The commitment of older learners to university-based learning: changing perspectives
Bibliographic record
Abstract
As the population ages, education in the post-retirement years will become an even more popular option and attention will need to be given to the contributions that older learners can make to our institutions and to our communities. This is a qualitative study of older learners who have enrolled at university for a degree. The case being studied is the Senior Citizen's Program at the University of Toronto, implemented in 1976. Since that date, several hundred older learners have completed their undergraduate programs. The qualitative approach for this study relied on data collected from interviews during two phases of research. The first phase focused on twenty-six seniors who had graduated between 1996 and 2001. This was an exploratory stage to identify this group of learners' motives for enrolling at university during this stage of their life. The second phase of the study examined in more depth the learning experiences for five respondents. Additional data sources included interviews with university administrators, both retired and current, university coordinators, family members, and university professors. An archival search for documents on the history of this program was also conducted through the University of Toronto library system. Current aging theory, adult learning principles, along with lifelong learning theory, were used as the conceptual frameworks and the lenses for the study. Six themes emerged from the case of the Seniors' Program: restitution for thwarted educational goals, the symbolic meaning of a degree, the importance of family and university support, struggles with the new technology, the importance of a mutually engaging teaching and learning environment, and benefits to the university and the older learners. This study addressed some of the limitations of existing research in this field as changing perspectives about the contributions that older learners can make needs to be addressed. Demographics indicate that more older learners will be accessing universities over the next decade and this study provides current information useful for future planning. What became apparent in this study is that older people have the capacity to contribute throughout the later years as part of their legacy of helping others. There are many healthy, active seniors who are an underused, untapped resource for our universities and the broader communities.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".