Scholarly article on topic 'Language Learning Strategy Use and its Impact on Proficiency in Academic Writing of Tertiary Students'

Language Learning Strategy Use and its Impact on Proficiency in Academic Writing of Tertiary Students Academic research paper on "Educational sciences"

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Abstract of research paper on Educational sciences, author of scientific article — Zakia Ali Chand

Abstract This research for a PhD dissertation on language learning strategies and academic writing skills of tertiary students in Fiji investigates the relationship between strategy preferences and proficiency in academic writing. It shows that the majority of students used language learning strategies with medium frequency. Metacognitive and cognitive strategies were used most frequently followed by social, compensation, memory and affective. There is a weak positive correlation between strategy use and academic language proficiency. Weaknesses in the undergraduate students‟ academic language cannot be attributed to their language learning strategies.

Academic research paper on topic "Language Learning Strategy Use and its Impact on Proficiency in Academic Writing of Tertiary Students"

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Procedia - Social and Behavioral Sciences 118 (2014) 511 - 521

SoLLs.INTEC.13: International Conference on Knowledge-Innovation-Excellence: Synergy in Language

Research and Practice

Language learning strategy use and its impact on proficiency in academic writing of tertiary students

Zakia Ali Chand*

_Fiji National University, Suva, Fiji_


This research for a PhD dissertation on language learning strategies and academic writing skills of tertiary students in Fiji investigates the relationship between strategy preferences and proficiency in academic writing. It shows that the majority of students used language learning strategies with medium frequency. Metacognitive and cognitive strategies were used most frequently followed by social, compensation, memory and affective. There is a weak positive correlation between strategy use and academic language proficiency. Weaknesses in the undergraduate students' academic language cannot be attributed to their language learning strategies.

Key words: language, learning, strategies, academic, proficiency

© 2013 The Authors.PublishedbyElsevier Ltd.

Selection and peer-review underresponsibilityofUniversitiKebangsaanMalaysia. 1. Introduction

Fiji is a multiethnic society and students come from bilingual or multilingual backgrounds. Being a former British colony, English has evolved over the last 200 years to become the country's main medium of communication for education, business, entertainment and politics. Because of its unique population mix, comprising Fijians, Indo-Fijians of Indian descent, other Pacific Islanders, Chinese and people of European descent with a variety of first languages, English has become the lingua franca. However, it is the native language of only 1% of Fiji's population (Tent, 2004, p.307). In spite of being immersed in English language for the better part of their lives, morphological, lexical, syntactic and mechanical errors continue to abound in tertiary students' writing. This study was conducted at the University of the South Pacific (USP) in Suva, Fiji to

Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 . E-mail address:

1877-0428 © 2013 The Authors. Published by Elsevier Ltd.

Selection and peer-review under responsibility of Universiti Kebangsaan Malaysia. doi: 10.1016/j.sbspro.2014.02.070

investigate the types of strategies students from Fiji use to learn English and if there is a relationship with proficiency in their academic language. Students differ in their proficiency levels as a result of many factors, including unequal opportunities to communicate in English, attitudes towards the language, learning styles, self esteem, age, gender, geographical locations, socio-economic backgrounds, religion and race. These variables influence the process of language acquisition and play a key role in their educational lives. Educators in Fiji tertiary institutes have found that in spite of eight years of primary and five years of secondary schooling with English as the medium of instruction, tertiary students continue to have weak academic writing skills. Although no comprehensive research data is currently available on the exact areas of weaknesses in academic writing of Fiji students, the researcher's thirty years of experience teaching English in secondary and tertiary institutions suggest that the most common errors in their written texts are: tense, subject-verb agreement, weak sentence structures, mechanics (in particular punctuation and spelling), usage of articles, vocabulary, connectives, participles, word forms, word choice, and direct and reported speech. According to Ferris (2002), errors in ESL writing can be categorized as "global" and "local." Global errors refer to "errors that interfere with the comprehensibility of a text" (p.22). These are errors concerning overall content, ideas, and organization of the writer's argument, while local errors refer to slips and lapses in grammar, spelling, or punctuation "that do not impede understanding" (p.22).

1.1 Key research question

The key research question that this study addresses is:

What is the impact of language learning strategies (LLS) of tertiary students in Fiji on their academic writing skills?

