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The Role of Automatised and Non-Automatised Explicit Knowledge in General L2 Proficiency

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Title: The Role of Automatised and Non-Automatised Explicit Knowledge in General L2 Proficiency
Language: English
Authors: Miki Satori (ORCID 0000-0002-5255-4430)
Source: Language Learning Journal. 2024 52(4):454-467.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 14
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Foreign Countries, Language Tests, English (Second Language), Second Language Learning, Grammar, Task Analysis, Decision Making, Teaching Methods, Second Language Instruction, Metalinguistics, Sentence Structure, Language Processing, Language Proficiency, Undergraduate Students, Timed Tests, Test Format, Psycholinguistics
Geographic Terms: Japan
Assessment and Survey Identifiers: Test of English for International Communication, Group Embedded Figures Test
DOI: 10.1080/09571736.2023.2207585
ISSN: 0957-1736
1753-2167
Abstract: This study examines the knowledge representation of Japanese university students assessed using grammaticality judgement tests (GJTs) and a metalinguistic knowledge test (MKT). The study also investigates the role of automatised and non-automatised explicit knowledge in general L2 language proficiency. Participants were 87 late learners of English as a foreign language (EFL) who completed the timed and untimed GJTs and MKT, modified Group Embedded Figures Test (GEFT), and the Test of English for International Communication (TOEIC). The principal component factor analysis results indicated that ungrammatical sentences in the GJTs loaded on non-automatised explicit knowledge, whereas grammatical sentences loaded on automatised explicit knowledge. The score for ungrammatical sections on the timed GJT was the most significant predictor of all components of the TOEIC. The results also indicated that the time pressure applied in the timed GJT could not sufficiently limit participants' access to explicit knowledge when they processed grammatical sentences. The findings suggest that non-automatised explicit knowledge may play a more significant role than automatised explicit knowledge in L2 proficiency in the case of Japanese EFL learners.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1428066
Database: ERIC
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  Value: <anid>AN0177840458;sdq01aug.24;2024Jun17.03:45;v2.2.500</anid> <title id="AN0177840458-1">The role of automatised and non-automatised explicit knowledge in general L2 proficiency </title> <p>This study examines the knowledge representation of Japanese university students assessed using grammaticality judgement tests (GJTs) and a metalinguistic knowledge test (MKT). The study also investigates the role of automatised and non-automatised explicit knowledge in general L2 language proficiency. Participants were 87 late learners of English as a foreign language (EFL) who completed the timed and untimed GJTs and MKT, modified Group Embedded Figures Test (GEFT), and the Test of English for International Communication (TOEIC). The principal component factor analysis results indicated that ungrammatical sentences in the GJTs loaded on non-automatised explicit knowledge, whereas grammatical sentences loaded on automatised explicit knowledge. The score for ungrammatical sections on the timed GJT was the most significant predictor of all components of the TOEIC. The results also indicated that the time pressure applied in the timed GJT could not sufficiently limit participants' access to explicit knowledge when they processed grammatical sentences. The findings suggest that non-automatised explicit knowledge may play a more significant role than automatised explicit knowledge in L2 proficiency in the case of Japanese EFL learners.</p> <p>Keywords: Automatised and non-automatised explicit knowledge; L2 listening; L2 Reading</p> <hd id="AN0177840458-2">Introduction</hd> <p>One of the key goals of language learning and instruction is to promote the ability to use the target language spontaneously (Çeçen and Erçetin [<reflink idref="bib2" id="ref1">2</reflink>]; Suzuki and DeKeyser [<reflink idref="bib30" id="ref2">30</reflink>], [<reflink idref="bib31" id="ref3">31</reflink>]; Zhang [<reflink idref="bib35" id="ref4">35</reflink>]); therefore, it is important to conceptualise the constructs underlying use of both implicit and explicit language knowledge and investigate their relationship with other cognitive constructs and language proficiency. Explicit knowledge is defined as conscious linguistic knowledge, whereas implicit knowledge is defined as unconscious linguistic knowledge that is difficult to measure directly (Ellis and Roever [<reflink idref="bib11" id="ref5">11</reflink>]). Many EFL teachers and researchers believe that explicit knowledge contributes to their learners' L2 proficiency. Some empirical studies (e.g. Elder and Ellis [<reflink idref="bib8" id="ref6">8</reflink>]; Erçetin and Alptekin [<reflink idref="bib13" id="ref7">13</reflink>]) have shown that L2 language proficiency, as measured by standard language tests such as TOEFL and IELTS, is related to both implicit and explicit knowledge, or indeed, to a combination of the two. However, investigating these different knowledge sources has been methodologically challenging for second language acquisition (SLA) researchers owing to controversy regarding the validity of the available measures (Çeçen and Erçetin [<reflink idref="bib2" id="ref8">2</reflink>]; Ellis and Roever [<reflink idref="bib11" id="ref9">11</reflink>]; Suzuki and DeKeyser [<reflink idref="bib30" id="ref10">30</reflink>], [<reflink idref="bib31" id="ref11">31</reflink>]; Zhang [<reflink idref="bib34" id="ref12">34</reflink>]). For example, Suzuki ([<reflink idref="bib29" id="ref13">29</reflink>]) showed that time-pressured form-focused tasks like timed grammaticality judgement tests (GJT) were hypothesised to measure automatised explicit knowledge, not implicit knowledge. Suzuki and DeKeyser ([<reflink idref="bib30" id="ref14">30</reflink>]; [<reflink idref="bib31" id="ref15">31</reflink>]) suggest that explicit knowledge can be automatised through extensive practice, and automatised explicit knowledge, functionally equivalent to implicit knowledge, still needs to be distinct from it.</p> <p>Many previous studies have investigated the relationship between implicit/explicit knowledge and cognitive variables such as cognitive aptitude (e.g. Suzuki and DeKeyser [<reflink idref="bib31" id="ref16">31</reflink>]) and working memory (e.g. Çeçen and Erçetin [<reflink idref="bib2" id="ref17">2</reflink>]). However, few empirical studies have investigated the relationship between cognitive styles and learners' linguistic knowledge (Ziętek and Roehr [<reflink idref="bib36" id="ref18">36</reflink>]). The analytic-holistic nature of cognitive styles is somewhat related to explicit and implicit language aptitude (Dörnyei and Skehan [<reflink idref="bib7" id="ref19">7</reflink>]; Granena [<reflink idref="bib14" id="ref20">14</reflink>]), attentional functioning (Guisande et al. [<reflink idref="bib15" id="ref21">15</reflink>]), and functioning of the visuospatial and executive component of working memory (Miyake, Witzki, and Emerson [<reflink idref="bib24" id="ref22">24</reflink>]). Field dependence/independence is the best-known dimension of cognitive style (Dörnyei and Ryan [<reflink idref="bib6" id="ref23">6</reflink>]; Ziętek and Roehr [<reflink idref="bib36" id="ref24">36</reflink>]) and has been found to relate to L2 learning and teaching (Meguro [<reflink idref="bib23" id="ref25">23</reflink>]; Rassaei [<reflink idref="bib27" id="ref26">27</reflink>]). However, its validity has repeatedly been called into question due to the methodological problems (Ziętek and Roehr [<reflink idref="bib36" id="ref27">36</reflink>]). Some empirical studies have shown that FL learners have an advantage in L2 listening comprehension (Khodadady and Zeynali [<reflink idref="bib20" id="ref28">20</reflink>]; Satori [<reflink idref="bib28" id="ref29">28</reflink>]).