35 research outputs found
ë¹íìì íµê³ì ì¶ëŠ¬ì íê°
íìë
Œë¬ž (ë°ì¬)-- ììžëíêµ ëíì : ìíêµì¡ê³Œ, 2015. 2. ìŽê²œí.In recent studies in statistics education, the teaching and learning of informal statistical inference have been emphasized as a precedence stage of formal statistical inference, which is taught at the tertiary level. Subsequently, there is an abundance of research identifying the meaning of informal statistical inference and exploring ways of improving students informal statistical inference abilities. As research on how to instruct informal statistical inference grows, how to assess students informal statistical inference abilities should be discussed. There has been a need for assessment methods that reflect and align with the characteristics of statistics, but substantive discussion about such assessments are still lacking. Thus, this study aims to clarify the nature of informal statistical inference and to propose appropriate assessment methods that reflect its nature.
Through an epistemological analysis of statistical inference, it is found that a statistical inference consists of two thinking components, abduction and induction. Statistical inference as induction can regulate its inherent characteristic according to how it deals with the uncertainty. To address the dilemmas posed by uncertainty, statistical inference uses the quantification of uncertainty using probability and applies modus tollens. Statistical inference as abduction is introduced to denote the importance of generating the simplest and most likely explanation of a hypothesis based on the characteristics and patterns of the sample and the context. Both induction and abduction serve as components of thinking in regard to statistical inference and need to be recognized as separate stages.
Through a didactical review of research on informal statistical inference, the treatment of essential concepts and thinking in informal statistical inference were examined. The essential concepts include descriptive statistics as expectation and variation, sample and population, the size of a sample, and sampling distribution, and the essential thinking includes abduction and induction. In informal statistical inference, abduction and induction are carried out as construction of argumentation and verification of argumentation, respectively. In particular, to address the uncertainty in verification of argumentation, students can use probabilistic representations, draw a conclusion by recognizing the importance of repeated sampling, and attempt to validate the argumentation by establishing