Running Head: Foreign Language Study Strategy
Foreign Language Study Strategy Use and Language Institute Attendance
by Korean EFL and CFL University
Students
Gregory C. Brundage B.A.
Konyang University, S. Korea
Deng Kui, M.S.
Nankai University,
China
Contact information:
Gregory
C. Brundage Konyang
University, Department of Foreign Language Education
Home
address:
101-1003 Buyong Apt.
Nonsan City,
Chungchongnam Province
South Korea 320-751
e-mail:
greg_brundage@yahoo.com
Home
telephone: (82) 041-735-1926
Mobile
phone: (82) 019-602-5099
Key words: Study strategies, Korean, EFL, CFL, Language Institutes
Abstract
This
paper compares foreign language study strategies as measured by the Strategic Inventory for Language Learning (SILL) used
by Korean university students (N = 276) who study either English as a Foreign Language (EFL) or Chinese as a Foreign Language
(CFL), and also have attended private foreign language institutes, to study strategies used by university students that haven’t
attended private foreign language institutes. Relationships between foreign language study strategy use, institute attendance
and gender, educational levels of parents, self-reported self confidence, and foreign language class grades earned in the
university are also examined. Results showed students that have attended private language institutes use significantly more
study strategies than students that haven’t attended institutes. Results are discussed in light of past and current
research.
Ti: Foreign
Language Study Strategy Use and Language Institute Attendance by Korean EFL and CFL
University Students
I. INTRODUCTION
Though
relationships between foreign language study strategies and many factors have been examined in previous research, the relationship
between study strategies and private language institute attendance has not been considered. This paper examines that relationship
in three university samples in South Korea,
as well as several other factors influencing foreign language study strategy use.
1. Korea
and private language institutes
Koreans
have an unbridled enthusiasm for education with roots wide and deep in its history and culture. Pertinent Confucian aphorisms
abound: “Ignorance is the night of the mind; a night without moon or stars,” “The scholar who cherishes
the love of comfort cannot be deemed a scholar,” and “Learning without thought is labor lost; thought without
learning is perilous.” These beliefs lie at the foundation of Korean mentality on this subject. Confucian philosophy
reinforces the concepts that “man is perfectible thorough education,” “only the educated should rule,”
and “knowledge enhances moral governance.”
During the past 20 years South Korea has catapulted to world leadership in many domains. The Secretary General
of the United Nations is Korean. Korean students rank first in the world in problem solving skills, and year after year score
in the top three on math and science aptitude tests (U.S. Department of Education, 2007). In 2006 South
Korea even usurped perennial first place winner Finland
in reading (OECD Executive Summary 2007). The South Korean economy is ranked 13th in the world according to GDP
(World Bank, 2008). In 1945, 75% of Koreans were illiterate. In the year 2000,
South Korea had a near 100% literacy rate.
In 2000, 58% of Korean students attended private after-school institutes.
By 2003 that percentage had jumped to 72%. It is estimated that Koreans spend 15 trillion Won ($14.4 billion dollars) each
year on private English language education (Song, 2008).
Though South Koreans have promoted equal educational opportunity
since independence in 1948, this is hard to maintain, given that more affluent parents can afford highly skilled tutors and
private institutes for their children while less affluent parents can not. This trend has undermined the common people’s
confidence in the educational system, based on the assumption that standard public and private school education isn’t
adequate to sufficiently prepare students for Korea’s
hyper-competitive society. Students compete to get into elite middle schools, repeat the process to get into elite high schools,
and repeat the process again to get into top universities.
However, according to a 2003 report by the Hong Kong based Political
and Economic Risk Consultancy, South Korea ranked lowest among 12 countries
in East Asia for communication in English (cited in “Does Korea,” 2007).
In spite of the enormity of this ever-growing industry in South Korea, very little research has been done on the educational,
social or psychological effects on students associated with private language academy attendance.
This study looks at the relationship between English as a Foreign
Language (EFL), and Chinese as a Foreign Language (CFL) university students’ attendance at private language institutes
and their use of study strategies as measured by the Strategic Inventory of Language Learning (SILL, Oxford, 1990), as well
as the relationships between these factors and gender, parents’ education levels, self-reported self-confidence, and
average grades earned in university foreign language classes.
2. The Strategic Inventory for Language Learning
(SILL)
Numerous studies have shown that proficient language learners use a wider range of learning
strategies than those who are less proficient in language learning (Ruben, 1975; Oxford, 1985, 1989, 1990; Green & Oxford,
1995, cited in Oxford, 1996; Wharton, 2000; Altan, 2004.
The most commonly used study strategy inventory at this time is
the SILL, Version 7.0 (Oxford, 1990). Numerous studies have
found its reliability to be between .91 and .95 (Oxford 1996).
