The power of critical thinking in learning and teaching. An interview with Professor Stephen D. Brookfield
Bibliographic record
Abstract
In this wide-ranging interview, we discuss continuities and watersheds of Professor Stephen Brookfield’s world-renowned and massive contributions to Higher Education and Adult Education. While Brookfield’s work demonstrates a remarkable continuity in terms of multi-angled perspectives on critical thinking and democratisation, there are also some notable changes through the years, such as a turn to self-directed learning (in the 1980s), a focus on power dynamics (in the 1990s), a theoretical turn (heavily influenced by Critical Theory, at the turn of the century) and a turn towards the importance of race relations (in the noughties). The extensive interview includes discussions of Brookfield’s four lenses (students’ eyes, colleagues’ perceptions, theory and personal experience); the power of failure; credibility and authenticity as key criteria of being a good teacher; the inevitable omnipresence of power and an open, pragmatic approach to learning and teaching methods; the importance of feedback and assessment’s key role as learning; use and abuse of technology in the classroom; MOOCs not being a disruptive innovation; Higher Education’s potential as an agent of liberation and prevailing counter-forces; how educational institutions can encourage skillful and critically-reflected teaching; and the connection between art and pedagogy. Since beginning his teaching career in 1970, Stephen Brookfield has worked in England, Canada, Australia, and the U.S., teaching in a variety of adult, community, organisational and higher education settings (the latter include Harvard University and Columbia University). In his endeavour to help adults learn to think critically about the dominant ideologies they have internalised, Professor Brookfield has written, co-written or edited 19 books on adult learning, teaching, critical thinking, discussion methods, critical theory and teaching race.
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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 | 0.019 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.022 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.014 |
| 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".