WO2018182072A1

WO2018182072A1 - System and method for extracting learning data from virtual reality content and augmented reality content

Info

Publication number: WO2018182072A1
Application number: PCT/KR2017/003531
Authority: WO
Inventors: 조용상
Original assignee: 한국교육학술정보원
Priority date: 2017-03-30
Filing date: 2017-03-31
Publication date: 2018-10-04
Also published as: KR20180112153A

Abstract

A method for extracting learning data from a virtual reality content and an augmented reality content according to an aspect of the present invention may comprise: a first step of executing an application on a device supporting at least one of a virtual reality and an augmented reality in order to display the virtual reality content and the augmented reality content in the device; a second step of requesting, by the device, checking of the virtual reality content and the augmented reality content from a content storage; a third step of receiving, by the device, the virtual reality content and the augmented reality content which have been checked in the content storage; a fourth step of displaying the received virtual reality content and augmented reality content on the device; a fifth step of extracting and binding, by the device, data relating to a learning activity performed in a learning environment in which the virtual reality content and the augmented reality content are displayed; a sixth step of transmitting the bound data relating to the learning activity to a learning data converter by the device; a seventh step of selecting, by the learning data converter, a conversion standard to be applied to the bound data relating to the learning activity; an eighth step of converting the bound data relating to the learning activity according to the conversion standard selected by the learning data converter; and a ninth step of transmitting the converted data relating to the learning activity to a storage by the learning data converter.

Description

System and method for extracting learning data from virtual reality and augmented reality contents

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. A system and method for converting a data format for learning analysis 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.

In general, 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.

As the terminal functions are diversified, for example, 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.

In order to support and increase the function of such a terminal, it may be considered to improve the structural part and / or the software part of the terminal.

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.

Nowadays, IT devices are closely related to our daily lives. It is because anyone can use the new technology that has been seen only in movies.

One of the representative new technologies is Augmented Reality (AR).

But not many people confuse Virtual Reality (VR) with Augmented Reality.

While virtual reality uses virtual images instead of reality, both augmented reality (AR) and augmented reality (AR) use a 3D virtual image superimposed on a real image or background. to be.

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.

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.

As a computer game, for example, in a virtual reality fighting game, a character substitute for me confronts a virtual enemy in a virtual space, but an augmented reality fighting game is a form in which a real enemy confronts a virtual enemy in a real space.

Therefore, virtual reality is superior to immersion reality compared to augmented reality, augmented reality is characterized by a superior reality than virtual reality.

In addition, 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.

On the other hand, in order to effectively utilize contents using augmented reality and virtual reality technologies in the education field, data related to learning activities should be collected, and the collected data should be analyzed and connected with the learning analysis system such as learning attitude and participation. have.

In the education field, personalized diagnosis and prescription are made through learning analytics that collect and analyze various types of learning activity data such as learning time, evaluation, discussion, media manipulation, and use of contents. Learning data generated while utilizing and manipulating content should also be able to be extracted for use in learning analysis.

In this specification, a method and method of extracting learning data from augmented reality and virtual reality content and converting it into a standardized learning data format for learning analysis will be presented.

An object of the present invention is to extract and convert learning data from virtual reality and augmented reality content. Specifically, 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.

On the other hand, the technical problems to be achieved in the present invention are not limited to the technical problems mentioned above, and other technical problems that are not mentioned are clearly to those skilled in the art from the following description. It can be understood.

In the method for extracting learning data from the virtual reality and augmented reality content that is an aspect of the present invention for achieving the above technical problem, 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 third step of receiving, by the device, the virtual reality and the augmented reality content of which the inquiry is confirmed in the content repository; A fourth step of displaying the received virtual reality and augmented reality content on the device; A fifth step of the device extracting and binding learning activity data performed in a learning environment in which the virtual reality and augmented reality content is displayed; A sixth step of the device transmitting the bound learning activity data to a learning data converter; A seventh step of selecting, by the learning data converter, a transformation standard to be applied to the bound learning activity data; An eighth step of converting the bound learning activity data according to a conversion standard selected by the learning data converter; And a ninth step of transmitting, by the learning data converter, the converted learning activity data to the storage.

In addition, the conversion standard selected by the training data converter may include xAPI and IMS Caliper.

In addition, in the eighth step, the learning data converter may convert classes and attributes of the learning activity data into classes and attributes according to the selected conversion standard.

In addition, in the eighth step, the learning data converter may further convert the meaning of the learning activity data into a meaning according to the selected conversion standard.

Further, in the eighth step, 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.

