• 한국직업건강간호학회지
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• 제29권4호
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• pp.354-362
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• 2020

Purpose: This study aimed to define and clarify learning transfer in nursing. Methods: This study used a hybrid model to analyze the concept of learning transfer in nursing through three phases. For the theoretical phase, learning transfer attributes were identified through a scoping literature review. In the fieldwork phase, in-depth focus group interviews were conducted to develop attributes. Purposive sampling was performed with ten participants(five nursing students, two nurses, three nursing faculty members). In the analysis phase, the attributes and final analysis of learning transfer in nursing were extracted and integrated from the previous two phases. Results: According to the analysis, learning transfer was represented in two dimensions with eight attributes. The development of competency dimension had three attributes: 1) theory acquisition, nursing skills, professional attitude, 2) integration, and 3) analysis competency. The competency change dimension had five attributes: 1) appropriateness in patient care, 2) proficiency in patient care, 3) satisfaction, 4) achievement, and 5) confidence. Conclusion: The concept analysis might provide a basic understanding of learning transfer, a development framework toward a measurement of nursing learning transfer and effective educational nursing strategies.

• 한국산업융합학회 논문집
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• 제25권1호
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• pp.37-45
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• 2022

It is well known that the Kano model measures customer satisfaction and classifies quality attributes into must-be, attractive as well as one-dimensional. The main purpose of this study is to investigate the dynamics of e-learning quality attributes by applying the proposed method using Kano's satisfaction index in the rapidly changing online learning environment. For this, the current study examined 27 e-learning quality attributes and conducted a comparative study using Kano's results obtained in 2013 and 2020. The result shows that the dynamics of quality attributes suggested by Kano(2001) is confirmed in the case of e-learning. The proposed approach shows better results in terms of Kano's direct classification method, and has potential application areas such as IPA(Importance-Performance Analysis) in the area of risk assemement. Some suggestions for better understanding of the proposed SI-DI diagram are also included in this study.

• 한국교육학연구
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• 제23권1호
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• pp.183-204
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• 2017

스마트 기술의 발달과 다양한 스마트 매체의 등장은 개인의 학습 환경과 학교교육 현장에 획기적인 변화를 가져왔다. 이에 정부는 학습 환경의 변화에 발맞추어, 2011년 스마트교육 추진 전략을 발표했고, 스마트교육이라는 용어를 공식적으로 등장시켰다. 이후 정부를 중심으로 스마트교육을 학교 현장에 도입하고자 다양한 정책이 실행되어 왔으며, 스마트교육에 관한 연구도 활발히 진행되어 왔다. 그러나 스마트교육이라는 개념이 등장한지 6여 년이 지난 지금까지도 스마트교육은 정체성에 대한 혼란을 안고 있으며, 스마트교육에 대한 명확한 이해가 부재한 상황에서 기술적 측면을 중심으로 정책이 추진되면서 일선현장의 교사들은 스마트교육을 단순히 스마트 기기를 활용한 수업으로 이해하고 있는 경우가 많으며, 이로 인해 스마트교육은 단순히 스마트 기기를 활용한 수업으로 자리잡아가고 있는 실정이다. 본 연구는 스마트교육의 이론적 기틀 마련을 위해 선행연구 분석을 토대로 스마트교육의 속성을 정의하였으며, 스마트교육 연구 가운데 실증 및 사례 연구를 검토 분석하여 실제 교육현장에서 스마트교육이 어떤 속성들을 중심으로 구현되고 있는지를 살펴보았다. 선행연구 분석 결과 도출된 스마트교육의 속성은 정보통신기술(T), 열린 학습 환경(E), 자기주도 학습(S), 맞춤형 학습(C), 소셜 러닝(SL)의 5가지로 나타났다. 실증 및 사례연구를 토대로 분석한 결과, 실제 교육현장에서 구현되고 있는 스마트교육의 속성은 정보통신기술을 기반으로 한 스마트기기 및 매체의 활용을 중심으로 나타났으며, 자기주도 학습, 맞춤형 학습은 스마트교육 환경에서 적절히 활용되지 못하고 있었다. 추후 연구를 통해 스마트교육이 가지고 있는 특성을 교육 현장에서 제대로 활용하여 교육적 효과를 제고하기 위한 연구가 필요하다는 시사점을 도출하였다.

