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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".