Introduction
Elicited imitation (EI) has a long, productive history in second language acquisition (SLA) as a means of assessing morphosyntactic knowledge (Erlam, Reference Erlam2006; Godfroid & Kim, Reference Godfroid and Kim2021; Suzuki et al., Reference Suzuki, Jeong, Cui, Okamoto, Kawashima and Sugiura2023) and second language (L2) global proficiency (Kostromitina & Plonsky, Reference Kostromitina and Plonsky2022; Yan et al., Reference Yan, Maeda, Lv and Ginther2016). The primary objective of EI is to measure one’s ability to reconstruct spoken language. The reconstruction process involves the use of working memory, comprehension, and knowledge of syntax, collectively reflecting individuals’ developing L2, known as their interlanguage. This interlanguage system is what researchers aim to measure through EI, serving as a proxy for implicit (or automatized explicit) linguistic knowledge in psycholinguistics and global L2 proficiency in the field of language assessment.
The goal of this study is to establish the validity of a low-fee, web-programmed EI that measures English morphosyntactic knowledge. To this end, we examined the comparability, in terms of construct validity, of an EI task delivered in a traditional in-person laboratory setting against an equivalent EI task administered on the web. Drawing on theoretical models and empirical work in L2 psycholinguistics and assessment, we formed two hypotheses. From a view of psycholinguistics, L2 grammar knowledge is composed of at least two types of knowledge: explicit (conscious) and implicit (unconscious). While the exact nature of the EI construct has long been debated (see Godfroid & Kim, Reference Godfroid and Kim2021; Suzuki et al., Reference Suzuki, Jeong, Cui, Okamoto, Kawashima and Sugiura2023), the meaning-focused and time-pressured element of EI requires largely automatic processing of sentences, relying on implicit knowledge (R. Ellis, Reference Ellis2005; Godfroid & Kim, Reference Godfroid and Kim2021) or more automatized version of explicit knowledge (Suzuki & DeKeyser, Reference Erlam, Akakura, Mackey and Marsden2015; Suzuki et al., Reference Suzuki, Jeong, Cui, Okamoto, Kawashima and Sugiura2023). If the web-based (WB) EI were to be as valid as the lab-based (LB) version, we expect to obtain a similar factor structure and loadings for EI scores when situated among several other measures of implicit and explicit morphosyntactic knowledge.
Second, L2 morphosyntactic knowledge, which is measured through the EI task in the current study, is a central component of L2 proficiency (Bachman & Palmer, Reference Bachman and Palmer1996; Canale & Swain, Reference Canale and Swain1980; Hulstijn, Reference Hulstijn2015); as such, we expected a significant and comparable relationship between both EIs and L2 proficiency. In reference to Hulstijn’s language proficiency model (2011, 2015), EI may pertain mostly to basic language cognition (BLC) while standardized proficiency tests, which were used as a measure of L2 proficiency in this paper, may extend beyond BLC to higher language cognition (HLC). The EI test, for instance, typically uses words and structures that frequently appear in communicative situations and is largely free of differences among L2 speakers in cognition factors such as phonological short-term memory capacity (Kim et al., Reference Kim, Tracy-Ventura and Jung2016; Park et al., Reference Park, Solon, Henderson and Dehghan-Chaleshtori2020). It is therefore common for native speakers of the target language to achieve near-perfect scores in EI, reflective of their proficiency in these linguistic elements. Standardized proficiency tests, on the other hand, require the use of HLC that contains more complex words and structures that need not be spoken, pertain to topics other than everyday matters (e.g., academic topics), and is subject to individual differences in education, literacy, and other cognitive abilities (Hulstijn, Reference Hulstijn2011, pp. 230–231). Importantly, because BLC is situated as a core component within HLC proficiency and is entirely encompassed by it, we expect a good amount of shared variance between standardized L2 proficiency test scores and EI.
Heeding calls to probe the validity of measures more rigorously in SLA research (Chapelle, Reference Chapelle2021; Révész & Brunfaut, Reference Révész, Brunfaut, Winke and Brunfaut2021; Sudina, Reference Sudina2021), we address this issue in the context of a web-programmed spoken production task. While WB spoken data collection brings many benefits, it does raise questions about its comparability to LB studies. Among these are construct-irrelevant factors, such as environmental distractions or suboptimal and/or varying audio playback conditions, which may threaten construct validity. Also, even when LB and WB versions of an instrument tap into the same construct, how similarly the construct is measured might still vary, influencing the interpretation of scores and study outcomes.
Acknowledging that establishing strict psychometric equivalence (or concordance; see Zumbo, Reference Zumbo2021) across different forms or administration conditions of a test is a difficult endeavor generally requiring carefully planned studies with large samples, our goal in the current study is to preliminarily compare the construct measurement of WB and LB EI scores by using existing data to examine (1) relationships with conceptually related constructs and (2) a relationship with a criterion variable (American Educational Research Association et al., 2014). For trait-type constructs, such as implicit morphosyntactic knowledge, a critical juncture in evaluating the validity of a test is what Chapelle (Reference Chapelle2021) describes as an explanation inference. This inference has to do with whether test scores are reflective of a well-defined target construct. Evidence to support such an inference can come from examining a nomological network of related concepts, among other sources. In our case, there is theoretical support to suggest that EI scores reflect implicit morphosyntactic knowledge that is distinguishable from explicit knowledge. Theory also suggests that (implicit) morphosyntactic knowledge ought to have a positive correlation of at least moderate strength with language proficiency (Hulstijn, Reference Hulstijn2011). This relationship should hold for subparts of an EI test composed of grammatical and ungrammatical items (see section, Elicited Imitation in SLA Research), although the relationship may be relatively stronger for grammatical items (Kostromitina & Plonsky, Reference Kostromitina and Plonsky2022; Yan et al., Reference Yan, Maeda, Lv and Ginther2016). Importantly, we wish to know whether these hypothesized relationships bear out in a similar fashion across WB and LB EI tests. Positive evidence of this would suggest that the scores from each version similarly indicate standing on the theoretical construct being measured, namely, implicit morphosyntactic knowledge.