norms for dealing with uncertainty during the communication. The characteristics of informal statistical inference include that it is an informal argumentation using natural language, that it is based on context, and that it occurs within an interaction. Therefore, the realization of informal statistical inference demands a situation of teaching and learning processes in which communication occurs based on argumentation using verbal language.
Due to the nature of informal statistical inference, assessments must occur in parallel with the teaching and learning process. For this reason, the meaning of integration of instruction and assessment was examined and several assessment models, such as the general assessment triangle model and assessment models based on interaction, were analyzed. As a result, an assessment model for informal statistical inference was developed. The assessment model includes the integration of instruction and assessment as a universal set and interaction between a teacher and students as two intersecting sets. The procedure of assessment is represented in the intersection, which consists of a teachers providing tasks, students initial responses, a teachers interpretation based on an assessment element, a teachers feedback, and students final responses. The characteristics of proper assessment tasks, the assessment elements, and the proper method for providing feedback for assessing informal statistical inference are described. Finally, the pedagogical implication of the study is discussed, and future research based on the assessment model developed in the study is suggested.CHAPTER 1. INTRODUCTION 1
CHAPTER 2. METHODS 10
CHAPTER 3. THE NATURE OF INFORMAL STATISTICAL INFERENCE 13
3.1. Epistemological Analysis of Statistical Inference 14
3.1.1. Thinking Components of Statistical Inference 14
3.1.2. Statistical Inference as Induction 16
3.1.3. Statistical Inference as Abduction 32
3.2. Didactical Analysis of Informal Statistical Inference 38
3.2.1. Definition and Components of Informal Statistical Inference 40
3.2.2. Concepts Emphasized in Informal Statistical Inference 47
3.2.3. Thinking Emphasized in Informal Statistical Inference 61
3.3. Discussion of the Nature of Informal Statistical Inference 74
3.3.1. Summary of the Nature of Informal Statistical Inference 74
3.3.2. Implication for Designing Assessment Model of Informal Statistical Inference 77
CHAPTER 4. DESIGNING AN ASSESSMENT MODEL OF INFORMAL STATISTICAL INFERENCE 79
4.1. Integration of Instruction and Assessment 80
4.1.1. Changes of Assessment Perspectives According to Instructional Perspectives 80