Related research questions this study sought to answer are:

1. What language learning strategies are used by Fiji students? Is there a significant difference between strategy use and gender/ethnicity?

2. What are the most common types of errors made in academic writing of Fiji students? Are there any implications in their frequency?

1.2 Significance of the study

Little research has been done on LLS and correlations with academic proficiency of students in Fiji and the Pacific. Errors of punctuation, tense, agreement, prepositions and use of articles have been identified by educators as the most problematic areas in students' writing but no research has yet been done to verify this. It is hoped that this research will assist teachers, textbook writers, curriculum developers and tertiary institutions to develop suitable courses, textbooks and teacher education programs to address these weaknesses in Fiji students' academic texts. This research will also add to the small volume of literature on this topic for this region and trigger further related research for other South Pacific island students.

2. Language learning strategies

Awareness of LLS was first created by Rubin (1975) and Stern (1975) followed by O'Malley et al (1985) and Ellis (1994). There have been many definitions of LLS. O'Malley and Chamot (1990) have defined it as "the special thoughts or behaviours that individuals use to help them comprehend, learn or retain new information" (p.1). Bialystok (1978), Chamot (1987), O'Malley & Chamot (1990), Oxford (1990), Rubin (1987), Rubin (1975) and Stern (1975) have attributed it to the learning processes used by learners to acquire knowledge while Skehan (1989, p.285, cited in Griffiths, 2004) has called them an "explosion of activity". Chamot (2004) calls it the thoughts and actions individuals employ to acquire a learning goal which can only be identified through self-reporting by the learner, while Ellis (1994, p.529) finds the concept "fuzzy." According to Griffiths (2004), the definition of "learning strategies" still remains unclear. In all of these definitions, there is an agreement in one aspect, that LLS are deliberate, conscious and well-thought out methods used by learners to learn or acquire a second language.

Oxford (1990) proposed six categories in the analysis of LLS, namely: memory, cognitive, compensation, metacognitive, affective, and social strategies. The first three are grouped as direct strategies and used in the direct learning of the target language while the three indirect strategies help learners to manage and support their learning without involving the target language directly (Ya-Ling Wu, 2008).

• Memory strategies help learners learn and store information for future use.

• Cognitive strategies involve the use of formal, direct steps to acquire knowledge or skill (Deny & Murphy, 1996; Rubin, 1997).

• Compensation strategies enable learners to make up their missing knowledge through guessing, switching to their native language and other compensatory methods.

• Metacognitive strategies help learners to take control of their learning.

• Affective strategies help learners to take control of their emotions while in the process of language learning.

• Social strategies allow learners to involve other people in their learning process by working together, asking questions and becoming aware of others' feelings.

They are measured using an LLS questionnaire which employs a Likert scale of one to five to rate strategy use. Much research has been done on strategy use of second language learners over the last twenty years. Evidence suggests that language performance is related to LLS (Dreyer & Oxford 1996) and that strategies can be taught. In a study by Ya-Ling Wu (2008), it was found that Taiwanese students who had a higher proficiency in English used strategies more frequently than those with lower proficiency.

Some research has been done on LLS of students in Fiji. Mangubhai (1990) conducted research on three subjects and the strategies they employed to do a cloze test. One of the strategies these participants given practice in was "think aloud," that is, to verbalize what was going on in their minds while they were attempting to do the cloze test. The three participants were put in separate classrooms and given the same set of instructions but no time limits were imposed. Their "think aloud" strategies were audio taped. This study revealed the more proficient subject used cognitive and metacognitive strategies. Their strategies correlated well with the categories the researcher had put them according to their proficiency levels: high, middle and low. In another research, Lal and Mangubhai (2000) studied three groups: secondary school students in fifth form and teacher trainees in year 1 and year 2 at the University of the South Pacific. This study revealed that the most common strategies used were metacognitive and social (p.61). It also revealed that the more advanced learners made a "greater overall use of language learning strategies" than those in lower levels. However, to date, no significant research has been done on correlations between the LLS and proficiency in academic writing of tertiary students from Fiji.

