Earticle

This study explores how smartphone applications can be used for Russian education. The progresses made in the ICT field, such as smartphones and the wireless Internet, have changed the environment of language education. The rapid development and implementation of smart technology in every aspect of society have led to the generation of the new learning environment called ‘smart learning’, applying those technologies in education. The definition of smart learning, however, is still evolving, while some attempts have been made slightly more in technology-oriented rather than pedagogy-oriented direction. But the importance of smart learning is that it can make it possible for learners to do self-directed learning utilizing ICT. Recently, in particular, smartphones have captured tremendous attention of foreign language teachers and learners as an ideal tool for satisfying new needs of the mobile learning era. Especially, the launch of iphone service has brought in a massive amount of educational applications to the market. Research has shown that many applications for Russian language education have been made in various categories. Despite some problems, using applications in learning the Russian language has bright prospects.

We introduce a deep reinforcement learning-based approach for smart city planning, designed to determine the optimal timing for constructing various smart city components such as apartments, base stations, and hospitals over a specified development period. Utilizing the Dueling Deep Q-Network (DQN), the proposed method aims to maximize the city’s population while maintaining a predetermined happiness level of residents in the smart city. This optimization is achieved through strategic construction of smart city components, considering that both the total population and happiness levels are influenced by the interplay between housing, communication, transportation, and healthcare infrastructures, as well as the population ratio. Specifically, we present two distinct formulations of the Markov Decision Process (MDP) for smart city planning to illustrate the practicality of applying reinforcement learning across different scenarios.

With growing use of smart phone, there has been increasing demands for utilizing it in the field of education in a variety of ways. Businesses have also rapidly begun to adopt and implement Smart-learning system as a part of HRD. The study aims to understand the effect of Smart-learning based on SNS technology, which can be adopted and utilized in the business area. For the purpose of this, an analysis has been conducted through reviewing definitions and concepts about technology-integrated learning, specifically Smart-learning. And then, factors for successful implementation of Smart-learning and strategies for its optimization are derived through the analysis. Another issue of the study focuses on a role of HRD in managing and implementing Smart-learning in the business field. For the meaningful results, a focus group interview and survey of 15 stakeholders in charge of Smart-learning system in a company has been made. As a result, five factors for successful implementation of Smart-learning in the business field are derived from a focus group interview and surveys; ‘CEO’s active involvement’, ’appropriateness for company policy’,’correct information’, ‘cyber security’ and ‘employees’ active participation’. The results of the study have significant implication in the field of HRD by suggesting several factors to consider in advance for successful implementation and management of Smart-learning.

세계 각 국은 현재 Smart 열풍 속에 Smart한 세상으로 변화하고 있다. 교육 또한 e-Learning의 지속적인 성장 발전을 위해 유비쿼터스환경에 적합한 학습형태인 모바일기반 의 Smart Learning으로 진화하고 있다. 본 연구는 e-Learning 성공요인으로 가장 주목받고 있 는 학 습자의 동기요인에 관하여 Keller의 ARCS모델에 근거한 학습자의 스마트러닝의 학업성과 와 관련 된 변인들 간의 관계를 분석한다. 학습자의 학습 동기전략 요소인 주의력(A:Attention), 관련 성 (R:Relevace), 자신감(C:Confidence), 만족감(S:Satisfaction)의 관측변수가 학업성과에 미치는 결 과를 알아보기 위해 학습자가 인식하는 콘텐츠교사에 대한 교사효능감과 학습자 스스로 자 각하는 스마트러닝에 대한 자아효능감을 조절변수로 설정하였다. 또한 학습몰입이 학업성과에 미 치는 매 개효과를 구조모형을 통해 확인해 본다.

With a craze for smart devices, the world is changing into a smarter one. And education is evolving to mobile-based smart-Learning suitable to the ubiquitous environment for sustainable e-Learning development. This study analyzed relations between variables related to smart-Learning performance based on Keller's ARCS model which is about learners' motivation factors and is regarded as the best factor of success in e-Learning. Efficacy for contents teachers recognized by learners and self-efficacy for smart-Learning recognized by learners themselves were set as a control variable in order to examine the effect of observation variables, or learners' learning motivation strategy factors, such as Attention(A), Relevance(R), Confidence(C) and Satisfaction (S), on learning performance. Besides, a mediating effect of learning absorption on learning performance is verified through a structural model.

