Earticle

A lot of learning objects have been distributed to schools, but they are not fully utilized because many teachers do not know where the data are and how to use them. Furthermore, the internet search engines produce large amount of data most of which are of no use. Thus, it is needed to develop an efficient method of finding data necessary for instruction and learning. In this study, a learning objects management system is constructed that utilizes these unused servers by connecting distributed databases using JDBC, which is a technology for distributed database. In addition, we developed an learning objects sharing function based on a metadata standard that we revised using the data collected from Sharable Content Object Reference Model(SCORM), a metadata standard from Korea Education & Research Information Service(KERIS).

본 연구는 초등수학 도형영역의 문제점을 해결하기 위한 방안의 하나로 초등학교 6학년 수학과 도형영역을 중심으로 현실세계에 가상 객체를 부가하여 학생 스스로 탐구하는 학습, 원리나 개념을 스스로 발견하게 하는 학습 객체를 만들었다. 본 연구에서 제안한 방법으로 서울 소재 M 초등학교 6학년 2급 학급을 선정하여 증강현실을 활용한 도형영역의 수업이 학생들의 학업성취도에 어느 정도의 영향을 미치는지 확인하기로 하였다. 그 결과 실험집단의 학습자들은 통제집단의 학습자보다 수업 흥미도 및 수업 만족도, 학업성취도에서 통계적으로 높게 나타났다. 이는 증강현실 도형 학습이 학습자에게 수업참여에 적극성을 가지게 하고, 도형 관련 개념 형성 및 학업성취도 관련이 있음을 알 수 있다.

In this study, our primary areas of mathematical shapes as a way to solve the problem of sixth grade math and geometry around the area in addition to the real world, the virtual objects to explore on their own learning, heuristic principles and learning concepts are developed. To this end, second-class sixth grade in Seoul class M is selected and the area of ​​Augmented Reality class shapes students' academic achievement sure to affect how much agreed. experimental study was developed and then applied to the actual class content across pre and post implementation evaluation, and subsequent academic achievement levels were compared and analyzed. As a result, learners in the experimental group and control group than the class of interested students and class satisfaction, a statistically higher achievement. Learning on augmented reality, which shapes have the gumption to participate in classes, and concepts related to shape the formation and indicates that academic achievement is related.

인류의 새로운 생활 형식으로 각광받고 있는 가상 공간에서의 활동을 위해, 전 세계 기업과 연구기관에 서는 가상 공간 내부에 존재하는 사물을 생성 및 동식물 환경을 조성하고 설정하는 것에 초점을 두고 있다. 가상 공간의 데이터 객체(Objects)들은 인간의 오감을 극대화할 수 있도록 보다 사실적인 모델링과 피드백 및 이를 통 한 후속적인 무브먼트가 향상될 필요성이 있다. 현실의 물리적 객체를 가상의 디지털 데이터로 전사하는 연구의 전처리 단계로 2차원 특정 객체 인식을 선행으로 요구한다. 본 연구는 딥러닝 기반 실시간 이미지 검출 시스템을 이용하여 객체 인식이 향상될 필요가 있는 회화 분야에서의 선행 작업 퍼포먼스를 향상시키는 것을 의도하였다. 현대 미술품들의 파라미터들과 차이가 있어 상대적으로 시행 데이터가 부족한 한국 전통 미술품들을 타깃으로 하여 미술품 내 객체 인식률 향상을 확인하였다. 입체적인 모델링을 형성하는 데에 있어 정확성을 높이는 연구가 추후 진행되어야 할 것이다.