As such, to investigate the comparability of the LB and WB EI tasks, we examined evidence (1) regarding relationships with conceptually related constructs and (2) of a relationship with a relevant criterion variable. In particular, we empirically tested the extent to which an EI administered in one’s home yielded relationships to measures of implicit (i.e., oral production [OP], timed grammaticality judgement test [TGJT]) and explicit (i.e., untimed grammaticality judgement test [UGJT] and metalinguistic knowledge test [MKT])] grammar knowledge and L2 proficiency (i.e., standardized language test) similar to those of an EI administered in a lab setting.
Elicited imitation in SLA research
Among several means of measuring L2 implicit knowledge, EI has a long, productive history in SLA (R. Ellis, Reference Ellis2005; Erlam, Reference Erlam2006; Godfroid & Kim, Reference Godfroid and Kim2021; Isbell & Rogers, Reference Isbell, Rogers, Winke and Brunfaut2020). As described by Vinther (Reference Vinther2002), R. Ellis (Reference Ellis2005), Erlam (Reference Erlam2006), and others, EI tasks are characterized by the following elements:
-
• Stimuli in the form of spoken sentences, designed to vary in overall length or complexity, or to target specific (morpho)syntactic features
-
• A pause between stimuli playback and subject response
-
• Scoring based on the accuracy of the repeated stimuli
The EI task format has been adapted to measure global L2 oral proficiency (e.g., Ortega et al., Reference Ortega, Iwashita, Norris and Rabie2002) and, in line with the focus of the current study, L2 (morpho)syntactic knowledge. Because of its fleeting audio stimuli and time constraints on production that require largely automatic processing of language with minimal opportunity to apply metalinguistic knowledge, EI tasks meet R. Ellis’ (Reference Ellis2005) criteria for operationalizing implicit knowledge.
A common design feature of EI tasks intended to measure morphosyntactic knowledge more narrowly is the introduction of ungrammatical stimuli, which are expected to be corrected in a test taker’s repetition as (further) evidence of automatic—and even subconscious—application of target L2 grammatical representations (Erlam, Reference Erlam2006; Erlam & Akakura, Reference Erlam, Akakura, Mackey and Marsden2015). Markman et al. (Reference Markman, Spilka and Tucker1975) provided early insights by emphasizing the difference between simple memory-based replication and the application of an “internalized grammar factor” (p. 37). They claim that learners often adjust linguistic structures in ways that align with their inherent understanding of a language’s grammar. Corrected imitation of ungrammatical sentences, then, becomes evidence of its assimilation into their internal grammar or interlanguage system, suggesting that learners are actively processing and understanding the content, rather than merely replicating it. Therefore, when a learner, armed with an accurate internal grammar system of the target language, confronts these items, their capacity to recognize and rectify them becomes a potent indicator of their deep-seated understanding of the language.
However, delving further into the literature reveals skepticism of the inconsistency in error correction, especially in the absence of explicit correction instructions, potentially undermining the effectiveness of ungrammatical sentences in EI tasks. In response to these concerns, Yan et al. (Reference Yan, Maeda, Lv and Ginther2016) conducted a meta-analysis of studies that included ungrammatical items (k = 9), and found that the grammaticality of EI stimuli had little impact on measurement: Both measures, with and without ungrammatical stimuli, were similarly sensitive measures of morphosyntactic knowledge thought to be implicit in nature. However, there was a trend of ungrammatical items being less discriminating across proficiency levels than grammatical items (g = 1.16 vs. g = 1.34), but these differences were comparable (i.e., Q[1] = 2.87, p = 0.09), suggesting that the inclusion of ungrammatical items neither improved nor harmed the sensitivity of EI. Kostromitina and Plonsky (Reference Kostromitina and Plonsky2022) echoed the concerns about the validity of ungrammatical items and expanded Yan et al.’s meta-analysis by including 4 additional studies (k = 15). They found ungrammatical items to be less discriminating than grammatical ones: mean effect sizes of studies that included and excluded ungrammatical items fall outside their respective confidence intervals (CIs) (r = .56, CI [0.46, 0.65] vs. r = .71, CI [0.66, 0.76]). The authors concluded that the repetition of ungrammatical sentences did not aid in EI’s capacity to assess learner proficiency, particularly when clear correction instructions are not provided. Their research adds another layer to the debate, highlighting the practical challenges in the administration of EI tasks with ungrammatical items.
In the realm of psycholinguistics, a series of factor analytic studies involving multiple measures of L2 morphosyntactic knowledge (R. Ellis, Reference Ellis2005; R. Ellis & Loewen, Reference Ellis and Loewen2007; Godfroid & Kim, Reference Godfroid and Kim2021; Kim & Nam, Reference Kim and Nam2017; Spada et al., Reference Spada, Shiu and Tomita2015), including measures designed to target implicit and those designed to target explicit knowledge, has yielded two key findings. First, EI-based measures of morphosyntax correlate more strongly with other measures of morphosyntax thought to tap into implicit knowledge (e.g., timed grammaticality judgments and oral narratives) compared to measures that clearly access explicit knowledge (e.g., metalinguistic knowledge tests). Second, EI measures load primarily on factors interpreted as reflecting implicit knowledge, though some researchers motivated by skill acquisition theory have argued that EI may be tapping into a third construct referred to automatized explicit knowledge (AEK) rather than truly implicit knowledge (Suzuki, Reference Suzuki2017; see also Suzuki & DeKeyser, Reference Suzuki and DeKeyser2015; Suzuki et al., Reference Suzuki, Jeong, Cui, Okamoto, Kawashima and Sugiura2023). Even so, the rapid and efficient access associated with AEK is thought to be largely indistinguishable from implicit knowledge when it comes to successful L2 communication.
