Conflict management styles assessment and feedback for student self-awareness and team development
Bibliographic record
Abstract
Teamwork skills are foundational for career success and student learning in group projects. One of the major challenges of teamwork involves managing conflict among team members. In the current research, we develop a conflict management styles (CMS) psychometric inventory that assesses students’ levels of the five styles of managing conflict based on the long-standing dual concern model: avoiding, accommodating, dominating, integrating, and compromising. Taking an educational approach, we embed the inventory in an automated web-based system that is freely accessible (www.ITPmetrics.com), and that provides students with developmental feedback upon survey completion. We present psychometric evidence for the new CMS instrument (Study 1), and develop, deploy, and qualitatively evaluate the utility of a workshop for debriefing the CMS results in student learning teams in a large engineering professional development course (Study 2). This second study finds that students reported various learning benefits and overall enjoyment of the experience, along with some suggestions for improvements. Based on this, we offer implications for engineering education, the scholarship of teaching and learning, conflict theory, and future research directions.
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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".