Zoom fatigue

Summary

Some recent studies have found effects of Zoom fatigue for Zoom use in business settings during the pandemic, and a few studies have reported analogous effects for Zoom in education. This survey study examined students doing English courses via Zoom during the pandemic, to examine Zoom fatigue effects specifically for second language teaching. Unlike past studies, personality factors were also included in this study. The results indicated that Zoom seemed advantageous for those with lower foreign language anxiety, those who already had friends in the course, those with good technology, and introverts or those who prefer passive learning. Zoom seemed disadvantageous for those with foreign language anxiety, extroverts or those who prefer active learning, those with mirror anxiety, and possibly for some female students (especially those who are more concerned with appearance and related anxiety). These results need to be considered for online classes, especially when circumstances mandate online courses for all students.

• 
  Methods: Survey design; correlation analysis in R
• 
  Results: Conference presentation, PPT

Conference presentation:

Sociocultural problems and challenges in Zoom instruction, MEESO 2022 International Conference, Seoul, Korea.
PPT

English pronunciation (2023)

Summary

Korean students struggle with English pronunciation, often because they were never taught well. For the main study, I created a one-month pronunciation training program for Korean college students, which cominbed phonetic explanations with some interactive activities. Though one month was too short for notable improvements in pronunciation, participant surveys showed an increased awareness of English pronunciation, and slightly improved motivation.

• 
  Methods: Pretest & posttest speaking test, pretest and posttest surveys, classroom experimental research design, hierarchical linear modeling using R
• 
  Results: Research article published; conference presentations in 2023; more and better teaching materials

Details

The main study investigates the effectiveness of a short-term pronunciation program for Korean students learning English, including the effectiveness of a short-term program for pronunciation improvement, possible effects of inductive learning, and improvements in motivation. Initial and post-surveys probed participants’ self-efficacy, motivation, and relevant personality factors. Pretest and post-test recordings were rated for pronunciation accuracy. In between were three classroom sessions involving group exercises on particular vowels, consonants, and prosody, such as lexical stress, rhythm, and linking. The sessions involved group learning, with some groups also experiencing more inductive style lessons. The results showed slight improvement in pronunciation, as well as in self-efficacy, despite the limited duration of the program. Some motivational and personality factors were correlated with their improvements. The more inductive style approach had no effect on outcomes, at least over the short term of this study. The study shows that phonetic explanations can be combined effectively with interactive activities, and that students’ motivation toward English speaking can improve with instruction. Also, better and more effective teaching materials resulted from this project.

In another study, Koreans listened to recorded samples of nonsense syllables, and the results indicated that they were not very aware of English stress and rhythm patterns. These results were presented at a conference.

Publications

• Lee, K. (2023). Improving English pronunciation accuracy among Korean adult learners. Modern English Education 24(2023) 272-283. https://doi.org/10.18095/meeso.2023.24.1.272

Conference presentations

• Lee, K. (2023). Pronunciation training in groups: Inductive learning and motivation. ELT conference, July 2023, Seoul.
• Lee, K. (2023). Koreans’ perceptions of English prosody. 2023 Aarhus International Conference on Voice Studies, August 2023, Aarhus, Denmark.

R code

# Import data set 
# Read the CSV file into a data frame
ratings <- read.csv(file="/home/kent/Dropbox/phon/2023.pron.L2.proj/raters/raters.csv", header=TRUE, sep=",")

impen <- read.csv(file="/home/kent/Dropbox/phon/2023.pron.L2.proj/results/pron2023.csv", header=TRUE, sep=",")

# DESCRIPTIVE STATS 
library(psych)
describe(mydata)
describe.by(mydata, group,...)

# CREATE new variable from others 
data$C <- (data$A - data$B)

# Change the reference level of the variable "group"
data$group <- relevel(data$group, ref = "treatment")

# CHI SQUARE
chisq.test(data_frame$treatment, data_frame$improvement, correct=FALSE)

chisq.test(impen$jNachPron, impen$jNachVowels) , impen$jNachCons, impen$jNachRhyhm) 

# Reshape the data into a contingency table
contingency_table <- table(data$jol1, data$jol2, data$jol3, data$jol4)

# Perform the chi-square test
result <- chisq.test(contingency_table)

# Print the results
print(result)

impentable <- table(impen$jNachPron, impen$jNachVowels, impen$jNachCons, impen$jNachRhyhm) 
result <- chisq.test(impentable(jNachPron, jNachVowels, jNachCons, jNachRhyhm))
result <- chisq.test(impentable)
print(result)


"jNachPron"	"jNachVowels"	"jNachCons"	"jNachRhyhm"

