Older Adults Learning Digital Skills Together: Peer Tutors’ Perspectives on Non-Formal Digital Support
Bibliographic record
Abstract
In later life, digital support is predominantly received outside of formal education from warm experts such as children, grandchildren, and friends. However, as not everyone can rely on this kind of informal help, many older adults are at risk of being unwillingly left without digital support and necessary digital skills. In this article, we examine non-formal digital support and peer tutoring as a way to promote digital and social inclusion through the acquisition of necessary digital skills. First, we ask: (a) What is peer tutoring, in the field of digital training, from the peer tutors’ point of view? Then, based on the first research question, we further ask (b) what are the key characteristics of peer tutoring in relation to other forms of digital support? Our thematic analysis is based on semi-structured interviews (n = 21) conducted in Central Finland in 2022 with peer tutors aged between 63 and 84. Peer tutors offered individual guidance by appointment and also supported their peers in group-based settings. Based on our study, we argue that from the peer tutors’ point of view, being a peer entails sharing an age group or a similar life situation and provides an opportunity for side-by-side learning. Although every encounter as a peer tutor is different and the spectrum of digital support is wide, these encounters share specific key characteristics, such as the experience of equality between the tutor and the tutee that distinguishes non-formal peer support from formal and informal learning.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".