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한의과대학 온라인 교육의 발전을 위한 제언 - COVID-19에 따른 온라인 교육 현황과 만족도 조사 사례를 바탕으로 - (Suggestions for the Development of Online Education at the College of Korean Medicine - Based on the Current Status of Online Education and Satisfaction Surveys due to COVID-19 -)

  • 위효선;양인준
    • 동의생리병리학회지
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    • 제35권5호
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    • pp.162-168
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    • 2021
  • This study was conducted to investigate the current status of online classes and evaluations during the COVID-19 pandemic and the satisfaction of students attending the College of Korean Medicine. A survey was conducted with students enrolled in Dongguk University's College of Korean Medicine. The questionnaire was divided into four areas asking about online lectures, laboratory practice, clinical practice, and evaluation experience. The items were composed of multiple-choice, a 5-point scale, and subjective type. After distributing the Google form address through SNS and LMS, only those who agreed to the questionnaire were responded anonymously. 149 out of 457 enrolled students responded. 98.7% of students experienced online lectures, and more frequently experienced real-time online lectures (98.6%) than recorded lectures (43.5%). Overall satisfaction with online lectures was 3.99 on average. 80.5% of the students experienced the online experiment and practice class, and the overall satisfaction with it was 3.29 on average. 1.3% of students experienced online clinical practice. 86.6% of students experienced online evaluation, and when asked about the fairness of the test, the average score was 3.99. Satisfaction with online lectures and evaluations is generally high, so it is expected to be used as an effective learning tool in the future. However, it seems that facility improvement and technical training of instructors are necessary. In experimental and practical education, the satisfaction level is lower than that of online lectures, so it seems necessary to develop a new online program and to prepare a safe offline education system.

Analysis and simulator implementation of Mighty, an advanced imperfect information game

  • Lee, Jeongwon;Kim, Kwihoon;Kim, Seung-Hyun
    • 한국컴퓨터정보학회논문지
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    • 제27권1호
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    • pp.9-21
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    • 2022
  • 마이티는 불완전 정보 게임으로, 국제적으로 대중적인 4인용 카드 게임 브리지와 유사하지만 게임 규칙 및 운영 측면에서 더욱 복잡한 특성을 가지고 있다. 마이티 게임의 전략을 탐구하고 분석하기 위한 환경이 필요하지만, 브리지 등 타 카드 게임의 전략 분석을 위한 시뮬레이터가 다수 개발된 것에 비해 마이티 게임의 분석 도구는 존재하지 않는다. 심지어 마이티 게임에 대한 학문 차원에서의 정의 및 이해가 부족한 상황이다. 이러한 문제를 해결하기 위해, 본 논문은 마이티 게임의 절차 및 규칙을 체계적으로 정의하였다. 그리고 이를 기반으로 마이티 게임을 학습하고 전략을 분석할 수 있는 시뮬레이터를 구현하였다. 시뮬레이터는 서비스의 활용성과 접근성을 고려하여 자바스크립트로 개발되었으며, 다양한 분석 기능을 PC/모바일 웹 환경에서 제공한다. 마지막으로, 관련 분야에서 연구 주제로 다루고 있는 다른 트릭테이킹 게임과의 비교 분석을 통해, 마이티 게임이 불완전 정보 게임으로서 연구 가치가 있으며 AI학습이 용이한 게임 특성이 존재함을 보였다.

SparkR을 이용한 R 기반 빅데이터 분석의 분산 처리 (Distributed Processing of Big Data Analysis based on R using SparkR)

  • 류우석
    • 한국전자통신학회논문지
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    • 제17권1호
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    • pp.161-166
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    • 2022
  • 본 논문에서는 데이터 분석 도구인 R을 이용하여 빅데이터 분석을 수행할 때 발생하는 문제점을 분석하고, 빅데이터의 분산 처리를 효과적으로 지원하는 스파크와 R을 연계한 SparkR을 이용한 분석의 유용성을 제시하고자 한다. 먼저, 대량의 데이터를 로딩하고 연산을 수행할 때 발생하는 R의 메모리 할당 문제점과 R과 비교한 SparkR의 특징 및 프로그래밍 환경을 분석한다. 그리고, 선형 회귀 분석을 각각의 환경에서 수행할 때의 실행 성능을 비교 분석한다. 분석 결과 SparkR을 통해 추가적인 언어 학습 없이도 R을 그대로 이용하여 데이터 분석에 활용할 수 있음을 보였으며, SparkR을 이용하여 R로 작성된 코드를 클러스터 내 노드 수의 증가에 따라 효과적으로 분산 처리할 수 있었다.

