• 의학교육논단
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• 제24권2호
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• pp.156-171
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• 2022

The purpose of this study was to establish a model for constructing longitudinal data for medical school, and to structure cohort and longitudinal data using data from Yonsei University College of Medicine (YUCM) according to the established input-environment-output (I-E-O) model. The study was conducted according to the following procedure. First, the data that YUCM has collected was reviewed through data analysis and interviews with the person in charge of each questionnaire. Second, the opinions of experts on the validity of the I-E-O model were collected through the first expert consultation, and as a result, a model was established for each stage of medical education based on the I-E-O model. Finally, in order to further materialize and refine the previously established model for each stage of medical education, secondary expert consultation was conducted. As a result, the survey areas and time period for collecting longitudinal data were organized according to the model for each stage of medical education, and an example of the YUCM cohort constructed according to the established model for each stage of medical education was presented. The results derived from this study constitute a basic step toward building data from universities in longitudinal form, and if longitudinal data are actually constructed through this method, they could be used as an important basis for determining major policies or reorganizing the curricula of universities. These research results have implications in terms of the management and utilization of existing survey data, the composition of cohorts, and longitudinal studies for many medical schools that are conducting surveys in various areas targeting students, such as lecture evaluation and satisfaction surveys.

• 정보교육학회논문지
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• 제21권4호
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• pp.393-401
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• 2017

본 연구의 목적은 학습자의 데이터 분석적 사고력 향상을 위한 딥러닝 기반 학습 시스템 개발 연구이다. 연구의 내용은 다음과 같다. 첫째, 데이터 분석적 사고력 향상을 위해 발견학습 모형에 딥러닝 기법을 적용하였다. 이는 데이터의 관계를 나타내주는 모델을 딥러닝 기법을 사용하여 생성하고, 새로운 데이터를 이 모델에 적용하여 데이터를 분석하는 과정을 경험할 수 있는 학습 방법이다. 둘째, 이 학습 방법에 따른 수업을 위한 딥러닝 기반 학습 시스템을 개발하였다. 딥러닝 기법을 사용하여 학습자가 입력한 데이터의 모델을 생성하고 적용할 수 있는 시스템을 개발하였다. 딥러닝을 적용한 발견학습 및 시스템 설계 연구는 데이터의 중요성이 더욱 커지는 미래 사회에서 학습자의 데이터 분석적 사고력을 향상시킬 수 있는 새로운 접근이 될 것으로 기대한다.

• 실천공학교육논문지
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• 제15권1호
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• pp.31-38
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• 2023

본 논문에서는 비전공자들을 위한 기초 머신러닝 모델 학습 및 활용교육 커리큘럼을 제안하고, Orange 머신러닝 모델 학습 및 분석 도구를 활용한 교육 방법을 제안하였다. Orange는 오픈 소스기반 머신러닝 및 데이터 시각화 도구로서, 복잡한 프로그래밍 없이 시각적인 위젯을 사용하여, 데이터를 학습시켜 머신러닝 모델을 만들 수 있다. Orange는 비전공자 학부생부터 전문가 그룹까지 다양하게 사용되는 플랫폼이다. 본 논문에서는 한 학기 분량의 기초 머신러닝 모델 학습 및 활용교육 커리큘럼과 주별 실습 내용을 제시하였다. 그리고, 머신러닝 모델 학습 및 활용에 대한 교육 내용 실체를 실증하기 위해, Orange 도구를 활용하여, 분류 데이터(Categorical Data) 표본과 수치 데이터(Numerical Data) 표본으로부터 머신러닝 모델을 학습시키고, 모델을 활용하여 모집단의 결과를 예측하는 활용 사례들을 제안하였다. 마지막으로 본 커리큘럼에 대한 교육 만족도를 비전공자 대상으로 조사 및 분석하였다.

