Designing Effective Experiential Curriculum: The Experiential Learning Map
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Designing experiential student exercises or course modules can be a daunting task for faculty members. Often, not knowing where to begin is a barrier that causes instructors to avoid developing meaningful, high-impact student exercises grounded in experience. Yet, we know that these can be incredibly powerful and transformative pedagogies. The Experiential Learning Map (ELM) is a curricular planning tool that instructors, learning consultants, or students can use to storyboard and develop an experiential lesson. Modelled after best practices in business model ideation, and informed by research about experiential learning, the ELM provides instructors with an easy-to-use curriculum planning tool. The ELM is designed to be flexible. Instructors can scale the pedagogy from a single-class interaction to a multi-session pedagogical arc. The ELM's value is that it provides instructors with a simple, iterative planning tool that can be used to scope and scale a learning experience.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it