With the growing interest in both public and private 5G services based on millimeter wave (mmWave) communication for indoor usage scenarios such as smart factories, site design specialists are seeking sophisticated methods and tools for simulating indoor radio coverage based on highly accurate path loss prediction models. Although machine learning approaches can be used in path loss modeling thanks to the highly accurate prediction capability, their performance can be limited by the size of available measurement data set used for training. In this paper, we propose new approaches to train path loss models in the few-shot learning scenarios of smart factories. The proposed approaches are based on meta-learning with slight modifications to perform fine-tuning over an entire train data set rather than a meta-test data set. It is shown that the indoor path loss models based on convolutional neural networks (CNNs) trained by meta-learning based on three different meta-train task assignment schemes outperform both a conventional CNN model and an empirical model.

본 연구는 표현활동 수업의 교육적 가능성을 확대하기 위한 방법으로 스마트러닝을 적용해보고, 이를 통해 스마트러닝이 표현활동 수업에 어떤 변화를 가져올 수 있으며 그것이 갖는 교육적 함의는 무엇인지 탐색해 보고자 하였다. 이를 위해 서울소재 Y초등학교 6학년 2개 학급을 대상으로 표현활동 영역 중 꾸미기체조를 선정하여 8차시의 스마트러닝 수업을 설계하고 적용하였다. 연구 결과, 스마트러닝을 통해 학생들은 풍부한 자료에 쉽게 접근하며 지식을 스스로 확장시켜나갔고, 학생간 또는 학생-교사간 활발한 상호작용을 하며 시공간을 초월한 소통을 하였다. 또한 스마트러닝은 동기를 유발하고 흥미를 지속시키면서 학습자로 하여금 수업에 몰입하도록 해주었고 결과적으로 자기주도적인 학습을 가능하게 하는 촉매로서 역할을 하였다. 그러나 스마트러닝의 구현에 있어 기술적 측면의 한계, 학습자 주도 수업에서 교육적 제한의 설정 미흡 등으로 동영상의 용량 초과와 그에 따른 제한적인 피드백, 대중가요에 치우친 음악선정 등이 개선되어야 할 문제로 지적되었다.

The purposes of this study was to apply smart learning as a way to expand the educational possibilities of teaching expressive activities, search for the changes and educational values. To do the research, sixth-grade students of two classes were selected from Y elementary school in Seoul, pyramid building of teaching expressive activities was selected and 8 section lessons of smart learning were designed and applied. The results are as follows: Students accessed plentiful learning resources and easily expanded the extent of their knowledge for themselves, interacted among students or student-to-teacher beyond space and time. Smart learning also played a role in motivation effect and keeping up interest, made students absorbed in their learning, as the catalyst for self-directed learning. But the technical limitations of smart learning practice, excess capacity of video and thus limited feedback as caused by educational unlimitedness in self-directed learning, music selection one-sided popular songs were pointed out as a problem to be improved.

Smart transportation systems implemented through vehicular ad hoc networks (VANET) offer significant potential to improve safety. However, the network faces critical challenges related to security, as well as inadequate spectrum sensing and management. To address these issues, researchers have utilized cognitive radio and machine learning technologies. Although, previous survey studies have provided a valuable foundation for understanding the use of cognitive radio in VANET, not all have systematically investigated its impact on mitigating spectrum sensing and management issues or the role of machine learning in supporting cognitive radio functionality. Furthermore, the effects of security issues on both VANET and cognitive radio enhanced VANET have not been consistently examined. This survey aims to systematically review the application of cognitive radio and machine learning approaches to address the identified challenges in smart transportation networks, offering valuable research opportunities for future investigations. The paper extensively explores state-of-the-art approaches and focuses on: (1) Assessing the impact of cognitive radio and machine learning on spectrum sensing and management in smart transportation networks and (2) Evaluating the impact of security issues on both VANET and cognitive radio enhanced VANET.

