Raising Students’ Self-Awareness of Their Conflict Communication Styles: Insights from an Intercultural Telecollaboration Project
Bibliographic record
Abstract
Intercultural communication is often affected by conflicts, which are not easy to resolve, mainly due to the clash of conflict communication styles. Direct/indirect ways to approach conflicts, emotional display/control, the ability to empathize and consider perspectives of others, cultural conventions, previous experiences with conflict, cooperativeness, and many other factors determine our conflict communication styles. It is important to acknowledge, though, that these styles are learned and are not rigid. They can differ depending on the context and situation. This article reports the results of an intercultural telecollaboration project, drawing on four sources of quantitative and qualitative data, i.e., the results of assessments conducted with the Interpersonal Reactivity Index, the Toronto Empathy Questionnaire, and a Conflict Styles Assessment based on the Thomas–Kilmann Conflict Mode Instrument, as well as students’ critical self-reflective feedback. The data were collected at a Mid-Atlantic minority-serving university from undergraduate students, who were invited to explore their conflict communication styles through a series of activities and then reflect on their experiences and the insights gained during this intercultural telecollaboration experience. As a result of this pedagogical intervention, most of the participants not only became aware of their conflict communication styles but also developed their empathy and ability to intervene to defend others who are discriminated against or attacked verbally.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".