Although research has generally supported EI as a measure of implicit L2 knowledge, until this point, most EI tasks have been administered in controlled lab settings, either on paper or by computer (see Erlam & Akakura, Reference Erlam, Akakura, Mackey and Marsden2015), in part due to the LB research tradition in the field of SLA and the complexity of setting up custom environments for running EI tests. Technically, such an environment requires the development of a web application that is, for instance, capable of user authentication, audio playback, and recording file storage/transmission between the user interface and the backend server as well as interactive interfaces for test takers and administrators. Recent advances in online experiment platforms and Internet communications technology more generally have ameliorated these difficulties, making precisely timed and faithfully recorded EI tasks possible online.
Web- and lab-based experimentation
WB experimentation was first introduced in the field of psychology on auditory perception (Welch & Krantz, Reference Welch and Krantz1996) and has been used with increasing frequency across disciplines (Birnbaum, Reference Birnbaum2000). The transition from traditional lab settings to online experimentation has offered researchers practical advantages (Table 1). For one, lifting restrictions on time and location allows data to be drawn from larger and more diverse sample pools, which may circumvent restrictions of traditional experiments, such as low statistical power and convenience sampling. WB experimentation is also more cost-efficient. Without the need for dedicated lab space and supervising and hiring staff, there is a significant reduction in cost.
Table 1. Benefits and costs of web experimentation
The flexibility and efficiency of online data collection, however, have raised questions about the validity of web-solicited data. Some claim that web-collected data are inherently different from LB data (Bauer et al., Reference Bauer, Iverson, Cernich, Binder, Ruff and Naugle2012), because a WB format may subvert the purpose of an experiment. It could also limit research approaches due to the differences in processing and response speed (Parsons et al., Reference Parsons, McMahan and Kane2018) and environmental distractors that may add noise to the data (Gagné & Franzen, Reference Gagné and Franzen2023). Despite some differences worthy of concern, accumulating findings suggest WB tasks are reliable alternatives to LB instruments. Comparable performances have been reported between lab- and web-delivered cognitive measures, such as working memory capacity and declarative memory (Ruiz et al., Reference Ruiz, Chen, Rebuschat and Meurers2019), cognitive functioning (Backx et al., Reference Backx, Skirrow, Dente, Barnett and Cormack2020; Houben & Wiers, Reference Houben and Wiers2008), and psycholinguistics measures, such as lexical decision task (Hilbig, Reference Hilbig2016).
In response to technological advances coupled with COVID-19 and the replication crisis (Klein et al., Reference Klein, Ratliff, Vianello, Adams, Bahník, Bernstein, Bocian, Brandt, Brooks, Brumbaugh, Cemalcilar, Chandler, Cheong, Davis, Devos, Eisner, Frankowska, Furrow, Galliani and Nosek2014; McManus, Reference McManus and Plonsky2021), online experimentation has only recently bloomed in L2 research. From recruitment to implementation, various topics of research have been carried out online. In L2 speech perception, for instance, researchers investigated the effectiveness of high variability phonetic training in which both the perception and production data were collected online (Saito et al., Reference Saito, Hanzawa, Petrova, Kachlicka, Suzukida and Tierney2022). In some studies, researchers crowdsourced listeners through Amazon Mechanical Turk (AMT) and sampled their transcription and speech ratings remotely (Loukina et al., Reference Loukina, Lopez, Evanini, Suendermann-Oeft, Ivanov and Zechner2015; Nagle, Reference Nagle2019; Nagle & Huensch, Reference Nagle and Huensch2020). In psycholinguistics, topics such as bilinguals’ agreement processing (Lee & Phillips, Reference Lee and Phillips2022), pragmatic inferencing by autistic learners (Van Teil & Kissine, Reference Van Teil and Kissine2018), and implicit L2 learning (Kerz et al., Reference Kerz, Wiechmann and Riedel2017), were investigated on the web, enabling the collection of both accuracy and reaction time data. For implementation, many studies have used a range of commercial services, such as PsychoPy, Prolific, Gorilla, LimeSurvey, and Unity. Alternatively, some programmed tasks created customized systems for web delivery by utilizing technologies such as HTML, CSS, JavaScript, SQL, and server-side programming. For crowdsourcing purposes, AMT, Prolific, and Appen (known as CrowdFlower until 2019), served to promote remote recruitment.
Despite the increased transition to WB research, few studies have directly explored the validity of web-solicited data. Hilbig (Reference Hilbig2016) investigated the reaction time effects of the lexical decision task by software (E-Prime vs. JavaScript) and environment (web vs. lab). To dissect such effects, participants were assigned to one of three conditions: 1) the lab using commercial software, E-Prime; 2) the lab using the JavaScript version; or 3) the web using the same JavaScript version. All conditions yielded the desired word frequency effects, demonstrating the comparability of effects across different conditions and environments. Likewise, results from Ruiz and colleagues (Reference Ruiz, Chen, Rebuschat and Meurers2019) suggested that the web version of a paired associates subtest of the Modern Language Aptitude Test yielded a strong correlation to its LB counterpart (r = .82), affirming its comparability to the web-version. Lastly, in SLA, Nagle and Rehman (Reference Nagle and Rehman2021) aimed to develop a reliable online method for recruiting listeners to rate L2 speech samples for previous research (Nagle, Reference Nagle2019). In their approach, they included both traditional local labs and the digital domain of AMT. Within AMT, participants were either exposed to the specific target dialect or a broader range of dialects. With quality controls included (i.e., prescreening and task timers), the study confirmed that three groups of raters (local lab and two AMT groups) had similar speech ratings, suggesting the high comparability between WB and LB participant recruitment with proper quality validation. Collectively, these results bring prospects for administering language research, including reaction time– and accuracy-based, cognitive, and speech-rating tasks on the web.
