• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Korean Library and Information Science Society
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• v.53 no.3
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• pp.263-285
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• 2022

This study presented the factor analysis on constructing the new factors affecting the middle school students' online learning behaviors from the questionnaires employed among middle school students. A total of 204 students participated and the data were collected in South Korea. The sample of middle school ninth-grade students was selected and used through purposive sampling. Findings from the factor analysis provided evidence for the eight-factor solution for the 35-items accounting for 66.15% of the shared variance. A wide range of factors has been considered to identify students' online learning behaviors. The appropriate experience and use of e-learning in the middle school period is also important as it will be a critical stepstone for future education. This research provides information that has been taken into account for advancing online learning to enhance the quality of e-learning systems for middle school students. The study results provided eight new factors affecting the middle school students' online learning behaviors; that is 1) communication using social media as a learning tool, 2) intention to share information using ICT, 3) addiction of technology, 4) adoption of technology, 5) seeking information using ICT, 6) use of social media learning, 7) information search using ICT, and 8) immersion of technology. This study confirmed that middle school students prefer communication using social media as a learning tool, and value intention to share information using ICT for the most part. The data obtained based on factor analysis can highlight the online learning behaviors towards a mixture of social media learning and ICT to ensure a new educational platform for the future of e-learning. This research expects to be useful for both middle schools of online learning to better understand students' online learning behaviors and design online learning environments and information professionals to better assist students who particularly need digital literacy.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.3
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• pp.643-660
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• 2013

The numbers of learning underachievers in college are gradually increasing. As a result, the need for extracurricular programs to increase learning in college is also growing. The purpose of this study was to analyze factors of learning difficulty and develop a model of learning consulting for college underachievers. This study also aimed to evaluate this model's validity. Using both 56 subscription forms of college underachievers and three sets of focus group interviews at B university, we found that students had difficulties in goal and career setting, management of grades and tests, learning methods, time management, failure overcome ability, lack of learning habit sustaining power and learning motivation, and so on. We developed a model of learning consulting for college underachievers based on these factors and applied the model to evaluate it's validity, testing it on 31 underachievers currently enrolled in college, five times every week. Let we say in conclusion that this model of learning consulting had positive effects on changing college underachiever's character, emotion, motivation, and behavior towards learning.

• Knowledge Management Research
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• v.9 no.4
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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.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.73-82
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• 2022

Chronic diseases such as hypertension require a differentiated approach according to age and life cycle. Chronic diseases such as hypertension require differentiated management according to the life cycle. It is also known that the cause of hypertension is a combination of various factors. This study uses machine learning prediction techniques to analyze various factors affecting hypertension by life cycle. To this end, a total of 35 variables were used through preprocessing and variable selection processes for the National Health and Nutrition Survey data of the Korea Centers for Disease Control and Prevention. As a result of the study, among the tree-based machine learning models, XGBoost was found to have high predictive performance in both middle and old age. Looking at the risk factors for hypertension by life cycle, individual characteristic factors, genetic factors, and nutritional intake factors were found to be risk factors for hypertension in the middle age, and nutritional intake factors, dietary factors, and lifestyle factors were derived as risk factors for hypertension. The results of this study are expected to be used as basic data useful for hypertension management by life cycle.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.5
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• pp.93-105
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• 2011

This study was performed to figure out the effects of perceived usefulness and ease of use in Technology Acceptance Model(TAM) affecting acceptance attitude and intention to use in smart learning. In addition, the study recognized the need for differentiation of learning time by analyzing the difference of effects influencing acceptance attitude of perceived usefulness and ease of use during learning time, which is at the beginning, midterm, and at the end of the term. As the results of the study, it showed that there were differences between the factors, the learning time of which was considered, affecting acceptance attitude and intention to use. Furthermore, in order to improve the effectiveness of building a smart campus, which is currently under the construction, the study argued that universities need to consider the learning relevance and subjective norm as important factors in perceived usefulness of smart learning. Finally, the need for the design of various smart learning types became accepted considering learning time.

• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.982-994
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• 2014

The purpose of this study was to find a way to improve the basic learning ability of vocational college students and to develop the examination of learning attitude. This examination can be used at Center for Teaching and Learning as a previous step to find the strategy and effectiveness of learning. The examination of learning attitude which consisted of 33 questions was surveyed by 984 college freshmen. The survey revealed that five factors; effective content arrangement, priority and self-management, Check and improve of study habits, goal setting and implementation, learning plans have high interrelation(.53~.72). Especially, the more positive aptitude for their majors students had, the higher score they achieved in those five factors. According to general characteristics, there was significant difference among five factors. Therefore, if it is considered to run a differentiated Teaching and Learning method individually, it is expected to be more effective learning strategies.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.3
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• pp.255-272
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• 2023

This study sought implications for further revitalizing student-centered classes by measuring and analyzing the effects of the operation of major courses applying blended learning for library and information sicence (LIS) students on learning immersion, result achievement, and learning satisfaction. The results are as follows: First, as a result of comparative analysis between the pre and post scores of learning immersion, result achievement, and academic satisfaction after operating the blended learning class, the average score of the post-survey was high. Second, among the factors of learning immersion, significant results were found in academic reasons, academic concentration, interest, and control. In particular, the effect of academic concentration and control was high. Third, among the factors of outcome achievement, significant results were found in achievement motivation, satisfaction, relationship utilization ability, and class attitude. The effect of satisfaction and relationship utilization ability was particularly high. Fourth, both general satisfaction and learning-related satisfaction were very effective in academic satisfaction factors. In sum, it was found that the operation of major courses applying blended learning was effective for LIS students.

• Journal of Engineering Education Research
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• v.15 no.6
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• pp.80-85
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• 2012

This study investigates significant factors regarding college freshman engineering students and analyses each factors influence on student satisfaction in the College of Engineering core curriculum. We carried out a survey targeting 505 students who completed their fundamental education for major course learning in the 2011 academic year while attending one college in Gwangju and ruled out inadequate respondents. A total sample size 437 students were analyzed. The seven independent variables are academic fees, academic term period, academic environment of the classroom, learning material content, time of lecture, student sincerity and student need for the program. The dependent variable is fundamental education satisfaction level. As a result of multiple regression analysis, the following factors were found to be significant in the following order: learning material content, time of lecture, student sincerity, student need for the program, academic fees and academic environment of the classroom. On the other side academic term period was not significant. For improving fundamental education satisfaction, there is a need for prudent consideration regarding learning material development and lecture times. Also further investigation should take place for policies necessary for increasing learner motivation and sincerity, and expand appropriate conditions for learners to become self-aware of the education they need within their major.

• Journal of The Korean Association of Information Education
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• v.16 no.1
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• pp.81-89
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• 2012

Quality in smart learning is composed of many factors, and it is more complicated than the traditional education. This study put emphasis on three aspects of the smart learning quality(contents, systems, services). This study depended mostly on literature review, supplemented by FGI(Focus Group Interview) for classification of the smart learning quality factors. On a 5 point Likert scale, the survey enables the users to rate the relative importance of factors, followed by another factor performance rating. The questionnaire were composed of 39 questions. 8 questionnaire sheets were excluded which were not properly filled in or unsuitable for the analysis, and therefore, a total of 112 questionnaires were used for the final analysis. Collected data was statistically analyzed using the SPSS 18.0 for Windows statistical package. Importance-performance analysis(IPA; gap between importance and performance) is used for the empirical test.