The SILL, originally organized by a factor analytic system, divides L2 study strategies into six broad categories: memory,
cognitive (using all mental processes), compensation (filling in missing knowledge), metacognitive (organizing and evaluating
learning), affective (managing emotions), and social learning with others.
Extensive research supports the proposition that numerous variables
influence a learner’s choice of strategy, including motivation, gender, type of task, age, subject matter, level of
L2, learning style and cultural background (Oxford, 1989).
Because there are sometimes dramatic inconsistencies in reported results on study strategy use and the aforementioned factors,
other variables need to be considered. Two such potential factors are parental education level and learning experiences outside
of school. Preliminary research by Hsu (2003, 2008) found there are significant differences in study strategy use by students
with more highly educated parents, and that students with parents who have more education tend to adopt cognitive and social
strategies more often. Also, learning experience outside of school fosters the use of more cognitive and compensation strategies.
a. Research on Memory and L2 study
There
are nine questions on the SILL relating to memory strategies. Phillips (1990, 1991, cited in Oxford, 2006) found a strong
relationship between English language proficiency and TOEFL scores, and that low scorers (generally beginning students) relied
heavily on memory (e.g. flash cards), affective, and social strategies. However, Green (1992, cited in Oxford, 1996) found no significant differences in the use of memory and affective strategies
at different proficiency levels as measured by ESLAT scores.
In a study by Chang, (cited in Yang, 2007), memory strategies were
found to be least used by Taiwanese students studying in the U.S. Similar results were found among Chinese students in Taiwan (Yang, 1993a, 1993b, cited in Yang, 2007) and Korean students (Oh, 1992).
b. Cognitive approaches to L2 study
Cognitive strategies are broadly defined as those “Using all mental processes” and include such strategies
as: using English words in many different ways, watching TV and movies in English, writing, finding patterns, interpreting
rather than just translating English, and making summaries.
Studying a group of 325 Korean secondary students, Lee (2003) found
that cognitive strategies correlated strongly with meta-cognitive and memory strategies. First-year secondary school students
were found to use more metacognitive, cognitive and memory strategies, whereas third-year students were found to use more
compensation and memory strategies.
c. Compensation strategies and L2 study
Compensation
strategies are used for making up for missing knowledge and include making guesses about unknown words, using gestures and
substituting words that are known for words that a student may not know.
Hsu (2003) found compensatory strategies to be most used by junior-college
students, and least used by elementary students in Taiwan.
Likewise, Yang (2007) found compensation strategies were most used by junior college students in Taiwan, followed by social, cognitive, metacognitive, affective and memory strategies.
d. Metacognitive
strategies and L2 study
Metacognitive strategies include noticing speaking mistakes, paying attention when someone
is speaking English, looking for people with whom to speak English, setting goals and thinking about progress.
Oh (1992) and Touba (1992,
cited in Oxford, 2008) found that adult learners most often
use metacognitive strategies for planning, organizing and evaluating their own L2 learning.
e. Affective strategies and L2 study
Affective
strategies deal with how to control fear and anxiety when speaking English via, for example, relaxation, writing down feelings,
talking to other people about feelings and so on. Memory and affective studies were found to be the least used strategies
by Yang (2007) in his study of Taiwanese students. Likewise, Park (1994, cited in Oxford, 1996)
found affective and compensatory strategies to be least used in his study using university students in Korea.
Green & Oxford (1995, cited in Oxford, 1996) found affective and memory categories to have no significant bearing upon proficiency
level. Lee & Oxford (2008) found affective strategies, followed by social strategies were least used by 1,000 Korean,
middle school, high school and university students.
f. Social strategies and L2 study
Some
studies found social strategies to be the least used by Chinese and Japanese students (Noguchi, 1991, cited in Oxford, 1996;
Politzer and McGroarty, 1985), however Yang (2007) found social strategies to be the second highest used category amongst
junior college students in Taiwan after
compensation strategies.
3. This study’s goals
This
paper addresses the following questions: to what extent are university students who study English and Chinese at three Korean
university using study strategies as measured by the SILL; what relationship exists between study strategies and institute
attendance; and how those variables relate to gender, parents educational level, self-reported self-confidence, and university
students’ average foreign language class grades?
II. METHODS
Korean
University students ( N = 276) studying EFL and CFL at Konyang University, Hankook University of Foreign Studies and Sangmyung
University were administered two questionnaires, the Student Institute Survey Questionnaire (prepared by authors), and a Korean
language translation of the Strategy Inventory for Language Learning (SILL Version 7.0, ESL/EFL, Oxford, 1990). Oxford (1996) reported that reliability of the ESL/EFL SILL goes down
when administered in the target language, thus, in this study the SILL was administered in the test groups’ native language,
Korean. This Korean language SILL was translated by Professor Kim Jae-Shin (2008, Konyang
University).
