• Journal of Fisheries and Marine Sciences Education
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• v.21 no.1
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• pp.16-27
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• 2009

The purpose of this study was to develop of learning strategy scale for college students. This study further classified several sub-areas and defined each concepts of learning strategy. Based upon the classification of each sub-areas, tentative test items were developed through the verification of validity by three professionals. A pilot study of the developed scale was administered to 239 college students. And the research collected major data from 1,012 college students. Data were analyzed to obtain item quality, reliability, and validity analysis. The results of this study were as follows. The scale for learning strategy was defined by eight factors and they were 'self-management strategy', 'examination-readiness strategy', 'cognitive strategy', 'memorizing strategy', 'reporting strategy', 'resource-utilization strategy', 'self-regulated strategy', 'cooperative learning strategy'. The results of the confirmatory factor analysis proved the eight factors in the learning strategy. And criterion validity evidence was also obtained from a correlation analysis of the level of academic achievement.

• Journal of Fisheries and Marine Sciences Education
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• v.26 no.1
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• pp.148-155
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• 2014

The purpose of this research was to analyze the effects of smart learning. By using meta analysis method, twenty MA and Ph.D degree papers published from 2006 to 2013 were analyzed and 104 effect sizes were calculated. Followings were the results of the research: (a) Smart learning turned out to be more statistically effective comparing to traditional education. The total mean effect size was .886 and the value of U3 was 66.53%. (b) All effect size of sub dependent variables(ie, academic achievement, learning satisfaction, learning attitude) were also effective by adapting smart learning. (c) The moderated variables likes learner characteristics, learning content, and interaction had high effect sizes. Operation system variable had a low effect size but it was not significant.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.295-301
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• 2019

Since the third artificial intelligence boom was triggered by deep learning, it has been 10 years. It is time to analyze and discuss the research trends of deep learning for the stable development of AI. In this regard, this study systematically analyzes the trends of research on deep learning over the past 10 years. We collected research literature on deep learning and performed LDA based topic modeling analysis. We analyzed trends by topic over 10 years. We have also identified differences among the major research countries, China, the United States, South Korea, and United Kingdom. The results of this study will provide insights into research direction on deep learning in the future, and provide implications for the stable development strategy of deep learning.

• Journal of the Korea Society of Computer and Information
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• v.21 no.7
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• pp.93-100
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• 2016

This paper shows what kind of influence the learning motivation factors have on the effectiveness of Flipped Learning Model through the case of operating a JAVA programming subject. The Flipped Learning Approach consisting of Before Class, Before or At Start of Class, and In Class provides the students with learning motivation as well as satisfies Keller's ARCS(Attention, Relevance, Confidence, Satisfaction) to keep them studying steadily. This research conducts the operation of Flipped Learning and gets Exploratory Factor Analysis and Reliability Analysis from the result of the course experience questionnaire at the end of the class. Given this survey result, Flipped Learning approach improves the learners' satisfaction in class and the effectiveness in the fields of understanding learning context more than does the previous lecture-based learning approach by pacing learning procedure and conducting self-directed learning.

• Journal of Fisheries and Marine Sciences Education
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• v.24 no.6
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• pp.935-949
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• 2012

The purpose of this study was to investigate the structural relationship among the self-efficacy, self-directed learning ability, school adjustment and learning flow in middle school students by the structural equation modeling analysis. The subjects of this study consisted of 553 middle school students. The data were analyzed with descriptive statistics, Pearson correlations and structural equation modeling analysis by using the SPSS 12.0 and AMOS 5.0 statistical program. The results of this study were as followed: First, there were significant correlations among the self-efficacy, self-directed learning ability, school adjustment and learning flow. Second, the self-directed learning ability and school adjustment directly affected the learning flow. Third, self-efficacy and school adjustment variables indirectly affected learning flow. The indices of the best fit model on these variable were adequate. This study shows that the self-efficacy, self-directed learning ability, school adjustment are the significant predictor for the learning flow during adolescent.

