Metadiscourse in Students’ Academic Writing: Case Study of Umaru Musa Yar’adua University and Al-Qalam University Katsina
Bibliographic record
Abstract
Studies on writing, thus, become crucial because when students make the transition from Secondary School to a Tertiary Institution, they encounter many challenges. One of them is the writing of B.A projects. Most of these undergraduate students both in L1 (English as a first language) and L2 (English as a second language) still find it difficult to argue, discuss or evaluate competently as well as persuasively in English essay writing. The present study aimed at exploring metadiscoursal choice and its influence on the success of students’ academic writing. The study was conducted within the framework of Appraisal Theory. The data was randomly generated from the written essays by thirty selected Level 400 students both from Umaru Musa Yar’adua University and Al-Qalam University Katsina. Also, the data was descriptively alaysed and presented. It was discovered that six (6) of the essays do not contain the relevant elements for this study, thus excluded from the analysis. To achieve the main objective of this study, the first six categories of the most successful essays and the least successful ones were taken for in-depth analysis. They were analysed paragraph by paragraph and then each interactional metadiscourse element was separately discussed as a whole. The findings showed that many of the students were not exposed to these elements, thus, they write academic essay the way they speak. It is against these findings that the present study unravels that embedding the teaching of metadiscourse in cumulative learning practices could consequently empower students to develop both linguistically and intellectually.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".