Virtual environment-based advanced training course resource adaptation method

Abstract

The invention discloses an advanced training course resource adaptation method based on a virtual environment, and the method comprises the steps: dynamically adjusting the task difficulty based on the performance of a student in each task, recommending the most suitable learning resource in real time, and guaranteeing that the challenging of the task is matched with the ability level of the student; by combining task difficulty adjustment and student behavior feedback, the problem that the task difficulty is not matched with the performance of a learner in the prior art can be solved, and then the participation degree of the student and the task completion quality are improved; besides, along with the proceeding of the task, learning resource recommendation can be continuously optimized according to the real-time feedback of the student in the task, and the student is helped to obtain the most appropriate support in the continuously adjusted learning situation, so that more efficient learning and skill mastering are realized; according to the scheme, the adaptability and individuation level of the virtual practical training course can be obviously improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent allocation of educational resources in a virtual environment, and in particular to a method for adapting higher education training course resources based on a virtual environment. Background Art

[0002] With the rapid development of information technology, virtual training has been widely used in the fields of education and training, especially in higher education, vocational skills training and engineering technology, and has become an important means to cultivate students' practical ability. The so-called virtual training is actually to provide students with a low-cost and low-risk learning platform by simulating a real working environment and using computer technology, virtual reality (VR) and augmented reality (AR) and other technologies. In the past, traditional virtual training systems mostly relied on preset teaching scenarios and resources, and teachers only needed to manually configure teaching content and tasks according to the syllabus and student conditions. However, with the gradual maturity of artificial intelligence (AI), big data and machine learning, intelligent virtual training systems have begun to emerge, which can dynamically adjust task content and difficulty according to students' personalized needs and task performance, and realize adaptive teaching. At present, some virtual training systems have begun to combine data mining and analysis technology to provide learners with personalized learning resources and task arrangements to improve learning efficiency and skill mastery.

[0003] However, the existing virtual training system still has some shortcomings, especially in terms of dynamic adjustment of task difficulty and resource recommendation. First, traditional virtual training platforms usually adopt static task setting and resource allocation mechanisms. The selection of task difficulty and teaching resources is often based on preset rules, and fails to fully consider the differences in students' abilities and real-time changes during task execution. This lack of flexibility limits students' learning progress and easily leads to a single learning experience. Some students may even feel that the task is too simple or too difficult, which affects their learning effect. Second, the resource recommendation mechanism of the existing system mostly relies on a static resource library, lacks dynamic feedback based on students' task completion and learning progress, and cannot achieve true personalized resource adaptation. Third, because current resource recommendations often focus on the initial stage of the task, they ignore the changes in students' performance during task execution and the dynamic changes in learning needs.

[0004] Therefore, how to adjust task difficulty and learning resource recommendations based on students' real-time learning performance and maintain challenging and personalized teaching has become the key to improving the effectiveness of virtual training systems. Summary of the invention

[0005] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides a method for adapting higher education practical training course resources based on a virtual environment to solve the problems raised in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for adapting higher education training course resources based on a virtual environment, comprising: Creating a virtual training course resource library, generating teaching tasks through virtual training courses, monitoring the teaching tasks and collecting multi-dimensional data of student behaviors, pre-processing the multi-dimensional data of student behaviors, and extracting key features of student behaviors as a model input set; Creating an adaptive model of task difficulty and student behavior performance, selecting learning resources suitable for the current virtual training course from the resource library and allocating them to corresponding students; The adaptive model is trained, and the completion status of students after adjusting the difficulty of the task after each assignment is used as a reference condition to optimize the resource library, so as to form a higher education training course resource adaptation method based on a virtual environment.

[0008] As a preferred solution of the method for adapting higher education practical training course resources based on a virtual environment described in the present invention, the method of creating a virtual practical training course resource library includes: Tutorial learning content, scenario simulation tools, and interactive feedback.

[0009] As a preferred solution of the method for adapting higher education practical training course resources based on a virtual environment described in the present invention, wherein: generating teaching tasks through virtual practical training courses, monitoring the teaching tasks and collecting multi-dimensional data of student behavior, including: The virtual training course is divided into multiple task types, and the teaching tasks to be issued are selected according to the task type and task difficulty. The task difficulty is defined according to the current student's historical behavior performance and current learning progress; According to the assigned teaching tasks, the operation time, operation accuracy and step execution status of students when completing the task are collected to obtain multi-dimensional data of students' behavior.

[0010] As a preferred solution of the method for adapting higher education practical training course resources based on a virtual environment described in the present invention, the multi-dimensional data of the student behavior is preprocessed, and the key features of the student behavior are extracted as a model input set, including: Normalizing the operation duration and operation accuracy, denoising the step execution status in the operation process, and filling in the missing values ​​of the operation duration and operation accuracy; Based on the preprocessed operation time, operation accuracy, and step execution of multi-dimensional data of student behavior during the operation, the total execution time of students in the task, the errors caused by the operation steps during the task execution, and whether the students have completed all necessary steps and followed the correct step sequence are considered to obtain the operation time feature, operation accuracy feature, and operation integrity feature; The operation duration feature, operation accuracy feature and operation integrity feature are used as an input set of the model.

