Crowd Context-Based Learning Process via IoT Wearable Technology to Promote Digital Health Literacy
Bibliographic record
Abstract
The crowd context-based learning process via IoT wearable technology [IoTW-driven Crowd context-based learning (CCBL)] is a new learning paradigm that integrates Technological Cybergogical Content Knowledge (TCACK) based on connectivism, cognitive tools and information processing theories to promote digital health literacy. In this study, the IoTW-driven CCBL was designed by incorporating content, cybergogical, and technological elements, which can become a sustainable solution in educational settings during the global COVID-19 pandemic. The researchers collected qualitative data by confirmatory focus groups from 12 experts who hold doctoral degrees or equivalent and have at least 3 years of relevant experience. The results of these studies found that the IoTW-driven CCBL involves three specific learning contents (healthcare, disease prevention and health promotion); five dynamic learning steps (context-aware alerting step; observing and questioning step; social information-seeking step; action step; and self-reflection step as all cognitive tools embedded in IoTW devices help promote all digital health literacy components); and four cognitive tools (notification tool; communication tool; searching tool; and monitoring tool). The IoTW-driven CCBL will allow learners to respond to real-life situations by utilising IoT devices to access, apprehend, appraise and apply health information from digital technologies in daily life for well-being, especially in educational settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".