스마트 러닝의 확대에 따라 다양한 스마트 교육 방법이 초·중등학교에서 시범· 적용되고 있다. 이에 본 연구 에서는 학급 SNS와 스마트폰을 활용한 Smart r-Learning 교수학습 모형을 개발․적용하였다. 무선 표준인 블 루투스 장비를 스마트폰과 연계하여 학생들이 스마트폰으로 로봇 프로그래밍을 하고 로봇을 제어할 수 있도록 함과 동시에 학급 SNS를 통해 학생들과 교사가 시간의 제약 없이 로봇 교육에 대한 다양한 의견을 서로 교환할 수 있도록 하는 데 주안점을 두었다. 개발된 Smart r-Learning 프로그램을 초등학생들에게 적용한 후 학생들의 창의적 사고력 향상 정도 및 수업 만족도를 조사한 결과 Smart r-Learning 수업에 대한 학생들의 만족도는 96%로 매우 높게 나타났으며 학생들의 창의적 사고력 또한 t-검증에서 유의미하게 향상 된 것으로 확인되었다.

Various methods of Smart education were tested on students in Elementary and Middle schools, as Smart learning expands. In this paper, we analyzed a Smart r- Learning-teaching model that utilizes Bluetooth and Class SNS, for students. We focused on all aspects that students found easy to program, on controlling the Bluetooth-connected robot with a smartphone, as well as on a diversity of opinions related to using Class SNS in the robot class without time constraints. Subsequently, we attempted to apply the developed a Smart r-Learning program to the classes of students, We undertook a t-test for students to find out whether it helped them enhance their creative thinking and investigated their satisfaction with the class. As a result, the study showed that students' satisfaction with a Smart r-Learning class is exceptionally high, at 96%, and students’ creative thinking and programming skills in order to control improved markedly.

The purpose of this study was to investigate the effect of learners' self-directed learning ability on their learning satisfaction using smart learning methods in national certification education for cosmetologist (makeup). A survey was conducted on 286 learners majoring in makeup who had recently received national certification education for hairdresser (makeup) for 5 years. The current status of cosmetologist (makeup) national certification education and the utilization of smart learning were investigated, and the subfactors of cosmetologist (makeup) national certification education awareness were composed of instructor, curriculum, educational content, and educational method, and the subfactors of smart learning self-directed learning ability were composed of openness, autonomy, learning motivation, and problem-solving ability, and the relationship between the factors was analyzed. The factors of cosmetologist (makeup) national certification education awareness, smart learning selfdirected learning ability, and learning satisfaction were all lower in the 20s and higher in the 40s, and were analyzed as higher for beauty teachers and instructors and lower for students in the profession. Among the perceptions of certification education, education content had a significant positive effect on learning satisfaction, and learning motivation had the most significant positive effect on learning satisfaction during self-directed learning using smart learning. It was confirmed that educational content had a significant positive effect on learning satisfaction and learning motivation had the most significant positive effect on learning satisfaction during smart learning self-directed learning. This is interpreted as learning motivation, such as learners' abilities and qualities, enjoyment of learning, reward of acquiring knowledge and skills, and internal satisfaction, are related to learning satisfaction. In addition, it can be said that smart learning methods have only recently been introduced, so there is a tendency to look at smart learning from a 'smartphone-centered' perspective. However, since smart devices are not the essence of smart learning, it is necessary to develop smart learning-related policies, continuous support, and infrastructure, and to develop various interesting smart learning-based teaching and learning models that can be used in beauty education smart learning classes to create an environment where learners can self-direct their own learning by actively utilizing these tools.

본 연구의 목적은 SMART 혁신교수법을 통해 2019년 발생한 코로나 바이러스 감염증(이하 COVID-19) 으로 인하여 달라진 교육환경에서 교수자들과 학습자들의 스마트러닝 경험과 향후 미래 역량강화를 위한 기초자료를 제공하는 데에 있다. 연구 방법은 양적연구와 질적연구를 혼합한 Mixed method로, C대학교에 재학 중인 학생들 중 본 연구의 참여에 동의한 스마트러닝 학습자 총 503명이다. 그 중 15명의 학습자와 15인의 교수자를 대상으로 질적 연구 방법인 심층 면담을 통하여 자료를 수집하였고 Colaizzi의 현상학적 연구분석방법에 근거하여 자료를 분석하였다. 본 연구결과 학습동기는 스마트러 닝 적용 후 3.18 ± 0.22에서 3.98 ± 0.22점으로 증가하였고, 통계적으로 유의미한 차이가 있었다(t = 16.28, p < .001). 스마트러닝의 경험에 대한 질적 분석결과 4개의 범주와 10개의 주제 모음, 26개의 주제가 나타났다. 본 연구의 결과를 토대로 학습자와 교수자 간 디지털 기기 사용의 격차를 줄이고 학습자의 눈높이에 맞는 맞춤형 교수학습법이 개발, 적용되어야 할 것으로 판단된다.

