• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5607-5609
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• 2014

Introduction: Although continuing education is necessary for practicing nurses, it is very difficult to organize traditional classes because of large numbers of nurses and working shifts. Considering the increasing development of mobile electronic learning, we carried out a study to compare effects of the traditional face to face method with mobile learning delivered as text messages by cell phone. Materials and Methods: Sixty female nurses working in our hospital were randomly divided into class and short message service (SMS) groups. Lessons concerning breast cancer screening were prepared as 54 messages and sent in 17 days for the SMS group, while the class group participated in a class held by a university lecturer of breast and cancer surgery. Pre- and post-tests were undertaken for both groups at the same time; a retention test also was performed one month later. For statistical analysis, the paired T test and the independent sample T test were used with SPSS software version 16; p<0.05 was considered significant. Results: Mean age and mean work experience of participants in class and SMS groups was $35.8{\pm}7.2$, $9.8{\pm}6.7$, $35.4{\pm}7.3$, and $11.5{\pm}8.5$, respectively. There was a significant increase in mean score post-tests (compared with pretests) in both groups (p<0.05). Although a better improvement in scores of retention tests was demonstrated in the SMS group, the mean subtraction value of the post- and pretests as well as retention- and pretests showed no significant difference between the 2 groups (p=0.3 and p =0.2, respectively). Conclusions: Our study shows that teaching via SMS may probably replace traditional face to face teaching for continuing education in working nurses. Larger studies are suggested to confirm this.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.285-301
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• 2023

Due to the increasing demand and importance of non-face-to-face education, open online learning platforms are getting interests both domestically and internationally. These platforms exhibit different characteristics from online courses by universities and other educational institutions. In particular, students engaged in these platforms can receive more learner autonomy, and the development of tools to assist learning is required. From the past, researchers have attempted to utilize process mining to understand realistic study behaviors and derive learning patterns. However, it has a deficiency to employ it to the open online learning platforms. Moreover, existing research has primarily focused on the process model perspective, including process model discovery, but lacks a method for the process pattern and instance perspectives. In this study, we propose a method to identify learning patterns within an open online learning platform using process mining techniques. To achieve this, we suggest three different viewpoints, e.g., model-level, variant-level, and instance-level, to comprehend the learning patterns, and various techniques are employed, such as process discovery, conformance checking, autoencoder-based clustering, and predictive approaches. To validate this method, we collected a learning log of machine learning-related courses on a domestic open education platform. The results unveiled a spaghetti-like process model that can be differentiated into a standard learning pattern and three abnormal patterns. Furthermore, as a result of deriving a pattern classification model, our model achieved a high accuracy of 0.86 when predicting the pattern of instances based on the initial 30% of the entire flow. This study contributes to systematically analyze learners' patterns using process mining.

• Tuberculosis and Respiratory Diseases
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• v.86 no.3
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• pp.203-215
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• 2023

Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R² and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R² value was 0.27 and in set II, LightGBM was the best model with the highest R² value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R² value of 0.56 and the lowest MSE of 174.07. Conclusion: The LightGBM model showed the best performance in predicting postoperative lung function.

• Journal of Korean Society on Water Environment
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• v.38 no.6
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• pp.292-305
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• 2022

The Yeongsan River, one of the four largest rivers in South Korea, has been facing difficulties with water quality management with respect to algal bloom. The algal bloom menace has become bigger, especially after the construction of two weirs in the mainstream of the Yeongsan River. Therefore, the prediction and factor analysis of Chlorophyll-a (Chl-a) concentration is needed for effective water quality management. In this study, Chl-a prediction model was developed, and the performance evaluated using machine and deep learning methods, such as Deep Neural Network (DNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Moreover, the correlation analysis and the feature importance results were compared to identify the major factors affecting the concentration of Chl-a. All models showed high prediction performance with an R² value of 0.9 or higher. In particular, XGBoost showed the highest prediction accuracy of 0.95 in the test data.The results of feature importance suggested that Ammonia (NH₃-N) and Phosphate (PO₄-P) were common major factors for the three models to manage Chl-a concentration. From the results, it was confirmed that three machine learning methods, DNN, RF, and XGBoost are powerful methods for predicting water quality parameters. Also, the comparison between feature importance and correlation analysis would present a more accurate assessment of the important major factors.

• Journal of The Korean Association of Information Education
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• v.18 no.4
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• pp.499-508
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• 2014

Developing Computational Thinking is getting attention as the importance of SW is emphasized. Also programming education is getting attention, especially, various researches that utilize robot in programming education are being carried out. This study focused on compensating the defects of the prior robot programming education and exploring the way of utilizing web based tool 2.0 while putting emphasis on communication and cooperation. This plan is based on $Gagn{\acute{e}}$ & Briggs nine events of instruction and can be used to implement cooperative learning with the Web 2.0 based tools at every instructional events. Tests for learner's cooperation were done before and after this new plan to evaluate its value. The result proves that this plan had a positive influence on improving learner's cooperative attitude.

