9. Language Diversity & Practice in Higher Education: Can Discipline-Specific Language Instruction Improve Economics Learning Outcomes?
Bibliographic record
Abstract
In the field of second language acquisition, discipline-specific language instruction is becoming widely known as Content and Language Integrated Learning. This method includes any activity that involves teaching a subject in a second language for the purpose of teaching both the subject content and the language. Research has shown that this two for one approach increases students’ content knowledge and language proficiency in both the short and long terms (Baik & Greig, 2009; Kasper, 1997; Song, 2006). These studies have been conducted using a variety of subjects in combination with several second languages, but the combination of economics and English has not been explored in the literature. Our research involved teaching English as an Additional Language (EAL) to international students taking an introductory economics course. Ten voluntary participants completed pre- and post-treatment assessments as well as exit interviews. Assessment results indicate that vocabulary instruction is correlated to success in economics although reading strategy instruction did not have the same impact.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".