Discipline-Specific Language Instruction for International Students in Introductory Economics
Bibliographic record
Abstract
This paper explores student perceptions of the effects of pairing discipline-specific language instruction with the traditional method of course delivery in economics. Our research involved teaching content-based English as an additional language (EAL) tutorials to a small group of ten international students taking first-year introductory economics courses. These voluntary participants completed pre- and post-treatment assessments with exit interviews at the end of the project. Assessment results and interviews suggest that students perceive that discipline-specific language instruction such as our EAL tutorials assists in the development of increased content and language proficiency. They also believe that vocabulary development is one of the most critical activities to support these goals; reading skills are also important but require more time and commitment than students can afford to give. Despite the students’ interest in the project, their heavy class schedules prevented many from participating; our group was limited to ten students which precludes any assurance of statistical significance. In spite of the limitations, we believe that the project can still contribute valuable qualitative lessons to the literature of content-based language instruction in which the discipline of economics has not been well represented. Cette communication explore la manière dont les étudiants perçoivent les effets du jumelage de l’enseignement de la langue spécifique à une discipline avec l’enseignement d’un cours d’économie selon la méthode traditionnelle d’enseignement. Notre recherche a porté sur l’enseignement en tutorat de l’anglais langue additionnelle (ALA) fondé sur le contenu à un petit groupe de dix étudiants internationaux inscrits dans des cours de première année d’introduction à l’économie. Ces participants bénévoles ont complété une évaluation avant et après le cours et ont été interviewés à la fin du projet. Les résultats de l’évaluation et les entrevues suggèrent que les étudiants ont le sentiment que l’enseignement de la langue spécifique à une discipline, tel que nos cours d’ALA en tutorat, les aident à développer une meilleure compréhension du contenu du cours et de la langue. Ils pensent également que l’acquisition du vocabulaire est l’une des activités les plus importantes pour réaliser ces objectifs. Les compétences en lecture sont également importantes mais requièrent davantage de temps et d’engagement que ce que les étudiants sont en mesure de fournir. Malgré l’intérêt des étudiants dans le projet, leur emploi du temps très chargé a empêché plusieurs d’entre eux d’y participer. Notre groupe a été limité à dix étudiants, ce qui écarte toute assurance de signification statistique. Malgré ces limites, nous croyons que ce projet peut malgré tout apporter une contribution qualitative appréciable à la documentation qui existe sur l’enseignement de la langue spécifique à une discipline dans laquelle l’économie n’a pas souvent été représentée.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".