Toward a Start-to-Finish Cross-Disciplinary Instructional Model for National and International Higher Education
Bibliographic record
Abstract
Using their cross-disciplinary review of Ideas that Work in College Teaching, the authors explore the pedagogical commonalities of fifteen higher education instructors from SUNY Potsdam (State University of New York at Potsdam) in an attempt to reveal the secrets of teaching success across thirteen academic disciplines—math, computer science, geology, modern languages, political science, philosophy, history, biology, psychology, sociology, physics, and art. While the specific instructional disciplines varied considerably in the content that was both studied and presented, the authors found that the principles of effective teaching were quite similar across each of these disciplines. The insights shared by these fifteen accomplished instructors provide pedagogical wisdom that all teachers can learn from regardless of context or developmental age and stage of student capability and competence. Common goals and principles associated with effective teaching in higher education are highlighted using specific examples from individual authors where appropriate. A new model of instruction is then introduced: Attention, Interact, Apply, Invite – Fact, Think, Feel, Do (AIAI-FTFD), as a potential start-to-finish approach to effective teaching in higher education. Implications for use of the model in both national and international higher education contexts are 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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".