Response sheets from the Student Institute Survey Questionnaire
were divided into two groups, those that attended foreign language institutes during their university years and those that
did not. Results from the two groups’ SILLS were further divided into EFL and CFL groups. These results were then analyzed for overall strategy use, and the six strategy categories (memory, cognitive,
compensatory…) according to gender, parents’ educational level, self-reported self confidence, and grades in foreign
language classes. Data was analyzed using the Statistical Package for the Social
Sciences (SPSS) to determine significant differences and correlations between the use of language learning strategies by the
sample groups.
III. RESULTS
First, the SILL and Institute Questionnaire variables were analyzed with EFL and CFL
student responses combined as both groups consist of Korean students. To test
for differences between EFL and CFL student strategy use in relationship to all variables, EFL and CFL student SILL and questionnaire
responses were then analyzed separately. A table of the overall results summary (not including the six SILL category results)
is presented in Appendix 1.
1. STUDENT GROUPS (EFL & CFL) COMBINED
a. Institute attendance and strategy use
Results indicate a significant difference in overall strategy use between university students that attended institutes
at some time in their life and those that did not, t (214) = 3.82, p = .000. Average
strategy use by university students that attended institutes (M = 3.09) was significantly greater than those that didn’t
attend institutes (M = 2.76). The strongest significant difference in SILL strategy categories between students that attended
institutes and those that didn’t was for social strategies (t = 4.33, p =
.000), while the lowest difference was for memory strategies (t = 2.07, p = .039).
[Table 1, here]
|
Strategy
Category |
EFL & CFL
Student groups combined |
EFL student results |
CFL student results |
|
Overall
means |
Went to
institute
Means |
No
Institute
Means |
Went
to
institute Means |
No Institute
Means |
Went to
institute Means |
No Institute
Means |
|
A.
Memory |
2.6956
N = 225 |
2.7835
N = 176 |
2.6077
N = 49 |
2.5438
N = 66 |
2.5018
N = 31 |
2.9273
N = 110 |
2.8472
N = 48 |
|
B.
Cognitive |
2.8634
N = 224 |
2.9975
N = 173 |
2.7292
N = 48 |
2.7879
N = 65 |
2.6024
N = 30 |
3.1237
N = 108 |
2.9807
N = 48 |
|
C.
Compen. |
3.35865
N = 225 |
3.4962
N = 176 |
3.2211
N = 49 |
3.4495
N = 66 |
3.1290
N = 31 |
3.5242
N = 110 |
3.3264
N = 48 |
|
D. Meta-
Cognitive |
3.1001
N = 222 |
3.3321
N = 174 |
2.8681
N = 48 |
3.1726
N = 65 |
2.8136
N = 31 |
3.4271
N = 109 |
3.1868
N = 47 |
|
E.
Affective |
2.6935
N = 225 |
2.8598
N = 176 |
2.5272
N = 49 |
2.7551
N = 66 |
2.4570
N = 31 |
2.9227
N = 110 |
2.8438
N = 48 |
|
F.
Social |
3.0231
N = 221 |
3.3081
N = 172 |
2.7381
N = 49 |
3.0215
N = 62 |
2.5108
N = 31 |
3.4697
N = 110 |
3.2021
N = 47 |
|
Overall
Means |
2.9282
N = 214 |
3.0934
N = 167 |
2.7630
N = 47 |
2.8743
N = 60 |
2.6567
N = 30 |
3.2163
N = 107 |
3.0383
N = 46 |
Table 1 Institute variable, CFL &
EFL groups and SILL category mean results
b. Gender and strategy use
No significant differences were found between male and female overall strategy use, t (255) = .28, p = .773) or between any of the six strategy categories in the combined EFL/CFL analysis.
c. Father’s education and strategy use
Significant
correlations were found between father’s educational level and overall mean strategy use (r = .165, significant at 0.01
level, 2 tailed, N = 248).