For activities in virtual space, which is in the spotlight as a new lifestyle of mankind, companies and research institutes around the world focus on creating objects inside the virtual space and creating and setting up animal and plant environments. Data objects in virtual space need to improve more realistic modeling and feedback and subsequent movements to maximize human five senses. Require two-dimensional specific object recognition as a preprocessing step for research to transfer real physical objects to virtual digital data. This study intended to improve the performance of prior work in the field of conversation, which needs to improve object recognition, by using a deep learning-based real-time image detection system. It was confirmed that the object recognition rate in the artwork was improved by targeting traditional Korean art that lacked relatively implementation data due to differences from the parameters of modern art. Research to increase accuracy in forming three-dimensional modeling should be conducted later.

본 연구는 3차원 오브젝트 기반의 가상현실 시뮬레이션을 활용한 치과방사선학 교육의 효과를 정량적으 로 분석하여, 새로운 교육 매체의 활용 가능성을 확인하고자 하였다. 실험참가자는 총 79명이었으며, 대조군(n=39) 과 실험군(n=40)으로 무작위 추출을 하였다. 각 집단은 360° 영상과 3차원 오브젝트를 이용한 가상현실 학습을 6 주간 진행하고, 학습성과를 평가하였다. 3차원 오브젝트 기반 가상현실 학습을 진행한 실험군(12.10 ± 2.74)은 대 조군(7.41 ± 3.05)에 비해 높은 기초이론 점수를 나타냈다. 3차원 오브젝트 기반 가상현실 학습을 한 등각촬영법 학습은 기초이론 이해와 기억력 향상에 긍정적인 영향을 미쳤다. 특히, 시뮬레이션의 상호작용과 피드백이 학습자 의 임상 기술 향상에 기여하였다. 그러나 이러한 교육적 접근 방식의 더 넓은 의미를 종합적으로 평가하기 위해서 는 실제 촬영 결과의 상관관계에 대한 추가 연구가 필요하다.

This study attempted to quantitatively analyze the effectiveness of dental radiology education using three dimensional object-based virtual reality simulation and confirm the usability of a new educational medium. There were a total of 79 experimental participants, and they were randomly selected into a control group (n=39) and an experimental group (n=40). Each group studied for 6 weeks using 360-degree virtual reality videos and virtual reality simulations, and their learning outcomes were evaluated. The experimental group (12.10 ± 2.74), which underwent three dimensional object-based virtual reality learning, showed higher basic theory scores than the control group (7.41 ± 3.05). Learning isometric imaging using a three dimensional object-based virtual reality simulation had a positive effect on understanding basic theory and improving memory, and in particular, the interaction and feedback of the simulation contributed to improving learners' clinical skills. However, additional research is needed in the future, such as the correlation with actual imaging results.

Ubiquitous learning and Calm Computing are hot research topics affecting the field of education for an information-rich world. Calm computing aims to reduce the "excitement" of information overload by letting the learner select what information is at the center of their attention and what information need to be at the peripheral. The objective of calm computing as a new delivery of education is to move e-learning and ubiquitous learning a step further from learning at anytime anywhere to be at the right time and right place with right learning resources and right learning functionalities and collaborative peers. In this paper we report on the adaptation of calm computing technologies in a ubiquitous educational setting with emphasis on the need to cater the preferences of the individual learner to respond to the challenge of providing truly- learner-centered, accessible, personalized and powered with the capability of collaborative learning within a social networking environment. Central to this vision is the development of Installable e-Learning Objects (Ie_LO) where learners are able to create, personalize or deploy them over the internet but they do not require the presence of the web browser.

A society will lose a lot of something in this field when the forest fire broke out. If a forest fire can be detected in advance, damage caused by the spread of forest fires can be prevented early. So, we studied how to detect forest fires using CCTV currently installed. In this paper, we present a deep learning-based model through efficient image data construction for monitoring forest fire smoke, which is unstructured data, based on the deep learning model YOLOv5. Through this study, we conducted a study to accurately detect forest fire smoke, one of the amorphous objects of various forms, in YOLOv5. In this paper, we introduce a method of self-learning by producing insufficient data on its own to increase accuracy for unstructured object recognition. The method presented in this paper constructs a dataset with a fixed labelling position for images containing objects that can be extracted from the original image, through the original image and a model that learned from it. In addition, by training the deep learning model, the performance(mAP) was improved, and the errors occurred by detecting objects other than the learning object were reduced, compared to the model in which only the original image was learned.