</p> <p>The present study explores whether grammatical and ungrammatical sentences in timed and untimed GJTs represent distinct nature of explicit knowledge by including field independence cognitive style in the factor analysis model.</p> <hd id="AN0177840458-3">Literature review</hd> <p></p> <hd id="AN0177840458-4">Implicit knowledge, automatised explicit knowledge, and explicit knowledge</hd> <p>The constructs of implicit and explicit knowledge and their role in language learning and use have generated considerable interest since the 1980s (DeKeyser [<reflink idref="bib5" id="ref30">5</reflink>]; Suzuki and DeKeyser [<reflink idref="bib31" id="ref31">31</reflink>]; Vafaee, Suzuki, and Kachisnke [<reflink idref="bib32" id="ref32">32</reflink>]). The interest was triggered particularly by Krashen's ([<reflink idref="bib22" id="ref33">22</reflink>]) non-interface position. While implicit knowledge refers to procedural, unanalysed, and intuitive linguistic knowledge retrieved without awareness, explicit knowledge refers to declarative, analysed, and conscious linguistic knowledge accessible through controlled processing (Çeçen and Erçetin [<reflink idref="bib2" id="ref34">2</reflink>]).</p> <p>SLA researchers assume three positions regarding the relationship between implicit and explicit knowledge: non-interface, strong interface, and weak interface positions (Vafaee, Suzuki, and Kachisnke [<reflink idref="bib32" id="ref35">32</reflink>]). The non-interface position rejects any interaction between implicit and explicit knowledge and posits that different processes access these two types of knowledge representation in different positions of the brain (Paradis [<reflink idref="bib26" id="ref36">26</reflink>]); it maintains that implicit knowledge develops subconsciously by processing input for meaning (Krashen [<reflink idref="bib22" id="ref37">22</reflink>]). Explicit knowledge develops from the intentional learning of language form or rules through instruction, which can never be transformed into implicit knowledge (Bowles [<reflink idref="bib1" id="ref38">1</reflink>]). By contrast, the strong interface position contends that explicit knowledge can be automatised through extensive practice and thus become implicit. This position is often associated with skill acquisition theory (DeKeyser [<reflink idref="bib4" id="ref39">4</reflink>]), which distinguishes between declarative and procedural knowledge. In this theory, learners first learn a specific feature of language as explicit declarative knowledge. Then declarative knowledge turns into procedural knowledge through repeated rehearsal and involvement in relevant tasks. Finally, procedural knowledge can be converted into automatised knowledge through further practice in meaningful communication. Suzuki and DeKeyser ([<reflink idref="bib31" id="ref40">31</reflink>]), however, suggested that implicit knowledge and automatised explicit knowledge are in fact distinct constructs. Suzuki ([<reflink idref="bib29" id="ref41">29</reflink>]) claims that the theoretical distinction between implicit and automatised explicit knowledge can be attributed to the difference in the degree of automatisation in L2 knowledge in different L2 learner populations' learning processes. For example, some L2 learners may first engage in explicit learning and practices until their linguistic knowledge is fully automatised, and their automatised explicit knowledge may directly impact the acquisition of implicit knowledge (Suzuki and DeKeyser [<reflink idref="bib31" id="ref42">31</reflink>]). In contrast, other L2 learner populations may be involved in implicit learning differently from the first group when their explicit knowledge is not automatised at all (Suzuki [<reflink idref="bib29" id="ref43">29</reflink>]). Khezrlou ([<reflink idref="bib19" id="ref44">19</reflink>]) also argues that developing implicit knowledge is desirable, but not all explicit knowledge can be automatised. Khezrlou ([<reflink idref="bib19" id="ref45">19</reflink>]) investigated the effectiveness of task-supported explicit grammar instruction on learning outcomes of explicit knowledge and automatised explicit knowledge. The results showed learning gains in explicit knowledge but no significant development of automatised explicit knowledge.</p> <p>Meanwhile, the weak interface position (Ellis [<reflink idref="bib9" id="ref46">9</reflink>]) claims that explicit knowledge of form-meaning association indirectly facilitates the generation of implicit knowledge. Ellis ([<reflink idref="bib9" id="ref47">9</reflink>]) suggested that conscious explicit registration (noticing) of relevant linguistic forms can promote implicit learning during meaning-focused processing.</p> <hd id="AN0177840458-5">The construct validity of grammaticality judgement tests</hd> <p>Implicit and explicit knowledge of a second language have frequently been measured by GJT. Many studies have examined construct validity as a measure of implicit and explicit knowledge (Bowles [<reflink idref="bib1" id="ref48">1</reflink>]; Ellis [<reflink idref="bib9" id="ref49">9</reflink>]; Gutiérrez [<reflink idref="bib16" id="ref50">16</reflink>]; Kim and Nam [<reflink idref="bib21" id="ref51">21</reflink>]; Suzuki [<reflink idref="bib29" id="ref52">29</reflink>]; Suzuki and DeKeyser [<reflink idref="bib30" id="ref53">30</reflink>], [<reflink idref="bib31" id="ref54">31</reflink>]; Vafaee, Suzuki, and Kachisnke [<reflink idref="bib32" id="ref55">32</reflink>]; Zhang [<reflink idref="bib34" id="ref56">34</reflink>]). For example, Ellis ([<reflink idref="bib9" id="ref57">9</reflink>]) created a battery of tests to measure implicit and explicit knowledge based on seven criteria – degree of awareness, time available, focus on attention, systematicity, certainty, metalanguage, and learnability – and conducted a series of factor analyses to distinguish between the grammatical and ungrammatical sentences in timed and untimed GJTs. In Ellis's ([<reflink idref="bib9" id="ref58">9</reflink>]) model, ungrammatical items in the untimed grammaticality judgement test (UGJT) were considered the best measures for tapping explicit knowledge, while the timed grammaticality judgement test (TGJT) was more likely to tap implicit knowledge (Timed/Untimed Model).</p> <p>Gutiérrez ([<reflink idref="bib16" id="ref59">16</reflink>]) also focused on whether L2 learners draw on different types of knowledge to judge grammatical and ungrammatical sentences in timed and untimed GJTs. The participants were 49 university students formally learning L2 Spanish at lower intermediate level (CEFR A2 to B1) at a Canadian university. The main focus of the study was the results from two GJTs, but a metalinguistic knowledge test (MKT) was also included in the study as a measure of explicit knowledge. The MKT included 16 ungrammatical sentences covering the same grammatical structures as the GJTs and required the participants to provide a written explanation in English of the rule violation in each sentence. Based on principal component factor analysis, it was found that the results for identifying ungrammatical sentences in both timed and untimed GJTs loaded on an 'explicit factor' together with results from MKT while the results for grammatical sentences in both timed and untimed GJTs loaded on an implicit factor. A second analysis undertaken by Gutierrez tested the model favoured by Ellis ([<reflink idref="bib9" id="ref60">9</reflink>]) and Bowles ([<reflink idref="bib1" id="ref61">1</reflink>]) where the key distinction was between accessing knowledge on a timed test (implicit) and an untimed test (explicit) but Gutierrez' data failed to confirm this model. Gutierrez' results thus suggest that L2 learners may process differently grammatical and ungrammatical sentences in the two types of GJTs, especially learners involved informal language learning with a focus on explicit grammar instruction. Zhang ([<reflink idref="bib34" id="ref62">34</reflink>]) also conducted a study to validate measures of implicit and explicit knowledge (Ellis [<reflink idref="bib9" id="ref63">9</reflink>], [<reflink idref="bib10" id="ref64">10</reflink>]) in a Chinese EFL context and found that his two CFAs confirmed R. Ellis's model, not Gutiérrez's.