Writing the Literature Review: Graduate Student Experiences
Bibliographic record
Abstract
Difficulties with academic writing tasks, such as the literature review, impact students’ timely completion of graduate degrees. A better understanding of graduate students’ perceptions of writing the literature review could enable supervisors, administrators, service providers, and graduate students themselves to overcome these difficulties. This paper presents a case study of graduate students at a secondary campus of a Canadian research university. It describes survey data and results from focus groups conducted between 2014 and 2015 by communications faculty, writing centre staff, and librarians. The focus group participants were Master’s and Doctoral students, including students situated within one discipline and those in interdisciplinary programs. The questions focused on the students’ experiences of writing the literature review as well as the supports both accessed and desired. Data analysis revealed four themes: (a) literature review as a new and fundamental genre; (b) literature review for multiple purposes, in multiple forms, and during multiple stages of a graduate program; (c) difficulties with managing large amounts of information; and (d) various approaches and tools are used for research and writing. Using an academic literacies approach, the paper addresses implications for campus program development and writing centre interventions and furthers research into graduate students’ experiences of writing literature reviews.
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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 | 0.025 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| 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".