This paper deals with machine learning through interaction with a person. We developed an interactive learning system (LTIP) by concentrating on a specific situation where a user and a robot cooperatively find a lost object in real world. The LTIP consists of a gathering candidate images process, a self-categorizing process based on SOM, and a cooperative and repeatable narrow down process so that the robotic system effectively learns the appearance of the target object that the user is looking for. We conducted several experiments to evaluate and analyze the process of interactive learning. We confirmed that preliminarily determining the effective features set for categorization is difficult. So our proposed LTIP scheme is useful for such a practical processes such as finding a lost object.

The purpose of this study came to start to be helpful in that the teachers in field produced evaluation subject about the mathematics and applied by analyzing the performance capacity for the students about the subject developed through the application of subject about the teaching-learning model that can utilize the learners' learning experience and the development of items that can evaluate it by utilizing various and concrete operational materials as an object of the first year at the middle schools. The concrete purpose of realizing it is as follows. 1. This study is to develop the teaching-learning model centered on activities adequate to utilize various and concrete operational materials. 2. This study is to develop evaluation data for utilizing the concrete operational materials by making construction through the application of developed teaching-learning model as possible. 3. This study is to present standards initiative for reliable and fair marking about the evaluation data and to analyze the students' practice capacity by using it effectively. For accomplishing the purpose of this research, this study is the work-oriented teaching and learning model using learning data that can be easily found around the surroundings and 8 evaluation programs in order that the experience-oriented learning based on circumstances learning among the learning models of constructivism. Also, it is to examine the result after applying that on a basis of 25 students, consisted of only on class in a year, in the first year at the small size of agricultural middle schools. The result of this study from this research is as follows. First, as a result of frequency survey, of response about the developed evaluation data it showed positive response more than 80% of all the items. The atmosphere of self-directed learning was produced because the developed evaluation data could induce activity-based learning naturally by stimulating the students' curiosity and promoting interest. Second, this study executed t-test for the result of questionnaire about the mathematics propensity, and there were significant differences in 19 items among 25 items. It presented that the application of data about the teaching-learning and evaluation directly using concrete operational materials to the constructional learning by level might have desirable affect on the students' mathematical propensity. It came to be a motive that could increase value recognition and interest on the subject of mathematics by investigating a mathematical principle rule and confirming it through activity learning using the concrete operational materials.

학습자와 학습객체간의 학습추론을 하기 위해서는 우선 학습자와 학습객체간의 연관성 정도를 파악해야 한다. 본 연구에서는 학습자가 전체 학습 중에 수강한 학습과 수강하지 않은 학습, 학습자가 수강한 학습을 전체 다른 학습자가 수강한 것과 수강하지 않는 정도, 학습자가 수강하지 않은 학습에 대한 전체 다른 학습자가 수강한 것과 수강하지 않는 정도를 가지고 2X2 메트릭스를 만들어 학습자와 학습객체간의 연관성 정도를 파악하여 이를 바탕으로 학습추론 시스템을 개발한다.

학습자와 학습객체간의 학습추론을 하기 위해서는 우선 학습자와 학습객체간의 연관성 정도를 파악해야 한다. 본 연구에서는 학습자가 전체 학습 중에 수강한 학습과 수강하지 않은 학습, 학습자가 수강한 학습을 전체 다른 학습자가 수강한 것과 수강하지 않는 정도, 학습자가 수강하지 않은 학습에 대한 전체 다른 학습자가 수강한 것과 수강하지 않는 정도를 가지고 2X2 메트릭스를 만들어 학습자와 학습객체간의 연관성 정도를 파악하여 이를 바탕으로 학습추론 시스템을 개발한다.

