Exploring Factors That Influence Technology-Based Distractions in Bring Your Own Device Classrooms
Bibliographic record
Abstract
Previous research on distractions and the use of mobile devices (personal digital assistants, tablet personal computers, or laptops) have been conducted almost exclusively in higher education. The purpose of the current study was to examine the frequency and influence of distracting behaviors in Bring Your Own Device secondary school classrooms. Quantitative and qualitative data were collected from 181 secondary school students (55 female and 126 male) enrolled in three schools across Canada. Almost 80% of the students reported being on task regularly when using mobile devices in class. However, students also engaged in at least one of five distracting activities occasionally or regularly with their mobile devices including emailing (64%), surfing the web (65%), using social media (52%), instant messaging (32%), and playing games (30%). Female students engaged with social media significantly more than male students, whereas male students played games significantly more than female students. Students were rarely distracted by peer use of mobile technology devices. Students were more distracted by their own use of mobile devices when engaged in independent or group work, and less distracted with lectures and student presentations. Students claimed that teacher and school restrictions were probably the most effective method to limit distracting behavior while learning.
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 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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".