#-----------------------------

# Cronbach's alpha 
alpha(raters[,2:4])
alpha(raters[,3:5], check.keys=TRUE)

# Perform a paired t-test on pretest and posttest columns
t.test(data$pretest, data$posttest, paired = TRUE)
t.test(posttest ~ TreatG, data = data)
anov <- aov(weight ~ group, data = mydata)

# Or equivalent GLM model 
model <- lmer(Posttest ~ TreatG + Pretest + (1|ID), data = data)

# HLM
library(lme4)
model <- lmer(DaccGen1 ~ sms01Intrinsic + sms01ExReg + sms01Amot + tipiOpen + hrStudied + cond + accGen1pre + (1 | ncode1), data = impen)  --- DID NOT WORK HERE


t.test(impen$EnBgS1, impen$EnBgS2, paired = TRUE)
t.test(impen$EnBgSpkS1, impen$EnBgSpkS2, paired = TRUE)
t.test(impen$EnBgListS1, impen$EnBgListS2, paired = TRUE)
t.test(impen$afs01Intrins, impen$afs02Intrin, paired = TRUE)
t.test(impen$afs01Auto, impen$afs02Auto, paired = TRUE)
t.test(impen$afs01Comp, impen$afs02Comp, paired = TRUE)
t.test(impen$afs01Rel, impen$afs02Rel, paired = TRUE)
t.test(impen$sms01Intrinsic, impen$sms02Intrinsic, paired = TRUE)
t.test(impen$sms01IdReg, impen$sms02IdReg, paired = TRUE)
t.test(impen$sms01ExReg, impen$sms02ExReg, paired = TRUE)
t.test(impen$sms01Amot, impen$sms02Amot, paired = TRUE)

t.test(impen$accGen1pre, impen$accGen1post, paired = TRUE)
t.test(impen$accVowel3pre, impen$accVowel3post, paired = TRUE)
t.test(impen$accCons4pre, impen$accCons4post, paired = TRUE)
t.test(impen$prosGen5pre, impen$prosGen5post, paired = TRUE)
t.test(impen$sylLeng6pre, impen$sylLeng6post, paired = TRUE)
t.test(impen$accStress7pre, impen$accStress7post, paired = TRUE)
t.test(impen$inton8pre, impen$inton8post, paired = TRUE)
t.test(impen$fluss9pre, impen$fluss9post, paired = TRUE)
t.test(impen$Comp2pre, impen$Comp2post, paired = TRUE)
t.test(impen$gramm10pre, impen$gramm10post, paired = TRUE)
t.test(impen$gramlexsof11pre, impen$gramlexsof11post, paired = TRUE)
 
 
#To mean center a variable:
 
mydata$Extroversion <- scale(mydata$Extroversion, center=TRUE, scale=FALSE)

# HLM:

model <- lmer(Diff ~ TreatG + Extroversion + Intrinsic + Extrinsic + AFS + (1|ID), data=mydata
 
# Mean center the pretest scores
data$pretest_mc <- scale(data$pretest, center = TRUE, scale = FALSE)

# Fit the hierarchical linear model with pretest as a covariate and TreatG as the main predictor
model <- lmer(posttest ~ TreatG + pretest_mc + Extroversion + Intrinsic + Extrinsic + AFS + (1 | ID), data = data)

# View the model summary
summary(model)

#empen : 2023 En. pron. factors
#-------------------------------------- 
nom	ncode1	age	gen	grade	enyears	cond	session	major	enCourses	rs1file	fs2file	DEnBg	DEnList	DEnSpk	EnBgS1	EnBgRdS1	EnBgWrS1	EnBgListS1	EnBgSpkS1	EnBgS2	EnBgListS2	EnBgSpkS2	Dafs	DafsAuto	DafsComp	DafsRel	afs01Intrins	afs01Auto	afs01Comp	afs01Rel	afs02Intrins	afs02Auto	afs02Comp	afs02Rel	DsmsInt	DsmsIdReg	DsmsExReg	DsmsAmot	sms01Intrinsic	sms01IdReg	sms01ExReg	sms01Amot	sms02Intrinsic	sms02IdReg	sms02ExReg	sms02Amot	tipiExtro	tipiAgr	tipiConsc	tipiStable	tipiOpen	LnAnxG	LnAnxInput	LnAnxProc	LnAnxOut	jolG	jol01	jolImprove	jolremem	jolDiffMat	DVorNach	VorDiff	Nach	jNachPron	jNachVowels	jNachCons	jNachRhyhm	Eff2	hrStudied	accGen1pre	Comp2pre	accVowel3pre	accCons4pre	prosGen5pre	sylLeng6pre	accStress7pre	inton8pre	fluss9pre	gramm10pre	gramlexsof11pre	accGen1post	Comp2post	accVowel3post	accCons4post	prosGen5post	sylLeng6post	accStress7post	inton8post	fluss9post	gramm10post	gramlexsof11post 
#--------------------------------------

describe(impen$jolG, impen$jolremem)

impen$DaccGen1 <- (impen$accGen1pre - impen$accGen1post)
chisq.test(impen$cond, impen$DaccGen1, correct=FALSE)
t.test(DaccGen1 ~ cond, data = impen)
model <- lmer(Posttest ~ TreatG + Pretest + (1|ID), data = data)
anov <- anova(DaccGen1 ~ session, data = impen)

impen$accGen1pre_mc <- scale(impen$accGen1pre, center = TRUE, scale = FALSE)