To our knowledge, no studies in SLA have explored the validity of web-delivered speech production tasks on the web. With the increased possibility of collecting spoken responses remotely using custom web platforms and services, empirical validation is needed to ensure accurate and reliable use of the web version of EI.
Current study
The current study aims to explore the validity and usefulness of a web-delivered EI task designed to measure English morphosyntactic knowledge. To this end, we examined the comparability, in terms of construct validity, of an EI task delivered in a traditional in-person lab setting against an equivalent EI task administered on the web. The following Research Questions (RQs) guided this study:
RQ1: To what extent do the EI scores administered in the lab and on the web relate to an English proficiency test?
RQ1a: Do grammatical and ungrammatical EI items administered in the lab and on the web exhibit similar relationships to an English proficiency test?
RQ2: To what extent do EI tasks administered in the lab and on the web relate to measures of implicit and explicit English morphosyntactic knowledge?
With regard to RQ1 and RQ1a, we anticipate EI-based measures of morphosyntactic knowledge to exhibit comparable correlations with English proficiency scores across modalities, for both grammatical and ungrammatical items. With regard to RQ2, we expect to obtain a similar factor structure and loadings for EI scores when situated among several other measures of implicit and explicit morphosyntactic knowledge.
Methods
This study drew on data from Kim’s dissertation (n = 149; Kim, Reference Kim2020; Kim & Godfroid, Reference Kim and Godfroid2023) and Godfroid et al.’s project (n = 151; Godfroid & Kim, Reference Godfroid and Kim2021; Godfroid et al., Reference Godfroid, Kim, Hui and Isbell2018). To perform the comparisons of interest, we used datasets of a standardized English proficiency measure and five linguistic measures: EI, OP, UGJT, TGJT, and MKT.
The same instruments of identical items were used in both Kim (Reference Kim2020) and Godfroid et al. (Reference Godfroid, Kim, Hui and Isbell2018), with the exception of the delivery mode of EI and OP. In Kim (Reference Kim2020), the two oral tasks (i.e., EI and OP) were delivered on the web, and other measures (i.e., UGJT, TGJT, and MKT) in the lab, while all data in Godfroid et al. (Reference Godfroid, Kim, Hui and Isbell2018) were collected from measures administered in a lab setting (see section, Web-based Environment, for details).
Participants
In both Kim’s WB study and Godfroid et al.’s LB study, the participants were English L2 speakers attending a large university in the American Midwest. Kim’s study recruited 149 participants, and Godfroid et al. initially recruited 160. Nine participants in Godfroid et al., who missed all five linguistic measures, were excluded from the analysis, resulting in a final sample of 151. For speaker L1, defined here as self-reported dominant language, the most common was reported as Chinese in both Kim (n = 53) and Godfroid et al. (n = 74), followed by any variety of Spanish (Kim, n = 14; Godfroid et al., n = 9), and Korean (Kim, n = 17; Godfroid et al., n = 10). The remaining 65 (Kim) and 58 (Godfroid et al.), reported 24 (Kim) and 30 (Godfroid et al.) other L1s. Table 2 outlines the demographic information of the participants in each project.
Table 2. Background information of the L2 speakers in the lab- and web-based environments
a Discrepancies in the number of participants are due to missing background data.
Target structures
Six grammatical features (Table 3) initially developed by Godfroid et al. (Reference Godfroid, Kim, Hui and Isbell2018) were included in both WB and LB research as target structures across five linguistic tasks: (1) third-person singular -s, (2) mass/count nouns, (3) comparatives, (4) embedded questions, (5) passive, and (6) verb complements. These structures were chosen due to their emergence at various stages of SLA, making them suitable for assessing a broad spectrum of English morphosyntactic knowledge.
Table 3. Six target structures
Instruments
Lab-based elicited imitation
The experimental stimuli of the LB EI developed by Godfroid et al. (Reference Godfroid, Kim, Hui and Isbell2018) consisted of 24 critical items (half grammatical and half ungrammatical) and eight filler items. The 24 critical items were counterbalanced by grammaticality yielding two counterbalanced lists (form A and form B). The sentences ranged from 6 to 13 words. Prior to the test phase, four practice sentences (two grammatical and two ungrammatical) were provided. Participants were instructed to listen to each sentence, judge its plausibility, and repeat the sentence in “correct English.” At the beginning of each trial, a fixation cross (+) appeared for 500 milliseconds. The sentence was then played with a speaker icon appearing on the screen. After each sentence, a plausibility question followed (e.g., Do you agree, disagree, or are you unsure about the content of the statement?). Participants had four seconds to decide. A microphone icon then appeared on the screen with a beep sound and the text, “Please repeat now.” They had eight seconds to repeat the sentence. The responses were recorded by a research assistant with a recorder. The task was programmed on Superlab 5.0. An overall accuracy score was calculated based on the correct usage of the target forms in obligatory contexts.
Web-based elicited imitation
In the WB EI, two changes were made from the LB EI. First, to enhance test item reliability, only one form of the counterbalanced versions of LB EI (form B) was employed. Second, we added four practice sentences (resulting in eight sentences, four grammatical and four ungrammatical) and model responses to two practice sentences. These changes were made to compensate for the lack of immediate assistance from researchers to address queries. Aside from these adjustments, all other conditions remained identical to the LB EI.
The task and the trial sequences of the WB EI were identical to the LB EI. During the repetition trial, an additional text, “Your voice is now being recorded.” was included under the microphone icon. We also designed a progress marker (e.g., 1/32) to inform participants of their progress throughout the experiment. At no point did the task instructions or the model responses include explicit guidance to focus on forms or provide metalinguistic feedback on the linguistic features. The task was programmed as a custom-made Java web application, which consisted of a backend server with Java Servlets doing user authentication and data storage in an SQL database as well as a web front-end accessible from the participants’ browsers. As in the LB EI, correct usage of the target forms in obligatory contexts was used as an outcome measure.
