Bibliographic record
Abstract
A bold commitment to EL positions UCalgary to be a leader in Canada, making learning-by-doing a cornerstone of the UCalgary experience.University of Calgary (2020) At the annual 2019 University of Calgary Conference on Postsecondary Learning and Teaching presenters and over 200 delegates shared their insights, experiences, and research on experiential learning (EL) in the classroom be it physical or virtual, a laboratory, clinical, field experience, or community placement.No matter the definition, perspective, or application of EL in higher education, this volume of Papers on Postsecondary Learning and Teaching (PPLT) expands on the conference theme of Exploring Experiential Learning and the commitment of the University of Calgary (2020).Dr. Norah McRae, the featured keynote speaker, began the discussion and set the stage for exploring experiential learning.She outlined models and a framework for work-integrated learning and challenged conference delegates to critically examine, explore, and expand on the scholarship and practice of teaching and experiential learning.This 4 th volume of PPLT contains 13 diverse papers from disciplines in archeology, business, chemistry, nursing, social work, and academic development.The authors each address the question "how do we transform education to spark curiosity, drive innovation and prepare students to thrive in their chosen careers?" (Conference on Postsecondary Learning and Teaching, 2019).While reading this volume, you will find various definitions, perspectives, and applications of EL including contributions from Rachel Braun, and Iffat Naeem and Fabiola E. Aparicio-Ting who set the EL landscape by introducing a definition of EL and offer both an institutional and graduate perspective of EL.Several authors present their work on game-based curriculum in EL and offer a range of activities from diverse disciplines (
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".