코로나19 발생 이후 일부 지역대학 응급구조학과의 비대면 교육 현황 (Status of non-face-to-face learning at selected regional universities for paramedicine since the coronavirus infectious disease 2019 outbreak: a cross-sectional survey on undergraduates)

  • 김사라;김철태
    • 한국응급구조학회지
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    • 제26권1호
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    • pp.71-85
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    • 2022
  • Purpose: This study aimed to investigate the current status of non-face-to-face education at universities for paramedicine and measure students' education satisfaction after the coronavirus infectious disease 2019 (COVID-19) outbreak. Methods: A cross-sectional survey was conducted for paramedic students at the Chungcheong and Honam areas using Google Forms. Convenience sampling was used. A structured questionnaire was created and modified according to Park and Choi's test tool developed to review online lectures and practical courses. Results: A total of 202 students responded to the survey. The satisfaction level of online lectures was 3.06±1.12 (n=202) out of 5. Students experiencing online lectures responded that it was difficult to focus on the class, and the overall quality and lecture delivery should be improved. They also experienced technical difficulties. The satisfaction level of practical course lectures was 3.24±1.04 (n=133) out of 5. It was higher than those of other types of classes because it was conducted by the more familiar face-to-face lecture. Conclusion: This study has shown that the universities and instructors have examined a variety of methods in paramedic education after the COVID-19 pandemic. However, further research and consideration are required to improve paramedic education during the COVID-19 pandemic.

가상현실을 이용한 모아간호 실습교육이 간호 대학생의 실습역량에 미치는 영향: 체계적 문헌고찰 (The effects of maternal-child nursing clinical practicum using virtual reality on nursing students' competencies: a systematic review)

  • 황성우;김현경
    • 여성건강간호학회지
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    • 제28권3호
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    • pp.174-186
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    • 2022
  • Purpose: This study aimed to investigate the effects of virtual reality used in maternal-child nursing clinical practicums on nursing students' competencies through a systematic review. Methods: The inclusion criteria were peer-reviewed papers in English or Korean presenting analytic studies of maternal-child nursing practicums using virtual reality. An electronic literature search of the Cochrane Library, CINAHL, EMBASE, ERIC, PubMed, and Research Information Sharing System databases was performed using combinations of the keywords "nursing student," "virtual reality," "augmented reality," "mixed reality," and "virtual simulation" from February 4 to 15, 2022. Quality appraisal was performed using the RoB 2 and ROBINS-I tools for randomized controlled trials (RCTs) and non-RCTs, respectively. Results: Of the seven articles identified, the RCT study (n=1) was deemed to have a high risk of bias, with some items indeterminable due to a lack of reported details. Most of the non-RCT studies (n=6) had a moderate or serious risk of bias related to selection and measurement issues. Clinical education using virtual reality had positive effects on knowledge, skills, satisfaction, self-efficacy, and needs improvement; however, it did not affect critical thinking or self-directed learning. Conclusion: This study demonstrated that using virtual reality for maternal-child nursing clinical practicums had educational effects on a variety of students' competencies. Considering the challenges of providing direct care in clinical practicums, virtual reality can be a viable tool that supplements maternal-child nursing experience. Greater rigor and fuller reporting of study details are required for future research.

