Evaluating the Use of Role Playing Simulations in Teaching Negotation Skills to University Students
Bibliographic record
Abstract
This paper critically evaluates the use of role-playing simulations in a negotiation course taught to graduate students. The course consisted primarily of a series of simulations involving the alternative dispute resolution (ADR) processes of negotiation, facilitation and mediation. Data were obtained from two sets of questionnaires completed by 41 students before and after the course. A review of previous research reveals that despite the widespread use of role-playing simulations in education, there has been very little empirical evaluation of their effectiveness, especially in conflict resolution and planning. Comparison of the data acquired from the two surveys generated findings regarding student understanding of ADR processes and key issues in conflict resolution; the educational value of simulations; the amenability of types of planning and planning goals to ADR; appropriate learning objectives; the importance of negotiation skills in planning; challenges in conducting effective simulations; the value of simulations in resolving real conflicts; the utility of negotiation theory; and obstacles to applying ADR to planning disputes. More generally, the paper concludes that role-playing simulations are very effective for teaching negotiation skills to students, and preparing them to manage actual conflicts skillfully and to participate effectively in real ADR processes. However, this technique is somewhat less valuable for teaching aspects of planning other than conflict resolution. Surprisingly, prior experience with simulations had no significant influence on the responses to the pre-course survey. Also surprising was the lack of a significant correlation between final exam scores and responses to relevant questions on the post-course survey.
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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 | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".