[0011] As a preferred solution of the method for adapting higher education practical training course resources based on a virtual environment described in the present invention, the adaptive model of task difficulty and student behavior performance is created, including: Evaluate the difficulty of the task and get: in, It is represented as the task difficulty of the i-th student, represents the historical performance of the i-th student, Represents the current learning progress of the i-th student; Considering the factors affecting the difficulty of the task by the student's behavior, the difficulty of the task is adaptively adjusted to obtain: Where n is the total number of students. Indicates the difficulty of the task after adjustment, It is represented as the behavioral performance of the i-th student, that is, the set of operation duration features, operation accuracy features, and operation integrity features.

[0012] As a preferred solution of the method for adapting higher education practical training course resources based on a virtual environment described in the present invention, wherein: selecting learning resources suitable for the current virtual practical training course from the resource library and allocating them to corresponding students includes: If the adjusted task difficulty value exceeds the original task difficulty value, it means that the student's ability is weak, but the task difficulty is high and has exceeded the student's ability range. In this case, learning resources of virtual training courses that are lower than or equal to the student's ability range are selected from the resource library. If the adjusted task difficulty value is less than the original task difficulty value, it means that the student has strong ability, but the task difficulty is low and cannot meet the student's learning ability. In this case, a learning resource of a virtual training course that is higher than or equal to the student's ability range is selected from the resource library; Among them, when selecting learning resources for virtual training courses in the resource library, set the expected value of learning progress of the corresponding learning resources.

[0013] As a preferred solution of the method for adapting higher education practical training courses resources based on a virtual environment described in the present invention, the adaptive model is trained, and the resource library is optimized based on the completion status of students after each adjustment of the task difficulty as a reference condition, including: If the student's current learning progress does not reach the expected learning progress after each adjustment of the task difficulty, the current student's behavior performance is recorded to update the student's historical performance, and the difficulty of the initially assigned task is reduced. The task difficulty is then re-evaluated until the current student's ability meets the learning resources allocated by the resource library, and the optimal learning resource allocation for the current student is output.