• Journal of Information Processing Systems
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• 제15권3호
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• pp.694-706
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• 2019

In this study, we applied the long short-term memory (LSTM) model to classify the cryptocurrency price time series. We collected historic cryptocurrency price time series data and preprocessed them in order to make them clean for use as train and target data. After such preprocessing, the price time series data were systematically encoded into the three-dimensional price tensor representing the past price changes of cryptocurrencies. We also presented our LSTM model structure as well as how to use such price tensor as input data of the LSTM model. In particular, a grid search-based k-fold cross-validation technique was applied to find the most suitable LSTM model parameters. Lastly, through the comparison of the f1-score values, our study showed that the LSTM model outperforms the gradient boosting model, a general machine learning model known to have relatively good prediction performance, for the time series classification of the cryptocurrency price trend. With the LSTM model, we got a performance improvement of about 7% compared to using the GB model.

• The Korean Journal of Physiology and Pharmacology
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• 제25권6호
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• pp.545-553
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• 2021

Fixed-dose combinations development requires pharmacokinetic drugdrug interaction (DDI) studies between active ingredients. For some drugs, pharmacokinetic properties such as long half-life or delayed distribution, make it difficult to conduct such clinical trials and to estimate the exact magnitude of DDI. In this study, the conventional (non-compartmental analysis and bioequivalence [BE]) and model-based analyses were compared for their performance to evaluate DDI using amlodipine as an example. Raw data without DDI or simulated data using pharmacokinetic models were compared to the data obtained after concomitant administration. Regardless of the methodology, all the results fell within the classical BE limit. It was shown that the model-based approach may be valid as the conventional approach and reduce the possibility of DDI overestimation. Several advantages (i.e., quantitative changes in parameters and precision of confidence interval) of the model-based approach were demonstrated, and possible application methods were proposed. Therefore, it is expected that the model-based analysis is appropriately utilized according to the situation and purpose.

• 한국환경교육학회지:환경교육
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• 제20권4호
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• pp.12-26
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• 2007

The purpose of this research is to develop a water environmental education (EE) program using streams, based on the core ideas of ENVISION and materializing elements that were extracted in this research. This research realized the elements and presented a model of the water EE program using a local stream. First, this research developed a basic model of a water EE program using streams by extracting 10 materializing elements and realizing the elements in 4 stage-procedural model. The 10 materializing elements were 1. experiencing the process of inquiry, 2. inquiring local environments, 3. self-directing learning and mutual interaction with colleagues, 4. collecting real data and interpreting, 5. utilizing the ICT(information and communication technology), 6. inquiring with the view point of the 'Environmental Studies for EE', 7. inquiring with the watershed concept, 8. inquiring with the integrating and the holistic view point, 9. pursuing the macroscopic understanding about environment, and 10. connecting the real world phenomena with the environmental concepts and theories. This research materialized these 10 elements in 4 stage model, following the previous ENVISION research, which are 1. preparing stage and visual assessment, 2. writing the report of the inquiry plan, 3. collecting the real data in the environment and performing the investigation, and 4. presenting the inquiry results. Second, with using this basic model, this research developed and presented a model of the specific water EE program using a case stream called 'Baig Cheon' stream, which is a local stream. This research is considered to have a considerable meaning in developing a EE program with ENVISION ideas for the watershed concept and inquiry with environmental science using local streams. The developed model can help the professional development of teachers and teacher education of water EE.

• 4차산업연구
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• 제3권2호
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• pp.19-26
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• 2023

Purpose - This study is to suggest a model of field education in the Assistant Nurses curriculum using edutech and to produce competent Assistant Nurses students reflecting the requirements of various medical fields. This model expects to upgrade the quality of the field education and to provide an Assistant Nurses school with standardized field education tools using edutech. Research design, data, and methodology - Throughout the review of the related thesis, most of them were studied on Assistant Nurses' job satisfaction, conflicts with other jobs in hospitals, and Assistant Nurses' job area in nursing hospitals. To study the current field education for Assistant Nurses students in hospitals, it used interviewing the heads of the hospital nursing department and reflecting on their interview results to develop the model of field education. Result - The field education model with edutech is processed with flipped class. Each area in flipped class is designed by applications and webs which is friendly to both teachers and students. Conclusion - This study presents a simple and easy process of field education using edutech. In the next study, it needs to find the precious results of comparison between students educated by the new model in field education in the Assistant Nurses' curriculum or not.

