Conventional flipped learning instructional models are operated in a blended learning environment online and offline. In contrast, this study moved onto fully online systems and explored how a sense of presence worked for students’ learning outcomes at university English writing courses. The two research questions for this study are: 1) What is the relationship between a sense of presence (teaching, cognitive, social presence) and learning outcomes (group cohesion, class satisfaction)? and 2) What are the variables among a sense of presence that affect group cohesion and class satisfaction? For the purposes of this study, 46 university students from English composition courses answered student questionnaires in the spring of 2021. Correlation and multiple-regression analyses were conducted to look into the relationships among the variables. Additionally, focus-group interviews were conducted and teaching journals were analyzed. The major findings were revealed as follows: Firstly, a sense of presence was significantly related to group cohesion and satisfaction. Secondly, social presence and cognitive presence only had a predictive power of group cohesion. Thirdly, cognitive presence and teaching presence were significant predictors of class satisfaction. Pedagogical implications are discussed for those interested in applying flipped learning in a fully online setting.

본 연구는 초등학생의 학습·사회성 부진에 영향을 미치는 요인을 분석하여, 부모 관계에 따른 아동기 심리적 안전감의 중요성을 밝히기 위한 연구이다. I시 J구의 초등학교 학부모 대상 안전 인 식 조사에서 주관적 건강, 성격 특성, 대인 관계, 정서·행동 특성의 심리적 안전감과 학교폭력 피 해 및 부모 관계를 통해 학습·사회성 부진의 영향요인을 분석하였다. 자료의 분석은 t-검증과 ANOVA를 활용하여 일반적 특성에 따른 차이를. 다중회귀분석으로 학습·사회성 부진의 영향요인을, 교차분석과 로지스틱 회귀분석으로 학습·사회성 부진의 위험도를 분석하였다. 분석의 결과는 첫째, 여아의 과민·반항성이 높았고, 6학년 아동의 개방성이 낮게 나타났으며, 부모의 학력이 높을수록 주관적 건강과 학교폭력 피해 인식이 높고, 월 300만 원대의 소득수준에서 학습·사회성 부진이 높 다. 둘째, 학습·사회성 부진의 영향요인으로 긍정적 관계에서는 불안·우울과 학교폭력 피해 경험으 로, 부정적 관계에서는 집중력과 불안·우울로 나타났다. 셋째, 학습·사회성 부진 위험도에 유의한 영향을 미치는 요인은 사회적 주도성, 불안·우울과 집중력 부진, 소득수준, 타인 이해, 공동체 의 식으로 나타나고 있다. 아동의 학습·사회성 부진은 불안·우울, 집중력 부진과 높은 상관성이 있고, 부정적 부모 관계에서 높은 영향을 받는다. 따라서 아동의 학습·사회성 향상을 위해서는 긍정적 부 모 관계와 심리적 안전감의 확보가 중요하다.

Due to COVID-19, changes in consumption trends are taking place in the distribution sector, such as an increase in non-face-to-face consumption and a rapid growth in the online shopping market. However, it is difficult for small and medium-sized export sellers to obtain forecast information on the export market by country, compared to large distributors who can easily build a global sales network. This study is about the prediction of export amount and export volume by country and item for market information analysis of small and medium export sellers. A prediction model was developed using Lasso, XGBoost, and MLP models based on supervised learning and deep learning, and export trends for clothing, cosmetics, and household electronic devices were predicted for Korea's major export countries, the United States, China, and Vietnam. As a result of the prediction, the performance of MAE and RMSE for the Lasso model was excellent, and based on the development results, a market analysis system for small and medium sellers was developed.

본 논문에서는 웹툰 캐릭터 영상에 대해서 심층학습에 기반한 3D 안면 재구성 기술을 제안한다. 본 연구에서 제안하는 방법은 기본 사항 모듈과 상세 사항 모듈로 구성된다. 입력 받은 웹툰 캐릭터 영상에 대해서 기본 사항 모듈의 요소인 Albedo 모듈을 적용해서 안면에 들어오는 빛의 양을 계산하여 Albedo 맵을 생성한다. 그 리고 기본 사항 모듈의 다른 구성 요소인 FLAME 모듈에서는 입력 영상에 대한 기본적인 3D 안면 형태를 생 성한다. 이와 동시에 상세사항 모듈을 적용해서 실제 사람과 다르게 이목구비가 변형된 웹툰 캐릭터 영상의 표정이나 얼굴 깊이와 같은 특징을 살리는 세부사항을 추출한다. 계산한 세부사항들을 토대로 세부사항 맵을 생성하여 앞서 FLAME 모듈에서 생성된 3D 안면 형태와 결합하여 세부사항 안면 형태를 생성한다. 그 후 Albedo 모듈에서 생성된 Albedo 맵까지 적용하면 최종적으로 웹툰 캐릭터 영상에 대한 3D 안면 재구성이 완 료된다. 본 연구에서는 웹툰 캐릭터뿐만 아니라 안면이 스타일화된 애니메이 션 캐릭터에 대해서도 결과를 생성하고, 이를 기존 연구와 비교하여 그 우수성을 입증한다.

