• Korean Medical Education Review
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• v.13 no.1
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• pp.45-49
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• 2011

This study was done to analyze students' learning and its lasting effect by teaching strategy involving questioning. This study was performed with 68 students who were enrolled in a physiology class of the Yonsei University College of Medicine in Seoul, Korea, in 2003. The students were randomly divided into 2 groups. One group was taught in a way where students asked questions and the instructor answered the questions. For the other group of students, the instructor asked questions, and the students answered the questions. We performed a pre-test before the study begins and post-tests immediately, 3 weeks, and 6 weeks after the study. The results were analyzed by using analysis of covariance and repeated measures analysis of variance. A higher learning effect was observed in a group where questions were asked by students compared with the other group. The post-test results showed no significant difference in the lasting effect of learning according to the teaching strategy. Students' learning significantly improved when students asked questions and the instructor answered the questions compared with the strategy of the instructor asking questions and students answering to the questions.

• Journal of Information Science Theory and Practice
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• v.10 no.3
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• pp.24-39
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• 2022

Online learning is becoming ubiquitous worldwide because of its accessibility anytime and from anywhere. However, it cannot be successfully implemented without understanding constructs that may affect its adoption. Unlike previous literature, this research extends the Unified Theory of Acceptance and Use of Technology with three well-known theories, namely compatibility, online self-efficacy, and knowledge sharing and acquisition to examine online learning adoption. A total of 264 higher education students took part in this research. Partial Least Squares-Structural Equation Modeling was used to evaluate the proposed theoretical model. The findings suggested that performance expectancy and compatibility were significant predictors of behavioral intention, whereas behavioral intention, facilitating conditions, and compatibility had a significant and direct effect on online learning's actual use. The results also showed that knowledge acquisition, knowledge sharing, and online self-efficacy were determinates of performance expectancy. Finally, online self-efficacy was a predictor of effort expectancy. The proposed model achieved a high fit and explained 47.7%, 75.1%, 76.1%, and 71.8% of the variance of effort expectancy, performance expectancy, behavioral intention, and online learning actual use, respectively. This study has many theoretical and practical implications that have been discussed for further research.

• Journal of Digital Convergence
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• v.16 no.4
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• pp.289-297
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• 2018

The purpose of this study was to examine the correlations of learning attitude, class participation, and learning satisfaction, and to identify the influencing factors on learning satisfaction of nursing students in fundamental nursing curriculum. Data were collected from 173 nursing students from September 1 to 8, 2017. Data were analyzed using Pearson's correlation coefficients and stepwise multiple regression. Learning attitude, class participation, and learning satisfaction according to the general characteristics commonly showed significant differences in satisfaction with major, satisfaction with school, friendship, last semester grade, and interest in fundamental nursing. Learning satisfaction showed significant positive correlations with learning attitude and class participation. Satisfaction with major, interest in fundamental nursing, and class participation, which accounted for 36% of the variance, were significant predictors influencing learning satisfaction in nursing students. It is necessary to increase majors' satisfaction, fundamental nursing interest, and class participation in order to improve learning satisfaction of nursing students.

• Korean Medical Education Review
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• v.23 no.2
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• pp.118-127
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• 2021

Due to the coronavirus disease 2019 pandemic, the new norm of online learning has been recognized as core to medical institutions for academic continuity, and students are expected to be motivated and engaged in learning while maintaining distance from other peers and educators. To facilitate students' and educators' newly defined roles in online medical education settings, it is crucial to understand how students are actively motivated and engaged in learning. Hence, this study explored medical students' motivational regulation profiles and examined the effects of motivational regulation strategies (MRS) on cognitive learning and learning engagement for online learning. Data were collected after the end of the first semester in 2020 from a sample of 334 medical students enrolled at a public university school of medicine. Latent profile analysis indicated three subgroups with different motivational regulation profiles: the low-profile, medium-profile, and high-profile groups. Regarding different MRS patterns in the high-profile group, mastery self-talk, performance approach self-talk, and the self-consequating strategy appeared to be most applicable for regulating learners' motivation. Analysis of variance showed that the profile groups with higher levels of MRS use were connected to a higher willingness to use cognitive learning strategies and a higher degree of engagement in online learning. The findings of this study emphasize the use of specific sets of MRS to support learning motivation and the need to design effective self-regulated learning environments in online medical education settings.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1995.04a
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• pp.587-599
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• 1995

This paper proposes a method for pattern recognition using spectral analysis and a hybrid genetic-neural-fuzzy networks. The feature vectors using spectral analysis on contour sequences of 2-D images are extracted, and the vectors are not effected by translation, rotation and scale variance. A combined model using the advantages of conventional method is proposed, those are supervised learning BP, global searching genetic algorithm, and unsupervised learning fuzzy c-method. The proposed method is applied to 10 aircraft recognition to confirm the performance of the method. The experimental results show that the proposed method is better accuracy than conventional method using BP or fuzzy c-method, and learning speed is enhanced.