Results also showed significant differences above 95% between two
levels of father’s education (graduated from high school and four-year university degree), overall study strategy means,
t (208) = 2.31, p = .022, and two categories of study strategy use, compensatory, t (208) = 3.33, p = .001 and social t (208) = 2.40, p = .017. Use of metacognitive strategies came in third place t
(208) = 1.85, p = .065, followed by cognitive, memory and affective strategies respectively.
d. Mother’s education and strategy use
No significant differences were found between mother’s educational level and strategy use t (216) = 1.56, p = .119, in the combined EFL/CFL group. However the use of compensatory strategies was found
to be borderline significantly different according to mother’s educational level, t
= 2.56, p = .011. In addition, a strong correlation was found between mother’s
level of education and strategy use on the SILL, r = .168, p< 0.01.
e. Students self reported grades and strategy use
Students self-reported grades strongly correlated with overall strategy use means (r
= .208, significant at the 0.01 level, two tailed, N= 263), as well as with five of the six categories of strategy use: memory
strategy use (.132, .05 significant level, N = 254), cognitive strategy use (r
= .203, significant at the 0.01 level, 2 tailed, N = 250), compensatory strategy use (r = .163, significant at 0.01 level,
2 tailed, N = 254), metacognitive strategy use (r = .225, significant at the
0.01 level, N = 251) and social strategy use (r = .165, significant at the 0.01 level, 2 tailed, N=248). Only affective study
strategies were not found to be significantly correlated with students self-reported grades in this study (r = .070, p<
.268, N = 254).
No significant correlation was found between students self-reported
grades in foreign language studies and institute attendance. (r = .075, p< .288, N
= 254).
f. Students self-reported self-confidence and strategy
use
Results indicate there were highly significant differences in intensity of strategy use
between higher and lower levels of students self-reported self-confidence in all study strategy categories above the 99% significance
level (overall t = 4.01, p = .000), except affective strategies, which yielded
a t score of 1.32 (p = .193).
A very strong Pearson’s Correlation was found between students’
self-reported confidence and intensity of study strategy use, r = .406, significant above the 0.01 level, (2-tailed, N = 241).
g. Students’ year in the university
In
an examination of students first and second years at their universities, the overall t
score was 1.29, barely significant at the .055 level (N = 189). Three categories of the SILL were found to be significantly
different between freshman and sophomore years: compensatory, t (189) = 2.85, p
= .005, metacognitive, t (189) = 3.22, p = .001 and affective, t (189) = 2.39, p = .018.Analysis of second, third and fourth year strategy use did not reveal significant differences
or correlations.
h. Hours per week studying foreign language outside of university and institutes
A
strong correlation was found between hours per week studying and overall mean study strategy intensity, r = .345, significant
at the 0.01 level (2-tailed, N = 252).
i. Most and least commonly used
individual strategies
The
single most commonly used SILL strategy used in this study by students was strategy 45, “If I do not understand something
in English, I ask the other person to slow down or say it again,” a social strategy (M = 3.87). The second most commonly
used strategy was strategy 10, “I write or say new English words several times,” a strategy in the cognitive category
(M = 3.85). The following are the next most commonly used SILL strategies: “When I can’t think of a word in conversation,
I use gestures,” (SILL number 25, compensatory) followed by SILL number 38: “I think about my progress in learning
English,” or “…Chinese” for CFL students, a metacognitive strategy).
The least frequently used strategies were: SILL number 6, “I
use flash cards to remember new English words (M = 1.76), a memory strategy, followed by number 43, “I write down my
feelings in a language learning diary,” (M = 2.00) an affective strategy and number 16, “I read for pleasure in
English,” (M = 2.11) a cognitive strategy.
2. EFL STUDENTS RESULTS
a. Institute attendance and strategy use
The
results indicate that there was a significant difference in study strategy use between EFL students that attended institutes
and those that didn’t, t (91) = 2.28, p = .025 and a weak correlation of
.198, p<0.061.
b. Gender and strategy use
The
results indicate a highly significant difference in overall strategy use between males and females in the EFL group, with
female students using strategies with greater intensity, t (109) = 2.69, p = .008.
The strongest significant difference between male and female EFL students in the categories of the SILL was in the memory
category, t = 2.46, p = .015, followed by cognitive and affective categories, t = 1.91, p = .058, and t = 1.87, p = .064,
respectively. The subcategory with the lowest significant differences between EFL males and females’ strategy use was
metacognitive, t = 1.65, p = .100.
There was also a significant correlation between male and female
use of different strategies on the SILL, r = 2.52, significant at the .008 level.
c. Father’s education and strategy use
A
significant correlation was found between father’s level of education and EFL students’ overall strategy use,
r = .198, p< 0.05.
d. Mother’s education and strategy use
No correlation was found in the EFL group between mother’s level of education and
EFL students’ overall strategy use.
e. Students self reported grades and strategy use
No significant correlations were found between EFL students self-reported grades in foreign
language classes and overall strategy use or strategy subcategory use.
f. Students self-reported self confidence and strategy
use
The correlation between EFL students self-reported self confidence and overall strategy
use was significant at the .01 level, r = .328, p< 0.01.