#GLM

model <- glm(accGen1post ~ EnBgS1 + afs02Rel + tipiOpen + accGen1pre_mc + cond, data = impen, family = gaussian(link = "identity"))

model <- glm(accGen1post ~ EnBgS1 + afs01Intrins + tipiOpen + accGen1pre_mc, data = impen, family = gaussian(link = "identity"))

model <- glm(EnBgS2 ~ EnBgS1 + afs01Comp + accGen1pre_mc, data = impen, family = gaussian(link = "identity"))

model <- glm(EnBgSpkS2 ~ EnBgSpkS1 + afs02Comp, data = impen, family = gaussian(link = "identity"))  

model <- glm(EnBgListS2 ~ EnBgListS1 + afs02Comp, data = impen, family = gaussian(link = "identity"))  

model <- glm(sms02Amot ~ session + sms01Amot, data = impen, family = gaussian(link = "identity"))  

impen$cond <- relevel(impen$cond, ref = "z")

model <- glm(jolImprove ~ EnBgS1 + afs02Comp + cond, data = impen, family = gaussian(link = "identity"))  

#Convert variable to factor and change reference category 
impen$cond <- factor(impen$cond)
impen$cond <- relevel(impen$cond, ref = "z")
 
 
#----------------------------
#Test for interaction effects
#----------------------------
 
model <- glm(sms02Intrinsic  ~ sms01Intrinsic * cond, data = impen, family = gaussian(link = "identity"))

model <- glm(sms02Intrinsic ~ afs02Rel + hrStudied + sms01Intrinsic + sms01Intrinsic*cond, data = impen, family = gaussian(link = "identity"))  

model <- glm(sms02Intrinsic  ~ sms01Intrinsic * condition + Amotivated * condition, data = impen, family = binomial)

model <- glm(sms02Amot ~  afs02Rel + sms01Amot + cond, data = impen, family = gaussian(link = "identity"))   

model <- glm(sms02Amot ~ afs01Rel + sms01Amot + cond, data = impen, family = gaussian(link = "identity"))  

model <- glm(sms02Amot ~ afs01Rel + sms01Amot + cond + sms01Amot*cond , data = impen, family = gaussian(link = "identity"))

model <- glm(sms02Amot ~ afs01Rel + sms01Amot + session + sms01Amot*session, data = impen, family = gaussian(link = "identity")) 


# Ordinal logistic regression 

library(MASS)

model <- glm(jNachPron ~ jNachVowels + jNachCons + jNachRhyhm, data = impen, family = gaussian(link = "identity"))  

jNachPron, jNachVowels, jNachCons, jNachRhyhm

model <- glm(sms02Amot ~ sms01Amot + hrStudied +  jNachRhyhm, data = impen, family = gaussian(link = "identity")) 

Professional development program

Summary

Korean graduate students face a difficult job market, and at some universities, the demands of taking graduate courses taught in English. The teaching and learning center at our university decided to create a non-credit professional development program for graduate students, which I undertook; the contents were designed and taught by me. Data were collected, such as data after each session, and upon their completion of the program. This included self-reports of professional readiness, assessment of their professional development and mentoring in their departments, and self-ratings of English skills and efficacy. The results showed a lack of sufficient professional guidance from their departments, especially regarding career options, weaknesses in various professional soft skills, and in their academic English readiness. This shows a need for more attention to academic English and professional development for graduate students in Korea.

• 
  Methods: Chi-square, correlation analyses, logistic regression in SAS & R (e.g., skills x motivation)
• 
  Results: Preliminary analysis led to a report for the departmental stakeholders. Three papers were published in 2017-2018 from the full analysis (I did the main work, including literature review and data analysis).
• Professional development program website

Publications

• Lee, K., & Lee, H. (2018). An EAP professional development program for graduate students in Korea. TESOL Quarterly, 52(4), 1097-1107. https://doi.org/10.1002/tesq.447
• Lee, K., & Lee, H. (2018). Korean graduate students’ self-perceptions of English skills and needs in an English-medium instruction context. Journal of Multilingual and Multicultural Development, 39(8), 715-728. https://doi.org/10.1080/01434632.2018.1438442
• Lee, K., & Lee, H. (2017). Korean graduate students’ perceptions of guidance and professional development. Higher Education, 73(5), 725–740. https://doi.org/10.1007/s10734-016-9988-9

