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WO2018182072A1 - Système et procédé d'extraction de données d'apprentissage à partir d'un contenu de réalité virtuelle et d'un contenu de réalité augmentée - Google Patents

Système et procédé d'extraction de données d'apprentissage à partir d'un contenu de réalité virtuelle et d'un contenu de réalité augmentée Download PDF

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Publication number
WO2018182072A1
WO2018182072A1 PCT/KR2017/003531 KR2017003531W WO2018182072A1 WO 2018182072 A1 WO2018182072 A1 WO 2018182072A1 KR 2017003531 W KR2017003531 W KR 2017003531W WO 2018182072 A1 WO2018182072 A1 WO 2018182072A1
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data
learning
virtual reality
augmented reality
content
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Korean (ko)
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조용상
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Korea Education And Research Information Service
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Korea Education And Research Information Service
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/25—Integrating or interfacing systems involving database management systems
    • G06F16/258—Data format conversion from or to a database
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/20—Education
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T19/00—Manipulating 3D models or images for computer graphics
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T19/00—Manipulating 3D models or images for computer graphics
    • G06T19/006—Mixed reality

Definitions

  • the present invention relates to a system and method for extracting and converting learning data from virtual reality and augmented reality content, and more particularly, by extracting learning data generated when utilizing virtual reality and augmented reality content for education purposes.
  • Terminals such as personal computers, laptops, mobile phones, etc. may be configured to perform various functions. Examples of such features include a variety of application-driven features, web browsing capabilities, data and voice communications capabilities, the ability to take photos or videos with the camera, voice recording capabilities, playback of music and audio files through the speaker system, images and videos Display function. Some terminals include additional functionality to play games, while others are implemented as multimedia devices. Moreover, recent terminals can receive a broadcast or multicast signal to watch a video or television program. All these functions can be used as a function for learning in the field of education.
  • terminals may be divided into mobile / portable terminals and stationary terminals according to their mobility.
  • the mobile terminal may be further classified into a handheld terminal and a vehicle mount terminal according to whether a user can directly carry it.
  • such a terminal is a multimedia player having a complex function such as taking a picture or a video, playing a music or video file, playing a game, or receiving a broadcast. Is being implemented.
  • Mobile terminals (100) and tablet PC crazes which have swept the world for several years, are changing their lifestyles and lifestyles, and recently, head-mounted displays have emerged for the consumption of virtual reality content.
  • AR Augmented Reality
  • augmented reality uses virtual images instead of reality
  • AR augmented reality
  • AR augmented reality
  • Augmented reality is also called Mixed Reality (MR), which was introduced to the world for the first time by adding a virtual image to the aircraft assembly process around 1990.
  • MR Mixed Reality
  • Augmented reality and virtual reality seem to be similar to each other, but it is clearly distinguished according to whether the subject is virtual or real.
  • an augmented reality fighting game is a form in which a real enemy confronts a virtual enemy in a real space.
  • virtual reality is superior to immersion reality compared to augmented reality
  • augmented reality is characterized by a superior reality than virtual reality.
  • augmented reality and virtual reality contents are more diverse than existing web and mobile contents, and new operation interfaces are added, so that interaction between users and contents occurs actively.
  • An object of the present invention is to extract and convert learning data from virtual reality and augmented reality content.
  • the present invention relates to a system and method for extracting learning data generated when utilizing virtual reality and augmented reality content for education and converting the learning data into a data format for learning analysis.
  • the virtual reality and augmented reality in a device that supports at least one of the virtual reality and augmented reality
  • a first step of executing an application on the device for displaying content Requesting, by the device, to query the virtual reality and augmented reality content to a content repository;
  • a sixth step of the device transmitting the bound learning activity data to a learning data converter;
  • the conversion standard selected by the training data converter may include xAPI and IMS Caliper.
  • the learning data converter may convert classes and attributes of the learning activity data into classes and attributes according to the selected conversion standard.
  • the learning data converter may further convert the meaning of the learning activity data into a meaning according to the selected conversion standard.
  • the learning data converter may include a structural and syntactic mapping instance table of classes and attributes of the learning activity data and a semantic instance table in which the meaning of the learning activity data is indicated according to an ontology rule. Can be.
  • a unique identification number may be assigned to each class and attribute of the structural and syntactic mapping instance table.
