• 한국학교보건학회지
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• 제32권2호
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• pp.67-76
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• 2019

Purpose: The purpose of this study was to explore self-directed learning ability and its affecting factors among undergraduate students. Methods: A descriptive cross-sectional study design was used. Using a convenience sampling method, data were collected from 196 undergraduate students enrolled in one national university. Negative psychological tendency (i.e. Type D personality), academic engagement, and self-directed learning ability were assessed using a structured questionnaire. Data were analyzed by descriptive statistics, t-test, and analysis of covariance, Pearson's correlation coefficients, and stepwise multiple linear regression, using SPSS/WIN 23.0 program. Results: The mean age of the students was $21.61{\pm}2.40years$ and 56.6% were male students. Approximately, one third (n=67, 34.2%) of the students had Type D personality. The average scores of academic engagement and self-directed learning ability were $3.01{\pm}1.14$ and $3.46{\pm}0.50$, respectively. After controlling for sociodemographic variables, the Type D personality and academic engagement were significant predictors of self-directed learning ability (${\beta}=.64$, p<.001; ${\beta}=-.13$, p=.021, respectively). This model explained 53.6% of the variance in self-directed learning ability. Conclusion: The study identified that Type D personality and academic engagement affect self-directed learning ability of undergraduate students, one in a negative way, the other in a positive way. Educators and educational policy makers need to make efforts to include interventions and strategies that increase academic engagement and change negative psychological dispositions such as D-type personality in the undergraduate education curriculum.

• 한국수학교육학회지시리즈E:수학교육논문집
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• 제35권3호
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• pp.341-358
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• 2021

2020년 처음 발생한 코로나는 사회 전반에 많은 변화를 초래했다. 이러한 변화에 있어 교육 역시 예외가 아니었다. 학교에서는 사상 유례없는 온라인 개학을 시행했고 학생들과 교사는 충분한 준비 없이 원격수업에 참여해야 했다. 원격수업과 관련하여 관련 자료 개발, 만족도 조사와 같은 연구들이 수행되었으나 실제 학교 현장을 기반으로 한 연구는 없었다. 따라서 본 연구는 2020년부터 원격수업에 참여한 고등학교 학생들을 대상으로 하여 원격수업의 방식 및 만족도를 조사하고 원격수업 만족도와 수학학습과 관련된 하위요소들과의 관계를 탐색하였다. 연구결과 수학 교과에서는 원격수업이 대체로 실시간 쌍방향 수업의 형태로 이루어졌으며 학생들은 수학학습에 만족을 느꼈다. 하지만 원격수업을 지속해야 하는 것의 문제에 있어서 어느 정도 상반된 의견을 표명하였다. 또한, 통계분석 결과 원격수업에 대한 만족도에 따라 수학학습 의지, 전략, 수학에 대한 가치, 학습의욕 등에서 차이가 나타나는 것으로 드러났다.

• 문화기술의 융합
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• 제6권4호
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• pp.9-14
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• 2020

이 연구에서는 장애학생을 위해 개발된 통합교육 과학과 자료 e-book이 보편적 학습설계 측면에서 적합하게 구현되었는지 살펴보았다. 분석을 위해 2015 개정 교육과정 초등학교 과학과 3~6학년까지 일반 교과서 내용을 학습하기 어려운 장애학생을 위해 교수 적합화(adaptation)한 국가 수준 자료인 「장애학생 통합교육 교수-학습 자료: 과학과」를 선정하였다. 과학과 3~6학년 영역은 기초과학탐구, 물질, 생명, 운동과 에너지, 지구와 우주로 총 40개 단원으로 구성되어 있다. 내용분석기준은 CAST(2018)에서 제시된 UDL 3개 원칙에 따른 9개 항목의 세부항목을 기준으로 하였다. 연구결과 통합교육 과학과 e-book은 UDL 원칙 중 전략적 네트워크가 가장 많았으며, 운동과 에너지 영역에서 가장 많이 나타났다. UDL 원칙별로 살펴보면, 인지적 네트워크는 학습자에 맞는 정보제시 구현, 전략적 네트워크는 자료탐색 방식 다양화 등(4.1~5.2/6.3), 정서적 네트워크는 난이도를 최적화한 다양한 자료제시가 가장 많이 나타났다.

• 정보교육학회논문지
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• 제11권2호
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• pp.205-218
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• 2007

본 연구는 서울 소재의 A사이버 대학에서 2002년 1학기부터 2005년 1학기까지의 누적입학생 가운데 제적, 자퇴한 중도탈락생의 배경변인을 조사하고 설문을 통해 사이버대학에서의 중도탈락의 원인을 알아보았다. 학습시간의 부족과 경제적 어려움이 가장 큰 원인으로 지적되었으나, 온라인교육에의 부적응과 사이버대학의 인지도에 대한 불안감도 적지 않은 학생들에게 중도탈락의 원인이 되었다. 특히 온라인교육에의 부적응이나 사이버대학의 인지도에 대한 불안감이 중도탈락의 원인으로 되었던 학생들은 다른 학생이나 교수와의 상호작용 부족을 온라인 학습의 가장 큰 어려움으로 꼽았다. 따라서 사이버대학의 중도탈락을 줄이기 위해서는 보다 효과적인 학습전략의 개발이나 등록금 지원 외에도 학생 상호간, 학생-교수 간의 상호작용 증대를 통하여 소속감을 고취하고 온라인 학습의 어려움을 나누는 노력이 필요한 것으로 드러났다.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.31-40
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• 2021

