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LINGUISTIC ACCURACY IN SPANISH ADOLESCENT LEARNERS' EFL WRITINGS.

1. Introduction

Linguistic accuracy or the "ability to be free from errors while using the language" (Wolfe-Quintero et al. 1998: 33) has been considered "an interesting, relevant construct for research in [...] second language acquisition, L2 writing assessment, and L2 writing pedagogy" (Polio 1997: 102). The study of linguistic accuracy may provide answers about learners' interlanguages under different conditions; it may yield useful information on learner language across testing situations; and it may shed light on the editing stage of L2 writing, so that certain guidelines for the implementation of successful pedagogical techniques may be developed. Additionally, EFL adolescent writers belong to "the most fraught and the most complex" of all contexts in which writing in L2/FL takes place (Leki et al. 2008: 17). Therefore, exploring their linguistic accuracy may be a challenging enterprise.

As a construct referring to the degree of conformity to certain norms, linguistic accuracy mainly involves the notion of error (Bui and Skehan 2018). The study of learner errors has long concerned Second Language Acquisition (SLA) scholars and language educators, since it has contributed to elucidate learners' knowledge of the target language and has been central to the issue of corrective feedback (Ur 1996; Ellis 2001, 2015; Ortega 2009; Harmer 2015). In this way, exploring the linguistic accuracy of EFL adolescent student writers may be revealing of their L2 proficiency in this language domain (cf. Bardovi-Harlig and Bofman 1989; Kroll 1990; Dagneaux et al. 1998; Darus and Khor 2009). This may provide information that could be useful for teachers in terms of L2 writing instruction and error treatment in the classroom, and for students regarding their own language learning process through writing.

Information on students' error frequencies and types might help teachers to make decisions on which perspective they should adopt in the classroom within the learning to write (LW) and writing to learn (WL) dimensions of L2 writing (Manchon 2011; Hirvela et al. 2016). Thus, if students' errors impede understanding of text content and are serious enough to make their texts unacceptable according to English language rules and conventions, teachers might decide to concentrate on teaching morphology, syntax and lexis. In this way, they may favour a writing to learn the language (WLL) approach instead of focusing on text type, purpose, audience, content, and organization as in LW perspectives like process-oriented and genre-based approaches (Manchon 2011). Additionally, teachers may employ more direct as opposed to indirect corrective feedback accompanied by metalinguistic explanation to enhance these low proficiency learners' understanding of their errors.

Knowledge of error number and type might also raise students' awareness of those areas in L2 writing in which they have language learning difficulties. In this way, they can make more adequate decisions on their own language learning process and perform specific actions that may help them progress. This information might also be valuable for teachers who may thus be better equipped to assist students by designing more tailor-made material and planning lessons more attuned to their needs.

In view of the above, this study aims to account for the linguistic accuracy of Secondary School Spanish students in personal opinion essays in EFL. More specifically, it addresses the following research questions: In which field do errors characterizing students' linguistic accuracy most frequently occur within morphology, lexis and syntax? (RQ1); and to what extent does the length of the writing task and time constraints influence the number of errors made? (RQ2 and RQ3 respectively). Time constraints were especially important, since "the relationship between time and essay quality has been chiefly overlooked" in FL writing (Kenworthy 2006: 2).

2. Theoretical background

In this section, an account of linguistic accuracy is offered as a measure of learner proficiency in L2/FL. The relevance of learner errors in linguistic accuracy and the SLA field is also highlighted, followed by a discussion of foreign language writing in relation to the context of this study.

2.1. Linguistic accuracy

Linguistic accuracy is a construct that has typically been related to complexity and fluency in second language research and pedagogy, giving place to what is known as CAF measures of language proficiency (Housen and Kuiken 2009; Bui and Skehan 2018). These three measures (complexity, accuracy and fluency) have been defined as a property of language performance both from the perspective of performance as a product and performance as a process (Pallotti 2009; Bui and Skehan 2018). They have also been argued to be independent from each other, since "learners might be strong on one or two, but not necessarily on all three", and what influences one of these areas might not affect the others (Bui and Skehan 2018: 1). Therefore, accuracy together with complexity and fluency has been invoked to assess learners' performance in oral and written tasks, their underlying proficiency, and their progress in L2/FL learning.

