IT Humanities Education Program to Improve Digital Literacy of the Elderly
Bibliographic record
Abstract
Due to the influence of the COVID-19 pandemic, more older people are exposed to Information Technology(IT) in their daily lives. However, due to the lack of digital literacy capabilities of the elderly, it is difficult to use digital devices, making it difficult to live. Therefore, this paper outlined the impact of the digital divide on daily life and the ability of the elderly to use digital information. Through this, we propose an educational program that combines IT and humanities to improve digital literacy in the elderly. IT and humanities do not seem to match, but the convergence of the two is essential. Hardware and software, technology and technology, technology and design are being combined. Accordingly, technology and humanities can combine competency is becoming more critical. Therefore, the educational program proposed in this thesis combines a decision tree with a game and allows the elderly to acquire IT knowledge while playing a game naturally. This educational program was conducted for 23 older adults in their 60s in J city, South Korea. The average satisfaction of the study participants in education was 4.13(±0.65). In addition, the post-test mean of the recognition area in digital literacy was 3.02(±0.64) (p<.05), and the post-test mean of the behavior area in digital literacy was 3.67(±0.59) (p<.01), which was statistically significant compared to the pre-test.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".