• 지식경영연구
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• 제9권4호
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• pp.1-16
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• 2008

The purpose of the present study is to explain the effects of academic self-efficacy, self-regulated learning and online task value on academic achievement and learning transfer in corporate cyber education. 202 students who completed S corporate's cyber courses in 2007 and responded to all survey participated in this study. A hypothetical model was proposed, which was composed of academic self-efficacy, online task value and self-regulated learning factors as prediction variables, and learning transfer as well as academic achievement factors as outcome variables. The results of this study through regression analysis as follows. First, learners' academic self-efficacy, self-regulated learning and online task value predict learners' academic achievement significantly. Second, except for academic self-efficacy, learners' self-regulated learning and online task value predict on learners' learning transfer significantly. Third, academic achievement plays a role as mediating value in predicting academic achievement by online task. It implies that learners' academic self-efficacy, online task value and self-regulated learning which predict learners' academic achievement and learning transfer should be considered in developing strategies for the design and operation of cyber courses.

• 한국콘텐츠학회논문지
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• 제21권2호
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• pp.262-270
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• 2021

본 연구는 간호대학생의 임상실습 중 학습전이 정도를 알아보고 학습전이에 영향을 미치는 요인을 파악하고자 시도하였다. 자료 수집은 2019년 6월부터 7월까지 S시에 위치한 일개 간호학과 재학 중인 3, 4학년 학생 113명을 대상으로 편의표집 하였다. 수집된 자료는 서술통계, 독립 t-검정, ANOVA 및 Scheffé test, Pearson's correlation coefficients, Stepwise multiple regression을 사용하여 분석하였다. 대인관계가 좋을수록, 전공만족도가 높을수록, 교내실습 만족도가 높을수록 학습전이가 높게 나타났고, 학습전이는 전이동기와는 강한 양의 상관관계(r=.60, p=<.001), 자기 주도적 학습능력사이에는 다소 강한 양의 상관관계(r=.46, p=<.001), 자기 주도적 학습능력 세부 영역에서는, 학습평가(r=.49, p=<.001), 학습계획(r=.41, p=<.001), 학습실행(r=.32, p=<.001) 순으로 양의 상관관계를 보였다. 학습전이의 영향요인은 전이동기(β=0.43), 학습평가 (β=0.21) 및 교내실습 만족도(β=0.22) 이었고 총 설명력은 43%이었다. 따라서, 실습중인 간호대학생의 학습전이를 높이기 위해서 전이동기를 높이고 자기주도적 학습평가를 높이는 교육전략과 교내실습 만족도를 높이기 위한 실습환경 및 교수학습법 적용이 필요하겠다.

• 디지털산업정보학회논문지
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• 제19권3호
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• pp.245-251
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• 2023

Facial expression recognition plays a significant role in understanding human emotional states. With the advancement of AI and computer vision technologies, extensive research has been conducted in various fields, including improving customer service, medical diagnosis, and assessing learners' understanding in education. In this study, we develop a model that can infer emotions in real-time from a webcam using transfer learning with TensorFlow.js and MobileNet. While existing studies focus on achieving high accuracy using deep learning models, these models often require substantial resources due to their complex structure and computational demands. Consequently, there is a growing interest in developing lightweight deep learning models and transfer learning methods for restricted environments such as web browsers and edge devices. By employing MobileNet as the base model and performing transfer learning, our study develops a deep learning transfer model utilizing JavaScript-based TensorFlow.js, which can predict emotions in real-time using facial input from a webcam. This transfer model provides a foundation for implementing facial expression recognition in resource-constrained environments such as web and mobile applications, enabling its application in various industries.

• 공학교육연구
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• 제27권3호
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• pp.26-33
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• 2024

This study aimed to verify the learning effectiveness of students who took courses in the field of new and renewable energy, which have been operated within a convergence university system. To achieve this, data were collected from 1,228 students who participated in 34 courses jointly developed and conducted by seven universities as part of standard curriculum offerings. The study analyzed learning effectiveness (course satisfaction, transfer motivation, learning transfer, creativity-convergence competency) using Excel 2018 and SPSS 25.0. It also examined inter-university differences in learning effectiveness and identified factors influencing creativity-convergence competency. The main findings are as follows: (a) Course satisfaction (M= 4.20), transfer motivation (M=3.62), learning transfer (M= 4.06), and creativity-convergence competency (M=3.92) were generally high. (b) Analysis of learning effectiveness differences between universities showed no significant differences among universities A, B, C, D, and E. University F was lower compared to other universities, while University G was significantly higher than others. (c) Sex, grade, number of courses taken, course satisfaction, transfer motivation, and learning transfer had effect on creativity-convergence competency. The results of this study provided implications for promoting activities to attract students, expanding transfer opportunities, and ensuring student agency.

