Pedagogical and Curricular Practices for Computer Science Education with English Language Learners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In many post-secondary institutions, English is the language of instruction in computer science, but our students may not be fluent in English. I am interested in exploring the pedagogical and curricular practices that computer science faculty members use when teaching students who are English Language Learners (ELL) and do not have a common native language. Identifying effective strategies for ELL students is one piece of creating an inclusive classroom. Inclusive teaching provides an accessible learning environment for all students. Universal Design for Learning is a framework that provides principles and guidelines which enable educators to create inclusive and accessible courses. Evidence suggests that strategies that remove barriers for a group of students also improve learning for all. I hypothesize that strategies that are effective for ELL students will also make computer science more accessible and inclusive for the entire student body. As the first step in this research, I intend to survey Canadian teaching-focused faculty members to investigate which teaching strategies they use, whether they think these strategies are effective for ELL students, and whether they think those strategies are inclusive.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 it