Applying an experiential learning cycle to inform a deeper understanding of teaching and learning in a changing Canadian community college
Bibliographic record
Abstract
The traditional, direct-from-high-school demographic is shifting to a greater mix of part-time learners, international students, and credential finishers in Canadian community colleges. This study explores how the learning lives of six faculty members and five mature students could help post-secondary administrators better understand experiential learning during a time of unprecedented change. Employing an applied phenomenological orientation, face-to-face workshops, faculty and student interviews, and online journaling were facilitated. These discussions and reflections were examined through the lens of an experiential learning model. This multi-dimensional approach enabled participants to share their experiences, observations, and feelings. In doing so, participants’ “everyday” learning lives, which are often assumed to be non-influential, led the process of more profoundly understanding personalized ownership of learning. \n The research question for this study was: To what extent can an experiential learning cycle inform a deeper understanding of teaching and learning for faculty and students in a changing Canadian post-secondary education environment? Barriers to adult education, learning cycles, learning communities, constructed learning, types of students and teachers, and self-study were examined. Transcript analysis software was used to analyze approximately 420 pages of semi-structured interview recordings and reflective, online journals. This resulted in the creation of 50 thematic code sets comprised of 417 codes. A framework emerged to interpret meaning associated with personalized ownership of learning. \n The data informed five calls for action. These focused on further implementing the model, helping administrators better understand experiential learning, ending labelling of non-traditional students, in-service opportunities for faculty, and growth of professional learning communities. This study celebrates how learning belongs to the individual, because when a student learns, new personalized knowledge is different than that of the teacher. Ultimately, this study establishes new opportunities for professional development as the learning lives of students and faculty intersect in a changing community college environment.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.024 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".