Automatically Assessing Lexical Sophistication: Indices, Tools, Findings, and Application

Kyle, Kristopher; Crossley, Scott A.

doi:10.1002/tesq.194

Cited by 399 publications

(383 citation statements)

References 53 publications

(123 reference statements)

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“…Clearly, the absence of significant correlation between percentage scores and oral ability in the study indicates potential problems with the frequency-based principle for assessing vocabulary knowledge (Nation & Webb, 2011). Therefore, in order to further investigate the relationship between vocabulary knowledge and L2 speaking ability, it is important to be aware of other aspects of vocabulary knowledge besides frequency such as L1 cognate (Bardel, Gudmundson, & Lindqvist, 2012), speed of lexical retrieval (Fitzpatrick & Izura, 2011;Miralpeix & Meara, 2014), abstractness (Crossley, Salsbury, & McNamara, 2009), sense relations (Crossley, Salsbury, & McNamara, 2010), richness and sophistication (Lu, 2012), and multi-word units (Kyle & Crossley, 2014).…”

Section: Productive Vocabulary Measurementioning

confidence: 99%

Exploring the relationship between productive vocabulary knowledge and second language oral ability

Uchihara

Saito

2016

The Language Learning Journal

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The current study investigated the extent to which L2 learners' productive vocabulary knowledge could predict multiple dimensions of spontaneous speech production. A total of 39 EFL participants with varying L2 proficiency levels first completed a productive vocabulary knowledge task (Lex30). Their spontaneous speech, elicited via a series of picture description task, was then assessed for comprehensibility (i.e., ease of understanding), accentedness (i.e., linguistic nativelikeness), and fluency (i.e., speech rate). The findings showed that the productive vocabulary scores significantly correlated with L2 fluency, but not with comprehensibility or accentedness. Such results might indicate that more proficient L2 learners, as indicated by their productive vocabulary scores, might be able to speak spontaneously without too many pauses and repetitions, and at a faster tempo. Finally, future research directions will be discussed with a focus on the relationships between vocabulary knowledge and speaking.Key words: productive vocabulary knowledge; L2 vocabulary; L2 speaking; comprehensibility; fluency Introduction Measuring Productive Vocabulary KnowledgeVocabulary knowledge has gained recent prominence in second language (L2) education, as research seeks to examine the relationship between vocabulary size measures and general language proficiency. This has much practical appeal since scores from vocabulary size measures might act as a proxy for general language proficiency (Meara, 1996). Vocabulary size measures tend to elicit vocabulary knowledge via receptive, or passive, vocabulary knowledge tests (e.g., Vocabulary Levels Test; Nation, 1983Nation, , 1990; the Eurocentres Vocabulary Size Test; Meara & Jones, 1990). Accordingly, researchers (e.g. Milton, Wade, & Hopkins, 2010;Milton, 2010Milton, , 2013 have generally adopted a wide range of receptive vocabulary tests (e.g., standard yes/no vocabulary size tests), in order to compare test scores to overall proficiency benchmarks (e.g., CEFR levels; Milton 2010), placement tests (e.g., Harrington & Carey, 2009), and standardized proficiency examinations (e.g., IELTS;Milton et al., 2010). Additionally, other research has examined the predictive power of L2 vocabulary knowledge for the four main language skills (i.e., reading, writing, listening, and, to a lesser extent, speaking) (e.g. Baba, 2009;Farvardin & Koosha, 2011;Laufer & Levitzky-Aviad, 2015;Meara & Jones, 1990;Meara & Milton, 2003;Milton, 2009Milton, , 2010Milton, , 2013Milton et al., 2010;Qian, 1999, 2002, Schoonen et al., 2003and, Staehr, 2008and, Staehr, , 2009). This brief summary highlights that a majority of the existing studies likely elicit vocabulary knowledge receptively, and compare such knowledge with reading, writing, and listening performance.

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Section: Productive Vocabulary Measurementioning

confidence: 99%

Exploring the relationship between productive vocabulary knowledge and second language oral ability

Uchihara

Saito

2016

The Language Learning Journal

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“…Meaningfulness is assessed based on how related a word is to other words. According to the definition in [30], words like "animal," for example, are likely to be more meaningful than field-specific terms like "equine". Lexical sophistication involves the "depth and breadth of lexical knowledge [30]."…”

Section: Scope Of Analysismentioning

confidence: 99%

“…According to the definition in [30], words like "animal," for example, are likely to be more meaningful than field-specific terms like "equine". Lexical sophistication involves the "depth and breadth of lexical knowledge [30]." It is usually assessed using word frequency indices, which look at the frequency by which words from multiple large-scale corpora appear in a body of text [30].…”

Section: Scope Of Analysismentioning

confidence: 99%

“…Lexical sophistication involves the "depth and breadth of lexical knowledge [30]." It is usually assessed using word frequency indices, which look at the frequency by which words from multiple large-scale corpora appear in a body of text [30]. More concrete or more sophisticated words were found to be associated with a greater probability of course completion, while more meaningful words were found to be associated with a lower probability of course completion.…”

Section: Scope Of Analysismentioning

confidence: 99%

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Studying MOOC completion at scale using the MOOC replication framework

Andrés

Baker

Gašević

et al. 2018

Proceedings of the 8th International Conference on Learning Analytics and Knowledge

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Research on learner behaviors and course completion within Massive Open Online Courses (MOOCs) has been mostly confined to single courses, making the findings difficult to generalize across different data sets and to assess which contexts and types of courses these findings apply to. This paper reports on the development of the MOOC Replication Framework (MORF), a framework that facilitates the replication of previously published findings across multiple data sets and the seamless integration of new findings as new research is conducted or new hypotheses are generated. In the proof of concept presented here, we use MORF to attempt to replicate 15 previously published findings across 29 iterations of 17 MOOCs. The findings indicate that 12 of the 15 findings replicated significantly across the data sets. Results contradicting previously published findings were found in two cases. MORF enables larger-scale analysis of MOOC research questions than previously feasible, and enables researchers around the world to conduct analyses on huge multi-MOOC data sets without having to negotiate access to data.

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“…For example, specialized programs are available to assess the readability (and cohesion) of a given piece of text (Coh-Metrix;McNamara et al, 2014), the syntactic complexity of a document (Syntactic Complexity Analyzer; Lu, 2010), and a document's level of lexical sophistication (TAALES; Kyle & Crossley, 2015).…”

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confidence: 99%

TACIT: An open-source text analysis, crawling, and interpretation tool

et al. 2016

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As human activity and interaction increasingly take place online, the digital residues of these activities provide a valuable window into a range of psychological and social processes. A great deal of progress has been made toward utilizing these opportunities; however, the complexity of managing and analyzing the quantities of data currently available has limited both the types of analysis used and the number of researchers able to make use of these data. Although fields such as computer science have developed a range of techniques and methods for handling these difficulties, making use of those tools has often required specialized knowledge and programming experience. The Text Analysis, Crawling, and Interpretation Tool (TACIT) is designed to bridge this gap by providing an intuitive tool and interface for making use of state-of-the-art methods in text analysis and large-scale data management. Furthermore, TACIT is implemented as an open, extensible, plugin-driven architecture, which will allow other researchers to extend and expand these capabilities as new methods become available.

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Automatically Assessing Lexical Sophistication: Indices, Tools, Findings, and Application

Cited by 399 publications

References 53 publications

Exploring the relationship between productive vocabulary knowledge and second language oral ability

Exploring the relationship between productive vocabulary knowledge and second language oral ability

Studying MOOC completion at scale using the MOOC replication framework

TACIT: An open-source text analysis, crawling, and interpretation tool

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