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Record W4230110372 · doi:10.22215/etd/2016-11701

Designing and Evaluating Training for Discipline-Specific Peer Writing Tutors

2016· dissertation· en· W4230110372 on OpenAlexaffabout
Shelley Appleby-Ostroff

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsCarleton University
Fundersnot available
KeywordsTUTORPeer tutorSet (abstract data type)Training (meteorology)Process (computing)Mathematics educationPedagogyEmpirical researchPsychologyWriting centerComputer scienceMedical educationMedicineEpistemology

Abstract

fetched live from OpenAlex

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.,

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.051
metaresearch head score (Gemma)0.129
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0510.129
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0040.003
Scholarly communication0.0050.003
Open science0.0040.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.244
GPT teacher head0.507
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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