Talk with Me: Student Pronoun Use as an Indicator of Discourse Health
Bibliographic record
Abstract
Identifying which online behaviours and interactions are associated with students’ perception of being supported will enable a deeper understanding of how those activities contribute to student learning experiences. Features of student language, especially verbally immediate behaviours, are one of the aspects of student interactions in need of greater exploration within discourse-based online learning environments. As a result, the verbally immediate behaviour of pronoun usage is explored within online courses where the primary learning mechanism is student discourse. Student posting behaviours and features of their language, specifically their use of different classes of pronouns, are explored from the perspective of how supported students felt in their courses as well as how their behaviours and pronoun usage changed over time. Findings suggest that students who were taking instructor-facilitated courses felt more supported which was associated with higher levels of interaction and increased consistency in student behaviours from week to week within the term. Those enrolled in peer-facilitated courses, who felt less supported, used pronouns differently than those who experienced greater levels of support, suggesting the potential for pronoun-based analytics.
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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 | 0.017 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".