Reinforcement learning (RL) is widely applied to various engineering fields. Especially, RL has shown successful performance for control problems, such as vehicles, robotics, and active structural control system. However, little research on application of RL to optimal structural design has conducted to date. In this study, the possibility of application of RL to structural design of reinforced concrete (RC) beam was investigated. The example of RC beam structural design problem introduced in previous study was used for comparative study. Deep q-network (DQN) is a famous RL algorithm presenting good performance in the discrete action space and thus it was used in this study. The action of DQN agent is required to represent design variables of RC beam. However, the number of design variables of RC beam is too many to represent by the action of conventional DQN. To solve this problem, multi-agent DQN was used in this study. For more effective reinforcement learning process, DDQN (Double Q-Learning) that is an advanced version of a conventional DQN was employed. The multi-agent of DDQN was trained for optimal structural design of RC beam to satisfy American Concrete Institute (318) without any hand-labeled dataset. Five agents of DDQN provides actions for beam with, beam depth, main rebar size, number of main rebar, and shear stirrup size, respectively. Five agents of DDQN were trained for 10,000 episodes and the performance of the multi-agent of DDQN was evaluated with 100 test design cases. This study shows that the multi-agent DDQN algorithm can provide successfully structural design results of RC beam.

Purpose: This study analyzed domestic studies on blended learning among nursing students to identify research trends and future directions. Methods: This scoping review was conducted following the process suggested by Arksey and O’Malley: 1) identifying the research questions; 2) searching for relevant studies; 3) study selection; 4) charting the data; 5) collating, summarizing, and reporting the results; and 6) optional consultation. Relevant studies were searched using the keywords “blended learning” and “nursing” in the domestic databases RISS, KISS, and DBpia. The selection and exclusion criteria were applied to 203 articles, and 34 were finally selected. Results: The final 34 studies included 30 quantitative and four qualitative studies. Most of the studies were experimental studies. In undergraduate nursing education, blended learning was utilized more in the classroom than in the lab or clinical setting. Furthermore, it was mainly applied by combining online and offline approaches. It improved learning satisfaction, critical thinking, clinical performance, and self-directed learning ability. Conclusion: Research on blended learning in nursing education has increased over time. Blended learning positively affects learning satisfaction and learning ability in nursing students; thus, it is expected that this could be effectively utilized in both the classroom and lab or in clinical teaching for nursing students. Therefore, future studies using various research methods should be expanded, and systematic reviews and meta-analyses are recommended.

The bandgap characteristics of semiconductor materials are an important factor when utilizing semiconductor materials for various applications. In this study, based on data provided by AFLOW (Automatic-FLOW for Materials Discovery), the bandgap of a semiconductor material was predicted using only the material’s compositional features. The compositional features were generated using the python module of ‘Pymatgen’ and ‘Matminer’. Pearson’s correlation coefficients (PCC) between the compositional features were calculated and those with a correlation coefficient value larger than 0.95 were removed in order to avoid overfitting. The bandgap prediction performance was compared using the metrics of R2 score and root-mean-squared error. By predicting the bandgap with randomforest and xgboost as representatives of the ensemble algorithm, it was found that xgboost gave better results after cross-validation and hyper-parameter tuning. To investigate the effect of compositional feature selection on the bandgap prediction of the machine learning model, the prediction performance was studied according to the number of features based on feature importance methods. It was found that there were no significant changes in prediction performance beyond the appropriate feature. Furthermore, artificial neural networks were employed to compare the prediction performance by adjusting the number of features guided by the PCC values, resulting in the best R2 score of 0.811. By comparing and analyzing the bandgap distribution and prediction performance according to the material group containing specific elements (F, N, Yb, Eu, Zn, B, Si, Ge, Fe Al), various information for material design was obtained.

