Evaluation method and device for primitive cognition, electronic equipment and storage medium

Abstract

The invention relates to the technical field of intelligent education, in particular to a primitive cognition evaluation method and device, electronic equipment and a storage medium, and the method comprises the steps: setting a plurality of primitive cognition micro-steps for a single primitive scene in a learning situation of a digital intelligence learning courseware platform; according to the first performance data, calculating a first proportion of successful completion of each primitive cognitive micro-step by the student; setting a plurality of element cognitive evolution levels for the whole teaching element scene; according to the second performance data, calculating a second proportion of each metagenetic cognitive micro-step in each metagenetic cognitive evolution level successfully completed by the student; and based on the first specific gravity and the second specific gravity, performing vitality and cognition evaluation. Student behavior data are collected and analyzed in real time through a digital means, and measurement and evaluation of the occurrence and evolution of primitive cognition in a digital intelligence learning courseware primitive scene are realized, so that teaching strategies are optimized.

Description

Technical Field

[0001] This application relates to the field of intelligent education technology, and in particular, to a method, device, electronic device, and storage medium for evaluating metacognitive ability in a meta-scenario. Background Art

[0002] Metacognition refers to an individual's awareness and control of their own cognitive processes. It plays a crucial role in the learning process as it involves understanding learning goals, selecting and implementing learning strategies, and monitoring and evaluating learning outcomes. In digital learning courseware, measuring and evaluating the metacognitive process is essential for understanding and improving learning effectiveness. However, existing evaluation methods often rely on manual scoring and subjective judgment. On the one hand, the manual scoring method not only consumes a large amount of human and time costs but also results in a lack of objectivity and consistency in scoring results. On the other hand, the evaluation method relying on subjective judgment lacks a scientific quantitative basis and cannot comprehensively and systematically analyze students' performance in each link of metacognition. As a result, it is impossible to adjust teaching strategies and optimize teaching resource allocation targetedly. Summary of the Invention

[0003] To overcome the deficiencies in the prior art, this application provides a method, device, electronic device, and storage medium for evaluating metacognitive ability in a meta-scenario, which can more scientifically and accurately evaluate students' metacognitive processes in the meta-scenario through digital means.

[0004] In a first aspect, this application provides a method for evaluating metacognitive ability in a meta-scenario, the method comprising the following steps:

[0005] In the learning context of a digital learning courseware platform, set multiple metacognitive micro-steps for a single meta-scenario;

[0006] Collect first performance data of students on each of the metacognitive micro-steps, and based on the first performance data, quantify the completion situation of students on each metacognitive micro-step to obtain the first proportion of students successfully completing each metacognitive micro-step;

[0007] Set multiple metacognitive evolution levels for the entire teaching meta-scenario;

[0008] Collect second performance data of students on each of the metacognitive micro-steps in each of the metacognitive evolution levels, and based on the second performance data, quantify the completion situation of students on each metacognitive micro-step in each metacognitive evolution level to obtain the second proportion of students successfully completing each of the metacognitive micro-steps in each of the metacognitive evolution levels;

[0009] Conduct a metacognitive evaluation based on the first proportion and the second proportion.

[0010] In a possible implementation, the multiple meta-cognitive micro-steps set for a single meta-scenario include scenario, task, problem, path, method, assessment, and expression.

[0011] In a possible implementation, the step of quantifying the completion of each meta-cognitive micro-step of the student based on the first performance data to obtain the first proportion of the student's successful completion of each meta-cognitive micro-step includes the following steps:

[0012] Obtain the first successful times and the first attempt times of the student's completion of each of the meta-cognitive micro-steps from the first performance data;

[0013] Based on the first successful times and the first attempt times, combine with the first preset rule to measure the possibility of the student's successful completion of each of the meta-cognitive micro-steps, obtain the first measurement value, and based on the first measurement value, obtain the first measurement sum of the student's successful completion of all the meta-cognitive micro-steps;

[0014] Determine the first proportion of the student's successful completion of each of the meta-cognitive micro-steps according to the first measurement value and the first measurement sum.

[0015] In a possible implementation, the step of combining the first successful times and the first attempt times, and combining with the first preset rule to measure the possibility of the student's successful completion of each of the meta-cognitive micro-steps to obtain the first measurement value includes the following steps:

[0016] Set the weight of each of the meta-cognitive micro-steps based on the first performance data;

[0017] Combine the first successful times, the first attempt times, and the weight, and measure the possibility of the student's successful completion of each of the meta-cognitive micro-steps according to the preset algorithm to obtain the first measurement value.

