Achieving digital inclusion of older adults through interest-driven curriculums
Bibliographic record
Abstract
One outcome of increased life expectancy is that older adults are leading active lives in their third age as they seize opportunities to learn new skills, pursue new interests and hobbies to challenge themselves. However, there are many misconceptions about older adults’ capabilities and aspirations, especially their attitudes towards technology. They are often misunderstood and seen to lack interest and motivation in the use of technology. Thus, this article examines interest-driven curriculums in order to achieve digital inclusion for older adults. Investigation methodology into this dilemma was best served with a mixed methods approach because, to date, there has been very little research about how technology could support older adults’ interests. The majority of the existing studies consulted were focused on school children in a classroom setting. Older adults can differ greatly in their general background and level of technical experience and knowledge. Consequently, it would be very difficult to conduct quantitative research with control groups to investigate single variables. In compensation, 131 older adults, five staff members and eight teachers participated in this study. Qualitative methods such as observations and interviews (one-on-one and focus group) provided a deeper insight into teachers’ experiences and teaching. Older adults were not always able to articulate their attitudes and problems with technology and consequently, observations were often a more effective means of data gathering. Finally, an Action Research approach was taken to trialling the concepts developed in the course of the investigation. This research comprised of four studies looked at expanding and extending on The Four-Phase Model of Interest Development by Hidi and Renninger (2006). The results show that when older adults are taught according to requests based on their pre-existing interests, it encourages long-term engagement of technology and ability to integrate technology into their everyday lives, thereby achieving digital inclusion amongst older adults.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".