Oral production
Both Kim’s and Godfroid et al.’s OP tasks instructed participants to read and retell a picture-cued short story containing the target structures (Godfroid et al., Reference Godfroid, Kim, Hui and Isbell2018; Godfroid & Kim, Reference Godfroid and Kim2021). The pictures provided illustrations of the main context and assisted in the later story retrieval. Each picture was followed by one to four sentences. Participants were asked to read the story twice without time constraints, with no note-taking allowed. Subsequently, they were asked to retell the story within 2.5 minutes, in as much detail as possible, while the pictures remained on the screen. The overall accuracy score was calculated by dividing the number of times a target structure was needed by the number of its correctly used instances. The WB OP tasks were programmed using the same technical setup as the EI tasks. In the current paper, only the WB EI was validated, given its wider use and greater technical sophistication compared to OP (i.e., prompt presentation and timing of responses).
Timed and untimed grammaticality judgment tests
The LB computerized written grammaticality judgment tests (GJTs) instructed participants to read a sentence and determine its grammaticality under timed or untimed conditions. The TGJT consisted of 24 critical items (half grammatical and half ungrammatical) and 16 filler items (Godfroid et al., Reference Godfroid, Kim, Hui and Isbell2018; Godfroid & Kim, Reference Godfroid and Kim2021). In the UGJT, we only included 12 ungrammatical items, following R. Ellis (Reference Ellis2005), and 16 fillers. The TGJT asked participants to make a decision under a time limit, which was set relative to the sentence lengths. The time limit was determined by computing the average audio length of sentences of the same length and adding 50% of its median (Godfroid et al., Reference Godfroid, Kim, Hui and Isbell2018; Godfroid & Kim, Reference Godfroid and Kim2021). The UGJT was equivalent to the TGJT, but a time constraint was not imposed. In this way, participants were able to employ explicit knowledge to help with their decisions. Correct responses in both tests were awarded one point.
Metalinguistic knowledge tests
The MKT asked participants to read 12 sentences, each containing one grammatical error. Participants were then asked to identify, correct, and provide detailed explanations for each error. Correct responses to each sentence earned one point, allowing a maximum score of 12 points on the MKT. Because explanations required participants to use their most explicit linguistic knowledge, we only considered the explanation part for analysis.
English proficiency measure
To obtain participants’ general English proficiency, individuals self-reported their most recent Test of English as a Foreign Language (TOEFL) scores through a background questionnaire. In the interest of inclusivity, the ordinal studies (Godfroid et al., Reference Godfroid, Kim, Hui and Isbell2018; Kim, Reference Kim2020) also accepted scores of other standardized tests. In Godfroid et al., the International English Language Testing System (IELTS) was included, and in Kim, IELTS and both the Common European Framework of Reference for Languages and the Test of English for International Communication were accepted. In all cases, the scores were converted into equivalent TOEFL scores using the conversion reference provided by the Educational Testing Service (https://www.ets.org/toefl/institutions/scores/compare/).
Procedure
Lab-based environment
Participants enrolled in the LB research (Godfroid et al., Reference Godfroid, Kim, Hui and Isbell2018; Godfroid & Kim, Reference Godfroid and Kim2021) completed the tasks one-on-one with a research assistant in a language research lab. Participants started with two tasks (i.e., OP and EI) that draw attention to the meaning of sentences and proceeded to measures that direct attention to forms (i.e., GJTs and MKT). This order was meant to minimize recognition of the target forms in the meaning-based, implicit knowledge measures.
Web-based environment
As previously mentioned, participants in the WB experiment (Kim, Reference Kim2020; Kim & Godfroid, Reference Kim and Godfroid2023) performed two oral tasks (i.e., OP and EI) on the web and the GJTs and MKT in the lab setting (Table 4). The decision to conduct the OP and EI tasks online was made to enhance research efficiency, especially by reducing the extensive labor involving one-on-one sessions with a research assistant. The GJTs and MKT were purposefully conducted in the lab to prevent learners from accessing external resources, such as metalinguistic explanations of grammar on websites or in grammar textbooks. Ten days prior to the lab visits, participants received an e-mail with step-by-step directions on accessing and completing the web tasks. This instructional tutorial was recorded using the New Screen Recording function in QuickTime Player (version 10.5). Seven days prior to the lab visits, participants received another e-mail containing a unique web link with a personalized code directed to an interface for the web tasks. On the interface, participants were given general instructions (e.g., find a quiet room for 30 minutes and use Chrome or Firefox). Following the completion of the consent form, background questionnaire, and OP, participants proceeded with the EI. The TGJT, UGJT, and MKT tasks were conducted in the lab after participants had completed the web tasks in the convenience of their homes.
Table 4. Sequence and settings of web- and lab-based tasks
Note: TFJT, timed grammatical judgment test; UGJT, untimed grammatical judgment test.
Analysis
To investigate RQ1 and RQ1a (i.e., the relationship between EIs and standardized English proficiency scores), Pearson correlation coefficients were conducted between the TOEFL scores and two versions of the EI—the WB and LB EIs—as well as their item types: grammatical items (EI_G) and ungrammatical items (EI_UG). We then examined the corresponding 95% CIs and performed a Fisher’s r-to-z transformation (Preacher, Reference Preacher2002) to statistically confirm the relationships (Cumming & Finch, Reference Cumming and Finch2005).
RQ2 addressed the extent to which both WB EI and LB EI are associated with other measures of implicit and explicit knowledge. To this end, we configured two separate confirmatory factor analysis (CFA) models with Kim’s WB dataset (Reference Kim2020) and Godfroid et al.’s LB dataset (Reference Godfroid, Kim, Hui and Isbell2018), respectively. Two latent constructs, implicit and explicit knowledge, were configured with the implicit knowledge factor indicated by EI, OP, and TGJT and the explicit knowledge factor indicated by UGJT and MKT (R. Ellis, Reference Ellis2005; R. Ellis & Loewen, Reference Ellis and Loewen2007).
