Impacts of a Digital Dialogue Game and Epistemic Beliefs on Argumentative Discourse and Willingness to Argue
Bibliographic record
Abstract
The goal of this study was to explore how students debate with their peers within a designed context using a digital dialogue game, and whether their epistemic beliefs are significant to the outcomes. Epistemic beliefs are known to colour student interactions within argumentative discourse, leading some students to hold back from interactions. By designing an online small group activity based around an issue both important and controversial to the students, with multiple viewpoints in each group and with the scaffolding provided by a dialogue game, it was examined whether these epistemic effects were still evident within their argumentative discourse. Furthermore, the study examined whether the activity design improves students' willingness to argue with each other, and their openness to attitudinal change. A pretest, posttest design was used with students who were assigned to groups of four or five and asked to argue on a controversial topic. Their aim was to explore various perspectives and to debate the pros and cons of the use of Genetically Modified Organisms (GMOs). While previous research has shown that some epistemic beliefs lead to less critical engagement with peers, the results presented here demonstrate that activity design is also an important factor in successful engagement within argumentative discourse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".