• 컴퓨터교육학회논문지
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• 제18권3호
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• pp.59-68
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• 2015

본 연구는 온라인 프로그래밍 학습 환경에 참여하는 학습자의 특성 및 학습양식과 성취도간의 관계를 분석하는데 목적이 있다. 분석을 위해, 중 고등학생 104명을 대상으로 Grasha-Reichmann의 학습양식 검사를 실시한 후, 12주간 C언어 프로그래밍 학습과 과제를 수행하였다. 먼저, 학습자 특성에 따른 학습양식 차이 결과, 성별에서 남학생이 여학생보다 의존형이 높았고, 학교급에서 중학생이 경쟁형과 회피형이 고등학생보다 높았다. 성취수준에서는 독립형과 참여형이 차이가 있었다. 학습양식과 성취도와의 관계를 분석한 결과, 독립형(r=.253, p<.01)과 참여형(r=.303, p<.01)이 정적 상관을 보여 두 분석 결과 독립형과 참여형이 성취도와 연관이 있는 학습양식임을 확인할 수 있었다. 또한 학습자들의 주 학습양식에 따른 학습 소감을 조사하여 학습유형별 특징을 분석하였으며, 조사 결과를 통해 학습양식별 온라인 프로그래밍 교수 학습 전략을 도출할 수 있었다.

• Safety and Health at Work
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• 제6권3호
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• pp.211-217
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• 2015

Background: E-learning-based programs have recently been introduced to the occupational safety and health (OSH) education for migrant workers in Korea. The purpose of this study was to investigate how the factors related to migrant workers' backgrounds and the instructional design affect the migrant workers' satisfaction with e-learning-based OSH education. Methods: The data were collected from the surveys of 300 migrant workers who had participated in an OSH education program. Independent sample t test and one-way analysis of variance were conducted to examine differences in the degree of learning satisfaction using background variables. In addition, correlation analysis and multiple regression analysis were conducted to examine relationships between the instructional design variables and the degree of learning satisfaction. Results: There was no significant difference in the degree of learning satisfaction by gender, age, level of education, number of employees, or type of occupation, except for nationality. Among the instructional design variables, "learning content" (${\beta}=0.344$, p < 0.001) affected the degree of learning satisfaction most significantly, followed by "motivation to learn" (${\beta}=0.293$, p < 0.001), "interactions with learners and instructors" (${\beta}=0.149$, p < 0.01), and "previous experience related to e-learning" (${\beta}=0.095$, p < 0.05). "Learning environment" had no significant influence on the degree of learning satisfaction. Conclusion: E-learning-based OSH education for migrant workers may be an effective way to increase their safety knowledge and behavior if the accuracy, credibility, and novelty of learning content; strategies to promote learners' motivation to learn; and interactions with learners and instructors are systematically applied during the development and implementation of e-learning programs.

• 한국정보시스템학회지:정보시스템연구
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• 제32권4호
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• pp.189-210
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• 2023

Purpose This study aims to investigate factors that positively influence the continuous Artificial Intelligence(AI) Learning Continuity of business major students. Design/methodology/approach To evaluate the impact of AI education, a survey was conducted among 119 business-related majors who completed a software/AI course. Frequency analysis was employed to examine the general characteristics of the sample. Furthermore, factor analysis using Varimax rotation was conducted to validate the derived variables from the survey items, and Cronbach's α coefficient was used to measure the reliability of the variables. Findings Positive correlations were observed between business major students' AI Learning Continuity and their AI Interest, AI Awareness, and Data Analysis Capability related to their majors. Additionally, the study identified that AI Project Awareness and AI Literacy Capability play pivotal roles as mediators in fostering AI Learning Continuity. Students who acquired problem-solving skills and related technologies through AI Projects Awareness showed increased motivation for AI Learning Continuity. Lastly, AI Self-Efficacy significantly influences students' AI Learning Continuity.

• International journal of advanced smart convergence
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• 제11권4호
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• pp.68-80
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• 2022

Since the launch of AI, technology development to implement complete and sophisticated AI functions has continued. In efforts to develop technologies for complete automation, Machine Learning techniques and deep learning techniques are mainly used. These techniques deal with supervised learning, unsupervised learning, and reinforcement learning as internal technical elements, and use the Big-data Analysis method again to set the cornerstone for decision-making. In addition, established decision-making is being improved through subsequent repetition and renewal of decision-making standards. In other words, big data analysis, which enables data classification and recognition/recognition, is important enough to be called a key technical element of AI function. Therefore, big data analysis itself is important and requires sophisticated analysis. In this study, among various tools that can analyze big data, we will use a Python program to find out what variables can affect addiction according to smartphone use in a decision tree environment. We the Python program checks whether data classification by decision tree shows the same performance as other tools, and sees if it can give reliability to decision-making about the addictiveness of smartphone use. Through the results of this study, it can be seen that there is no problem in performing big data analysis using any of the various statistical tools such as Python and R when analyzing big data.

