Exploring Ethical Issues with Using Wearable Technology in K-12 Classrooms: A Review of the Literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Smartwatches and other miniaturized wearable devices are continuously evolving and are being used to capture and analyze the body data of young students. The body data being captured includes physical location and body movement, heart rate, stress and arousal, as well as academic emotions. With this information teachers are incorporating student-generated body data into creative learning activities to make learning student-centred and more engaging. However, there are unique challenges for teachers. These challenges are premised on applying sound pedagogical practices when implementing wearables and being informed about ethical considerations when using the personal data generated from student bodies. Our systematic review combines the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach with a thematic narrative analysis to review 29 articles from 2003 to 2019. Our findings include three major thematic sections dealing with ethical issues when using wearable technologies in K-12 education. The ethical issues we discuss are awareness of unintended outcomes, ethics of data ownership, and the risk of statistical solutionist management of student bodies.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| 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 it