Use of Metadiscourse in the Persuasive Writing of Nigerian Undergraduates
Bibliographic record
Abstract
Persuasive writing is a very important prerequisite for undergraduates in their academic life endeavour. For the students to effectively compose good persuasive writing, they need to understand and employ metadiscourse appropriately in their writing. However, a large number of Nigerian undergraduates face lots of challenges in using metadiscourse in their writing. Therefore, this study investigated the use of metadiscourse in the persuasive writing of Nigerian undergraduates, by examining the relationship between the frequency of metadiscourse used and the persuasive writing quality. The participants of the study are second-year students of English in one of the Nigerian Universities. The data used in the study were collected through the participants’ written persuasive essays. The essays were analyzed by highlighting all the metadiscourse used in the texts. The findings indicate that the participants’ persuasive essays have a low deployment of metadiscourse which also correlates with their persuasive writing quality. It was observed that almost all the metadiscourse markers were underutilized by the participants such as endophoric markers, evidential, code glosses, hedges and self-mention. Some other metadiscourse were left out in some of the participants' persuasive essays. The study highlights some benefits of the use of metadiscourse and some implications that would improve the teaching and learning of metadiscourse, particularly in the Nigerian setting.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".