We evaluated the CFA models based on the global goodness of fit, the indices of which provide a (global) summary of the acceptability of the model; that is, whether the model has been properly specified. Model fit indices considered were the χ2 statistic (and corresponding degree of freedom and p value); root mean square error of approximation (RMSEA), corrected for model complexity, taking sample size into account, and its 90% CI; standardized root mean square residual (SRMR); and comparative fit index (CFI), which compares the fitted model to a base model with no parameter restrictions. We followed Hu and Bentler’s (Reference Hu and Bentler1999) and Kline’s (Reference Kline2016) guidelines for fit interpretation (i.e., RMSEA lower-bound CI ≤ 0.06, which yields a nonsignificant p value, SRMR ≤ 0.08, and CFI ≥. 95). Full-information maximum likelihood estimation was used to evaluate CFA models, and robust maximum likelihood estimator method was used to accommodate multivariate normality assumption violations. All CFA analyses were carried out in R version 4.2.0, using the lavaan package.
Results
We first present the descriptive statistics of the TOEFL scores and the linguistic measures in both WB and LB conditions (Table 5). The average TOEFL score for the participants was 93.03 (SD = 13.11) in the WB condition and 96.77 (SD = 8.86) in the LB condition. The reliability scores of the linguistic tasks ranged from. 51 to. 96, and the skewness and kurtosis values were within the acceptable ranges (±2), indicating the normality of most of the tasks.
Table 5. Descriptive results of the lab- and web-based tasks
Note: EI = elicited imitation; Max = maximum; Min = minimum; MKT = metalinguistic knowledge test, only the rule was used; OP = oral production; TOEFL = Test of English As a Foreign Language; TGJT = timed grammatical judgment test; UGJT = untimed grammatical judgment test, only ungrammatical items were used.
a Alpha;
b Pearson r inter-rater reliability;
c Several items were removed due to poor discrimination.
RQ1 and RQ1a: Relationship with English proficiency
In addressing RQ1, we carried out correlation analyses between the TOEFL scores and two versions of the EI tasks separately for the grammatical (EI_G), ungrammatical (EI_UG), and combined EI items.
Before stratifying by grammaticality, we first observed the overall EI scores that combined both grammatical and ungrammatical items. Moderate and significant correlations were found between TOEFL and LB EI (r = .60, 95% CI [0.44, 0.72], n = 83) as well as TOEFL and WB EI (r = .52, 95% CI [0.39, 0.63], n = 139). Figure 1 visually represents these relationships. The correlation between LB -EI and TOEFL (r = .60) falls within the 95% CI of the corresponding WB EI and TOEFL pair (95% CI [0.39, 0.63]). Similarly, the WB EI’s relation to the TOEFL score (r = .52) falls within the CI of the LB EI and TOEFL pair (95% CI [0.44, 0.72]). Their comparable associations were supported by the nonsignificant Fisher’s test results (p = .393), failing to reject a null hypothesis (H0 = the strength of EI-TOEFL correlations are comparable between the administrative conditions).
Figure 1. TOEFL and EI correlational values, along with a 95% CI and Fisher’s r-to-z transformation, by conditions (left) and by item types (right).
Note: EI = elicited imitation; EI_G = EI with only grammatical items; EI_UG = EI with only ungrammatical items.
When stratified by grammaticality, different patterns were observed. As shown in Figure 1 (left), the grammatical items in both EI versions yielded similar associations to their respective TOEFL scores, returning nonsignificant Fisher’s test results (p = .164). However, we observed differences between the lab and web versions of the ungrammatical EI items (although not reaching statistical significance, p = 0.062), with the web version correlating weaker to TOEFL scores (r = .37, 95% CI [0.22, 0.50]) than those of the lab version (r = .57, 95% CI [0.41, 0.70]). This trend of ungrammatical items showing less discrimination than the grammatical items is pronounced only in the web condition and not in the lab condition (Figure 1, right), suggesting that the administrative environment of the web might introduce variability in how ungrammatical items are processed and performed by participants.
RQ2: relationship with explicit and implicit measures
To address RQ2 (i.e., the relationship between EI and explicit and implicit L2 knowledge measures), we first explored the associations between EIs and the five linguistic measures. Figure 2 presents the correlation results for all five linguistic measures separately for the WB (top) and LB (bottom) tasks. Small to moderate relationships were found among most measures (.332 < r <. 430, p < 0.001), except for the correlations between UGJT and OP in the LB versions (r = .168) and the MKT and three implicit knowledge measures, which were either insignificant or weakly correlated (.146 < r <. 226, 0.01 < p < 0.05). Importantly, no statistically significant differences in the strength of correlation were found between LB and WB EI measures with different linguistic tests, with Fisher’s r-to-z transformation with a range of 0.19 < p < 0.72 (see Figure S1, Online Supplementary Materials).
Figure 2. Correlations of linguistic tasks performed in the web (top) and in the lab (bottom).
Note: *p < 0.05, **p < 0.01, ***p < 0.001. The diagonal cells plot the standardized score distribution of corresponding tests.
To explore the relationships more fully, we submitted all measures to a series of CFAs separately for the WB (n = 149) and the LB (n = 151) tasks. As previously mentioned, for each task version, we configured a model with EI indicated as an implicit knowledge construct along with two implicit knowledge measures (i.e., OP and written TGJT). Table 6 presents fit indices for the WB and LB conditions. Both CFA models of the lab and web versions demonstrated excellent fit to the data, with all fit indices (i.e., CFI, SRMR, and RMSEA lower bound) within the acceptable range and χ2 returning nonsignificant values. The reliability (𝜔) of the implicit factor was .69 (WB) and .67 (LB), and for the explicit factor, .66 (WB) and .66 (LB). Parameter estimates for the models are detailed in Figure 3. All indicators in both models were significant. Collectively, no significant deviation of the WB EI and LB EI scores was observed in relation to a battery of different explicit and implicit knowledge measures. Such mirroring of the CFA results of the WB and LB measures reaffirms WB EI as a measure sharing a similar underlying construct as the LB EI.