• 시큐리티연구
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• 제51호
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• pp.61-78
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• 2017

이 연구는 해양경찰 조직의 학습전이풍토가 교육훈련의 전이효과에 미치는 영향을 경험적으로 검증하고 이를 바탕으로 전이효과를 높이기 위한 정책적 제언을 목적으로 수행되었다. 이를 위해서 해양경찰교육원의 협조를 받아 해양경찰교육원에 입교한 해양경찰 공무원 526명을 대상으로 설문을 실시하였다. 먼저 선행연구를 바탕으로 학습전이풍토의 하위요인 상사의 지원, 동료의 지원, 조직의 변화가능성을 독립변수로 설정하여 회귀분석을 실시하였다. 그 결과 상사의 지원이 높을수록, 동료의 지원이 높을수록, 조직의 변화가 능성이 높을수록 교육훈련의 전이효과가 높아지는 것으로 나타났다. 이러한 연구결과를 바탕으로 교육훈련 설계에 있어 실무자와 감독자의 참여, 장기적인 교육계획의 수립 등을 교육훈련의 전이효과를 높일 수 있는 방안으로 제시하였다. 이 연구는 판단적 표집을 사용하여 연구결과를 전체 해양경찰관에 대해 일반화하는데 일정한 한계가 있으나 해양경찰을 대상으로 조직의 학습전이풍토가 교육훈련에 미치는 영향에 대하여 최초로 경험적 연구를 수행하였고 교육훈련의 전이효과를 높이기 위한 정책적 제언을 했다는 점에서 연구의 의의가 있다고 할 것이다.

• 대한수학교육학회지:수학교육학연구
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• 제12권1호
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• pp.29-48
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• 2002

The purpose of this paper is to investigate the transfer in mathematics learning, especially focussing on arithmetic and algebra. There are many obstacles at the stage of transfer in learning. In the case of mathematics, each learning contents are definitely categorized by the learning level, therefore these obstacles are more happened than other subjects. First of all, this paper investigates the historical transfer from arithmetic to algebra by Sfard's perspectives. And we define prealgebra as the stage between arithmetic and algebra, which may be revised obstacles or misconceptions happened in the early algebra learning. Also, this paper discusses various obstacles and concrete examples happened in the transfer from arithmetic to algebra. To advance the understanding in the learning of algebra, we consider the core contents of the algebra learning which should be stressed at the prealgebra stage. Finally we present the teaching units of (pre)algebra which are sequenced from the variable concepts to equations.

• 한국콘텐츠학회논문지
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• 제13권8호
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• pp.482-489
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• 2013

본 연구는 일개 대학에서 e-Learning 수업을 듣고 있는 간호학과 학생 239명을 대상으로 학습전이 인식수준과 만족도에 영향을 미치는 e-Learning 교수설계 변인을 밝히고자 시행된 서술적 조사연구이다. 그 결과, 대상자의 학습전이 인식수준에 영향을 미치는 도입의 하부영역은 학습상황 및 방향제시, 학습자 초기 동기화로 나타났으며, 이는 41%의 설명력이 있었다(F=81.16, p<.001). 대상자의 학습전이 인식수준에 영향을 미치는 학습객체의 하부영역은 동기화, 학습목적 일치, 접근성, 피드백 및 적합으로 나타났으며, 이는 46%의 설명력이 있었다(F=50.69, p<.001). 대상자의 만족도에 영향을 미치는 도입의 하부영역은 학습상황 및 방향제시, 학습자 초기 동기화로 나타났으며, 이는 33%의 설명력이 있었다(F=59.32, p<.001). 대상자의 만족도에 영향을 미치는 학습객체의 하부영역은 동기화, 표현설계, 상호작용 유용성, 피드백 및 적합, 학습목적 일치, 콘텐츠 품질로 나타났으며, 이는 52%의 설명력이 있었다(F=43.93, p<.001). 이를 통해 대학 e-Learning 프로그램의 교수설계 요인이 학습자의 학습전이와 만족도에 영향을 미치고 있다는 것을 알 수 있었다. 이는 e-Learning 프로그램 교수설계 전략을 개발하기 위한 기초자료로서 활용될 수 있을 것이다.