In this paper, we introduce the sharable and reusable learning objects which are suitable for XML Web services in e-learning systems. These objects are extracted from the principles of pedagogical paradigms for reusable learning units. We call them LIO (Learning Item Object) objects. Existing models, such as Web-hosted and ASP-oriented service model, are difficult to cooperate and integrate among the different kinds of e-learning systems. So we developed the LIO objects that are suitable for XML Web services. The reusable units that are extracted from pedagogical paradigms are tutorial item, resource, case example, simulation, problems, test, discovery and discussion. And these units correspond to the LIO objects in our learning object model. As a result, the proposed model is that learner and instruction designer should increase the power of understanding about learning contents that are based on pedagogical paradigms. By using XML Web services, this guarantees the integration and interoperation of the different kinds of e-learning systems in distributed environments and so educational organizations can expect the cost reduction in constructing e-learning systems.

학습시스템의 컨텐츠 제공방식은 제작된 컨텐츠를 모든 수강생에게 일괄적으로 제공하는 방식을 사용한다. 본 논문에서는 학습자의 정보를 이용하여 학습자를 분류한 후 학습자의 수준에 따라 컨텐츠를 제공하기 위해 학습자 정보를 저장하고, 또한 컨텐츠의 개별 내용을 모듈화하여 학습자에 따라 컨텐츠를 새롭게 구성할 수 있는 시스템을 구현하였다.

본 연구는 SCORM 기반 학습 객체의 메타데이타 생성 즉 Asset, SCO, Contents Aggregation과 Contents Package에 대한 메타데이터를 생성하는 시스템(MetaGene)을 개발한다. SCORM 을 지원하는 LMS내 API 어댑터와 인터페이스를 위한 학습 객체 내에 API 활성화 함수를 내장시키고, 데이터 모델을 기반으로 학습 과정을 트래킹 하는 코드도 포함 시킨다. 또한 학습 객체들이 LMS에 전송되게 PIF(Package Interchange File)로 패키징 시킨다. MetaGene에 생성된 학습객체의 메타데이터와 컨텐츠 패키지의 manifest file을 $SCORM^{(TM)}$ Conformance Testsuite을 이용하여 유효성을 검증한다.

This study develops the System(MetaGene) to create meta-data for learning object based on SCORM including meta-data of Assets. SCO, Contents Aggregation and metadata of Contents Package. API function cocle is embeded in Learning Object for interfacing API adopter in LMS to support SCORM and for tracking on learning process based on data models. Also, the learning objects are packaged the PIF(Packaged Interchange File) to transmit with LMS. MetaGene is verified by $SCORM^{(TM)}$ Conformance TestSuite for meta-data of learning objects, manifest file of Contents Packaging.

웹은 양적으로 폭발적인 성장을 이루게 되었지만 맥락적 의미의 결여로 사용자에게 검색에 대한 지적 부담을 높이고 있다. 정보의 양은 많으나 사용자에게 적합한 정보를 얻기 위하여 많은 노력을 기울여 검색 용어를 찾고 검색된 각 웹 문서들을 다시 살펴보아야 한다. 본 연구에서는 시맨틱 웹(Semantic Web)에서 중요하게 다루어지고 있는 온툴로지를 이용하여 특정 도메인 컴퓨터 하드웨어 - 에 관한 맥락적 지식을 표현하고, 이를 이용한 e-Learning 학습 자료 검색 시스템을 설계 및 구현함으로써 검색의 주체인 학습자에게 적합한 교수-학습 자료와 그에 연관된 멀티미디어 자료들을 제공하도록 하였다. 또한 유사어나 철자 오류를 보정하여 검색하는 기능, 온톨로지 상의 클래스, 인스턴스, 프로퍼티를 이용하여 검색하는 등 다양한 부가 기능들을 추가함으로써 학습자 중심의 효율적인 검색시스템을 구현하였다.

