Interpersonal Relationships in Education: Exploring Teacher Interpersonal Communication
Bibliographic record
Abstract
In this conceptual presentation the author will introduce the Teacher Interpersonal Circle , formerly known as the Model for Interpersonal Behaviour (Wubbels et al., 1985). The paper will review the Questionnaire for Teacher Interaction (Brekelmans, den Brok, van Tartwijk, and Wubbels, 2005; Wubbels & Brekelmans, 1998; Wubbels, Creton, and Hooymayers, 1985), an instrument for mapping interpersonal teacher behaviour. The results can be “mapped” visually into one of eight styles, generating a specific profile of a teacher’s teacher-student relationship style. In this presentation the author will explore the questions “Which teacher-student relationships styles are most effective for cognitive and affective outcomes?” and “How can the QTI and TIC support the development of effective teacher-student relationships?” The TIC suggests that two of the styles are more effective than the others. The model has been carefully researched over the past 30 years, and is used across the world. Studies exploring the reliability and validity of the QTI have been very encouraging (Brekelmans, Wubbels, & Creton, 1990; den Brok, 2001; Wubbels & Levy, 1991). This paper will explore implications for teacher-student relationships and teacher interpersonal behaviour and communication, considering how the instrument can provide important feedback for teachers, teacher educators, and educational leaders.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".