In addition, a unique identification number may be assigned to each class and attribute of the structural and syntactic mapping instance table.

Further, in the eighth step, when the attribute of the learning activity data is not included in 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

Further, in the eighth step, when the class or attribute of the learning activity data does not correspond to the mapping instance table or the meaning of the learning activity data does not correspond to the semantic instance table, the learning data converter May exception-process the first learning activity data.

Further, in the ninth step, 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.

On the other hand, 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; And 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. In detail, 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.

Meanwhile, the effects obtainable in the present invention are not limited to the above-mentioned effects, and other effects not mentioned above will be clearly understood by those skilled in the art from the following description. Could be.

The following drawings, which are attached to this specification, illustrate one preferred embodiment of the present invention, and together with the detailed description thereof, serve to further understand the technical spirit of the present invention. It should not be construed as limited.

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.

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.

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.

Hereinafter, a portable terminal according to the present invention will be described in detail with reference to the drawings. The suffixes "module" and "unit" for components used in the following description are given or used in consideration of ease of specification, and do not have distinct meanings or roles from each other.

As smartphones and social networks become more common, the types and amounts of data generated by billions of users around the world are growing at an explosive rate, and waking leaders in government, the public sector, and private companies are paying attention to the use of this diverse data. .

In particular, 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.

In general, meaningful data are generated by various learning activities using information and communication technology (ICT) in the classroom and online, but these data are discarded or partially extracted and recorded and managed when the activity or process is completed.

As a result, it is not easy to provide a personalized learning environment or support learning by level, because the average level of materials and follow-up activities are required in a situation where the learner's level of understanding and process are not accurately understood.

For example, in the existing offline education, only the final evaluation data such as test results are recorded by hand, and since meaningful activity records are not accumulated in the learning process, students face limitations of learning by level and individual learning.

Moreover, because students measure their academic level only through standardized tests, they may have difficulty in providing personalized feedback such as learning habits and cognitive levels.

In order to overcome these limitations and to support and motivate individual learners, new data-based services, such as learning analytics, are being actively developed around international standards bodies and organizations.

However, the reality is that it is difficult to collect data systematically and accurately because the learning environment of users to collect data, that is, the platform and the software, is very diverse.

In order to solve this problem, some standardization organizations have established a systematic data collection system by standardizing data profiles and data collection APIs for data collection, but in some situations, since standardized data collection systems are composed of heterogeneous structures and APIs. One educational institution or service provider faces the use of heterogeneous data collection standards.

Through this specification, the inventors propose a rule, procedure, and method for securing interoperability of heterogeneous data collection systems and data collected by APIs.

Specifically, 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.

Both xAPI and IMS Caliper standards describe data in a triple structure.

Triple is a conceptual representation of RDF (Resource Description Framework), which is composed of <Subject, Predicate, and Object.

Referring to FIG. 1, 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 is also the home of the Sharable Content Object Reference Model (SCORM), one of the e-learning content standards.

xAPI is also known as 'TinCan API'. 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.

In the education field, 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.

2A illustrates a concept for describing an xAPI (Experience API), which is an international standard for collecting the aforementioned learning data.

IMS Caliper, on the other hand, 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.

Referring to FIG. 2B, 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.

Next, with respect to the method for analyzing the learning data collection system, the data flow will be described based on the data binding structure of FIG. 1 described above.

That is, FIG. 3 shows the process of collecting data into a data store from the time when the learning data is generated.

Referring to FIG. 3, the transformation system 100 through the training data mapping and matching according to the present invention 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.

However, since the components illustrated in FIG. 3 are not essential, the data mapping and matching system 100 having more components or fewer components may be implemented.

In addition, the components shown in FIG. 3 are interdependently connected, and each component may be implemented separately or integrally, unlike those shown in FIG. 3.

Hereinafter, the components will be described in order.

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. Can be operated as. That is, data generated according to IMS Caliper Metric Profile 21 may be operated by IMS Caliper Sensor APIs 31, and data generated according to xAPIs Recipes 22 may be operated by xAPIs 32.

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. In learning analytics services, 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.

In the meantime, 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.

In particular, in FIG. 3, 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.

This means that the environment that collects data with xAPI and the environment that collects data with IMS Caliper Sensor API are different from the data point of view.

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.

There is a data mapping and matching process between the two repositories, which transform the data.

Meanwhile, 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.

First, the procedure before heterogeneous data exchange is performed is described.

The structural and syntactic information of the data profile to be converted is registered in the data mapping and matching instance 60 (S110).

In other words, it corresponds to an identification scheme process that assigns an identification number, such as a URI, to classes and attributes of a data profile.

