Attachment Representations and Emotions in Teaching as Antecedents to Teaching Styles in Higher Education
Bibliographic record
Abstract
The current study explored how relational antecedents and emotional experiences were associated with faculty-centered versus student-centered approaches to teaching in higher education. One hundred and forty-one faculty members from two institutions of higher education in the United States completed self-report surveys regarding an undergraduate course they were teaching that semester. Path analyses showed that faculty reports of a secure attachment style were positively correlated with positive teaching-related emotions and, in turn, with greater use of a student-centered, inquiry-based approach to teaching emphasizing engagement with course material and restructuring of students’ knowledge. Faculty reports of avoidant and anxious-ambivalent attachment styles were correlated with greater negative teaching-related emotions and, in turn, with greater use of a faculty-centered, direct instruction teaching approach. These findings suggest that attachment theory is a useful lens through which to understand why faculty might feel more positively or negatively about their teaching and, in turn, the teaching approaches they employ. We discuss how our findings might inform the re-design of faculty training programs to encourage reflection on relationship styles and greater positive emotions about teaching.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".