</p> <p>Vafaee, Suzuki, and Kachisnke ([<reflink idref="bib32" id="ref65">32</reflink>]) later proposed a further two-factor model, including two more online-processing measures – a word-monitoring task (WMT) and a self-paced reading task (SPRT) – as more fine-grained measures to capture implicit knowledge. In this case, responses to ungrammatical sentences in both the timed and untimed GJTs and results of the MKT loaded onto what seemed to be an explicit factor, while WMT and SPRT loaded onto an implicit knowledge factor. Results for grammatical sentences in timed and untimed GJTs correlated only with each other and not with any other measure. Vafaee, Suzuki, and Kachisnke's ([<reflink idref="bib32" id="ref66">32</reflink>]) results suggest that ungrammatical sentences of GJTs would be a more valid measure of L2 explicit knowledge and that GJTs are too blunt an instrument to measure implicit knowledge effectively. Vafaee, Suzuki, and Kachisnke ([<reflink idref="bib32" id="ref67">32</reflink>]) argue that GJTs require learners to direct attention to form in the sentences to make judgements, and time conditions do not necessarily mean that L2 learners are unable to access their explicit knowledge. Taking on board this variety of results, Ellis and Roever ([<reflink idref="bib11" id="ref68">11</reflink>]) concluded that GJTs of any type – timed or untimed – could really only be used to distinguish between automatised and non-automatised explicit knowledge. Ellis and Roever ([<reflink idref="bib11" id="ref69">11</reflink>]) argue that distinguishing actual implicit knowledge from automatised explicit knowledge may not be possible by using GJTs. However, Ellis and Roever ([<reflink idref="bib11" id="ref70">11</reflink>]) suggest that psycholinguistic tests such as WMT may be adaptable to distinguish implicit knowledge from automatised explicit knowledge for grammar.</p> <hd id="AN0177840458-6">Relationship between L2 explicit knowledge and L2 proficiency</hd> <p>Empirical studies have examined the relationship between L2 proficiency and implicit and explicit knowledge. However, these studies have also shown mixed results, depending on how knowledge is measured (Gutiérrez [<reflink idref="bib17" id="ref71">17</reflink>]). Many studies have used the measures created by Ellis ([<reflink idref="bib9" id="ref72">9</reflink>], [<reflink idref="bib10" id="ref73">10</reflink>]), which comprise the oral elicited imitation test (OEIT) and TGJT as measures of implicit knowledge, and the UGJT and MKT as measures of explicit knowledge. Elder and Ellis ([<reflink idref="bib8" id="ref74">8</reflink>]), for example, had different results regarding the relationship between these latter measures of explicit knowledge and two slightly different measures of proficiency. One of their studies found that explicit knowledge had a stronger relationship than implicit knowledge with results on all components of the TOEFL test. In contrast, a second study indicated that implicit knowledge (EIT and TGJT) was a significant predictor of IELTS speaking and writing scores, while explicit knowledge (UGJT and MKT) was a significant predictor of IELTS listening and reading scores. Elder and Ellis ([<reflink idref="bib8" id="ref75">8</reflink>]) concluded that the TGJT was not a valid measure of implicit knowledge as participants were likely to draw on both implicit and explicit knowledge to perform well on language proficiency tests. However, when Zhang ([<reflink idref="bib35" id="ref76">35</reflink>]) explored the role of implicit and explicit knowledge in L2 proficiency as measured by the listening and grammar parts of the Oxford Placement Test (OPT) with 49 Chinese EFL learners, regression results showed that only implicit knowledge (EIT and TGJT) was a significant predictor of L2 proficiency, and the TGJT was the only significant predictor of L2 listening proficiency; explicit knowledge (UGJT and MKT) yielded no significant relationship with L2 listening. Zhang's ([<reflink idref="bib34" id="ref77">34</reflink>]) results thus seem to contradict those of Elder and Ellis ([<reflink idref="bib8" id="ref78">8</reflink>]). Zhang ([<reflink idref="bib35" id="ref79">35</reflink>]) suggested that the difference may be attributed to the difference in time pressure in the proficiency tests in the two studies. This finding suggests that the role of implicit and explicit knowledge in L2 proficiency may differ according to the task characteristics of proficiency tests.</p> <p>Some other studies (Çeçen and Erçetin [<reflink idref="bib2" id="ref80">2</reflink>]; Erçetin and Alptekin [<reflink idref="bib13" id="ref81">13</reflink>]) have explored the relationship between L2 reading and L2 implicit/explicit knowledge, as well as that between L2 reading and L2 working memory capacity. Erçetin and Alptekin ([<reflink idref="bib13" id="ref82">13</reflink>]) showed that only explicit knowledge, measured by the UGJT, and L2 working memory, measured by L2 Reading Span Test (RST) capacity, were significantly related to L2 reading comprehension among advanced-level EFL learners in Turkey. Çeçen and Erçetin ([<reflink idref="bib2" id="ref83">2</reflink>]) further investigated the role of L2 linguistic knowledge and working memory in L2 reading among 84 late EFL learners in Turkey. Their results, however, differed considerably from those of Erçetin and Alptekin ([<reflink idref="bib13" id="ref84">13</reflink>]), showing that L2 reading comprehension was significantly correlated with OEIT, TGJT, and MKT but not with UGJT. The principal component factor analysis results indicated that TGJT and EOI (implicit knowledge measures) showed some loading on the same factor as the processing component of working memory, whereas the UGJT and MKT (explicit knowledge measures) did not. In addition, MKT has no significant relationship with the processing component of working memory, which is related to controlled attention (Engle [<reflink idref="bib12" id="ref85">12</reflink>]). Çeçen and Erçetin ([<reflink idref="bib2" id="ref86">2</reflink>]) suggest that the MKT might have been less cognitively challenging in terms of attentional resources for participants with high levels of explicit knowledge as prospective English teachers. In contrast, the link between implicit knowledge measures and the processing function of working memory may suggest that the EOI and TGJT involve less automatic processing than the UGJT and MKT; thus, they are inadequate measures of implicit knowledge.</p> <p>Gutiérrez ([<reflink idref="bib17" id="ref87">17</reflink>]) investigated knowledge and knowledge of metalanguage – the two facets of explicit knowledge – and analysed their relationship to L2 proficiency among intermediate-level L2 Spanish learners. The UGJT measured analysed knowledge, while the MKT measured knowledge of metalanguage. L2 proficiency was measured by two compositions, an oral exam, and two exams containing listening, reading, vocabulary, and grammar and writing sections. The results showed that the analysed component of explicit knowledge is more significant than metalanguage knowledge in different skills and aspects of L2 proficiency.</p> <hd id="AN0177840458-7">Effect of cognitive styles on language learning</hd> <p>Field dependence and independence are among the most widely researched constructs to assess cognitive styles in SLA (Dörnyei and Ryan [<reflink idref="bib6" id="ref88">6</reflink>]). Cognitive styles are language-independent (Granena [<reflink idref="bib14" id="ref89">14</reflink>]) and are related to the perceptual or cognitive ability to control how individuals process linguistic information and interact with external referents (Chapelle and Green [<reflink idref="bib3" id="ref90">3</reflink>]). It is claimed that field independent (FI) learners are likely to process linguistic information analytically (search for rules), detect important contextual information, and learn language systematically. In contrast, field dependent (FD) learners are said to tend to deal with linguistic information and learn language holistically (Granena [<reflink idref="bib14" id="ref91">14</reflink>]; Ortega [<reflink idref="bib25" id="ref92">25</reflink>]).