[0018] In a possible implementation, the multiple meta-cognitive evolution levels set for the entire teaching meta-scenario include generativity, rooting, growth, derivation, ecology, creation, and vitality.

[0019] In a possible implementation, the step of quantifying the completion of each meta-cognitive micro-step of the student in each meta-cognitive evolution level based on the second performance data to obtain the second proportion of the student's successful completion of each meta-cognitive micro-step in each meta-cognitive evolution level includes the following steps:

[0020] Obtain the second successful times and the second attempt times of the student's completion of each meta-cognitive micro-step in each meta-cognitive evolution level from the second performance data;

[0021] Based on the second number of successes and the second number of attempts, in combination with a second preset rule, measure the likelihood of a student successfully completing each of the metacognitive micro-steps in each of the metacognitive evolution levels to obtain a second measurement value, and based on the second measurement value, obtain the second measurement sum of the student completing all of the metacognitive micro-steps in each of the metacognitive evolution levels;

[0022] Based on the second measurement value and the second measurement sum, determine the second proportion of the student successfully completing each of the metacognitive micro-steps in each of the metacognitive evolution levels.

[0023] In a possible implementation manner, the metacognitive evaluation based on the first proportion and the second proportion includes the following steps:

[0024] According to the distribution of the first proportion, determine the metacognitive micro-steps where the student is weak;

[0025] According to the distribution of the second proportion, determine whether the student develops evenly in each metacognitive evolution level.

[0026] In a second aspect, the present application provides a metacognitive evaluation device, and the device includes:

[0027] A first setting module, configured to set a plurality of metacognitive micro-steps for a single metascene in the learning scenario of a digital learning courseware platform;

[0028] A first quantification module, configured to collect first performance data of a student on each of the metacognitive micro-steps, and perform quantification processing on the completion situation of the student on each metacognitive micro-step based on the first performance data to obtain the first proportion of the student successfully completing each metacognitive micro-step;

[0029] A second setting module, configured to set a plurality of metacognitive evolution levels for the entire teaching metascene;

[0030] A second quantification module, configured to collect second performance data of the student on each of the metacognitive micro-steps in each of the metacognitive evolution levels, and perform quantification processing on the completion situation of the student on each metacognitive micro-step in each metacognitive evolution level based on the second performance data to obtain the second proportion of the student successfully completing each of the metacognitive micro-steps in each of the metacognitive evolution levels;

[0031] An evaluation module, configured to perform metacognitive evaluation based on the first proportion and the second proportion.

[0032] In a third aspect, the present application provides an electronic device, including: a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the steps of the meta-cognitive evaluation method according to any one of the first aspects are executed.

[0033] In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the steps of the meta-cognitive evaluation method according to any one of the first aspects are executed.

[0034] A meta-cognitive evaluation method, device, electronic device, and storage medium provided in this embodiment set multiple meta-cognitive micro-steps for a single meta-scenario in the learning context of a digital learning courseware platform; collect first performance data of students on each of the meta-cognitive micro-steps, and perform quantitative processing on the completion of each meta-cognitive micro-step by students based on the first performance data to obtain the first proportion of students successfully completing each meta-cognitive micro-step; set multiple meta-cognitive evolution levels for the entire teaching meta-scenario; collect second performance data of students on each of the meta-cognitive micro-steps in each of the meta-cognitive evolution levels, and perform quantitative processing on the completion of each meta-cognitive micro-step by students in each of the meta-cognitive evolution levels based on the second performance data to obtain the second proportion of students successfully completing each meta-cognitive micro-step in each of the meta-cognitive evolution levels; perform meta-cognitive evaluation based on the first proportion and the second proportion. Thus, by means of digital means, real-time collection and analysis of students' behavior data are realized, and the measurement and evaluation of the occurrence and evolution of meta-cognition in the meta-scenario of digital learning courseware are achieved, which helps to optimize teaching strategies. Description of the Drawings

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use 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 limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0036] Figure 1 Shows a flowchart of the meta-cognitive evaluation method according to an embodiment of the present application;

[0037] Figure 2 Shows a schematic diagram of multiple meta-cognitive micro-steps set for a single meta-scenario according to an embodiment of the present application;

[0038] Figure 3The structural schematic diagram of the meta-cognitive evaluation device according to an embodiment of the present application is shown;

[0039] Figure 4 The structural block diagram of the electronic device according to an embodiment of the present application is shown. Specific embodiments

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. It should be understood that the accompanying drawings in the present application only serve the purposes of illustration and description, and are not used to limit the protection scope of the present application. In addition, it should be understood that the schematic drawings are not drawn according to the actual scale. The flowcharts used in the present application show the operations implemented according to some embodiments of the present application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical context relationships may be reversed or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart or remove one or more operations from the flowchart under the guidance of the content of the present application.