In general, linguistic accuracy refers to the extent a learner converges with or diverges from the conventions or rules of the target language. Therefore, it is necessarily related to errors (Wolfe-Quintero et al. 1998; Housen and Kuiken 2009; Bui and Skehan 2018). Regarding complexity, there are two types: lexical and syntactic. Lexical complexity, often called "lexical richness", "covers practically all lexical constructs and their associated measures--including but not limited to lexical diversity and lexical sophistication" (Jarvis 2013: 89). Lexical diversity has been measured through indices that concentrate on type-token ratios, whereas indices of lexical sophistication refer to the ratio of high frequency to low frequency words (Crossley et al. 2011) according to frequency lists, e.g. the New General Service List (NGSL) (Browne et al. 2013). Syntactic complexity is typically calculated through general complexity indices like ratio of subordination or length of a clause per T-unit, and specific complexity indices based on a range of grammatical structures (Bui and Skehan 2018). Finally, fluency concerns ease of expression "usually ensconced in some qualitative-temporal requirement performance" (Dormer 2016: 275). Commonly used indices of fluency in written communication include number of words per T-unit, number of correctly spelled words per sentences or letter-sequences, rate of composition, length of proposed text, output 'chunk' size and pausing (Dormer 2016).

These three notions (accuracy, complexity and fluency) have not been uncontroversial, since they have commonly been operationalized and measured as uni-dimensional, linear, and static units without any reported validity and reliability in the literature (Polio 1997, 2001; Wolfe-Quintero et al. 1998; Housen and Kuiken 2009; Larsen-Freeman 2009; Pallotti 2009; Skehan 2009; Polio and Shea 2014). Researchers in the field have therefore proposed to use more socially-oriented specific measures of performance along with general measures (Larsen-Freeman 2009), supplement the measures employed by measures of lexical use (Skehan 2009), and use appropriateness to communicative goals and situations as a way to interpret these constructs (Pallotti 2009).

Notwithstanding this criticism, linguistic accuracy has featured as "the oldest, most transparent, and most consistent construct of the triad" (Housen and Kuiken 2009: 3). It has commonly been measured through number of errors per one hundred words, presence of errors per T-unit, and ratio of error-free clauses to all clauses. With regards to L2 writing, it has been described as "a broad term that has to do with the absence of errors" (Polio 2001: 94). However, focusing on the presence of errors rather than their absence can contribute to depict this notion in a more detailed manner, since information is offered on error type (cf. Polio 1997). In this paper, we have assumed a relationship between students' errors in their writings, which are partly illustrative of their linguistic accuracy, and their knowledge of the target language, which is revealing of their interlanguage systems. In particular, we have taken errors to depict "L2 knowledge representation and [...] the level of analysis of internalized linguistic information" (Housen and Kuiken 2009: 2).

2.2. Learner errors

Presence of learner errors in terms of number and types has been used as a measure of linguistic accuracy in many studies of L2 writing along with holistic measures, error free units, and measures of error severity (Polio 1997; Wolfe-Quintero et al. 1998; Polio and Shea 2014). Learner errors have thus been essential in the conceptualization of linguistic accuracy in L2 writing research, and they have also been the focus of a vast amount of research in SLA for years. Errors started to be investigated through Error Analysis (EA), a specific type of linguistic analysis, which emerged as a reaction to Contrastive Analysis (CA), and offered a broader range of possible explanations for errors beyond L1 interference. Errors were mostly related to the version of the target language used or known by the learner, i.e. his/her interlanguage (Selinker 1972), and therefore they were considered by-products of his/her learning process (Corder 1967, 1981).

Although EA provided a starting point for the systematic study of learner language and SLA, it has generally been criticized on different grounds (cf. Taylor 1986; Lennon 1991; Dagneaux et al. 1998; James 1998; Ellis 2001); it is based on heterogeneous learner data; error categories are fuzzy; it cannot cater for a learner's avoidance of performance when s/he perceives an area of difficulty in L2/FL; it is restricted to what the learner cannot do; and it offers a static picture of L2/FL learning. These criticisms "need not spell the death of error analysis" (Taylor 1986: 145). EA can still be a valid enterprise and may be used with other techniques, (e.g. corpus- and computer-based analysis) to provide an answer to certain research questions or test specific hypotheses besides enquiring into learner interlanguage systems (cf. Taylor 1986; Bardovi-Harlig and Bofman 1989; Dagneaux et al. 1998; James 1998; Ellis and Barkhuizen 2005; Castillejos 2009; Mendikoetxea et al. 2010; Mediero and Robles 2012).

