Bridging the digital divide: tailoring learning platforms for the elderly based on learning styles and digital skills
Bibliographic record
Abstract
This study examines the relationship between learning styles and digital skills among older adults in Thailand to offer insights for the development of customized online learning platforms. A study was conducted on 706 participants aged between 60 and 78 years to evaluate their digital skills in four areas and learning styles according to Kolb’s model. Confirmatory factor analysis was performed to validate the four-factor model of digital skills. MANOVA results demonstrated significant variations in digital skills among learning styles, with convergers displaying superior proficiency in most areas. A matrix was created to outline the recommended functions of a learning platform with the objective of aligning pedagogical approaches with individual learning preferences and specific digital competencies. The results emphasized the significance of tailored methods in educating older adults on digital literacy and offered a framework for creating digital learning environments that are more comprehensive and efficient. This study intended to address the digital divide and improve the quality of life of older adults in a world that is increasingly becoming digital. Lastly, it posed implications for educational policy and practice.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".