Connectives in L2 writing

Summary

Some studies have shown that Korean students learning English struggle with nuances of academic or professional style. A study was conducted, comparing Korean writers with native English writers at the college level. This study offered some refinements over previous corpus studies. It was found that Korean writers overused and underused various connectors compared to the native writers, specifically in their use of contrastive markers, topic transitionals, presentational ‘there is’ and ‘there are’, markers for enumerating sequences, and exemplifiers. They also certain expressions used incorrectly as connectors due to apparent transfer errors. A similar study with Chinese writers found similar patterns. These data indicate a number of issues for ESL/EFL writing teachers to address, and some suggestions for teaching are discussed.

• 
  Methods: Corpus research, loglinear regression in SAS
• Results: Two published research papers
 

Publications

• Lee, K. (2020). Chinese ESL writers’ use of English contrastive markers. English Language Teaching, 32(4), 89-110. https://dx.doi.org/10.17936/pkelt.2020.32.4.5
• Lee, K. (2013). Koreans’ use of English connectors and topic management in writing. English Language Teaching, 25(2), 81-103.

Student motivation in EMI

Summary

Students at some Korean universities are required to take some major courses in English, regardless of major. This is according to EMI or English medium instruction at such universities. In this survey study, data were collected from 500+ undergraduates at a major university in Seoul, to assess their English self-efficacy and readiness for EMI courses. The results show insufficient English skills or weak self-efficacy, especially for some groups. This calls into question the validity of EMI courses, as they have been enacted, as many students are not ready for EMI requirements.

• 
  Methods: Survey data collection, statistical analysis in SAS (hierarchical linear modeling)
• 
  Results: Conference presentation with recommendations; PPT

Details

Students at some Korean universities are required to take some courses in English within their majors, regardless of major; this is in addition to required first-year courses in general English that are required for most students. This policy of required major courses in English is known as EMI, or English medium instruction, and has been common at some higher-ranked universities in Korea.

In this survey study, data were collected from over 500 undergraduates at a major university in Seoul, to assess their English self-efficacy and readiness for EMI courses. Data came from students in many majors in required first-year English courses (for students at a general or intermediate level of English, and for those at a lower or remedial level), elective English courses, and an undergraduate linguistics course. The results show insufficient English skills or efficacy overall, especially for some groups. Those in more advanced or elective English courses (and the linguistics course) tended to have higher self-ratings of their English skills and better self-efficacy. The majority of respondents (intermediate English) had significantly lower skills and efficacy, and lower levels of intrinsic motivation toward English. Certain subgroups had particularly poor self-ratings, efficacy, and motivation issues: those in the lower or remedial level English courses, and foreign students from other Asian nations.

This calls into question the validity of EMI courses, as they have been enacted, as many students are not ready for EMI requirements. They will naturally encounter difficulties taking courses and doing coursework in their major of the courses are taught in English.
The recommendations made based on this study include: modifying EMI requirements, especially for those in certain majors who tend to have lower English skills, and for whom academic English will not be so necessary for their careers; an extended sequence of general English courses for those in the lower or remedial category; stricter English requirements for foreign students; and more support services for students at risk of doing poorly due to weak English skills.

Presentations

• Lee, K. (2022). Language ability and motivation issues in English medium instruction courses in Korea. 9th International Conference on Language, Literature & Linguistics 2022, July 2022, Singapore.
• Lee, K. (2020). Motivation and motivational components among Korean university learners. AsiaTEFL conference, November 2020, Goyang, Korea. PPT

SAS code

FILENAME CSV "/emi.study.ku.st.survey.csv" TERMSTR=LF; 
 
PROC IMPORT DATAFILE=CSV 
		    OUT=ug
		    DBMS=CSV 
		    REPLACE; 
RUN; 
*PROC PRINT DATA=det14; *RUN; 
 
data ug;  
infile '/folders/myshortcuts/d/researchdata/emi.study.ku.st.survey.csv'  truncover firstobs=2 dsd; *dlm=" " n=1300;     
input  sem $ yr $ id $ kurs $ kursniv $ fach $ sex $ nat $natstatus $ l1 $ en_allg en_read en_writ en_list en_speak emi 
M1acINTall M1acINTauto M1acINTable M1acINTsoc M2aeINTall M2aeINTauto M2aeINTable M2aeINTsoc M3enINT M3aeEXT 
M1ac_capable M1ac_belong M1ac_stres M1ac_pax M1ac_friends M1ac_pressure M1ac_competent M1ac_desire M1ac_uptight M1ac_emotclose M1ac_improve M1ac_decidde M1ac_free 
M2ae_capable M2ae_belong M2ae_stress M2ae_pax M2ae_friends M2ae_pressure M2ae_competent M2ae_desire M2ae_uptight M2ae_emotclose M2ae_improve M2ae_decide M2ae_free 
M3en_interest M3en_mygood M3en_oblig M3en_noraison M3en_pleasant M3en_gutforme M3en_haveto M3en_notworth M3en_fun M3en_decide M3en_nochoice M3en_dunno M3en_feelgut M3en_important M3en_fhaveto M3en_notpursue;