2.2 Academic language proficiency

Academic writing requires conscious effort and practice in composing, developing, and analysing ideas. Students writing in a second language have to acquire proficiency as well as appropriate writing strategies, techniques and skills when compared to students writing in their native language. Research has shown that ESL learners write quite differently compared to learners whose L1 is English (Maasum et al, 2012, p.428).

Both social and cognitive factors affect language learning. Exploration of social factors gives us some idea of why learners differ in their rate of L2 learning, in proficiency type, for example between speaking and writing abilities, and in ultimate proficiency (Ellis, 1994). Their negative attitudes may be strengthened by a lack of success (McGroarty, 1996) or by a lack of interest. According to Lipstein and Renninger (2007), "... students say that their interest for writing is often influenced by their teachers and classroom practice" (p.79).

Since the ultimate goal of higher learning is academic proficiency, this study will present findings related to strategy use and English proficiency. Research conducted in this area has shown an association between strategy use and proficiency in English (Oxford and Burry-Stock, 1995 cited in Nisbet et al, 2005).

2.3 The relationship between strategy use and academic proficiency

The relationship between LLS and academic proficiency has been the subject of much research over the last twenty years. According to Green and Oxford (1995), the picture is not crystal clear because a lot of research has focused on overall strategy use only and not taken into account individual strategy use or variations in gender. In their study of university students at different course levels in Puerto Rico, Green and Oxford (1995) found that there was a positive correlation between strategy use and academic proficiency (p.275). When data was analyzed, seventeen items of the SILL questionnaire showed a positive correlation with the more academically advanced students. "By far the commonest type of significant variation across course levels was positive variation, indicating greater strategy use by more proficient, more successful learners" (p.278). Their research showed that the basic and intermediate categories of students used LLS less frequently.

The issue of causality between strategy use and proficiency has been a subject of debate for some time. Skehan (1989, cited in Bremner, 1999, p.494) and Rees-Miller (1993, cited in Bremner, 1999, p.494) believe that a correlation between the two does not necessarily suggest a cause-and-effect relationship, while McIntyre (1994, p.188, cited in Bremner, 1999, p.494) feels that "...either proficiency influences the choice of strategies or that strategy choice is simply a sign of proficiency level." It may be plausible that LLS have no influence on language proficiency, or that they are features of it. Whatever the argument, research has shown that proficiency in language skills is enhanced by the use of these strategies.

In a study done by Saricoban and Sarocaoglu (2008) in Turkey, it was found that compensation strategies had a positive correlation with academic achievement (p. 172) while affective strategies were negatively correlated. Students who used affective strategies were less successful than others.

Griffiths (2004), in a study at a private language school in Auckland, found that "there was a significant relationship between strategy use and language proficiency" (p. 82). The study showed that the "Advanced students reported a higher average frequency of use of each strategy than did elementary students" (p.78). The implication of these studies is that we can raise levels of proficiency by teaching these strategies. These studies may not have shown a clear causality in any direction between language proficiency and strategy use; however, it can be logically concluded that there are significant relationships between the two.

3.0 Methodology

3.1 Participants

A sample of 88 undergraduate USP Fiji students was randomly selected from the first year studying the English for Academic Purposes (EAP) course. An additional ten students were randomly selected from the final year Bachelor of Arts programme majoring in English Language and Literature for a one-year longitudinal study. In spite of random selection, nearly half of the participants were female Indo-Fijians. The I-Taukei (indigenous Fijians) comprised one-third of the sample. This is because on the day of data collection, all Fiji students who were present in their EAP classes volunteered to participate in this research and, coincidentally, there were more females and more Indo-Fijian students in attendance. The table below shows a breakdown of all participants according to gender and ethnicity:

Figure 3.1.1 Gender * Ethnicity Cross tabulation



Non IT non IF


12% 19% 31%

17% 48% 65%

1% 3% 4%

3.2 Instrumentation

For language learning strategies, data was collected using two methods: firstly, SILL questionnaires, version 7.0 used for second language learners (see Appendix 1), were distributed to the participants during class time in October, 2011.

The second method used for data collection for language learning strategies was interviews with 18 students who volunteered to be interviewed and participated in a yearlong longitudinal study. Data for proficiency in academic language were collected from three sources: a diagnostic test given at the beginning of the semester before writing strategies were taught, final exam answer scripts and assignments.

