The value of interactional metadiscourse in university level writing: Differences between high and low performing undergraduate business students
Bibliographic record
Abstract
This study investigates the use of interactional metadiscourse within a third-year Human Resources course at a large North American university. Analysing final individual writing assignments, higher-performing (grades 80 and above) and lower-performing (grades 74 and below) students were compared in terms of how they differ in their use of interactional metadiscourse. The Authorial Voice Analyzer (Yoon, 2017) was employed to extract interactional metadiscourse features, including hedges, boosters, attitude markers, self-mentions, and engagement markers. Intergroup differences were then assessed using Cohen's d . Key findings include higher-performing students employing a greater variety of hedge types and using self-mentions more frequently, while lower-performing students relied more heavily on reader engagement markers, particularly by way of reader pronouns. These results suggest that higher-graded students in business courses may be more adept at managing interactional metadiscourse to present an appropriate authorial stance, while lower-graded students tend to over-engage with the reader. Pedagogical implications include the need for writing instructors to focus on teaching students how to strategically employ hedges and self-mentions to improve the quality and authority of their writing in business-related disciplines. These insights can help shape targeted writing interventions aimed at improving student performance in content-focused courses, such as Human Resources. • This study explored metadiscourse in a university level Human Resources course. • Individual writing assignments were grouped into higher- and lower-graded papers. • Higher graded papers used a greater variety of hedge types. • Lower graded papers used more reader engagement markers.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".