In this study, exploring errors as a measure of linguistic accuracy in the writings of Spanish adolescent EFL students has been deemed relevant, since it may shed light on areas of difficulty for these students in this language domain. As previously discussed, information on error frequency and type may provide both teachers and students with a clearer picture of the latter's writing proficiency in EFL, since "accuracy is certainly part of writing quality" (Polio and Shea 2014: 24). In this way, students can make more appropriate decisions about their language learning process, and teachers may shape their teaching practices more precisely to meet their needs.

2.3. Foreign language writing

In spite of the L2 bias pervading FL writing (Manchon 2009), the latter should be distinguished from L2 writing in that the manner in which writing is learnt and taught in FL contexts is subjected to a series of social and material conditions that differ from those in L2 contexts. Learners in FL writing contexts have less contact with the language under study outside the classroom than those in L2 settings, albeit still considerable in our increasingly globalised world. Therefore, they may be less used to the writing conventions of English. Learning and giving instruction on FL writing thus requires more careful planning and greater use of materials and resources that help FL learners not only to learn to write in the target language for academic or professional reasons, but to write to learn the language (Manchon 2009, 2011). In this regard, LW approaches with beginner FL writers may not be helpful, since they need to learn to verbalise their thoughts in the target language first before handling more complex textual functions. Researchers such as Roca de Larios et al. (2007) have demonstrated that Spanish Secondary School students' low proficiency in EFL affects the interaction of formulation with other composing processes (i.e. revising and planning) since such students spend a great deal of time on the former due to their limited linguistic repertoire and lack of more automated processes of information retrieval.

In this study, we have adopted a product-oriented perspective on writing, whereby we have considered the formal and grammatical features of learner texts and good writing a demonstration of "linguistic accuracy and clear exposition" on the part of the learner (Hyland 2011: 22). Product-centred perspectives have frequently been criticised for primarily paying attention to the surface features of texts, and viewing written products as one-shot static discourses rather than dynamic interactions between the writer, the reader(s), and the context of communication (Manchon 2001; Hyland 2011). However, we believe that a product-oriented approach may be appropriate for the purposes of this study, and it may also be useful for low proficiency learners beginning to write in EFL as long as they are also encouraged to attend, in a very simple manner, to the content of their communication, the recipient and the context (cf. Leki 1992; Ferris 2002). Certainly, process-oriented and genre perspectives to FL writing should be sought after in the long run within the communicative language classroom.

We shall further argue that a product-based perspective can be suitable in FL writing for an error-based analysis of linguistic accuracy to help students improve their writing skills (cf. e.g. Kobayashi and Rinnert 1992; Frantzen 1995; Dagneaux et al. 1998; Darus and Khor 2009). As advocated by Myles (2002: 2), a product-oriented approach to FL writing is relevant for error treatment, since students first need sufficient feedback on their errors before moving to a "process approach to instruction, with its emphasis on the writing process, meaning-making, invention and multiple drafts". Therefore, writing to learn with these students, especially writing to learn the language, may be a more adequate approach than learning to write.

3. Methods

3.1. Participants

The participants in this research are a group of Spanish students (N = 28), aged 17-18, 15 male and 13 female, in their last year of Secondary School with an average A1-A2 level of English. Writing and linguistic accuracy in this year is strongly emphasized due to the imminent College entrance examination they have to sit. Thus, last year students' productions were highly suitable for the purpose of this study.

The teacher was an L1 Spanish speaker with seven years of experience in EFL teaching at Secondary School and other educational levels.

3.2. Data and data collection procedures

The data for this research are 54 opinion essays produced by the above mentioned students (two essays each) on two different topics: risky driving and car accidents, and technology and its influence on everyday life. The essays respond to two writing tasks: a homework assignment, and a test performed in class (see Appendix). The students had previous knowledge on the essay topics, since they had been discussed in class. Therefore, they were already familiar with certain content, terms and expressions. However, these topics were simply designed as part of the syllabus rather than explicitly proposed to help students to generate ideas for their essays. Opinion essays were considered appropriate for this research because they enable the students to express themselves freely, and therefore may illustrate their linguistic accuracy more faithfully.

