On Becoming an Experiential Educator
Bibliographic record
Abstract
Background Becoming an experiential educator involves more than just being a facilitator or matching learning style with teaching style. Experiential education is a complex relational process that involves balancing attention to the learner and to the subject matter while also balancing reflection on the deep meaning of ideas with the skill of applying them. Aim To describe a dynamic matching model of education based on Experiential Learning Theory and to create a self-assessment instrument for helping educators understand their approach to education. Method A dynamic matching model for “teaching around the learning cycle” describes four roles that educators can adopt to do so—facilitator, subject expert, standard-setter/evaluator, and coach. A self-assessment instrument called the Educator Role Profile was created to help educators understand their use of these roles. Results Research using the Educator Role Profile indicates that to some extent educators do tend to teach the way they learn, finding that those with concrete learning styles are more learner-centered, preferring the facilitator role; while those with abstract learning styles are more subject-centered preferring the expert and evaluator roles. Conclusion A model for the practice of dynamic matching of educator roles, learner style, and subject matter can aid in the planning and implementation of educational experiences. With practice, both learners and educators can develop the flexibility to use all educator roles and learning styles to create a more powerful and effective process of teaching and learning—in Mary Parker Follett’s words to, “. . . free the energies of the human spirit . . . the highest potentiality of all human association.”
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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".