Innovations in Teaching Adult Education: Living History Museums and Transformative Learning in the University Classroom
Bibliographic record
Abstract
The difficult times in which we live require innovative, creative, and hopeful pedagogies of adult education. This article describes a nontraditional experiential, “empathy-invoking” approach to the teaching of a graduate course on the theory and research of adult learning. The approach begins with the building of a safe learning community, a familiar “knowledge curriculum,” and a structured syllabus with academic readings, small group discussions, student “theory-to-practice” facilitation of learning activities, and an academic mid-term paper. Both the teacher and students design and lead learning activities which elaborate, “unpack,” and critique readings, and develop students’ capacity for experiential, emotional, spiritual, arts-based, and bodily learning as well as group process, all the while reinforcing trust, deeper relationships, cooperation, and better knowledge of each others’ lives, personalities, capabilities, and identities. The class culminates in creative presentations where learners transform the classroom into “living history museums” representing the sites of adult learning they have investigated in field research. Visitors to living history museums engage in a rich array of informal adult learning; they gain new knowledge, participate in hands-on learning and role playing, and at times even experience transformative learning. In this class, the museum and its learning opportunities come into the classroom, and are created by learners themselves.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".