g. Students’ year in the university
The
only significant differences in strategy use by students’ year in the university in the EFL sample were found between
freshman and sophomore year students. The results indicate that there was a highly significant difference in intensity of
overall strategy use between freshman and sophomore EFL students, t (92) = 2.45,
p = .016, with sophomore students using significantly more strategies in all categories of the SILL. In regards to the categories,
the strongest significant differences between freshmen and sophomores were found in meta-cognitive and compensatory strategies,
t = 3.09, p = .003 and t = 2.95, p =
.004, respectively. The smallest difference between use of strategy categories between freshman and sophomores was in the
use of memory strategies t = .250, p = .803. The least used strategy category was
freshman use of affective strategies (M = 2.55), while the highest mean found in a strategy category was sophomore use of
compensatory strategies (M = 3.51).
No significant correlations were found between students’
year in the university and overall strategy use in the EFL group.
h. Hours per week studying foreign language outside
of university and institutes
The results indicate that there was a highly significant difference in study strategy
intensity between students that spent more time studying (13 hours per week) outside the classroom and those that spent less
time studying outside the classroom (two hours per week), t (54) = 4.10, p = .000.
There was a very strong correlation between time spent studying and study strategy intensity means, r = .321, significant
at the .01 level.
A correlational analysis between institute attendance and time
spent studying revealed there was no correlation between these two variables (r = .062, p<.546). Though foreign language
study outside the classroom appears to relate strongly with study strategy intensity, and institute attendance relates strongly
with study strategy intensity, these factors appear to be independent.
3. CFL STUDENT RESULTS
a. Institute attendance and strategy use
Results indicate there was a significant difference in strategy use between students
that attended an institute and those that didn’t, t (153) = 1.99, p = .049.
There was also a significant correlation between CFL students’ attendance at a foreign language institute, and study
strategy use, r = .211, significant at the .05 level.
b. Gender and strategy use
Unlike the EFL group, there was no significant correlation between gender and study strategy
use in the CFL group, r = .081, p< .323, N = 153.
c. Father’s education and strategy use
Unlike
the EFL group, there was no significant correlation between father’s level of education and strategy use, r = .120,
p< 0.158, N = 153.
d. Mother’s education and strategy use
Unlike
the EFL group, there was a significant correlation between mother’s level of education and strategy use, r = .187, significant
at the .05 level, N = 153. Though there wasn’t a significant difference between CFL students overall strategy use based
on level of mother’s education, t (120) = 1.09, p = .275, a significant difference
was found in the compensatory category (t = 2.52, p = .013) and a borderline significant
difference was also found in cognitive (t = 1.92, p = .057) and affective (t = 1.73, p = .085) strategy categories. Significant correlations were found between mother’s education
and cognitive strategies (.05 level), compensation (.01 level), and metacognitive (.05 level) strategies.
e. Students self reported grades and strategy use
There was a highly significant correlation between CFL students’ self-reported
grades and mean strategy use, r = .301, significant at the .01 level.
f. Students self-reported self confidence and strategy use
Like
the EFL group, there was a strong positive correlation between self-reported self-confidence and study strategy use, r = .433,
significant at the .01 level, N = 153. There was also a very strong correlation in the CFL group between confidence and hours
of target language study outside the classroom, r = .374, p< .000, N = 152.
g. Students’ year in the university
Unlike the EFL group, a significant correlation was found between year of university
study and study strategy use, r = .202, significant at the .05 level, N = 153.
h. Hours per week studying foreign language outside
of university and institutes
There
was a borderline significant difference t (86) = 1.82, p = .072, in student SILL
means between students who reported studying an average of two hours per week and students who reported studying 8.5 hours
per week. There was a significant difference, t (73) = 4.38, p. = .000, in SILL
means between students who reported studying two hours per week and students who reported studying 13 hours per week. There was a very strong correlation (r = .337, p<.000,
N = 152) between hours per week studying a foreign language and study strategy mean use although there was a rather radical
dip in study strategy usage at the 18.5 hours per week level in the CFL group.
IV. DISCUSSION
1. Overview of strategy use
As Table 1 illustrates, the overall Grand Mean for all students in this study was 2.9 (on a scale of 1 – 5).
Compensatory strategies were most used by this total sample of Korean students (M = 3.36), consistent with results by Lee
(2003) and Oxford (2008) in their studies on Korean students’
study strategy use. Oxford (2008) wrote that: “Since 1969, Korean students have taken
multiple-choice entrance examinations, equivalent to the Scholastic Aptitude Test in the U.S. and we speculate that such examinations might promote compensatory strategies
for guessing the right choice from the context, even if the details are not fully understood.” Yang (2007) and Hsu (2003)
also found that compensatory strategies were most frequently used in their studies.
