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Record W4386663194 · doi:10.37237/120203

Student, Faculty, and Graduate Teaching Assistant Perceptions of Support Provided by a Graduate Student Writing Centre

2021· article· en· W4386663194 on OpenAlexaffabout
Victoria Handford, Joe Dobson, Yuhang Liu

Bibliographic record

VenueStudies in Self-Access Learning Journal · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsGraduate studentsMedical educationGraduate educationPerceptionPsychologyStudent engagementPedagogyMedicine

Abstract

fetched live from OpenAlex

The purpose of this mixed-methods study was to identify key factors in a discipline-specific, self-access graduate writing centre that both contribute to student success and that indicate needed improvements. The centre is located in a graduate education program at a university in Canada. Findings indicate the centre contributes to student success most directly by helping students improve their writing, which leads to an overall sense of confidence and engagement. Relationships with other students were also enhanced and found to be important. Faculty similarly noted that the improvements in writing and the strengthening of the graduate student culture were important gains. Graduate teaching assistants working in the centre said they benefited from improvements with their writing, which they linked to supporting students, as well as personal gains in their instructional skills. Suggested improvements included increasing appointment availability, adding workshops on new topics, increasing the availability of workshops and events, and increasing interaction between students and faculty at social events. These results indicate that providing targeted supports led by students but guided by faculty input and oversight can increase graduate student success and benefit graduate programs in general.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.332
GPT teacher head0.599
Teacher spread0.267 · 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 teacher head, not a consensus.

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

Citations3
Published2021
Admission routes2
Has abstractyes

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