• 한국정보통신학회논문지
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• 제11권3호
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• pp.619-626
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• 2007

건설적 귀납법은 사례들이 갖고 있는 속성들에 적합한 연산자를 적용하여 이들 사례들을 좀 더 효율적으로 분류할 수 있는 새로운 속성들을 도출해 내는 기법이다. 본 논문에서는 주어진 사례의 집합으로부터 PROSPECTOR에서 사용한 규칙 형태의 분류 규칙을 습득하는 유전 알고리즘 기반 귀납적 학습 환경을 위한 건설적 귀납법을 제시한다. 속성 결합 연산자와 유도된 속성의 유용성을 평가하기 위한 방법을 중심으로 건설적 귀납법에 대해 자세히 설명하고 다양한 사례 집합을 이용하여 건설적 귀납법이 유전 알고리즘 기반 학습 환경에 미치는 영향을 평가하였다.

• 전자공학회논문지CI
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• 제48권5호
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• pp.83-87
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• 2011

나이브 베이시안은 기계학습에서 많이 사용되고 상대적으로 좋은 성능을 보인다. 하지만 전통적인 나이브 베이시안 학습의 환경은 두 가지의 가정을 기반으로 학습을 수행한다: (1) 각 속성들의 값은 서로 독립적이다. (2) 각 속성들의 중요도는 동일하다. 본 연구에서는 각 속성의 중요도가 동일하다는 가정에 대하여 새로운 방법을 제시한다. 즉 각 속성은 현실적으로 다른 중요도를 가지며 본 논문은 나이브 베이시안에서 각 속성의 중요도를 계산하는 새로운 방식을 제안한다. 제안된 알고리즘은 다수의 데이터를 이용하여 기존의 나이브 베이시안과 SBC 등의 다른 확장된 나이브 베이시안 방법들과 비교하였고 대부분의 경우에 더 좋은 성능을 보임을 알 수 있었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3685-3707
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• 2021

In recent years, facial attribute editing has been successfully used to effectively change face images of various attributes based on generative adversarial networks and encoder-decoder models. However, existing models have a limitation in that they may change an unintended part in the process of changing an attribute or may generate an unnatural result. In this paper, we propose a model that improves the learning of the attention mask by adding a spatial attention mechanism based on the unified selective transfer network (referred to as STGAN) using semi-supervised learning. The proposed model can edit multiple attributes while preserving details independent of the attributes being edited. This study makes two main contributions to the literature. First, we propose an encoder-decoder model structure that learns and edits multiple facial attributes and suppresses distortion using an attention mask. Second, we define guide masks and propose a method and an objective function that use the guide masks for multiple facial attribute editing through semi-supervised learning. Through qualitative and quantitative evaluations of the experimental results, the proposed method was proven to yield improved results that preserve the image details by suppressing unintended changes than existing methods.

• 한국농공학회논문집
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• 제65권6호
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• pp.15-22
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• 2023

In this study, We conduct a comparative study of deep learning-based classification of agricultural field attributes using Tagged Image File (TIF) and Enhanced Compression Wavelet (ECW) images. The goal is to interpret and classify the attributes of agricultural fields by analyzing the differences between these two image formats. "FarmMap," initiated by the Ministry of Agriculture, Food and Rural Affairs in 2014, serves as the first digital map of agricultural land in South Korea. It comprises attributes such as paddy, field, orchard, agricultural facility and ginseng cultivation areas. For the purpose of comparing deep learning-based agricultural attribute classification, we consider the location and class information of objects, as well as the attribute information of FarmMap. We utilize the ResNet-50 instance segmentation model, which is suitable for this task, to conduct simulated experiments. The comparison of agricultural attribute classification between the two images is measured in terms of accuracy. The experimental results indicate that the accuracy of TIF images is 90.44%, while that of ECW images is 91.72%. The ECW image model demonstrates approximately 1.28% higher accuracy. However, statistical validation, specifically Wilcoxon rank-sum tests, did not reveal a significant difference in accuracy between the two images.