4.1.2. Meaning of Integration of Instruction and Assessment 87
4.1.3. Models of Integration of Instruction and Assessment 95
4.2. Assessment Model for Informal Statistical Inference 109
4.2.1. Design of Assessment Model 109
4.2.2. Characteristics of Components in Assessment Model 112
CHAPTER 5. SUMMARY AND CONCLUSION 130
References 137
Appendix 155
Abstract in Korean 165Docto
ê³ ë±íìì ì 첎íëì ë°ë¥ž ìë©Žì ì§ ë° ì¬ë°ë³ìŽë
ê°íží곌/ìì¬ì²ìë
êž°ë ì 첎ì , ì ì ì ìŒë¡ ì±ì¥ìëê° ë¹ ë¥Žë©°, ìžì ìê·¹ ëë í겜ì 믌ê°í ìêž°ìŽêž° ë묞ì 걎ê°êŽëŠ¬ê° ë§€ì° ì€ìíë€. 걎ê°ì ìí¥ì 믞ì¹ë ìì ì€ì íëê° ìŽë, ìë©Ž, ì€ížë ì€ìžë° ê³ ë±íììŽ ì€íìì ë¹íŽ ì 첎íëìŽ ì ê³ , ìë©Žìê°ë 짧ìŒë©° ì€ížë ì€ë ìëì ìŒë¡ ë§ìŽ ë°êž° ë묞ì 걎ê°êŽëŠ¬ê° íìíë€. í¹í ììšì 겜ê³ë í겜 ë³íì ì믌íê² ë°ìíë êž°êŽìŒë¡ íëì ì 겜ê³ê° íì±í ëê±°ë ì íëë©Ž íìì±ì ë¶ê· íìŒë¡ ì§ë³ìŽ ìŽëëë€. ë°ëŒì 걎ê°ì ì ì§íë €ë©Ž ììšì 겜ê³ê° ê· íì ìŽë£šìŽìŒ íë©° ììšì 겜ê³ì êž°ë¥ì ì¬ë°ë³ìŽë륌 íµíŽ íìží ì ìë€.볞 ì°êµ¬ë ê³ ë±íìì ì 첎íëì ë°ë¥ž ìë©Žì ì§, ì¬ë°ë³ìŽë륌 íìžíŽ ë³Žê³ ì ìíë í¡ëšì ì¡°ì¬ì°êµ¬ìŽë€. ì°êµ¬ëììë ììžì§ììì¬ Yê³ ë±íêµ 1, 2íë
íìë€ë¡ ì¬ì¥, ì ì¥ ë± ì§ë³ìŒë¡ ìœì ë³µì©íë íì, ìµê·Œ 1ê°ìê° ê³šì ë±ê³Œ ê°ì ì¬ê³ ë¡ ì 첎íëì ì íìŽ ìë íìë€ì ì ìží 118ëª
ìê² ì€ë¬žì¡°ì¬ì ì¬ë°ë³ìŽë ê²ì¬ë¥Œ ìííìë€. ìŽ 105ëª
íìì ì€ë¬žì§ì ì¬ë°ë³ìŽë ê²ì¬ 결곌ì§ë¥Œ ìµì¢
ë¶ìì íì©íìê³ ìì§ë ìë£ë SPSS 21.0 íë¡ê·žëšì ìŽì©íì¬ ë¶ìíìë€. ëììì ìŒë°ì í¹ì±, ì 첎íë, ìë©Žì ì§, ì¬ë°ë³ìŽë륌 íì
íêž° ìíŽ íê· , íì€ížì°š, ë¹ë, ë°±ë¶ìšì ì€ìíìê³ , ìŒë°ì í¹ì±ì ë°ë¥ž ì 첎íë, ìë©Žì ì§, ì¬ë°ë³ìŽë륌 íì
íêž° ìíì¬ ë
늜 t-test, x²-test, ë¶ì°ë¶ì ë° ScheffeÌ test륌 ì€ìíìë€. ê·žëŠ¬ê³ ëììì ì 첎íëì ë°ë¥ž ìë©Žì ì§, ì¬ë°ë³ìŽëì ì°šìŽë¥Œ íì
íêž° ìíì¬ ë¶ì°ë¶ìì
ì€ìíìë€.볞 ì°êµ¬ ëììì ìŒë°ì í¹ì±ì ìŽíŽë³Žë©Ž, ì¬íìì 53.3%, 1íë
ì 52.4%, ê³Œì²Žì€ íìì 33.3%, ì ì²Žì€ íìì 16.2%ìŽìë€. ì륎ë°ìŽíž 겜íìŽ ìë íìì 24.8%, ì죌ë 14.3%, í¡ì°ì 6.7%ìŽìë€. 걎ê°ìíê° ì¢ë€ê³ ìê°íë íìì 59.0%, íë³µí íìì 58.1%, ì€ížë ì€ë¥Œ ë§ìŽ ë°ë íìì 42.9%ìŽìë€. íê· ì¬ë°ëì(mean Heart Rate, mean HR)ë 77.3í, ì¬ë° íì€ížì°š(Standard Deviation of Normal to Normal R-R Intervals, SDNN)ë 50.64msìŽë©°, ìë©Žì ì§ì ìŽì 21ì ë§ì ì íê· 6.10ì ìŽë©° 8ì ìŽíë ìë©Žì ì§ìŽ ì¢ì ìí륌 ì믞íë€. ì 첎 íì ì€ ìë©Žì ì§ìŽ ì¢ì§ ìì íìì 18ëª
(17.1%)ìŽìë€.ëšíìì ê²œì° í룚 90ë¶ì ìŽê³Œíì¬ ì 첎íë íë íììŽ ì¬íìë³Žë€ ì ìíê² ë§ìë€(x²=8.864, p=.012). ì륎ë°ìŽížë¥Œ íë íìì ê²œì° í룚 180ë¶ ìŽê³Œì ì 첎íëì íë íì ë¹ìšìŽ ì륎ë°ìŽížë¥Œ íì§ ìë íìë³Žë€ ì ìíê² ëìë€(x²=7.560, p=.023).ìë©Žì ì§ì ë¹í¡ì° íì(t=-2.009, p=.047)ìŽê³ , 걎ê°íë€ê³ ìê°í ìë¡(F=6.778, p=.002), íêµìíì ë§ì¡±í ìë¡(F=3.313, p=.040), íë³µí ìë¡(F=4.667, p=.012), ì€ížë ì€ ì ëê° ë®ììë¡(F=8.330, p<.001) ëìë€. ì¬ë°ë³ìŽëììë ì¬íìì mean HRìŽ ì ìíê² ëìŒë©°(t=3.321, p=.001), SDNNì ëšíììŽ ì ìíê² ëìë€(t=2.120, p=.036). ì 첎íë ë°ë¥ž ìë©Žì ì§, ì¬ë°ë³ìŽëììë ì ìí ì°šìŽê° ììë€.ìŽìì 결곌ìì ì 첎íë, ìë©Žì ì§, ì¬ë°ë³ìŽë ì¬ìŽì ìêŽêŽê³ë ììŒë ì±ë³ì ë°ëŒ ì 첎íë, ì¬ë°ë³ìŽëì ì ìí ì°šìŽê° ììì íìžíìë€. ëí íêµìí ë§ì¡±ë, íë³µì ë, ì€ížë ì€ ë± ì ìì ìž ë¶ë¶ì ë°ëŒ
