Using the Teaching Perspectives Inventory (TPI) to examine the relationship between teaching perspectives and disciplines in higher education
Bibliographic record
Abstract
This study examined the relationship between different views of teaching and dimensions that differentiate academic disciplines. A total of 114 academics within Greece and 127 academics from similar disciplines but other countries were compared using the Teaching Perspectives Inventory (TPI). Distinctions among disciplines for both groups were based on Biglan’s 3-dimensional classification (Pure/Applied; Hard/Soft; Life/non-Life). The objective of this study was to examine to what extent the teaching perspectives of the academics differ from one classification category to another. Greek academics represented participants in 15 departments across 9 universities throughout Greece, while the international sample was drawn from the TPI database. Statistical analysis indicated better differentiation of Biglan’s classification for its Life/non-Life dimension than for Pure/Applied or Hard/Soft between the combined samples for four Teaching Perspectives: Transmission, Developmental, Nurturing and Social Reform. No perspectives differences existed for Biglan’s Hard/Soft categorisation, although Greek professors were significantly higher in Soft rather than Hard disciplines in the Developmental, Nurturing and Social Reform perspectives compared to their global counterparts. They also demonstrated overall higher ‘Transmission’ scores. The authors conclude that disciplinary differences are real, but that they are small compared to the interpersonal ones or even to the international ones. The relationships between the teaching perspectives and Biglan’s classifications are further discussed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".