• Asia Marketing Journal
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• 제15권3호
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• pp.159-172
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• 2013

In the present study, we apply the multiple cue probability learning (MCPL) paradigm to examine consumer learning from feedback in repeated trials. This paradigm is useful in investigating consumer learning, especially learning the relationships between the overall quality and attributes. With this paradigm, we can analyze what people learn from repeated trials by using the lens model, i.e., whether it is knowledge or consistency. In addition to introducing this paradigm, we aim to demonstrate that knowledge people gain from repeated trials with feedback is robust enough to weaken one of the most often examined contextual effects, the asymmetric dominance effect. The experiment consists of learning session and a choice task and stimuli are sport rafting boats with motor engines. During the learning session, the participants are shown an option with three attributes and are asked to evaluate its overall quality and type in a number between 0 and 100. Then an expert's evaluation, a number between 0 and 100, is provided as feedback. This trial is repeated fifteen times with different sets of attributes, which comprises one learning session. Depending on the conditions, the participants do one (low) or three (high) learning sessions or do not go through any learning session (no learning). After learning session, the participants then are provided with either a core or an extended choice set to make a choice to examine if learning from feedback would weaken the asymmetric dominance effect. The experiment uses a between-subjects experimental design (2 × 3; core set vs. extended set; no vs. low vs. high learning). The results show that the participants evaluate the overall qualities more accurately with learning. They learn the true trade-off rule between attributes (increase in knowledge) and become more consistent in their evaluations. Regarding the choice task, there is a significant decrease in the percentage of choosing the target option in the extended sets with learning, which clearly demonstrates that learning decreases the magnitude of the asymmetric dominance effect. However, these results are significant only when no learning condition is compared either to low or high learning condition. There is no significant result between low and high learning conditions, which may be due to fatigue or reflect the characteristics of learning curve. The present study introduces the MCPL paradigm in examining consumer learning and demonstrates that learning from feedback increases both knowledge and consistency and weakens the asymmetric dominance effect. The latter result may suggest that the previous demonstrations of the asymmetric dominance effect are somewhat exaggerated. In a single choice setting, people do not have enough information or experience about the stimuli, which may lead them to depend mostly on the contextual structure among options. In the future, more realistic stimuli and real experts' judgments can be used to increase the external validity of study results. In addition, consumers often learn through repeated choices in real consumer settings. Therefore, what consumers learn from feedback in repeated choices would be an interesting topic to investigate.

• 스마트미디어저널
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• 제13권4호
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• pp.57-64
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• 2024

패션 이미지에 포함된 소재, 색상, 핏 등의 속성은 소비자가 의류를 구매하는 데 있어서 중요한 요인이다. 그러나 의류 속성을 분류하는 과정은 많은 인력을 필요로 하고, 작업자의 주관적인 판단에 의존하기 때문에 일관성이 떨어진다. 이러한 문제를 완화하기 위해 인공지능을 활용하여 패션 이미지의 의류 속성을 분류하는 연구가 필요하다. 기존 연구에서는 주로 상의 또는 하의 중 하나의 항목에 대한 의류 속성을 분류하는 것에 초점을 두고 있기 때문에 전신 패션 이미지의 경우에는 상의와 하의의 속성을 동시에 파악할 수 없다는 한계가 있었다. 본 연구는 패션 이미지의 상의와 하의를 구분하여 각 항목의 카테고리와 의류 소재의 속성을 분류할 수 있는 딥러닝 모델을 제안한다. 본 연구에서 딥러닝 모델 ResNet과 EfficientNet를 이용하였고, 학습에 활용한 데이터셋은 패션 이미지 1,002,718장과 의류 카테고리와 소재 속성을 포함한 라벨 총 125개를 사용하였다. Weighted F1-Score를 기준으로 ResNet은 0.800, EfficientNet는 0.781로 ResNet이 더 우수한 성능을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권8호
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• pp.3966-3991
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• 2018

Due to the lack of visualization services and organic combinations between public and private buildings data, the usability of the basic map has remained low. To address this issue, this paper reports on a solution that organically combines public and private data while providing visualization services to general users. For this purpose, factors that can affect building prices first were examined in order to define the related data attributes. To extract the relevant data attributes, this paper presents a method of acquiring public information data and real estate-related information, as provided by private real estate portal sites. The paper also proposes a pretreatment process required for intelligent machine learning. This report goes on to suggest an intelligent machine learning algorithm that predicts buildings' value pricing and future value by using big data regarding buildings' spatial information, as acquired from a database containing building value attributes. The algorithm's availability was tested by establishing a prototype targeting pilot areas, including Suwon, Anyang, and Gunpo in South Korea. Finally, a prototype visualization solution was developed in order to allow general users to effectively use buildings' value ranking and value pricing, as predicted by intelligent machine learning.