[0041] In addition, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative efforts fall within the protection scope of the present application.

[0042] It should be noted that the term "including" will be used in the embodiments of the present application to indicate the existence of the features stated thereafter, but does not exclude adding other features.

[0043] In view of the technical problems proposed in the background art, the present application provides a meta-cognitive evaluation method, device, electronic device, and storage medium, which can more scientifically and accurately evaluate the meta-cognitive process of students in the meta-scene through digital means.

[0044] In one embodiment, referring to the attached drawings of the specification Figure 1 , a meta-cognitive evaluation method provided by the present application, the method includes the following steps:

[0045] S1. In the learning context of the digital learning courseware platform, set multiple meta-cognitive micro-steps for a single meta-scene;

[0046] S2. Collect the first performance data of the students on each of the above-mentioned meta-cognitive micro-steps, and quantitatively process the completion of the students on each meta-cognitive micro-step based on the first performance data to obtain the first proportion of the students successfully completing each meta-cognitive micro-step.

[0047] S3. Set multiple meta-cognitive evolution levels for the entire teaching meta-scene.

[0048] S4. Collect the second performance data of the students on each of the above-mentioned meta-cognitive micro-steps in each of the above-mentioned meta-cognitive evolution levels, and quantitatively process the completion of the students on each meta-cognitive micro-step in each meta-cognitive evolution level based on the second performance data to obtain the second proportion of the students successfully completing each of the above-mentioned meta-cognitive micro-steps in each of the above-mentioned meta-cognitive evolution levels.

[0049] S5. Conduct a meta-cognitive evaluation based on the first proportion and the second proportion.

[0050] To clearly understand the technical solution of the embodiments of the present invention, the technical features "meta-scene" and "meta-cognition" will be described first. Among them, in the field of motion change analysis, a meta-scene (MetaScene) refers to a series of indivisible basic units obtained by dividing a continuous motion change process. These units constitute the basic constituent elements of the entire change process. For example, a 2-minute long motion scene can be divided into 120 one-second long meta-scenes. Each meta-scene represents the state or event at a specific time point in the entire scene. In a meta-scene, new cognitive activities or processes that occur can be defined as meta-cognition (Meta-Cognition). There is a one-to-one correspondence between meta-cognition and meta-scene, that is, each meta-scene may trigger specific meta-cognitive activities. This relationship can be regarded as a functional relationship or a probabilistic discrete relationship in mathematics, where the meta-scene is the independent variable and the meta-cognition is the dependent variable. In this way, we can conduct a detailed analysis and understanding of complex motion change processes, so as to perform precise intervention or adjustment at each time point.

[0051] Specifically, in step S1, in the learning context of the digital intelligent learning courseware platform, a single meta-scene is the smallest scene unit that carries a complete and independent learning activity of a student. For example, in the time interval from 10 to 11 minutes of the course, the student focuses on using the animation demonstration on the platform to understand the proof process of the Pythagorean theorem, and the learning activity of the student within this 1 minute constitutes a single meta-scene.

[0052] See the appended Figure 2, in this application, based on the integrity of the learning process, the laws of cognitive development, and the setting of educational teaching goals, each single meta-scenario is divided into seven meta-cognitive micro-steps, namely: Meta-cognitive micro-step R1 situation, which refers to the situation of students' learning activities under specific created environmental conditions; Meta-cognitive micro-step R2 task, which means that in a specific scenario, students will be assigned specific learning tasks; Meta-cognitive micro-step R3 problem, which means that students will encounter various problems to complete the task; Meta-cognitive micro-step R4 path, which means that in the process of completing the task, students will experience a series of path guides to reflect their thinking process; Meta-cognitive micro-step R5 method, which means that students will use various specific methods to complete the task; Meta-cognitive micro-step R6 evaluation, which means that through various evaluation methods, the learning progress and achievements of students will be understood; Meta-cognitive micro-step R7 expression, which means that after the evaluation, students need to express their thinking process in a specific way.