ìë©Žì ì§ìŽ ë¬ëŒì§ë ê²ì ì ì ììë€. ì±ë³ì ë°ë¥ž ì 첎íë ì°šìŽë¥Œ ì€ìŽêž° ìíŽ ì 첎íëì ì€ìì±ì ëí êµì¡ê³Œ íšê» ê°ëšíê³ ìœê² ì°žì¬í ì ìë ì¬íìë§ì ìí ì€í¬ìž íë¡ê·žëšìŽ íìíë€. ëí ìë©Žì ì§ í¥ì ë° ì ì 걎ê°ì ìíŽ ìì ì ì€ížë ì€ ìì€ì íì
íê³ ëª
ì, ìì
ê°ì, ìŽë ë± ê±Žì í ë°©ë²ìŒë¡ ì€ížë ì€ë¥Œ íŽìí ì ìëë¡ êµì¡ê³Œ íë¡ê·žëš ì ê³µìŽ íìíë€. ì¬ë°ë³ìŽëë ìŽë, ꞎì¥, í¥ë¶, ì²Žìš ë³í ë± ë€ìí ììžì ìí¥ì ë°êž° ë묞ì íì ì°êµ¬ììë ìŽë ì , í ëë ì€í군곌 ëì¡°êµ°ì ì€ì íì¬ ìŽë ê°ëì ë°ëŒ ìë©Žì ì§ê³Œ ì¬ë°ë³ìŽë륌 ë¹êµíë ì€íì°êµ¬ë¥Œ ì ìžíë€.prohibitio
ìŒë³ž ìžêµì ì± ì ë³í: ìŒë³žì ìŒì¹Žì¿ /ëì€ìë€ì€ ë¶ììëìì ì± ì ì€ì¬ìŒë¡
íìë
Œë¬ž (ìì¬)-- ììžëíêµ êµì ëíì : êµì í곌, 2014. 8. ë°ì² í¬.Japan has been conducting so-called quiet diplomacy for a long time, especially in terms of dealing with territorial conflicts. However, in the 2010 collision incident between Chinese fishing crawler and Japanese Coast Guards patrol boat, Japan chose to respond in more assertive manner, by detaining the Chinese captain, hence shifting its policy from hands off to proactive defence. Why has Japan shifted its policy?
Examining the relationship from the Japanese point of view, this paper presents how Japans hands off policy approach to the territorial and maritime dispute has enabled China to effectively prevent Japan from exercising sovereignty over the disputed islands while pushing its own claim further.Table of Contents
Chapter 1. Introduction 1
1-1 Puzzle 2
1-2 Existing Literature 2
1-3 Argument. 3
1-4 Methodology 4
Chapter 2. Senkaku/Diaoyu islands disputeResults 5
2-1 Re-emergence of the dispute in 1990 5
2.2 1996 Lighthouse Recognition 7
2.3 Maritime Issues 11
2-4 Lighthouse and Leasing 15
2-5 Arrests made by Japan 16
Chapter 3. Conclusion 21
Bibliography 22
Appendix 25
Abstract in Korean 37Maste
êž°íë³íë¡ ìží êž°ììžì ë³íì ì ìí¹ì±ìŽ ëìœì ìì€ ëë ë³ìŽì ëŒì¹ë ìí¥
íìë
Œë¬ž (ìì¬)-- ììžëíêµ í겜ëíì : í겜ê³íí곌, 2014. 8. ìŽëì.êž°íë³íë íí묌ì§ì í겜 ì€ ëíì ìí¥ì 죌ë ê²ìŒë¡ ìë €ì ž ìë€. 볞 ì°êµ¬ììë êž°íë³íì ë°ë¥ž êž°ììžìì ë³íì ì ìì í¹ì±ìŽ ìì€ ëìœ ëëì ë³ìŽì ìŠê°ì ëŒì¹ë ìí¥ì ë€ë§€ì²Ž 몚íì ì¬ì©íì¬ íê°íê³ ì íìë€. ëšíì ì§ìì ëììŒë¡ íë KPOP-CC ë€ë§€ì²Ž 몚íì RCP8.5 êž°íë³í ìë늬ì€ë¥Œ ì ì©íì¬ êµëŽìì ì¬ì©ëë ëìœ ì€ ìììíê³ ê±Žê°ì ìí¥ì ì€ ê°ë¥ì±ìŽ ìë€ê³ íëšëë 4ì¢
ì ëìœ(Butachlor, Carbofuran, Chlorpyrifos, Endosulfan) 묌ì§ì ëíŽ ê³Œê±° 구ê°(1956-2005)ììì ëëì 믞ë 구ê°(2006-2100)ììì ëë륌 ë¹êµíìë€.
