Employing the intellectual virtues to better understand argumentation interventions in education
Bibliographic record
Abstract
Argumentation-based classroom interventions are a growing alternative for stimulating conceptual learning, thinking, and communicative skills. However, not all classroom argumentation is desired, nor does every argumentation design lead students to develop their abilities and understanding. In the educational literature, productive argumentation has been associated particularly with deliberation due to the design properties that deliberative practices demand from students, such as collaborating towards a goal, revising one’s own opinion, listening to others, and changing their minds when it is necessary to arrive at a collective decision or problem resolution. We contend that what makes deliberation productive is not argumentation in itself, but how a certain type of design scaffolds students into virtuous-like behavior, which can be the enabling condition for productive argumentation in classroom activities. Through the exploration of three cases of classroom argumentation and discussion experiences, we hypothesize that virtuous-like behavior may serve as an enabling condition for each intervention. In particular, intellectually humble behaviors could be scaffolded within these interventions because all three create the proper environment for students to revise their own positions, listen carefully to others, and change their minds in light of appropriate reasoning or new evidence. Employing an intellectual virtues framework, advances our understanding of how to design classroom environments for productive argumentation. This paper thus presents a novel and pioneering approach to understanding argumentation in the classroom by incorporating the concept of intellectual virtues, bridging the gap between virtues and traditional research, and offering fresh perspectives on the field.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".