• 문화기술의 융합
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• 제9권2호
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• pp.381-388
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• 2023

본 연구는 빅데이터 분석을 활용한 지역 맞춤형 교육프로그램 선정 모형 개발을 주요 목적으로 한다. 우선, 문헌 고찰을 통해 빅데이터 및 교육의 개념 및 특성 그리고 빅데이터 기술과 연구 활용 등의 이론을 분석하여, 이를 평생교육 빅데이터 활용을 위한 선결과제와 기초 연구자료로 제공한다. 아울러 교육 데이터 수집의 방법과 교육의 특성에 적정한 빅데이터 활용 방법을 제시하고 이를 활용한 지역 맞춤형 교육프로그램 선정 모형을 개발하였다. 지역 맞춤형 교육프로그램 선정 모형 개발은 총 6단계로 진행되었다. 본 연구에서 제시한 맞춤형 교육프로그램 모델은 실질적 활용 면에 있어, 국가승인통계인 '평생학습 개인 실태조사' 처럼 1년 후에 분석하지 않고 실시간으로 데이터가 제공되는 방식으로 활용 부분에 있어서도 선택적 분석이나 미래예측 등 자유도가 매우 높아 교육 분야에 빅데이터가 충분한 필요성과 가치가 있음을 알 수 있다. 뿐만 아니라 표본 모형에 사용되고 있는 모든 프로그램은 무료로 제공되고 있으며, 프로그래밍 특성상 커뮤니티 또한 활발하게 교류가 이루어지고 있어 추후 수정 및 보완 시에도 매우 용이하여 더욱 완성도 높은 교육프로그램 개발 모형을 개발할 수 있다.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제20권1호
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• pp.59-70
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• 2013

The purpose of this study was to develop a zero-inflated Rasch (ZI-Rasch) model, a combination of the Rasch model and the ZIP model. The ZI-Rasch model was considered in this study as an appropriate alternative to the Rasch model for zero-inflated data. To investigate the relative appropriateness of the ZI-Rasch model, several analyses were conducted using PROC NLMIXED procedures in SAS under various simulation conditions. Sets of criteria for model evaluations (-2LL, AIC, AICC, and BIC) and parameter estimations (RMSE, and $r$) from the ZI-Rasch model were compared with those from the Rasch model. In the data-model fit indices, regardless of the simulation conditions, the ZI-Rasch model produced better fit statistics than did the Rasch model, even when the response data were generated from the Rasch model. In terms of item parameter ${\lambda}$ estimations, the ZI-Rasch model produced estimates similar to those of the Rasch model.

• International Journal of Computer Science & Network Security
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• 제22권10호
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• pp.246-256
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• 2022

Recently, parents and teachers consider physical education as a minor subject for students in elementary and secondary schools. Physical education performance has become increasingly significant as parents and schools pay more attention to physical schooling. The sports mining with distribution analysis model considers different factors, including the games, comments, conversations, and connection made on numerous sports interests. Using different machine learning/deep learning approach, children's athletic and academic interests can be tracked over the course of their academic lives. There have been a number of studies that have focused on predicting the success of students in higher education. Sports interest prediction research at the secondary level is uncommon, but the secondary level is often used as a benchmark to describe students' educational development at higher levels. An Automated Student Interest Prediction on Sports Mining using DL Based Bi-directional Long Short-Term Memory model (BiLSTM) is presented in this article. Pre-processing of data, interest classification, and parameter tweaking are all the essential operations of the proposed model. Initially, data augmentation is used to expand the dataset's size. Secondly, a BiLSTM model is used to predict and classify user interests. Adagrad optimizer is employed for hyperparameter optimization. In order to test the model's performance, a dataset is used and the results are analysed using precision, recall, accuracy and F-measure. The proposed model achieved 95% accuracy on 400^th instances, where the existing techniques achieved 93.20% accuracy for the same. The proposed model achieved 95% of accuracy and precision for 60%-40% data, where the existing models achieved 93% for accuracy and precision.