4.0 Data collection and analysis procedures

Data from the SILL questionnaire was analysed using the Statistical Package for Social Sciences (SPSS software). Descriptive analyses of the SILL questionnaires were done to estimate the mean and standard deviations which identified the frequency of language learning strategies used by the research subjects. Further analysis of data was done using Pearson's correlation, one way ANOVA and independent samples t-test in order to identify the relationship between language learning strategies and academic language proficiency. Data for academic language proficiency was collected in three successive stages (Corder, 1973): recognition, description and explanation. Errors were classified and analysed using the rubrics from a software package called Markin version 4. Markin is a Windows program which is available on the Internet and can be used to mark students' essays that have been electronically submitted. The following errors were considered in the analysis: Sense: incomprehensible text; (cut) unnecessary text; vague reference; paragraphing problem. Grammar: subject verb agreement error; article error; count/non count error; sentence fragment; missing word or words; misplaced or dangling modifier; parallel construction problem; singular/plural error; verb form; verb tense; wrong or misused preposition. Punctuation: capitalization error; punctuation.

Vocabulary: poor word choice; Linking: conjunction/Transition; Mechanics: formatting problem; Content: inaccurate quotation; Style: repetition of information or phrase; Editing: missing space; Spelling: spelling; Morphology: word form; Syntax: word order.

Markin has been used by Darus et al (2007) and Darus and Subramaniam (2009) as a research instrument to analyse the errors of 400 essays in the 2007 study and 72 in the 2009 study.

Since the number of non I-Taukei and non Indo-Fijian subjects was only 3, data from this group was not used in the final analysis.

5.0 Data analysis and discussion

5.1 SILL questionnaire analyses Research question 1

1. What language learning strategies are used by Fiji students? Is there a relationship between strategy use and gender/ethnicity?

The mean scores of the entire group were calculated. According to Oxford (1990, p.300), mean scores that occur between 1.0 and 2.4 can be classified as "low", 2.5 and 3.4 as "medium" and 3.5 and 5.0 as "high" strategy use..

Although there are differences, they all fall within the medium range as defined by Oxford (1990) except for the affective strategy which falls in the least frequent use category. As the table below indicates, metacognitive strategies were the most frequently used followed by cognitive, social, compensation and memory.

Table 5.1.1 Mean strategy use in each of the strategy groups

Strategy Mean Rank

Memory 2.5 5

Cognitive 2.9 2

Compensation 2.7 4

Metacognitive 3 1

Affective 2.4 6

Social 2.8 3

Is there a relationship between strategy use and gender/ethnicity? Table 5.1.2 Correlations between strategy use and gender and ethnicity

Errors tot LLS total Gender Ethnicity

Errors tot Pearson Correlation 1 .220* -.063 -.296**

Sig. (2-tailed) .040 .557 .005

N 88 88 88 88

LLS total Pearson Correlation .220* 1 .205 -.130

Sig. (2-tailed) .040 .055 .229

N 88 88 88 88

Gender Pearson Correlation -.063 .205 1 .054

Sig. (2-tailed) .557 .055 .615

N 88 88 88 88

Ethnicity Pearson Correlation -.296** -.130 .054 1

Sig. (2-tailed) .005 .229 .615

N 88 88 88 88

* Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed).

Table 5.1.2 above shows that the correlation coefficient between strategy use and gender has a value of 0.205. This is a weak correlation which means there is not really any relationship between gender and strategy use. Ethnicity shows a negative correlation which can be interpreted as meaningless data because it means that as more students from different ethnic groups use the strategies, there is a reduction in ethnicity. Both males and females have very closely related means of strategy use, as shown in Table 5.1.3 below. This is further evidence that there is very little difference in strategy use between the two genders.