[0053] Among them, the purpose of meta-cognitive micro-step R1 is to set a realistic situation for students to understand the application of the knowledge they have learned in real life and stimulate students' learning interest; the purpose of meta-cognitive micro-step R2 is to clarify the specific tasks that students need to complete in this learning process and provide students with clear learning goals; the purpose of meta-cognitive micro-step R3 is to raise questions related to the task, guide students to think and explore, and stimulate students' curiosity; the purpose of meta-cognitive micro-step R4 is to provide students with ideas and methods to solve problems and guide students to learn; the purpose of meta-cognitive micro-step R5 is to introduce specific learning methods and skills to help students complete tasks efficiently; the purpose of meta-cognitive micro-step R6 is to set evaluation criteria to evaluate students' learning achievements to ensure that students achieve the expected goals; the purpose of meta-cognitive micro-step R7 is to summarize the process and results of this learning, strengthen students' cognition, and provide inspiration for subsequent learning.

[0054] In step S2, in order to calculate the first proportion of students successfully completing each of the meta-cognitive micro-steps, it is necessary to collect the first performance data of students on each meta-cognitive micro-step through the digital intelligent learning courseware platform. In one embodiment, to simplify the calculation process, it is assumed that the successful completion of each meta-cognitive micro-step is independent and the importance of each meta-cognitive micro-step is the same, that is, it is not necessary to consider the influence of the previous meta-cognitive micro-step on the current meta-cognitive micro-step and the weight difference. Here, the first successful times and the first attempt times of students completing each of the meta-cognitive micro-steps are obtained from the first performance data, and then the first metric value of students successfully completing each of the meta-cognitive micro-steps is calculated based on the first successful times and the first attempt times. For example, for each meta-cognitive micro-step R i , divide the corresponding first successful times by the first attempt times to obtain the first metric value of students successfully completing this meta-cognitive micro-step R iThe probability P(R i ), and then calculate the first metric sum of the student successfully completing all the metacognitive micro-steps based on the first metric value, and calculate the first proportion of the student successfully completing each metacognitive micro-step based on the first metric sum. For example, for each metacognitive micro-step Ri, calculate its first proportion through the following formula: Finally, standardize the first proportions of all the metacognitive micro-steps calculated so that the sum of all the first proportions is 1, in order to intuitively compare the relative proportion of each metacognitive micro-step in the learning process of the student.

[0055] In other embodiments, when calculating the first metric value of the student successfully completing each metacognitive micro-step, considering that each metacognitive micro-step may depend on the successful completion of the previous metacognitive micro-step, calculate through the formula P(R i |R i-1 ). In addition, if the importance of each metacognitive micro-step is different, it is also necessary to set the weight Wi of each metacognitive micro-step Ri, and calculate through the formula P(R i )*Wi.

[0056] Among them, the weight Wi of each metacognitive micro-step Ri can be set according to the first performance data of the student on each metacognitive micro-step. The first performance data also includes the time spent by the student on each metacognitive micro-step, the correct / error rate, and the participation degree (for example, the number of clicks, the interaction frequency, etc.). For example, the time spent can be used to evaluate the efficiency or difficulty perception of the student in completing a certain metacognitive micro-step, the correct / error rate can assist in judging the mastery degree of the student on a certain metacognitive micro-step, and the participation degree can reflect the initiative and investment degree of the student. For example, if the student spends a lot of time on a certain metacognitive micro-step but has a high error rate and low participation degree, it may imply that there is an obstacle in the student's cognition of this metacognitive micro-step, so the weight of this metacognitive micro-step can be appropriately adjusted.

[0057] In step S3, the present application defines seven metacognitive evolution levels for the entire educational meta-scene to characterize the characteristics of different learning stages, which are: generativity, rooting, growth, derivation, ecology, creation, and symbiosis.

[0058] Among them, the generative level of metacognitive evolution is represented by E1. This stage refers to the time when new concepts, skills, or understandings first emerge during the learning process. Teachers help students start exploring and understanding new knowledge areas by providing appropriate stimuli and activities; the rooting level of metacognitive evolution is represented by E2. At this stage, students begin to internalize new knowledge and establish a solid understanding of basic concepts and principles. Teachers help students consolidate learning outcomes through repeated practice, review, and reinforcement teaching; the growing level of metacognitive evolution is represented by E3. This stage involves the further development and deepening of students' knowledge and skills. Teachers encourage students to think and apply at a higher level by providing more challenging learning tasks and projects; the derivative level of metacognitive evolution is represented by E4. At this stage, students apply the knowledge they have learned to new situations and spread their understanding and insights through communication, cooperation, and presentation. Teachers encourage students to innovate and share, such as through demonstrations, reports, or creative works; the ecological level of metacognitive evolution is represented by E5. This stage emphasizes that students place learning in a broader context and understand how knowledge interacts with other fields and daily life. Teachers help students understand the social and cultural significance of learning through interdisciplinary learning and reflection activities; the creative level of metacognitive evolution is represented by E6. This stage may refer to students generating new ideas and solutions through creative thinking and problem-solving. Teachers stimulate students' creative potential through open-ended tasks and an environment that encourages innovation; the persistent level of metacognitive evolution is represented by E7. This stage may emphasize the persistence and long-term impact of students' learning, including cultivating students' habits and awareness of lifelong learning. Teachers help students establish sustainable learning paths by paying attention to the sustainability of learning outcomes and students' personal development.