Regarding the primary use of compensatory strategies, Yang wrote: “It is only natural for students to make greater
use of compensation strategies as these can allow them to guess the meaning of what they have heard or read or to remain in
the conversation despite their limited grammatical and vocabulary knowledge.” This study found metacognitive to be the
second most used strategy (M = 3.10), also consistent with Oxford
(2008).
Following that similarity however, the results from this study
diverged from Oxfords, but were consistent with most contemporary research on study strategy use in Asia
(see Table 2).
|
This
study (South Korea) |
Oxford, 2008
(South Korea) |
Yang,
2007
China |
Hsu,
2003
Taiwan |
Lengkanawati,
2004
Indonesia |
|
Compensation |
Compensation |
Compensation |
Compensation |
Memory |
|
Metacognitive |
Metacognitive |
Social |
Affective |
Cognitive |
|
Social |
Cognitive |
Cognitive |
Social |
Compensation |
|
Cognitive |
Memory |
Metacognitive |
Metacognitive |
Affective |
|
Affective |
Affective |
Affective |
Cognitive |
Social |
|
Memory |
Social |
Memory
|
Memory |
Metacognitive |
Table 2. Most Commonly used strategy categories in five studies in Asia
Memory was found to be the least used strategy in this and other
recent studies, compared to older research by Politzer and McGroarty (1985); Tyacke & Mendelsohn (1986); Huang & Van
Naerrsen (1987); O’Mally and Chamot (1990) and Lengkanawati (2004), which suggested that Asians tend to prefer memorization
and other rule-bound strategies.
One possible explanation for these differences could be that most
of the research that found Asians prefer memory based strategies was done back in the 1980s, when older systems of language
teaching were used. In regards to Lengkanawati’s (2004) results, he wrote: “Indonesian students have the habit
of rote learning behavior. This behavior has become the cultural habit in studying.”
Studies by Chang (1990); Bedell (1993, cited in Oxford,
1996), Yang (1993a, 1993b, cited in Yang, 2007); Biggs, 1994; Cortazi and Jin, 1996; Littlewood (2000) and Lee and Oxford (2008) raise questions about preconceptions regarding cultural
influences on learning strategies. Lee and Oxford for example,
found that memory strategies were not more frequently used by Korean students.
2. Variables and strategy use
a. Institute attendance and strategy use
Students that attended private language institutes at sometime in their life demonstrated
significantly greater study strategy intensity use in this study, in combined and separate EFL and CFL groups. However, correlations
and significant differences do not necessarily imply causation, as some intervening variable such as motivation may explain
both increased probability to attend a language institute and greater intensity of study strategy use.
b. Gender and strategy use
That highly significant differences and a significant correlation in strategy use
by males and females should be found in the EFL group but not the CFL group is consistent with contradictory results of other
studies. Clearly other variables are at work here. Numerous investigators have
found results similar to the EFL group in this study, i.e. females make more frequent use of strategies than males, i.e. Lee
(2003) and Hsu (2008). In her 1996 study, Oxford
cites 14 studies that examined study strategy use and gender, concluding that “results usually favored females as more
frequent users of strategies.” However, in her 2008 study conducted with Korean students, she found gender by itself
did not affect strategy use significantly. Bedell (cited in Yang, 2007) and Watanabe (cited in Yang, 2007) found that females
utilize different patterns of strategy use than males. Bedell (1993) and Green and Oxford (1993,
cited in Oxford, 1996) found that males surpass females in
some specific strategy items, but not whole categories.
c. Father’s education and strategy use
Whereas
this study found significant differences between student use of compensatory and social strategies based on their fathers’
education, Hsu (2008) found parents’ education most influenced cognitive and social strategy use. That the EFL group
should have a significant correlation between fathers’ level of education and strategy use whereas the CFL group did
not, is curious. Hsu (2008) looked at “parents” education as a unity and didn’t differentiate between fathers’
and mothers’ educational levels. He defined educational level as “low” when parents’ education was
equal or below senior high school (the same as this study) and “high” when the parents’ education level
was Junior college or better, whereas in this study we considered “high” to be a four-year university bachelors
degree. In attempting to explain the differences in study strategies resulting from having parents with different educational
backgrounds, Hsu wrote: “It is because parents with higher education level respect children’s education more.”
d. Mother’s education and strategy use
In the combined EFL/CFL analysis, mother’s education had a strong correlation with student strategy use. That
the CFL group produced a significant correlation between mother’s level of education and strategy use, whereas the EFL
group did not, while the EFL group showed a correlation between father’s education and strategy use, but the CFL group
did not, warrants further research.
