Designing and Evaluating Training for Discipline-Specific Peer Writing Tutors
Bibliographic record
Abstract
This qualitative empirical study develops a set of theory-supported criteria for designing effective training programs for peer tutors in discipline-specific writing centres.It then assesses whether the criteria are present in a writing tutor-training program at an Eastern Ontario law school.The study draws on theories about writing-centre pedagogy, the writing process, and effective training for peer writing tutors in developing the criteria.Measuring the law school's writing tutor-training program against the theory-supported criteria reveals the presence of most of the criteria.The only significant shortcomings identified are that the law school's program does not select tutors on the basis of their personal attributes, focuses more on practice than theory, and does not include regular observation and self-evaluation activities.These findings suggest that the program is effective in training law-student tutors to provide discipline-specific writing support to their peers. Chapter 1: IntroductionPeer feedback is an integral part of academic writing instruction in North American universities.Writing centre conferences, in-class tutoring, peer writing groups, and writing fellows 1 are now common features of many undergraduate and graduate writing programs (Fitzgerald & Ianetta, 2016).These collaborative learning approaches to writing instruction actively involve students in their own learning and aim to help writers enter new discourse communities.Evolved from the grammar "fix-it shops" of the 1970s, writing centres offering individualized academic writing instruction have become fixtures at most North American universities (Carino,1995).During writing centre conferences, students trained as writing tutors 2 offer personalized writing support to their peers primarily through Socratic questioning and active listening techniques.These techniques help tutors guide student writers in recalling knowledge they already have, but have trouble accessing, as well as in constructing new knowledge.Typically, these discovery-based "one-to-one writing conferences" (Harris, 1986), focus on helping students become better writers rather than only on fixing a particular piece of writing (e.g., North, 1984). 1 Writing fellows are students who tutor their peers "in specific courses, often in collaboration with the instructors of these courses" (Fitzgerald & Ianetta, 2016, p. 157) Writing fellows "are probably most likely to work continuously and in depth with disciplinary genres" (Fitzgerald & Ianetta, 2016, p. 157). 2 Some Canadian university writing centres employ professional writing instructors (e.g.,
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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 | 0.051 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".