proc sort; by natstatus kursniv; 
proc means mean n mean std median ; var en_allg M1acINTall M1acINTauto M1acINTable M1acINTsoc M2aeINTall M2aeINTauto M2aeINTable M2aeINTsoc M3enINT M3aeEXT ;
 by natstatus kursniv; run; 

proc sort; by kursniv;  
proc means mean mean n mean std median  ; var en_allg M1acINTall M1acINTauto M1acINTable M1acINTsoc M2aeINTall M2aeINTauto M2aeINTable M2aeINTsoc M3enINT M3aeEXT ; 
 by kursniv; run;

proc sort; by natstatus; run; 
proc freq data=ug order=data ;  by natstatus; 
 tables en_allg M1acINTall M1acINTauto M1acINTable M1acINTsoc M2aeINTall M2aeINTauto M2aeINTable M2aeINTsoc M3enINT M3aeEXT  / nocol nocum nopercent;  
 run;
 
proc univariate data=ug;
 class natstatus; 
 var en_allg M1acINTall M2aeINTall M3enINT M3aeEXT;
 run;
 
proc glm data=ug ;
 class yr kursniv fach sex nat natstatus l1;
 model en_allg = kursniv emi fach  l1; 
 run;
 
proc glm data=ug ; 
 class yr kursniv fach sex nat natstatus l1; 
 model M1acINTall  = yr kursniv emi fach natstatus l1;  
 run; 
 
proc glm data=ug ; 
 class yr kursniv fach sex nat natstatus l1; 
 model M2aeINTall  = yr kursniv emi fach natstatus l1;  
 run; 
 
proc glm data=ug ; 
 class yr kursniv fach sex nat natstatus l1; 
 model M3enINT  = yr kursniv emi fach natstatus l1;  
 run;  
 
proc glm data=ug ; 
 class yr kursniv fach sex nat natstatus l1; 
 model M3aeEXT  = yr kursniv fach sex emi natstatus l1;  
 run;  

Pedagogical coaching

Summary

New professors at many universities in Korea are required to meet with a pedagogical coach for coaching and help with teaching skills. Since one particular university mandated that most new professors should teach their courses in English, I was brought on board as an educational consultant. I created a fully developed consulting program, to help professors with pedagogical skills and English efficacy, which included coaching sessions. Notes from coaching sessions were analyzed for a needs analysis. Various pedagogical needs and issues that arose were categorized, as well as language-related issues. This helped inform and improve our coaching program, and provides data that can inform other teaching and learning centers, especially in an EMI (English-medium instruction) context.

• 
  Methods: Qualitative analysis of coaching reports; descriptive statistics
• 
  Results: An initial report to unit stakeholders; and a published research paper of the full analysis.

Details

At one particular university in Seoul, new professors are required to meet with a pedagogical coach at the university’s teaching and learning center, for coaching and help with teaching skills. Since the university mandates that most new professors should teach their courses in English (according to English-medium instruction, or EMI policy), I was brought on board as an educational consultant. Before that, the coaching program was under-developed and inconsistent. I created a fully developed consulting program, to help professors with pedagogical skills and English efficacy, which included coaching sessions.

Notes from coaching sessions were then analyzed for a needs analysis, so we could better understand clients’ needs. Various pedagogical needs and issues that arose were categorized, including classroom management, lecture skills, professional development, balancing teaching and research, and others. Language-related issues also arose, as many were not confident or highly proficient in English. Pedagogical and language issues were categorized for a qualitative analysis. This needs analysis helped inform and improve our coaching program, and the paper published from the data analysis that can inform other teaching and learning centers in Korea or in Asian countries, especially those that must deal with an EMI (English-medium instruction) context.

Publication

• Lee, K. A. & Lee, H. (2023). Faculty coaching and faculty needs in Korean universities. Journal of Pan-Pacific Association of Applied Linguistics, 27(2), 27-52). http://www.paal.kr/html/sub04_03.asp

Delimiters project

Summary

Definite / indefinite articles (noun delimiters) and bare nouns are problematic for many learners and teachers of EFL/ESL. My research on this, from cognitive semantic perspectives, makes possible more intuitive and sensible approaches to teaching, explaining, and practicing these grammatical juggernauts. Several smaller studies have been published (Phase 1), based mainly on analysis of student responses to pragmatic questionnaires. Two major data sets await analysis for large-scale data analysis projects (Phase 2). This has led so far to a more comprehensive understanding of noun phrase patterns, and better teaching methods and materials.

