present application also provides a digital intelligence courseware platform, which executes the steps of the user ability assessment method described in any of the above embodiments when running.

[0170] Embodiment 4

[0171] Based on the same application concept, see Fig. 9 As shown, Fig. 9 FIG. 4 shows a schematic diagram of the structure of a computer device provided by Embodiment 4 of the present invention, wherein: Fig. 9 As shown, a computer device 900 provided in Embodiment 4 of the present application includes:

[0172] A processor 901, a memory 902 and a bus 903, wherein the memory 902 stores machine-readable instructions executable by the processor 901. When the computer device 900 is running, the processor 901 communicates with the memory 902 via the bus 903. When the processor 901 is running, the machine-readable instructions execute the steps of the user capability assessment method shown in the above-mentioned embodiment 1.

[0173] Embodiment 5

[0174] Based on the same application concept, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the user capability assessment method described in any one of the above embodiments are executed.

[0175] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0176] The computer program product for user capability assessment provided in the embodiment of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be found in the method embodiment and will not be repeated here.

[0177] The user capability assessment device provided in the embodiment of the present invention can be specific hardware on the device or software or firmware installed on the device. The device provided in the embodiment of the present invention has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brief description, the parts not mentioned in the device embodiment can refer to the corresponding contents in the aforementioned method embodiment. It can be clearly understood by technicians in the relevant field that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0178] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0179] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0180] In addition, each functional unit in the embodiment provided by the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0181] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0182] It should be noted that similar numbers and letters represent similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0183] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can still modify the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or perform equivalent replacements on some of the technical features thereof; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. They should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.< / rule> < / rule> < / rule> < / rule> < / feature> < / dataset> < / rule> < / feature> < / dataset> < / rule>

Claims

1. A user capability assessment method, characterized in that: The method comprises: Acquire original user data, and determine user capability characteristic data based on the original user data; Associating the capability characteristic data with a predefined knowledge entity to obtain a capability tag of the user; Constructing a capability knowledge graph of the user based on the associations between the capability tags; The user's capabilities are evaluated based on the capability knowledge graph.

2. The method according to claim 1, characterized in that The determining the capability characteristic data of the user based on the original user data includes: Preprocessing the original user data to obtain target user data, wherein the preprocessing includes abnormal information correction and duplicate data cleaning; Extracting features from the target user data to obtain user feature data, wherein the user feature data includes keywords, entities, named entities, topics, and discourse; The user characteristic data is expanded to obtain the capability characteristic data.

3. The method according to claim 1, characterized in that The performing capability assessment on the user based on the capability knowledge graph of the user includes: Determining the sub-scores of the user in each capability dimension according to the capability knowledge graph; The total ability score of the user is determined according to the sub-scores of the user in each ability dimension to implement user ability assessment.

4. The method according to claim 3, characterized in that The determining, based on the capability knowledge graph, the sub-scores of the user in each capability dimension includes: Determine the original score of the user in each capability dimension according to the capability knowledge graph; Configure dimension weights for each capability dimension; Calculate the product of the raw score of each ability dimension and the dimension weight of each ability dimension; The product corresponding to each capability dimension is determined as the sub-score possessed by the user in each capability dimension.

5. The method according to claim 3, characterized in that: Determining the total ability score of the user according to the sub-scores of the user in each ability dimension includes: Calculate the sum of the sub-scores of each ability dimension; The sum of the sub-scores of each capability dimension is determined as the total capability score of the user.

6. The method according to claim 1, characterized in that The method further comprises: Obtaining the total ability scores of the user at different time points, and evaluating the change in the ability of the user based on the total ability scores of the user at each time point; Alternatively, feedback suggestions are generated based on the total ability scores of the user at each time point, and the feedback suggestions are displayed to the user.

7. A user capability assessment device, characterized in that: The device comprises: A feature data determination module, used to obtain original user data and determine the user's capability feature data based on the original user data; A capability label determination module, used to associate the capability characteristic data with a predefined knowledge entity to obtain the capability label of the user; A capability knowledge graph construction module, used to construct the capability knowledge graph of the user based on the association between each capability label and each capability label; A capability assessment module is used to assess the capability of the user based on the capability knowledge graph.

8. A digital courseware platform, characterized in that: When the digital intelligence courseware platform is running, the steps of the user ability assessment method as described in any one of claims 1 to 6 are executed.

9. A computer device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor and the memory communicate via the bus, and when the machine-readable instructions are executed by the processor, the steps of the user capability assessment method as described in any one of claims 1 to 6 are performed.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, executes the steps of the user capability assessment method as described in any one of claims 1 to 6.

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