Table 6. Model fit indices for the web- and lab-based conditions
Note: χ2 = chi-square; χ2 p = chi-square test p value; CFI = comparative fit index; df = degree of freedom; RMSEA = root mean square error of approximation; SRMR = standardized root mean square residual.
Figure 3. Two-factor CFA model, lab-version (left) and web-version (right).
Note: EI = elicited imitation; EI = EI; MKT = metalinguistic knowledge test; OP = oral production; TWGJT = timed grammaticality judgment test; UWGJT = untimed grammaticality judgment test.
Discussion
The goal of this study was to verify the validity of a low-fee, web-programmed EI task that measures English morphosyntactic knowledge. Drawing on theoretical and empirical work in L2 assessment and psycholinguistics, we hypothesized that, first, EI scores will yield significant associations with standardized L2 proficiency test scores as L2 grammar knowledge is a central component of L2 proficiency. Second, linguistic knowledge consists of explicit and implicit knowledge, with the EI task operationalizing as either implicit or automatized explicit knowledge; hence, EI is expected to exhibit more meaningful relationships to time-pressured accuracy measures (implicit or automatized explicit knowledge) than untimed linguistic measures (explicit knowledge). To empirically test the validity of web-programmed EI in relation to these assumptions, we compared the extent to which an EI administered on the web yielded a relationship to English proficiency scores and measures of implicit (i.e., OP and TGJT) and explicit (i.e., UGJT and MKT) grammar knowledge similar to that of EI administered in a lab setting.
In this study, we observed a good amount of shared variance between general English proficiency scores and the two versions of the EI scores. We found relationships of medium to large, comparable associative strength between TOEFL and WB EI (r = .52, 95% CI [0.39, 0.63]) and LB EI (r = .60, 95% CI [0.44, 0.72]), providing initial support for the similar importance of grammar competence in overall language proficiency. Previous research has reported strong relationships between standardized English proficiency and EI scores, demonstrating the use of morphosyntactic knowledge in language proficiency tests (e.g., Elder & R. Ellis, Reference Elder, Ellis, Ellis, Loewen, Elder, Reinders, Erlam and Philip2009; Erlam, Reference Erlam2006, Reference Erlam, Ellis, Loewen, Elder, Reinders, Erlam and Philip2009; Spada et al., Reference Spada, Shiu and Tomita2015). For instance, in Erlam’s studies (2006, 2009), scores of EI measuring 17 features of English grammar were strongly linked to the overall IELTS scores (r = .76), with the writing component showing the weakest (r = .46) and the listening component yielding the strongest (r = .72) correlations. Including only one target feature in the EI, Spada and colleagues (Reference Spada, Shiu and Tomita2015) used a C-test to measure learners’ English proficiency and found one’s knowledge of the English passive voice was moderately related to their general English proficiency (r = .50). In a recent meta-analysis (Kostromitina & Plonsky, Reference Kostromitina and Plonsky2022), studies using binary scoring, as our study, showed a correlation of . 53 (95% CI [0.43, 0.63]) to proficiency scores, which aligns closer to our findings. On par with these findings, our study, which also used only six features of English, yielded a medium correlation to general English proficiency in both versions of the EIs, thus establishing preliminary evidence for the comparable validity of online EI vis-à-vis in-person EI tasks.
Intriguingly, when the EI items were grouped by grammaticality, we observed some evidence of modality effects. A trend of differences emerged in the correlations between TOEFL scores and the ungrammatical EI items (although not reaching statistical significance, p = 0.062), with a weaker correlation for the web version (r = .37, 95% CI [0.22, 0.50]) compared to that of the lab version (r = .57, 95% CI [0.41, 0.70]). This pattern, where the ungrammatical tasks were less consistent but neither improved nor harmed the overall effectiveness of EI, matches the findings of Yan et al. (Reference Yan, Maeda, Lv and Ginther2016). In our context, such a trend was specific to the WB format, suggesting that the online administrative context might introduce variability in participant responses to ungrammatical items. The lab setting (more specifically, the affordance of direct researcher interaction) allowed for immediate feedback on participants’ questions on “correct English” responses when task instructions were given. Although the web version attempted to address this by modeling correction of ungrammatical items, the less controlled condition of the web setting might not have achieved the same effectiveness, thereby potentially affecting the results. We note that the observed trend of modality effect does not necessarily diminish the validity of ungrammatical items as a whole (because the observed effects are not statistically meaningful; and the LB version did show a fairly robust correlation to the TOEFL scores, in fact, numerically slightly better than grammatical items; see Figure 1, right). Instead, our findings caution users of WB EI about the potential for misinterpreting instructions, which could reduce the effectiveness of ungrammatical sentences in EI tasks.
Second, the effectiveness of the online EI was further corroborated by a series of CFA results. Based on a well-established explicit-implicit knowledge model by R. Ellis (Reference Ellis2005) and its extensions (Godfroid & Kim, Reference Godfroid and Kim2021; Godfroid et al., Reference Godfroid, Kim, Hui and Isbell2018), another goal was to assess the association between the web-programmed EI with different implicit and explicit knowledge measures. To this end, in both WB and LB versions, we configured CFA models with the same structure, anticipating comparable structural validity. As expected, we found excellent model fits for both WB and LB CFA models, with significant loadings for EI scores and other four linguistic measures. Such mirroring of the CFA results adds additional support for both EIs zeroing in on a common construct of L2 knowledge type.