해부 실습 비교과프로그램이 보건의료계열 대학생의 전공효능감 및 만족도에 미치는 영향 (The Effects of Anatomy Extracurricular Program on Efficacy of Major and Satisfaction level of Medically Inclined College Students)

  • 송나리;김대훈
    • 융합정보논문지
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    • 제12권5호
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    • pp.271-277
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    • 2022
  • 본 연구에서는 해부 실습 비교과프로그램이 의료보건계열 학생의 전공효능감 및 만족도에 미치는 영향을 알아보았다. 연구 도구는 전공효능감 조사와 프로그램 만족도 조사를 활용하였으며, 전공효능감 조사는 해부학 실습 참여 전-후 향상도, 해부학 그림 참여 전-후 향상도를 비교 분석하였으며, 비교과프로그램 종료 후 해부학 실습과 해부학 그림 간 만족도조사를 비교 분석하였다. 두 그룹간 비교 분석 결과, 전공효능감에 미치는 영향은 프로그램 진행 전보다 후에 유의한 결과가 나타났으며, 만족도의 경우 해부학 실습 그룹의 만족도가 해부학 그림 그룹의 만족도보다 높게 나타난 것을 알 수 있었다. 이와 같은 연구 결과를 통해 해부 실습 비교과프로그램을 운영할 때, 해부학 그림 작성 보다 해부학 실습 프로그램을 운영하는 것이 전공효능감과 만족도 점수를 향상시키는 것으로 생각된다.

Prediction of Wave Transmission Characteristics of Low Crested Structures Using Artificial Neural Network

  • Kim, Taeyoon;Lee, Woo-Dong;Kwon, Yongju;Kim, Jongyeong;Kang, Byeonggug;Kwon, Soonchul
    • 한국해양공학회지
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    • 제36권5호
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    • pp.313-325
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    • 2022
  • Recently around the world, coastal erosion is paying attention as a social issue. Various constructions using low-crested and submerged structures are being performed to deal with the problems. In addition, a prediction study was researched using machine learning techniques to determine the wave attenuation characteristics of low crested structure to develop prediction matrix for wave attenuation coefficient prediction matrix consisting of weights and biases for ease access of engineers. In this study, a deep neural network model was constructed to predict the wave height transmission rate of low crested structures using Tensor flow, an open source platform. The neural network model shows a reliable prediction performance and is expected to be applied to a wide range of practical application in the field of coastal engineering. As a result of predicting the wave height transmission coefficient of the low crested structure depends on various input variable combinations, the combination of 5 condition showed relatively high accuracy with a small number of input variables defined as 0.961. In terms of the time cost of the model, it is considered that the method using the combination 5 conditions can be a good alternative. As a result of predicting the wave transmission rate of the trained deep neural network model, MSE was 1.3×10-3, I was 0.995, SI was 0.078, and I was 0.979, which have very good prediction accuracy. It is judged that the proposed model can be used as a design tool by engineers and scientists to predict the wave transmission coefficient behind the low crested structure.

한국어판 간호학생 간호실무준비도 측정도구의 타당도와 신뢰도 (The Validity and Reliability of the Korean Version of Readiness for Practice Survey for Nursing Students)

  • 이태화;지윤정;윤예슬
    • 대한간호학회지
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    • 제52권6호
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    • pp.564-581
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    • 2022
  • Purpose: This study aimed to evaluate the validity and reliability of the Korean version of the Readiness for Practice Survey (K-RPS). Method: The English Readiness for Practice Survey was translated into Korean using the Translation, Review, Adjudication, Pretesting, and Documentation (TRAPD) method. Secondary data analysis was performed using the dataset from the New Nurse e-Cohort study (Panel 2020) in South Korea. This study used a nationally representative sample of 812 senior nursing students. Exploratory and confirmatory factor analyses were also conducted. Convergent validity within the items and discriminant validity between factors were assessed to evaluate construct validity. Construct validity for hypothesis testing was evaluated using convergent and discriminant validity. Ordinary α was used to assess reliability. Results: The K-RPS comprises 20 items examining four factors: clinical problem solving, learning experience, professional responsibilities, and professional preparation. Although the convergent validity of the items was successfully verified, discriminant validity between the factors was not. The K-RPS construct validity was verified using a bi-factor model (CMIN/DF 2.20, RMSEA .06, TLI .97, CFI .97, and PGFI .59). The K-RPS was significantly correlated with self-esteem (r = .43, p < .001) and anxiety about clinical practicum (r = - .50, p < .001). Internal consistency was reliable based on an ordinary α of .88. Conclusion: The K-RPS is both valid and reliable and can be used as a standardized Korean version of the Readiness for Practice measurement tool.

