Full Paper: Improving Educational Equity and Outcomes in a First-Year Engineering Programming Course through a Content and Language Integrated Approach
Bibliographic record
Abstract
Non-native English speakers may encounter unique challenges when learning computer programming for the first time.It could be argued that these students must navigate the complexities of two foreign languages simultaneously.This work aimed to address some of the language-related barriers faced by a cohort of ESL (English as a Second Language) international students in a first-year engineering programming course.The objective was to achieve educational equity for this group of non-native English-speaking students and support their learning outcomes and success in the course.In an interdisciplinary effort, resulting from the collaboration between an engineering instructor and an academic English instructor, several learning modules on computer programming topics were designed through a linguistic lens.To inform our pedagogy and assess the effectiveness of our approach, low-stakes assessments were incorporated into each module, and anonymous student feedback was collected.The results suggest that our approach contributed to improved student performance in the course and increased confidence in programming.Nonetheless, further work is needed to refine linguistic support strategies and enhance comprehension of complex programming tasks.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".