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.

코로나 로 고등학교와 대학교에서 장기간 원격교육이 19 실시된 이후, 다수의 선행 연구는 각각의 교육 기관에서 교육 불평등이 전반적으로 심화되었음을 밝혔다. 그러나 교육 결과의 불평등에 한하여 상이한 결과가 확인되었는데 고등학교는 학력 격차가 커지면서 불평등이 악화되는 양상을 보였으나 대학교는 학 력 격차가 감소하면서 불평등이 완화되는 양상을 보였다. 고등학생과 대학생 모두 졸업 이후 대학생 혹은 직장인이 되어 전에 비해 상당한 양의 자유와 책임을 갖는다는 점을 감안할 때, 사회적 맥락에서 두 집단 은 유사한 기능을 담당한다고 볼 수 있다. 따라서 고등학교와 대학교의 기능상 유사성에도 불구하고 원격 교육 이후 교육 결과의 불평등에서 상이한 결과가 나타난 원인을 탐색할 필요가 있다. 본 연구는 이를 밝 히기 위해 원격교육이 실시되는 기간 동안 고등학교 혹은 대학교를 다닌 학생을 대상으로 인터뷰를 진행 하고 구성주의적 근거이론을 기반으로 인터뷰 결과를 분석하였다. 그 결과 불평등 해소를 위한 학교의 역 할에 대한 인식 차이가 교육 정책에 영향을 미쳐 궁극적으로 교육 결과의 불평등에 영향을 미친 것으로 드러났다. 나아가 본 연구는 학생들이 불평등 해소에 있어 구조의 역할에 대한 선명한 인식을 견지하는 것이 교육 정책에 학생들의 의견을 반영하는 출발점이 될 수 있음을 시사한다.

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.

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

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.

This paper explores possibilities of using language learning autobiographies as a form of identity texts in order to produce suggestions for critical teacher education. Specifically, we examined what insights we might gain by reframing Korean pre-service teachers’ autobiographies as a form of identity texts to develop reflective teachers, who affirm their identities and language learning histories. We thematically analyzed autobiographical reflection papers on English learning experiences of 73 undergraduate students enrolled in a compulsory elementary English education course at a public university in South Korea. The analysis revealed the predominance of referral to English private education experiences and a sense of linguistic insecurity felt by these pre-service teachers, who would later teach in public schools. Based on these findings, we provide suggestions for critical teacher education to foster critical language awareness and identity investment for the students. We conclude that language learning autobiographies, while incorporating characteristics of identity texts, might be a useful tool for critical pedagogies.

Interactional modification is important in SLA research because it involves correcting problematic L2 use. However, not all modifications will lead to pedagogical changes. Participants in conversational interactions are not always oriented to linguistic forms or functions. One way to address this dilemma is to examine the process by which participants come to terms with problematic L2 use in interactional exchanges. “Language moments” refer to cases in which L2 forms and functions are objects of interactive exchanges in L2 interactions. Through conversation analysis, the present study uncovered four different types in which participants in L2 interaction discovered and acted on language moments in terms of the degree of explicitness in recognizing and addressing problematic L2 use. This study used data from ESL classroom interactions that featured native teachers of English and L2 learners in an US context. This descriptive account of interactional processes might complement prior research studies that have focused on effects of interactional modification.

The study aims to understand personal changes experienced through Korean language learning and the significance of these changes to members of the new Korean Language and Culture Community by examining the life histories of foreign graduate students majoring in Korean language education and Korean literature. To this end, six Vietnamese students who are studying at a graduate school specializing in Korean language participated in this study and their life histories were examined, focusing on the development of their identities. The analysis focused on the (re)construction of identity in the learning processes of participants and showed that participants achieved complex identity development by learning and using Korean in various formal and informal contexts. In this study, changes were described in terms of improving Korean language skills, changing the self-image during early adulthood, and personal growth.

본 연구는 뷰티산업 현장 실무 전문가를 양성하기 위한 피부미용 이론 및 실기 수업을 통해 문 제중심학습(PBL) 수업 개발 및 적용하고자 한다. 학습자들에게 피부미용 현장에서 일어나는 실제적인 문제 를 제시하고, 그 문제를 해결하기 위해 학습자들 상호간에 공동으로 문제를 해결하는 방안을 강구하고자 한다. 학습자들에게 문제중심학습(PBL)을 적용한 피부미용 수업을 진행하고, 수강 후 그 효과에 대해 분석 한다. 본 연구 결과로 현장 실제의 문제를 제시함으로써 학습자들이 실제 전문가들이 하는 일을 이해할 수 있었고, 주어진 문제에 대한 다양한 해결방법을 도출할 수 있다는 부분을 알게 되었다고 하여 문제해결 능 력 신장을 기대 해 볼 수 있었다. 이와 같은 결과는 문제 탐구와 문제를 해결하기 위한 문제중심학습(PBL) 수업 과정은 학습자의 문제해결능력 향상에 긍정적으로 작용하고 있음을 시사하고 있다.

본 연구는 대학의 피부미용 전공교육에서 문제중심학습(PBL) 적용에 따른 학습효과를 알아보 고, 피부미용 교육경쟁력을 강화할 수 있는 교수·학습방법 개발을 위한 기초자료 제공을 목적으로 하였다. 이에 본 연구는 H대학교에 개설된 피부미용 전공 교과목인 “동양요법” 수강 학생 15명을 대상으로 PBL 수업을 적용하여 실증적 교육효과 분석을 실시하였다. 그 결과 PBL 수업을 참여한 후 학습자들의 문제해 결 능력과 협력적 자기효능감의 평균값이 상승하여 유의미한 차이를 보였으며, 이에 따라 PBL 교수학습법 의 효과성이 확인되어 피부미용 교육 현장에서 학습자들의 문제해결 능력과 협력적 자기효능감 상승을 위 한 효과적인 교수학습법으로 제안하는 바이다.

The forms and demands of language learning are changing in the pandemic era. Learners no longer rely solely on a formal language curriculum. Instead, they are using various informal language learning (ILL) channels. Although informal language learning has been in the spotlight and is growing, more research on ILL is needed. In this study, ILL taking place through an online community was analyzed. Articles from the Korean learning community (r/Korean in Reddit) were collected, and the topic modeling technique, Latent Dirichlet Allocation, was conducted on the collected data. As a result, seven major topics were selected. The most common topics in all posts were issues faced by beginner learners, followed by vocabulary and sentence meanings, interest in Chinese characters and applications of Korean language skills, culture and daily life, translation, online learning materials, and Korean phonics. Through this, the interests of ILL learners and the characteristics of learners could be identified. Due to the nature of ILL, in which a formal curriculum does not exist, it was found that questions about general strategies for learning and questions that could not be solved in formal language education were most prominent. In addition, the characteristics of ILL learners who actively sought learning content and materials were also found.