The answer may lie in Korea’s traditional culture. For example, older generation people in Korea tend to adhere much more closely to traditional values
associated with sex-role socialization than younger people. Also, English language is considered the modern way of the future,
whereas Chinese is associated with traditional values. Consequently it’s possible that highly educated mothers may consciously
or unconsciously encourage their daughters towards learning Chinese language studies and educated fathers may consciously
or unconsciously steer their sons towards learning English language.
e. Students self reported grades and strategy use
This
study found significant positive correlations between foreign language class grades at the university and SILL scores, consistent
with Park (1994) who used the SILL and TOEFL with 332 Korean university students. His
results indicated that the high strategy use group had TOEFL scores significantly higher than that of the medium strategy
use group, though the medium and low strategy use groups only had slight language proficiency differences as measured by the
TOEFL. Mullins study (as cited in Oxford, 2008) of language strategy use and English proficiency with 110 Thai students, found
significant correlations between compensation and metacognitive strategy use and English ability using course grades and language
placement scores. A study by Green (cited in Oxford, 1996) using ESLAT proficiency scores with Spanish speaking EFL students
in Puerto Rico found moderate and significant differences in strategy use between higher and lower English proficiency students.
Green and Oxford (1995, cited in Oxford,
1996) found that language proficiency level had significant effects on compensatory, cognitive, metacognitive and social strategies.
f. Students self-reported self confidence and strategy
use
Self confidence was the second most highly correlated factor with study strategies
obtained in this study. Oxfords 2008 study conducted in Korea
included a measure she called “English learning self-image.” This quality was measured by response to the question:
“How do you rate your overall English proficiency as compared with the proficiency of other student in your class.” (Italics were used in the questionnaire.) Oxford’s
“English learning self-image” question sounds similar to self-confidence in the context of an EFL or CFL class.
Oxfords 2008 study found a strong relationship between “English learning self-image” and study strategy use, consistent
with findings in this study.
g. Students’ year in the university
It
is curious that the EFL students should show a significant increase between freshman and sophomore years, and then level off
for the next three years compared to the CFL students that showed a slow but steady increase throughout their university career.
This result may be an anomaly of different admissions standards at the university, or reflection of different cultures of
learning (i.e. Cortazzi and Jin, 1996) within the respective departments. Numerous studies have shown correlations between
age and study strategy use. For example, Oxford 2008 found significantly higher strategy use in Korean university students
compared to high school and middle school students.
Conversely, Hsu (2008) found that Taiwanese elementary school children
used study strategies with greater intensity in all strategy categories than did two-year junior college students. A number
of intervening variables could account for this discrepancy, for example, the age at which the college students started learning
English could influence their strategy usage. Some influence on foreign language
study strategy use by the males in this study after the freshman year (irrespective of language studied) could be the result
of the mandatory two year military service all young men must undergo at the end of their freshman year (except those who
enter an ROTC program). Students with a more traditional philosophical attitude, inculcated in Chinese philosophy (and to
some extent language) might recuperate more quickly from the potential trauma of forced military service than someone with
expectations of the freedoms that are woven through most modern societies and the international language of English. Such reasoning is highly speculative and further research is required to see if these
results are consistent across South Korea.
h. Hours per week studying foreign language outside of university and institutes
EFL
and CFL groups both produced high correlations between strategy use and study outside the classroom, although the radical
dip in strategy usage in the CFL group at the 18.5 hour level was unexpected. It could be that students who felt above average
need for study did so because their proficiency level was low, possibly due to limited use of study strategies. EFL strategy
use had a minor dip at 5 hours per week of outside the classroom study and a major dip at 9 hours a week of outside the classroom
study. There was a peak in strategy use in the 18 hours a week of outside class study in the EFL group.
The mean study time of students who attended institutes at some
time in life was higher than that of students that never attended an institute (M =
4.0 hours per week vs. 3.4 hours per week), though the differences were not significant. An intervening variable such
as motivation could explain the difference. Attending an institute at some time in life probably does not cause greater time
in study. However, the motivation to spend more time studying could help “cause” attendance at an institute.
In Hsu’s 2008 study he included a factor he referred to as:
“Learning experiences outside of school.” Though he didn’t elaborate on the meaning of this phrase, his
groups on this factor were divided into “less than one year” and “more than one year.” He may have
been referring to institutes, tutoring, independent study or some combination of the three.
Whatever the case, he found significant differences in study strategy use between students with more and less than
one year of “learning experience outside of school.”
3. Suggestions for further research
The results of this study raised several questions. For example, why should mother’s
level of education correlate highly with CFL students mean strategy intensity use, but not with EFL students strategy use,
and why did fathers educational level correlate highly with EFL students study strategy use, and not with CFL students study
strategy use? A replication study needs to be done to ascertain if this pattern is consistent in South Korea, or if it is an anomaly of these students at these three universities.