From these texts, a specific sample, namely, a sample that belongs to a limited number of learners, and an incidental sample, i.e. a sample produced by single learners (Ellis 2001; Ellis and Barkhuizen 2005), were selected to investigate the relationship between errors and length of writing task (RQ2), and errors and time constraints (RQ3). These samples were representative of long and short essays (specific sample) and homework and test essays (incidental sample) in the data. Taking into account essay length and time constraints, we established a distinction between long essays (LE) (130-150 words and beyond) and short essays (SE) (less than 130 words), and homework writing assignment (HWA) and test writing assignment (TWA). SE did not comply with the task instructions, and therefore offered a measure of essay length below task requirements.

Our specific sample included the most representative 6 long essays (n = 6) and short essays (n = 6) of our corpus, produced by 12 students, whilst our incidental sample included HWA (n = 6) and TWA (n = 6), produced by 6 students, one assignment type each.

Other data in this study include: fieldnotes based on classroom observation; other written samples of learner language that were obtained from different writing tasks students performed in their textbooks; and conferencing the classroom in ELF on their own errors. These interviews were brief and were conducted individually in the context of the classroom after delivering the texts to the students with feedback on their errors. Only those students whose errors and mistakes were not clear to us were interviewed. All these data were collected with the aim of obtaining information that could help further interpret our results and were analysed following content analysis (Krippendorff and Bock 2009).

3.3. Analysis

Error identification, explanation and classification were not devoid of problems. In order to recognise errors, we followed Lennon's (1991: 182) definition of error: "a linguistic form or combination of forms which, in the same context and under similar conditions of production, would, in all likelihood, not be produced by the speakers' native speaker counterparts". Grammaticality was then our main criterion for error identification.

Kroll's "syntactic reconstruction" criterion for error identification and classification was also applied, and so, we determined "what 'syntactic reconstruction' could most easily and economically render the sentence into acceptable English given the context" (1990: 143). We focused on content words for lexical errors, which amounted to a search for the wrong word. For instance, using the wrong verb in a sentence like "This technologies can't ocuppe all the time" was coded as a lexical error. However, if a student used the right base with the wrong ending (e.g. consumist for consumerist), we coded the error as both lexical and morphological.

Errors were distinguished from mistakes by contrasting learner language in our written samples with learner language from the other written samples already mentioned. These comparisons were made at the same time as the students' essays were analysed. If fluctuation on a linguistic form was observed in a student's texts, erroneous instances were considered mistakes. However, if a form was consistently wrong across the student's writings, instances of its erroneous usage were considered errors. The students' interviews on their own performance also added to the distinction between errors and mistakes in the data, since the students were not able to self-correct their errors, but did correct their mistakes.

For our error-based analysis, we used Corder's (1981) framework. Therefore, our analysis included: error identification; error description (morphological, lexical, and syntactic); and error explanation, whereby certain errors were considered the result of L1 transfer and could sometimes be phrasing errors (transfer errors), whilst others were not related to L1 transfer (non-transfer errors) (cf. Ellis 2000; Mediero and Robles 2012). Transfer errors were further classified as follows (Ellis 2000):

a. False friends and false cognates: the student uses an item incorrectly because it shares features with an item in their L1, for instance, the incorrect use of "embarrassment" for "pregnancy" by an L1 Spanish speaker because of analogy with the term embarazo: "She was very happy with her embarrassment" ("She was very happy with her pregnancy").

b. Transfer of linguistic feature: the student uses an L1 feature (lexical, grammatical, pragmatic), rather than a target language feature: In Spanish, present simple is used to convey an immediate future action, whereas in English the auxiliary "will" is used: "I take an aspirin" ("I'll take an aspirin").

Following Ellis (2000), non-transfer errors qualified as errors of:

a. Over-generalization, in which the learner creates a deviant structure in the L2/FL based on their experience in other target language structures, e.g.: "The cat eated the fish" ("The cat ate the fish").

b. Ignorance of rule restrictions, which may occur at the level of syntax or lexis, and can be caused by analogy or by rote learning of rules, for instance: "The children are full of energy" ("Children are full of energy") or "He runs fastly" ("He runs fast").

c. Incomplete application of rules, which illustrates the degree of development in the rules required to produce acceptable utterances: "I washed me" ("I washed myself").

d. False concepts hypothesized, which indicates faulty comprehension of certain distinctions in the target language: "I have just done a cake" ("I have just made a cake").

Lastly, to ensure inter-reliability, another trained coder analysed most of the data following these error categories. This coder was a doctoral student doing research on errors and linguistic transfer. Our analyses were then compared, and those cases classified under more than one category were discussed. If appropriate, the error was coded more than once. Ambiguous cases were discarded. 92% agreement was reached.