[0059] In step S4, in order to calculate the second proportion of students successfully completing each of the metacognitive micro-steps in each of the metacognitive evolution levels, it is necessary to collect the second performance data of students on each of the metacognitive micro-steps in each of the metacognitive evolution levels through the digital learning courseware platform. Here, the second number of successful completions and the second number of attempts of students completing each of the metacognitive micro-steps in each of the metacognitive evolution levels are obtained from the second performance data, and then the second metric value of students successfully completing each of the metacognitive micro-steps in each of the metacognitive evolution levels is calculated based on the second number of successful completions and the second number of attempts. For example, for each metacognitive evolution level E i each metacognitive micro-step R in i , divide the corresponding second number of successful completions by the second number of attempts to obtain the second metric value of the student completing this metacognitive micro-step R i in this metacognitive evolution level E i Among them, Represents the second number of successes. Represents the second number of attempts. Further, based on the second metric value, calculate the second metric sum of the student successfully completing all the meta-cognitive micro-steps in each of the meta-cognitive evolution levels, and calculate the second proportion of the student successfully completing each meta-cognitive micro-step in each of the meta-cognitive evolution levels based on the second metric sum. For example, for the meta-cognitive evolution level E i in the meta-cognitive micro-step R i , calculate its second proportion through the following formula: Finally, standardize the second proportion of each meta-cognitive micro-step in each of the meta-cognitive evolution levels calculated so that the sum of all the second proportions is 1, in order to intuitively compare the relative proportion of each meta-cognitive micro-step in each meta-cognitive evolution level during the student's learning process.

[0060] In step S5, through the standardized first proportion, the student's mastery of each meta-cognitive micro-step can be intuitively reflected. For example, in the meta-cognitive micro-step R5, if the student's success probability is 0.8, it indicates that the student has a good mastery of this learning method; if the probability is only 0.3, it means that the student has great difficulties in applying this method and needs more guidance.

[0061] By synthesizing the second proportions of each meta-cognitive micro-step in the meta-cognitive evolution level, the cognitive development stage of the student can be judged. For example, in the meta-cognitive evolution growth level E3, if the second proportions of the student in most meta-cognitive micro-steps are relatively high, such as the second proportions of the meta-cognitive micro-step R3 problem, the meta-cognitive micro-step R4 path, the meta-cognitive micro-step R5 method, etc. are all above 0.7, it indicates that the student is making smooth progress in knowledge expansion and deepening and has well adapted to the requirements of this evolution level; if the probabilities are generally low, it means that the student encounters obstacles in this meta-cognitive evolution growth level E3 and the cognitive development lags behind.

[0062] Thus, through the probability of the student successfully completing the meta-cognitive micro-steps and the micro-steps in the evolution level for meta-cognitive evaluation, multi-dimensional and well-structured evaluation results can be obtained, providing a basis for educators to comprehensively understand the students' cognitive levels and development from aspects such as individual student performance, group differences, and the correlation between micro-steps and evolution levels.

[0063] In addition, it should be noted that when judging which meta-cognitive micro-step the student is currently in, this application needs to collect and analyze the student's performance data in the learning process in real time through digital means. For example, the digital learning courseware platform will record the student's interactive behavior at each micro-step, such as the dwell time, click or operation frequency, and task completion accuracy of a certain link. If a student frequently participates in problem discussions or tries to answer questions many times in the meta-cognitive micro-step R3 problem stage, the platform will identify that he may be in the meta-cognitive micro-step R3 stage based on these behavioral characteristics. And there is a logical dependency between the micro-steps (such as completing the scene setting R1 before entering the task clarification R2), and the current step can be dynamically inferred based on the completion of the previous steps. Similarly, it is also the same to determine which evolutionary level the student is currently in. For example, the metacognitive evolution generative level E1 focuses on the first appearance of new concepts. If students frequently explore new situations and put forward preliminary insights in the metacognitive micro-step R1 (scenario), they may be at the E1 level; while E2 (rootedness) requires observing the stable application of basic knowledge and the completion of repeated practice in the metacognitive micro-steps R2 (task) and R5 (method). If students can independently design solutions in the metacognitive micro-step R4 (path step) and systematically summarize knowledge in the metacognitive micro-step R7 (expression), they may enter the metacognitive evolution generative level E4 (derivativeness), reflecting the ability of knowledge transfer and innovative application.