• Educational Technology International
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• 제20권2호
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• pp.169-194
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• 2019

This study aims to compare and analyze the trends of research on mobile learning conducted in Korea and China to suggest future directions and multifaceted subject areas in its study field. To achieve this purpose, 620 Chinese papers from CNKI (CSSCI and CSCD) database and 205 Korean papers from RISS database (KCI and KCI candidate) published between 2009 and 2018 were selected to be analyzed through applying the frequency analysis and visualized semantic network analysis. The criteria for analysis used in this study are four types: publication years, research subjects, research methods, and keywords. The results of this study are as follows. Firstly, in relation to the year of publication, Korea entered the peak of mobile learning research in 2016 (33 papers), and China reached high publications (94 papers) in 2017. Secondly, with regard to the research subjects, the most frequently studied subjects in Korea and China were targeted to college students, followed by general adult groups. Thirdly, in terms of research methods, quantitative research accounted for a high proportion in Korea, but in China, literature research showed a high frequency. Fourthly, the high frequency keywords appearing in mobile learning research of the two countries were mainly reflected in language learning. Based on the findings, several directions of future research for both countries were suggested.

• 한국항공운항학회지
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• 제28권3호
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• pp.41-51
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• 2020

This research is accomplished to verify objectively how flight instructor's transformative leadership affects student pilot's psychological stabilities and learning satisfactions. Flight instructor's transformative leadership factor divided into individual consideration, intellectual stimulus and charisma from exploring factor analysis. Psychological stability factor subdivided into happiness, concentration and satisfaction. Learning satisfaction factor subdivided into participation, recommendation, persistence, accomplishment and relationship. According to the analysis of flight instructor's transformative leadership effect on psychological stability, it showed that it has statistical significance on happiness, concentration and satisfaction. It also has positive influence on happiness and concentration. The result from regression analysis showed that individual consideration and charisma affected happiness and concentration in order. However, satisfaction from individual consideration, intellectual stimulus and charisma didn't show statistical significance to student pilot's satisfaction. Analysis of flight instructor's transformative leadership on student pilot's learning satisfaction showed statistical significance between them. Intellectual stimulus and charisma had positive influence on student pilot's learning satisfaction. Regression analysis showed charisma and intellectual affect student pilot's learning satisfaction in order.

• International Journal of Advanced Culture Technology
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• 제11권1호
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• pp.283-289
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• 2023

AI is bringing about drastic changes not only in the aspect of technologies but also in society and culture. Medical AI based on deep learning have developed rapidly. Especially, the field of medical image analysis has been proven that AI can identify the characteristics of medical images more accurately and quickly than clinicians. Evaluating the latest results of the AI-based medical image processing is important for the implication for the development direction of medical AI. In this paper, we analyze and evaluate the latest trends in AI-based medical image analysis, which is showing great achievements in the field of medical AI in the healthcare industry. We analyze deep learning models for medical image analysis and AI-based medical image segmentation for quantitative analysis. Also, we evaluate the future development direction in terms of marketability as well as the size and characteristics of the medical AI market and the restrictions to market growth. For evaluating the latest trend in the deep learning-based medical image processing, we analyze the latest research results on the deep learning-based medical image processing and data of medical AI market. The analyzed trends provide the overall views and implication for the developing deep learning in the medical fields.

• 디지털산업정보학회논문지
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• 제19권2호
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• pp.79-87
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• 2023

On-line learning has been adopted as a major educational method due to the COVID-19 pandemic. Students and faculties got accustomed to on-line educational environment as they experienced it during the COVID-19 pandemic. Development of various technologies and social requirement for educational renovation lay groundwork for on-line learning as well. Therefore, on-line learning or blended learning will be likely to go on after the end of COVID-19 pandemic and it is necessary to prepare the guidelines for effective utilizing on-line learning. The primary purpose of this study is to examine the learning behaviors and the learning effects by using LMS data. Learning behaviors were measured in terms of learning time and access frequency for pre-recorded video lectures targeting computer-practice classes. The results of empirical analysis reveal that frequency was the significant predictor of course achievements but learning time was not. The findings of empirical analysis will provide insights that the effective planning and designing on-line classes based on learning behaviors are key to enhancing learning effects and learner's satisfaction.

• 인터넷정보학회논문지
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• 제8권6호
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• pp.127-133
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• 2007

u-러닝 환경은 수 없이 많은 단계를 거쳐 발전되어 왔으며, 현재에는 학습자의 학습 결과 분석과 양적인 사용, 질적인 평가 등을 통하여 정립되고 있다. 일반적으로 개선된 학습 효과와 학습자의 학습 결과분석을 위하여 대부분의 학습 시스템이 문항분석방법을 이용되고 있다. 그러나 오늘날 학습 시스템은 문항분석이론 대신에 문항반응이론을 사용하고 있다. 문항분석이론은 시험에 대한 각각의 가능한 응답에 대한 확률을 위해 명확한 모델을 제시한다. 따라서 본 연구에서는 문항반응이론을 이용한 경량 컴포넌트 기반의 u-러닝 시스템을 제시하고자 한다. u-러닝에 적용된 기기는 윈도우 모바일 5.0 환경의 PDA로 하였다.

• 무역학회지
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• 제46권6호
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• pp.191-209
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• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.