4. Results and discussion

This section reports and discusses the findings of this study in terms of the most frequent non-transfer and transfer errors made by students within morphology, lexis and syntax. A discussion of the influence of essay length and time constraints in students' linguistic accuracy follows.

4.1. Linguistic accuracy and learner errors in EFL writing

Regarding our RQ1, the mean score of errors per word in the data was M = 0.103, which indicates that students made 10 errors per 100 words. The total number of errors found was N = 774, and student essays amounted to 7,467 words, with a minimum and maximum word length of 83 and 205 words respectively. Linguistic accuracy in student texts was mainly characterised by non-transfer errors (59.81%, n = 463) versus transfer errors (40.19%, n = 311). Non-transfer errors mainly affected morphology (38.88%, n = 301). However, transfer errors outnumbered non-transfer errors in syntax (19.12%, n = 148) and lexis (12.14%, n = 94) (Table 1).

Morphology therefore emerged as the linguistic area most affected by error in the data. These results support previous research that depicts non-transfer errors as more frequent in EFL compositions than in translation tasks (Ellis 2001); and depicts morphology as the weakest language system (Bardovi-Harlig and Bofman 1989).

Nevertheless, one needs to be cautious about these findings, since the proportion of non-transfer and transfer errors varies considerably in light of a student's age, language level, tasks type, and the kind of language samples collected (Ellis 2001). Thus, in their analysis of High School L1 Spanish learners' writings in EFL, Ibanez and Hernandez (2011) found that transfer errors were predominant in their data, and grammar-syntax was the linguistic area most affected by error. A plausible explanation for these different findings from ours might be that they only considered tasks consisting of exams, and their grammar-syntax category includes units that qualify under morphology in our study (i.e. verbal tense, gerunds and infinitives, and pronouns). By contrast, Mediero and Robles (2012) obtained similar results to ours, since college EFL learners with different proficiency levels made more morphological errors than syntactic and lexical errors at lower proficiency levels.

Within morphology, problematic elements concerned verb-related units (38.37%, n = 142), prepositions (21.89%, n = 81), and articles and determiners (14.86%, n = 5; 11.08%, n = 41) (Table 2).

Of the verb-related units, verbal tense and voice (40.14%, n = 57), gerunds and infinitives (24.64%, n = 35), subject pronouns (the -s in the third person singular) (10.56%, n = 15), and negative forms (9.15%, n = 13) were the most difficult for students (Table 3).

As can be observed, non-transfer errors were more abundant than transfer errors in all the aforementioned categories except for verbal tense and voice, in which transfer errors were more frequent. This indicates that students erroneously used L1 verbal tense and voice to build their sentences in English more often than making other kinds of errors in the verbal tense and voice category. This is in keeping with the high predominance of transfer versus non-transfer errors in syntax in this study.

The results in Table 3 also back the findings of investigations like Darus and Khor's (2009), who observed more difficulties with tenses and prepositions than with other elements in EFL writings of L1 Chinese schoolers. Kenworthy (2006) also notes that subject-verb agreement, word choice and verb use are troublesome for L1 Cantonese intermediate-level college EFL writers. Ferris (2002) argues that the most problematic issues for ESL learners regarding morphology are verbal tense and aspect, and James (1998) establishes that verb-related units are the most frequent and persistent morphological problems for EFL writers.

However, studies like Dagneaux et al.'s (1998) and Bardovi-Harlig and Bofman's (1989) evince a higher proportion of errors in articles than in verbal elements and pronouns in written essays from L1 French EFL learners, and EFL learners across proficiency levels and L1 backgrounds respectively. It should be noted that learners in these studies were advanced EFL college students, and that the errors analysed come from a variety of writing tasks including translation assignments.

Non-transfer errors were mainly errors of overgeneralization (53.56%, n = 248) and ignorance of rule restrictions (27.86%, n = 129) versus errors based on false concepts hypothesized (11.66%, n = 54) and incomplete application of rules (6.91%, n = 32) (see Figure 1).

Example (1) illustrates a morphological overgeneralization type of error. This extract is part of a student's essay on how risky driving and car accidents can be reduced. In offering a solution to these problems, the learner refers to the idea of "having safe roads", but makes an intralingual morphological error of overgeneralization when using this expression, since she extends the rule "use the indefinite article with singular countable nouns" to plural countable nouns.
(1) HWA.LE. Topic: risky driving and car accidents.
... everybody should help to avoid car accidents in this way to have a
safe roads.
Syntactic reconstruction: to have safe roads.
Possible source: interlingual error, overgeneralization.


