Discourse Moves and Emotion in Knowledge Building Discourse and Metadiscourse
Bibliographic record
Abstract
This paper explores the possibility that knowledge building metadiscourse-discourse about knowledge building-can produce a positive feedback loop, with positive emotional state and knowledge advancement serving to increase each other. Grades 2 and 3 students’ utterances over several months were analyzed as a unit of study, starting with identification of each discourse move and corresponding emotion, defined as a state. These states were then analyzed over time, with a focus on metadiscourse sessions in which students reflected on earlier discourse to identify questions and ideas to be pursued in greater depth. Each discourse move-emotional state was analyzed to determine frequency, transition from one state to another, and spread of each state such as “reflection and positive” and “proposing new directions for inquiry and curiosity.” These two states were among the most frequently occurring in the metadiscourse sessions and virtually absent in other discourse sessions. Transition rates indicated that reflection tended to trigger more reflection, and proposing a new direction led to more proposals for new directions. Sequential pattern analysis suggested sub-sequences specific to metadiscourse sessions. Overall, results indicate that engaging in metadiscourse contributes to students’ productive KB and positive emotions.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".