Lexical Dimensions and BEAM-Lex Vocabulary Assessment
A vocabulary assessment and item-design project using lexical dimensions such as frequency, complexity, proximity, polysemy, and diversity.
Overview
This project builds on my ongoing research on lexical dimensions: the measurable features of words and word meanings that shape vocabulary knowledge, item difficulty, and reading comprehension. The work connects corpus-based word features, item response modeling, and AI-supported item development.
BEAM-Lex is the applied assessment strand of this work. It focuses on building vocabulary measures that sample words and meanings more systematically and provide better information about what learners know.
Why this matters
Vocabulary assessment often treats words as interchangeable units, but words differ in frequency, complexity, semantic proximity, polysemy, and distribution across contexts. These features affect how difficult items are and what they reveal about learners. Better vocabulary assessment requires better models of words, meanings, and item features.
Current work
- Developing AI-supported vocabulary items targeted to specific word meanings.
- Using lexical dimensions to sample words and interpret item difficulty.
- Connecting vocabulary assessment to adolescent reading comprehension.
- Building tools that support rapid but principled assessment development.
Guiding questions
- Which lexical features best explain vocabulary item difficulty?
- How can AI-generated items be evaluated for quality, fairness, and meaning specificity?
- How can vocabulary assessments provide information that is useful for instruction?