[0064] In some embodiments, the machine learning model can learn historical data to accurately identify the corresponding relationship between behavior patterns and meta-cognitive evolution levels and meta-cognitive micro-steps, thereby updating the student's status judgment in real time. This is not the core point of this application and will not be described in detail here.

[0065] It can be seen that the meta-cognitive evaluation method provided by this application captures, analyzes and evaluates students' meta-cognitive processes in the meta-scene of digital learning courseware in real time through digital means, which can measure them more accurately. Through in-depth analysis of the meta-cognitive process, educators and researchers can better understand students' thinking patterns and learning needs, thereby optimizing teaching strategies and resource allocation.

[0066] Based on the same inventive concept, a meta-cognitive evaluation device is also provided in the embodiment of the present application. Since the principle of solving the problem by the device in the embodiment of the present application is similar to the above-mentioned meta-cognitive evaluation method in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0067] As the instruction manual Figure 3 As shown, an embodiment of the present application provides a meta-biological cognitive evaluation device, the device comprising:

[0068] The first setting module 301 is used to set multiple meta-cognitive micro-steps for a single meta-scene in the learning scenario of the digital learning courseware platform;

[0069] The first quantification module 302 is used to collect the first performance data of students on each of the meta-cognitive micro-steps, and calculate the first proportion of students successfully completing each of the meta-cognitive micro-steps according to the first performance data;

[0070] The second setting module 303 is used to set multiple meta-cognitive evolution levels for the entire teaching meta-scene;

[0071] The second quantification module 304 is used to collect the second performance data of students on each of the meta-cognitive micro-steps in each of the meta-cognitive evolution levels, and calculate the second proportion of students successfully completing each of the meta-cognitive micro-steps in each of the meta-cognitive evolution levels according to the second performance data;

[0072] The evaluation module 305 is used to conduct meta-cognitive evaluation based on the first proportion and the second proportion.

[0073] In one embodiment, the multiple meta-cognitive micro-steps set for a single meta-scene include scenario, task, question, path, method, assessment, and expression.

[0074] In one embodiment, the first quantification module 302 calculates the first proportion of students successfully completing each of the meta-cognitive micro-steps from the first performance data, including: obtaining the first number of successes and the first number of attempts of students completing each of the meta-cognitive micro-steps from the first performance data; calculating the first metric value of students successfully completing each of the meta-cognitive micro-steps based on the first number of successes and the first number of attempts, and calculating the total first metric of students successfully completing all the meta-cognitive micro-steps based on the first metric value; calculating the first proportion of students successfully completing each of the meta-cognitive micro-steps based on the total first metric.

[0075] In one embodiment, the first quantification module 302 calculates the first metric value of students successfully completing each of the meta-cognitive micro-steps based on the first number of successes and the first number of attempts, including: setting the weight of each of the meta-cognitive micro-steps based on the first performance data; calculating the first metric value of students successfully completing each of the meta-cognitive micro-steps based on the first number of successes, the first number of attempts, and the weight.

[0076] In one embodiment, the multiple meta-cognitive evolution levels set for the entire teaching meta-scene include generativity, rooting, growth, derivation, ecology, creativity, and symbiosis.

[0077] In one embodiment, the second quantization module 304 calculates the second proportion of each meta-cognitive micro-step in each meta-cognitive evolution level that the student successfully completes according to the second performance data, including: obtaining the second number of successful attempts and the second number of attempts of the student to complete each meta-cognitive micro-step in each meta-cognitive evolution level from the second performance data; calculating the second metric value of the student to successfully complete each meta-cognitive micro-step in each meta-cognitive evolution level based on the second number of successful attempts and the second number of attempts, and calculating the total second metric of all the meta-cognitive micro-steps in each meta-cognitive evolution level that the student completes based on the second metric value; calculating the second proportion of each meta-cognitive micro-step in each meta-cognitive evolution level that the student successfully completes based on the total second metric.