The Web is evolving quantitatively into an explosive development. However, users usually have heavy burden of searching information because of the absence of contextual meaning on the Web. Due to an enormous amount of information, users have to endure for finding strong cohesive keywords by themselves and read each of the documents with enduring effort. This paper proposes an efficient method of searching more relative documents than current KEM-based searching systems on the Web by using contextual meaning. We designed a domain ontology on computer hardware, and a searching system which was searching those e-Learning objects. Owing to the Ontology-applied search system, information such as educational materials and related multimedia can be easily provided to the users. Further, learners could be informed of relationship of knowledge, e.g., class hierarchy, properties and values, and so on. The request results are semantically related to users' needs, and thus the system provides a learner-centered searching.

E-learning의 학습 컨텐츠는 점차 멀티미디어화 되고, 시스템 환경은 유비쿼터스 네트워크를 기반으로 고도화될(T-learning 및 U-learning) 것으로 예측된다. 학습자가 요구하는 멀티미디어 학습객체를 유비쿼터스 네트워크 환경에서 쉽게 찾아 학습자에게 효율적으로 전달하기 위해서는 메타데이터 관리 시스템의 도움이 필요하다. 이를 위해서는 멀티미디어 학습객체의 특성을 정확하게 기술하는 메타데이터 모델이 필요하고 효율적인 메타데이터 관리 시스템의 개발이 필요하다. 본 논문에서는 멀티미디어 학습객체를 위한 새로운 메타데이터 모델을 제안하고, 효율적인 메타데이터 관리를 위한 새로운 시스템을 설계하였다.

최근 컴퓨터의 대중화와 인터넷환경의 구축으로 인하여 웹기반 코스웨어들의 필요가 급속히 늘고 있다. 그러나 수학 교과의 입체도형 편에 있어서는 웹기반 3D 코스웨어들의 개발은 부진한 편이다. 기존의 코스웨어들은 저작도구를 활용한 2D 위주였고, 최근 연구되어진 3D 코스웨어들은 상호작용이 부족하여 다양한 학습자의 욕구를 충족시키지 못하고 있다. 따라서 본 연구에서는 3D 입체도형의 학습과 학습자의 자극에 반응하는 상호작용적 체험학습이 되도록 VRML 과 JAVA를 이용하여 구현하였다.

This study focuses on developing a building object recognition technology for efficient use in the remodeling of buildings constructed without drawings. In the era of the 4th industrial revolution, smart technologies are being developed. This research contributes to the architectural field by introducing a deep learning-based method for automatic object classification and recognition, utilizing point cloud data. We use a TD3D network with voxels, optimizing its performance through adjustments in voxel size and number of blocks. This technology enables the classification of building objects such as walls, floors, and roofs from 3D scanning data, labeling them in polygonal forms to minimize boundary ambiguities. However, challenges in object boundary classifications were observed. The model facilitates the automatic classification of non-building objects, thereby reducing manual effort in data matching processes. It also distinguishes between elements to be demolished or retained during remodeling. The study minimized data set loss space by labeling using the extremities of the x, y, and z coordinates. The research aims to enhance the efficiency of building object classification and improve the quality of architectural plans by reducing manpower and time during remodeling. The study aligns with its goal of developing an efficient classification technology. Future work can extend to creating classified objects using parametric tools with polygon-labeled datasets, offering meaningful numerical analysis for remodeling processes. Continued research in this direction is anticipated to significantly advance the efficiency of building remodeling techniques.