Or, identification entities and properties for the API model.

Next, structural and syntactic mapping between heterogeneous data profiles is performed (S120).

This is the process of structural and syntactic mapping for data transformation in the following steps.

Although the data collected by xAPI and IMS Caliper are both systematically and accurately collected, they are interpreted as mechanically heterogeneous data because they use different data models.

Therefore, to convert these data mechanically, it is necessary to map in advance what the attributes of each data mean.

As shown in Fig. 1, triples of xAPI and IMS Caliper are mapped first, and then contents corresponding to contexts are mapped.

For example, this process can be performed by mapping the attributes of each data model by time, app, user-generated data, and learning environment.

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.

As illustrated in FIG. 5, 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.

If only two data conversion targets exist, IMS Caliper and xAPI, the data conversion step can be designed as a structural / syntax mapping and semantic matching step as shown in FIG.

The assumption here is to integrate and interpret the data model known as xAPI's Recipe into one type.

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.

Next is the semantic transformation, which is the process of determining whether the value of the mapped property is the same, similar, or different.

For example, suppose the word used in the predicate is IMS Caliper completed, but xAPI is the same, but you cannot determine if it means the same.

To solve this problem, you need to use an ontology table. If registered and finished are used in the ontology table with the same meaning, accurate conversion can be performed semantically.

Specifically, 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. As illustrated in FIG. 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.

On the other hand, if there are a large number of data transformation targets, if the information model of xAPI needs to be interpreted as different information models, the transformation process may be applied through a process of performing a semantic filter and a mapper function. .

Here, the functions performed by the filter and the mapper cannot be performed only by predefined rules, so further functions should be considered.

For example, structural / syntactic mapping can be done with predefined rules, but the semantic transformation step should reflect the learning rules that update the rules.

Returning to FIG. 4 again, the start of the sequence is carried out in defining structural / syntactic transformation rules and ontology rules for semantic transformations in advance.

In addition, 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.

Before performing the actual conversion process, the metadata of the repository is checked because it is necessary to determine the data model applied to the repository.

After the storage is identified, as described above, after performing the structural / syntactic mapping and semantic matching, the transformed result is transmitted to the requested storage.

If an exceptional situation occurs where the conversion rule is not applicable, an exception is handled and sent.

Specifically, after step S120, semantic matching is performed (S130).

That is, in the following steps, the meanings of the vocabularies and sentences used in each data profile for data conversion are analyzed to match the degree of agreement and significance.

For semantic mapping, the instance table 140 to which mapping rules for vocabularies used in each data profile are applied may be used.

Next, a procedure for collecting and storing data will be described.

Referring to FIG. 4, first, the data collection API 30 is notified of occurrence of a learning activity (S210).

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.

Next, 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).

That is, the generated data is temporarily stored by binding the data according to the data profile.

Next, the data collection API 30 requests authentication to transmit the temporarily stored data to the data storage 40 (S310).

This is a procedure for authenticating between the data collection API 30 and the data store 40, and the data store 40 may approve the data collection API 30 when it is authenticated.

Thereafter, the data temporarily stored by the data collection API 30 is transmitted to the data storage 40 (S320).

Next, 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.

Hereinafter, a process of converting data stored in a data store will be described.

When mutual data exchange is required in the storage 40 where heterogeneous data is stored, when transferring data from one storage 40 to another storage 40, or from several data storages 40 to one storage 40 When collecting data in a batch, first, the data store 40 makes a data conversion request to the data mapping and matching instance 60 (S510).

Next, 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).

In other words, according to which data profile the data stored in the data store is generated, and where the data is stored.

Next, the data store 40 transmits the data requesting the transformation to the data mapping and matching instance 60 (S530).

Next, 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.

Thereafter, 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.

Next, the data mapping and matching instance 60 transmits the converted data to the data store 40 to be transmitted (S540).

Thereafter, 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.

Meanwhile, 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. In this case, 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.

Meanwhile, 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.

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.

Referring to FIGS. 7 and 8, first, a step S711 of a user executing an application of a device to utilize virtual reality and augmented reality content is performed.

Thereafter, a search for a content and a query are sent to the content repository, and the virtual reality or augmented reality content list is searched in the content repository (S712).

Thereafter, the corresponding content is downloaded from the content repository, and the user plays the corresponding content on the device or the terminal (S713).

Thereafter, the above-described binding process is performed by extracting the running data (S714), and then, the running data is transmitted to the learning data converter or the utility (S715).

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.

If the xAPI is selected, 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.

Thereafter, 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).