</p> <p>Many previous studies have investigated the relationship between language learning and FD/FI cognitive styles as measured by the Group Embedded Figures Test (GEFT), in which participants trace simple visual figures embedded inside more complex visual figures. For example, Rassaei ([<reflink idref="bib27" id="ref93">27</reflink>]) investigated which type of recast, implicit or explicit, would be more effective for FI and FD learners. The results showed that only FI learners benefited from recasting and significantly improved their L2 writing skills after receiving recast. Meguro ([<reflink idref="bib23" id="ref94">23</reflink>]) examined whether learners' FD and FI cognitive styles and analogical reasoning would predict learning gain from explicit instruction on English relative adverbs with 46 Japanese senior high school students. The learning of the target forms was assessed by a fill-in-the-gap task and analogical reasoning was measured by an online nonverbal analogy test. The results indicated that learners with a high FI tendency and higher ability to discern meaningful patterns across multiple forms of relations could learn complicated syntactic rules effectively under explicit learning conditions. The finding suggests that the analytic nature of field independence may be more strongly associated with non-automatised explicit knowledge than implicit knowledge or automatised explicit knowledge.</p> <hd id="AN0177840458-8">The current study</hd> <p>This study aims to understand the nature of Japanese EFL learners' explicit knowledge representations, as measured by Ellis's ([<reflink idref="bib9" id="ref95">9</reflink>], [<reflink idref="bib10" id="ref96">10</reflink>]) GJTs and MKT, and including field independence as a cognitive variable related to attentional resources. The differences in syntactic structures between Japanese and English (e.g. word order) and Japanese EFL participants' learning experience may yield a different pattern of knowledge representation to that of other ESL learners. Based on the study by Vafaee, Suzuki, and Kachisnke ([<reflink idref="bib32" id="ref97">32</reflink>]) and Ellis and Roever ([<reflink idref="bib11" id="ref98">11</reflink>]), the present study assumes that responses to grammatical and ungrammatical sentences in GJTs may correlate differently with various constructs (such as GEFT) and load on two distinct factors (one for automatised explicit knowledge and the other for non-automatised explicit knowledge). Thus, this study also investigates how general L2 proficiency can be explained by automatised and non-automatised explicit knowledge. The following research questions (RQs) were investigated:</p> <p></p> <ulist> <item> To what extent do grammatical and ungrammatical sentences, in timed and untimed GJTs, form relatively independent subsets loading for principal component factor analysis?</item> <p></p> <item> How do time pressure and grammaticality in the sentences of the GJTs affect L2 learners' accuracy on the test?</item> <p></p> <item> In measures of general L2 proficiency with Japanese beginner and intermediate EFL learners, how much variance is attributable to automatised and non-automatised explicit knowledge factors?</item> </ulist> <hd id="AN0177840458-9">Materials and methods</hd> <p></p> <hd id="AN0177840458-10">Participants</hd> <p>The 87 participants–66 female and 21 male–were Japanese first-year (<reflink idref="bib32" id="ref99">32</reflink>) and second-year (<reflink idref="bib55" id="ref100">55</reflink>) students of English as a foreign language (EFL) from four English language classes at a Japanese university. The majority comprised 19–20-year-olds with homogeneous social and educational backgrounds. The participants had completed six years of compulsory English language education before arriving at university. On average, they received six weekly hours of English language instruction at the university. Their English proficiency level ranged from 195 to 890 on the TOEIC (average = 521.61, <emph>SD</emph> = 127.99). Based on their TOEIC scores, their proficiency was estimated to range from CEFR A2 to B2 according to the level mapping of the Test of English for International Communication – Common European Framework of Reference for Languages (TOEIC-CEFR) provided by the Educational Testing Service (ETS), so from 'basic user' to 'independent user'.</p> <hd id="AN0177840458-11">Instruments</hd> <p></p> <hd id="AN0177840458-12">Group embedded figure test</hd> <p>FD/FI cognitive styles were measured using a modified version of the GEFT developed by Itoi ([<reflink idref="bib18" id="ref101">18</reflink>]). This test was a timed paper-and-pencil instrument that required participants to accurately find a simple geometric figure embedded in a more complicated pattern within seven minutes. FI individuals were assumed to complete tasks more quickly than individuals with FD. Itoi's ([<reflink idref="bib18" id="ref102">18</reflink>]) GEFT version includes more sets of figures (50 items) than Witken et al.'s ([<reflink idref="bib33" id="ref103">33</reflink>]) original GEFT (18 items). Field independence was assessed by counting the number of items perceived and traced correctly; scores could range from 0 to 50. For two reasons, Meguro ([<reflink idref="bib23" id="ref104">23</reflink>]) employed this modified GEFT to examine the effects of Japanese learners' differences in field dependence/independence for L2 classroom instruction. First, this instrument can produce more variance than the original GEFT of Witkin et al. (1971) due to more testing items. Second, the test performance is unlikely to be affected by the storage component of working memory because this GEFT version presents both simple and complex figures simultaneously, unlike the original version. The internal consistency analyses conducted by Meguro ([<reflink idref="bib23" id="ref105">23</reflink>]) yielded fairly good results (Cronbach's α =.90).</p> <hd id="AN0177840458-13">Timed grammaticality judgement test</hd> <p>The timed grammaticality judgement test (TGJT) (Ellis [<reflink idref="bib9" id="ref106">9</reflink>], [<reflink idref="bib10" id="ref107">10</reflink>]) contains 68 common sentences tapping knowledge of 17 morphosyntactic structures, such as verb complements, modal verbs, ergative verbs, relative clauses, and adverb placement. Half of the sentences were grammatical, and the other half were ungrammatical. There were two grammatical and two ungrammatical sentences for each of the 17 target structures. The sentences were presented on an automated PowerPoint slide show. Participants had to indicate on the Google answer form whether each sentence was grammatical or ungrammatical within a time limit. The time allowed for each sentence was adapted from the same range as that in a previous study (Çeçen and Erçetin [<reflink idref="bib2" id="ref108">2</reflink>]), in which sentences were shown for 3.5 seconds. Each item was scored dichotomously as correct or incorrect; unanswered items were scored as incorrect.</p> <hd id="AN0177840458-14">Untimed grammaticality judgment test</hd> <p>The untimed grammaticality judgement test (UGJT) (Ellis [<reflink idref="bib9" id="ref109">9</reflink>], [<reflink idref="bib10" id="ref110">10</reflink>]) includes the same 68 sentences as the TGJT. The participants were required to read the sentences on a sheet of Google documents and indicate on the Google answer form whether each sentence was grammatical or ungrammatical with no time limit. Each item was scored dichotomously as correct or incorrect; unanswered items were scored as incorrect.