Feel, think, teach – Emotional Underpinnings of Approaches to Teaching in Higher Education
Bibliographic record
Abstract
The paper investigates relations between higher education teachers’ approaches to teaching and their emotions during teaching, as well as their emotion regulation strategies. Based on the assumption that the approaches hinge on emotional experiences with higher education teaching and learning, three studies assessed teachers’ emotions, their emotion regulation strategies and their approaches to teaching with questionnaires. Study 1, with n = 145 German university teachers and teaching assistants, found relations between positive emotions and the student-oriented approach to teaching, but not with negative emotions. In addition, cognitive reappraisal and expressive suppression were related to the student-oriented approach. Study 2, with n = 198 German teachers, replicated these findings and, in addition, found relations between perspective taking, empathic concern and personal distress, and the student-oriented approach. Study 3, with n = 76 Australian and New Zealand teachers, again replicated and extended the findings by establishing a relation between negative emotions and the content-oriented approach to teaching. The results of all studies together indicate a significant emotional component of the approaches to teaching. Positive emotions are not only directly related to the student-oriented approach, but also partially mediate the relation between cognitive reappraisal and the student-oriented approach. This link seems to generalize to emotional components of empathy. In addition, the cultural-educational context seems to moderate the relations between negative emotions and the content-oriented approach to teaching. Limitations and directions for future research and educational practice are discussed.
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 | 0.002 | 0.005 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".