Examining the Use of Wearable Technologies for K-12 Students: A Systematic 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
Wearable technologies such as smartwatches, smart clothing, smart glasses, fitness trackers, and brain senor headbands are wireless body sensors designed to record physiological and physical data. Since 2015, their use has increased in K-12 classrooms, but a comprehensive investigation of student impact yet to be conducted. In this paper, we conducted a systematic review of the literature focussing on the benefits and challenges of using wearable technologies for K-12 students. Using the PRISMA approach and a thematic narrative analysis, we analyzed 29 peer-reviewed articles from 2003 to 2019. The benefits of using wearable technologies for K-12 students included providing students with voice, ownership of learning and reflection, increasing engagement and relevance, improving learning, building social presence, increasing accessibility, and differentiated instruction. The challenges of using wearable technologies for K-12 students were health and safety as well as diminished perceptions of self-worth. Finally, we explored future research directions for wearable technologies in K-12 classrooms, including improved wearables-based pedagogy, data analysis methods, data ethics, and security policies.
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 | 0.002 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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