Learning and Retaining Technical Vocabulary with Enhancement Activities in an ESP Course
Bibliographic record
Abstract
The purpose of this paper is to explore the effectiveness of vocabulary enhancement activities on vocabulary learning in an ESP course. In designing the activities, technical terms on journal entries were chosen for the acquisition of language necessary for the successful implementation of accounting major’s professional tasks. The desirable difficulty approach and the four strands principle，focused input, meaning-focused output, language-focused learning and fluency development, were guidelines in combining subject matter and English language learning. To test the result of the activities, the Vocabulary Knowledge Scale was employed to measure students’ knowledge of 50 vocabulary items. Subjects of the ESP course in discussion comprised 200 accounting juniors in Guangdong University of Foreign Studies in China. Half of them in Group A read the texts and did matching exercises and translation exercises. The other half in Group B read the texts and practiced journal entry activity, targeting at accounting concepts and terminologies. The results revealed that Group B gained better results than Group A at a post-test. After the test a reflection on the vocabulary activities was gathered among the participants of Group B. The feedback further proved that the students did benefit from the enhancement activities on selected technical terms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".