Example (2) shows a morphological error due to ignorance of rule restrictions, in which the learner does not know that the adjective "responsible" requires the preposition "for", and uses the preposition "to" because of analogy with similar phrases in English (e.g. "we are happy to announce"). This was observed in another short written sample by him.
(2) HWA.LE. Topic: risky driving and car accidents.
... So we are responsible to try to reduce this large number.
Syntactic reconstruction: we are responsible for trying to reduce this
number.
Error type: non-transfer, ignorance of rule restrictions.


The following extract exemplifies an error based on false concepts hypothesized, since the learner shows faulty comprehension of the distinction between the indefinite article "a" and the numeral "one". Other errors in the example are transfer of linguistic feature errors affecting verbal tenses.
(3) TWA.SE. Topic: technology and everyday life.
I have one phone since I'm 11 years old.
Syntactic reconstruction: I have had a phone since I was 11 years old.
Error type: non-transfer, false concepts hypothesized.


Finally, Example 4 illustrates a non-transfer error due to incomplete application of rules regarding verbal tense sequence in English, as the learner uses "won't let" instead of "lets". Another error observed in the example is a non-transfer ignorance of rule restrictions error in the use of the double negative "isn't possible" and "won't let".
(4) HWA.LE. Topic: risky driving and car accidents.
But we all know that this isn't possible, unless the car won't let you
do it.
Syntactic reconstruction: But we all know that this isn't possible,
unless the car lets you do it.
Error type: non-transfer, incomplete application of rules.


Although linguistic accuracy was primarily shaped by non-transfer errors, transfer errors outnumbered these in syntax and lexis, as previously mentioned. Transfer errors in syntax (19.12%, n = 148) generally consisted of transfer of linguistic feature together with false friends and false cognates (cf. Chan 2004; Ibanez and Hernandez 2011). Example (5) illustrates a transfer of linguistic feature error in the data. In this extract, the learner describes the type of accident that people find out about by saying "... car accidents where one, two or three people are injured". S/he used the subordinate clause conjunction "where" instead of the relative pronoun "which" with the preposition "in", which is a more appropriate option. This syntactic error can be explained by transfer of the structure donde +Subject +Verb from the learner's L1.
(5) HWA.LE. Topic: risky driving and car accidents.
We always notice about car accidents where one, two or three people are
injured Syntactic reconstruction: We always find out about car
accidents in which one, two or three people are injured.
Error type: transfer, transfer of linguistic feature.


Transfer errors in syntax mainly referred to word order (72.97%, n = 108), the use of subordinate clauses (20.27%, n = 30), clause connectors (4.05%, n = 6), and subject omission (2.7%, n = 4) (Figure 2).

These findings are in tune with Ibanez and Hernandez's (2011) results, which evince that wrong word order was one of the most common transfer errors in Spanish Secondary School EFL students. Bardovi-Harlig and Bofman (1989) also found that a typical syntactic error of EFL learners with different L1s and proficiency levels was word order. By contrast, divergent results were obtained in other studies. Chan (2004) shows that transfer errors in the syntax of L1 Chinese high school and college EFL learners with low and upper intermediate proficiency levels mostly referred to confusion with verb transitivity, inability to use the "there be" structure, failure to employ relative clauses, lack of control of the copula, and incorrect placement of adverbs. L1 Chinese children, on the other hand, were observed to make mostly transfer errors regarding subject-verb agreement in their EFL essays (Darus and Khor 2009).

Transfer errors in lexis (12.14%, n = 94) mostly consisted of "overextension of analogy" errors based on the similarity between the English term and the word in the learner's L1. In example (6) the learner makes an "overextension of analogy" error by using the term "ocuppe", which is similar to the Spanish word "ocupar", as opposed to the right term "take" in expressing the idea that technological devices should not take all people's time.
(6) TWA.LE. Topic: technology and everyday life.
This technologies can't ocuppe all the time
Syntactic reconstruction: These technologies can't take all people's
time.
Error type: transfer, overextension of analogy.


