A Lexical Profile Analysis of a Diagnostic Writing Assessment: The Relationship between Lexical Profiles and Writing Proficiency
Bibliographic record
Abstract
Effective use of vocabulary contributes to academic language proficiency and academic success (Douglas, 2013).Sophisticated vocabulary use relates to increased quality of writing (Laufer & Nation, 1995;Kyle & Crossley, 2015).However, it is unclear which lexical sophistication characteristics contribute to writing quality assessments.Furthermore, previous studies focused on general assessments, rather than English for Specific Purposes diagnostic assessments which aid in early intervention for academic support.The present study investigates the relationship between vocabulary sophistication indices and writing scores (N = 353) on a post-entry university diagnostic test for engineers (Fox & Artemeva, 2017).Multiple lexical sophistication approaches were compared to writing scores and differences in lexical profile characteristics of successful and unsuccessful students were compared.Results indicated that samples of successful writing have a higher presence of tokens, types, lexical stretch, academic vocabulary and formulaic language.The findings have pedagogical implications for remedial writing instruction of engineering students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".