Should these results be consistent across South Korea,
answers may be found in cultural attitudes towards Chinese and/or English speaking people.
Another question which remains unanswered is: Why was there a correlation
between gender and study strategy use in the EFL group, but not the CFL group? Are different personality types attracted to
studying different languages?
Likewise, that the EFL students’ mean strategy use leveled
off after the freshman year whereas the CFL students showed a slow steady increase.
Again, further research is required to understand the mechanisms for this result.
Not much research has been done on motivation as a factor in study
strategy use, even though Oxford’s 1990 SILL came complete
with a questionnaire that included a motivation measure. Oxford
and Nyikos (1989) found motivation and foreign language study strategy use to be related. Oxford,
Talbott and Halleck (1990, cited in Oxford, 1996) found a
significant relationship between strategy use and motivation. Oxford,
Park-Oh, Ito, and Sumrall (1993a, 1993b) found motivation to be a significant factor along with study strategy use in Japanese
language learning. Hsu (2003) found motivation to be the greatest predictor of
variance of EFL proficiency in his study.
Culhane (2004) cites research by Gardner and Lambert (1959) which identified five motivational attributes affecting L2 acquisition:
the learner’s reasons for learning the L2; degree of anomie, or dissatisfaction with one’s place and role in society;
level of ethnocentrism; the degree to which the first culture is preferred over the second and attitudes held toward the target
language and culture. Research on these factors and their interaction with study strategies might help teachers provide curricula
and assignments tailored to lift up students’ weak motivational factors and contribute to second language learning.
Cross factor analysis between Gardner and Lamberts five motivational attributes and the independent
variables in this study could produce valuable insights and answers to bigger questions regarding foreign language learning
in Asia.
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Appendix
1 Results summary by sample group, variable, overall significance and statistic*
|
|
EFL & CFL COMBINED |
EFL STUDENT RESULTS |
CFL STUDENT RESULTS |
|
a. Institute attendance
and strategy use |
Sig. diff.
Sig.
corre-
lation |
t = 3.82
p = .000
r = .200
p< .01 |
Sig. diff.
Weak
corre-
lation |
t = 2.28
p = .025
r = .198
p< 0.061 |
Sig. diff.
Sig. corre-
lation |
t = 1.99
p = .049
r = .211
p< 0.05 |
|
b. Gender and strategy
use |
No
sig. diff.
No corre-
lation |
t = .288
p = .773
r = .020
p< .752 |
Sig. diff.
Strong
corre-
lation |
t = 2.69
p = .008
r = .252 p<
0.008 |
No sig.
diff.
No corre-lation |
t = 1.10
p = .271
r = .081
p< 0.323 |
|
c. Father’s
education and strategy use |
Sig. diff.
Strong corre-lation |
t = 2.31
p = .022
r = .165
p< 0.01 |
Sig. diff.
Sig. corre-lation |
t = 2.10
p = .038
r = .198 p<
0.05 |
No sig.
diff.
No corre-lation |
t = 1.26
p = .210
r = .120
p< 0.158 |
|
d. Mother’s
education and strategy use |
No
sig. diff.
Strong
corre-
lation |
t = 1.56
p = .119
r = .168
p< 0.01 |
No sig.
diff.
No corre-lation |
t = 1.34
p = .184
r = .127 p< 0.192 |
No sig. diff.
Sig. corre-lation |
t = 1.09
p = .275
r = .187
p< 0.05 |
|
e. Students’
self-reported grades and strategy use |
Sig. diff.
Strong
corre-lation |
t = 3.27
p = .002
r = .208
p< 0.01 |
No corre-lation
|
r = .087
p< 0.405 |
Strong corre-lation |
r = .301
p< 0.01 |
|
f. Students’
self-reported self-confidence and strategy use |
Sig. diff.
Strong
corr. |
t = 4.01
p = .000
r = .406
p< 0.00 |
Strong corre-lation |
r = .328
p< 0.01 |
Strong corre-lation |
r = .433
p< 0.00 |
|
g. Students year
in the university(1-2) |
Sig. diff. |
t = 1.29
p = .055 |
Sig. diff.
|
t = 2.45
p = .016 |
Strong corre-lation |
r = .202
p< 0.05 |
|
h. Hours per week
studying foreign language outside of classroom |
Sig. diff.
Strong corre-lation |
t = 4.05
p = .000
r = .345
p< 0.00 |
Sig. diff.
Strong
corre-
lation |
t = 4.10
p = .000
r = .321
p< .01 |
Sig diff.
Strong
corre-
lation |
t = 4.38
p = .000
r = .337
p< .0.00 |
* For SILL category
results, see text.
