Supplementary material from "Collaborative learning of new information in older age: a systematic review"
Bibliographic record
Abstract
Ageing is accompanied by a multitude of changes in cognitive abilities, which in turn affect learning. Learning collaboratively may benefit older adults by negating some of these age-related changes. However, studies on collaborative learning in older age differ in their methodology and findings. This systematic review provides an overview of the current research on collaborative learning in older age, exploring what factors influence collaborative learning in this age group. The titles and abstracts of imported 6629 works were screened, as well as four works added manually, which resulted in 29 studies. These studies were conducted across five countries (Canada, United States, United Kingdom, Switzerland and Belgium) between 1993 and 2023. Most studies were quantitative with a non-randomized (n = 16) design. Of the 29 studies, almost all studied collaboration in pairs (n = 28). The results suggest that the benefits of collaborating in older age may depend on the type of learning material, that familiarity between partners does not affect learning, and that age differences appear to decrease or disappear when older adults are provided with adequate time or trials. In addition, this systematic review identifies several gaps in the literature that future research should investigate further.This study was preregistered prior to its commencement on 21 January 2022. The accepted Stage 1 manuscript, unchanged from the point of in-principle acceptance, may be viewed at https://osf.io/tj4w7/. The data and materials of this study can be found at https://osf.io/8xvqf/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.617 | 0.037 |
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".