4ì¢
ëìœì ëê²œì§ í ì ëëë ëë¶ë¶ì ì êµ ì ììì ê°ìíììŒë©°, ë³íìšì ìµì -70%ê¹ì§ ëíë¬ë€. ìì€ ëëë ì°íê· ì ê²œì° ëë ·í ìŠê° í¹ì ê°ì 겜í¥ì 볎ìŽì§ ìììŒë, ìëì ìŒë¡ ëëê° ëì 5ìë¶í° 8ìê¹ì§ì êž°ííê· ëë CMJJAë ê°ìíìë€. ê·žë¬ë ìŒë¶ ì첎구ê°ììë CMJJA ëëê° 40%ê¹ì§ ìŠê°íìì§ë§, 곌거 구ê°ìì ìëì ìŒë¡ ê³ ëëìž ì§ì ì€ êž°íë³íë¡ ìžíŽ ëëì ìŠê°ìšìŽ 컀ì§ë 겜ì°ë ê±°ì ììë€. ëìœë¥ ì€ìŒë¬Œì§ì ì첎ë¡ì ì ì
ì ê°ì¥ í° ìí¥ì 죌ë ìŽë êž°ìì ìë¥ë¡ë¶í° ì ì
ìŽ ìë ì첎구ê°ì 겜ì°ë ìŽë¥, ìë¥ë¡ë¶í° ì ì
ìŽ ìë 구ê°ì 겜ì°ë í ì¬ì ì¶(carbofuranì ê°ì°ì ì¶)ìŽìë€. êž°íë³íë¡ ìží ëìœë¥ ëëì ìŠê° ëë ê°ìì ê°ì¥ í° ìí¥ì 죌ë ì첎ë¡ì ì ì
êž°ìì í ì¬ì ì¶ ë° ìŽë¥ìž ê²ìŒë¡ ëíë¬ìŒë, ìŽë€ì ìŠê°ìšìŽ ë°ëì ëëì ìŠê°ë¡ ìŽìŽì§ë ê²ì ìëìë€. êž°íë³íë¡ ìží ëìœ ë¬Œì§ì ê³ ëë ë³í륌 íê°íêž° ìíŽ ì°ê° 3ìŒ íê· ëë ìì 10% ê°ë€ì êž°ííê· ì AT10-3dayë¡ ì ìíì¬ ê·ž ë³í륌 ë¶ìíìë€. CMJJA ëëê° ëë¶ë¶ì ì첎 구ê°ìì ê°ìíë ë°ë©Ž, 4ì¢
ëìœì AT10-3dayë ë§ì ì ììì ìŠê°íìë€.
êž°íë³íë¡ ìžíì¬ ì첎ìì ëìœì ìì ëëê° ìŠê°í ì ìì í¹ì§ì ì첎 ë¶íŒì ëí ì ì ëŽ ëê²œì§ ë©Žì ìŽ ëìŒë©°, í ì¬ì ì¶ìëê° ìŠê°íì¬ ë겜ì§ë¡ë¶í°ì ë¹ì ì€ìŒ ë¶íëìŽ ìŠê°íë ì§ììŽë€. ëí, ìë¥ë¡ë¶í°ì ì ì
ìŽ ìë ë³žë¥ êµ¬ê°ì 겜ì°ìë ìŽë¥ì ìí ì ì
ìŽ ì§ë°°ì ìŽë¯ë¡ ìë¥ì ëê²œì§ ë©Žì ë° ì ì¶ìë ë³íê° íë¥ì ëìœ ë¬Œì§ ëë ë³íì ìí¥ì 죌ë ê²ìŒë¡ 볎ìžë€.