  • the learning data converter adds the attribute not included in the structural and syntactic mapping instance table. You can add
  • the learning data converter May exception-process the first learning activity data.
  • the repository performs a suitability check to determine whether the transformed learning activity data has a class, an attribute, and a meaning according to a preset criterion, and if appropriate, the converted learning activity You can save the data and throw an exception if it doesn't fit.
  • the system for extracting learning data from the virtual reality and augmented reality content that is another aspect of the present invention for achieving the above technical problem
  • the content storage that is requested to query the virtual reality and augmented reality content from the outside; Supports at least one of the virtual reality and the augmented reality, receives the virtual reality and augmented reality content is confirmed in the content repository, and displays the received virtual reality and augmented reality content, the virtual reality and augmented
  • a device for extracting and binding learning activity data performed in a learning environment in which real content is displayed, and transmitting the bound learning activity data;
  • a learning data converter which receives the learning activity data from the device, selects a transformation standard to apply to the bound learning activity data, and converts the bound learning activity data according to the selected transformation standard;
  • a storage configured to receive and store the converted learning activity data from the learning data converter.
  • the present invention is to effectively extract the learning data from the virtual reality and augmented reality content.
  • it is possible to provide interoperability with the learning data by effectively extracting the learning data generated when utilizing the virtual reality and augmented reality contents for education and converting the learning data into a data format for the purpose of learning analysis.
  • FIG. 1 conceptually illustrates a data binding format for effectively expressing training data, and illustrates a structure for descriptive data representation.
  • 2A illustrates a concept for describing an xAPI (Experience API), which is an international standard for collecting learning data.
  • xAPI Experience API
  • 2B illustrates a concept describing IMS Caliper, an international standard for collecting training data.
  • FIG. 3 represents a procedure for interconverting heterogeneous data generated according to the standards described in FIGS. 2A and 2B.
  • FIG. 4 illustrates a flow of operation of the conversion system through heterogeneous training data mapping and matching according to the procedure of FIG. 3.
  • FIG. 5 illustrates an example in which heterogeneous data is structurally and syntactically mapped according to the procedure illustrated in FIG. 3.
  • FIG. 6 illustrates an example in which heterogeneous data is structurally and syntactically mapped according to the data represented procedure illustrated in FIG. 5.
  • FIG. 7 illustrates a procedure of extracting training data generated while utilizing virtual reality and augmented reality content, converting the training data into a standardized data collection API, and transmitting and storing the same.
  • FIG. 8 is a flowchart illustrating a procedure of extracting training data generated while executing virtual reality and augmented reality content, converting the training data into a standardized training data format, and transmitting the same.
  • big data is more like a picture that expresses individual needs and actions, rather than data representing a group, and is expected to be very useful in all fields for personalized customized service.
  • ICT information and communication technology
  • the inventors propose a rule, procedure, and method for securing interoperability of heterogeneous data collection systems and data collected by APIs.
  • this specification describes xAPI and IMS Caliper Sensor APIs known as representative data collection systems in the education field, analyzes the learning data collection system, and describes the data conversion system design.
  • Triple is a conceptual representation of RDF (Resource Description Framework), which is composed of ⁇ Subject, Predicate, and Object.
  • RDF Resource Description Framework
  • FIG. 1 conceptually illustrates a data binding format for effectively expressing training data, and illustrates a structure for descriptive data representation.
  • both the xAPI and the IMS Caliper data adopt a triple structure, and additional information is expressed in a manner of adding as context information.
  • the contextual information includes used apps, time information, courseware information, learning results, and user-generated data.
  • the Experience API is called xAPI for short, and the organization that developed this data collection system standard is Advanced Distributed Learning (ADL) under the US Department of Defense.
  • ADL Advanced Distributed Learning
  • ADL is also the home of the Sharable Content Object Reference Model (SCORM), one of the e-learning content standards.
  • SCORM Sharable Content Object Reference Model
  • TinCan API was the project name of the research stage, and after the research period was over, the name was changed from ADL to Experience API and released to the public.
  • xAPI defines a data structure that can describe the user's activities in order to systematically understand the activity stream of activities performed in various domains as well as education.
  • xAPI is mainly used to collect log data generated when using SCORM-based contents.
  • the data collected through xAPI is collected into a designated learning record store (LRS) and sent to a learning management system or through an analysis step and delivered to a reporting tool.
  • LRS learning record store
  • 2A illustrates a concept for describing an xAPI (Experience API), which is an international standard for collecting the aforementioned learning data.