• 
  Methods used: Pragmatic judgment questionnaires, statistical analysis to be conducted in SQL and R. Previous small-scale initial studies with logistic regression in SAS.
• 
  Results: Several published research papers; two major research papers to be submitted in later 2024; teaching materials with more intuitive explanations and coverage of noun phrse patterns; a forthcoming online grammar course will be created, to appear in 2025.

Details

Definite / indefinite articles (noun delimiters, as I call them) and bare nouns have been problematic for many learners and teachers of EFL/ESL (English as a second / foreign language). Few linguists actually understand how these noun phrase (NP) patterns work, and teachers generally do not know how to teach them. My research approaches this from cognitive semantic perspectives, whjich makes possible more intuitive and sensible approaches to teaching, explaining, and practicing these grammatical juggernauts.

I have conducted some smaller, initial studies with smaller data sets. This mainly consists of learner responses to pragmatic judgment questionnairs (filling in blanks with appropriate delimiters), which have been analyzed with loglinear / logistic regression. Also, qualitative survey data show that Korean students do not really understand these NP patterns or the rules they were taught; they find that the traditional textbook rules do not work, or they cannot apply them. My qualitative and quantitative analyses of NP patterns indicate that these do not follow traditional rules, but the use of delimiters or bare NPs follow a set of core and extended semantic patterns. This includes semantic extensions of definiteness for various specialized uses of ‘a’ and ‘the’, and for bare singular and plural NPs. NPs followed by post-modifers also warrant separate attention, as they often are specialized patterns.

Several smaller studies have been published (Phase 1), based mainly on analysis of student responses to pragmatic questionnaires, and a qualitative survey. Two major data sets await analysis for large-scale data analysis projects (Phase 2). This includes a full data set of learner and L1 responses to pragmatic judgment questionnaires, to be analyzed in SQL and R; and a large L1 corpus data set, to be analyzed in Python and/or R.

So far, this research has led so far to a more comprehensive understanding of noun phrase patterns, and better teaching methods and materials. The teaching methods and explanations developed so far explain NP patterns much more clearly for students, and explain NP patterns far better than traditional rules.

Publications

• Lee, K. (2023). Schematic semantic conceptualization of English definite noun phrases. Poster presented at 16th International Cognitive Linguistics Conference, August 2020, Düsseldorf.
• Lee, K. (2021). A study on the viability of a cognitive approach to teaching articles. Studies in English Education, 26(1), 29-55. https://dx.doi.org/10.22275/SEE.26.1.02
• Lee, K. (2021). Teaching articles: Addressing university students’ misconceptions. Journal of Learner-centered Curriculum and Instruction, 21(2), 1-25. https://dx.doi.org/10.22251/jlcci.2021.21.2.1
• Lee, K. (2020). Definite and indefinite articles with abstract nouns in L2. Modern English Education, 21(4), https://dx.doi.org/1-10. 10.18095/meeso.2020.21.4.1
• Lee, K. (2017). A “the” or the “a”? L2 learner problems and patterns. Korea TESOL Journal, 13(2), 25-48.

Sample SAS code

PROC IMPORT DATAFILE=CSV
		    OUT=det14
		    DBMS=CSV
		    REPLACE;
RUN;
*PROC PRINT DATA=det14; *RUN;

data det14; 
infile '/folders/myshortcuts/d/researchdata/detsurvey2014.all.csv'  truncover firstobs=2 dsd; *dlm=" " n=1300;    
input subj $ l1$ item $ wort $ age gender $ major $ enyrs ausland pt1time pt2time group $ nfall syn1 $ syn2 $ syn3 $
 synpost $ num $ antwort $ orig $ agr $ concrete semvar lfreq fam;