• 한국산학기술학회논문지
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• 제20권10호
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• pp.332-340
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• 2019

본 연구는 시뮬레이션 교육을 받은 일 대학 간호학생의 학습동기와 전이풍토, 학습자기효능감 및 전이동기의 정도와 변수들 간의 상관관계를 확인하기 위한 서술적 조사연구이다. 연구대상은 일 간호대학에서 시뮬레이션 교과목을 이수한 4학년 학생이며, 자기보고식 설문지를 이용해 자료를 수집하였고, 수집된 자료는 SPSS 21.0 program을 이용하여 분석하였다. 연구 결과, 간호학생의 학습동기, 전이퐁토의 하위변인인 상사의 지지와 동료의 지지 및 전이기회, 그리고 학습자기효능감과 전이동기는 높은 수준인 것으로 나타났으며, 대상자의 학습동기, 학습자기효능감 및 전이동기는 모두 각각 사회적 입학동기(Z=6.04, p=.049; Z=6.92, p=.031; Z=9.16, p=.010)와 전공만족도(Z=8.55, p=.036; Z=12.55, p=.006; Z=13.47, p=.004)에 따라, 전이기회는 사회적 입학동기(Z=6.27, p=.043)에 따라 유의한 차이가 있었다. 이들 변수는 모두 서로 양의 상관관계를 보였으며, 특히 전이동기는 학습동기(r=.58, p=.002), 상사의 지지(r=.60, p=.004), 동료의 지지(r=.58, p<.001), 전이기회(r=.56, p=.002) 및 학습자기효능감(r=.79, p=.002)과 상관관계가 있었다. 본 연구결과를 토대로 전이동기와 관련된 변인 간의 구조적 인과관계를 파악하는 후속 연구 및 전이동기를 북돋기 위한 효과적인 시뮬레이션 교육 프로그램 개발이 필요하다.

• 동아시아경상학회지
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• 제5권4호
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• pp.20-38
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• 2017

Purpose - this study analyzes the impact of restaurant startup education on transfer effects in learning and entrepreneurial intentions based on previous research. Also, problems and ways to provide effective business startup education for a restaurant founder will be proposed based on the result. Research design, data, methodolog - this study collected surveys by conducting direct investigation. From July 20th of 2016 to September 20th of 2016 (approximately 60 days), the survey was collected. Out of 540 surveys, 520 were collected. And excepting 9 surveys which were untrustworthily conducted, total 511 surveys were used for the analysis. Results - First, as a result of the impact of which factor of a restaurant founder's startup education has a positive impact on transfer effect in learning (the satisfaction of startup education and learning transfer), law education, entrepreneurship education and business district analysis education and practical education have turned out be positively related variables. Secondly, as a result of the impact of a restaurant founder's startup education satisfaction on transfer in learning, it has been identified that startup education has a positive impact. Lastly, by conducting an analysis to find out which factor from a restaurant founder's transfer effect in learning has an impact on entrepreneurial intention, all variables, including startup education satisfaction and transfer effect in learning, are positively influencing factors. Conclusions - as startup education satisfaction of a restaurant founder is increasing, there is a higher level of transfer effect in learning. Moreover, as transfer effect of startup business is getting higher, it has an impact on entrepreneurial intention.

• 스마트미디어저널
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• 제11권4호
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• pp.30-37
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• 2022

Disease threatens plant growth and recognizing the type of disease is essential to making a remedy. In recent years, deep learning has witnessed a significant improvement for this task, however, a large volume of labeled images is one of the requirements to get decent performance. But annotated images are difficult and expensive to obtain in the agricultural field. Therefore, designing an efficient and effective strategy is one of the challenges in this area with few labeled data. Transfer learning, assuming taking knowledge from a source domain to a target domain, is borrowed to address this issue and observed comparable results. However, current transfer learning strategies can be regarded as a supervised method as it hypothesizes that there are many labeled images in a source domain. In contrast, unsupervised transfer learning, using only images in a source domain, gives more convenience as collecting images is much easier than annotating. In this paper, we leverage unsupervised transfer learning to perform plant disease recognition, by which we achieve a better performance than supervised transfer learning in many cases. Besides, a vision transformer with a bigger model capacity than convolution is utilized to have a better-pretrained feature space. With the vision transformer-based unsupervised transfer learning, we achieve better results than current works in two datasets. Especially, we obtain 97.3% accuracy with only 30 training images for each class in the Plant Village dataset. We hope that our work can encourage the community to pay attention to vision transformer-based unsupervised transfer learning in the agricultural field when with few labeled images.