The increasing number of credit card fraud cases has become a considerable problem since the past decades. This phenomenon is due to the expansion of new technologies, including the increased popularity and volume of online banking transactions and e-commerce. In order to address the problem of credit card fraud detection, a rule-based approach has been widely utilized to detect and guard against fraudulent activities. However, it requires huge computational power and high complexity in defining and building the rule base for pattern matching, in order to precisely identifying the fraud patterns. In addition, it does not come with intelligence and ability in predicting or analysing transaction data in looking for new fraud patterns and strategies. As such, Data Mining and Machine Learning algorithms are proposed to overcome the shortcomings in this paper. The aim of this paper is to highlight the important techniques and methodologies that are employed in fraud detection, while at the same time focusing on the existing literature. Methods such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), naïve Bayesian, k-Nearest Neighbour (k-NN), Decision Tree and Frequent Pattern Mining algorithms are reviewed and evaluated for their performance in detecting fraudulent transaction.

• 간호행정학회지
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• 제22권1호
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• pp.1-10
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• 2016

Purpose: A descriptive survey was used in this study to identify the relationship between nurses' critical thinking disposition, self-directed learning readiness and professional nursing competency and to examine factors that influence professional nursing competency. Methods: The data were collected from 188 nurses and analyzed using t-test, ANOVA, $Scheff{\acute{e}}$' test, Pearson correlation, and stepwise multiple regression analysis with the SPSS/WIN 20.0 PC program. Results: Professional nursing competency was found to have significant pure correlations with critical thinking disposition (r=.59, p<.001), and self-directed learning readiness (r=.54, p<.001). The most influential factor influencing nurses' professional nursing competency was critical thinking disposition, followed by self-directed learning readiness (${\beta}=.25$, p=.003), work department (${\beta}=.19$, p=.001), total clinical career (${\beta}=.19$, p=.003), and position (${\beta}=.12$, p=.040), and these factors explained 43.8% of the variance in professional nursing competency. Conclusion: The findings indicate the necessity of developing and applying strategies and educational programs to enhance individual nurse's critical thinking disposition and self-directed learning readiness. Furthermore, exploration is needed on ways to enhance professional nursing competency.

• Smart Structures and Systems
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• 제29권1호
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• 정보교육학회논문지
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• 제16권4호
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• pp.439-450
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• 2012

본 연구는 정보통신기술의 발달에 따른 초등교사에 대한 사회적 요구를 반영하고 대학 강의의 질을 개선하기 위하여 컴퓨터 교과교육에서 웹 2.0 기반의 문제중심학습을 통합 적용하고 그 효과를 확인하고자 하였다. 학습자들은 4회에 걸쳐 문제중심학습을 수행하였다. 그 학습 과정은 클라우드 환경의 구글 오피스를 이용하여 작성하였으며, PBL 과정 및 결과에 관한 경험을 반성 저널에 작성하였다. 학습자들의 문제 해결 과정과 반성저널은 질적 분석 방법을 통하여 분석하였으며 그 결과는 다음과 같다. 웹 2.0 기반의 문제중심학습은 컴퓨터교과교육에 대한 이해의 증진, ICT 활용 능력의 향상, 웹 2.0 기반의 교수 학습 전략의 습득, 그리고 수행평가로서 e-포트폴리오를 경험하는 등 21세기 교사에게 필요한 역량을 향상시킬 수 있었다.

• 영어어문교육
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• 제12권2호
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• pp.61-85
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• 2006

The purpose of the present study is to compare Korean EFL learners' speech corpus (i.e. oral productions) with their composition corpus (i.e. written productions). Four college students participated in the study. The composition corpus was collected through a writing assignment, and the speech corpus was gathered by audio-taping their oral presentations. The results of the data analysis indicate that (i) As for error frequency, young adult low-intermediate Korean EFL learners showed high frequency in determiners (mostly, indefinite articles), vocabulary (mostly, semantic errors), and prepositions. The frequency order did not show much difference between the speech corpus and the composition corpus; and (ii) When comparing the oral productions with the written productions, there were not many differences between them in terms of the contents, a style (i.e., colloquial vs. literary), vocabulary selection, and error types and frequency. Therefore, it is assumed that the proficiency in oral presentation of EFL learners at this learning stage heavily depends on how much/how well they are able to write. In other words, EFL learners' writing and speaking skills are closely co-related. It implies that the teacher does not need to separate teaching how to speak from teaching how to write. The teacher may use the same methods or strategies to help the learners improve their English speaking and writing skills. Furthermore, it will be more effective to teach writing before speaking since they have more opportunities to write than speak in the EFL contexts.

• 가족자원경영과 정책
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• 제18권2호
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• pp.37-54
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• 2014

This study investigates fathers' demands for fathering education. Data from eight married, working men was collected through in-depth interviews. The interviews were intended to examine their father-role, working life, balance between work and family, and demand for the educational programs for fathers with particular regard to the themes, contents, and arrangement strategies they desire of the programs. The results show that fathers have a high demand for learning detailed methods of childrearing such as how to effectively sooth and play with their child(ren). Further, it was discovered that job flexibility is the major variable that determines a man's satisfaction with his parental role. Various information about possible arrangements of fathering education was collected from the interview data, such as desired themes, number of sessions, size of the educational program, volunteer role of participants, and focus of the course (e.g., many indicated interest in focusing on gender equality). This study reveals that greater detail, more effective contents, and efficient managerial strategies are required in fathering education in order to impart broader perspectives and knowledge about how to enhance the relationship between father and child(ren).