CN113963578A - An adaptive training system and training method for knowledge service and skill drill - Google Patents
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Abstract
本发明公开了一种面向知识服务与技能演练的自适应培训系统及培训方法，所述培训系统：包括应用层、服务层、模型层和数据层；所述应用层：用于采集学习行为数据，以及用于根据服务层反馈的自适应培训信息进行学习交互展示；所述模型层：用于为服务层提供模型支撑；所述数据层：用于为模型层和服务层提供数据支撑；所述服务层：用于根据所采集的学习行为数据从模型层及数据层采集培训相关服务数据，自适应生成与用户学习行为数据相匹配的培训信息并反馈至应用层；其中，所述培训相关服务数据包括：知识资源、学习资源、任务信息、用户信息、教学方式和服务方式。本发明能够根据用户对培训知识的掌握情况自适应提供培训内容，有助于提高培训效率和培训质量。
The invention discloses an adaptive training system and training method oriented to knowledge service and skill drill. The training system includes an application layer, a service layer, a model layer and a data layer; the application layer is used for collecting learning behavior data , and for interactive display of learning based on the adaptive training information fed back by the service layer; the model layer: used to provide model support for the service layer; the data layer: used to provide data support for the model layer and the service layer; The service layer: used to collect training-related service data from the model layer and the data layer according to the collected learning behavior data, adaptively generate training information that matches the user's learning behavior data, and feed it back to the application layer; Service data includes: knowledge resources, learning resources, task information, user information, teaching methods and service methods. The present invention can adaptively provide training content according to the user's mastery of the training knowledge, thereby helping to improve training efficiency and training quality.
Description
Technical Field
The invention relates to a self-adaptive training system and a training method for knowledge service and skill drilling, and belongs to the technical field of simulation training.
Background
At present, to personnel's training, mainly adopt theoretical knowledge training, master and slave's band, utilize real modes such as real training of equipment to develop knowledge skill training work, though can satisfy personnel's training demand to a certain extent, still exist and lack not enough such as systematicness, convenience and pertinence, the concrete expression is in following several aspects:
(1) in the aspect of theoretical knowledge training, a classroom centralized teaching and network learning platform mode is mainly adopted. The classroom centralized teaching adopts an infusion type passive learning mode, the form is boring, one side of thousands of people is dead and remembered, the back is hard, the training is easy to be performed, the learning and the practicing are disjointed, the training efficiency is not high, and the effect is not ideal. The existing network learning platform simply moves off-line learning resources to the on-line, and training content is one of thousands of people, so that interaction is lacked; the learning resource management is extensive, and the knowledge is not organized systematically; the individual knowledge mastering degree of the user is not accurately evaluated, and the individual knowledge level and the learning style are not accurately depicted;
(2) in the aspect of skill training, three modes of carrying on brothers by a master, utilizing practical training of real equipment and training of simulation software are mainly adopted. The master-slave type training mode is that a master conducts knowledge and skill teaching on a slave in actual work according to rules and experiences, standardization and systematization are difficult, the training effect depends on the capability and the responsibility center of the master, and the problems that equipment abnormity and fault types are limited in quantity, the training period is long, and a large number of students cannot be supported to conduct training at the same time exist in the actual work. The practical training mode using real equipment has the problems of large occupied area, high investment cost, difficulty or incapability of disassembling some equipment, incapability of enabling students to deeply understand and learn internal structures and operation mechanisms, difficulty in presenting abnormal and fault phenomena on the real equipment, easiness in damaging the equipment, incapability of supporting a large number of personnel to simultaneously carry out practical training due to site limitation, low expandability, incapability of supporting anytime and anywhere learning and drilling and the like. The simulation software training mode is based on the characteristics that a virtual training environment is established for personnel, the reusability is high, a large number of students can be supported to simultaneously carry out training and the like, the simulation software training method plays an important role in the aspects of skill training, examination and identification, accident prevention exercise and the like, but some outstanding problems exist in application, the networking simulation training through a mobile terminal is not supported, and the students cannot carry out skill exercise at any time and any place by utilizing fragmentation time; the skill drilling behavior tracking, recording, analyzing and feedback are lacked, the skill level of an individual cannot be accurately evaluated, a training teaching plan needs to be made manually before training, and drilling content and a drilling scene cannot be generated in a self-adaptive mode according to the personal skill mastering degree; lack of intelligent guidance and automatic evaluation, incapability of enabling trainees to obtain help in real time in the drilling process, and the like.