• Journal of information and communication convergence engineering
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• v.18 no.4
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• pp.239-244
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• 2020

This study analyzed the influencing factors of learning engagement and teaching presence in online programming practice classes. The subjects of this study were students enrolled in an industrial specialized high school, who practiced creating Arduino circuits and programming using a web-based virtual practice tool called Tinkercad. This research adopted a tool that can measure task value, learning flow, learning engagement, and teaching presence. Based on this analysis, learning flow had a mediating effect between task value and online learning engagement, as well as between task value and teaching presence. Increasing learning engagement in online classes requires sensitizing the learners about task value, using hands-on platforms available online, and expanding interaction with instructors to increase learning flow of students. Furthermore, using virtual hands-on tools in online programming classes is relevant in increasing learning engagement. Future research tasks include: confirming the effectiveness of online learning engagement and teaching presence through pre- and post-tests, and conducting research on various practical subjects.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.89-103
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• 2022

The purpose of this study is to explore professors' perception of blended learning. For this purpose, the reflective journals written by 56 university professors was analyzed using the keyword network analysis method. The results of this study are as follows: First, as a result of keyword frequency analysis for the blended learning, the keywords showed the highest frequency in the order of (1) 'instructional design', 'student', 'instructional method', 'learning objective' in the area of learning, (2) 'importance', 'instruction', 'feeling', 'student' in the area of feeling, and (3) 'semester', 'plan', 'weekly', and 'instruction' in the area of action plan. Second, the results of analyzing the degree, closeness centrality, and betweenness centrality of network connection are as follows. (1) The keywords 'instruction', 'instructional method', 'instructional design', and 'learning objective' in the area of learning, (2) the keywords 'instruction', 'importance', and 'necessity' in the area of feeling, and (3) 'instruction', 'plan', and 'semester' in the area of action plan showed high values in degree, closeness centrality, and betweenness centrality. Based on the research results, implications for blended learning and professors' perception were discussed.

• International Journal of Internet, Broadcasting and Communication
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• v.10 no.2
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• pp.60-67
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• 2018

The purpose of this study was to examine the effects of internet-use Anatomy class, as one of the Flipped learning method, on critical thinking disposition. The class for this study was conducted from March 1 to April 10, 2018. The study involved a total of 180 people in the first year of a University located in C province. Data collection was carried out before and after the Flipped learning method application. Frequency analysis, Paired t-test, Pearson correlation, and Regression analysis were used for the analysis. According to the analysis, 28.3% of men and 71.1% of women and before applying the program analysis of correlation between Flipped learning perception and critical thinking disposition showed a significant correlation between confidence(sub-component of critical thinking) only (p<.005). Comparing the scores of critical thinking before and after the program, it was found that Truth seeking (p<.001), Open-mindness (p<.005), Confidence (p<.001), Systematicity (p<.005), Analyticity (p<.001), and Inquisitiveness (p<.001) scores had increased significantly except Maturity (p>.005). And the regression analysis of Flipped learning method applying influence on critical thinking disposition were significantly affected (p<.001). Based on the results of this study, it was possible to determine that Flipped learning method had a positive effect on critical thinking disposition.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.193-197
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• 2021

The pandemic situation caused by COVID-19 has a large and small impact on our society socially, economically, psychologically, and other aspects. In order to prevent the spread of COVID-19, various countries, including Korea, have entered into long-term home care and distance learning systems. However, distance learning experiments conducted in many countries have raised whether face-to-face education can be replaced by distance learning. Therefore, in this study, public opinion, social perception, and field trends were analyzed based on media reports on distance learning. For this purpose, 2,600 articles from 11 newspapers and four broadcasters related to distance learning were collected in this study. Based on this data, keyword trend analysis, topic modeling analysis, sentiment analysis were performed.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.55-65
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• 2023

In this paper, we apply KeyBERT(Keyword extraction with Bidirectional Encoder Representations of Transformers) algorithm-driven topic extraction and topic frequency analysis to deep learning and reinforcement learning research to discover the rapidly changing trends in them. First, we crawled abstracts of research papers on deep learning and reinforcement learning, and temporally divided them into two groups. After pre-processing the crawled data, we extracted topics using KeyBERT algorithm, and then analyzed the extracted topics in terms of topic occurrence frequency. This analysis reveals that there are distinct trends in research work of all analyzed algorithms and applications, and we can clearly tell which topics are gaining more interest. The analysis also proves the effectiveness of the utilized topic extraction and topic frequency analysis in research trend analysis, and this trend analysis scheme is expected to be used for research trend analysis in other research fields. In addition, the analysis can provide insight into how deep learning will evolve in the near future, and provide guidance for select research topics and methodologies by informing researchers of research topics and methodologies which are recently attracting attention.