University of Illinois Urbana-Champaign
Analyzing lexical features to predict Indian ITAs’ performance in a university oral English assessment
Abstract
dc:descriptionThis 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.
Degree
thesis:*- Name thesis:degree_name
- M.A.
- Level thesis:degree_level
- Thesis
- Discipline thesis:degree_discipline
- Teaching of English Sec Lang
- Grantor
- University of Illinois Urbana-Champaign
- Year dc:date
- 2025
Author and committee
dc:creator, dc:contributor.*- Author dc:creator
-
- Rahman, Raihan
- Contributors dc:contributor
-
- Yan, Xun
- Zhang, Qiusi
Subjects
dc:subject × 8Rights
dc:rights- Statement dc:rights
-
- Copyright 2025 Raihan Rahman
- Language dc:language
- en, eng
Identifiers
dc:identifier.*- Handle dc:identifier
- https://hdl.handle.net/2142/129522