Integration of English Language Modules into the Introduction to Engineering Design Course in the Vantage College 1st Year Engineering Program at the University of British Columbia
Bibliographic record
Abstract
Abstract – Vantage College at the University of British Columbia (UBC) offers a unique undergraduate first-year program to international students, built around the integration of English language education with academic degree-focused courses. The particular emphasis of this program is on creating an effective learning environment for students with varying levels of English language proficiency and diverse cultural backgrounds. In the engineering design course, which demands a high level of student engagement and collaboration, this Academic English support in an adjunct course is tailored towards the improvement of presentation skills, verbal and written literacy, and group work. This paper provides an overview of the partnership between the engineering design course and its linked language-enrichment course. Some of the language-based practices are described and their successes are evaluated based on observations and student feedback through a survey. The overall student response on the effectiveness of language activities is found to be positive and oral presentation exercises are considered to be particularly helpful in improving the students’ experience in the design course.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".