볞 ì°êµ¬ì 결곌ì ìíë©Ž, ì ì¶ìëì ë³íê° í¬ê³ ëšìì첎ë¶íŒ ë¹ ìë¥ê¹ì§ ê³ ë €í ëê²œì§ ë©Žì ìŽ í° ì§ìì ëíŽì ëìœ ì¬ì©ì ëí ê·ì 륌 ì격í íë ê²ìŽ ë°ëì§íë€.â
. ìë¡ 1
1. ì°êµ¬ì 배겜 ë° ëª©ì 1
2. ì°êµ¬ì ë²ì ë° ë°©ë² 4
1) ì°êµ¬ì ë²ì 4
2) ì°êµ¬ì ë°©ë² 8
3. ì°êµ¬ íëŠë 10
â
¡. ìŽë¡ ì 배겜 ë° ì°êµ¬ ì€ê³ 11
1. êž°íë³íê° ì íŽííë¬Œì§ ëí ë³íì 믞ì¹ë ìí¥ 11
2. ë€ë§€ì²Žëªšíì°êµ¬: KPOP-CC 13
1) 몚í ì°êµ¬ 13
2) ì¬ì©ë ì°ì 16
â
¢. ë¶ì결곌 19
1. êž°íë³í ìë늬ì€ì ë°ë¥ž êž°ì ë³í ë¶ì 19
2. 몚íì íê° 30
3. ëìœì ì êµ íê· ëë ë³í 33
1) ì°íê· ëë ë³í 33
2) ìë³ ëë ë³í 36
4. ì êµ ì첎구ê°ììì ëìœ ëë ë³í 39
5. ëìœë¥ 묌ì§ì ìŽë êž°ì ë¶ì 44
6. ëìœë¥ 묌ì§ì ê³ ëë ì¶í 53
â
£. ê²°ë¡ 59
1. ê²°ë¡ 59
2. ì°êµ¬ì íê³ ë° ì¶í ì°êµ¬ì íìì± 60
â ì°žê³ ë¬ží 61Maste
ê°ìžì ì¬êµì¡ ìë¹ì ìŽììŽ ë¯žì¹ë ìí¥ /
íìë
Œë¬ž (ìì¬)-- ììžëíêµ ëíì : 겜ì íë¶, 2016. 8. ìŽìžíž.This paper characterizes the eects of neighbourhoods on the consumption
of private education by studying those who moved passing districts for
a reason other than children's education in Korea. In this study, I present a
quasi-experimental evidence that people adjust the consumption considering
the average of newly given neighbourhoods. Households increase their expenditure
by about 50,000 won after moving to a new region where the average
consumption is relatively high. On the other hand, households which moved
to where with lower consumption decrease the expenditure by 50,000 won.
This pattern also holds when the consumption is measured in unit, not by the
expenditure, implying that the dierence in price level among regions can't
fully explain this phenomenon. Also, households adjust tutoring and private
institute consumption rather than after school and complementary courses
consumption, which supports that some private education are more positional
than others.1 Introduction 4
2 Previous literature 7
3 Data, The Quasi-experiment and Empirical strategy 9
3.1 Data 9
3.2 The Quasi-experiment 10