Table 5.1.3 Group Statistics by gender and strategy use

Gender N Mean Std. Deviation Std. Error Mean

Mem strat Male 30 61.4 13.9 2.5

Female 58 64.5 10.2 1.3

Cog strat Male 30 70.0 10.1 1.9

Female 58 74.3 7.9 1.0

Comp strat Male 30 65.4 13.7 2.5

Female 58 68.8 12.5 1.6

Meta strat Male 30 71.1 17.3 3.2

Female 58 78.2 9.9 1.3

Affec strat Male 30 56.1 18.0 3.3

Female 58 61.0 16.3 2.1

Soc strat Male 30 67.6 16.9 3.1

Female 58 70.4 17.5 2.3

Errors tot Male 30 8.6 5.6 1.0

Female 58 8.1 3.4 .5

LLS total Male 30 65.3 11.2 2.0

Female 58 69.6 9.0 1.2

5.2 Data from error analysis Research question 2

What are the most common types of errors made in academic writing of Fiji students? Are there any implications in the frequency of these errors?

Twenty six grammatical areas were considered in the analysis. A total of 4,466 errors were found in the 88 final exam scripts. The table below shows the highest number of errors was made in punctuation (15.1%) followed by errors in word choice (10.5%). The third highest errors occurred in repetition of information or phrases (9.11%). The fourth highest errors were made in unnecessary or irrelevant information (8.4%). This was followed by errors in the use of singular and plural (7.1%).

Table 5.2.1 Breakdown of errors in final examination

Error Pun WCh Rep Cut Plu Mis Mod Unn Prep Agr WFo Spl VTe

% 15.1 10.5 9.11 8.4 7.1 5.8 5.2 5 4.9 4.7 3.7 3.4 2.6

Error VFo Art Con Cap Qot Vag Frg WOr Par Spa Prg Cou For

% 2.4 2.4 2.2 1.8 1.5 1.4 1.1 0.5 0.4 0.4 0.2 0.2 0.04

Other types of errors such as missing words, misplaced modifiers, prepositions, agreement, word forms, spelling, verb tense, articles, use of conjunctions and sentence fragments occurred less frequently. Almost negligible were errors in word order, paragraphing and count/non-count nouns. There were no errors in formatting. The top five categories of errors were punctuation, word choice, repetition, unnecessary text (cut) and singular/plural. 50% of all errors were made in these five categories.

Are there any implications in the frequency of these errors?

In dealing with pedagogical improvements, it is imperative to examine the causes and sources of these errors and how they can be minimized so that students can produce academic texts acceptable at an undergraduate level. Such analysis is beneficial if done in the classroom by teachers as it diagnoses the problems faced by the students, and teachers can provide remedial action immediately. Students' written work in the classroom provides a wealth of data for teachers to help them design better remedial programmes and build an inventory of errors for remedial work. It can also help teachers to individualize instruction and deal with each student according to his/her linguistic needs.

Some errors may be attributed to the learner's L1 and some to the target language. The differences in the structure of L1 and L2 have a huge impact on L2 learners. As a result there are many aspects of the structure of English which are difficult to master for L2 learners. The high frequencies of errors in punctuation, word choice, repetition of information and phrases, irrelevant information and singular/plural usage indicate the inadequacies present in both the teaching and learning systems of English at lower levels.

When combined with LLS, the analysis of errors provides teachers with information on how strategies can be used to improve the academic language of their students. Instead of focusing on "quantity," teachers can focus on quality in students' written tasks.

5.3 Correlation between strategy use and academic language Key research question

What is the impact of language learning strategies of tertiary students in Fiji on their academic writing skills?

This study used Pearson's correlation and analysis of variance (ANOVA) to determine a relationship between language learning strategies and academic language proficiency, similar to studies by Green and Oxford (1995) and Bremner (2000). For one way ANOVA, there is a dependent and an independent variable [factor].In this study gender and ethnicity were used as independent variables and strategies and errors were dependent variables. This is because the aim of this study is to see if strategy use has an effect on proficiency. To determine significance, the standard p<0.05 is used.

Table 5.3.1 Correlations between overall strategy use and errors

Errors tot LLS total

Errors tot Pearson Correlation 1 .220*

Sig. (2-tailed) .040

N 88 88

LLS total Pearson Correlation .220* 1

Sig. (2-tailed) .040

N 88 88

*Correlation is significant at the 0.05 level (2-tailed).

