Teaching and Learning in a First-Year Writing Skills Transfer Course: Investigating College Professor and Student Experiences
Bibliographic record
Abstract
When Seneca Polytechnic replaced EAC150, an essay-based English course, with COM101, a first-semester writing course based on writing skills transfer, we saw the opportunity to investigate both professors’ and students’ experiences of the new approach. Specifically, we wanted to know how professors conceptualized and taught COM101 and also how students connected their writing for COM101 with other writing they did at Seneca, their workplaces, and in their personal lives. From 2018–2020 we conducted qualitative surveys with professors and mixed-method surveys with students and applied inductive, thematic coding to all qualitative data. The data results were encouraging: student responses indicated that COM101 positively affected their writing and reported transferring writing skills to other areas of their lives. In addition, professor responses indicated that they actively engaged with skills transfer pedagogy, despite the fact that COM101 demanded a significant change in approach. In professor responses that indicated resistance to the new approach we found valuable lessons about the core ideas of transfer, including negative transfer, and the difficulties that anyone – professors and students alike – face in new learning situations.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".