Won Yong-guk

Considering the recent emergence of voice chatbots as substitutes for human interlocutors in eliciting spoken responses during English oral proficiency interviews, this study examines how interlocutor type affects both fluency and holistic scores. Data were collected from 32 Korean college students, yielding 128 audio recordings across four distinct topics of varying complexity, with each topic administered via both chatbot and human interlocutors. Fluency features were analyzed using Praat soft

Despite the benefits of performance-based oral communication tests, a plethora of variables, as illustrated in Ockey and Li’s (2015) model of oral communication assessment, can create construct-irrelevant variance in test scores. In relation to human participants in the oral communication tests, previous studies mostly focused on the direct effect of the rater group variable on test scores. Little attention has been paid to the interaction of raters with interviewers in oral communication tests.

Appropriate use of reporting verbs plays an important role in building arguments in academic writing. Many studies have investigated the use of reporting verbs in academic writing, but little attention has been paid to the comparison of reporting verb use in academic writing with more informal writing. To address this research gap, this study aims to compare the use of reporting verbs in academic writing and in blog writing. Two comparable academic and blog corpora were prepared: the academic co

This paper proposes a method for designing a large recording script for open domain English speech synthesis. For read-aloud style text, 12 domains and 294 sub-domains were designed using text contained in five different news media publications. For conversational style text, 4 domains and 36 sub-domains were designed using movie subtitles. The final script consists of 43,013 sentences, 27,085 read-aloud style sentences, and 15,928 conversational style sentences, consisting of 549,683 tokens and

Abstract This study examines the validity of WER as a proxy for pronunciation quality in EFL contexts. Human ratings of comprehensibility and accentedness were compared with WER and automated pronunciation scores from six ASR systems — Kaldi, wav2vec 2.0, HuBERT, Whisper (Base and Large-v3), and Microsoft Azure — using 190 read-aloud recordings by Korean elementary learners. With respect to pronunciation scoring, Azure’s phoneme-level accuracy scores demonstrated moderate correlations with human

Test score variability associated with characteristics of raters is considered as a construct-irrelevant variance in test scores. Several studies have tried to identify the effect of rater characteristics on test score variances in performance-based oral communication tests. However, little attention has been paid to the effect of raters’ familiarity with speech topics in an oral communication test. This study investigates how raters’ prior knowledge of test takers’ presentation topics affects r

The primary objective of this study is to identify the types of errors made by Korean college students in an oral proficiency interview in relation to specific task topics, and to examine how these errors affect their lexico-grammatical proficiency scores. Ninety-six two-minute-long audio clips of 32 Korean college students on three different topics were transcribed. Lexico-grammatical errors were then coded for statistical analysis and lexico-grammatical scores were estimated using many-facet R

The primary purpose of this study is to investigate how well vocabulary features, such as collocation, word family, etc., predict vocabulary scores in writing graded by human raters, and the degree to which they explain holistic scores. Forty-nine writings by first-year non-native English-speaking international students at a mid-western U.S. university were analyzed by using two multiple regression models. Data includes both holistic and analytic vocabulary scores given by four human raters and

본 연구는 2015년부터 2024년까지 10년 간의 영어교육 관련 언론 기사를 대상으로 빅데이터 분석을 실시하여 영어교육에 대한 사회적 관심의 변화를 탐색하였다. 한국언론진흥재단의 빅카인즈 데이터베이스에서 수집한 32,348건의 기사를 대상으로 Kiwi 형태소 분석기를 활용한 텍스트 전처리, TF-IDF 벡터화, 비음수 행렬 분해(NMF) 알고리즘 기반 토픽모델링, 그리고 시계열 분석을 실시한 결과, 영어교육 관련 기사는 ‘영어학습/교육’(55.7%), ‘영어교육환경’(29.7%), ‘영어입시/평가’(14.6%) 세 주제 영역으로 구분되었다. 각 영역은 다시 세 개의 하위 토픽으로 세분화되었으며, 시기별로 차별화된 변화 양상을 보였다. 특히 환경맥락과 교수자 관련 주제가 크게 증가한 반면, 평가 중심의 주제들은 감소하는 추세를 보였다. 2020년을 전후로 AI와 온라인 교육 관련 키워드가 급증하였으며, ChatGPT 등장 이후에는 AI 관련 보도가 증가하였다. 한편, 학술논문과의 비

본 논문에서는 최근 10년(2013년-2022년) 동안 한국연구재단 등재 학술지에 게재된 영어교육 논문들을 조사하여 양적 분석과 함께 논문의 영문 초록을 데이터로 토픽 모델링 알고리즘을 활용하여 영어교육 주제들을 분석하였다. 연구에서의 분석 대상 논문은 12,915편으로 이들 논문은 영어교육 전문 학술지, 영어 관련 학술지, 일반 학술지에 게재되었는데, 일반 학술지에 게재된 논문의 비율이 66%로 두드러지게 컸다. 영어교육 논문 게재 현황은 분석 기간 동안 점차 증가하다가 최근에 감소세를 보였다. 영어교육 논문에서 많이 나타난 연구 주제는 학교 상황에서의 교수 및 학습과 관련된 것으로 모든 유형의 학술지에서 동일한 결과를 보여주었다. 다음으로, 영어 기능과 관련된 주제인 쓰기, 통사, 어휘와 코퍼스 및 담화, 발음과 말하기에 대한 연구가 많은 것으로 나타났다. 영어교육 전문 학술지의 경우 학교급에 따른 다양한 연구가 진행되고 있는 것을 알 수 있었다. 영어 관련 학술지와 일반 학술지