The purpose of this study is to provide basic data for future competency reinforcement after confirming whether instructors and learners experience smart learning and learners have core competencies such as creativity, collaboration ability and convergent thinking in the educational environment changed due to COVID-19. The research method was a Mixed method, combination of quantitative research and qualitative research. 503 students from freshmen to seniors enrolled in “C” college participated from September 01 to December 04, 2020. We analyzed the data based on Colaizzi’s phenomenological research method. We found that learning motivation increased, after applying smart learning, which is statistically significant (t=16.28, p<.001). Self-directed learning ability and self-efficacy increased from 3.21 ± 0.32 to 4.01 ± 0.22, which was also statistically significant (t+21.03, p<.001). Pessimistic thinking tendencies increased from 3.14 ± 0.21 to 3.24 ± 0.19, but there was no statistical significance (t=-.93, p=.259). As a result of qualitative analysis of the experience of smart learning, we found 10 themes and 4 theme clusters. Based on the results, we like to say that we should reduce the gap in the use of digital devices between learners and instructors.

This study empirically verified the factors affecting the learning outcomes of English as a Foreign Language (EFL) learners in a smart learning (s-learning) environment. Based on social cognitive theory (SCT), six independent constructs (facilitating conditions, social influence, positive attitude toward s-learning, self-efficacy, social interaction, and knowledge) were proposed in the research model. The data collected from 455 s-learning EFL learners were analyzed in order to test the causal relationships between these six constructs and s-learning performance. For the analysis, the structural equation model (SEM) with AMOS 22.0 was used, and all six constructs were found to have a significant effect on s-learning performance. Important implications are discussed based on the study results.

최근 교육용 로봇 하드웨어 발달로 연산 처리 속도 및 확장성이 매우 좋아졌다. 이에 따라 로봇 하드웨어에 MBL용 온도 센서나 자이로 센서도 호환되어 데이터 로깅이 가능해졌다. 데이터 로깅이 가능한 교육용 로봇으로 학생들은 과학적인 탐구 예측, 수집, 데이터 분석이 가능한 실험을 할 수 있게 된 것이다. 이에 본 연구에서는 학급 SNS와 스마트폰을 활용한 ‘Smart r-Learning’에 데이터로깅이 가능한 교육용 로봇을 도입하여 과학 프로젝트 수업을 개발하고 적용했다.데이터 로깅 활용 Smart r-Learning 프로젝트 수업을 초등학교 5학년 학생들에게 적용한 결과 논리적 사고력 6개 영역 중 4개 영역이 유의미하게 향상 된 것으로 나타났다.

Due to the recent development of educational robot hardwares, processing speed and scalability have been greatly improved. Thus, the robot hardwares that are compatible with temperature sensor for MBL and gyro sensor made a data logging possible. Students can conduct an experiment on scientific research and prediction, collecting and data analysis with robots that can process data logging. Therefore this research constructed and adopted science project class that introduced a Smart r- Learning that utilizes Class SNS and smartphone. As a result of applying a data logging smart r-Learning to elementary school 5th graders, it has shown that the students' logical thinking ability four of the six areas have been improved in t-test.