proc freq data=det14 order=data;  
 tables item*antwort/ nocol nocum nopercent; 
 tables gender*major; *tables wort; 
 tables orig * agr / chisq nocum ; *plots(only)=(freqplot mosaicplot);  
 tables antwort * orig / chisq nocum nocol; *plots(only)=(freqplot mosaicplot); 
 tables orig*num/ chisq nocum nopercent;
 tables ausland*l1/ nocol nocum nopercent; 
 tables l1*agr / chisq nocum  nocol; *plots(only)=(freqplot mosaicplot); 
 tables syn2*agr  / chisq nocum  nocol; *plots(only)=(freqplot mosaicplot); 
 tables synpost*agr  / chisq nocum  nocol; *plots(only)=(freqplot mosaicplot);  
 tables agr*ausland  / chisq nocum nocol; * plots(only)=(freqplot mosaicplot);  
 tables agr*gender / chisq nocum nocol; 
 tables nfall*agr / chisq nocum nopercent nocol; 
 tables num*agr / chisq nocum nopercent nocol; 
proc logistic data=det14 order=data ; 
 class antwort num l1 orig agr syn2 synpost;
 model antwort = orig l1 ausland num nfall syn2 lfreq  / l=clogit stb expb rsq lackfit expb corrb parmlabel cl aggregate ; 
 oddsratio orig;    
 *contrast 'syn2' syn2 1 1 0 -2 0;  contrast 'a z' orig 1 0 -1; 
 contrast 'the z' orig 0 1 -1; contrast 'a the' orig 1 -1 0; 
 contrast 'num' num 1 -1; 
 contrast 'Korean cf. Chinese' l1 -1 0 0 1; 
run;
 
proc logistic data=det14 order=data; 
 class l1 antwort num orig agr syn1 syn2 synpost ;
 model agr = l1 syn2 synpost nfall synpost lfreq  / l=logit stb expb rsq parmlabel cl ; 
 *oddsratio orig;    
 contrast 'syn2-a1-n' syn2 1 0 0 -1 0;  contrast 'syn2-a2-n' syn2 0 1 0 -1 0 ; contrast 'syn2-a4-n' syn2 0 0 1 -1 0 ;
 contrast 'L1' l1 1 0 0 -1; contrast 'L1-kr-others' l1 3 -1 -1 -1; 
run;
 
proc catmod data=det14 order=data;
 direct ausland lfreq concrete semvar lfreq fam; 
 model agr = l1 syn2 synpost nfall num  lfreq   ; 
 *contrast 'syn2' syn2 1 0 0 -1 0 ; *contrast 'syn2b' syn2 -1 .5 .5 0 0 ; 
 contrast 'syn2-a1-n' syn2 1 0 0 -1;  contrast 'syn2-a2-n' syn2 0 1 0 -1 ; contrast 'syn2-a4-n' syn2 0 0 1 -1 ;
 contrast 'L1'  l1 1 0 1 ;
run; 

Community group demographic analysis

Summary

I conducted and analyzed a demographic survey for a religious organization in Seoul – specifically, for a large English-language church. NOTE: This is NOT intended to endorse or promote any religion (as I am not religious), but to simply showcase my work. The organization stakeholders wanted to better understand attendees, their needs, and satisfaction with the church. I created a survey, and after it was administered, I used correlational and logistic regression functions in SAS to analyze the data. Very detailed reports were then provided to the stakeholders, which helped them to understand attendees and their needs, and to make changes accordingly.

• 
  Methods used: Survey development, correlation and logistic regression in SAS
• 
  Result: Lengthy, detailed reports with quantitative and qualitative analysis for stakeholders, which informed decision making and implementation of changes based on the results.

Details

Stakeholders at a large English church in Seoul asked me to lead a survey project, so they could better undeerstand the organization’s attendees, needs, and satisfaction with the church. I researched church surveys online and adapted questions from church polling organizations from the US. I drafted a survey, which was modified in consultation with the stakeholders. The surveys were administered during service times. This was done in 2009, and again in 2011. The survey included the following question types.

• Basic demographics: age, gender, marital status, nationality, ethnicity, language background, length of time in Korea, intended length of stay in Korea
• Religious identity and practices: membership status, activities and degree of involvement in the organization, several questions on religious identity, several questions on personal religious practices, reasons for attending, several questions on whether their needs were met
• Open-ended questions: strengths and weaknesses of the organization, improvements to be made, other comments

A lengthy report was produced (45+ pages, in both 2009 and 2011), with detailed descriptive statistics for each close-ended question, including Excel graphs, and cross-tabulations of various variables. Open-ended responses were sorted in Excel by key demographic categories for a qualitative analysis and understanding of attendees’ needs. Correlational analyses and logistic regression in SAS (proc freq, genmod, catmod) showed specific relationships among variables that were informative to the stakeholders.

The quantitative analysis showed interesting trends. For example, attendees’ status in the country correlated with membership status or plans, which is relevant, since many attendess are Korean-American, foreign Koreans, or non-Korean foreigners. Correlations were found among attendees’ personal religious practices, degree of involvement in the organization, religious experiences, satisfaction with one’s personal life, and satisfaction with the organization and its services. Finally, grouping responses to open-ended questions by key demographic variables helped the stakeholders to better understand different subgroups and subpopulations at the services, and their needs.

The stakeholders found the reports to be extremely helpful, and implemented some changes soon thereafter based on the survey data. The results helped inform their decision making and strategies thereafter.