• Journal of Industrial Technology
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• v.29 no.B
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• pp.25-31
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• 2009

The necessity of university's educational change is rising up, along with a social environment that keeps changing. This study deals with a newly educational style by recognizing the necessity of variance for engineering education, according to engineers' diversified roles within a company. The study was conducted by 7 members of middle-sized group, replaced from 5 members of small-sized and each was given his task by his job such as CEO, CMO, CSO, CIO, CCO, CQO or Auditor. On the basis of four theoretical backgrounds, this paper investigated students' level of learning satisfaction shown before and after task undertaking through a questionnaire and appraised the result made before and after task undertaking. This study is expected to improve a teaching method of university engineering education.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.20 no.4
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• pp.438-448
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• 2013

Purpose: The purpose of this study was to investigate factors influencing clinical competence in nursing students. Method: The participants were 125 nursing students and data were collected from October 8 to December 18, 2010 using questionnaires with. Collected data were analyzed using descriptive statistics, t-test, ANOVA, Pearson's correlation coefficient and stepwise multiple regression. Results: There were significant correlations for creativity, problem-solving ability, self-directed learning ability, and clinical competence. The factor influencing clinical competence the most was creativity, followed by problem-solving ability, self-directed learning ability, and grade point average score. The regression model explained 37% of variance in clinical competence. Conclusion: The results indicate that for improvement in the clinical competence of nursing students, it is necessary to develop strategies and education programs to enhance creativity, problem-solving ability, and self-directed learning ability.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.10
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• pp.141-150
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• 1997

분산은 확률모델을 표현하는 유용한 변수중 하나이다. 입력변수에 대한 함수로 표현되는 조건부 분산을 학습하는 신경회로망에 대한 많은 연구가 있어왔다. VALEAN이라는 신경회로망 역시 이러한 많은 연구중 하나인데 이것은 기본적으로 feedforward 다층 퍼셉트론 구조를 가지며 새롭게 제시된 에너지 함수를 사용하고 있다. 이 논문에서는 이 에너지 모델에 의해 결정되는 피드백에러(델타)가 신경망의 transient, steady state에서 미치는 영향을 다루었다. 과도 상태 분석에서는 델타와 수렴성, 안정성에 관한 내용을 다루고 모의 실험을 하였으며 정상 상태 분석에서는 신경회로망의 정상상태 에러의 크기와 델타의 크기사이의 상관관계에 대하여 다루었다. 학습 알고 리듬이 확률적이므로 정상상태 역시 확률적인 상태를 나타낸다. 따라서 델타의 크기에 따른 정상 상태 에러의 최대치는 확률적인 모델을 가지게 된다. 여기서는 이 확률 관계를 분석적으로 규명하고 이에 따라 원하는 신뢰도로 정상 상태 에러를 제어하기 위해 필요한 델타의 크기를 예측할 수 있는 이론적 배경을 마련하게 된다.

• Journal of Technologic Dentistry
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• v.43 no.3
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• pp.132-137
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• 2021

Purpose: To study the perception and satisfaction of in-person and online classes for dental technology students. Methods: A total of 420 questionnaires were distributed to dental technology students between June 1 and June 30, 2021. Of these, 225 questionnaires were assessed using frequency analysis, one-way analysis of variance, Pearson's Chi-squared test, and independent t-tests via IBM SPSS Statistics ver. 22.0 (IBM). Results: For theory subjects, satisfaction was higher for online classes than in-person classes (p=0.001). For practical subjects, satisfaction was higher for in-person classes than online classes (p=0.002). Both the learning effect and motivation for learning were higher for in-person classes than online classes (p=0.001). Conclusion: When in-person and online classes become coexistent, there should be educational guidelines for improving the quality and effectiveness of learning in these different contexts.

• Computers and Concrete
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• v.30 no.1
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• pp.33-42
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• 2022

Experimentally predicting the compressive strength (CS) of concrete (for a mix design) is a time-consuming and laborious process. The present study aims to propose surrogate models based on Support Vector Machine (SVM) and Gaussian Process Regression (GPR) machine learning techniques, which can predict the CS of concrete containing nano-silica. Content of cement, aggregates, nano-silica and its fineness, water-binder ratio, and the days at which strength has to be predicted are the input variables. The efficiency of the models is compared in terms of Correlation Coefficient (CC), Root Mean Square Error (RMSE), Variance Account For (VAF), Nash-Sutcliffe Efficiency (NSE), and RMSE to observation's standard deviation ratio (RSR). It has been observed that the SVM outperforms GPR in predicting the CS of the concrete containing nano-silica.