• Journal of Science Education
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• v.34 no.2
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• pp.225-236
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• 2010

The purpose of this study is to analyze and evaluate the Cyber Home Learning contents of Earth science area in the basic course of the $10^{th}$ grade. For this purpose, we applied the 'Cyber Home Study Content Quality Control Tool' presented in Elementary Secondary Education e-Learning Quality Management Guidelines (Ver.2.0)' of Korea Education & Research Information Service(2008). The results of Cyber home learning contents analysis are as follow: First, it was presented that the study guide introduced the contents which should be studied for one class, properly. And it was not analyzed that the diagnosis assesment was not completed in the initiative study; Second, it was possible to study choosing the contents fitting the learner's level of learning in the main study, it was comprised of about 10 minutes. Third, it was performed without feedback for incorrect answers in the learning assessment, just the number of wrong questions. And the learning arrangement present the important contents learned in that class, summarizing and arranging again. The results of evaluating the contents in Cyber Home Learning are as follows: First, in evaluation section of instructional design, many text materials which were so difficult for learners to read were explained, being provided. Besides, the systematic structures leaves much to be desired, in view of learners' learning experience, contents, and environment. And in evaluation section of learning contents, the error of contents caused the learning contents not to appear, the amount of learning in each section was found too much. Second, in evaluation section of the strategy for Teaching and Learning, when we mention the strategy of Self Directed Learning, the environment to make learners search for information free and self-study possible was not possessed well. And in evaluation section of interaction, it was found that a simple click caused the learning to go on. Third, in evaluation section of evaluating, it was evaluated that there was wanting in consistency in learning aims, contents, evaluation contents.

• Phonetics and Speech Sciences
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• v.2 no.2
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• pp.3-16
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• 2010

This study aims at setting the hierarchy of difficulty of the 7 Korean monophthongs for Mongolian learners of Korean according to Prator's theory based on the Contrastive Analysis Hypothesis. In addition to that, it will be shown that the difficulties and errors for Mongolian learners of Korean as a second or foreign language proceed directly from this hierarchy of difficulty. This study began by looking at the speeches of 60 Mongolians for Mongolian monophthongs; data were investigated and analyzed into formant frequencies F1 and F2 of each vowel. Then, the 7 Korean monophthongs were compared with the resultant Mongolian formant values and are assigned to 3 levels, 'same', 'similar' or 'different sound'. The findings in assessing the differences of the 8 nearest equivalents of Korean and Mongolian vowels are as follows: First, Korean /a/ and /$\wedge$/ turned out as a 'same sound' with their counterparts, Mongolian /a/ and /ɔ/. Second, Korean /i/, /e/, /o/, /u/ turned out as a 'similar sound' with each their Mongolian counterparts /i/, /e/, /o/, /u/. Third, Korean /ɨ/ which is nearest to Mongolian /i/ in terms of phonetic features seriously differs from it and is thus assigned to 'different sound'. And lastly, Mongolian /$\mho$/ turned out as a 'different sound' with its nearest counterpart, Korean /u/. Based on these findings the hierarchy of difficulty was constructed. Firstly, 4 Korean monophthongs /a/, /$\wedge$/, /i/, /e/ would be Level 0(Transfer); they would be transferred positively from their Mongolian counterparts when Mongolians learn Korean. Secondly, Korean /o/, /u/ would be Level 5(Split); they would require the Mongolian learner to make a new distinction and cause interference in learning the Korean language because Mongolian /o/, /u/ each have 2 similar counterpart sounds; Korean /o, u/, /u, o/. Thirdly, Korean /ɨ/ which is not in the Mongolian vowel system will be Level 4(Overdifferentiation); the new vowel /ɨ/ which bears little similarity to Mongolian /i/, must be learned entirely anew and will cause much difficulty for Mongolian learners in speaking and writing Korean. And lastly, Mongolian /$\mho$/ will be Level 2(Underdifferentiation); it is absent in the Korean language and doesn‘t cause interference in learning Korean as long as Mongolian learners avoid using it.

• Computers and Concrete
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• v.32 no.3
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• pp.233-246
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• 2023

Accurate prediction of concrete compressive strength can minimize the need for extensive, time-consuming, and costly mixture optimization testing and analysis. This study attempts to enhance the prediction accuracy of compressive strength using stacking ensemble machine learning (ML) with feature engineering techniques. Seven alternative ML models of increasing complexity were implemented and compared, including linear regression, SVM, decision tree, multiple layer perceptron, random forest, Xgboost and Adaboost. To further improve the prediction accuracy, a ML pipeline was proposed in which the feature engineering technique was implemented, and a two-layer stacked model was developed. The k-fold cross-validation approach was employed to optimize model parameters and train the stacked model. The stacked model showed superior performance in predicting concrete compressive strength with a correlation of determination (R²) of 0.985. Feature (i.e., variable) importance was determined to demonstrate how useful the synthetic features are in prediction and provide better interpretability of the data and the model. The methodology in this study promotes a more thorough assessment of alternative ML algorithms and rather than focusing on any single ML model type for concrete compressive strength prediction.

