Technology-Supported Curriculum-Based Measurement for Vocabulary Development in ESP: A Case Study in Marketing Education
Bibliographic record
Abstract
In this study we investigate the development and instructional application of a curriculum-based English vocabulary list tailored for marketing majors at a technological university in Taiwan. Combining corpus linguistics and curriculum-based measurement (CBM), we aimed to address the lexical challenges that low-proficiency learners in English for Specific Purposes contexts (ESP) face. We constructed a specialised corpus from open-access marketing textbooks and web-based content. We selected the Marketing Word List (MWL), comprising 208 high-frequency domain-specific terms, through frequency analysis and expert validation using the Delphi method. We subsequently integrated these terms into an online learning platform that delivered bilingual instructional materials, self-paced quizzes and weekly formative assessments. A two-month intervention showed a statistically significant improvement in students’ vocabulary performance. Preliminary evidence suggested that the MWL enhanced domain-specific vocabulary acquisition, and learners reported increased confidence and perceived relevance of the word list to their studies. The findings highlight CBM-aligned vocabulary instruction’s pedagogical potential, supported by technology-enhanced delivery systems, in tertiary education. This approach offers a replicable model for aligning assessment, instruction and content in ESP instruction for low-proficiency English learners.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".