Bibliographic record
Abstract
How many times have we as teachers been confronted with situations in which we really were not sure what to do? We “flew by the seat of our pants,” usually doing with our learners what had been done with us. It would be useful to be able to turn to a set of guiding principles based on evidence, or at least on long term successful experience. ![][1] Fortunately, a body of theory exists that can inform practice. An unfortunate gap between academics and practitioners, however, has led to a perception of theory as belonging to an “ivory tower” and not relevant to practice. Yet the old adage that “there is nothing more practical than a good theory” still rings true today. This chapter describes several educational theories and guiding principles and then shows how these could be applied to three case studies realting to the “real world.” Malcolm Knowles introduced the term “andragogy” to North America, defining it as “the art and science of helping adults learn.” Andragogy is based on five assumptions—about how adults learn and their attitude towards and motivation for learning. #### Andragogy—five assumptions about adult learning ![][2] Learners need to feel safe and comfortable expressing themselves Knowles later derived seven principles of andragogy. Most theorists agree that andragogy is not really a theory of adult learning, but they regard Knowles' principles as guidelines on how to teach learners who tend to be at least somewhat independent and self directed. His principles can be summarised … [1]: /embed/graphic-1.gif [2]: /embed/graphic-2.gif
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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 | 0.048 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".