The higher number of transfer errors over non-transfer errors in lexis concurs to some extent with research that signals the prevalence of the former in the phonological and lexical planes (Ellis 2001). For example, Castillejos (2009) found a significant number of lexical errors within the transfer category in the writings of advanced EFL non-peninsular Spanish students.

4.2. Linguistic accuracy and essay length

In order to answer RQ2, a specific sample of 12 essays was analysed: 6 exceeding the word limit imposed by the teacher (130-150 words), and 6 that did not reach this limit (less than 130 words). These essays were representative of long and short essays in the data.

There is the idea that lower proficiency learners writing shorter sentences, and by extension, shorter texts, might actually make fewer errors than more advanced students (cf. Polio and Shea 2014). Our results disprove this idea, since error occurrence in our data was higher in SE (46.7%, n = 362) than LE (53.29%, n = 412) (see Figures 3 and 4).

As illustrated in these figures, short essays of less than 100 words show the highest number of errors in comparison with more than 100 word texts within the short and long categories: e.g. an 89 word essay with 26 errors (29%) or a 98 word writing with 17 errors (17.34%) versus a 127 word essay with 13 errors (10.23%), and a 205 word writing with 8 errors (3.9%).

In spite of these differences, essay length was not observed to have an influence on students' linguistic accuracy in light of other written samples they produced. Students who produced short and highly error-laden essays were also observed to produce other longer writings with a great number of errors. However, more advanced students in the group produced longer texts in general with the presence of fewer errors. Therefore, we may conclude that the learner's proficiency level affected essay length and linguistic accuracy in our data (cf. Frantzen 1995; Ferris 2002), and as a consequence, the length of the writing task did not affect students' linguistic accuracy in terms of the number of errors.

4.3. Linguistic accuracy and time constraints

For our RQ3, an incidental sample of 12 essays--6 HWA and 6 TWA--featuring as the most representative of each task type in the data was used to check whether home essays were more accurate than test essays. Although performing the writing task as homework undoubtedly offers more time to write than a classroom writing test does, this task modality also affects other conditions for production such as consulting references, talking to classmates, experts, etc. Therefore, the results for RQ3, which are illustrated in Table 4 below, should be interpreted with caution.

These results indicate that students made a similar number of errors in both task types (49.1%, n = 82 in HWA and 50.89%, n = 85 in TWA). These findings lend support to the general idea that linguistic accuracy in FL writing is likely to increase when the learner is afforded more time to monitor their production in the target language (Ferris 2002). Therefore, higher overall linguistic accuracy can be expected from home than from in-class essays (cf. Kroll 1990; Kenworthy 2006).

However, the results of empirical research regarding time as a task variable are inconclusive when linguistic accuracy is narrowly conceptualised. Dagneaux and colleagues (1998) encountered a greater proportion of errors in untimed versus timed written activities of French EFL learners. Kenworthy (2006), however, found more grammatical errors in manually written test essays than in computer-based home writings. Lastly, Kroll (1990) found no significant differences between errors in timed in-class assignments, and untimed home assignments for ESL learners. Therefore, considering these research findings and the fact that home assignments affect other conditions for writing besides time, we should be cautious about the results obtained for RQ3 in this study.

5. Conclusions

In this research, we have attempted to account for the linguistic accuracy of a group of Secondary School Spanish students by exploring their errors in personal opinion essays produced in EFL. The results of this study evince a predominance of non-transfer errors of a morphological kind in these texts. Transfer errors were less abundant and were mainly syntactic and lexical errors. Therefore, students in this study showed a weak morphological (grammar) system in the target language, but relatively stronger syntax and lexis.

Along the lines of other investigations on FL writers across L1 backgrounds and proficiency levels, difficulties in verbal tense and aspect were salient in these students coupled with the use of prepositions, articles and other determiners. Syntactic errors were mainly related to word order, subordinate clauses, clause connectors, and subject omission, whilst lexical errors were fewer and primarily consisted of "overextension of analogy" errors based on the similarity between the word in English and the term in the learner's L1. Essay length and time constraints did not appear to influence these students' linguistic accuracy. Learners with limited accuracy were found to produce consistently a large number of errors across shorter and longer writings, whilst the reverse was the case with learners with higher accuracy levels. No difference in the number of errors was found in home (untimed) versus in-class (timed) writings either. However, as previously mentioned, these findings concerning time constraints need to be regarded with caution.