이 연구는 한국어를 학습하는 외국인 학습자들을 위하여 인지언어학적 인 관점으로 접근하여 신체 관련 한국어 감정 관용 표현에 나타나는 은 유와 개념화의 방식을 살펴보는 데 목적이 있다. 관용 표현은 그 언어를 사용하는 사람들에 의해 그 의미가 형성된다. 따라서 관용 표현을 생성 한 한국인의 개념화 방식을 알아야 한다. 이에 따라 분노와 불안의 근원 영역을 ‘온도/색깔/명암/전복/분출/경직’으로 분류하였다. 근원 영역이 ‘온도’일 때 개념적 은유는 ‘분노는 뜨거움’과 ‘불안은 차가움’이며 ‘색깔’ 일 때의 개념적 은유는 ‘불안은 죽음’, ‘명암’의 근원 영역에서는 ‘불안은 어두움’이라는 개념화가 일어나고 ‘전복’의 영역에서는 ‘분노는 뒤집함’이 라는 개념적 은유가 일어난다. 또한 ‘분출’은 ‘불안은 땀’이라는 개념적 은유를, ‘경직’은 ‘불안은 정지’라는 개념적 은유를 통해 감정 관용 표현 을 생성하였다. 본고는 신체 관련 한국어의 감정 관용 표현 통해 한국인 의 개념화 방식을 정리하였으며 이 연구가 한국어교육 현장에 기초 자료 로도 활용될 수 있다는 점에서 의의가 있다

본 연구는 교수자가 학습자를 위해 긍정적 가치탐색을 효과적으로 적용할 수 있도록 4D 프로세스 기반 학습모형을 개발하고 학습유형을 분류하여 연구하는 것을 목적으로 하였다. 긍정적 가치탐색 교육 방법은 학습자의 사고방식과 행동 변화에 효과적이다. 또한, 의미와 가치발견에 중점을 둔 강점 기반 접근을 통해 학습 참여를 증진하고 지속 가능한 학습 환경과 배움을 실현할 수 있다. 이러한 교육적 효과는 긍정적 가 치 탐색의 4D 프로세스를 토대로 한 활동으로 이루어진다. 교육 현장에서 긍정적 가치탐색 4D 프로세스 를 보다 유용하게 활용하기 위해서는 교육목표와 지향하는 역량개발에 따라 4D 프로세스에 적합한 학습 유형 분류와 체계적이고 구조화된 학습모형 개발이 필요하다. 본 연구는 4D 프로세스 기반 4가지 학습유 형을 구조화하여 학습모형을 개발하고 모형타당화를 진행하였다. 4D 프로세스 기반 학습모형 구성요소 도 출은 선행 문헌의 검토와 분석을 통해 이루어졌고, 구성요소의 구조화는 사례연구를 통해 진행하였다. 그 리고 해당 분야 전문가 검토를 통한 타당성 평가를 3차에 걸쳐 실시하였다. Discover, Dream, Design, Destiny 4D 프로세스는 탐색과 발견, 사고와 상상, 공유와 구성, 발표와 실천으로 개선되어 적용되었다. 학습에 적합하도록 보완된 4D 프로세스는 도달할 학습 목표와 개발할 학습자의 역량에 따라 탐구형, 창의 형, 과제해결형, 실천형으로 세분화하여 개발되었다. 개발된 학습모형에서의 학습유형은 다양한 교육 환경 에 맞게 긍정적 가치탐색 활동이 선택적으로 운영될 수 있다는 이점이 있다.

This study was conducted to estimate the damage of Whole Crop Corn (WCC; Zea Mays L.) according to abnormal climate using machine learning as the Representative Concentration Pathway (RCP) 4.5 and present the damage through mapping. The collected WCC data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. The machine learning model used DeepCrossing. The damage was calculated using climate data from the automated synoptic observing system (ASOS, 95 sites) by machine learning. The calculation of damage was the difference between the dry matter yield (DMY)normal and DMYabnormal. The normal climate was set as the 40-year of climate data according to the year of WCC data (1978-2017). The level of abnormal climate by temperature and precipitation was set as RCP 4.5 standard. The DMYnormal ranged from 13,845-19,347 kg/ha. The damage of WCC which was differed depending on the region and level of abnormal climate where abnormal temperature and precipitation occurred. The damage of abnormal temperature in 2050 and 2100 ranged from -263 to 360 and -1,023 to 92 kg/ha, respectively. The damage of abnormal precipitation in 2050 and 2100 was ranged from -17 to 2 and -12 to 2 kg/ha, respectively. The maximum damage was 360 kg/ha that the abnormal temperature in 2050. As the average monthly temperature increases, the DMY of WCC tends to increase. The damage calculated through the RCP 4.5 standard was presented as a mapping using QGIS. Although this study applied the scenario in which greenhouse gas reduction was carried out, additional research needs to be conducted applying an RCP scenario in which greenhouse gas reduction is not performed.

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald’s (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.