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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".