In sum, the results of this study underscore the need to foster these students' development of EFL morphology and to pay attention to syntax and lexis. This involves a WLL approach that focuses on developing their micro writing skills through focus on form tasks versus a LW perspective that emphasizes macro writing skills, meaning, text type, purpose, audience, and organization. WLL does not exclude a LW approach, which should be expected in the long run in the communicative FL classroom. Additionally, teachers should provide these students with sufficiently clear feedback on their errors, favouring in this way direct corrective feedback with metalinguistic explanation, at least with low proficiency learners.

Finally, it should be pointed out that the findings of this research are limited due to our focus on a specific genre and the small size of our sample. A larger sample would thus be necessary to draw more solid conclusions, and statistical tests should be conducted to further clarify the relationship between linguistic accuracy and task variables, and check whether any ensuing differences are statistically significant. Our findings also underscore the relevance of considering other means of measuring of linguistic accuracy besides the presence of errors (e.g. the ratio of error-free clauses to all clauses) as well as some means of measuring of syntactic and lexical complexity (previously indicated) to evaluate, positively and negatively, students' writings more efficiently.

Acknowledgements

I would like to express my gratitude to Ronan Miller and the two anonymous reviewers of this article for their enlightening comments. I am also greatly indebted to Teodora Selistean for her invaluable help in the development of this study.

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Appendix

Writing task (homework)

Due date: 8th March 2010

Instructions: Can we avoid risky driving and car accidents? Write an opinion essay on this topic (130-150 words).

Try to use your previous knowledge on the topic to write your essay (content, words and expressions you know). Do not use external resources (dictionaries, your student book or workbook, etc.). Do not use your computer to write your essay. Your text should be handwritten.

Writing task (test)

Due date: 12th April 2010

Instructions: Could you imagine your life without the latest technology (mobile phones, i-pods, computers etc.)? Write an opinion essay on this topic (130-150 words).

Try to use your previous knowledge on the topic to write your essay (content, words and expressions you know). Do not use external resources (dictionaries, your student book or workbook, etc.). Do not use your computer to write your essay. Your text should be handwritten.

MARIA DOLORES GARCIA-PASTOR

Universidad de Valencia

Maria.D.Garcia@uv.es

Received: 2 May 2018

Accepted: 4 October 2018
Table 1. Distribution of non-transfer and transfer errors in the
students' writing

    Non-transfer            %      Transfer  %      Total  %

Morphology  301           38.88    69       8.91   370     47.8
Syntax       95           12.27   148      19.12   243     31.4
Lexis        67            8.65    94      12.14   161     20.8
Total       463           59.81   311      40.19   774    100

Table 2. Distribution of non-transfer and transfer errors in linguistic
elements of a morphological nature

Morphological  Non-transfer    %      Transfer    %      Total  %
element

Verb (total)    87           23.51    55      14.86  142    38.37
Preposition     42           11.35    39      10.54   81    21.89
Article         45           12.16    10       2.70   55    14.86
Determiner      41           11.08     0       0.00   41    11.08
Noun            17            4.59     8       2.16   25     6.75
Adjective       12            3.24     0       0.00   12     3.24
Pronoun          9            2.43     0       0.00    9     2.43
(referencing)
Adverb           5            1.35     0       0.00    5     1.35
Total          258           69.72  11 2      30.27  370   100

Table 3. Distribution of non-transfer and transfer errors in
verb-related elements

Verb-related           Non-transfer   %      Transfer    %
elements

Tenses/voice           18            12.67  39         27.46
Gerund and infinitive  22            15.49  13          9.15
3rd person -s          15            10.56   0          0.00
Negative forms         13             9.15   0          0.00
Modal verbs            10             7.04   2          1.40
Verb-subject            6             4.22   1          4.92
agreement
There is/there are      3             2.11   0          0.00
                       87            61.24  55         38.76

Verb-related                  Total     %
elements

Tenses/voice                  57      40.14
Gerund and infinitive         35      24.64
3rd person -s                 15      10.56
Negative forms                13       9.15
Modal verbs                   12       8.45
Verb-subject                   7       4.92
agreement
There is/there are             3       2.11
                             142     100

Table 4. Distribution of errors in HWA and TWA
              Errors
Student  HWA   %      TWA    %

A        16   9.58    17   54.07
B        13   7.78    11    6.58
C        12   7.1 8   15    8.98
D        11   6.58    18   10.77
E        16   9.58    9     5.38
F        14   8.38    15    8.98
Total    82   49.1    85   50.89
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