Sample SAS code

options ls=256 ps=1000 nocenter;
data oem ;
infile 'h:\oem\survey\oem09survey.sas.txt' firstobs=2 lrecl=613    ;
input form … 

proc genmod ;
class … ; /* Over 20 variables here from the survey */
model q15f = q06 q03 q18 q10bible q10pray / dist=mult type3 ;
* q06 q03 q17a q18 q10bible q05sg q10pray q10med q10mem q08 q09 ;
run;

proc catmod data=oem;
response logits ;
model q15f = q06 q03 q18 q10bi q10pr ;
/* contrast '00m-06m' q03 1 -1 0 0 0 0 ; contrast '06m-12y' q03 1 -1 0 0 0 ; contrast '12-23y' q03 0 1 -1 0 0 ;
contrast '23-45y' q03 0 0 1 -1 0 ; contrast '45-6y' q03 0 0 0 1 -1 ;
quit ;

proc freq ; missing ;
tables q06*q15a q03*q15a / measures trend cmh chisq nocol ; test scorr;
run;

proc freq ; missing ;
tables q06*q15a q03*q15a q06*q15b q03*q15b q06*q15c q03*q15c q06*q15d q03*q15d q06*q15e q03*q15e q06*q15f q03*q15f / nocum nopercent nocol ;
run;

/* Q14 feelings of belonging */
proc genmod ;
class ... ;
model q14 = q06 q03 q05sg q18 q09 / dist=mult type3 ;
run;

proc catmod data=oem;
response logits ;
model q14 = q06 q03 q05sg q09 q18 ; *q10pr q10med ;
/* contrast '00m-06m' q03 1 -1 0 0 0 0 ; contrast '06m-12y' q03 1 -1 0 0 0 ; contrast '12-23y' q03 0 1 -1 0 0 ;
contrast '23-45y' q03 0 0 1 -1 0 ; contrast '45-6y' q03 0 0 0 1 -1 ; */
quit ;

proc freq ; missing ;
tables q06*q14 q03*q14 / measures trend cmh chisq nocol ; test scorr;
run;

/* Q12 quality of personal life */

proc genmod ;
class … ;
model q13 = q06 q03 q08 q10 / dist=mult type3 ;
run;

proc catmod data=oem;
response logits ;
model q13 = q06 q03 q08 q10 ;
/* contrast '00m-06m' q03 1 -1 0 0 0 0 ; contrast '06m-12y' q03 1 -1 0 0 0 ; contrast '12-23y' q03 0 1 -1 0 0 ;
contrast '23-45y' q03 0 0 1 -1 0 ; contrast '45-6y' q03 0 0 0 1 -1 ; */
quit ;

proc freq ; missing ;
tables q06*q13 q03*q13 / measures trend cmh chisq nocol expected ; test scorr;
run;

/* MEMBERSHIP factors */

proc genmod ;
class … ;
model q06 = q03 q04 q05sg q10 q16b q18 / dist=mult type3 ; * q17a q18 q19o q20a q03*q06q q03 q18 q05sg q08 q09 q10bible;
run;

proc catmod data=oem;
response logits;
model q06 = q03 q04 q05sg q10 q16b ;
contrast 'lg' q16b 1 -1 0 0 0 0 ;
/* contrast '00m-06m' q03 1 -1 0 0 0 0 ; contrast '06m-12y' q03 1 -1 0 0 0 ; contrast '12-23y' q03 0 1 -1 0 0 ;
contrast '23-45y' q03 0 0 1 -1 0 ; contrast '45-6y' q03 0 0 0 1 -1 ; */
quit ;

proc freq ; missing ;
tables q15a*q06/ measures trend cmh chisq nocol expected ; test scorr;
run;

proc genmod ;
class … ;
model q15a = q06 q03 q18 / dist=mult type3 ;
run;

proc catmod data=oem;
response logits; model q15a = q06 ;
*model q15a = q06 q03 q18;
/* contrast '00m-06m' q03 1 -1 0 0 0 0 ; contrast '06m-12y' q03 1 -1 0 0 0 ; contrast '12-23y' q03 0 1 -1 0 0 ;
contrast '23-45y' q03 0 0 1 -1 0 ; contrast '45-6y' q03 0 0 0 1 -1 ; */
quit ;

proc sort out=oem15a; by q15a q03 q06; run;
proc freq data=oem15a; missing ; by q15a; tables q15a q03*q06 / out=oem15a1; run;
proc print data=oem15a1; run;

proc logistic data=oem15a1 ;
class q15a q03 q06; freq count;
model q15a=q03 q06 ;
run;

proc catmod data=oem15a1;
direct count;
model count=q03 q06; quit;