The table above shows Pearson's correlation R=0.22 which is a very positive weak correlation (or linear association) between strategy use and errors. Because 0.22 is very close to 0 this means that the relationship is inclining towards negligibility. This study has shown that the LLS have very little association or connection with language proficiency of the subjects. The significance or p-value of each correlation coefficient is also displayed in the correlation table above. If the significance is <0.05 then the null hypothesis, which states that there is no significant correlation, is rejected. The data shows a p-value of 0.04 which is <0.05. This means that the relationship between strategy use and errors is statistically significant. As the use of strategies increases, the number of errors made in the written texts increases. However, the increase is marginal, almost negligible.

Table 5.3.2 Correlations between individual strategies and errors

Mem Cog Comp strat Meta Affec Soc Errors LLS

strat strat strat strat strat tot total

Mem Pearson 1 .582** .245* .585** .459** .456** .116 .722**

strat Correlation

Sig. (2-tailed) .000 .021 .000 .000 .000 .281 .000

N 88 88 88 88 88 88 88 88

Cog Pearson .582** 1 .296** .595** .347** .474** .016 .693**

strat Correlation

Sig. (2-tailed) .000 .005 .000 .001 .000 .885 .000

N 88 88 88 88 88 88 88 88

Comp Pearson .245* .296** 1 .154 .320** .242* .186 .503**

strat Correlation

Sig. (2-tailed) .021 .005 .151 .002 .023 .083 .000

N 88 88 88 88 88 88 88 88

Meta Pearson .585** .595** .154 1 .701** .577** .128 .821**

strat Correlation

Sig. (2-tailed) .000 .000 .151 .000 .000 .234 .000

N 88 88 88 88 88 88 88 88

Affec Pearson .459** .347** .320** .701** 1 .639** .268* .832**

strat Correlation

Sig. (2-tailed) .000 .001 .002 .000 .000 .011 .000

N 88 88 88 88 88 88 88 88

Soc Pearson .456** .474** .242* .577** .639** 1 .170 .805**

strat Correlation

Sig. (2-tailed) .000 .000 .023 .000 .000 .114 .000

N 88 88 88 88 88 88 88 88

Errors Pearson .116 .016 .186 .128 .268* .170 1 .220*

tot Correlation

Sig. (2-tailed) .281 .885 .083 .234 .011 .114 .040

N 88 88 88 88 88 88 88 88

LLS Pearson .722" .693" .503" .821" .832" .805" .220* 1

total Correlation

Sig. (2-tailed) .000 .000 .000 .000 .000 .000 .040

N 88 88 88 88 88 88 88 88

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Results in Table 5.3.2 above show that the six categories of language learning strategies were significantly correlated with one another. Where the values of the Pearson's correlation are above 0.5, it shows a moderate positive correlation. It can be seen that all the strategies have a positively moderate correlation with each other except compensation strategy which has a weak positive correlation with all other strategies. The absolute values of the correlation coefficient indicate the strength of the relationship, with larger absolute values indicating stronger relationships. The correlation coefficients on the main diagonal are always 1 because each variable has a perfect positive linear relationship with itself. All strategies have a p-value of <0.05 with each other. This means that the correlation is statistically significant and the two variables are linearly related. The exception occurs with compensation and metacognitive strategies which have a p-value >0.05, that is 0.151. In this case, the correlation is not statistically significant and has a non- linear relationship. The correlation between errors and LLS shows a very weak positive association. The weakest correlation is with cognitive strategies with a value of 0.016 and the greatest is with affective strategies of 0.268. The results show that strategy use does not have a strong impact on students' proficiency in academic language.

6.0 Conclusion and Recommendations

This study has examined the LLS used by USP students of Fiji and if their strategy use impacts on proficiency in their academic language. Using a 50-item SILL, it was found that language learning strategies were used at a medium level by most students. The most frequently used strategies were metacognitive and cognitive. The study has found that all strategies have a weak positive relationship with students' academic language. This means that the LLS of Fiji students have a negligible effect on their academic language proficiency. Errors in their academic writing are weakly correlated with strategy use. This is a unique set of findings as many studies, including Green and Oxford (1995), Bremner (2000) and Al-Hebaishi (2012) have found that cognitive and metacognitive strategies have a positive impact on academic language proficiency while affective strategies have had an inverse relationship; no single strategy has a strong, direct impact in this investigation. Therefore, this study now recommends further research into LLS of Fiji students with a larger sample size and from institutions at all levels: primary, secondary and tertiary.


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