스마트 홈은 민감하고 이질적인 데이터를 생성하여 중앙 집중식 학습에 프라이버시 위험, 대역폭 비용, 지연 시간 등의 부담을 가중시킨다. 프라이버시를 보호하면서 신속하고 확장 가능한 학습을 가능하게 하는 연합학 습 프레임워크 구축에 노력하고 있다. 개인정보 보호 및 저지연 에지 클라우드(PLEC) 연합 학습 프레임워크를 제안한다. 본 프레임워크는 기기 측 차등적 프라이버시 확률적 경사 하강법 (DP-SGD), 에지 측 보안 집계, 통신 효율적 업데이트 메커니즘을 통합하며, 적응형 클라이언트 스케줄링과 결합된다. 에너지 예측, 키워드 탐지, 활동 인식 작업 전반에 걸쳐 적용된다. 제안한 방식은 에너지 예측, 키워드 탐지, 활동 인식 작업 전반에 걸쳐 PLEC 연합 학습은 평균 제곱 오차 0.219, 정확도 94.3%, F1 점수는 93.9%를 보였다. 동시에 라운드당 업링크 데이터를 4.2MB로 줄이고 단일 라운드 지연 시간을 1.1초로 단축하여 성능이 크게 향상되었다.

Smart homes generate sensitive, heterogeneous data that strain centralized learning with privacy risks, bandwidth costs, and latency. We are committed to building a federated framework that enables rapid, scalable learning while safeguarding privacy. We proposed the Privacy -preserving and Low-latency Edge Cloud (PLEC) federated learning framework, integrating device side Differentially Private Stochastic Gradient Descent (DP-SGD), edge side secure aggregation, and communication efficient update mechanisms, coupled with adaptive client scheduling. Across energy forecasting, keyword detection, and activity recognition tasks. The PLEC federated learning achieved a mean squared error of 0.219, an accuracy rate of 94.3%, and F1 score of 93.9%. It simultaneously reduces per round uplink data to 4.2MB and single round latency to 1.1s, significantly outperforming.

본 연구에서는 영단어 학습 콘텐츠 개발 필요에 따른 새로운 스마트 영단어 암기방법을 설계 제안한다. 이 방법은 스마트 폰에서 효과적으로 영단어 학습을 지원하는 콘텐츠로 개발 가능하다. 본 연구의 핵심 아이디어는 첫째, 30개의 단어를 하루에 3분씩 10회(학습 1회 및 복습 9회)로 나누어 학습한다. 둘째, 망각주기를 고려하여 최초학습 1일 후, 10일 후, 30일 후 등의 3회 반추 복습을 제안한다. 본 콘텐츠의 개발과정은 크게 앱ID 생성부, 앱 접속부, 알람 설정부, 단어학습 처리부, 학습결과 모니터링부 등 5개의 단계로 이루어져 있다. 제안된 방법은 에빙하우스 주기적 반복 학습전략으로 최적화되어 있어 사용자의 영단어 학습 만족도를 높일 수 있다.

This paper suggests designing how to acquire English vocabularies on the smart devices based on the research that a ground-breaking English Vocabulary Learning Contents needs developing. The method makes it possible to develop the contents which helps the learners to master English vocabularies effectively on the smart phone. The core idea of this paper is as in the following: 1) English learners learn 30 vocabularies for three minutes 10 times (one is for a new learning and the other nine ones are for reviews about the first learning) a day. 2) Considering Ebbinghaus Forgetting Curve, the reflection study proposes to provide the learners with three times' reviews: one day, 10days, and 30days later from which they learn the first 30 vocabularies. This contents is mainly made up of 5 developing sections ①to generate App ID, ②to access App, ③to set up Alarm, ④to process Word learning, and ⑤to monitor the result of learning. This proposed idea is optimized to enhance the memory by Ebbinghaus Periodic Repetitive Method, which makes the learners satisfied with their English vocabulary learning.

분산형 농업 IoT 애플리케이션에서 데이터 프라이버시 문제는 스마트 온실 시스템이 생성하는 방대한 양의 민감한 환경 및 작물 관련 데이터로 인해 제기된다. 본 연구는 온실 환경에서의 데이터 보호를 개선하기 위해 차등 프라이버시를 통합한 프라이버시 강화 FL 아키텍처를 제안한다. 이 접근법은 특히 라즈베리 파이와 같은 제한된 자원을 가진 에지 디바이스를 위해 설계했다. 실제 온실 환경 데이터셋을 대상으로 한 실험에 따르면, 중앙 집중식 훈련은 최대 정확도(92.4%)를 달성하지만 프라이버시 보호 기능이 전혀 제공되지 않는다. 정확도, 프라이 버시 예산, 시스템 효율성 간의 균형 잡힌 절충점을 달성하기 위해 제안된 계층적 FL+DP 전략은 모델 업데이트를 크게 안정화하고 통신 오버헤드를 낮춘다. 이러한 결과는 높은 데이터 기밀성과 정확한 예측이 필요한 프라이버시 민감형 스마트 온실 애플리케이션에 FLDP 프레임워크가 적합함을 보여주었다.

