Effects of Persuasion and Discussion Goals On Writing, Cognitive Load, and Learning in Science
Bibliographic record
Abstract
Argumentation can contribute significantly to content area learning. Recent research has raised questions about the effects of discussion (deliberation) goals versus persuasion (disputation) goals on reasoning and learning. This is the first study to compare the effects of these writing goals on individual writing to learn. Grade 7 and 8 students learned about buoyancy through argument writing. A 2 x 2 x 2 between-subjects pretest-post-test randomized experiment was used to investigate the effects of two types of argument writing goals (persuasion versus discussion), two distributions of writing sub-goals (segmented versus clustered), and two levels of writing achievement (low versus high) on bias/balance in reasoning, cognitive load, and learning. Results showed that segmented sub-goals were rated less difficult than clustered sub-goals. In a three-way interaction, for high-achieving writers, sub-goal segmentation reduced cognitive load in discussion writing, but increased it in persuasive writing. Argument goal type and sub-goal distribution affected bias/balance in claims and inferences. These results suggest that the effects of argument goal type are moderated by sub-goal distribution and previous writing achievement.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".