[0014] Compared with the prior art, the invention has the following beneficial effects: 1. The present invention monitors the students' operation accuracy, operation time and the execution of steps in the operation process in real time, so that the system can accurately judge the suitability of the task and adjust the challenge of the task according to the students' progress, thereby avoiding the problem of fixed task difficulty and inability to adapt to individual ability differences in traditional virtual training, ensuring that students learn within an appropriate difficulty range, and will not lose learning motivation due to too simple tasks, nor will they feel frustrated due to too difficult tasks; 2. Through the two-way adaptation of task difficulty and learning resources, the students' learning experience has been significantly improved. The difficulty of the task and the learning resources can be optimized in real time according to the feedback of the students' abilities, so that students can get the most suitable challenges and support in each learning link, thereby improving their learning efficiency and task completion quality. In addition, personalized learning resource push can help students make up for weak links, ensure that every learning resource in the resource library can effectively meet the needs of students at different task stages, strengthen students' mastery of core knowledge points, and thus improve the overall task completion and skill mastery. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them: Figure 1 The present invention is an overall flow chart of a method for adapting higher education practical training course resources based on a virtual environment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0018] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0019] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0020] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0021] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0022] Reference Figure 1 , which is the first embodiment of the present invention, and provides a method for adapting higher education training course resources based on a virtual environment, comprising: S1. Create a virtual training course resource library, generate teaching tasks through virtual training courses, monitor the teaching tasks and collect multi-dimensional data of student behavior, pre-process the multi-dimensional data of student behavior, and extract key features of student behavior as a model input set; Furthermore, a virtual training course resource library is created, including tutorial learning content, scenario simulation tools, and interactive feedback on operations; Specifically, tutorial learning content refers to the theoretical basis support for students to complete practical training course tasks, including textbooks, handouts, documents, videos, etc.; Specifically, scenario simulation tools refer to the provision of virtual laboratories or equipment through technologies such as VR, AR, and 3D simulation, allowing students to simulate operations and practices in the actual environment in a virtual environment; Specifically, the interactive feedback refers to the system automatically generating an evaluation report (in duplicate, one uploaded to the system administrator and the other printed to the virtual training interface) based on the student's performance in the virtual training. By reading and analyzing the evaluation report, the system gives the student's deficiencies in the virtual training and provides suggestions; Furthermore, the virtual training courses are divided into multiple task types, and the teaching tasks to be issued are selected according to the task type and task difficulty; Specifically, tasks are divided into different types according to the objectives of the virtual training course, such as operation tasks, decision-making tasks, combination tasks, etc. For example, in mechanical engineering training, the task can be "assembling a certain device" or "debugging a certain machine"; It should be noted that each task type also includes a task description (such as "complete the assembly of a certain device"), the requirements and goals of each step in the task (such as "Step 1: Install the gear with an accuracy of less than 5 mm"), and the resource requirements of the task itself (such as the equipment to be simulated, the time preset for the simulation, etc.) Specifically, the initial task difficulty is defined based on the current student’s historical behavioral performance and current learning progress; Furthermore, according to the teaching tasks issued, the operation time, operation accuracy and step execution status of students in completing the tasks are collected to obtain multi-dimensional data of students' behavior; Furthermore, the operation duration and operation accuracy are normalized, the execution of steps in the operation process is denoised, and the missing values ​​of the operation duration and operation accuracy are filled; It should be noted that the denoising operation is to remove the noise caused by step operation errors during the virtual training process, the normalization is to extract key features and send them to the model for processing, and the missing values ​​are to prevent the missing features when the key features are extracted; because the preprocessing operations (normalization, denoising, and filling missing values) can be achieved through existing methods, they will not be elaborated here; Furthermore, based on the pre-processed operation time, operation accuracy, and step execution of the multi-dimensional data of student behavior during the operation, the total execution time of the student in the task execution, the errors caused by the student's operation steps during the task execution, and whether the student has completed all necessary steps and followed the correct step sequence are considered, and the operation time feature, operation accuracy feature, and operation integrity feature are obtained; Furthermore, the total execution time of the task is expressed as: in, is the total task execution time, The task end time, The task start time; Furthermore, the error caused by the operation steps of students during task execution is expressed as: in, and It is expressed as the student's operating position, represented by the coordinates x and y axes; and It is represented as the target position, and similarly it is represented by the coordinates x and y axes; It is expressed as operating error; It should be noted that the feature extraction method for obtaining the operation duration feature, the operation accuracy feature and the operation integrity feature can be implemented by using machine learning methods such as clustering, regression, decision tree, etc.; Furthermore, the operation duration feature, operation accuracy feature, and operation completeness feature are used as the input set of the model; S2. Create an adaptive model of task difficulty and student behavior performance, and select learning resources suitable for the current virtual training course from the resource library and assign them to corresponding students; Furthermore, when creating an adaptive model of task difficulty and student behavior performance, it is necessary to evaluate the task difficulty and obtain: in, It is represented as the task difficulty of the i-th student, represents the historical performance of the i-th student, Represents the current learning progress of the i-th student; Furthermore, considering the impact of students' behavior on the task difficulty assessment, it is necessary to adaptively adjust the task difficulty, and obtain: Where n is the total number of students. Indicates the difficulty of the task after adjustment, It is represented as the behavior performance of the i-th student, that is, the set of operation duration characteristics, operation accuracy characteristics, and operation integrity characteristics; Furthermore, the rules for selecting learning resources suitable for the current virtual training course from the resource library are as follows: Rule 1: If the adjusted task difficulty value exceeds the original task difficulty value, it means that the student's ability is weak, but the task difficulty is high and has exceeded the student's ability range. In this case, select the learning resources of the virtual training course that is lower than or equal to the student's ability range from the resource library; Rule 2: If the adjusted task difficulty value is less than the original task difficulty value, it means that the student has strong ability, but the task difficulty is low and cannot meet the student's learning ability. In this case, select the learning resources of the virtual training course that is higher than or equal to the student's ability range from the resource library; Rule 3: If the adjusted task difficulty value is the same as the original task difficulty value, it means that the learning resources selected from the resource library are just suitable for the student; Among them, when selecting learning resources of virtual training courses in the resource library, set the expected value of learning progress of the corresponding learning resources; Specifically, the expected learning progress value of the learning resource needs to be set according to the original learning progress value and is a dynamic value; It should be noted that by adaptively adjusting the learning resources supporting the virtual training course according to the difficulty of the task, the personalized learning ability of each student can be customized; in addition, the expected value of the learning progress of the learning resources is set to observe whether the learning resources selected from the resource library that are suitable for the student's ability range can fit the original learning progress; for example, when it is detected that the ability range of student A exceeds the current task difficulty, a learning resource that matches or exceeds the ability range of student A is selected from the resource library. If the learning progress of student A using the learning resource that matches or exceeds his ability range is the same as the learning progress of the task difficulty value before adjustment (i.e., the original learning progress value), then it means that student A's learning ability can reach the current adjusted task difficulty, indicating that the allocation of learning resources in the resource library is reasonable; S3, training the adaptive model, and optimizing the resource library based on the completion status of students after adjusting the difficulty of each assignment, so as to form a resource adaptation method for higher education training courses based on a virtual environment; Furthermore, if after each adjustment of the task difficulty, the student's current learning progress does not reach the expected learning progress (indicating that the student's learning ability cannot fully cover the learning resources allocated after the adjustment of the task difficulty, then the current task difficulty needs to be fine-tuned), then the current student's behavior performance is recorded to update the student's historical behavior performance, and the initial assigned task difficulty is reduced, and the task difficulty is re-evaluated until the current student's ability is consistent with the learning resources allocated by the resource library, and the optimal learning resource allocation for each student is output.