Concerns with data privacy in dispersed agricultural IoT applications are raised by the massive amounts of sensitive environmental and crop-related data generated by smart greenhouse systems. In order to improve data protection in greenhouse settings, this study suggests a privacy -enhanced federated learning architecture that incorporates differential privacy. In order to achieve a balanced trade-off between accuracy, privacy budget, and system efficiency, the suggested hierarchical FL+DP strategy significantly stabilizes model updates and lowers communication overhead. The suggested model shows a favorable privacy-accuracy trade-off when compared to ordinary federated learning, achieving up to 88.7% prediction accuracy under moderate noise circumstances (σ = 0.3–0.5) while offering explicit differential privacy guarantees. These findings show that the FLDP framework is a good fit for privacy-sensitive smart greenhouse applications that need high data secrecy and accurate prediction.

Emergency exit signs are crucial during misfortunate events such as fire, earthquakes or even human caused events such as robbery and, bombing. However, these signs are of no use to the visually impaired people. During emergency scenarios, the blind people need to rely on other individuals and sometimes they may even be left helpless. This raises a need for some assistive device that could benefit the visually impaired people during the time of emergency. In this paper, we propose a concept of smart glasses that could be tremendously beneficial to the blind people. These glasses will have camera and headphone speakers embedded to them. The device will be capable of detecting emergency signs using modern deep learning techniques during the times of need and could notify the user regarding the direction where the exit is.

과학 기술의 발달과 더불어 학습자들에게 맞는 맞춘 교수 방법을 찾기 위한 연구를 부단히 노력해 오고 있다. 그러한 교수법 중의 하나로써 많은 연구자들은 플립드 러닝 방식을 제시하고 있다. 이 논문의 목적은 플립드 러닝방식이 i) 전통적 교육방식 수업에 비해 학업 성취도 면에서 유용한지 ii) 학생들의 영어성적에 상관없이 모든 영어레벨의 학생에게 적용되어 질수 있는지 iii) 학습자들의 학습에 대한 동기유발을 일으키는데 도움이 되는 교수법인지를 알아보고자 한다. 학업성취에 있어서 플립드 러닝 수업을 받은 학생 그룹 중 가장 성적이 좋은 그룹 학생들은 학업에 있어 많은 향상을 보인 반면 학업 성적이 가장 낮은 그룹 학생들은 오히려 성적이 낮아지는 결과로 나타났다. 플립드 러닝 교수법으로 수업을 진행 할 시에는 학습자들의 수준에 따른 콘텐츠를 교수자가 개발해야한다고 본다. 학습자들의 수준에 맞는 맞춤형 콘테츠 개발 이 학습자들의 학습 성취도를 높일 것으로 본다.

With advances in technology, many researchers have made an effort to find out educational methods with customized instruction. The purpose of the research is to investigate i) if flipped learning is beneficial for the students taking intermediate-level English grammar and writing class compared with the traditional class, ii) if the flipped learning class is advantageous for all the score level students in terms of student achievement and iii) if the students feel motivated with the flipped learning class. T-test was utilized to determine any differences between pretest and posttest in student achievement. The result in terms of the academic achievement revealed that the flipped classroom approach for the low score group was found to be the least effective among others. In the case of flipped learning teaching method, the instructor should develop contents according to the level of learners. The development of customized contents tailored to the level of learners will enhance learners' learning achievement.