  • xAPI Experience API
  • IMS Caliper is a standard that defines a metric profile for measuring learning activities.
  • the API that performs the function of collecting data is called the IMS Caliper Sensor API.
  • the standard was developed by the IMS Global Learning Consortium, a leading standardization body in education.
  • the hallmark of the IMS Caliper standard is to define the metrics for each type of learning activity to increase the accuracy and efficiency of the data.
  • 2B illustrates a concept describing IMS Caliper, an international standard for collecting training data.
  • the types of learning activities vary, such as evaluation, media use, reading, assignments, session information, and the like.
  • the IMS Caliper standard also limits the scope of the standard to collecting data and sending it to an event store.
  • FIG. 3 represents a procedure for interconverting heterogeneous data generated according to the standards described in FIGS. 2A and 2B.
  • FIG. 3 shows the process of collecting data into a data store from the time when the learning data is generated.
  • the transformation system 100 through the training data mapping and matching includes a learning environment 10, a data profile 20, a data collection API 30, a data store 40, and a data store profile. 50, and data mapping and matching process 60, and the like.
  • the data mapping and matching system 100 having more components or fewer components may be implemented.
  • the learning environment 10 may be composed of various environments 11 and 12, and a user may generate learning data while participating in a learning activity by utilizing contents, services, web links, and software provided by the learning environment 10. do.
  • This training data is generated according to the standardized data profile 20.
  • Examples of the data profile 20 include IMS Caliper Metric Profile 21, xAPIs Recipes 22, and the like.
  • the data profile 20 may be used in various ways by educational institution, region, and country.
  • Data generated according to the data profile 20 is acquired, stored and transmitted by the data collection API 30, which also collects and stores heterogeneous forms 31 and 32 according to the diversity of the data profile 20.
  • Data sent by the data collection API 30 is preserved in the data collection store 40, where data exchange and matching instances 60 need to be exchanged between the stores 41 and 42 that preserve the heterogeneous data.
  • a conversion request can be made to receive the converted data.
  • the data mapping and matching instance 60 looks up the metadata 51 and 52 to obtain a profile for the data profile and location applied to the requested repositories and transforms the data according to the conversion rules for the data profile to be converted. Afterwards, the data interoperability providing function is returned to the data storage 60.
  • a representative example of the system is a learning analysis service.
  • service providers typically provide a learning environment (10) that operates on a variety of devices, and IMS Caliper (21) or xAPI (data collection schemes provided by standardization organizations such as the IMS Global Learning Consortium and Advanced Distributed Learning). Data is generated and stored using data collection APIs 31 and 32 that partially or fully comply with standards such as 22).
  • the data collected may include session information, quiz and test results, reading activity history, assignment performance history, media utilization, etc., and these data are stored in the learning data stores 41 and 42 and analyzed and then displayed in a dashboard form.
  • the visualization information is processed and delivered to the user.
  • the data profile 20 uses different data profiles and vocabularies, and in particular, the xAPI standard encourages users to create and use their own profiles under the name xAPIs Recipes 22. Therefore, heterogeneous data is generated by the data collection API.
  • both xAPI and IMS Caliper collect and transmit data in the same flow, but since the transmitted data contain heterogeneous models and contents, a process for converting between storages should be located.
  • the learning environment 11 is classified into different environments according to the data collection API.
  • Data collected according to IMS Caliper's metrics is collected in the event repository, and data collected by the xAPI's collection model is collected in the learning record repository 42.
  • FIG. 4 illustrates a flow of operation of the conversion system through heterogeneous learning data mapping and matching according to the procedure of FIG. 3.
  • the structural and syntactic information of the data profile to be converted is registered in the data mapping and matching instance 60 (S110).
  • an identification scheme process that assigns an identification number, such as a URI, to classes and attributes of a data profile.
  • triples of xAPI and IMS Caliper are mapped first, and then contents corresponding to contexts are mapped.
  • this process can be performed by mapping the attributes of each data model by time, app, user-generated data, and learning environment.
  • FIG. 5 is an example showing such a mapping result. That is, FIG. 5 illustrates an example in which heterogeneous data is structurally and syntactically mapped according to the procedure of FIG. 3.
  • the classes and attributes used for each data profile have an identification system that can be mapped to an N: M relationship by being given a unique identification value as a URI.
