Alternative sources of feedback and second language writing development in university content courses
Bibliographic record
Abstract
Abstract Despite a strong intuitive sense held by instructors that feedback practices can help scaffold L2 writers’ composition processes a number of questions remain concerning the manner best suited to deliver this feedback and its ultimate impact on literacy development. This paper presents findings from on an eight-month longitudinal ethnographic case study of five international Japanese undergraduate students and their efforts to navigate the writing requirements of their content courses at a large Canadian university. While confirming the importance of instructor based feedback practices and their potential as valuable language learning experiences, findings from this research also highlight language learners’ perceived importance of “alternative sources of feedback” for their L2 writing development. Friends, roommates, and writing center tutors amongst others, were seen as valuable sources of advice on writing that could compensate for perceived problems with content instructor’s feedback while offering feedback opportunities which were more closely associated to students ideal representation of this pedagogic tool. Implications focus on the advantages of widening our focus when understanding of feedback practices to also include paying closer attention to the impact of the ‘invisible partners’ which also help shape students' literacy development and the bridges that might be built between these and more formal modes of instruction.
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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 | 0.013 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".