This study aimed to explore the characteristics and dimensions of of systematic functional gestures employed by pre-service Earth science teachers during instructional sessions. Data were collected from eight students enrolled in a university’s Department of Earth Science Education. The data included lesson plans, activity sheets, and recordings of one class session from participants. The analysis, conducted using the systemic functional multimodal discourse analysis framework, categorized gestures into scientific and social functional dimensions. Further subdivision identified meta gestures, analytical gestures, and interrelated gestures. Additionally, pre-service teachers used gestures to explain scientific concepts, concretely represent ideas and facilitate communication during instruction. This study emphasizes the nonverbal strategies used by pre-service Earth science teachers, highlighting the importance of noverbal communication in teachers’ professional development and the need for its integration into education. It also establishes a systematic conceptual framework for understanding gestures in the instructional context.

The purpose of this study was to examine the impacts of AI-integrated MALL on Korean students’ TOEIC preparation, by comparing with AI-integrated CALL. A total of 496 freshmen students participated in this study. They were arbitrarily assigned to AI CALL (n = 190), AI MALL (n = 164), and the control (n = 132) groups. During a 2021 academic semester, the two experimental groups studied TOEIC through computers or mobile phones, integrated with AI technology. The control group was taught by a human teacher, in a traditional classroom setting. Before and after the treatment, TOEIC listening and reading tests were administered. Paired samples t-tests and one-way ANOVAs, were used to analyze collected data. Findings show that all groups significantly increased listening and reading test scores. Group comparison results show that the AI CALL group outperformed the control group for both tests. This group also outperformed the AI MALL group for the reading test. Based on this, pedagogical implications are invaluable

This study has been attempted to find out how the learning with the introduction of the physical environmental variant in earth science teaching-learning scene in high school can be approached to the goals of science education. And it also aims to seek for an effective teaching method of earth science education. The effectiveness of learning between two groups has been compared: the experimental group and the control group. According to the result of this research, the effectiveness of learning of the experimental group in the science branch of high school has been much higher than the control group while in the humanities branch, the effectiveness between the two groups is not conspicuous. In conclusion, the result of introducing suitable environmental variant into teaching-learning scene enhances the effectiveness of learning, and can be one of the desirable approaches to science education.

본 논문에서는 컴퓨터 매개 의사소통(Computer-Mediated Communication, CMC)의 전반적인 효과성을 메타분석하였다. 국내에서 출간된 29편의 연구를 바탕으로 총 218개의 개별효과크기를 추출하였으며, 이를 연구대상, 연구기간, 집단크기, 연구 영역 및 세부 하위영역에 따라 효과크기를 분석하였다. 연구에 활용한 논문은 “CMC”, “컴퓨터 매개 학습”이라는 검색어를 사용하여 RISS 학술연구정보서비스 및 구글 등을 통해 수집하였다. 분석 결과, “CMC”를 활용한 영어학습은 전체적으로 중간 크기의 효과를 나타내어(ES=.69) 학습자에게 긍정적인 효과를 미친 것으로 나타났다. 세부적으로는, 정의적 영역의 효과크기(ES=.71)가 인지적 영역의 효과크기(ES=.69)보다 약간 더 높았다. 특히, CMC 기반 영어수업은 어휘 및 문법(ES=.87)과 쓰기(ES=.81)에서 더 효과적이었다. 한편, 정의적 영역의 태도 측면에서는 긍정적인 결과를 보였으나(ES=.92), 학습전략 영역에서는 유의한 효과가 없었다. 또한, CMC는 중·고등학생보다 대학생과 초등학생에게, 중간 기간(5주-11주) 과 장기간(12주 이상)일 때, 그리고 소집단일 때 더 효과적인 것으로 나타났다. 현재 영어교육에서는 컴퓨터를 포함하여 다양한 멀티미디어 기기나 플랫폼 및 SNS 기기들이 활용되고 있는데, 본 연구에서 나타난 결과들을 감안하여 수업을 설계한다면 보다 큰 성과를 거둘 수 있을 것이라 생각된다.

Collecting a rich but meaningful training data plays a key role in machine learning and deep learning researches for a self-driving vehicle. This paper introduces a detailed overview of existing open-source simulators which could be used for training self-driving vehicles. After reviewing the simulators, we propose a new effective approach to make a synthetic autonomous vehicle simulation platform suitable for learning and training artificial intelligence algorithms. Specially, we develop a synthetic simulator with various realistic situations and weather conditions which make the autonomous shuttle to learn more realistic situations and handle some unexpected events. The virtual environment is the mimics of the activity of a genuine shuttle vehicle on a physical world. Instead of doing the whole experiment of training in the real physical world, scenarios in 3D virtual worlds are made to calculate the parameters and training the model. From the simulator, the user can obtain data for the various situation and utilize it for the training purpose. Flexible options are available to choose sensors, monitor the output and implement any autonomous driving algorithm. Finally, we verify the effectiveness of the developed simulator by implementing an end-to-end CNN algorithm for training a self-driving shuttle.

The purpose of this study was to examine the concept of r-learning based on existing studies of r-learning. It also aimed to analyze r-learning environments in an effort to determine prerequisites for the successful entrenchment of r-learning in material(technology and infrastructure), huma (young children and teacher) and institutional(law and policy) aspects. This study intended to suggest some of the right directions for the revitalization of r-learning. In conclusion, the position of r-learning and its interrelationship with related systems in the ecosystem of early childhood education should accurately be grasped to accelerate the integration of r-learning into kindergarten education to maximize the effects of the convergence of the two. Intensive efforts should be made from diverse angles to expedite the spread and enrichment of r-learning.