The effect of planning sub-processes on L2 writing fluency, grammatical complexity, and lexical complexity

Johnson, Mark D.; Mercado, Leonardo A.; Acevedo, Anthony

doi:10.1016/j.jslw.2012.05.011

Cited by 123 publications

(113 citation statements)

References 42 publications

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“…However, these results run counter to the previous studies that found favorable impacts of preplanning on syntactic complexity (Ellis & Yuan, 2004Yuan & Ellis, 2003). The results lend support to the outcomes of previous examinations that revealed no effect of preplanning on complexity (Johnson et al, 2012;Ong & Zhang, 2010).…”

Section: Discussioncontrasting

confidence: 90%

The Combined Effects of Pre-task and Careful Online Planning on EFL Learners' Written Discourse

Behyar

Nabilou

2019

IJREE

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The present study aimed at examining the combined effects of pre-task and online planning time on complexity, accuracy, and fluency in narrativebased texts produced by Iranian intermediate EFL learners. Overall, the implemented studies in this regard point to the facilitative impacts for both pre-task and careful online planning on complexity, accuracy, and fluency with some recorded trade-off effects. Using a between-groups design, sixty homogeneous participants were randomly assigned to four performance conditions: no planning (NP), careful online planning (OLP), pre-task planning (PTP), and both pre-task and careful online planning (POLP). The findings revealed that pre-task and careful online planning don't have any significant effects on the level of complexity. The results also suggested that whereas the provision of abundant online planning time increases the accuracy level, the opportunity to plan prior to performance leads them to generate more fluent written discourse. In addition, compared with the NP condition, pre-task in tandem with careful online planning time enable the participants to produce more accurate, and fluent written discourse that lend support to the Dual-Mode system and Limited Attentional Capacity Model. The findings are of pedagogical significance in that they speak of the efficacy of planning as an important metacognitive learning strategy capable of helping teachers achieve the desirable pedagogical objective of enhanced complexity, accuracy, and fluency of learners' task-based production. Theoretically, the results underscore the effectiveness of planning time in helping language learners overcome the limitation of their attentional capacity and direct them towards aspects of form and meaning.

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Section: Discussioncontrasting

confidence: 90%

The Combined Effects of Pre-task and Careful Online Planning on EFL Learners' Written Discourse

Behyar

Nabilou

2019

IJREE

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“…Coh-metrix is an online tool that computes the readability of a text using 108 indices (Graesser, McNamara, & Kulikowich, 2011;Graesser, McNamara, Louwerse, & Cai, 2004). In language assessment, Coh-metrix has been used to measure language proficiency in terms of cohesion and coherence (Crossley, Greenfield, & McNamara, 2008;Crossley & McNamara, 2011), L2 writing proficiency (Guo, Crossley, & McNamara, 2013), vocabulary use in L2 (Crossley & McNamara, 2009), and fluency and lexical complexity in L2 writing (Johnson, Mercado, & Acevedo, 2012).…”

Section: Automated Assessment Of Learner Developmentmentioning

confidence: 99%

Corpora and Language Assessment: The State of the Art

Park

2014

Language Assessment Quarterly

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This article outlines the current state of and recent developments in the use of corpora for language assessment and considers future directions with a special focus on computational methodology. Because corpora began to make inroads into language assessment in the 1990s, test developers have increasingly used them as a reference resource to become well versed in terms of the linguistic characteristics of expert and novice speakers' usage and identify the test construct. In regard to developing and validating language tests, large representative corpora, learner corpora, and specialized corpora have been actively used, as these corpora have made it possible to systematically compare the linguistic features associated with expert users with those found in learner language. Recent advances in computational approaches to assessment can facilitate this comparison to a great extent using technologies in automated essay scoring and learner language analysis. As an emerging area in the field of language assessment, corpus-based research should extend to less explored areas including compilation and longitudinal analysis of developmental corpora, fine-grained microanalysis of learner's development, and assessment attuned to individual learners who use different linguistic varieties. INTRODUCTIONThis article outlines the current state of and recent developments in the use of corpora for language assessment and considers future directions with a special focus on computational approaches to assessment. A corpus is a principled collection of language materials, spoken or written, compiled into an electronic database for the purpose of linguistic analysis (Biber, Conrad, & Reppen, 1998;Sinclair, 2004). Early discussions on the use of corpora for the purpose of assessing language use dates back to the mid-1990s when the potential of corpora for assessment was first recognized (e.g., Alderson, 1996). Since then, corpora have been increasingly used for large-scale standardized tests as well as for more holistic and developmental assessments. The present article has two related purposes: (a) to review the use of corpora in language assessment and (b) the potential contribution of corpora to language assessment, which include exploratory endeavors drawing on longitudinal, qualitative, and computational techniques. In the following sections, this article offers a survey of related research areas that are beginning to make a mark on the field and computational approaches to assessment drawing on natural language processing (NLP) technology.Correspondence should be sent to Kwanghyun Park,