[0023] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0024] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0025] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0026] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0027] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0028] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for adapting higher education training course resources based on a virtual environment, characterized in that: include: Creating a virtual training course resource library, generating teaching tasks through virtual training courses, monitoring the teaching tasks and collecting multi-dimensional data of student behaviors, pre-processing the multi-dimensional data of student behaviors, and extracting key features of student behaviors as a model input set; Creating an adaptive model of task difficulty and student behavior performance, selecting learning resources suitable for the current virtual training course from the resource library and allocating them to corresponding students; The adaptive model is trained, and the completion status of students after adjusting the difficulty of the task after each assignment is used as a reference condition to optimize the resource library, so as to form a higher education practical training course resource adaptation method based on a virtual environment.

2. The method for adapting higher education training course resources based on a virtual environment as claimed in claim 1, characterized in that: The creation of a virtual training course resource library includes: Tutorial learning content, scenario simulation tools, and interactive feedback.

3. The method for adapting higher education training course resources based on a virtual environment as claimed in claim 1, characterized in that: Generate teaching tasks through virtual training courses, monitor the teaching tasks and collect multi-dimensional data of student behavior, including: The virtual training course is divided into multiple task types, and the teaching tasks to be issued are selected according to the task type and task difficulty. The task difficulty is defined according to the current student's historical behavior performance and current learning progress; According to the assigned teaching tasks, the operation time, operation accuracy and step execution status of students when completing the task are collected to obtain multi-dimensional data of students' behavior.

4. The method for adapting higher education training course resources based on a virtual environment as claimed in claim 3, characterized in that: Preprocess the multi-dimensional data of the student behavior and extract key features of the student behavior as a model input set, including: Normalizing the operation duration and operation accuracy, denoising the step execution status in the operation process, and filling in the missing values ​​of the operation duration and operation accuracy; Based on the preprocessed operation time, operation accuracy, and step execution of multi-dimensional data of student behavior during the operation, the total execution time of students in the task, the errors caused by the operation steps during the task execution, and whether the students have completed all necessary steps and followed the correct step sequence are considered to obtain the operation time feature, operation accuracy feature, and operation integrity feature; The operation duration feature, operation accuracy feature and operation integrity feature are used as an input set of the model.

5. The method for adapting higher education training course resources based on a virtual environment as claimed in claim 4, characterized in that: Create an adaptive model of task difficulty and student performance, including: Evaluate the difficulty of the task and get: ； in, It is represented as the task difficulty of the i-th student, represents the historical performance of the i-th student, Represents the current learning progress of the i-th student; Considering the factors affecting the difficulty of the task by the student's behavior, the difficulty of the task is adaptively adjusted to obtain: ； Where n is the total number of students. Indicates the difficulty of the task after adjustment, It is represented as the behavioral performance of the i-th student, that is, the set of operation duration features, operation accuracy features, and operation integrity features.

6. The method for adapting higher education training course resources based on a virtual environment as claimed in claim 2 or 5, characterized in that: Select learning resources suitable for the current virtual training course from the resource library and assign them to corresponding students, including: If the adjusted task difficulty value exceeds the original task difficulty value, it means that the student's ability is weak, but the task difficulty is high and has exceeded the student's ability range. In this case, learning resources of virtual training courses that are lower than or equal to the student's ability range are selected from the resource library. If the adjusted task difficulty value is less than the original task difficulty value, it means that the student has strong ability, but the task difficulty is low and cannot meet the student's learning ability. In this case, a learning resource of a virtual training course that is higher than or equal to the student's ability range is selected from the resource library; Among them, when selecting learning resources for virtual training courses in the resource library, set the expected value of learning progress of the corresponding learning resources.

7. The method for adapting higher education training course resources based on a virtual environment as claimed in claim 6, characterized in that: The adaptive model is trained, and the resource library is optimized using the student completion status after each adjustment of the task difficulty as a reference condition, including: If the student's current learning progress does not reach the expected learning progress after each adjustment of the task difficulty, the current student's behavior performance is recorded to update the student's historical performance, and the difficulty of the initially assigned task is reduced. The task difficulty is then re-evaluated until the current student's ability meets the learning resources allocated by the resource library, and the optimal learning resource allocation for the current student is output.

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