TY - JOUR
T1 - Predicting the proficiency level of language learners using lexical indices
AU - Crossley, Scott A.
AU - Salsbury, Tom
AU - McNamara, Danielle S.
N1 - Funding Information:
This research was supported in part by the Institute for Education Sciences (IES R305A080589 and IES R305G20018-02). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the IES. The authors would also like to thank Christie Collins and Richard Raymond at Mississippi State University who provided valuable resources without which this study could not have happened. The authors would also like to thank the three anonymous reviewers who provided critical and welcome feedback on this study.
PY - 2012/4
Y1 - 2012/4
N2 - This study explores how second language (L2) texts written by learners at various proficiency levels can be classified using computational indices that characterize lexical competence. For this study, 100 writing samples taken from 100 L2 learners were analyzed using lexical indices reported by the computational tool Coh-Metrix. The L2 writing samples were categorized into beginning, intermediate, and advanced groupings based on the TOEFL and ACT ESL Compass scores of the writer. A discriminant function analysis was used to predict the level categorization of the texts using lexical indices related to breadth of lexical knowledge (word frequency, lexical diversity), depth of lexical knowledge (hypernymy, polysemy, semantic co-referentiality, and word meaningfulness), and access to core lexical items (word concreteness, familiarity, and imagability). The strongest predictors of an individual's proficiency level were word agability, word frequency, lexical diversity, and word familiarity. In total, the indices correctly classified 70% of the texts based on proficiency level in both a training and a test set. The authors argue for the applicability of a statistical model as a method to investigate lexical competence across language levels, as a method to assess L2 lexical development, and as a method to classify L2 proficiency.
AB - This study explores how second language (L2) texts written by learners at various proficiency levels can be classified using computational indices that characterize lexical competence. For this study, 100 writing samples taken from 100 L2 learners were analyzed using lexical indices reported by the computational tool Coh-Metrix. The L2 writing samples were categorized into beginning, intermediate, and advanced groupings based on the TOEFL and ACT ESL Compass scores of the writer. A discriminant function analysis was used to predict the level categorization of the texts using lexical indices related to breadth of lexical knowledge (word frequency, lexical diversity), depth of lexical knowledge (hypernymy, polysemy, semantic co-referentiality, and word meaningfulness), and access to core lexical items (word concreteness, familiarity, and imagability). The strongest predictors of an individual's proficiency level were word agability, word frequency, lexical diversity, and word familiarity. In total, the indices correctly classified 70% of the texts based on proficiency level in both a training and a test set. The authors argue for the applicability of a statistical model as a method to investigate lexical competence across language levels, as a method to assess L2 lexical development, and as a method to classify L2 proficiency.
KW - frequency
KW - language proficiency
KW - lexical competence
KW - lexical diversity
KW - second language acquisition
KW - word familiarity
KW - word imagability
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U2 - 10.1177/0265532211419331
DO - 10.1177/0265532211419331
M3 - Article
AN - SCOPUS:84860161784
SN - 0265-5322
VL - 29
SP - 243
EP - 263
JO - Language Testing
JF - Language Testing
IS - 2
ER -