• Journal of Digital Convergence
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• v.18 no.11
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• pp.311-318
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• 2020

This study was done to explore the correlation among self-determination, learning commitment and learning outcomes and identify factors related learning outcomes under academic credit bank system. The survey was conducted self-report questionnaire. The data collection period was form April to November 2018. Participants were 144 registered nurses working currently under academic credit bank system. Collected data were analyzed using SPSS/WIN 24.0. Learning outcomes had a positive correlation with self-determination and learning commitment. learning commitment. Self-determination was positively correlated learning commitment. Factors affecting learning outcomes included self-determination and learning commitment. And self-determination and learning commitment accounted for 32.1% of the variance in learning outcomes. Therefore, developing learning outcomes support for improving self-determination and learning commitment. Future research will be needed to clarify the effects of learning outcomes promotion program on self-determination and learning commitment.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.109-126
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• 2023

Early detection of forest fires is essential in preventing large-scale forest fires. Predicting forest fires serves as a vital early detection method, leading to various related studies. However, many previous studies focused solely on climate and geographic factors, overlooking human factors, which significantly contribute to forest fires. This study aims to develop forest fire prediction models that take into account human, weather and geographical factors. This study conducted a comparative analysis of four machine learning models alongside the logistic regression model, using forest fire data from Gangwon-do spanning 2003 to 2020. The results indicate that XG Boost models performed the best (AUC=0.925), closely followed by Random Forest (AUC=0.920), both of which are machine learning techniques. Lastly, the study analyzed the relative importance of various factors through permutation feature importance analysis to derive operational insights. While meteorological factors showed a greater impact compared to human factors, various human factors were also found to be significant.

• Journal of Engineering Education Research
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• v.19 no.5
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• pp.3-12
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• 2016

This paper aims to apply two ways of student evaluation, i.e. peer- and self-evaluation to TBL(team based learning) class and to explore the difference between two evaluations by gender and grade as well as their relationships, and finally to provide an explanation for the improvement of evaluation ways in TBL class. There has been much research about TBL and its related factors. However, according to the examination of both domestic and overseas researches concerning the application of peer- and self-evaluation to TBL class, few studies have focused on them in terms of the engineering curriculum. This study was conducted with 251 engineering students at P University, and peer- and self-evaluation in TBL class have been measured. Our findings show that firstly, there were significant grade differences in self-evaluation of engineering students. Second, there were no significant gender and grade differences in peer-evaluation. Third, we found a significant correlation between the two factors, self- and peer-evaluation. Also there was a significant correlation among variables of subcategories. Based on these findings, it is expected to provide an explanation for the application of peer- and self-evaluation in TBL class and will be useful for the improvement plans of the related courses in engineering school.

• Journal of Korean Elementary Science Education
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• v.28 no.3
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• pp.352-360
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• 2009

In this study, we investigated the relationships among elementary school teachers' perceptions of constructivist science assessment, views on science teaching and learning, science teaching efficacy belief, and the perceptions of constructivist science learning environment. An exploratory factor analysis was conducted to validate the factor structure of the perceptions of constructivist science assessment test. The test consisting of 3 factors with 21 questions in the previous research was reconstructed as one consisting of 2 factors with 22 questions as a result of the factor analysis. A stepwise multiple regression analysis was also conducted to predict the explanatory powers of the variables on perceptions of constructivist science assessment. The results indicated that the perceptions of constructivist science learning environment, views on constructivist science teaching and learning, and personal science teaching efficacy belief were the significant predictors of the perceptions of constructivist science assessment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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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.

• Journal of Korean society of Dental Hygiene
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• v.22 no.5
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• pp.375-382
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• 2022

Objectives: This study analyzes factors related to English communication skills in the dental health services of clinical dental hygienists who provide dental medical services to foreigners. Methods: Surveys were conducted to measure students' English communication skills. Participants comprised 195 clinical dental hygienists working at dental English study cafes or who provided dental medical services to foreigners. After analyzing the differences in English communication skills, hierarchical multiple regression analysis was performed on the factors related to English communication skills. Results: English communication skill of dental hygienists was 1.96 points out of 5 points. The factors related to the English communication skill of the clinical dental hygienists were foreign patient care, language training experience, overseas living experience, and certified English proficiency. The adjusted explanatory power of this model was 53.0%. Conclusions: Dental hygienists in charge of foreign patients have experience in language training and overseas residence, have a language qualification certificate, and have higher English communication skills. It is necessary to develop English language learning programs based on metaverse to develop the English communication skills of dental hygienists who provide dental health services to foreign patients and to operate a creative educational environment to increase interest in learning English.