(3) In the aspect of field work auxiliary technology, the intelligent interaction guide and accurate knowledge presentation service by utilizing real-time interaction information aiming at work tasks and work scenes is lacked in the current work field, and the guidance and the help are mainly realized by a master with a brother.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a self-adaptive training system and a training method for knowledge service and skill drilling, can self-adaptively provide training content according to the mastering condition of a user on training knowledge, and is beneficial to improving the training efficiency and the training quality.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the invention provides an adaptive training system, comprising an application layer, a service layer, a model layer and a data layer;
the application layer: the system is used for collecting learning behavior data and performing learning interactive display according to self-adaptive training information fed back by a service layer;
the model layer: the system is used for providing model support for the service layer;
the data layer: the data support is provided for the model layer and the service layer;
the service layer: the system comprises a model layer, a data layer, an application layer and a data layer, wherein the model layer is used for acquiring training related service data from the model layer and the data layer according to the acquired learning behavior data, adaptively generating training information matched with the learning behavior data of a user and feeding the training information back to the application layer;
wherein the training-related service data comprises: knowledge resources, learning resources, task information, user information, teaching modes and service modes.
With reference to the first aspect, further, the molding layer includes:
a domain model: for visualizing a domain-specific abstract knowledge system;
the user model comprises the following steps: the system is used for describing and recording the individual characteristics of the users so as to reflect the individual differences among the users;
the teaching model comprises the following steps: the system comprises a domain model, a user model and a teaching recommendation rule, wherein the domain model is used for combining the characteristics of the domain model and the characteristics of the user model to provide the teaching recommendation rule suitable for a user;
the field work auxiliary service model: the method is used for selecting learning, practicing and working auxiliary resources contained in the knowledge points to form teaching and service contents aiming at teaching strategies of different types of knowledge points.
With reference to the first aspect, further, the data layer includes:
a knowledge base: the knowledge point extraction method is used for extracting knowledge points from the structured, semi-structured and unstructured knowledge resources of the trained profession and constructing the association relation among the knowledge points so as to form a professionally-specialized knowledge base;
a learning database: the system is used for storing and labeling relevant learning data in a centralized manner;
learning behavior library: the framework structure is used for describing acquisition, data storage and management of knowledge learning behaviors, skill exercise behaviors and field work auxiliary interaction behaviors of students by adopting a network learning behavior acquisition standard xAPI standard of an international advanced distributed learning organization.
With reference to the first aspect, further, the service layer includes:
a core service unit: the system comprises a database, a database server and a database server, wherein the database is used for providing core services including an analysis service, an evaluation service, a model updating service, a skill exercise service and a knowledge learning service;
service interface: the adaptive learning content is fed back to the application layer;
service management: for providing management functions including service registration management, service lifecycle management.
With reference to the first aspect, the learning behavior collection is further performed by receiving learning, practicing and work assistance contents by using various terminal devices, and transmitting generated activity data to a behavior database by using an xAPI protocol and a specification;
the learning interaction display comprises data visualization and evaluation result visualization of knowledge learning and skill drilling scenes.
In a second aspect, the present invention provides a training method of the adaptive training system according to any one of the first aspect, the training method comprising:
collecting learning behavior data of a user;
calling training related service data according to the learning behavior data of the user, and adaptively generating training information matched with the learning behavior data of the user;
and feeding the training information back to the user, so that the user can continue learning according to the training information, and new learning behavior data are generated until the user grasps all knowledge points.
With reference to the second aspect, the method further includes performing adaptive evaluation according to the learning behavior data of the user, and determining whether the user grasps all knowledge points according to an adaptive evaluation result.
Compared with the prior art, the invention has the following beneficial effects:
a training system comprising an application layer, a service layer, a model layer and a data layer is built, the training mode is more standardized and systematized, matched training contents can be generated in a self-adaptive mode according to the learning behaviors of the user, and the training efficiency and the training quality are improved.
Drawings
FIG. 1 is a block diagram of an adaptive training system according to an embodiment of the present invention;
fig. 2 is a flowchart of a training method of the adaptive training system according to the second embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The first embodiment is as follows:
fig. 1 shows an adaptive training system according to an embodiment of the present invention, which includes a data layer, a model layer, an adaptive engine (service layer), and an application layer.
The data layer comprises a knowledge base, a learning database and a learning behavior base and provides data support for the model layer and the adaptive engine (service layer). The knowledge base is a knowledge base which is formed by extracting knowledge points from the structured, semi-structured and unstructured knowledge resources of the trained profession and constructing the association relation among the knowledge points. The learning database is formed by storing relevant learning data of video, audio, electronic book and the like in the same profession in a centralized way and manually marking labels. The learning behavior library is a framework structure adopting the network learning behavior acquisition standard xAPI specification of the international advanced distributed learning organization and describes acquisition, data storage and management of user knowledge learning behaviors, skill exercise behaviors and field work auxiliary interaction behaviors.