</p> <hd id="AN0177840458-15">Metalinguistic knowledge test</hd> <p>The metalinguistic knowledge test (MKT) (Ellis [<reflink idref="bib9" id="ref111">9</reflink>], [<reflink idref="bib10" id="ref112">10</reflink>]) comprises two parts. The first part presented 17 ungrammatical sentences, targeting the same 17 morphosyntactic structures as in the TGJT and the UGJT. The participants had to select the rule that best explained each error from the four options. The second part comprised two sections. In Section 1, the participants had to read a short passage and find examples of 19 specific grammatical features (e.g. definite articles and prepositions). Section 2 contained four sentences, and the participants were required to identify the parts corresponding to each sentence's grammatical features (e.g. subject and indirect object). In this test, all the answer options of the first section and the grammatical terms in the second section (e.g. infinitive) were translated into Japanese because most participants had learned the grammatical rules and grammatical terms in Japanese. Each item was scored dichotomously as correct or incorrect. The unanswered items were scored as incorrect. The maximum total score for the MKT was 40.</p> <hd id="AN0177840458-16">General L2 proficiency test</hd> <p>L2 proficiency was measured using the computer-based TOEIC IP test offered by the ETS. The test is a computer adaptive test (CAT) comprising two units divided into a listening section (25 min) and a reading section (37 min). Unit 1 presented the same 25 questions to all participants, and Unit 2 presented 20 questions adapted according to the participants' performance in Unit 1. The questions in the listening section refer to Photographs (3 items), Question-Response (9 items), Conversations (18 items), and Talks (15 items), and each question has three or four options. The questions in the reading section referred to Incomplete Sentences (12 items), Text Completion (8 items), and Reading Comprehension (15 items), and each question had four options. Participants completed both listening and reading sections during their normal classes. Each participant received scores on a scale of 5–495 points for reading and listening comprehension. The combined scores provided the total score on a scale from 10 to 990 points.</p> <hd id="AN0177840458-17">Procedure and analyses</hd> <p></p> <hd id="AN0177840458-18">Data collection procedure</hd> <p>The data collection period spanned two weeks. Participants first took TOEIC in one session during the first week and GEFT in another session. In addition, the participants took TGJT, UGJT, and MKT tests in one session during the second week.</p> <hd id="AN0177840458-19">Data analysis</hd> <p>After the initial preparation, statistical analyses were performed using SPSS (19.0) for Windows. Cronbach's alpha, skewness, and kurtosis coefficients were used to examine the reliability and distribution of the scores. In addition, Pearson's correlation among the measures was obtained to examine the interrelationships between the various test measures. A principal component factor analysis was then carried out to investigate RQ1. Next, a paired-sample <emph>t</emph>-test analysis was performed to address RQ2. Finally, a hierarchical regression analysis was conducted to examine RQ3.</p> <hd id="AN0177840458-20">Results</hd> <p>The descriptive statistics are presented in Table 1. All variables had skewness and kurtosis within the range of ±1.0; therefore, the scores for all variables were approximately normally distributed. Cronbach's alpha coefficient for all tests was good or acceptable for this population.</p> <p>Table 1. Descriptive statistics for scores on the tests (N = 87).</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Tests</td><td><italic>k</italic></td><td><italic>M</italic></td><td><italic>SD</italic></td><td><italic>n</italic></td><td>Skewness</td><td>Kurtosis</td><td><italic>α</italic></td></tr></thead><tbody><tr><td>GEFT</td><td char=".">50</td><td char="(">33.95 (67.9%)</td><td char=".">7.64</td><td char=".">87</td><td char=".">-.06</td><td char=".">-.88</td><td char=".">.94</td></tr><tr><td>TGJT</td><td char=".">68</td><td char="(">42.10 (61.9%)</td><td char=".">5.50</td><td char=".">87</td><td char=".">.44</td><td char=".">.16</td><td char=".">.61</td></tr><tr><td>UGJT</td><td char=".">68</td><td char="(">50.26 (73.9%)</td><td char=".">6.59</td><td char=".">87</td><td char=".">.08</td><td char=".">-.41</td><td char=".">.77</td></tr><tr><td>MKT</td><td char=".">40</td><td char="(">30.69 (76.7%)</td><td char=".">4.26</td><td char=".">87</td><td char=".">-.79</td><td char=".">.61</td><td char=".">.71</td></tr><tr><td>TOEIC</td><td char=".">100</td><td char="(">521.61</td><td char=".">127.99</td><td char=".">87</td><td char=".">.26</td><td char=".">.14</td><td char=".">—</td></tr><tr><td>TGJT G</td><td char=".">34</td><td char="(">27.15 (79.9%)</td><td char=".">3.63</td><td char=".">87</td><td char=".">-.21</td><td char=".">-.57</td><td char=".">.62</td></tr><tr><td>TGJT UG</td><td char=".">34</td><td char="(">14.95 (44.0%)</td><td char=".">4.66</td><td char=".">87</td><td char=".">-.15</td><td char=".">-.14</td><td char=".">.68</td></tr><tr><td>UGJT G</td><td char=".">34</td><td char="(">29.22 (85.9%)</td><td char=".">3.267</td><td char=".">87</td><td char=".">-.69</td><td char=".">.52</td><td char=".">.66</td></tr><tr><td>UGJT UG</td><td char=".">34</td><td char="(">21.05 (61.9%)</td><td char=".">5.26</td><td char=".">87</td><td char=".">-.30</td><td char=".">-.31</td><td char=".">.78</td></tr><tr><td>TOEIC Listening</td><td char=".">50</td><td char="(">308.79</td><td char=".">71.12</td><td char=".">87</td><td char=".">.13</td><td char=".">-.35</td><td char=".">—</td></tr><tr><td>TOEIC Reading</td><td char=".">50</td><td char="(">212.82</td><td char=".">72.83</td><td char=".">87</td><td char=".">.32</td><td char=".">-.20</td><td char=".">—</td></tr></tbody></table> </ephtml> </p> <p>1 Note<emph>.</emph> GEFT = Group Embedded Figure Test; TGJI = Timed Grammaticality Judgement Test; UGJT = Untimed Grammaticality Judgement Test; MKT = Metalinguistic Knowledge Test; G = Grammatical sentences; UG = Ungrammatical sentences</p> <p>Table 1 shows that the participants scored higher on the UGJT than on the TGJT. They also scored considerably higher on grammatical sentences than ungrammatical ones in both the TGJT and UGJT.</p> <p>A correlation analysis was performed to examine whether a linear relationship existed between any two test variables (Table 2). As shown in Table 2, overall scores for TGJT and UGJT, scores on ungrammatical sentences in both the TGJT, UGJT, and GEFT scores yielded significant moderate correlations with all TOEIC test scores. Scores for grammatical sentences in the UGJT correlated significantly with the TOEIC total scores and reading scores. However, the strength of the relationship was weak compared to the relationships between the ungrammatical sections and L2 proficiency. Moreover, there was no significant relationship between grammatical sentences in the TGJT and TOEIC scores. MKT scores correlated to the UGJT scores more strongly than the TGJT scores and yielded a weak correlation only with TOEIC reading scores. The GEFT scores correlated significantly only with scores for ungrammatical sentences on the TGJT.</p> <p>Table 2. Summary of correlations between the tests (N = 87).</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Tests</td><td>1</td><td>2</td><td>3</td><td>4</td><td>5</td><td>6</td><td>7</td><td>8</td><td>9</td><td>10</td></tr></thead><tbody><tr><td>1 GEFT</td><td char=".">–</td><td char="." /><td char="." /><td char="." /><td char="." /><td char="." /><td char="." /><td char="." /><td /><td /></tr><tr><td>2 TGJT</td><td char=".">.21</td><td char=".">–</td><td char="." /><td char="." /><td char="." /><td char="." /><td char="." /><td char="." /><td /><td /></tr><tr><td>3 UGJT</td><td char=".">.15</td><td char=".">.63***</td><td char=".">–</td><td char="." /><td char="." /><td char="." /><td char="." /><td char="." /><td /><td /></tr><tr><td>4 MKT</td><td char=".">.13</td><td char=".">.34**</td><td char=".">.63***</td><td char=".">–</td><td char="." /><td char="." /><td char="." /><td char="." /><td /><td /></tr><tr><td>5 TOEIC</td><td char=".">.31**</td><td char=".">.51***</td><td char=".">.46***</td><td char=".">.21</td><td char=".">–</td><td char="." /><td char="." /><td char="." /><td /><td /></tr><tr><td>6 TGJT G</td><td char=".">.01</td><td char=".">.54***</td><td char=".">.34**</td><td char=".">.28**</td><td char=".">.09</td><td char=".">–</td><td char="." /><td char="." /><td /><td /></tr><tr><td>7 TGJT UG</td><td char=".">.24*</td><td char=".">.76***</td><td char=".">.47***</td><td char=".">.18</td><td char=".">.53***</td><td char=".">-.14</td><td char=".">–</td><td char="." /><td /><td /></tr><tr><td>8 UGJT G</td><td char=".">.06</td><td char=".">.42***</td><td char=".">.62***</td><td char=".">.54***</td><td char=".">.23*</td><td char=".">.51***</td><td char=".">.10</td><td char=".">–</td><td /><td /></tr><tr><td>9 UGJT UG</td><td char=".">.15</td><td char=".">.52***</td><td char=".">.87***</td><td char=".">.45***</td><td char=".">.43***</td><td char=".">.11</td><td char=".">.53***</td><td char=".">.15</td><td>–</td><td /></tr><tr><td>10.TOEIC Listening</td><td char=".">.31**</td><td char=".">.32**</td><td char=".">.27*</td><td char=".">.10</td><td char=".">.89***</td><td char=".">-.02</td><td char=".">.39***</td><td char=".">.08</td><td char=".">.29**</td><td>–</td></tr><tr><td>11.TOEIC Reading</td><td char=".">.25*</td><td char=".">.59***</td><td char=".">.54***</td><td char=".">.27*</td><td char=".">.89***</td><td char=".">.18</td><td char=".">.56***</td><td char=".">.33**</td><td char=".">.48***</td><td>.58***</td></tr></tbody></table> </ephtml> </p> <ulist> <item>2 Note<emph>.</emph> *<emph>p </emph><.05, **<emph>p </emph><.01, ***<emph>p </emph><.001</item> <item>3 GEFT = Group Embedded Figure Test; TGJI = Timed Grammaticality Judgement Test; UGJT = Untimed Grammaticality Judgement Test; MKT = Metalinguistic Knowledge Test; G = Grammatical sentences; UG = Ungrammatical sentences</item> </ulist> <hd id="AN0177840458-21">Results for RQ1</hd> <p>A principal component factor analysis was conducted to investigate the extent to which results for grammatical and ungrammatical sentences in the GJTs, for MKT, and for FI cognitive style formed relatively independent subsets loading for principal component factor analysis. Oblique rotation (direct oblimin) was chosen because the factors were correlated, as shown in Table 2. The Kaiser-Meyer-Olkin (KMO) measure (KMO =.52) confirmed that the sample size was adequate for analysis, and Bartlett's test of sphericity was significant (<emph>p</emph> <.001).</p> <p>Two components with eigenvalues greater than 1 (Table 3) were extracted, which explained 45.8% of the total variance. According to the rotated solution (Table 4), grammatical and ungrammatical sentences in both timed and untimed GJTs loaded on separate components. It should be noted that field independence had a lower but noticeable loading on the second component. The MKT loaded on both components but had a higher loading on the first component than on the second.</p> <p>Table 3. Principal component factor analysis.</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Component</td><td>Eigenvalue</td><td>Variance (%)</td><td>Cumulative (%)</td></tr></thead><tbody><tr><td>1</td><td char=".">1.74</td><td char=".">29.02</td><td char=".">29.02</td></tr><tr><td>2</td><td char=".">1.01</td><td char=".">16.76</td><td char=".">45.78</td></tr></tbody></table> </ephtml> </p> <p>Table 4. Loading for principal component factor analysis.</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Tests</td><td>Component 1</td><td>Component 2</td></tr></thead><tbody><tr><td>UGJT Grammatical</td><td char="."><bold>.</bold><bold>82</bold></td><td char=".">.19</td></tr><tr><td>TGJT Grammatical</td><td char=".">.<bold>61</bold></td><td char=".">-.02</td></tr><tr><td>MKT</td><td char=".">.<bold>62</bold></td><td char=".">.44</td></tr><tr><td>UGJT Ungrammatical</td><td char=".">.25</td><td char=".">.<bold>73</bold></td></tr><tr><td>TGJT Ungrammatical</td><td char=".">-.00</td><td char=".">.<bold>73</bold></td></tr><tr><td>GEFT</td><td char=".">.06</td><td char=".">.<bold>27</bold></td></tr></tbody></table> </ephtml> </p> <p>4 Note<emph>.</emph> UGJT = Untimed Grammaticality Judgement Test; TGJI = Timed Grammaticality Judgement Test; MKT = Metalinguistic Knowledge Test; GEFT = Group Embedded Figure Test</p> <hd id="AN0177840458-22">Results for RQ2</hd> <p>A paired-sample <emph>t</emph>-test was performed to examine the effects of time pressure and grammaticality in the sentences on participants' performance on the GJTs. Table 5 shows the results of the <emph>t-</emph>tests.</p> <p>Table 5. Paired-samples <emph>t</emph>-test.</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Tests</td><td>Paired Differences</td><td><italic>t</italic></td><td><italic>df</italic></td></tr><tr><td>M</td><td>SEM</td></tr></thead><tbody><tr><td>TGJT G vs. TGJT UG</td><td char=".">12.20</td><td char=".">.68</td><td char=".">18.06***</td><td char=".">86</td></tr><tr><td> UGJTG vs. UGJT UG</td><td char=".">8.17</td><td char=".">.62</td><td char=".">13.24***</td><td char=".">86</td></tr><tr><td>TGJT G vs. UGJT G</td><td char=".">−2.07</td><td char=".">.37</td><td char=".">−5.63***</td><td char=".">86</td></tr><tr><td>TGJT UG vs. UGJT UG</td><td char=".">−6.09</td><td char=".">.52</td><td char=".">−11.70***</td><td char=".">86</td></tr></tbody></table> </ephtml> </p> <ulist> <item>5 Note<emph>.</emph> ***<emph>p </emph><.001</item> <item>6 TGJI = Timed Grammaticality Judgement Test; UGJT = Untimed Grammaticality Judgement Test; G = Grammatical sentences; UG = Ungrammatical sentences</item> </ulist> <p>As shown in Table 5, the differences between the means were significant at the <emph>p</emph> <.001 level. The mean difference and effect sizes were larger when comparing grammatical versus ungrammatical sentences than when comparing timed and untimed tests. In addition, the mean difference and effect sizes of grammatical sentences were the smallest when comparing the timed and untimed tests.</p> <hd id="AN0177840458-23">Results for RQ3</hd> <p>A hierarchical regression analysis was performed to further investigate the role of L2 automatised and non-automatised explicit knowledge in general L2 proficiency. The scores for the TOEIC test performance were set as the dependent variable. Grammatical and ungrammatical sentences in the TGJT and UGJT were entered in the model in Step1, and GEFT was entered in Step2 using the stepwise method. The regression analysis results for the TOEIC total, listening test, and reading test scores are presented in Tables 6–8, respectively.</p> <p>Table 6. Results of hierarchical regression: TOEIC.</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Predictor</td><td><italic>β</italic></td><td><italic>t</italic></td><td>Sig.(<italic>p</italic>)</td><td><italic>R<sup>2</sup></italic></td><td>Adjusted <italic>R<sup>2</sup></italic></td><td><italic>F</italic></td></tr></thead><tbody><tr><td>TGJT Ungrammatical</td><td char=".">.42</td><td char=".">3.98</td><td char=".">.000</td><td char=".">.32</td><td char=".">.30</td><td char=".">19.53***</td></tr><tr><td>UGJT Ungrammatical</td><td char=".">.21</td><td char=".">2.00</td><td char=".">.048</td></tr></tbody></table> </ephtml> </p> <ulist> <item>7 Note<emph>. ***p <.001</emph></item> <item>8 TGJI = Timed Grammaticality Judgement Test; UGJT = Untimed Grammaticality Judgement Test</item> </ulist> <p>Table 7. Results of hierarchical regression: TOEIC Listening.</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Predictor</td><td><italic>β</italic></td><td><italic>t</italic></td><td>Sig.(<italic>p</italic>)</td><td><italic>R<sup>2</sup></italic></td><td>Adjusted <italic>R<sup>2</sup></italic></td><td><italic>F</italic></td></tr></thead><tbody><tr><td>TGJT Ungrammatical</td><td char=".">.33</td><td char=".">3.30</td><td char=".">.001</td><td char=".">.20</td><td char=".">.18</td><td char=".">10.37***</td></tr><tr><td>GEFT</td><td char=".">.23</td><td char=".">2.25</td><td char=".">.027</td></tr></tbody></table> </ephtml> </p> <ulist> <item>9 Note<emph>. ***p <.001</emph></item> <item>10 TGJI = Timed Grammaticality Judgement Test; GEFT = Group Embedded Figure Test</item> </ulist> <p>Table 8. Results of hierarchical regression: TOEIC Reading.</p> <p> <ephtml> <table><thead valign="bottom"><tr><td>Predictor</td><td><italic>β</italic></td><td><italic>t</italic></td><td>Sig.