• Biomolecules & Therapeutics
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• v.20 no.2
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• pp.207-213
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• 2012

The present study examined the effects of krill-derived phosphatidylserine (Krill-PS) on the learning and memory function and the neural activity in rats with trimethyltin (TMT)-induced memory deficits. The rats were administered vehicle (medium-chain triglyceride: MCT) or Krill-PS (50, 100 mg/kg, p.o.) daily for 21 days. The cognitive improving efficacy of Krill-PS in TMT-induced amnesic rats was investigated by assessing the Morris water maze test and by performing choline acetyltransferase (ChAT), acetylcholinesterase (AChE) and cAMP responsive element binding protein (CREB) immunohistochemistry. The rats with TMT injection showed impaired learning and memory of the tasks and treatment with Krill-PS produced a significant improvement of the escape latency to find the platform in the Morris water maze at the $2^{nd}$ and $4^{th}$ day compared to that of the MCT group (p<0.05). In the retention test, the Krill-PS+MCT groups showed increased time spent around the platform compared to that of the MCT group. Consistent with the behavioral data, Krill-PS 50+MCT group significantly alleviated the loss of acetylcholinergic neurons in the hippocampus and medial septum compared to that of the MCT group. Treatment with Krill-PS significantly increased the CREB positive neurons in the hippocampal CA1 area as compared to that of the MCT group. These results suggest that Krill-PS may be useful for improving the cognitive function via regulation of cholinergic marker enzyme activity and neural activity.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.205-208
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• 2003

대학이 신입생을 선발하는 기준은 해당전공을 공부하는데 적합한 지원자를 선발하는 것이며, 해당모집단위에 많이 지원하게 하여 신입생 충원을 용이하게 하는 것 등이다. 대학은 입학전형을 거쳐서 신입생을 선발하게 되며, 선발기준으로 전형요소를 활용하게 된다. 전형요소 활용방법은 신입생 선발에 영향을 주기 때문에 전략적으로 중요하다. 2005학년도 신입생 선발에는 고등학교 7차 교육과정을 이수한 지원자들이 지원하게 되므로 전형요소에 있어서 변화가 있다. 대학입학수학능력 시험의 체제는 수험생들이 영역이나 과목을 선택하여 응시하는 방향으로 변화한다. 즉, 수리 영역을 가형 및 나형으로 응시하고, 하나의 탐구영역을 응시하되 사회탐구영역 및 과학탐구영역은 4과목 이내에서 선택하여 응시한다. 또한 수학능력시험의 성적표는 각 영역별 및 과목별 표준점수, 백분위점수 및 등급을 표시하여 통지한다. 본 연구는 변화된 수학능력시험의 체제와 고등학교 교육과정을 어떠한 방법으로 반영하여 학생을 선발하는 것이 바람직한가에 대한 전략을 도출하는 틀을 제시하고, 각 전형요소 활용의 대안과 문제점을 도출하고자 한다. 2005 수능시험 결과는 표준점수로 통지하기 때문에 만점 개념을 적용하기 어렵고, 표준점수를 전형요소로 활용할 때 전형총점 개념을 도입하기 어렵다. 또한 복수영역 및 과목의 선택에서 유리함과 불리함이 나타나게 된다. 과거의 수능시험결과를 분석하여 전형총점개념 도입의 방법과 불리함을 보정하여 주는 방법을 제시하고, 신입생을 선발하는 목적에 적합한 전형요소 결정전략을 도출하고자 한다.2; Learning Decisions, 2001) 연구모형을 설정하고 이를 근거로 실증연구를 수행 중에 있다.7.2 $e^{0.101}$x/, y = 70.01 $e^{0.030}$x/, 반감기는 12.0, 6.86, 23.0 일이고 폐장, 간장, 신장의 회복기간(x)별 크롬농도(y)의 소실속도 상관계수 (노출농도 0.50 mg/㎥군의 경우)는 y = 1808 $e^{0.004}$93x/, y = 12.02 $e^{0.029}$7x/, y = 67.61 $e^{0.029}$2x/ 반감기는 140.6, 23.3, 23.7 일로 평가되었다. 4. 고찰 : 실험동물의 전혈, 혈청, 뇨에서의 크롬농도와 시험물질 노출농도는 밀접한 상관을 가졌으나 농도에 정비례하지는 않았다. 뇨 중 흡수된 크롬의 경우 회복기간 초기 (12시간 내)에 대부분 배설이 일어나는 것으로 나타났다. 폐장이 간장, 신장 등 다른 장기에 비해 높은 축적량을 보였으며 축적된 크롬농도가 높을수록 크롬의 소실속도는 현저히 저하하는 경향을 보였다. 노출농도가 높을수록 각 장기조직 내 크롬의 소실속도 (clearance)는 크게 감소경향이 있었으며 이는 체내 과부하시 자정작용이 감소하는 것으로 판단되었다. 본 연구 결과 SD rat를 이용 반복흡입노출의 경우 생체의 무유해영향농도 (NOAEL)는 0.2mg/㎥이하이며 발암물질을 감안하여 안전계수를 100으로 할 경우 사람에 대한 NOAEL은 0.002mg/㎥이하로 판단되