According to the configuration of the present invention described above, it is possible to effectively extract the learning data from the virtual reality and augmented reality content.

In detail, 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.

Systematic data collection is a critical success factor (CSF) for accurate learning analytics services.

If data is mixed with inaccurate or ambiguous meanings or if a large number of data are such data, real-time analysis will be an unexpected service because it requires a lot of time and effort to refine the data.

The importance and standardization of the data collection system is great. However, it is unreasonable to expect a single standard system for data collection in various computing environments these days.

An alternative is to establish an exchange mechanism within the scope of the standard that can ensure data interoperability, and this study was pursued for that purpose.

The data transformation process is largely divided into structural / syntactic transformation stages and semantic transformation stages that match the meaning of actual data.

We have designed these two steps of data mapping and matching processes.

In addition, the subject of 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. For example, embodiments of the present invention may be implemented by hardware, firmware, software, or a combination thereof.

For implementation in hardware, 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.

In the case of an implementation by firmware or software, 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.

The detailed description of the preferred embodiments of the invention disclosed as described above is provided to enable those skilled in the art to implement and practice the invention. Although the above has been described with reference to preferred embodiments of the present invention, those skilled in the art will understand that various modifications and changes can be made without departing from the scope of the present invention. For example, those skilled in the art can use each of the configurations described in the above-described embodiments in combination with each other. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The invention can be embodied in other specific forms without departing from the spirit and essential features of the invention. Accordingly, the above detailed description should not be interpreted as limiting in all aspects and should be considered as illustrative. The scope of the invention should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the invention are included in the scope of the invention. The present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. In addition, the claims may be combined to form an embodiment by combining claims that do not have an explicit citation relationship or may be incorporated as new claims by post-application correction.

Claims

In the method for extracting the training data from the virtual reality and augmented reality content,

A first step of executing an application on the device for displaying the virtual reality and the augmented reality content in a device supporting at least one of the virtual reality and the augmented reality;

Requesting, by the device, to query the virtual reality and augmented reality content to a content repository;

A third step of receiving, by the device, the virtual reality and the augmented reality content of which the inquiry is confirmed in the content repository;

A fourth step of displaying the received virtual reality and augmented reality content on the device;

A fifth step of the device extracting and binding learning activity data performed in a learning environment in which the virtual reality and augmented reality content is displayed;

A sixth step of the device transmitting the bound learning activity data to a learning data converter;

A seventh step of selecting, by the learning data converter, a transformation standard to be applied to the bound learning activity data;

An eighth step of converting the bound learning activity data according to a conversion standard selected by the learning data converter; And

And a ninth step of transmitting, by the learning data converter, the converted learning activity data to the storage.
The method of claim 1,

The conversion standard selected by the training data converter includes xAPI and IMS Caliper.
The method of claim 1,

In the eighth step,

And the learning data converter converts the classes and attributes of the learning activity data into classes and attributes according to the selected conversion standard.
The method of claim 3, wherein

In the eighth step,

And the learning data converter further converts the meaning of the learning activity data into a meaning according to the selected conversion standard.
The method of claim 4, wherein

In the eighth step,

The learning data converter,

Learning data in virtual reality and augmented reality content comprising a structural and syntactic mapping instance table of the class and attribute of the learning activity data and a semantic instance table in which the meaning of the learning activity data is represented according to an ontology rule. How to extract.
The method of claim 5,

Method for extracting learning data from the virtual reality and augmented reality content, characterized in that each of the class and attribute of the structural and syntactic mapping instance table is assigned a unique identification number.
The method of claim 6,

In the eighth step,

If the attribute of the learning activity data is not included in the structural and syntactic mapping instance table,

And the learning data converter adds the not included attribute to the structural and syntactic mapping instance table.
The method of claim 6,

In the eighth step,

When the class or attribute of the learning activity data does not correspond to the mapping instance table, or the meaning of the learning activity data does not correspond to the semantic instance table,

The learning data converter extracts the learning data from the virtual reality and augmented reality content, characterized in that the exception processing for the first learning activity data.
The method of claim 6,

In the ninth step,

The reservoir is

A suitability check is performed to determine whether the transformed learning activity data has a class, an attribute, and a meaning according to a preset criterion.

If appropriate, store the transformed learning activity data,

Method for extracting the training data from the virtual reality and augmented reality content, characterized in that the exception processing if not appropriate.
In the system for extracting learning data from virtual reality and augmented reality content,

A content repository receiving a request for inquiring of 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; And

And storing the received learning activity data from the learning data converter and storing the converted learning activity data.