The first purpose of this study is to distinguish difficult chapters in 'Speed of objects' chapter and find the factors which give difficulty to the teachers and students. Also, it attempts to compare the students' assessment scores with the degree of difficulty in teaching and also with the degree of difficulty in learning. This report is expected to help science teachers develop their PCK(Pedagogical Content Knowledge) for teaching the chapter professionally. 15 teachers who had taught the 'Speed of Objects' chapter and their 386 students took part in the survey to acquire information about the difficulties in teaching and learning. 386 students also received a test to examine their understandings of the chapter. The results of this study are as follow; First, the degree of teachers' and students' difficulty is only affected by the contents, and the degree of onerousness felt by teachers is higher than that of students. Second, The topics caused higher difficulty to teachers were 'Understanding the meaning of motion(2nd lesson)', 'Understanding the meaning and unit of speed(5th lesson)', 'Changing unit of speed(6th lesson)', 'Drawing a distance-time graph(7th lesson)', and 'Understanding the relative motion(10th). The topics that led higher difficulty to students were the contents of 5th, 6th, and 7th lessons. Third, the 'Speed of Objects' chapter can be divided into 4 types of difficulty according to the degree of teaching and learning; 'Strong difficulty', 'Learning difficulty', 'Weak difficulty', and 'Teaching difficulty'. Last, students showed low achievement to the tasks that were related with 'Strong difficulty' and 'Teaching difficulty'.

배경 및 목적: 본 연구는 신경언어장애군의 동사 중재 자극으로 쓰이는 동사의 주격 및 목적격 명사의 한국어 특징을 빅데이터 기반으로 분석하였다. 또한 분석한 원자료는 향후 동사 중재 관련 연구에 활용할 수 있도록 클라우드에 배포하여 공유하였다. 방법: 교과서 말뭉치에서 목표 동사와 결합한 주격 및 목적격 명사 간 출현빈도수 및 유형수 차이를 분석하여 주어생략현상을 확인하였다. 또한 목표동사로부터 주격 및 목적격 명사까지의 의미거리를 기계학습으로 산출하여 의미거리 분포 차이를 분석하였다. 더불어 기계학습으로산출한 의미거리와 한국어 화자들이 인식하고 있는 의미거리 간 상관관계를 살펴보았다. 결과: 주격 명사는 목적격 명사보다 유의하게출현빈도수 및 유형수가 낮았으며, 동사와의 기계학습 기반 의미거리가 더 멀었다. 기계학습으로 산출한 의미거리는 행동데이터와 강한 정적 상관관계를 보였다. 논의 및 결론: 본 연구에서 밝힌 한국어의 주어생략현상은 한국어를 사용하는 신경언어장애군의 특징을설명하는 근거자료로 활용될 수 있다. 주격 명사와 동사 간 기계학습 기반 의미거리가 멀다는 결과는 앞으로 한국어를 사용하는 신경언어장애군을 위한 동사 중재에서 주격 명사의 활용 방안이 재고될 필요가 있음을 시사하였다. 더불어 기계학습으로 산출한 의미거리를중재자극선정에서하나의기준으로사용할수있는가능성을확인하였으나, 차후정밀한추가검증이필요하다.

Objectives: Subjects and objects associated with treatment verbs are common stimuli in verb treatment using the argument structure for neurogenic patients. This study investigated Korean nouns from subjects and objects associated with the target verbs in the corpus to suggest the characteristics of subjects and objects for developing Korean-specific verb treatment stimuli. In addition, we shared raw data through cloud so that anyone can use the data for clinical or academic purposes. Methods: We used Korean textbook corpus to investigate the differences between subjects and objects in terms of frequency, number of type, and machine-learning based semantic distance to the target verbs. We also examined how machine-learning based semantic distance is correlated with behavioral rating semantic distance. Results: Subjects significantly showed less frequency, less number of types, and farther machine-learning based distance to the target verbs than objects did. Machine-learning based semantic distance was strongly correlated with behavioral rating. Conclusion: The results demonstrated strong evidence of the subject ellipsis phenomenon in Korean. The weak semantic relation, as proven by the machine-learning based semantic distance, indicated that subjects as verb stimuli for Korean-speaking neurogenic patients need to be reconsidered. We confirmed the possibility of machine-learning based semantic distance as a criterion in selecting the treatment stimuli, but more detailed verification is required for future studies.