The role of conflicts in the learning process
Bibliographic record
Abstract
Tutoring strategies have evolved from direct learning to cooperative learning involving various agents, which are either computer simulated or real human beings. During these learning sessions conflicts then arise since the student must interact with several simulated participants such as the tutor, the companion, or the troublemaker (a companion able to mislead the learner). We call these conflicts external conflicts . Some of them are accidental but others are intentional in order to test the learner's self-confidence and to detect internal conflicts that oppose new knowledge to existing learner knowledge. In this article, we highlight the usefulness of conflicts in various cooperative learning strategies, showing that they contribute with social interaction to the development of cognition. In particular, we discuss the advantage of an intentional external conflict caused by a difference of opinion between the student and the troublemaker. This difference of opinion is introduced in order to get the student to evaluate his own opinion and cognitive schema. If the learner presents a cognitive dissonance (discord between ideas) a dialogue with the troublemaker will help him correct his internal conflicts. Then, the tutor and the troublemaker cooperate to manage a learning session. We present experimental results that show the gain brought by the troublemaker conflicts in learning improvement.
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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 | 0.012 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".