  • the data conversion step can be designed as a structural / syntax mapping and semantic matching step as shown in FIG.
  • the transformation step first performs a structural / syntactic transformation as described in FIG.
  • This step is performed by the rules of transformation between the data model and the model.
  • FIG. 6 shows an example in which heterogeneous data are structurally and syntactically mapped according to the data represented procedure illustrated in FIG. 5.
  • FIG. 6 conceptually shows a data mapping and matching sequence composed of two steps when there are two data profiles for collecting data in an educational field according to an embodiment of the present invention.
  • This relationship mapping maps the identification value 111 assigned to the class and the identification values 112 for the attributes of each class into a hierarchical structure.
  • the transformation process may be applied through a process of performing a semantic filter and a mapper function.
  • the functions performed by the filter and the mapper cannot be performed only by predefined rules, so further functions should be considered.
  • structural / syntactic mapping can be done with predefined rules, but the semantic transformation step should reflect the learning rules that update the rules.
  • the start of the sequence is carried out in defining structural / syntactic transformation rules and ontology rules for semantic transformations in advance.
  • the process of transmitting the data collected through the data collection API to the storage should include an authentication step to protect personal information.
  • IMS Caliper performs a conformance test before storing data in the repository, but xAPI does not do conformance testing, so this step is optional.
  • the metadata of the repository is checked because it is necessary to determine the data model applied to the repository.
  • the transformed result is transmitted to the requested storage.
  • step S130 semantic matching is performed (S130).
  • mapping rules for vocabularies used in each data profile may be used.
  • the data collection API 30 is notified of occurrence of a learning activity (S210).
  • the learning environment 10 When the user participates in learning, the learning environment 10 is used. When a learning activity is performed in the learning environment 10, the data collection API 30 is notified that data is generated in a synchronous or asynchronous manner.
  • the data collection API 30 recognizing that the data is generated is bound to the generated data according to the data profile and stored in the temporary storage module (end-point) of the API (S220).
  • the generated data is temporarily stored by binding the data according to the data profile.
  • the data collection API 30 requests authentication to transmit the temporarily stored data to the data storage 40 (S310).
  • the data storage 40 may perform a conformity check to test whether the received data is created according to the data profile (S330). However, step S330 may be selectively performed.
  • the data store 40 makes a data conversion request to the data mapping and matching instance 60 (S510).
  • the data mapping and matching instance 60 queries the profile metadata of the data store in order to grasp the data profile applied to each data store, transmission and reception location information, and the like before receiving the data (S520).
  • the data store 40 transmits the data requesting the transformation to the data mapping and matching instance 60 (S530).
  • the data received by the data mapping and matching instance 60 is converted into classes and attributes to be converted (S610).
  • the data mapping and matching instance 60 converts the received classes and attributes of the received data into classes and attributes to be converted using the structural and syntactic mapping instance tables.
  • the data mapping and matching instance 60 matches the meaning of the received data (S620).
  • the data mapping and matching instance 60 extracts a vocabulary and a sentence used in the received data and the transformed data, and matches the same meaning or meaning with the ontology rules of the instance table.
  • the data mapping and matching instance 60 may exception the received data when the mapping in step S510 or the matching in step S520 is not performed.
  • the data mapping and matching instance 60 transmits the converted data to the data store 40 to be transmitted (S540).
  • the data storage 40 checks the suitability of the received data, and stores the nonconforming data by exception processing (S550).
  • the data store 40 selectively performs suitability check on the received converted data, and stores an exception if it is not correctly mapped or semantically matched.
  • new structures, attributes, and vocabularies may be received when data is transmitted from the data store 40 to the data mapping and matching instance 60 to perform the structural, syntactic mapping (S610) and semantic matching (S620) processes.
  • the data mapping and matching instance 60 may update the semantic filter and mapper 113 illustrated in FIG. 5B using an ontology rule.
  • Ontology rules can be updated in an automated manner using the structure, syntax, and vocabulary arrangement of initially constructed data and newly received data.
  • the present invention may extract the training data generated while utilizing the virtual reality and the augmented reality content, convert it into a standardized data collection API, and transmit and store the data.
  • FIG. 7 illustrates a procedure of extracting training data generated while utilizing virtual reality and augmented reality content, converting the training data into a standardized data collection API, and transmitting and storing the same.
