Analyzing lexical features to predict Indian ITAs’ performance in a university oral English assessment
Rahman, Raihan
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https://hdl.handle.net/2142/129522
Description
Title
Analyzing lexical features to predict Indian ITAs’ performance in a university oral English assessment
Author(s)
Rahman, Raihan
Issue Date
2025-04-18
Director of Research (if dissertation) or Advisor (if thesis)
Yan, Xun
Committee Member(s)
Zhang, Qiusi
Department of Study
Linguistics
Discipline
Teaching of English Sec Lang
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.A.
Degree Level
Thesis
Keyword(s)
Oral English assessment
Lexical features
lexical diversity
lexical sophistication
collocational use
Indian ITAs
language proficiency
TESL
Abstract
This study investigates the extent to which lexical features can predict human rater assessments of high-performing Indian International Teaching Assistants (ITAs), examining whether metrics of lexical diversity, sophistication, and collocational use predict between Level 4 and Level 5 ratings. To address this question, sixty-six speaking test transcripts from Indian ITAs were analyzed using computational tools that generate quantitative measures of vocabulary use, including TAALES and TAALED. Binary logistic regression analysis revealed that while lexical diversity measures did not significantly predict between advanced proficiency levels, lexical sophistication measures (word frequency) and collocational metrics demonstrated moderate predictive power. The findings show a complex pattern where Level 5 candidates tend to use less frequent individual words but more conventional multi-word expressions, suggesting sophisticated vocabulary deployment that balances precision with accessibility. These results contribute to both the theoretical understanding of lexical development at advanced proficiency levels and the practical assessment of ITAs, suggesting that beyond a certain threshold, lexical sophistication and collocational competence play more decisive roles than diversity in predicting highly proficient speakers.
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