3.3 Empirical Strategy 12
4 Empirical Results 15
4.1 Summary Statistics of Each Group 15
4.2 The Impact of Neighborhoods on the Private Education Consumption 17
5 Conclusion 21
References 23
Figure and Table 26Maste
Art as an investment : economics of taste : marketing strategy for contemporary Korean art
Thesis(masters) --ììžëíêµ ëíì :겜ìí곌(SNU Global MBA),2008. 8.Maste
Pharmacokinetics of Oral Doxofylline in Korean Asthma or Chronic Obstructive Pulmonary Disease Patients
Thesis(master`s)--ììžëíêµ ëíì :ìœí곌 ìììœí ì ê³µ,2006.Maste
ê³ ë±íìì ì 첎íëì ë°ë¥ž ìë©Žì ì§ ë° ì¬ë°ë³ìŽë
ê°íží곌/ìì¬ì²ìë
êž°ë ì 첎ì , ì ì ì ìŒë¡ ì±ì¥ìëê° ë¹ ë¥Žë©°, ìžì ìê·¹ ëë í겜ì 믌ê°í ìêž°ìŽêž° ë묞ì 걎ê°êŽëŠ¬ê° ë§€ì° ì€ìíë€. 걎ê°ì ìí¥ì 믞ì¹ë ìì ì€ì íëê° ìŽë, ìë©Ž, ì€ížë ì€ìžë° ê³ ë±íììŽ ì€íìì ë¹íŽ ì 첎íëìŽ ì ê³ , ìë©Žìê°ë 짧ìŒë©° ì€ížë ì€ë ìëì ìŒë¡ ë§ìŽ ë°êž° ë묞ì 걎ê°êŽëŠ¬ê° íìíë€. í¹í ììšì 겜ê³ë í겜 ë³íì ì믌íê² ë°ìíë êž°êŽìŒë¡ íëì ì 겜ê³ê° íì±í ëê±°ë ì íëë©Ž íìì±ì ë¶ê· íìŒë¡ ì§ë³ìŽ ìŽëëë€. ë°ëŒì 걎ê°ì ì ì§íë €ë©Ž ììšì 겜ê³ê° ê· íì ìŽë£šìŽìŒ íë©° ììšì 겜ê³ì êž°ë¥ì ì¬ë°ë³ìŽë륌 íµíŽ íìží ì ìë€.볞 ì°êµ¬ë ê³ ë±íìì ì 첎íëì ë°ë¥ž ìë©Žì ì§, ì¬ë°ë³ìŽë륌 íìžíŽ ë³Žê³ ì ìíë í¡ëšì ì¡°ì¬ì°êµ¬ìŽë€. ì°êµ¬ëììë ììžì§ììì¬ Yê³ ë±íêµ 1, 2íë
íìë€ë¡ ì¬ì¥, ì ì¥ ë± ì§ë³ìŒë¡ ìœì ë³µì©íë íì, ìµê·Œ 1ê°ìê° ê³šì ë±ê³Œ ê°ì ì¬ê³ ë¡ ì 첎íëì ì íìŽ ìë íìë€ì ì ìží 118ëª
ìê² ì€ë¬žì¡°ì¬ì ì¬ë°ë³ìŽë ê²ì¬ë¥Œ ìííìë€. ìŽ 105ëª
íìì ì€ë¬žì§ì ì¬ë°ë³ìŽë ê²ì¬ 결곌ì§ë¥Œ ìµì¢
ë¶ìì íì©íìê³ ìì§ë ìë£ë SPSS 21.0 íë¡ê·žëšì ìŽì©íì¬ ë¶ìíìë€. ëììì ìŒë°ì í¹ì±, ì 첎íë, ìë©Žì ì§, ì¬ë°ë³ìŽë륌 íì
íêž° ìíŽ íê· , íì€ížì°š, ë¹ë, ë°±ë¶ìšì ì€ìíìê³ , ìŒë°ì í¹ì±ì ë°ë¥ž ì 첎íë, ìë©Žì ì§, ì¬ë°ë³ìŽë륌 íì
íêž° ìíì¬ ë
늜 t-test, x²-test, ë¶ì°ë¶ì ë° ScheffeÌ test륌 ì€ìíìë€. ê·žëŠ¬ê³ ëììì ì 첎íëì ë°ë¥ž ìë©Žì ì§, ì¬ë°ë³ìŽëì ì°šìŽë¥Œ íì
íêž° ìíì¬ ë¶ì°ë¶ìì
ì€ìíìë€.볞 ì°êµ¬ ëììì ìŒë°ì í¹ì±ì ìŽíŽë³Žë©Ž, ì¬íìì 53.3%, 1íë