To our knowledge, this is one of the first studies to validate the effectiveness of EIs deployed on the web. Our findings show promise for utilizing web-programmed EI as an alternative to its LB counterpart bringing potential to language science and education. Notably, the capacity to reliably record utterances via the Internet opens great prospects for research that necessitate elicited production data. Research, such as L2 oral proficiency (e.g., Ortega et al., Reference Ortega, Iwashita, Norris and Rabie2002) and explicit-implicit knowledge development (e.g., R. Ellis, Reference Ellis2005; Erlam, Reference Erlam2006; Suzuki & DeKeyser, Reference Suzuki and DeKeyser2015; Suzuki et al., Reference Suzuki, Jeong, Cui, Okamoto, Kawashima and Sugiura2023) that entails an experimental design similar to that of the current study, may benefit from utilizing web-programmed EI in eliciting speech data. Relatedly, our web-programmed EI may serve as an efficient tool for researchers to conduct equitable and statistically powered research. With increased calls for diverse representation of L2 learners in SLA research (Andringa & Godfroid, Reference Andringa and Godfroid2020; Godfroid & Andringa, Reference Godfroid and Andringa2023), this online task, which enables wider and larger recruitment of participant samples, could hold promise for conducting equitable yet reliable research with unconventional learners (e.g., non-WEIRD populations: Western, Educated, Industrialized, Rich, and Democratic) on a larger scale. Beyond EIs used for research purposes, there is potential for their use in educational applications. With EI demonstrating its effectiveness as a language program placement test (Yan et al., Reference Yan, Maeda, Lv and Ginther2016), this WB program, along with the potential for automated scoring of EIs (Isbell et al., Reference Isbell, Kim and Chen2023), may make the administration and scoring of EIs feasible with limited staff and resources. For researchers and educators interested in web-programming their own EI test, see Appendix S1 (Online Supplementary Material) and Kim et al. (Reference Kim, Chen and Liu2024.) for the coding guidelines and a tutorial on autoscoring English grammar forms.
Limitations and future directions
As previously mentioned, in the WB condition, the OP task was administered online alongside the EI task. This decision was made deliberately to optimize limited resources in Kim’s longitudinal study. However, for the specific focus of this paper (i.e., the validation of online vs. offline EIs), having online OP exclusively in the WB condition and not in the LB condition might introduce variations. To check for potential confounding effects, we looked into how OPs, in both conditions, relate to the three linguistic tests (i.e., TGJT, UGJT, and MKT), all of which were conducted in the lab for both conditions. We then used Fisher’s r-to-z transformation to test for parity between the two conditions. If online modality mediated test performance, the strength of associations between OP and its paired linguistic tasks would yield meaningful differences between conditions. All paired relationships between OP and the linguistic tests were comparable across the conditions, with p values ranging from 0.117 to 0.763 (Figure S2, Online Supplementary Materials). While these results offer some support that the differences in modality between OPs are not meaningful, in future research, it is recommended to eliminate any discrepancies between conditions to facilitate more direct comparisons.
Another important limitation of this study is that participants’ English proficiency scores were self-reported. The secondary data we reanalyzed (Godfroid et al., Reference Godfroid, Kim, Hui and Isbell2018; Kim, Reference Kim2020) primarily came from university students, many of whom provided the scores achieved at the commencement of their academic programs. Therefore, their entered language proficiency score may not accurately represent their current proficiency levels. To test whether test-taking time changes the associations between EI and standardized proficiency scores, we broke the analysis down by test administration year (Figure S3 and Tables S1–3, Online Supplementary Materials). In essence, there is a gradual decline in correlation strength as the time since taking the test increases. Specifically, among the 139 participants who reported the years of test administration, those who took the English proficiency test within 2 years from the year of experimentation showed a correlation score of r = .57 (95% CI [0.33, 0.74], n = 51) to the EI scores. This coefficient is comparable to those who have taken the tests between 3 and 4 years (r = .55, 95% CI [0.33, 0.72], n = 63). The correlational strength dropped drastically when the years of test-taking dated after 5 years from 2019 (r = .30, 95% CI [–0.11, 0.62], n = 25). Given the strength weakens as the time since test-taking increases, we anticipate a stronger correlation between EI and language proficiency scores when the scores more accurately reflect learners’ current proficiency level. Future studies should incorporate language proficiency tests into the experimental design, if not using secondary data.
The current WB EI program used in the study served as a data collection instrument. Nevertheless, its scalability was limited by the reliance on manual procedures for test item development and response scoring. To address this, a more comprehensive system integrating item development, test administration, and automated scoring is envisioned. With the aid of natural language processing technologies, a system could be created to generate or select sentences from a corpus containing target grammatical constructs, both with and without errors. The system would store participants’ responses and employ automatic speech recognition and other natural language processing tools for automated scoring (Isbell et al., Reference Isbell, Kim and Chen2023; Kim et al., Reference Kim, Chen and Liu2024). Such a comprehensive WB EI test system has the potential to greatly benefit the SLA and language learning community.
As also suggested by a reviewer, we encourage future studies to utilize a planned within-subjects design, where all participants engage in both offline and online versions of EIs. The reanalytic nature of our study precluded such a design, but the proposed within-subjects approach offers a rigorous way to investigate the impact of modalities (online vs. offline) on test scores while mitigating potential sources of noise, such as sampling error and participant variability.
Conclusion
The aim of this study was to validate the WB EI test by comparing its performance with the traditional LB delivery mode in terms of its correlation with established overall proficiency and linguistic knowledge tests. Using secondary data from Kim (Reference Kim2020) and Godfroid et al. (Reference Godfroid, Kim, Hui and Isbell2018), we found moderate correlations between the EI test results and the TOEFL test for both delivery modes. The factor loadings of the EI test were also similar between the two modes, indicating comparable structural validity in the CFA model. Thus, we show promise for utilizing web-programmed EI as an alternative to its LB counterpart bringing potential to language science and education. The WB delivery mode offers numerous benefits, including logistical convenience, easier participant recruitment, and lower running costs.
Supplementary material
The supplementary material for this article can be found at http://doi.org/10.1017/S0272263124000214.
Open access