Determining the reliability of diagnosis and treatment using artificial intelligence software with panoramic radiographs

  • Kaan Orhan;Ceren Aktuna Belgin;David Manulis;Maria Golitsyna;Seval Bayrak;Secil Aksoy;Alex Sanders;Merve Onder;Matvey Ezhov;Mamat Shamshiev;Maxim Gusarev;Vladislav Shlenskii
    • Imaging Science in Dentistry
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    • 제53권3호
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    • pp.199-207
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    • 2023
  • Purpose: The objective of this study was to evaluate the accuracy and effectiveness of an artificial intelligence (AI) program in identifying dental conditions using panoramic radiographs(PRs), as well as to assess the appropriateness of its treatment recommendations. Materials and Methods: PRs from 100 patients(representing 4497 teeth) with known clinical examination findings were randomly selected from a university database. Three dentomaxillofacial radiologists and the Diagnocat AI software evaluated these PRs. The evaluations were focused on various dental conditions and treatments, including canal filling, caries, cast post and core, dental calculus, fillings, furcation lesions, implants, lack of interproximal tooth contact, open margins, overhangs, periapical lesions, periodontal bone loss, short fillings, voids in root fillings, overfillings, pontics, root fragments, impacted teeth, artificial crowns, missing teeth, and healthy teeth. Results: The AI demonstrated almost perfect agreement (exceeding 0.81) in most of the assessments when compared to the ground truth. The sensitivity was very high (above 0.8) for the evaluation of healthy teeth, artificial crowns, dental calculus, missing teeth, fillings, lack of interproximal contact, periodontal bone loss, and implants. However, the sensitivity was low for the assessment of caries, periapical lesions, pontic voids in the root canal, and overhangs. Conclusion: Despite the limitations of this study, the synthesized data suggest that AI-based decision support systems can serve as a valuable tool in detecting dental conditions, when used with PR for clinical dental applications.

머신러닝을 활용한 유역단위 하이브리드모델 개발 및 평가 (Development and evaluation of watershed hybrid model using machine learning)

  • 박상준;이관재;이서로;정연지;금동혁;류지철;박운지;임경재
    • 한국수자원학회:학술대회논문집
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    • 한국수자원학회 2023년도 학술발표회
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    • pp.212-212
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    • 2023
  • 비점오염원관리와 같이 장기적인 유역 관리 계획에서 유역 내 오염원 평가는 정말 중요하다. 유역 내 오염원 평가는 강우 유출에 의한 비점오염 발생원이 어디서 얼마나 발생시키는지에 대한 정량적인 조사가 필요하다. 유역 내의 오염원에 대한 정량적인 조사는 많은 비용과 시간이 필요하다. 이러한 비용과 시간을 줄이기 위해 유역단위 수리 수문 모델을 사용하고 있다. 유역단위 수리수문 모델은 HSPF (Hydrological Simulation Program in Fortran), SWAT (Soil and Water Assessment Tool), L-THIA ACN-WQ(The Long-term Hydrologic Impact Assessment Model with Asymptotic Curve Number Regression Equation and Water Quality model)등 다양한 모델이 사용되고 있다. 하지만 유역 모델을 통한 모의는 다양한 연산 과정을 진행하여 모의까지 많은 시간이 필요하다는 단점이 있다. 이에 따라 데이터 기반 모델링 기법(머신러닝/딥러닝)을 이용한 유출 및 수질 예측 연구가 많이 이루어지고 있다. 단순 머신러닝/딥러닝 기반 모델링 기법은 점(최종유출구)에서의 예측만 가능하여 최적관리 기법 적용 등과 같은 유역관리 방안을 적용하기 힘들다는 문제점이 있다. 따라서 본 연구에서 머신러닝/딥러닝을 통해 일부 수문 프로세스를 대체하고 소유역별 하도추적 기법을 연계하여 유량 및 수질 항목들의 모의가 가능한 하이브리드 모델을 개발하였다. 이는 머신러닝/딥러닝이 유역 모델의 일부 연산 과정을 대체하여 모의시간이 빠르며, 기존 머신러닝/딥러닝 예측 모델에서 평가가 어려웠던 유역 관리 방안 및 최적관리기법 적용 평가에도 활용이 가능할 것으로 판단이 된다.

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