(<italic>p</italic>)</td><td><italic>R<sup>2</sup></italic></td><td>Adjusted <italic>R<sup>2</sup></italic></td><td><italic>F</italic></td></tr></thead><tbody><tr><td>TGJT Ungrammatical</td><td char=".">.42</td><td char=".">4.27</td><td char=".">.000</td><td char=".">.42</td><td char=".">.40</td><td char=".">20.20***</td></tr><tr><td>UGJT Grammatical</td><td char=".">.25</td><td char=".">2.98</td><td char=".">.004</td></tr><tr><td>UGJT Ungrammatical</td><td char=".">.22</td><td char=".">2.23</td><td char=".">.028</td></tr></tbody></table> </ephtml> </p> <ulist> <item>11 Note<emph>. ***p <.001</emph></item> <item>12 TGJI = Timed Grammaticality Judgement Test; UGJT = Untimed Grammaticality Judgement Test</item> </ulist> <p>Scores for ungrammatical sentences on the TGJT were the most significant predictors of all test scores. Field independence was a significant predictor of the TOEIC listening section. Ungrammatical sentences in the GJTs and grammatical sentences in the UGJT were significant predictors of TOEIC reading scores, explaining 40% of the variance.</p> <hd id="AN0177840458-24">Discussion</hd> <p>This study aimed to investigate whether grammatical and ungrammatical sentences in GJTs function as distinct measures of automatised and non-automatised explicit knowledge and whether they correlate differently with measures of L2 proficiency and other cognitive variables involving consciousness, such as cognitive style.</p> <p>The correlational results showed that the relationship between ungrammatical sentences and measures of L2 proficiency was slightly more substantial than between grammatical sentences and L2 proficiency. The GEFT measure of field independence, assumed to be testing ability to analyse language, correlated significantly only with participants' performance on ungrammatical sentences in the TGJT. Processing ungrammatical sentences under timed conditions represents a high cognitive demand, as does the GEFT, requiring them to direct selective attention to the specific structures under timed conditions. Participants' accuracy on ungrammatical sentences in the TGJT may represent analysed and conscious knowledge accessed through controlled processing (Çeçen and Erçetin [<reflink idref="bib2" id="ref113">2</reflink>]).</p> <p>In contrast, MKT yielded a significant correlation only with the TOEIC reading scores and no significant relationship with the TOEIC total or the listening scores. The absence of a significant relationship between MKT and L2 listening supports Zhang ([<reflink idref="bib35" id="ref114">35</reflink>]). In addition, there was no significant relationship between MKT and spotting ungrammatical sentences on the TGJT, or with field independence. The absence of a significant relationship between MKT and field independence supports Çeçen and Erçetin ([<reflink idref="bib2" id="ref115">2</reflink>]), who argued that MKT is cognitively less challenging than TGJT regarding controlled attention. The MKT comprises two parts. All the questions in the second part (21 items) required the participants to read a short sentence and a passage and then identify parts corresponding to specific grammatical features. The participants in the present study began learning English after puberty and had received extensive form-focused grammar instruction before entering university. If they knew the grammatical terms (e.g. infinitive verb) and were able to identify corresponding examples (e.g. to arrive), they could activate their knowledge in long-term memory effortlessly and choose correct answers quickly. In this regard, the task characteristics of the second part of the MKT may be more related to long-term memory than controlled attention.</p> <p>Regarding RQ1, field independence loaded on the same component as ability to identify ungrammatical sentences in the GJTs. The results confirm that these two subsets share similar underlying resources. The results also showed that identifying grammatical and ungrammatical sentences, respectively, were relatively independent of each other. The results support Vafaee, Suzuki, and Kachisnke ([<reflink idref="bib32" id="ref116">32</reflink>]) and suggest that grammatical sentences in GJTs behave differently from ungrammatical sentences. Considering the link between ungrammatical sentences in the TGJT and field independence, ungrammatical sentences in the GJTs appeared to load on the non-automatised explicit factor. In contrast, grammatical sentences in both GJTs loaded on the automatised explicit factor; that is, the participants in the present study processed grammatical sentences in GJTs more automatically. However, they processed ungrammatical sentences with less automation because the controlled process of checking explicit knowledge made high demands of their attentional resources. The results partially support Gutiérrez ([<reflink idref="bib16" id="ref117">16</reflink>]) and Vafaee, Suzuki, and Kachisnke ([<reflink idref="bib32" id="ref118">32</reflink>]) and suggest that ungrammatical sentences in GJTs are a more valid measure of <emph>non-automatised</emph> explicit knowledge of target structures. On the other hand, the type of knowledge assessed in identifying grammatical sentences in GJTs appears to be tapping <emph>automatised</emph> explicit linguistic knowledge. The MKT, meanwhile, loaded more strongly on Component 1 (along with grammatical sentences) than on Component 2 (along with ungrammatical sentences). The result suggests that the MKT also taps <emph>automatised</emph> explicit knowledge, rather than <emph>non-automatised</emph> explicit knowledge.</p> <p>RQ2 investigated the effects of time pressure and grammaticality on the participants' performance on the GJTs. The <emph>t</emph>-test results showed that the participants judged grammatical sentences more accurately than ungrammatical ones in both the TGJT and UGJT. The difference in accuracy between the grammatical and ungrammatical sentences supports Gutiérrez ([<reflink idref="bib16" id="ref119">16</reflink>]) and indicates that grammaticality may affect participants' performance on the GJTs regardless of the time limit. They also performed better on the UGJT than on the TGJT for both grammatical and ungrammatical sentences. However, the mean differences between grammatical and ungrammatical sentences were more significant than between timed and untimed tests. The results suggest that grammaticality had a more significant effect on participants' accuracy than time pressure. In addition, the mean difference (−2.1%) and effect size (<emph>t</emph> = −5.63) of grammatical sentences between the timed and untimed tests were smaller than the mean difference (−6.1%) and the effect size (<emph>t </emph>= −11.70) of ungrammatical sentences in the timed and untimed tests. The results suggest that the effect of time pressure on grammatical sentences in the TGJT was smaller than on ungrammatical sentences in the test. The results tend to agree with DeKeyser ([<reflink idref="bib5" id="ref120">5</reflink>]) and Suzuki and DeKeyser ([<reflink idref="bib30" id="ref121">30</reflink>]) and suggest that the time pressure applied in the TGJT is unlikely to prevent Japanese EFL learners from accessing their explicit knowledge when they process grammatical sentences in the TGJT. In contrast, it is likely they were more strongly affected by time pressure when they processed ungrammatical sentences in the TGJT, which seems to be a cognitively more challenging task.</p> <p>In terms of the role of automatised and non-automatised explicit knowledge in L2 proficiency, as investigated in RQ3, the ungrammatical section of the TGJT (tapping <emph>non-automatised</emph> explicit knowledge) was the most significant predictor of all components of the TOEIC test. The participants involved in this study would have received explicit exam-directed instructions since they entered university. In addition, they would often have been encouraged to consciously access explicit knowledge when taking language proficiency tests such as the TOEIC. Thus, it seems unlikely that they would be using their grammatical knowledge implicitly (without awareness). We conclude, therefore, that in the case of these late EFL learners, activating their explicit knowledge representations more elaborately may well play a significant role in tests of general L2 proficiency.