본 연구의 목적은 스마트 테크놀로지를 활용한 팀 기반 학습(Smart technology-enhanced Team-Based Learning, S-TBL) 모형 설계를 위한 디자인 원칙과 S-TBL의 개요, 절차 및 활동을 개념화하는 것이다. 이를 위 해 기존의 팀 기반 학습(Team-Based Learning, TBL) 모형을 기반으로 모바일 테크놀로지, 협력학습, 문제해결학 습과 다양한 평가 모형들을 종합한 학습 모형을 설계하였다. 기존의 TBL 모형을 기반으로 스마트 테크놀로지 학 습 환경에서 적용 가능한 학습 모형을 설계함에 있어 1) 학습 자원, 평가 도구, 문제해결상황과 문제해결과정을 통 합하는 총체적인 학습 환경을 제공하고, 2) 팀 구성원 간 및 교수자와 학습자 간 협력과 커뮤니케이션을 증대시킬 수 있는 환경 개발에 중점을 두었다. 이러한 S-TBL 모형은 1) 개별 학습과 협력적인 팀 학습을 통합하고, 2) 개념학습과 문제 해결 및 비판적 사고력 신장을 위한 학습을 통합하며, 3) 개별 평가와 그룹 평가를 통합하고, 4) 자기주 도적 학습과 강의식 설명 학습을 통합하고, 5) 개인적 성찰과 산출물의 공유를 통합할 수 있도록 설계되었다.

The purpose of this study is to explore and develop a new instructional approach to a technology-enhanced, collaborative learning environment called Smart technology-enhanced Team-Based Learning (S-TBL). We designed a novel instructional model that combines mobile technology, collaborative teamwork, a problem-solving process, and a variety of evaluation techniques from the viewpoint of a conventional team-based model. Based on the traditional TBL model, we have integrated smart learning technologies: 1) to provide a holistic learning environment that integrates learning resources, assessment tools, and problem solving spaces; and 2) to enhance collaboration and communication between team members and between an instructor and his or her students. The S-TBL instructional approach combines: 1) individual learning and collaborative team learning; 2) conceptual learning and problem-solving & critical thinking; 3) both individual and group assessment; 4) self-directed learning and teacher-led instruction; and 5) personal reflection and publication.

본 논문은 도시 교통 혼잡 문제를 해결하기 위해 IoT 기반 스마트 폴 인프라와 DQN 강화학습 알고리즘을 결합한 엣지지능형 교통 신호 최적화 프레임워크를 제안한다. 제안 방법은 교차로에 설치된 스마트 폴을 통해 차량 대기열, 교통 밀도, 대기시간 등의 데이터를 실시간으로 수집하고, 엣지 계층에서 DQN 기반 신호 제어 정책을 수행하도록 설계하였다. 또한 VEINS 기반 공동 시뮬레이션 환경에서 SUMO와 OMNeT++를 연동하여 고정시간 제어 및 감응식 제어 방식과 성능을 비교하였다. 또한 자율주행차량 환경에서 V2X 통신의 성능을 최적화하고, 높은 이동성과 네트워크 불안정성이 특징인 VANET환경에서 효과적인 지능형 교통 시스템(ITS)을 구현하기 위한 SDVN 기반 설계 방법을 제안한다. 기존의 VANET은 분산형 구조로 인해 패킷 손실, 지연 증가, 네트워크 단절 등 여러 한계가 존재하며, 이를 해결하기 위해 본 연구는 SDN 아키텍처를 차량 통신망에 통합하였다. SDN의 중앙집중 제어 특성을 활용하여 네트워크 토폴로지를 실시간으로 관리하고, 트래픽 상황에 따라 최적의 경로를 동적으로 설정할 수 있도록 하였다.

This paper proposes an edge-intelligent traffic signal optimization framework that integrates IoT-based smart pole infrastructure with a DQN reinforcement learning algorithm to address urban traffic congestion problems. The proposed approach is designed to collect real-time traffic information, including vehicle queue lengths, traffic density, and waiting times, through smart poles installed at intersections, while executing DQN-based signal control policies at the edge layer. Furthermore, a VEINS-based co-simulation environment integrating SUMO and OMNeT++ was employed to evaluate the performance of the proposed framework in comparison with conventional fixed-time and actuated traffic signal control methods. In addition, this study proposes an SDVN-based design methodology to optimize V2X communication performance in autonomous vehicle environments and to implement an effective ITS in VANET environments characterized by high mobility and network instability. Conventional VANETs suffer from several limitations, including packet loss, increased latency, and frequent network disconnections due to their distributed architecture. To overcome these challenges, this research integrates SDN architecture into vehicular communication networks. By leveraging the centralized control capabilities of SDN, the proposed approach enables real-time management of network topology and dynamic configuration of optimal communication routes according to changing traffic conditions.