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“…Factor analysis Saeed et al (2014), Grant et al (2012), Rojas et al (2016), Dörnyei and Chan (2013), Rodriguez and Shepard (2013), Kurt and Bilginer (2016), Kartchava (2016), Huang (2017), and Kieffer and Lesaux (2012) Regression Cakir and Solak (2014), Alzubaidi, Aldridge, and Khine (2014), Baghaei and Ravand (2015), Ghadirzadeh, Hashtroudi, and Shokri (2013), Guntzviller, Yale, and Jensen (2016), Swanson, Kudo, and Guzman-Orth (2016), Wang and Cheng (2016), Sohsah et al (2014), Chen and Adesope (2016), and Johnson, Leonardo, and Anthony (2012) Correlation mining Dörnyei and Chan (2013), Rouhani (2013), Guntzviller et al (2016), Alkharusi (2013), François and 3rd workshop on NLP for Computer-assisted Language Learning (2014), Vyatkina (2012), Vyatkina (2013), and Sharifi and Ahangary (2015) Causal data mining Hong et al (2014), Winke (2013), Gu and Cheung (2016), Yu (2013), Hong et al (2015), Kim and Kang (2014), Robson (2015), and Silverman et al (2015) Path mining Tragant et al (2013), Winke (2013), Gu and Cheung (2016), Kim and Kang (2014), Robson (2015), Silverman et al (2015), ...…”

Section: Edm Methods Referencesmentioning

confidence: 99%

Data mining in foreign language learning

Bravo‐Agapito

Bonilla

Seoane

2018

WIREs Data Min & Knowl

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Educational data mining (EDM) combines the techniques of data mining with educational data in order to provide students, instructors, and researchers with knowledge that can benefit academic processes. Due to globalization, foreign language learning (FLL) has become increasingly important. This work seeks to gain insight as to how data mining (DM) is being used to benefit FLL. For this purpose, an advanced review of pertinent research published from 2012 to 2017 was performed. After applying our screening method, 208 papers were selected for the exhaustive analysis. This analysis was divided into four aspects: context (educational environments, educational level), number of items, DM methods, and DM applications. The results indicated that 54% of studies were conducted in traditional environments, while only 3% of studies were performed in an m‐learning environment. In addition, 25 and 72% of the research was conducted in either a primary or secondary level, or in tertiary or adult level, respectively. Likewise, 76% of studies contained datasets of less than 1,000 items. The most utilized EDM methods were: factor analysis, regression, text mining, correlation mining, and causal DM. In addition, the studies analyzed showed that DM is mainly employed to predict the performance of students, to check learners' motivation, and to provide feedback for instructors. These results seem to indicate that although DM has much to offer the increasing number of language students, it is not being used to its full potential. This article is categorized under: Application Areas > Education and Learning Fundamental Concepts of Data and Knowledge > Data Concepts

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The effect of planning sub-processes on L2 writing fluency, grammatical complexity, and lexical complexity

Cited by 123 publications

References 42 publications

The Combined Effects of Pre-task and Careful Online Planning on EFL Learners' Written Discourse

The Combined Effects of Pre-task and Careful Online Planning on EFL Learners' Written Discourse

Corpora and Language Assessment: The State of the Art

Data mining in foreign language learning

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