이 연구는 미얀마인 한국어 학습자의 한국어 사용 오류 양상을 살펴서 미얀마인 한국어 학습자들에게 좀 더 효율적으로 한국어교육을 할 수 있 는 방안에 대해 제언하는 것을 논의의 목적으로 하였다. 이 연구의 목적 을 달성하기 위해 미얀마인 외국인 유학생들 63명의 작문 과제를 음소, 문법, 어휘 등의 측면에서 분석하였다. 그 결과 음소 관련 오류에서는 파 찰음, 평음과 경음의 대치 오류가 많았으며, 'ㅓ'와 'ㅏ', 'ㅐ'와 'ㅏ', 'ㅐ' 와 'ㅔ'의 대치 오류도 많이 나타났다. 문법 관련 오류에서는 연결어미 ‘-고’와 ‘-아서/어서’의 대치 오류가 가장 많았으며, 어휘 관련 오류에서 는 어휘 의미 혼동, 미얀마어의 영향에 따른 의미 과잉 적용 등의 오류 가 많이 나타났다. 이 연구과 같은 한국어와 미얀마어의 대조 분석 관점 에서의 오류 연구가 계속 된다면 미얀마 한국어 학습자의 한국어교육에 유의미한 기여를 할 수 있을 것이다.

Bee traffic at the hive entrance can be used as an important indicator of foraging activity. We investigated patterns of honeybees and bumblebees near their hives as a basis for calculating bee traffic using the image deep learning. The flight pattern near the hive differed significantly according to bee at entering and leaving the hive. Honeybees mainly showed flight that changed flight direction more than once (69.5%), whereas bumblebees mainly performed straight flight (48.7%) or had a single turn (36.5%) in flight. When bees entered the hive, honeybees primarily showed one-turn or two-turn flight patterns(88.5%), and bumblebees showed a one-turn flight pattern (48.0%). In contrast, when leaving the hive, honeybees primarily showed a straight flight pattern (63.0%), and bumblebees primarily showed a straight or one-turn pattern (90.5%). There was a significant difference in flight speed according to the flight pattern. The speed of straight flight (0.89±0.47 m/s) was 1.5 to 2.1 times faster than flight where direction changed. Therefore, our results can help determine the capturing and recognizing the flying image of bees when calculating bee traffic by image deep learning.

This study investigated which factors impact esports viewership for League of Legends matches through Live and highLight streaming video. This research anaLyzed League of Legends viewership factors' differences between the three Leagues (LCK, LCS, and LEC) where viewership factors infLuence esports viewership demand through LOL’s Live and highLight streaming video. League of Legends not onLy has the highest audience among different various esports, aLso has formed a huge market from diverse countries. In order to soLve each League's unbaLance viewership trend issue between the three Leagues, and facing worLdwide viewership decrease, this study examined viewership impact factors from three Leagues with the highest viewership ratings: Korea's LCK, North America's LCS, and Europe's LEC. After that, differences in viewership impact factors for the research were anaLyzed and compared to each League. SpecificaLLy, this study investigated which factors impact esports viewership for League of Legends (LOL) matches. Data (N=1581) of reguLar season matches from the LCK, LCS, and LEC Leagues (2020 spring to 2021 summer) were coLLected and anaLyzed. This study anaLyzed the average number of visitors per minute of Twitch Live streaming and the number of views on YouTube highLights. Five main viewership factors(the uncertainty of match, consumer preferences, match importance, team attributes, match content, and controL variabLes), and seventeen independent variabLes were verified by muLtipLe regression anaLysis using STATA 15. As a resuLt of the anaLysis, the modeL with the number of reaL-time audience as the dependent variabLe demonstrated different trends between Leagues in terms of the uncertainty of match factors (rank difference, win rate difference, goLd difference, kiLL difference, object difference), and consumer preference factors(fan, operating period of both teams, number of worLd championship wins, the number of championship semifinaL advances). WhiLe the match importance factors(week, top-rank matches) and match content factors (the sum of kiLLs, the sum of assists, the sum of objects) indicated somewhat simiLar tendencies between Leagues. In the modeL with the number of highLights views as the dependent variabLe, consumer preference factors and match importance factors tended to be somewhat simiLar, but match content factors prone to be sLightLy different between Leagues. In addition, the uncertainty of the match factors did not show a significant infLuence across the three Leagues. Findings from this research expand and appLy the variabLes derived from the demand determination theory of traditionaL sports to e-sports demand research. This study heLps stakehoLders understand various esports cuLtures from different regions. Furthermore, it is meaningfuL in that it provides information that wiLL contribute to the growth of the entire League of Legends market through baLanced deveLopment between Leagues.