The model layer comprises a field model, a user model, a knowledge learning teaching model, a skill rehearsal teaching model and a field work auxiliary service model, and provides model support for the adaptive engine (service layer). The domain model is the visualization presentation of an abstract knowledge system of a specific domain, and is the basis for realizing the self-adaptive presentation of knowledge resources based on the user model by the self-adaptive learning system. The domain model consists of a collection of domain knowledge points and their associations. The most widespread technology for domain model building is the semantic web ontology technology, which provides a formalized way to represent and share knowledge. The semantic web technology allows interaction between a shared knowledge module and a user, and can realize the opening of an adaptive learning system, and the existing semantic web technology passing the W3C standard comprises the following steps: RDF, OWL, SPARQL, etc., and the ontology technology can improve the operability and reusability of the domain model. The user model describes and records the individual characteristics of the users and reflects the individual differences among the users. The construction of the system can comprehensively refer to IMS LIP specification, IEEE PAPI specification and CELTS-11 specification, and specifically comprises 6 main parts of personal information, user preference, user document, user score, user relationship and the like. The knowledge learning and skill drill teaching model comprises a series of recommendation rules, and the overall decision result of the rules determines when, in what way and which content the adaptive learning system recommends to the user. Namely, the teaching model needs to combine the characteristics of the field model and the user model to provide a set of teaching recommendation rules suitable for the user. The field work auxiliary service model is used for selecting learning, practicing and work auxiliary resources contained in the knowledge points to form teaching/service contents aiming at teaching strategies of different types of knowledge points.
The self-adaptive engine (service layer) is composed of five core services, namely a behavior analysis service, an evaluation service, a model updating service, a skill drilling service and a knowledge learning service, a service interface and a service management. The core service part is the most key component in the adaptive engine, and can call corresponding data of knowledge resources, learning resources, task information, user information, teaching modes, service modes and the like from a knowledge base, a learning resource base, a field model, a user model, a knowledge learning skill drilling teaching model and a field work auxiliary service model according to application requirements to realize the functions of adaptive knowledge service and skill drilling. The service interface is used as an outlet for feeding back the adaptive content to the power transformation operation and maintenance personnel, and the service management mainly provides various functions in the adaptive engine and management functions of the service, including service registration management and service life cycle management.
The application layer mainly comprises two types of applications of knowledge learning and skill drilling, and mainly realizes two functions: 1. and a learning behavior acquisition function. 2. And learning an interactive display function. The learning behavior acquisition mainly comprises the steps that various terminal devices are utilized to enter a knowledge service and skill drilling system to receive learning/drilling/working auxiliary contents (including videos, documents, simulation cases, auxiliary information and the like), and generated activity data are transmitted into a behavior database through an xAPI protocol and specifications. The learning interaction display mainly comprises data visualization and evaluation result visualization of knowledge learning and skill drilling scenes.
The self-adaptive training system provided by the embodiment of the invention can be constructed and obtained by adopting the following method:
firstly, establishing a data layer (comprising a knowledge base, a learning database and a learning behavior base);
secondly, constructing a model layer (comprising a domain model, a user model, a knowledge learning and skill rehearsal teaching model and a field work auxiliary service model);
thirdly, constructing an adaptive engine (comprising a behavior analysis service, an evaluation service, a model updating service, a knowledge learning and skill drilling service);
finally, an application layer (comprising a skill drilling and knowledge learning system) is constructed.
Example two:
as shown in fig. 2, a training method provided in an embodiment of the present invention can be implemented by using the adaptive training system described in the first embodiment, and the method includes the following steps:
the user logs in the training system through the application layer: for unregistered users, the users need to be registered in advance, and the system to be trained can log in the training system after user information is added; for registered users, after logging in the system, a knowledge learning application and a skill drilling application can be selected to enter related learning and training processes, so that corresponding learning behavior data are generated;
after acquiring learning behavior data of a user, an adaptive engine (namely a service layer) acquires training related service data from a data layer and a model layer respectively, and adaptively generates training information (which can comprise learning content, learning path and the like) matched with the learning behavior data of the user;
and the user continues to learn according to the training information fed back by the self-adaptive engine (namely the service layer), new learning behavior data are generated, and the next round of learning training process is started until the user grasps all knowledge points.
In order to test the mastery degree of the user on the knowledge points, the training method provided by the embodiment of the invention further comprises the step of starting the self-adaptive evaluation program after the user implements one or more times of training and learning so as to perform self-adaptive training on the user. After the evaluation is passed, the user can quit by himself.
The invention can lay a foundation for the research and development of professional field knowledge service and skill drill systems, and greatly improve the efficiency and quality of professional field knowledge learning, skill drill and field work auxiliary system development.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
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