ì 52.4%, ê³Œì²Žì€ íìì 33.3%, ì ì²Žì€ íìì 16.2%ìŽìë€. ì륎ë°ìŽíž 겜íìŽ ìë íìì 24.8%, ì죌ë 14.3%, í¡ì°ì 6.7%ìŽìë€. 걎ê°ìíê° ì¢ë€ê³ ìê°íë íìì 59.0%, íë³µí íìì 58.1%, ì€ížë ì€ë¥Œ ë§ìŽ ë°ë íìì 42.9%ìŽìë€. íê· ì¬ë°ëì(mean Heart Rate, mean HR)ë 77.3í, ì¬ë° íì€ížì°š(Standard Deviation of Normal to Normal R-R Intervals, SDNN)ë 50.64msìŽë©°, ìë©Žì ì§ì ìŽì 21ì ë§ì ì íê· 6.10ì ìŽë©° 8ì ìŽíë ìë©Žì ì§ìŽ ì¢ì ìí륌 ì믞íë€. ì 첎 íì ì€ ìë©Žì ì§ìŽ ì¢ì§ ìì íìì 18ëª
(17.1%)ìŽìë€.ëšíìì ê²œì° í룚 90ë¶ì ìŽê³Œíì¬ ì 첎íë íë íììŽ ì¬íìë³Žë€ ì ìíê² ë§ìë€(x²=8.864, p=.012). ì륎ë°ìŽížë¥Œ íë íìì ê²œì° í룚 180ë¶ ìŽê³Œì ì 첎íëì íë íì ë¹ìšìŽ ì륎ë°ìŽížë¥Œ íì§ ìë íìë³Žë€ ì ìíê² ëìë€(x²=7.560, p=.023).ìë©Žì ì§ì ë¹í¡ì° íì(t=-2.009, p=.047)ìŽê³ , 걎ê°íë€ê³ ìê°í ìë¡(F=6.778, p=.002), íêµìíì ë§ì¡±í ìë¡(F=3.313, p=.040), íë³µí ìë¡(F=4.667, p=.012), ì€ížë ì€ ì ëê° ë®ììë¡(F=8.330, p<.001) ëìë€. ì¬ë°ë³ìŽëììë ì¬íìì mean HRìŽ ì ìíê² ëìŒë©°(t=3.321, p=.001), SDNNì ëšíììŽ ì ìíê² ëìë€(t=2.120, p=.036). ì 첎íë ë°ë¥ž ìë©Žì ì§, ì¬ë°ë³ìŽëììë ì ìí ì°šìŽê° ììë€.ìŽìì 결곌ìì ì 첎íë, ìë©Žì ì§, ì¬ë°ë³ìŽë ì¬ìŽì ìêŽêŽê³ë ììŒë ì±ë³ì ë°ëŒ ì 첎íë, ì¬ë°ë³ìŽëì ì ìí ì°šìŽê° ììì íìžíìë€. ëí íêµìí ë§ì¡±ë, íë³µì ë, ì€ížë ì€ ë± ì ìì ìž ë¶ë¶ì ë°ëŒ
ìë©Žì ì§ìŽ ë¬ëŒì§ë ê²ì ì ì ììë€. ì±ë³ì ë°ë¥ž ì 첎íë ì°šìŽë¥Œ ì€ìŽêž° ìíŽ ì 첎íëì ì€ìì±ì ëí êµì¡ê³Œ íšê» ê°ëšíê³ ìœê² ì°žì¬í ì ìë ì¬íìë§ì ìí ì€í¬ìž íë¡ê·žëšìŽ íìíë€. ëí ìë©Žì ì§ í¥ì ë° ì ì 걎ê°ì ìíŽ ìì ì ì€ížë ì€ ìì€ì íì
íê³ ëª
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ê°ì, ìŽë ë± ê±Žì í ë°©ë²ìŒë¡ ì€ížë ì€ë¥Œ íŽìí ì ìëë¡ êµì¡ê³Œ íë¡ê·žëš ì ê³µìŽ íìíë€. ì¬ë°ë³ìŽëë ìŽë, ꞎì¥, í¥ë¶, ì²Žìš ë³í ë± ë€ìí ììžì ìí¥ì ë°êž° ë묞ì íì ì°êµ¬ììë ìŽë ì , í ëë ì€í군곌 ëì¡°êµ°ì ì€ì íì¬ ìŽë ê°ëì ë°ëŒ ìë©Žì ì§ê³Œ ì¬ë°ë³ìŽë륌 ë¹êµíë ì€íì°êµ¬ë¥Œ ì ìžíë€.prohibitio
ìì 죌ìì ì믌ì±ì ëëêµì¡ì íšì ì°êµ¬
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Pre-service Teachers Understanding of Variance
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Œë¬ž(ìì¬) --ììžëíêµ ëíì :ìíêµì¡ê³Œ,2010.2.Maste