• Safety and Health at Work
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• v.13 no.4
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• pp.379-386
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• 2022

Emergency medical services (EMS) personnel are at high risk for adverse mental health outcomes during disease outbreaks. To support the development of evidence-informed mitigation strategies, we conducted a scoping review to identify the extent of research pertaining to EMS personnel's mental health during disease outbreaks and summarized key factors associated with mental health outcomes. We systematically searched three databases for articles containing keywords within three concepts: EMS personnel, disease outbreaks, and mental health. We screened and retained original peer-reviewed articles that discussed, in English, EMS personnel's mental health during disease outbreaks. Where inferential statistics were reported, the associations between individual and work-related factors and mental health outcomes were synthesized. Twenty-five articles were eligible for data extraction. Our findings suggest that many of the contributing factors for adverse mental health outcomes are related to inadequacies in fulfilling EMS personnel's basic safety and informational needs. In preparation for future disease outbreaks, resources should be prioritized toward ensuring adequate provisions of personal protective equipment and infection prevention and control training. This scoping review serves as a launching pad for further research and intervention development.

• Journal of The Korean Association For Science Education
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• v.22 no.1
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• pp.141-157
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• 2002

The purpose of this study was to analyze teacher's teaching-learning strategies and young children's concepts related to mathematics and science in integrated mathematics and science education activities. To achieve this purpose, actual class episodes were analyzed. The episodes were derived from 7 sessions of interaction between teacher and 4 kindergartners in integrated mathematics and science education activities. As a result of the study, children's concepts related to mathematics and science in integrated mathematics and science education activities occurred three factors: the relationship between weight, shape and movement, the relationship between weight and size, and the concept of measurement. In teacher's teaching-learning strategies, three factors were revealed: teacher's questioning, use of teaching materials, and children grouping.

• The Mathematical Education
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• v.55 no.3
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• pp.299-316
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• 2016

The abilities for 21st learners have recently changed and learners' engagement is emphasized. In flipped classroom, students learn the prerequisite concepts of the lecture online in advance and perform various types of activities based on interaction and engagement. As students in flipped classroom construct knowledge actively, students' engagement is very important. Therefore, I conducted a research of flipped mathematics class to help teachers to better understand students' engagement in flipped mathematics class. The flipped mathematics class was conducted for about 3 weeks with 29 middle school students and one teacher. Video and audio recordings, completed student worksheets and interview data were collected and analyzed using the qualitative method. The results of this study showed that students' engagement is influenced by diverse factors. Engagement factors were categorized by teacher factors, community factors, material factors, tasks and strategy factors, classroom culture factors. Each factor facilitates or suppresses behavioral, emotional, cognitive, agentic engagements, and sometimes several factors are related. The results of this study increase understanding of engagement through the example of a case study on flipped mathematics class.

• Journal of the Korean Society of Safety
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• v.36 no.1
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• pp.56-63
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• 2021

The narrative texts of industrial accident reports help to identify accident risk factors. They relate the accident triggers to the sequence of events and the outcomes of an accident. Particularly, a set of related keywords in the context of the narrative can represent how the accident proceeded. Previous studies on text analytics for structuring accident reports have been limited to extracting individual keywords without context. We proposed a context-based analysis using a Natural Language Processing (NLP) algorithm to remedy this shortcoming. This study aims to apply Word2Vec of the NLP algorithm to extract adjacent keywords, known as word embedding, conducted by the neural network algorithm based on supervised learning. During processing, Word2Vec is conducted by adjacent keywords in narrative texts as inputs to achieve its supervised learning; keyword weights emerge as the vectors representing the degree of neighboring among keywords. Similar keyword weights mean that the keywords are closely arranged within sentences in the narrative text. Consequently, a set of keywords that have similar weights presents similar accidents. We extracted ten accident processes containing related keywords and used them to understand the risk factors determining how an accident proceeds. This information helps identify how a checklist for an accident report should be structured.