</p> <p>Regarding the role of automatised explicit knowledge in L2 proficiency, identifying grammatical sentences in the UGJT was a significant predictor of the TOEIC reading test performance. Together with identifying ungrammatical sentences in both timed and untimed GJTs, this represented 40.1% of the variance in TOEIC reading scores. The results suggest that both automatised and non-automatised explicit knowledge may be involved in the sentence and text completion tasks in the TOEIC reading section. These included questions requiring participants to choose the best answer from the four options to complete the incomplete sentence and text. Some questions targeted the complex syntactic structures for Japanese learners of English. In contrast, others were easy to understand and complete, such that the participants could access their explicit grammatical knowledge more automatically. Thus, both automatised and non-automatised explicit knowledge could have significantly contributed to TOEIC reading test performance. These findings support Zhang ([<reflink idref="bib35" id="ref122">35</reflink>]) and suggest that the role of automatised and non-automatised explicit knowledge may differ according to the task characteristics of proficiency tests.</p> <p>In contrast, MKT was not a significant predictor of any component of L2 proficiency, as measured by the TOEIC test. The results are in line with Gutiérrez ([<reflink idref="bib17" id="ref123">17</reflink>]) and support the more significant role of the analysed component of explicit knowledge in L2 proficiency than knowledge of metalanguage. Learners' explicit knowledge of correct grammar rules may not directly contribute to L2 proficiency.</p> <hd id="AN0177840458-25">Conclusion</hd> <p>As described in the introduction, controversy exists regarding the validity of the measures of the distinct nature of explicit knowledge and its relative contribution to L2 proficiency. The overall results of the present study confirmed that the grammatical and ungrammatical sentences in the GJTs are distinct in the nature of explicit knowledge that they tap, and operate differently with L2 language proficiency. These two different types of sentences appear to distinguish use of automatised and non-automatised explicit knowledge (Ellis and Roever [<reflink idref="bib11" id="ref124">11</reflink>]). The results also showed that the time pressure applied in the TGJT could not be sufficient to prevent Japanese EFL learners from accessing explicit knowledge when they processed grammatical sentences. Therefore, the TGJT did not function as a test of implicit knowledge.</p> <p>Most importantly, this study makes a significant contribution to L2 language learning and teaching by indicating that non-automatised explicit knowledge may play a more significant role than automatised explicit knowledge in general L2 proficiency as measured by standard proficiency tests in the case of late EFL learners.</p> <p>In conclusion, the findings of this study highlight the significance of intentional learning of language forms or rules as explicit declarative knowledge in the EFL context. Regarding pedagogical implications, explicit or form-focused grammar instruction might be effective for Japanese EFL learners to achieve advanced performance on standard language proficiency tests. Furthermore, as Suzuki and DeKeyser ([<reflink idref="bib31" id="ref125">31</reflink>]) suggested, explicit declarative knowledge may be converted into automatised explicit knowledge through repeated practice and involvement in the relevant communicative tasks.</p> <p>The role of field independence in L2 proficiency was not the main focus of the study. However, it should be noted that field independence accounted for the unique variance in TOEIC and listening scores independently of L2 linguistic knowledge. This finding supports the previous studies' (Khodadady and Zeynali [<reflink idref="bib20" id="ref126">20</reflink>]; Satori [<reflink idref="bib28" id="ref127">28</reflink>]) results and highlight the significance of field independence in L2 proficiency as a language-independent cognitive variable.</p> <hd id="AN0177840458-26">Limitations and future studies</hd> <p>The present study had certain limitations. First, the study investigated only the role of automatised and non-automatised explicit knowledge and L2 proficiency due to a lack of pure measures of implicit knowledge. It is challenging to measure implicit knowledge directly (Ellis and Roever [<reflink idref="bib11" id="ref128">11</reflink>]), especially for late L2 learners who receive explicit instruction in the formal EFL context. However, future studies may investigate the role of implicit knowledge in L2 proficiency by including instruments such as the WMT and SPRT as more fine-grained measures to capture implicit knowledge (Suzuki and DeKeyser [<reflink idref="bib31" id="ref129">31</reflink>]; Vafaee, Suzuki, and Kachisnke [<reflink idref="bib32" id="ref130">32</reflink>]). The second limitation is the operationalisation of the TOEIC reading test, consisting of grammar and reading comprehension sentences. It is worth investigating the role of explicit knowledge separately from syntactic processing, and literal and global reading comprehension. Automatised and non-automatised explicit knowledge may relate differently to lower-and higher-level language processing.</p> <hd id="AN0177840458-27">Disclosure statement</hd> <p>No potential conflict of interest was reported by the author(s).</p> <ref id="AN0177840458-28"> <title> References </title> <blist> <bibl id="bib1" idref="ref38" type="bt">1</bibl> <bibtext> Bowles, M. 2011. Measuring implicit and explicit linguistic knowledge: what can heritage language learners contribute? Studies in Second Language Acquisition 33, no. 2 : 247 – 271. doi: 10.1017/S0272263110000756.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref1" type="bt">2</bibl> <bibtext> Çeçen, S., and G. Erçetin. 2016. Interrelationships among L2 linguistic knowledge, working memory functions, and L2 reading. In Bilingual Processing and Acquisition: Vol. 3. 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  Data: The Role of Automatised and Non-Automatised Explicit Knowledge in General L2 Proficiency
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  Data: This study examines the knowledge representation of Japanese university students assessed using grammaticality judgement tests (GJTs) and a metalinguistic knowledge test (MKT). The study also investigates the role of automatised and non-automatised explicit knowledge in general L2 language proficiency. Participants were 87 late learners of English as a foreign language (EFL) who completed the timed and untimed GJTs and MKT, modified Group Embedded Figures Test (GEFT), and the Test of English for International Communication (TOEIC). The principal component factor analysis results indicated that ungrammatical sentences in the GJTs loaded on non-automatised explicit knowledge, whereas grammatical sentences loaded on automatised explicit knowledge. The score for ungrammatical sections on the timed GJT was the most significant predictor of all components of the TOEIC. The results also indicated that the time pressure applied in the timed GJT could not sufficiently limit participants' access to explicit knowledge when they processed grammatical sentences. The findings suggest that non-automatised explicit knowledge may play a more significant role than automatised explicit knowledge in L2 proficiency in the case of Japanese EFL learners.
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        Type: general
    Titles:
      – TitleFull: The Role of Automatised and Non-Automatised Explicit Knowledge in General L2 Proficiency
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Miki Satori
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 0957-1736
            – Type: issn-electronic
              Value: 1753-2167
          Numbering:
            – Type: volume
              Value: 52
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Language Learning Journal
              Type: main
ResultId 1