Developing literature review writing and citation practices through an online writing tutorial series: Corpus-based evidence
Bibliographic record
Abstract
Writing a literature review (LR) in English can be a daunting task for non-native English-speaking graduate students due to the complexities of this academic genre. To help graduate students raise genre awareness and develop LR writing skills, a five-unit online tutorial series was designed and implemented at a large university in Canada. The tutorial focuses on the following features of the LR genre: logical structure, academic vocabulary, syntax, as well as citation practices. Each tutorial unit includes an interactive e-book with explanations, examples, quizzes, and an individual or collaborative LR writing assignment. Twenty-nine non-native English-speaking graduate students from various institutions participated in the tutorials and completed five writing tasks. This study reports on their developmental trajectories in writing performance in terms of cohesion, lexical features, syntactic features, and citation practices as shown in three individual writing tasks. Corpus-based analyses indicate that noticeable, often non-linear, changes are observed in several features (e.g., use of connectives, range and frequency of academic vocabulary) across the participants' writing samples. Meanwhile, citation analysis shows a steady increase in the use of integral citations in the participants' writing samples, as measured with occurrence by the number of sentences, along with a more diverse use of reporting verbs and hedges in their final writing samples. Pedagogical implications are discussed.
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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 | 0.066 | 0.321 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".