  • FIG. 8 is a flowchart illustrating a procedure of extracting training data generated while executing virtual reality and augmented reality content, converting the training data into a standardized training data format, and transmitting the same.
  • a step S711 of a user executing an application of a device to utilize virtual reality and augmented reality content is performed.
  • the corresponding content is downloaded from the content repository, and the user plays the corresponding content on the device or the terminal (S713).
  • the learning data converter or the utility that receives this converts the running data (S716), and selects a data conversion target standard in step S716.
  • the conversion is performed to the xAPI data format (S810). If the IMS Caliper is selected, the conversion is performed to the IMS CaIiper data format.
  • the data converted to the xAPI data format or the IMS CaIiper data format is transmitted to the running data store, and the data store stores the data (S717).
  • CSF critical success factor
  • the data transformation process is largely divided into structural / syntactic transformation stages and semantic transformation stages that match the meaning of actual data.
  • heterogeneous data-to-data conversion rules and methods is a technology that is of great interest not only in Korea but also in international standardization organizations that have developed xAPI and IMS Caliper.
  • the invention proposed in the present specification may provide a technical basis for securing interoperability for exchanging heterogeneous learning data.
  • Embodiments of the present invention described above may be implemented through various means.
  • embodiments of the present invention may be implemented by hardware, firmware, software, or a combination thereof.
  • a method according to embodiments of the present invention may include one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), and Programmable Logic Devices (PLDs). It may be implemented by field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, and the like.
  • ASICs Application Specific Integrated Circuits
  • DSPs Digital Signal Processors
  • DSPDs Digital Signal Processing Devices
  • PLDs Programmable Logic Devices
  • FPGAs field programmable gate arrays
  • processors controllers, microcontrollers, microprocessors, and the like.
  • the method according to the embodiments of the present invention may be implemented in the form of a module, a procedure, or a function that performs the functions or operations described above.
  • the software code may be stored in a memory unit and driven by a processor.
  • the memory unit may be located inside or outside the processor, and may exchange data with the processor by various known means.

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Abstract

L'invention concerne un procédé d'extraction de données d'apprentissage à partir d'un contenu de réalité virtuelle et d'un contenu de réalité augmentée qui peut comprendre: une première étape d'exécution d'une application sur un dispositif prenant en charge au moins l'une d'une réalité virtuelle et d'une réalité augmentée afin d'afficher le contenu de réalité virtuelle et le contenu de réalité augmentée dans le dispositif; une deuxième étape consistant à demander, par le dispositif, une vérification du contenu de réalité virtuelle et du contenu de réalité augmentée à partir d'un stockage de contenu; une troisième étape consistant à recevoir, par le dispositif, le contenu de réalité virtuelle et le contenu de réalité augmentée qui ont été vérifiés dans le stockage de contenu; une quatrième étape consistant à afficher le contenu de réalité virtuelle reçu et le contenu de réalité augmentée sur le dispositif; une cinquième étape d'extraction et de liaison, par le dispositif, de données relatives à une activité d'apprentissage effectuée dans un environnement d'apprentissage dans lequel le contenu de réalité virtuelle et le contenu de réalité augmentée sont affichés; une sixième étape de transmission des données liées relatives à l'activité d'apprentissage à un convertisseur de données d'apprentissage par le dispositif; une septième étape de sélection, par le convertisseur de données d'apprentissage, une norme de conversion à appliquer aux données liées relatives à l'activité d'apprentissage; une huitième étape consistant à convertir les données liées relatives à l'activité d'apprentissage selon la norme de conversion sélectionnée par le convertisseur de données d'apprentissage; et une neuvième étape consistant à transmettre les données converties concernant l'activité d'apprentissage à une mémoire par le convertisseur de données d'apprentissage.
PCT/KR2017/003531 2017-03-30 2017-03-31 Système et procédé d'extraction de données d'apprentissage à partir d'un contenu de réalité virtuelle et d'un contenu de réalité augmentée Ceased WO2018182072A1 (fr)

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KR102660530B1 (ko) * 2020-12-22 2024-04-24 인피니텀주식회사 센싱 데이터 기반의 학습 데이터 분석 시스템 및 그 방법
KR102557077B1 (ko) * 2020-12-23 2023-07-19 주식회사 버블콘 분산학습(Distributed Learning) 환경하에서 xAPI 템플릿을 활용한 교수 학습법 중심의 교육과정/콘텐츠 품질 분석 시스템

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