[0078] In one embodiment, the evaluation module 305 performs meta-cognitive evaluation based on the first proportion and the second proportion, including: determining the meta-cognitive micro-steps where the student is weak according to the distribution of the first proportion; judging whether the student develops evenly at each meta-cognitive evolution level according to the distribution of the second proportion.

[0079] A meta-cognitive evaluation device provided by the present application sets multiple meta-cognitive micro-steps for a single meta-scene in the learning context of a digital learning courseware platform through a first setting module; collects the first performance data of the student on each meta-cognitive micro-step through a first quantization module, and performs quantization processing on the completion situation of the student on each meta-cognitive micro-step based on the first performance data to obtain the first proportion of the student to successfully complete each meta-cognitive micro-step; sets multiple meta-cognitive evolution levels for the entire teaching meta-scene through a second setting module; collects the second performance data of the student on each meta-cognitive micro-step in each meta-cognitive evolution level through a second quantization module, and performs quantization processing on the completion situation of the student on each meta-cognitive micro-step in each meta-cognitive evolution level based on the second performance data to obtain the second proportion of the student to successfully complete each meta-cognitive micro-step in each meta-cognitive evolution level; performs meta-cognitive evaluation through an evaluation module based on the first proportion and the second proportion. Thus, by means of digitalization, the student behavior data is collected and analyzed in real time, and the measurement and evaluation of the occurrence and evolution of meta-cognition in the meta-scene of digital learning courseware are realized, so as to help optimize teaching strategies.

[0080] Based on the same concept of the present invention, the accompanying Figure 4As shown in the figure, the structure of an electronic device 400 provided by an embodiment of the present application. The electronic device 400 includes: at least one processor 401, at least one network interface 404 or other user interfaces 403, a memory 405, and at least one communication bus 402. The communication bus 402 is used to realize the connection and communication between these components. The electronic device 400 optionally includes a user interface 403, including a display (such as a touch screen, LCD, CRT, holographic imaging, or projector, etc.), a keyboard, or a pointing device (such as a mouse, trackball, touchpad, or touch screen, etc.).

[0081] The memory 405 may include a read-only memory and a random access memory, and provide instructions and data to the processor 401. A part of the memory 405 may also include a non-volatile random access memory (NVRAM).

[0082] In some embodiments, the memory 405 stores the following elements, executable modules, or data structures, or subsets thereof, or extended sets thereof:

[0083] An operating system 4051, including various system programs, used to implement various basic services and process hardware-based tasks;

[0084] An application module 4052, including various applications, such as a launcher, a media player, a browser, etc., used to implement various application services.

[0085] In the embodiment of the present application, by calling the programs or instructions stored in the memory 405, the processor 401 is used to execute the steps in a meta-cognitive evaluation method, and can more scientifically and accurately evaluate the meta-cognitive process of students in a meta-scene by digital means.

[0086] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the steps in the meta-cognitive evaluation method.

[0087] Specifically, the storage medium can be a general storage medium, such as a mobile disk, a hard disk, etc. When the computer program on the storage medium is run, it can execute the above-mentioned meta-cognitive evaluation method.

[0088] In the embodiments provided in the present application, it should be understood that the disclosed device and method can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For another 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 displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces. The indirect coupling or communication connection of devices or units can be in electrical, mechanical or other forms.

[0089] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] In addition, each functional unit in the embodiments provided in the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0091] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in the various embodiments of the present application. The aforementioned storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs and other various media that can store program codes.

[0092] Finally, it should be noted that the above embodiments are only specific implementation manners of the present application, used to illustrate the technical solutions of the present application, rather than limiting it. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that any person skilled in the technical field of the present application can still modify the technical solutions recorded in the foregoing embodiments, or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application. All should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for evaluating metacognition, characterized in that, The method includes the following steps: In the learning context of the digital learning courseware platform, set multiple meta-cognitive micro-steps for a single meta-scene; Collect the first performance data of students on each of the meta-cognitive micro-steps, and quantitatively process the completion of each meta-cognitive micro-step by students based on the first performance data to obtain the first proportion of students successfully completing each meta-cognitive micro-step; Set multiple meta-cognitive evolution levels for the entire teaching meta-scene; Collect the second performance data of students on each of the meta-cognitive micro-steps in each of the meta-cognitive evolution levels, and quantitatively process the completion of each meta-cognitive micro-step by students in each of the meta-cognitive evolution levels based on the second performance data to obtain the second proportion of students successfully completing each of the meta-cognitive micro-steps in each of the meta-cognitive evolution levels; Conduct meta-cognitive evaluation based on the first proportion and the second proportion.

