Bibliographic record
Abstract
This qualitative study examines student engagement in transformative learning in an undergraduate university setting. Although transformative learning using Mezirow's model (1975, 2000) has been described in adults, it has received little attention in relation to traditional age undergraduate students. This is surprising in light of developmental potential at this age and purposes attributed to undergraduate education. In keeping with concerns around Mezirow's adult transformation theory, Freire's philosophy of liberatory education, and other gaps in the literature, this exploratory study examines three main questions: (1) What are traditional age students' experiences of transformative learning in an undergraduate liberal arts university setting? (2) What factors contribute to transformative learning for traditional age undergraduate university students? and (3) What role do discrete student-centred courses have in the process of transformative learning? The empirical basis is the beginning of engagement in transformative learning for a volunteer sample of twelve traditional age students in three, 13-week undergraduate courses across disciplines at a small liberal arts university in Canada. For students in the study, engaging in the process of transformative learning involved seeing personal relevance in course issues; sensing tensions among perspectives; and digging deeper to explore underlying assumptions, agendas, and contexts. Rather than being an individual process as implied by Mezirow's model, student descriptions highlight a social process built on sharing experiences and working to understand the assumptions underlying the experiences. Students described changes in assumptions, as a result of their experiences, that involved cognitive-affective reflection and cognitive-behavioural action. Factors facilitating transformative learning included considering multiple perspectives (challenging traditionally assumed authority of the professor, discipline, and academic knowledge), shaping course experiences (challenging more traditional organization of courses), and developing relationships (focussing on interpersonal support). Through tapping into the power of experience, these student-centred courses moved beyond dichotomies critiqued in the literature to integrate emotion and cognition in learning, link individual and social learning and change, and offer students a different experience of power. The concept of power in transformative learning is developed further and implications for practice and theory around the role of universities in transformative learning and future research are discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".