2. The meta-cognitive evaluation method according to claim 1, wherein Wherein, The multiple meta-cognitive micro-steps set for a single meta-scene include scenario, task, question, path, method, evaluation, and expression.

3. The method for metacognitive evaluation according to claim 2, wherein, The quantitatively processing the completion of each meta-cognitive micro-step by students based on the first performance data to obtain the first proportion of students successfully completing each meta-cognitive micro-step includes the following steps: Obtain the first number of successful attempts and the first number of attempts of students to complete each of the meta-cognitive micro-steps from the first performance data; Based on the first number of successful attempts and the first number of attempts, measure the possibility of students successfully completing each of the meta-cognitive micro-steps in combination with a first preset rule to obtain a first measurement value, and obtain the first measurement sum of students successfully completing all the meta-cognitive micro-steps based on the first measurement value; Determine the first proportion of students successfully completing each of the meta-cognitive micro-steps according to the first measurement value and the first measurement sum.

4. The metacognitive evaluation method according to claim 3, wherein The measuring the possibility of students successfully completing each of the meta-cognitive micro-steps in combination with a first preset rule based on the first number of successful attempts and the first number of attempts to obtain a first measurement value includes the following steps: Set the weight of each of the meta-cognitive micro-steps based on the first performance data; Combine the first number of successful attempts, the first number of attempts, and the weight, and measure the possibility of students successfully completing each of the meta-cognitive micro-steps according to a preset algorithm to obtain a first measurement value.

5. The metacognitive evaluation method according to claim 1, wherein Wherein, The multiple meta-cognitive evolution levels set for the entire teaching meta-scene include generativity, rooting, growth, derivation, ecology, creation, and vitality.

6. The metacognitive evaluation method according to claim 5, wherein The quantitatively processing the completion of each meta-cognitive micro-step by students in each of the meta-cognitive evolution levels based on the second performance data to obtain the second proportion of students successfully completing each of the meta-cognitive micro-steps in each of the meta-cognitive evolution levels includes the following steps: Obtain the second number of successful attempts and the second number of attempts of students to complete each of the meta-cognitive micro-steps in each of the meta-cognitive evolution levels from the second performance data; Based on the second number of successes and the second number of attempts, in combination with a second preset rule, measure the likelihood of a student successfully completing each of the meta-cognitive micro-steps in each of the meta-cognitive evolution levels to obtain a second measurement value, and based on the second measurement value, obtain the second measurement sum of the student completing all of the meta-cognitive micro-steps in each of the meta-cognitive evolution levels; Based on the second measurement value and the second measurement sum, determine the second proportion of the student successfully completing each of the meta-cognitive micro-steps in each of the meta-cognitive evolution levels.

7. The metacognitive evaluation method according to claim 6, wherein The meta-cognitive evaluation based on the first proportion and the second proportion includes the following steps: According to the distribution of the first proportion, determine the meta-cognitive micro-steps that the student is weak in; According to the distribution of the second proportion, judge whether the student develops evenly in each meta-cognitive evolution level.

8. A meta-cognitive evaluation device, characterized in that, The device includes: A first setting module, configured to set multiple meta-cognitive micro-steps for a single meta-scenario in the learning context of a digital learning courseware platform; A first quantification module, configured to collect first performance data of a student on each of the meta-cognitive micro-steps, and based on the first performance data, perform quantification processing on the completion situation of the student on each meta-cognitive micro-step to obtain the first proportion of the student successfully completing each meta-cognitive micro-step; A second setting module, configured to set multiple meta-cognitive evolution levels for the entire teaching meta-scenario; A second quantification module, configured to collect second performance data of the student on each of the meta-cognitive micro-steps in each of the meta-cognitive evolution levels, and based on the second performance data, perform quantification processing on the completion situation of the student on each of the meta-cognitive micro-steps in each of the meta-cognitive evolution levels to obtain the second proportion of the student successfully completing each of the meta-cognitive micro-steps in each of the meta-cognitive evolution levels; An evaluation module, configured to perform meta-cognitive evaluation based on the first proportion and the second proportion.

9. An electronic device, characterized in that, Includes: A processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device runs, the processor communicates with the memory through the bus. When the machine-readable instructions are executed by the processor, the steps of the meta-cognitive evaluation method according to any one of claims 1 to 7 are executed.

10. A computer-readable storage medium, characterized in that, A computer program is stored on this computer-readable storage medium. When the computer program is run by a processor, the steps of the meta-cognitive evaluation method according to any one of claims 1 to 7 are executed.

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